Efficient System Research Articles

Non-mechanical beam steering for high-speed optical wireless communications via electrowetting on dielectric

Electrowetting on dielectric (EWOD) is used for non-mechanical optical beam steering (OBS) in optical communication systems. High-capacitance ion gel is used to construct an efficient electrowetting interface that facilitates dynamic OBS. This integration facilitates precise control of beam steering and data transmission efficiency in optical wireless communication systems. An EWOD-based liquid prism (LP) manipulates beam direction via electrowetting. The theoretical framework is supported by the Young and Young-Lippmann equations for precise optical path control. We present a theoretical and experimental demonstration of a two-dimensional beam steering system using an EWOD-based LP, with beam steering up to 14.82° and 14.39° along the X and Y axes, respectively. The system achieves data rates of 1.9 Gbps in free-space optics (FSO) and 1.7 Gbps in underwater wireless optical communication (UWOC) systems, with a measured bit error rate that adheres to the standard threshold of the forward error correction limit. Our results suggest that the EWOD-based LP system offers a compact, efficient, and versatile design for optical devices in both FSO and UWOC systems.

Smart manufacturing implementation: identifying barriers and their related stakeholders and components of technology

Purpose One of the achievements of the fourth industrial revolution is smart manufacturing, a manufacturing system based on Industry 4.0 technologies that will increase systems' reliability, efficiency and productivity. Despite the many benefits, some barriers obstruct the implementation of this manufacturing system. This study aims to analyze these barriers. Design/methodology/approach One of the measures that must be taken is to identify and try to remove these barriers, which involves identifying the stakeholders and components of technology associated with each barrier. As such, the primary purpose of this paper is to present a systematic literature review in the field of smart manufacturing with a focus on barriers to implementation related to the stakeholders and components of technology. Findings This research conducted a systematic literature review in Scopus and Web of Science databases and considered the studies published until 2021 were examined. The central question of this paper is answered based on this literature review, in which 133 related studies and 15 barriers were identified. Practical implications The significant gap observed in the literature review is that no research has been conducted to determine the stakeholders and components of technology related to the barriers, making it a potentially worthwhile subject for future research. In addition, the results of this study may help managers to implement smart manufacturing. Originality/value This study provides two main originalities. The former is helpful information for managers to make effective decisions when they face smart manufacturing barriers. The latter is related to identifying critical research gaps through systematic literature review.

Efficiency optimization of fuel cell systems with energy recovery: An integrated approach based on modeling, machine learning, and genetic algorithm

Maximizing the efficiency of fuel cell systems (FCS) with energy recovery capabilities is crucial for advancing high-efficiency FCS technology. This research aims to explore the maximization of FCS efficiency through the optimization of operational parameters and to elucidate the synergistic effects between parameter optimization and energy recovery. Employing an integrated approach that combines model simulation, experiments, machine learning, and genetic algorithm (GA), this work addresses key technical challenges in developing an efficient FCS model and a high-precision surrogate model. It also tackles the critical problem of achieving maximum system efficiency through the optimization of operational parameters and energy recovery. The findings indicate that the data-injection-based model simplifies the computational process and effectively validated over long time scales. Feature selection and network quality assessment ensure the precision and reliability of the long short-term memory (LSTM) network. Optimization using GA in conjunction with the LSTM surrogate model enables a rapid and accurate determination of the system's maximum net output power and corresponding operational conditions. The results demonstrate a 6 % increase in the system's net output power, with the system efficiency consistently surpassing 55 %. Moreover, energy recovery consistently boosts optimized system efficiency by about 1.6–1.7 %.

