Future Systems Research Articles

Estimation of the lateral mis-registrations of the GRAVITY+ adaptive optics system. Perturbative method with open-loop modal correlation and non-pertubative method with temporal correlation of closed-loop telemetry

The GRAVITY+ upgrade implies a complete renewal of its adaptive optics (AO) systems. Its complex design, featuring moving components between the deformable mirrors and the wavefront sensors, requires the monitoring and auto-calibrating of the lateral mis-registrations of the system while in operation. For preset and target acquisition, large lateral registration errors must be assessed in open loop to bring the system to a state where the AO loop closes. In closed loop, these errors must be monitored and corrected, without impacting the science. With respect to the first requirement, our method is perturbative, with two-dimensional modes intentionally applied to the system and correlated to a reference interaction matrix. For the second requirement, we applied a non-perturbative approach that searches for specific patterns in temporal correlations in the closed loop telemetry. This signal is produced by the noise propagation through the AO loop. Our methods were validated through simulations and on the GRAVITY+ development bench. The first method robustly estimates the lateral mis-registrations, in a single fit and with a sub-subaperture resolution while in an open loop. The second method is not absolute, but it does successfully bring the system towards a negligible mis-registration error, with a limited turbulence bias. Both methods proved to robustly work on a system still under development and not fully characterised. Tested with Shack-Hartmann wavefront sensors, the proposed methods are versatile and easily adaptable to other AO instruments, such as the pyramid, which stands as a baseline for all future AO systems. The non-perturbative method, not relying on an interaction matrix model and being sparse in the Fourier domain, is particularly suitable to the next generation of AO systems for extremely large telescopes that will present an unprecedented level of complexity and numbers of actuators.

Impacts of inter-satellite links on the ECOM model performance for BDS-3 MEO satellites

Inter-satellite link (ISL) plays an essential role in current and future Global Navigation Satellite System (GNSS). In this study, we investigate the impact of ISL observations on precise orbit determination for BeiDou-3 Navigation Satellite System (BDS-3) Medium Earth Orbit (MEO) satellites based on different Extended CODE Orbit Models (ECOM). Thanks to the better observation geometry of the Ka-band ISL data compared to the L-band data for BDS-3 MEO satellites, the ISL solution substantially reduces Orbit Boundary Discontinuity (OBD) errors, except for C30, which suffers from unstable Ka-band hardware delay. From the external quality analysis, ISL significantly enhances the reliability of the orbit of MEO satellites manufactured by the China Academy of Space Technology (CAST). The standard deviation (STD) of the satellite laser ranging (SLR) residuals is approximately 2.5 cm, and the root mean square (RMS) is reduced by 10–23% compared to L-band solutions. Besides, the Sun-elongation angle dependent systematic error in SLR residuals nearly vanishes based on the reduced 5-parameter ECOM (ECOM1) or extended 7-parameter ECOM (ECOM2) with ISL data. This is because the ISL reduces the correlation between state parameters and solar radiation pressure (SRP) parameters as well as those among SRP parameters, leading to a more accurate estimation of both orbit and SRP perturbations, particularly those along B direction. This confirms that the deficiency of the SRP models for BDS-3 CAST satellites can be compensated by using better observation geometry from ISL data. On the other hand, for the satellite manufactured by Shanghai Engineering Center for Microsatellites (SECM), the ISL allows for a more accurate estimation of the Bc1 parameter in the ECOM1 model. This only reduces linear systematic error, possibly because the impact generated by the satellite bus cannot be entirely absorbed by the B-direction parameters.

Blockchain with optimal deep learning assisted secure data sharing and classification on future healthcare systems

