Environmental Efficiency Research Articles

Decentralized Machine Learning Framework for the Internet of Things: Enhancing Security, Privacy, and Efficiency in Cloud-Integrated Environments

The Internet of things (IoT) presents unique challenges for the deployment of machine learning (ML) models, particularly due to constraints on computational resources, the necessity for decentralized processing, and concerns regarding security and privacy in interconnected environments such as the Internet of cloud. In this paper, a novel decentralized ML framework is proposed for IoT environments characterized by wireless communication, dynamic data streams, and integration with cloud services. The framework integrates incremental learning algorithms with a robust decentralized model exchange protocol, ensuring that data privacy is preserved, while enabling IoT devices to participate in collaborative learning from distributed data across cloud networks. By incorporating a gossip-based communication protocol, the framework ensures energy-efficient, scalable, and secure model exchange, fostering effective knowledge sharing among devices, while addressing the potential security threats inherent in cloud-based IoT ecosystems. The framework’s performance was evaluated through simulations, demonstrating its ability to handle the complexities of real-time data processing in resource-constrained IoT environments, while also mitigating security and privacy risks within the Internet of cloud.

Development of Machine Learning-Based Production Forecasting for Offshore Gas Fields Using a Dynamic Material Balance Equation

Offshore oil and gas fields pose significant challenges due to their lower accessibility compared to onshore fields. To enhance operational efficiency in these deep-sea environments, it is essential to design optimal fluid production conditions that ensure equipment durability and flow safety. This study aims to develop a smart operational solution that integrates data from three offshore gas fields with a dynamic material balance equation (DMBE) method. By combining the material balance equation and inflow performance relation (IPR), we establish a reservoir flow analysis model linked to an AI-trained production pipe and subsea pipeline flow analysis model. We simulate time-dependent changes in reservoir production capacity using DMBE and IPR. Additionally, we utilize SLB’s PIPESIM software to create a vertical flow performance (VFP) table under various conditions. Machine learning techniques train this VFP table to analyze pipeline flow characteristics and parameter correlations, ultimately developing a model to predict bottomhole pressure (BHP) for specific production conditions. Our research employs three methods to select the deep learning model, ultimately opting for a multilayer perceptron (MLP) combined with regression. The trained model’s predictions show an average error rate of within 1.5% when compared with existing commercial simulators, demonstrating high accuracy. This research is expected to enable efficient production management and risk forecasting for each well, thus increasing revenue, minimizing operational costs, and contributing to stable plant operations and predictive maintenance of equipment.

Environmental sustainability, energy efficiency and uncertainty analysis of agricultural residue-based bioethanol production: A comprehensive life cycle assessment study

Bioethanol produced from agro-residue provides a sustainable alternative to fossil fuels, mitigating pollution, reducing waste, and promoting a circular economy. However, challenges arise in sustainable sourcing, resource conflicts, water quality concerns, and managing environmental footprints to maximize agro-residue-based bioethanol benefits. The environmental impacts, net energy ratio (NER), net energy value, water footprint, and uncertainties associated with bioethanol production from agro-residues are analyzed in this study. The analysis followed a cradle-to-gate approach using 1 L of bioethanol produced as a functional unit (FU), allowing for a direct comparison of the environmental performance of the different feedstocks. The result shows that rice straw-based bioethanol exhibited higher environmental impacts, particularly in terms of ecotoxicity, ionizing radiation, human toxicity, and ozone layer depletion, except for greenhouse gas emission, which is lowest for corn stover (0.37 kg CO2 eq. per FU) and highest for cassava straw (1.1 kg CO2 eq. per FU). Furthermore, the Cumulative Energy Demand analysis highlighted a significant reliance on fossil fuels from agricultural biomass throughout the bioethanol production process. The energy balance analysis shows that the agricultural residues are feasible for bioethanol production with NER of 1.20, 3.10, and 3.28 for rice straw, corn stover, and cassava straw, respectively. To enhance the accuracy and reliability of the LCA results, Monte Carlo simulation was employed to account for uncertainty and variability in the data. This methodology provides a better understanding of the impacts of bioethanol production, with a focus on sustainability and advancements in reducing our reliance on fossil fuels.