Optimizing solar power efficiency in smart grids using hybrid machine learning models for accurate energy generation prediction

The fourth energy revolution is characterized by the incorporation of renewable energy supplies into intelligent networks. As the world is shifting towards cleaner energy sources, there is a need for efficient and reliable methods to predict the output of renewable energy plants. Hybrid machine learning modified models are emerging as a promising solution for energy generation prediction. Renewable energy generation plants, such as solar, biogas, hydropower plants, wind farms, etc. are becoming increasingly popular due to their environmental benefits. However, their output can be highly variable and dependent on weather conditions, making integrating them into the existing energy grid challenging. Smart grids with artificial intelligent systems have the potential to solve this challenge by using real-time data to optimize energy production and distribution. Although by incorporating sensors, analytics, and automation, these grids can manage energy demand and supply more efficiently, reducing carbon emissions, increase energy security, and improve access to electricity in remote areas. However, this research aims to enhance the efficiency of solar power generation systems in a smart grid context using machine learning hybrid models such as Hybrid Convolutional-Recurrence Net (HCRN), Hybrid Convolutional-LSTM Net (HCLN), and Hybrid Convolutional-GRU Net (HCGRN). For this purpose, this study considers various parameters of a solar plant such as power production (MWh), irradiance or plane of array (POA), and performance ratio (PR). The HCLN model demonstrates superior accuracy with the RMSE values of 0.012027 for MWh, 0.013734 for POA and 0.003055 for PR, along with the lowest MAE values of 0.069523 for MWh, 0.082813 for POA, and 0.042815 for PR. The obtained results suggest that the proposed machine learning models can effectively enhance the efficiency of solar power generation systems by accurately predicting the required measurements.

Navigation Based on Hybrid Decentralized and Centralized Training and Execution Strategy for Multiple Mobile Robots Reinforcement Learning

In addressing the complex challenges of path planning in multi-robot systems, this paper proposes a novel Hybrid Decentralized and Centralized Training and Execution (DCTE) Strategy, aimed at optimizing computational efficiency and system performance. The strategy solves the prevalent issues of collision and coordination through a tiered optimization process. The DCTE strategy commences with an initial decentralized path planning step based on Deep Q-Network (DQN), where each robot independently formulates its path. This is followed by a centralized collision detection the analysis of which serves to identify potential intersections or collision risks. Paths confirmed as non-intersecting are used for execution, while those in collision areas prompt a dynamic re-planning step using DQN. Robots treat each other as dynamic obstacles to circumnavigate, ensuring continuous operation without disruptions. The final step involves linking the newly optimized paths with the original safe paths to form a complete and secure execution route. This paper demonstrates how this structured strategy not only mitigates collision risks but also significantly improves the computational efficiency of multi-robot systems. The reinforcement learning time was significantly shorter, with the DCTE strategy requiring only 3 min and 36 s compared to 5 min and 33 s in the comparison results of the simulation section. The improvement underscores the advantages of the proposed method in enhancing the effectiveness and efficiency of multi-robot systems.

Thermodynamic evaluation of decarbonized power production based on solar energy integration

This study aims to create an environmentally friendly system by utilizing solar and biomass energies as renewable sources, employing high temperature and pressure ranges for analysis. A new energy system is introduced, using biomass to produce hydrogen and other valuable commodities. The study evaluates power and hydrogen production technologies from a thermodynamic perspective, including conventional biomass gasification, calcium and iron thermochemical looping cycles, and gasification with supercritical water, incorporating carbon capture techniques. It assesses and compares the performance of locally available biomass material, such as cotton stalk, during gasification. Results show that employing the supercritical water gasification cycle yields the highest hydrogen production (8330 kg/h) and carbon capture rate (92.91 %), with potential for significant reduction in CO2 emissions. Supercritical water gasification also demonstrates electricity, heating, and CO2 production rates of 51.48 MW, 23.3 MW, and 16.6 MW, respectively. Integrating supercritical water gasification with solar energy enhances syngas productivity and system efficiency. Additionally, the highest performance is demonstrated in the SCWG case, which exhibits the lowest irreversibility at 320.32 MW. In conclusion, the system with supercritical water gasification emerges as the most efficient biomass conversion method, with higher energy and exergy efficiencies of 67.59 % and 49.74 %, respectively, under specific operating conditions. The developed model facilitates integration of solar energy into biomass gasification, enabling prediction and comparison of performance in terms of energy and exergy efficiencies.