In the future of healthcare, Blockchain (BC) technology holds immense potential for improving the security and privacy of data. By allowing the secure and immutable storage of medical files and healthcare-related transactions, BC ensured that sensitive medical data remains tamper-proof and open only to authorized parties. Patients have greater control over their data's development, revoking or granting access as required, but healthcare workers can streamline data sharing and ensure the integrity of important data. The decentralized nature of BC networks decreases the risk of centralized data breaches, eventually fostering trust and transparency in the healthcare ecosystems. Conversely, deep learning (DL) has great to revolutionize healthcare diagnostics in the future, offering quick and extremely accurate estimates of medical conditions. This technology has greatly enhanced patient solutions, decreased medical expenses, and improved the burden on medical staff by providing appreciated insights into an extensive range of conditions, from cancer to neurological disorders. With this stimulus, this study presents a novel BC with optimal DL-based secure data sharing and classification (BCODL-SDSC) technique in the future healthcare system. The goal of the BCODL-SDSC technique is to secure and thoroughly examine healthcare data using BC and DL techniques. Primarily, the BCODL-SDSC technique enables BC technology to store and maintain the patient’s data from the procedure of several transactions and enable access control to the various stakeholders. For the security of the medical images, the BCODL-SDSC technique applies the Fractional Order Lorenz system (FOLS) based encryption technique with tuna swarm optimization (TSO) algorithm based optimal key generation process. Finally, a multi-stage process performs the classification of the medical images: MobileNetv1 feature extractor, artificial rabbit’s optimization (ARO) based hyperparameter tuning, and stacked recurrent neural network (SRNN) based classification. The experimental outcome of the BCODL-SDSC technique was examined on a benchmark medical image database. An extensive comparative study reported that the BCODL-SDSC technique reaches an effective performance with other models with a maximum accuracy of 99.11%.

Life cycle assessment of electric kick scooters: consolidating environmental impact quantification and concluding climate-friendly use options

BackgroundThe environmental impact of electric scooters has been the subject of critical debate in the scientific community for the past 5 years. The data published so far are very inhomogeneous and partly methodologically incomplete. Most of the data available in the literature suffer from an average bias of 34%, because end-of-life (EOL) impacts have not been modelled, reported or specified. In addition, the average lifetime mileage of shared fleets of e-scooters, as they are operated in cities around the world, has recently turned out to be much lower than expected. This casts the scooters in an unfavourable light for the necessary mobility transition. Data on impact categories other than the global warming potential (GWP) are scarce. This paper aims to quantify the strengths and weaknesses of e-scooters in terms of their contribution to sustainable transport by more specifically defining and extending the life cycle assessment (LCA) modelling conditions: the modelling is based on two genuine material inventories obtained by dismantling two different e-scooters, one based on a traditional aluminium frame and another, for the first time, based on plastic material.ResultsThis study provides complete inventory data to facilitate further LCA modelling of electric kick scooters. The plastic scooter had a 26% lower lifetime GWP than the aluminium vehicle. A favourable choice of electric motor promises a further reduction in GWP. In addition to GWP, the scooter's life cycles were assessed across seven other impact categories and showed no critical environmental or health impacts compared to a passenger car. On the other hand, only the resource extraction impact revealed clear advantages for electric scooters compared to passenger cars.ConclusionsUnder certain conditions, scooters can still be an important element of the desired mobility transition. To assure a lifetime long enough is the crucial factor to make the electric scooter a favourable or even competitive vehicle in a future sustainable mobility system. A scooter mileage of more than 5400 km is required to achieve lower CO2eq/pkm emissions compared to passenger cars, which seems unlikely in today's standard use case of shared scooter fleets. In contrast, a widespread use of e-scooters as a commuting tool is modelled to be able to save 4% of greenhouse gas (GHG) emissions across the German mobility sector.

Electropolymerization of a Carbonyl-Modified Dihydropyrazine Derivative for Aqueous Zinc Batteries with Ultrahigh Cycling Stability.

The design of aqueous zinc-ion batteries (ZIBs) that have high specific capacity and long-term stability is essential for future large-scale energy storage systems. Cathode materials with extended π-conjugation and abundant active sites are desirable to enhance the charge storage performance and the cycling stability of the aqueous ZIB. Based on this concept, 6,9-dihydropyrazino[2,3-g]quinoxaline-2,3,7,8(1H,4H)-tetrone was chosen as the monomer to be electropolymerized onto carbon cloth (PDHPQ-Tetrone/CC). When used as the cathode material for aqueous ZIBs, an exceptional cycling life (>20,000 cycles) at a current density of 10 A g-1 was achieved, with the specific capacity maintained at 82.8% and with the Coulombic efficiency at around 100% throughout cycling. At the charge-discharge current density of 0.1 A g-1, the ZIB with PDHPQ-Tetrone/CC achieved a high specific capacity of 248 mAh g-1. Kinetic analyses showed that both surface-capacitive-controlled processes and semi-infinite diffusion-controlled processes contribute to the stored charge. The charge storage mechanism was investigated with ex situ characterizations and involves the redox processes of carbonyl/hydroxyl and amino/imino groups coupled with insertion and extraction of both Zn2+ and H+.