Blockchain in Supply Chain Management: Enhancing Transparency, Efficiency, and Trust

Supply chain management has become an area of focus as organizations around the world work to leverage blockchain technology to increase supply chain visibility, productivity, and reliability. As a novel approach to innovation, this paper aims at assessing whether or not blockchain technology could help overcome problems that have attributed lack of consensus between SCM and its stakeholders including, inadequate information flow, complex logistics, ad inadequate inter-organizational trust. As such, using scientific data I investigate how innovations built on the blockchain enhance efficiency, contain costs, and create instant permanent transparency, including in the supply chain. The research methods include a literature search and analysis, calculations of blockchain benefits in relation to the most important SCM indicators: transaction time, cost cutting, and transparency of suppliers. From our research, it can be suggested that advanced use of blockchain results in improved efficiency by around 20-25% of OM costs, while the error rate will be decreased up to 30%. In addition, by enhancing the history of each product, blockchains decrease fraud rates in high-risk sectors, including the pharmaceutical marketplace, by a half. These findings emphasize the positive impact that blockchain implements to alter the supply chain by promoting trust between the chain’s participants and allowing for secure transfer of necessary information. This research helps to complete the existing literature by giving concrete information about blockchain’s efficiency in the real business environment which could be useful for those companies and government agencies which are aiming to implement blockchain-based solutions in supply chain management. Finally, the ethical issues and implications related to the practical realization of blockchain in SCM are highlighted and analyzed with the help of corresponding recommendations.

A Fair MAC Protocol Based on Dual Bandwidth Allocation Iterations in Underwater Acoustic Sensor Networks

ABSTRACTMost existing passive allocation‐based media access control (MAC) protocols for underwater acoustic sensor networks (UASNs) overlook the fairness issue in channel bandwidth allocation, resulting in certain nodes occupying channel resources for extended periods, thereby leading to low channel utilization. To address this issue, this paper proposes a fair MAC protocol based on dual bandwidth allocation iterations (FBA‐MAC). Firstly, by limiting the maximum number of packets and the maximum number of available time slots that the host node can receive in each communication cycle, the protocol reduces collisions and collisions more effectively, thus controlling the communication delay. Secondly, in the process of channel bandwidth allocation, a dual bandwidth allocation iterative method was used to allocate appropriate channel bandwidth for each member node according to its communication requirements and the generation time of its generated data packets. Finally, in order to minimize the time interval of each packet arriving at the host node to reduce the communication delay, the transmission scheduling scheme was adaptively adjusted by combining the results of channel bandwidth allocation to maximize the channel utilization. Simulation results indicate that compared to the other four MAC protocols, FBA‐MAC demonstrates significant advantages in terms of fairness (10% higher), achieving higher network throughput (10% higher) and shorter end‐to‐end delay. Therefore, FBA‐MAC exhibits superior adaptability and efficiency in dynamic network environments.

Delineation of Management Zones for Site‐Specific Soil Nutrient Management for Sustainable Crop Production

ABSTRACTSoil nutrients deficiencies are one of the major causes of soil degradation in different parts of World, adversely impacting crop production. Delineation of soil nutrients management zones (MZs) is one of the commonly used techniques for evaluating spatial distribution pattern of soil parameters for adoption of site‐specific nutrient management. We, therefore, conducted the present study to understand the spatial distribution pattern of soil nutrients and their associated soil properties, and to delineate soil nutrients MZs in a north‐western Indian Himalayan (NWIH) region. A total of 18,930 representative surface (0–15 cm depth) soil samples were collected and processed. The processed soil sample were analyzed for pH, and electrical conductivity (EC), soil organic carbon (SOC), available N (AN), available P (AP), available potassium (AK), exchangeable Ca (Ex. Ca), exchangeable Mg (Ex. Mg), available S (AS), available Zn (AZn), available Fe (AFe), available Cu (ACu), available Mn (AMn) and available B (AB). The values of studied soil parameters varied widely with coefficient of variation ranging from 11.8% to 156%. Semivariogram analysis revealed stable, exponential and Gaussian best‐fit models for different soil parameters with weak (AP and AB), moderate (rest of soil parameters) and strong (AS) spatial dependence. Varied distribution pattern of soil parameters was visualized from ordinary kriging interpolation. Five soil nutrient management zones (MZs) were identified (using fuzzy performance index and normalized classification entropy values) by employing the techniques of principal component analysis and fuzzy c‐means clustering. Principal components with Eigen value > 1 were considered for further analysis. The soil parameters of identified MZs differed significantly. Thus, the study highlighted the usefulness of MZ delineation technique for site‐specific soil nutrient management in different cultivated areas for sustainable crop production. The developed MZ maps could suitably be used for efficient management of agronomic inputs especially fertilizer nutrients for improved environmental and economic efficiency.