Study on novel control method for small wind – solar hybrid power system using photovoltaic cooperating system in parallel connection mode

Currently, a small-scale wind turbine can be connected to the Power Conditioning System (PCS) of the solar power system by simulating the technical characteristics of the solar panels to enhance the efficiency of the PCS on cloudy or rainy weather days. However, the working efficiency of the entire system has not been optimized because the use of traditional control techniques. P-I control has several problems of high starting overshoot, and slow response to sudden disturbances. Besides, P&O MPPT control has fluctuations in output power and slow response time at peak solar cell power due to weather effects. Therefore, this research proposes a novel control system including Artificial Neural Networks (ANN) MPPT control and digital slide mode control (DMSC) for the power conversion circuit to connect small-scale wind turbine power with the PCS of the solar power system in the parallel mode. The novel control solution in this study can minimize the disadvantages of PI control and P&O MPPT control. The study results showed that DSMC controller has better performance than the PI controller when controlling the PVCS in parallel mode with more 17.5 % power, no static error, 20 % less chattering of the power value of PVCS in the steady state, and 20 % less static error of power. Besides, the ANN MPPT controller also works better when changing solar radiation and has a harvested power of about 0.6 % higher than the power generated by the P&O MPPT controller.

Enhanced Energy Transfer Efficiency for IoT-Enabled Cyber-Physical Systems in 6G Edge Networks with WPT-MIMO-NOMA

The integration of wireless power transfer (WPT) with massive multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) networks can provide operational capabilities to energy-constrained Internet of Things (IoT) devices in cyber-physical systems such as smart autonomous vehicles. However, during downlink WPT, co-channel interference (CCI) can limit the energy efficiency (EE) gains in such systems. This paper proposes a user equipment (UE)–base station (BS) connection model to assign each UE to a single BS for WPT to mitigate CCI. An energy-efficient resource allocation scheme is developed that integrates the UE–BS connection approach with joint optimization of power control, time allocation, antenna selection, and subcarrier assignment. The proposed scheme improves EE by 24.72% and 33.76% under perfect and imperfect CSI conditions, respectively, compared to a benchmark scheme without UE–BS connections. The scheme requires fewer BS antennas to maximize EE and the distributed algorithm exhibits fast convergence. Furthermore, UE–BS connections’ impact on EE provided significant gains. Dedicated links improve EE by 24.72% (perfect CSI) and 33.76% (imperfect CSI) over standard connections. Imperfect CSI reduces EE, with the proposed scheme outperforming by 6.97% to 12.75% across error rates. More antennas enhance EE, with improvements of up to 123.12% (conventional MIMO) and 38.14% (massive MIMO) over standard setups. Larger convergence parameters improve convergence, achieving EE gains of 7.09% to 11.31% over the baseline with different convergence rates. The findings validate the effectiveness of the proposed techniques in improving WPT efficiency and EE in wireless-powered MIMO–NOMA networks.

Investigation of Removing Asphaltene-Resin-Paraffin Deposits by Chemical Method for Azerbaijan High-Paraffin Oil Production Process

Asphaltene-resin-paraffin deposition (ARPD) is a complicated and prevalent issue in the oil and gas industry, impacting the efficiency and integrity of petroleum extraction, production, transportation and processing systems. Considering all witnessed ARPD problems in Azerbaijan oil fields, this paper proposed a chemical method and optimized the type and concentration of chemical inhibitors. Then, the effect of selected chemical reagents on inhibiting the ARPD amount and thus enhancing oil recovery was detected by reservoir simulation during both waterflooding and CO2 flooding production. Three new chemical compounds (namely, Chemical-A, Chemical-B and Chemical-C) were examined in laboratory conditions, and their impact on rheological properties of high-paraffin oilfield samples of Azerbaijan (X, Y and Z) were investigated. Experimental results show that Chemical-C with a concentration of 600 g/t has the best efficiency for alleviating the problems. After adding Chemical-C to the crude oil, the freezing point of oil was decreased from 12 °C to (−4) °C, the ARPD amount declined from 0.185 to 0.016 g, and oil effective viscosity was reduced from 16.2 mPa·s to 3.1 mPa·s. It was determined that for water and CO2 flooding, higher injection pressure resulted in reduced asphaltene precipitation. Adding the selected ARPD inhibitor, the oil recovery for waterflooding can increase from 52% to 62%, while it can rise from 55% to 68% for CO2 flooding.