Synchrotron x-ray spectra characterisation for radiation therapy applications at the ESRF - ID17 biomedical beamline

Objective. Radiation therapy requires reliable dosimetry protocols to deliver successful treatments with high accuracy and precision. In this context, accurate knowledge of the beam’s energy spectra is mandatory. The goal of this study was to validate the synchrotron x-ray spectrum of the ID17 beamline at the European Synchrotron Radiation Facility (ESRF). The modification of the synchrotron storage ring and beamline in recent years necessitates a new characterisation of the radiation spectra of the ID17 beamline. The validated spectra will be a starting point for possible future clinical applications. Approach. The half value layer method was used to measure the attenuation of the x-ray spectrum in Al and Cu. Experimental data was validated against theoretical data produced using OASYS; an in-house developed software for calculating beamline spectra. Two different spectral configurations, ‘conventional’ and ‘clinical’, were investigated. The characterised spectra were used to perform dosimetric validation of depth dose profiles measured in a water-equivalent phantom. The dose profile was measured using two different detectors and compared with calculations generated using two different Monte Carlo algorithms. Main results. The results showed good agreement between measured and predicted half value layers, with differences of less than 1% in most cases. Excellent dosimetric agreement to within 3% was obtained, an agreement that satisfies the requirements in conventional radiotherapy for approvable treatment planning. Significance. Accurate spectra have been defined and validated for the ESRF—ID17 Biomedical beamline. The validated spectra can be used as input for future dosimetric studies and treatment planning systems in the context of preclinical studies and possible future clinical trials.

Analysis of the COVID-19 pandemic on preferences of transport modes

The COVID-19 pandemic has affected the entire world and changed many aspects of daily life, including transportation behaviour and preferences. This study examines the situation of transportation behaviour and preferences before and after the COVID-19 pandemic process through a survey study. A total of 471 people participated in the survey, and thirty questions were asked to participants regarding transportation modes preference, daily usage habits, public transportation usage rate, income status, perceived risk level of transportation systems, preferences before and after the pandemic process, and more. The answers were classified, evaluated, and statistically analysed. The results showed that users were significantly affected by the COVID-19 process and changed their transportation mode preferences. The use of buses in urban journeys decreased, and the use of private vehicles significantly increased after the COVID-19 process. Hygiene was the key factor in travel, and it was followed by the vehicle occupancy rate, ventilation, fare, and travel time factors, respectively. The perceived risk levels of public transportation systems were ranked as metrobus, minibus (paratransit), bus, metro, tram, and ferry. The study suggests that future transportation system designs should consider user preferences, as well as changes in education and working conditions, and pandemic conditions.

Risk-averse electrolyser sizing in industrial parks: An efficient stochastic-robust approach

Hydrogen is called to be one of the most important energy vectors in future energy systems. Nowadays, its use in the industry sector is prominent, finding multiple applications in fertilizer production or oil refining. In this sense, some industry sectors demand a considerable amount of hydrogen for their processes. In many cases, hydrogen must be purchased externally, which supposes a challenge due to hydrogen transportation is costly and few efficient. In this context, local hydrogen production through mature electrolysis technology may suppose an attractive alternative in industrial parks. This paper focuses on this topic, in particular, a risk-averse electrolyser sizing methodology is developed. The new approach accounts for uncertainties in electricity prices as well as local renewable generation and demand through an original hybrid stochastic-robust model. The developed uncertainty modelling is integrated into a novel four-level optimization framework, whose main result is the optimal electrolyser rated power. To efficiently attain the solution, an original hybridization of the Benders' decomposition and the Column and Constraint Generation Algorithm is proposed. The developed methodology results efficient in an illustrative three-industry park, showing that installing local hydrogen generation may reduce the amount of hydrogen purchased externally (by 38%) and project costs by 2.5%. Furthermore, increasing the robustness level leads to increase the project cost by 8%, while assuming unfavourable realization of uncertainties. Moreover, the developed tool is further validated in larger parks, involving an increasing number of industries, showing that the proposed methodology scales well with the size of the park.