Environmental and economic assessment of energy projects.

The energy industry has a significant impact on the scarce fossil hydrocarbon resources and on the environment. The burning of natural energy carriers by traditional energy facilities is one of the factors increasing the content of greenhouse gases in the atmosphere that entails serious climate changes. Evaluating the efficiency of energy enterprises and the implementation of energy projects requires an integrated approach that considers not only technical and economic aspects, but also the environmental impact. Such approach is especially important in the context of the energy transition and the implementation of circular economy principles. Despite the attention of scientists to the tasks of scientific, methodological, and practical determination of the economic efficiency and environmental consequences of energy projects, the issue of environmental and economic assessment remains relevant, in particular considering resource and environmental efficiency. The purpose of this study is to improve the methodological tools of environmental and economic assessment of energy projects. The authors propose an approach to the assessment of energy projects using an integral indicator. This indicator is calculated based on a system of specific indicators of the natural resource capacity and the environmental compatibility of energy production at the energy facility, considering the regional environmental conditions. The formed approach can be used for environmental and economic assessment and comparison of energy projects, fast-acting measures, and projects for the development of energy enterprises.

Recycled Aggregates Influence on the Mechanical Properties of Cement Lime-Based Mortars

The current framework for managing construction waste, guided by European Union regulations, calls for an integrated waste management system. However, the reuse of old plaster waste, particularly from deteriorated facades, remains underexplored. This study investigates the potential of repurposing old plaster waste as a substitute for aggregates and cement in mortars, with the aim of promoting environmental sustainability and resource efficiency. Three mortar mixes were analyzed: a control mix, a mix with 45% waste replacing aggregates, and a mix with 10% waste replacing cement. Results show that replacing 45% of aggregates with plaster waste led to a 30% reduction in flexural strength, while the 10% cement replacement increased flexural strength by 6%. Compressive strength dropped by 27% and 38% for cement and aggregate replacements, respectively. Despite these reductions, the waste replacement remained within acceptable limits for structural integrity. Further microscopic analysis revealed that the incomplete integration of portlandite particles from the waste contributed to non-uniform bonding and crystal formation, weakening the mortar’s structure. This research demonstrates the feasibility of reusing old plaster waste, offering a novel approach to reducing construction waste and promoting a circular economy. It contributes to filling the knowledge gap on the reuse of plaster mortars while aligning with sustainable construction goals.

Dynamics‐Oriented Underwater Mechanoreception Interface for Simultaneous Flow and Contact Perception

The lack of a sufficient and efficient way to simultaneously perceive general underwater mechanical stimuli, physical contact, and fluidic flow has been a bottleneck for many aquatic applications. To address this challenge, dynamics‐oriented underwater mechanoreceptor interface (DOUMI), a bioinspired mechanoreception system that realizes simultaneous contact and flow perception using a single receptor, is introduced. This receptor, response‐elevated‐and‐expanded hair‐like tactile mechanoreceptor (REEM), is inspired by the mechanoreceptive mechanism of aquatic arthropods. REEM combines structural features from different mechanoreceptive sensilla, enabling it to capture a wide range of stimulus dynamics. Under different stimuli, REEM encodes stimuli dynamics as its oscillations with distinct spectral attributes. Those oscillations are efficiently transferred through mechanical processes and imaging, enabling vision‐based extraction and further analysis. Therefore, by evaluating the oscillation dynamics with tailored wavelet‐based indices, DOUMI can distinguish between contact‐ and flow‐induced oscillations at each receptor unit with 90.5% accuracy. Furthermore, DOUMI provides comprehensive 2D mechanoreception with a scalable array of REEMs, delivering capabilities like stimuli spatiotemporal visualization, flow trend detection, and scenario classification with an accuracy of 99.5%. With its robustness and operational efficiency in underwater environments, DOUMI can be easily adapted to existing applications using common materials and hardware, establishing a new, streamlined paradigm for underwater general mechanoreception.