Impact of Temperature and Optical Error on the Combined Optical and Thermal Efficiency of Solar Tower Systems for Industrial Process Heat

Concentrating solar thermal (CST) power towers can provide high flux concentrations at commercial scale. As a result, CST towers exhibit potential for high-temperature solar industrial process heat (SIPH) applications. However, at higher operating temperatures, thermal radiation losses can be significant. This study explores the trade-off between thermal and optical losses for SIPH applications using a collection of three case studies at operating temperatures that range from 900-1,550 °C. We assume blackbody radiation to represent the thermal losses at the receiver and we use ray tracing to estimate the optical losses. The results show the impact of process temperature on the maximum attainable system efficiency, as well as the higher flux concentration requirements as the temperature increases.

Anti-turbulent efficiency of oil-soluble polymer solutions and colloid systems flowing through cylindrical channel

Relevance. The use of anti-turbulent additives for transporting hydrocarbon liquids through main pipelines allows reducing significantly the energy consumption of pumping power stations. Aim. Comparative analysis of the anti-turbulent efficiency of high-molecular polymers and compositions of surfactants. Methods. Laboratory-scale experimentation aimed to study the flow of dilute polymer solutions and dispersed surfactant systems through a cylindrical channel of a turbulent rheometer. Results. The author has carried out the comparative experimental studies of the anti-turbulent efficiency of extremely dilute solutions of polymers and colloidal systems. The results were obtained that suggest a higher anti-turbulent efficiency of high-molecular-weight polymers compared to micellar surfactant systems. Solutions of high molecular weight polybutadiene and aluminum polyhydroxydicarboxylates in gasoline were used as samples for the experimental comparison of hydrodynamic efficiency. The paper describes the laboratory setup, on which the studies were carried out, and introduces the formulas used for quantitative calculations. The structure of polymer solutions and colloidal systems is considered and a theoretical explanation is given for the preferential use in industrial practice of high-molecular polymers in extremely low concentrations in real pipelines. It was found out that the mechanisms of degradation of antiturbulent properties of polymer solutions and dispersed surfactant systems are different. This is due to the difference in the structure of macromolar coils of polymer with an immobilized solvent and that of micelles from low molecular amphiphilic compounds. The paper introduces the arguments that explain the degradation of the antiturbulent properties of polymers not by the destruction of carbon-chain macromolecules, but by decomposition in a turbulent flow of the original large associates, consisting of a large number of chains, into individual and smaller macromolecular coils with an immobilized solvent.

Pairing voltage-source converters with PI tuning controller based on PSO for grid-connected wind-solar cogeneration

This paper introduces a novel method to improve the efficiency of grid-connected wind-solar cogeneration systems. It involves the integration of Voltage-Source Converters (VSCs) with a Proportional-Integral (PI) tuning controller that has been optimized using Particle Swarm Optimization (PSO). Integrating renewable energy sources into power grids presents a set of challenges in terms of ensuring stability and optimizing power flow. VSCs are essential for effectively managing power flow between renewable sources and the grid. The PI controller is vital in maintaining voltage levels and ensuring stable operation. Conventional PI controller tuning methods commonly rely on heuristic approaches, which might only partially optimize performance across various operational conditions. In order to tackle this issue, PSO is utilized to fine-tune the parameters of the PI controller automatically. This results in a significant reduction in system deviations and a notable improvement in efficiency, even when faced with fluctuating wind and solar conditions. The simulation results conducted in MATLAB/Simulink confirm the effectiveness of the proposed approach in enhancing system stability and reducing response times. The PI controller optimized using PSO showcases exceptional adaptability to varying environmental and grid conditions, resulting in minimized power fluctuations and improved grid reliability. This study makes a valuable contribution to the field of renewable energy integration. It provides valuable insights into enhancing the performance of grid-connected wind-solar cogeneration systems through advanced control techniques and optimization algorithms such as PSO.