Photonic quasi-crystal fiber electro-optical modulator

The integration of graphene with optical fiber is considered to be a new interdisciplinary research hotspot for functional fiber. In this paper, an electro-optical modulator based on a six-fold Stampfli-type photonic quasi-crystal fiber (PQF) is theoretically proposed with a sandwiched graphene/hexagonal boron nitride/graphene (Gr/hBN/Gr) film covering all the hole walls. This design exhibits a strong light-graphene interaction with an excellent modulation depth of ∼64 dB mm−1 at 1550 nm by applying an external bias voltage (below 30 V) on both graphene layers. As the Fermi level of the graphene changes with voltage, the fiber shows ‘On’ and ‘Off’ states, serving well as a light-switch. For the modulator performance, the dependence of modulation depth on multiple factors is studied in terms of the layer numbers of graphene and hBN films, the incident wavelength, and the structure parameters. Interestingly, an attenuation peak occurs due to the epsilon-near-zero effect in graphene and shows a linear relationship between the wavelength and the Fermi level. This design provides a guidance for the integration of PQF and graphene, and holds great promise for future all-fiber systems.

Neural Network-Based Detection of OCC Signals in Lighting-Constrained Environments: A Museum Use Case

This research presents a novel approach by applying convolutional neural networks (CNNs) to enhance optical camera communication (OCC) signal detection under challenging indoor lighting conditions. The study utilizes a smartphone app to capture images of an LED lamp that emits 25 unique optical codes at distances of up to four meters. The developed CNN model demonstrates superior accuracy and outperforms traditional methodologies, which often struggle under variable illumination. This advancement provides a robust solution for reliable OCC detection where previous methods underperform, particularly in the tourism industry, where it can be used to create a virtual museum on the Unity platform. This innovation showcases the potential of integrating the application with a virtual environment to enhance tourist experiences. It also establishes a comprehensive visible light positioning (VLP) system, marking a significant advance in using CNN for OCC technology in various lighting conditions. The findings underscore the effectiveness of CNNs in overcoming ambient lighting challenges, paving the way for new applications in museums and similar environments and laying the foundation for future OCC system improvements.

Chemical mapping of xyloglucan distribution and cellulose crystallinity in cotton textiles reveals novel enzymatic targets to improve clothing longevity

Pilling is a form of textile mechanical damage, forming fibrous bobbles on the surface of garments, resulting in premature disposal of clothing by consumers. However, our understanding on how the structural properties of the cellulosic matrix compliment the three-dimensional shape of cotton pills remains limited. This knowledge gap has hindered the development of effective ‘pillase’ technologies over the past 20 years due to challenges in balancing depilling efficacy with fabric integrity preservation. Therefore, the main focus here was characterising the role of cellulose and the hemicellulose components in cotton textiles to elucidate subtle differences between the chemistry of pills and fibre regions involved in structural integrity. State-of-the-art bioimaging using carbohydrate binding modules, monoclonal antibodies, and Leica SP8 and a Nikon A1R confocal microscopes, revealed the biophysical structure of cotton pills for the first time. Identifying regions of increased crystalline cellulose in the base of anchor fibres and weaker amorphous cellulose at dislocations in their centres, enhancing our understanding of current enzyme specificity. Surprisingly, pills contained a 7-fold increase in the concentration of xyloglucan compared to the main textile. Therefore, xyloglucan offers a previously undescribed target for overcoming this benefit-to-risk paradigm, suggesting a role for xyloglucanase enzymes in future pillase systems.

Sustainable propulsion systems for future aircraft systems

ZusammenfassungDie steigende Erderwärmung und die erforderlichen Maßnahmen, um diese einzudämmen, sind Themen, für die möglichst bald von den Naturwissenschaftler:innen und Ingenieur:innen Lösungen gefunden werden müssen. Die Diskussion um die Art der künftigen Mobilität hat mittlerweile alle Bereiche erfasst, von Autos, Lastkraftwagen und Schiffen bis hin zu Flugzeugen. Leider ist das Thema sehr komplex, systemübergreifend, politisch und ideologisch besetzt. Vieles ist nur mithilfe gemeinsamer internationaler Anstrengungen zu lösen.