Sustainability Strategies in Municipal Wastewater Treatment

The European Parliament adopted a legislative resolution of 10 April 2024 on the proposal for a directive of the European Parliament and of the Council concerning urban wastewater treatment. The reduction in pollution in discharged treated wastewater in the parameters of BOD5, total nitrogen, and total phosphorus was emphasized. Based on these results, it stated that the impacts on the quality of lakes, rivers, and seas in the EU are visible and tangible. At the same time, it was emphasized that the sector of urban wastewater removal and treatment is responsible for 0.8% of total electricity consumption and about 0.86% of all greenhouse gas emissions in the entire EU. Almost a third of these emissions could be prevented by improving the treatment process, better use of sewage sludge, and increasing energy efficiency, as well as a higher rate of use of renewable resource technologies. It is also necessary to integrate treatment processes into the circular economy. Sludge management and water reuse are suboptimal as too many valuable resources are still being wasted. This article focuses on sustainable municipal wastewater treatment, innovative and new wastewater treatment processes and technologies (combined and hybrid processes, ANAMMOX, etc.) and their use in practice with the aim of increasing environmental and energy efficiency and reducing the carbon footprint. The research is focused on the possibilities of increasing the efficiency of energy processing of sludge, reuse of nitrogen and phosphorus, sludge, and reuse of treated wastewater.

The Challenge of Energy Efficiency in Social Housing: An Evaluation of Envelope's Improvements and their Economic Effects

Objective: This study aims to propose a combination of modifications to the envelope of a Social Housing (SH) project and evaluate their impacts on thermal load reduction and cost increase, staying within the budgetary constraints. Theoretical Framework: The Minha Casa Minha Vida Program (PMCMV) prioritizes architectural solutions focused on large-scale production and reduced unit costs, at the expense of environmental comfort and energy efficiency. However, efforts to raise the standards of habitability and quality of life are evident in the new 2023 program version and its regulations for new projects. Method: This case study assesses the envelope classification of an SH project applied to 20 sets of construction modifications and 4 deployment orientations, using the Inmetro Normative Instruction for Building Energy Efficiency (INI-R). Thermal load reduction and cost increment analysis will be performed. Results and Discussion: For each deployment orientation, the modifications that resulted in the greatest reduction in thermal load. This reduction ranged between 20% and 26.5% for the four orientations, with an additional cost ranging from 6% to 16%. Research Implications: Energy efficiency measures, even if they entail higher costs, may provide economic benefits during the operation and maintenance phases of the properties within PMCMV standards. Originality/Value: This study contributes to the dissemination of the simplified method of INI-R for envelope analysis in buildings and may influence improvements in public policies related to environmental quality in housing programs.

Exploiting RSM and ANN modeling methods to optimize phosphate ions removal using LDH/alginate composite beads