Numerical simulation and optimization of biological nanocomposite system for enhanced oil recovery

Abstract Nanofluid flooding is a novel technology with potential for enhanced oil recovery. In this study, a biological nanocomposite system was formed by mixing hexamethyldisilazane-modified hydrophobic nano-SiO2 with a biosurfactant produced by Bacillus. The stability of the system, its influence on rock wettability, and fluid interfacial tension were investigated experimentally. Numerical simulation methods were employed to simulate the displacement efficiency of the biological nanocomposite system and optimize the injection parameters. Finally, the application effects of the system in the field were evaluated. Results indicated that the biological nanocomposite system could change rock wettability and significantly reduce the interfacial tension to 1.8 mN/m at low concentrations. The core flooding results showed that the maximum oil recovery factor of the system reached 47.07%. Numerical simulations optimized the optimal injection concentration to be 7,000 ppm and the volume of injection to be 1.75 × 10–2 pore volumes, resulting in an oil increment exceeding 10,000 m3 in field application. This study provides a solution for the green development of oil reservoirs and provides effective technical support for the numerical simulation and process scheme optimization of biological nanocomposite systems.

Application of DPC to improve the integration of DFIG into wind energy conversion systems using FOPI controller

While the direct power control (DPC) approach has proven effective in improving the efficiency of wind energy conversion systems (WECS) using doubly fed induction generators (DFIG), its applicability is currently confined to a single usage and has not been extended to meet numerous applications. This work aimed to modify the implementation of DPC in WECS-DFIG for several objectives. This is accomplished by updating the reference power of the conventional DPC method into an adapted one to achieve two goals independently. The first objective is to track the maximum power during wind speed variations. This tracking is performed by updating the reference power to match the maximum available power at the current wind speed. The second purpose is to ensure that the WECS remains connected to the grid and continues to operate smoothly even in the event of faults; supporting fault-ride through (FRT) capability. That is achieved by reducing the reference power during these faults. The discrimination between these two objectives is based on the voltage level at the point of connecting WECS to the grid. The controller provided is an improved fractional order PI controller developed using arithmetic optimization technique (AOA). A comparison between the AOA and cuckoo search is presented. The results demonstrate the efficacy of the suggested configuration and regulator in enhancing the performance of integrating DFIG into the WECS in the presence of wind fluctuations and short circuit faults occurring. It is worth noting that AOA is better than cuckoo search in fine-tuning the settings of the FOPI controller.

Pension Management System of PRBS in the Philippine National Police: Ba-sis for Policy Enhancement

This study assessed the pension management system of the PRBS in the Philippine National Police, highlighting specific challenges and deficiencies in different areas. These challenges include ensuring the completeness and legality of supporting documents, addressing delays in the issuance of updated documents from the Philippine Statistics Authority (PSA), improving the process of updating and issuing records, and enhancing the feedback mechanism and incorporation of results in the PRBS Program Review and Analysis. There are variations in beneficiaries' perceptions across different aspects of the pension management system, indicating room for improvement. Recommendations include establishing clear guidelines, enhancing communication channels, streamlining processes, and prioritizing solutions favored by beneficiaries. The study also recommends implementing clear guidelines, standardized checklists, training programs, and streamlined processes to address the challenges. These measures, if adopted, could significantly improve the efficiency and effectiveness of the pension management system. Enhancing communication and coordination with the PSA, implementing technological solutions, and establishing a feedback mechanism are also advised. Further, analysis is suggested to understand specific problems and differences in perception. Recommendations also focus on improving access to claim status information, prioritizing beneficiary-favored solutions, and enhancing policy implementation of the Pension Management System. These changes, if implemented, could lead to a more transparent, efficient, and beneficiary-friendly pension management system. Your active participation in this process is crucial and highly valued. In summary, the study highlights the challenges, recommendations, and conclusions related to the pension management system in the Philippine National Police, emphasizing the need for improvements in various aspects to enhance efficiency, effectiveness, and satisfaction among pensioners and stakeholders.