An innovative information accumulation multivariable grey model and its application in China's renewable energy generation forecasting

Reducing greenhouse gas emissions is urgent for the global community with rising climates. Considering the importance of renewable energy in mitigating climate warming, forecasting renewable energy generation is vital for the Chinese government's future low-carbon and green development plan. This paper proposes a novel multivariable grey model based on historical data on China's renewable energy generation and three industries. A novel information accumulation mechanism with two adaptive factors is designed to improve the traditional multivariable grey modeling defect. Based on the proposed mechanism, this paper optimizes the initial and background values and nonlinear model structure with the whale optimization algorithm. The forecasting results show that the fitting MAPE is 1.13%, comprehensive MAPE is 2.60%, MSE is 50.86, and RMSE is 7.13, which significantly improve the forecasting accuracy of traditional GM(1,N) and are better than other compared models. The forecasting results show that China's renewable energy generation will gradually increase to 5834.02 TWh. The Chinese government should keep the previous Five-Year Plans rising trend of the three industries in the future Five-Year Plans to support renewable energy industries. In China's future energy system, it is necessary to promote incentive policies and capital investment for actively accelerated development to make renewable energy the leading force.

Fenugreek seed mucilage-based active edible films for extending fresh fruit shelf life: Antimicrobial and physicochemical properties

Trigonella foenum-graecum, known as fenugreek, belongs to the leguminous family of wild growth in Western Asia, Europe, the Mediterranean, and Asia; its ripe seeds contain a pool of bioactive substances with great potential in the food industry and medicine. In this study, fenugreek seed mucilage (FSM) was extracted and characterized in its structural properties by X-ray diffraction, nuclear magnetic resonance, and high-performance liquid chromatography. Then, the applicability of FSM as an antimicrobial agent was demonstrated via the development of novel, active, edible FSM-based biofilms containing carboxymethyl cellulose and rosemary essential oil (REO). Incorporating REO in the biofilms brought about specific changes in Fourier-transform infrared spectra, affecting thermal degradation behavior. Scanning electron microscopy and atomic force microscopy morphography showed an even distribution of REO and smoother surfaces in the loaded films. Besides, the solubility tests evidenced a reduction in water solubility with increasing REO concentration from 1 to 3 wt%. The biological assay evidenced the antimicrobial activity of REO-loaded biofilms against Staphylococcus aureus and Escherichia coli. Finally, whole apples were dip-coated with FSM-based solutions to showcase future edible systems. The REO-loaded biofilms extended the shelf life of apples to 30 days, demonstrating their potential for sustainable and active coatings.

Unlocking the potential of rooftop solar panels: An incentive rate structure design

With the continuous maturity of distributed generation technology, rooftop solar panels have come to play an increasingly critical role in the construction of future energy systems. However, there remains an open question as to how to design adequate rate structure and provide proper incentives for distributed solar production. In this paper, we propose an incentive rate structure for rooftop solar panels to fairly unlock the potential of such distributed energy resources. The interaction of residential consumers and utilities is formulated as a tri-level optimization model to evaluate the impact of the rate structure design on the welfare transfer and investment behavior of residential consumers. An incentive rate structure is designed in which the supply-side effects of rooftop solar panel investment are considered by explicitly modeling wholesale market dynamics. Contrary to conventional belief, we indicate that the integration of rooftop solar panels can increase the expected profits of the utility company in some usual cases. Case studies demonstrate that through adequate rate structure design, the market operator can achieve the established social goals while balancing the welfare transfer effect among different market entities. Hopefully, our work can provide novel insights and a theoretical reference for the relevant policy design.

Auxiliary management and control channel aided crosstalk online monitoring in multi-core fibers

As a stochastic perturbation, the inter-core crosstalk (IC-XT) severely distorts the signal in multi-core fibers (MCF), especially for long-haul transmission. How to quickly measure and monitor the IC-XT online for an MCF-based space division multiplexing (SDM) system is of special importance. In this paper, we introduce the technology of auxiliary management and control channel (AMCC) to online monitor the IC-XT of MCF, in which the unique advantage of low-frequency auxiliary management and control signal is fully utilized with the limited influence on high-speed data transmission. Specifically, two orthogonal sequences are chosen as monitoring signals for the signal-channel core and the crosstalk-channel core, respectively, followed by digital signal processing (DSP) for the received signal to evaluate the real-time crosstalk accurately. The experimental verifications of XT online monitoring confirm the effectiveness of our proposed method with very small monitoring error (mostly < 0.5 dB) for both heterodyne XT and homodyne XT in the C and L bands, showing its great potential for future SDM systems.