In the present work, layered double hydroxide (MgAl) and alginate composite beads (LDH/alginate) were developed and used as low-cost and environmentally friendly adsorbent for phosphate ions removal. The successful incorporation of sodium alginate into the LDH structure was confirmed through SEM analysis. The dried beads exhibited a notably rough surface, which promotes molecular movement and potentially enhances pollutant adsorption. The pH of zero charge point (pHPZC) of the composite was found to be 7.41. This result implies that negative ions have a tendency to draw positive charges on the adsorbent surface via electrostatic interaction forces when the pH is lower than the pHPZC. On the other hand, a surface that has a pH higher than pHPZC mostly has a negative charge. The percentage removal and adsorption capacity were investigated as a function of contact time.Experiments were carried out by simultaneously varying three factors, in order to evaluate and compare the predictive capabilities of the response surface methodology (RSM) and the artificial neural network approach (ANN) for the adsorption process. Both methods demonstrated a strong ability to accurately predict the adsorption process. However, the response surface methodology exhibited a lower prediction error compared to the artificial neural network approach.The central composite design within response surface methodology (CCD-RSM) was employed to optimize the experimental conditions for the adsorption process. The model, which considers three factors: adsorbent dose (A), initial concentration (B), and contact time (C), proved to be significant. Among these, the quadratic term (B2) had the most substantial impact on the phosphate ion adsorption rate. The analysis yielded an R2 value of 0.94, indicating an excellent fit to the data. The findings suggest that increasing contact time and reducing the initial concentration improve phosphate ion removal efficiency, while the adsorbent dose has little to no effect. The Artificial Neural Network (ANN) model effectively predicted the adsorptive remediation of phosphate ions onto LDH/alginate composite beads, achieving a high coefficient of determination (R2 = 0.984) between the model outputs and the experimental data. The study highlights the significant potential of LDH/alginate composite beads as a natural adsorbent for the removal of phosphate ions from aqueous solutions. These results underscore the environmental friendliness and efficiency of the adsorbent used for phosphate adsorption.

Enhanced Organic Solvent Nanofiltration Membranes with Double Permeance via Laser‐Induced Graphitization of Polybenzimidazole

AbstractThis study investigates the fabrication of organic solvent nanofiltration (OSN) membranes through laser‐induced graphitization of polybenzimidazole (PBI). Employing a CO2 laser, the polymer is converted into graphene, resulting in controlled submicron‐scale porous 3D structures, a feat not achievable with traditional methods such as chemical crosslinking. The effectiveness of this process hinges on precise adjustments of laser parameters, such as fluence, to attain the ideal graphitization levels. The findings indicate that partial graphitization, as opposed to excessive, is crucial for preserving the membrane's microstructure and enhancing its functional properties. The partially graphitized PBI‐LIG (Polybenzimidazole ‒ Laser‐induced Graphene) membranes achieved up to 94% rejection of Congo red from ethanol, with an ethanol permeance rate of 12.14 LMH bar−1—nearly twice that of standard PBI membranes. Additionally, these membranes showcased outstanding chemical stability and solvent resistance, maintaining over 99% structural integrity and experiencing <1% weight loss after prolonged exposure to various industrial solvents over a week. These results highlight the potential of laser‐graphitized PBI membranes for applications in harsh chemical conditions, paving the way for further optimization of high‐performance OSN membranes. This research advances membrane technology, merging laser engineering with materials science, and contributes to environmental sustainability and industrial efficiency.

Z-Scheme Ag2S-Ag-In2O3 Heterostructure with Efficient Antibiotics Removal under Natural Sunlight.

The widespread distribution of antibiotics in natural waters is a great threat to human health. Photocatalytic degradation is an environmentally friendly technology to remediate antibiotic-polluted waters, driven by endless solar energy. Herein, a Z-scheme Ag2S-Ag-In2O3 heterostructure photocatalyst is prepared to remove antibiotics under environmental conditions. Under natural sunlight (light intensity: ∼78 mW/cm2) irradiation, the optimal Ag2S-Ag-In2O3 (10-ASAIO) exhibits considerable performance for decomposing diverse antibiotics, including norfloxacin (NOR), tetracycline hydrochloride, sulfisoxazole, ciprofloxacin, chlortetracycline hydrochloride, and ofloxacin. The NOR photodegradation rate constant of 10-ASAIO reaches 0.025 min-1, which is 12.50, 5.00, and 6.25 times higher than that of In2O3 (0.002 min-1), Ag-In2O3 (0.005 min-1), and Ag2S-In2O3 (0.004 min-1), respectively. This performance of the 10-ASAIO photocatalyst for decomposing NOR under natural sunlight exceeds most of the previously reported photocatalysts under a xenon lamp. Particularly, due to the intermittency of natural sunlight, a light-emitting diode (LED) lamp (light intensity: 5.1 mW/cm2) is also used as a light source, and 72.20% of NOR can be degraded with irradiation for 12 h. The effects of many water characteristics (water bodies, coexisting inorganic anions, pH, and humic acid) on the degradation performance of 10-ASAIO have been investigated, which exhibits stable degradation efficiency in variable aquatic environments. A 10-ASAIO catalyst-coated frosted glass sheet is fabricated to settle the problem of recovery of powder photocatalysts, and the immobilized catalyst shows outstanding activity and stability to decompose NOR. The photocatalytic mechanism and pathway of degrading NOR over 10-ASAIO have also been systemically investigated and proposed. The ecotoxicity (phytotoxicity and biotoxicity) of the 10-ASAIO photocatalyst and treated NOR solution have been tested by their toxic effects on cabbage seeds and Staphylococcus aureus (S. aureus). This work provides a feasible photocatalytic system for environmental pollutant remediation under natural sunlight or an LED lamp.