Screening HFC/HFO and ionic liquid for absorption refrigeration at the atomic scale by the prediction model of machine learning

Absorption refrigeration is a highly effective method for utilizing renewable energy, as it can be driven by low-grade heat sources such as industrial waste heat, solar energy, and geothermal energy. The development of new working pairs, particularly hydrofluorocarbon/hydrofluoroolefin refrigerants combined with ionic liquids, has been pivotal in enhancing the cooling efficiency of absorption refrigeration systems. These systems rely on the solubility difference between the generator and absorber, making solubility a crucial factor in determining their efficiency. In this context, we have established an advanced solubility estimation model. This model employs the Attention E(n)-equivariant Graph Neural Network (AEGNN) applied to disconnected graphs, enabling comprehensive learning from both topological and Euclidean structural information. Our atomic-scale model demonstrates significantly higher accuracy than traditional group contribution methods, with an average absolute deviation of 0.003 mol/mol from experimental data. Moreover, it encompasses a much broader range of working pairs. Through extensive screening, we have identified working pairs with high estimated solubility differences. Compared to the high-efficiency working pair identified in the literature, the best-screened working pairs exhibit an improvement in solubility differences by more than 0.3 mol/mol under common operating conditions.

To the problem of the shielding systems efficiency in constructions of extra-high voltage electrical apparatus

In the article, the authors proposed a method for estimating the electric load parameters to calculate the distribution of the electric field strength of the solid insulating structure of gas-filled high-voltage devices with axial symmetry. This structure consists of a support and insulating busbar filled with SF6 gas as an internal insulating medium. This technique allows to evaluate the parameters of the electrical load (voltage and field strength) depen-ding on the design features of the device and the shielding system used to equalize the electric field. The proposed methodology allows to evaluate the effectiveness of the influence of design parameters of the protection system for gas-filled high-voltage equipment (instrument transformers, arresters, surge arresters, etc.) when designing and improving individual design solutions. The calculation results are in good agreement with the data of experimental studies and statistical information obtained as a result of monitoring the operation of insulating structures, taking into account actual operating conditions and the influence of external factors. The implementation of this technique allows us to take into account the influence of external factors and operational characteristics inherent in instrument transformers and surge suppressors. In the proposed methodology, as an example, we consider a supporting insulating coating that is in operation under the most unfavorable conditions, such as external pollution, moisture and their combination, overvoltage of various origins, etc. The theoretical conclusions are confirmed by the results of calculations using the example of the support-insulating cover of the surge suppressor of the OПН-500 series. A more accurate determination of the effectiveness of the proposed methodology for predicting the field strength distribution parameters under the influence of a shielding system can be achieved by conducting an additional series of calculations and experimental tests of specific insulating structures. Thus, it was concluded that the obtained results can be used to evaluate the external insulation characteristics of both surge suppressors and gas-filled instrument transformers, as well as similar high-voltage equipment of switchgears and transformer substations in conditions of ultra- and transformer substations ultra-high voltages

A novel hybrid efficiency prediction model for pumping well system based on MDS–SSA–GNN