Unveiling Milan’s Navigli and Underground Water Heritage through Integrated Urban (Water) Design

Historic water systems have become iconic features of cities like Venice and Amsterdam. The Navigli of Milan were constructed to channel groundwater for various purposes and a consequence was the desiccation of the surrounding marshy land. As the city faced new water challenges amid imminent water needs, its water identity was affected by the covering up of the historic water system. Climate change poses new challenges to preservation and planning in this historical water city. This article highlights the importance of history and water heritage for future interventions, by evoking the Navigli vistas that were once the cityscape of Milan. It discusses the current challenges of the hydrographic network, including more frequent and severe floods, and proposes the daylighting of the canals to inspire and adapt modern and future water systems to climate impact. The goal is to reclaim Milan’s identity as a “city of water” through a deliberate design methodology informed by the city’s history.

Sensitivity analysis for knowledge discovery in scramjet intake design optimization using deep-learning flowfield prediction

Scramjet engines are a promising propulsion technology for future space transportation systems, and its design exploration represents a significant task toward development of high-performance scramjets. While sensitivity analysis is expected to facilitate acquisition of physical insights for effective scramjet design, the substantial computational cost incurred due to numerical simulations required for statistical analysis poses a challenge to the application, limiting the capability of this analysis. The present study has conducted sensitivity analysis using deep-learning-based flowfield prediction that can significantly reduce the computational cost for numerical simulations providing flowfield data. Global sensitivity analyses have been employed to investigate influential design variables on the flowfield and performance. These have allowed for identifying the design variables that dominantly influence or determine the performance parameters. Local sensitivity analyses have been performed to elucidate the design rationales and the characteristic flow structures for high-performance intake designs. The sensitivity analysis methods in conjunction with flowfield prediction have enabled generation of rich insights that would otherwise be difficult to acquire without this approach, demonstrating the capabilities of the proposed methodology.

A comprehensive review on unleashing the power of hydrogen: revolutionizing energy systems for a sustainable future.

Population growth and environmental degradation are major concerns for sustainable development worldwide. Hydrogen is a clean and eco-friendly alternative to fossil fuels, with a heating value almost three times higher than other fossil fuels. It also has a clean production process, which helps to reduce the emission of hazardous pollutants and save the environment. Among the various production methodologies described in this review, biochemical production of hydrogen is considered more suitable as it uses waste organic matter instead of fossil fuels. This technology not only produces clean energy but also helps to manage waste more efficiently. However, the production of hydrogen obtained from this method is currently more expensive due to its early stage of development. Nevertheless, various research projects are underway to develop this method on a commercial scale.

Impact of memory on beamforming optimization for DDPG-assisted RIS-multi-user system

Recently, reconfigurable intelligent surfaces (RIS) have garnered considerable attention as indispensable components driving the evolution of future 6G wireless communication systems. This heightened interest is primarily attributed to notable advancements in programmable meta-material fabrication, which enable the creation of highly versatile and adaptable surfaces. These surfaces, often referred to as intelligent reflecting arrays, represent a significant departure from the conventional capabilities of massive multiple-input multiple-output (MIMO) systems, thereby catalyzing the emergence of intelligent radio environments characterized by enhanced flexibility and efficiency. Our study is dedicated to the comprehensive exploration of coordinated design strategies that encompass both the transmission beamforming matrix at the base station and the phase shift matrix at the RIS. Leveraging recent breakthroughs in deep reinforcement learning (DRL), our approach harnesses the power of a Long Short-Term Memory (LSTM) based DRL algorithm. This algorithm orchestrates the joint design process through iterative interactions with the environment, strategically guided by predefined rewards operating within a continuous framework of states and actions. While recent research has underscored the efficacy of LSTM-based architectures in bolstering the learning capacity of reinforcement learning (RL) algorithms and simplifying the search process, our investigation reveals a nuanced insight. Contrary to prevailing trends, we find that the integration of memory into the Deep Deterministic Policy Gradient (DDPG) with LSTM (DDPG-LSTM) algorithm yields unexpected consequences, negatively impacting the system's overall performance. Through preliminary simulation results, we empirically demonstrate the adverse effect of memory on DDPG performance when applied to RIS systems. This finding not only sheds light on the complexities of optimizing 6G wireless communication systems but also underscores the importance of careful algorithmic design and parameter tuning in achieving desired outcomes in emerging technologies.