Активизация пластовой микрофлоры для повышения нефтеотдачи

Constant optimization of technological processes of oil production is one of the integral parts of rational subsurface use. The study and implementation of new developments and technologies in the oil production process is primarily due to the economic effect. Methods of increasing oil recovery are of particular interest in the practical implementation of innovative technologies in the oil and gas industry. To date, a huge number of technologies have been developed and tested in field conditions aimed at preserving and increasing the volume of produced products, the classification of which is very diverse. The primary criteria for choosing a method or combination of methods of influencing a productive reservoir in order to increase oil recovery are the geological conditions of the productive horizon, as well as the composition and physico-chemical properties of reservoir fluids. Currently, of all the many methods of increasing oil recovery, the microbiological method is the least studied, as a result of which it is extremely rarely used in enterprises. The relevance of research on the effects of microorganisms on the reservoir rock is beyond doubt. The possibility of widespread application of oil recovery enhancement technology based on bacterial life processes is interesting both from the point of view of environmental safety and economic efficiency. One of the microbiological methods of increasing oil recovery is the activation of the existing reservoir microflora by feeding a nutrient medium into the reservoir. In the course of their vital activity, some types of bacteria are capable of destroying clay rocks, which entails changes in the filtration and capacitance properties of the reservoir rock and, as a result, the volume of products produced increases significantly. The advantage of this method is the absence of the need for forced “contamination” of the reservoir formation, since the necessary bacteria are already in it.

Digitalization of Mining Enterprise: A Key Factor in Ensuring Economic Security

The level of competitiveness of mining companies is primarily determined by productivity and operational excellence. Digitalization is becoming a key factor that enables companies in this industry to remain competitive in the future. In the mining industry, the transition to new and better ways of working has been slow due to the scale and complexity of production processes, as well as the significant costs of modernization. As a result, some enterprises are not actively implementing digital technologies. However, leading global and Kyrgyz mining companies are actively investing in the development of modern technologies in the field of energy and automation in order to increase production volumes, improve environmental efficiency, reduce the use of manual labor, costs and energy consumption. It is obvious that digital technologies open new opportunities for a significant increase in labor productivity and profit growth. The article discusses the main directions of digital transformation of mining enterprises, reveals the key advantages and potential threats. The author analyses both foreign and Kyrgyz examples of effective digitalization in this industry.

Speed planning and control in complex road conditions based on vehicle driving capability

In order to solve the problem of vehicle speed planning that meets the constraints of safety and efficiency in complex road environments (large curvature and low adhesion), a differential equation planning method based on vehicle dynamics analysis is proposed. First, the structural parameter expression of the limit speed that meets the lateral tire force constraint during steady-state steering is derived. Secondly, the tire force execution space of the front and rear wheels is combined into an FF diagram, and an implicit differential equation that considers load transfer and driving mode factors is obtained. The differential equation is solved to obtain the limit speed along the path, and a limit speed calculation method for discrete path information is given. Finally, a lateral and longitudinal collaborative model predictive controller is designed, and a CarSim and Simulink joint simulation platform is built. Trajectory tracking simulation experiments are carried out on both continuous and discrete information paths with the planned limit speed. The results show that the limit speed planning method proposed in this paper can complete the trajectory tracking task in complex road environments as quickly as possible while controlling the tire force within the range of the stable friction circle.