AbstractThe prediction of the efficiency of oil well pumping systems plays an important role in optimizing the energy efficiency parameters of these systems. Currently, the prediction of oil well pumping system efficiency relies primarily on mechanistic models, but these models are often overly complex in predicting efficiency. Some researchers have attempted to use deep learning to predict system efficiency, but due to insufficient consideration of influencing factors and the causal relationships between these factors and system efficiency, they often include irrelevant variables as influencing factors, leading to less accurate prediction models. In this paper, a hybrid model (MDS–SSA–GNN) is proposed for the prediction of pumping well system efficiency. The model consists of six parts: Pearson's product moment correlation coefficient (PPMCC), multidimensional scaling (MDS) transform, maximum–minimum normalization, sparrow optimization algorithm (SSA), graph neural network (GNN), and maximum–minimum inverse normalization. First, the size of the correlation coefficient between each influencing factor and the system efficiency is quantitatively calculated by using PPMCC. Second, the main influencing factors are downscaled by using MDS and normalized based on the principle of maximum–minimum normalization. Third, the GNN algorithm is used for the prediction of the pumping unit system efficiency, and the SSA algorithm is used for the optimization of the initial values of the network parameters. Finally, the prediction results are obtained by the maximum–minimum antinormalization. To validate the model's accuracy, this study randomly selected 100 actual oil wells for comparative analysis and analyzed the impact of structural parameters of the hybrid algorithm on the prediction accuracy of system efficiency. The analysis results demonstrate that the proposed model can effectively predict system efficiency and has a certain role in improving the accuracy of oil well pumping system efficiency predictions.

Riboflavin-induced microbial succession for enhanced tellurite reduction in wastewater treatment

Tellurite is a common component of industrial wastewater; thus, its efficient removal is crucial for environmental protection and wastewater treatment. Although the efficiency is low, the activated sludge process (ASP) is frequently used to treat wastewater by reducing tellurite (Te(Ⅳ)) to harmless elemental tellurium (Te(0)). Riboflavin is a biostimulant that when added to ASP can significantly shape the structure of microbial communities and improve the efficiency of treating telluride-containing wastewater. However, the underlying biological mechanism remains unclear. Herein, two groups—control and biostimulation—of sequencing batch reactors (SBRs) were constructed and run under the same conditions. In the control group (CG), only tellurite was added, whereas biostimulation group (BS) was treated with tellurite and riboflavin. After adding riboflavin, chemical oxygen demand, tellurite removal rates, and extracellular polymeric substance generation increased. The 16 S rRNA high-throughput sequencing results revealed that the succession of microbial communities was effectively promoted in the ASP by riboflavin treatments and that telluride-reducing bacteria—such as Sphingopyxis, Shinella, Brevundimonas, and Brucella—were significantly increased. Key topological properties suggested that riboflavin treatments changed the co-occurrence patterns of SBR microbial communities and strengthened microbial interactions, considerably improving the stability and efficiency of the tellurium-containing wastewater treatment system. In summary, we explored the effect of adding riboflavin to the ASP to stimulate the succession of sludge microbial communities for improved tellurite reduction. The findings of this research contribute to our understanding of the mechanism of riboflavin in optimizing wastewater treatment for removing recalcitrant pollutants.

Admissibility of Lawsuits Based on Interest under Algerian Civil and Administrative Procedures

This study examines the impact of interests on the admissibility of civil and administrative lawsuits in Algeria under the Administrative and Civil Procedures Code in the country. The research utilized a normative research method that integrated legal concepts and approaches. It drew on primary legal sources such as laws, regulations, and court decisions, as well as secondary sources including documents, literature, and articles. The study aims to raise awareness among both legal professionals and the general public about the significance of interests in civil lawsuits and how it can impact the admissibility or inadmissibility of lawsuits filed before the court. This will help enhance judicial efficiency by allowing courts to prioritize cases with significant impact on the parties or broader societal relevance. The research manifested that interests are pivotal in determining a lawsuit's validity and that for a lawsuit to be heard, interests must be legal, legitimate, and actual. It also highlighted that the concept of interest in civil lawsuits acts as a crucial mechanism to safeguard the integrity, fairness, and efficiency of the judicial system. Objections based on the unenforceability of interests serve as a key procedural tool, enabling the dismissal of lawsuits that do not meet this criterion. Thus, interest is essential not only for forming the basis of a lawsuit but also for maintaining judicial integrity and effectiveness.