The Green Extraction of Blueberry By-Products: An Evaluation of the Bioactive Potential of the Anthocyanin/Polyphenol Fraction.

In the context of a circular economy, this study explores the valorization of blueberry pomace (BP) as a source of bioactive compounds using sustainable extraction methods. Microwave-assisted extraction (MAE) and microwave-assisted subcritical water extraction (MASWE) were employed to obtain two distinct fractions: MAE 1° and MASWE 2°. The first extract, MAE 1°, obtained at 80 °C, had a high total anthocyanin content (21.96 mgCya-3-glu/gextract), making it suitable as a natural pigment. Additionally, MAE 1° exhibited significant enzyme inhibition, particularly against α-amylase and β-glucosidase, suggesting potential anti-diabetic and anti-viral applications. The second extract, MASWE 2°, obtained at 150 °C, contained a higher total phenolic content (211.73 mgGAE/gextract) and demonstrated stronger antioxidant activity. MASWE 2° showed greater inhibition of acetylcholinesterase and tyrosinase, indicating its potential for use in Alzheimer's treatment, skincare, or as a food preservative. MASWE 2° exhibited cytotoxicity against HeLa cells and effectively mitigated H2O2-induced oxidative stress in HaCat cells, with MAE 1° showing similar but less pronounced effects. A tested formulation combining MAE 1° and MASWE 2° extracts in a 3:2 ratio effectively enhanced anthocyanin stability, demonstrating its potential as a heat-stable pigment. The extract characteristics were compared with a conventional method (MeOH-HCl in reflux condition), and the protocol's sustainability was assessed using several green metric tools, which provided insights into its environmental impact and efficiency.

Impact of Ad Blockers on Computer Power Consumption while Web Browsing: A Comparative Analysis

This study explores the impact of various ad blockers on power consumption during web browsing, focusing on different types of online content. By analyzing power use across ten popular websites, the study assesses the performance of five widely utilized ad blockers: AdBlock, AdBlock Plus, uBlock, uBlock Origin, and uBlock Origin Lite. Power consumption was measured under controlled conditions, comparing scenarios with and without ad blockers to gain insight into their efficiency. The findings indicate substantial differences in power savings, with some ad blockers significantly reducing power usage, particularly on media-heavy sites, while others unexpectedly increased consumption under certain conditions. The study underscores the potential of ad blockers to enhance power efficiency in digital environments, highlighting the importance of optimizing ad-blocking techniques to reduce the environmental impact of online activities. Through comprehensive analysis and comparison, this research offers insights into selecting effective ad blockers to minimize power consumption, promoting more sustainable web browsing practices.

Enhancing Edge Environment Scalability: Leveraging Kubernetes for Container Orchestration and Optimization

ABSTRACTKubernetes is an open‐source container orchestration platform, offers a comprehensive suite of features for managing containerized applications effectively. These features encompass horizontal scaling, per‐node‐pool cluster scaling and automated resource request adjustments. This research endeavors to harness these capabilities to address the limitations experienced by fog servers in edge environments, particularly those arising from restricted network connectivity and scalability challenges. In this research paper, the primary focus is on Kubernetes role of enhancing scalability, providing a robust framework for managing containerized applications. The proposed approach involves creating a predefined number of pods and containers within a Kubernetes cluster, specifically designed to efficiently handle incoming requests while optimizing CPU and memory usage. This method implements a microservice architecture for the web tier, with separate pods for the front end, back end and database, ensuring modular and scalable design. All pods communicate and integrate through REST APIs, facilitating seamless interaction and data exchange between the services. When handling web requests, the approach enables and controls both internal and external networks, ensuring secure and efficient communication. The analysis then examines the CPU and memory utilization of the pods, as well as node bandwidth, to provide a comprehensive evaluation of container scalability and performance within the Kubernetes cluster. These findings effectively demonstrate Kubernetes' capability in managing container scalability and optimizing resource utilization, highlighting its efficiency and robustness in a microservice environment.