Mitigation Challenges Research Articles

Energy–Carbon Coupling Modeling of Integrated Energy Systems in Low-Carbon Parks

In integrated energy system modeling, extant research predominantly addresses single-energy system optimization or carbon emission flow models, failing to adequately elucidate the mechanisms of combined energy and carbon flow modeling in complex energy systems. This deficiency hampers a thorough analysis of the coupling relationships between energy and carbon flows, thereby posing significant challenges for resource allocation and carbon mitigation within integrated energy systems. This paper presents an innovative energy–carbon coupling model, constructing a unified framework for energy and carbon flow modeling centered on the energy hub, thereby overcoming the limitations of traditional approaches that are unable to model both energy and carbon flows concurrently. The model comprehensively examines the coupling nodes and carbon density correlations among energy conversion devices within multi-energy systems, precisely quantifying carbon emission paths and distribution across devices. This provides a novel methodology for carbon emission management in integrated energy systems. Case studies on typical integrated energy systems demonstrate the proposed model’s efficacy in low-carbon economic dispatch. The energy–carbon coupling model developed in this study offers a high-adaptability solution for integrated energy systems in multi-energy, low-carbon parks, achieving an optimal balance between economic efficiency and environmental performance under dual objectives of energy demand and carbon emission minimization.

Chain of Risks Evaluation (CORE): A framework for safer large language models in public mental health.

Large language models (LLMs) have gained significant attention for their capabilities in natural language understanding and generation. However, their widespread adoption potentially raises public mental health concerns, including issues related to inequity, stigma, dependence, medical risks, and security threats. This review aims to offer a perspective within the actor-network framework, exploring the technical architectures, linguistic dynamics, and psychological effects underlying human-LLMs interactions. Based on this theoretical foundation, we propose four categories of risks, presenting increasing challenges in identification and mitigation: universal, context-specific, user-specific, and user-context-specific risks. Correspondingly, we introduce CORE: Chain of Risk Evaluation, a structured conceptual framework for assessing and mitigating the risks associated with LLMs in public mental health contexts. Our approach suggests viewing the development of responsible LLMs as a continuum from technical to public efforts. We summarize technical approaches and potential contributions from mental health practitioners that could help evaluate and regulate risks in human-LLMs interactions. We propose that mental health practitioners could play a crucial role in this emerging field by collaborating with LLMs developers, conducting empirical studies to better understand the psychological impacts on human-LLMs interactions, developing guidelines for LLMs use in mental health contexts, and engaging in public education.

The Evolving Role of Artificial Intelligence in Recruitment: Efficiency, Bias Mitigation, and Ethical Challenges

The Evolving Role of Artificial Intelligence in Recruitment: Efficiency, Bias Mitigation, and Ethical Challenges

Advanced Persistent Threats (APTs) Analysing: Current Detection Techniques and Emerging Countermeasures

Advanced persistent threats (APTs) are a significant problem for organisational cyber security because of their sophistication and advanced attacks. This research article analyses the state of the art in current APT detection techniques and cutting-edge countermeasures. Different detection approaches, such as signature-based, behaviour-based, and AI-driven methods, are studied. The study points out the drawbacks of traditional signature-based detection and showcases the rising significance of behavioural analysis and machine learning for spotting sophisticated patterns of APT. Federated learning, blockchain technology for secure communication, deception techniques, and advanced forms of APT defence are explored. The article also assesses what tools and frameworks exist for APT detection, with an open-source versus proprietary comparison. We then discuss key APT detection and mitigation challenges, including detecting stealthy and polymorphic threats, data privacy problems, and resource constraints in real time. Future directions for the research are proposed, centred on explainable AI, collaborative defence mechanisms, and more effective detection in resource-constrained environments. This further advances the body of current work focused on developing more robust and adaptive security procedures against the tricky sophistication of cyber threats, proving beneficial to security experts and researchers attempting to strengthen organisational resiliency to APTs.

The Challenge of Invisible Enemies and Risk Mitigation for Parents in Educating Children in the Modern Era

This study explores the hidden challenges faced by parents in educating children in the modern era, referred to as “invisible enemies”, focusing on digital technology, individualism, academic pressure, peer influence and consumerist culture. It develops adaptive and responsive strategies to address these hidden challenges as a guide for parents. Employing a literature study with a qualitative-exploratory-descriptive approach, this research analyses the risks of the modern era in child education using Ulrich Beck's risk society theory framework. The findings reveal that parents face various hidden risks, such as exposure to inappropriate digital content, social pressures that undermine collective values, and challenges in balancing child protection with fostering independence. These align with the risk society theory, characterized by the risks arising from technological advancements and social changes, which are not only physical but also psychosocial. In this context, parents act as risk mitigation agents. Their role encompasses not only protecting children from the negative impacts of these risks but also equipping them with adaptive skills, instilling collective values, providing support for emotional balance and interests and fostering independence. This study contributes scientifically to the development of parenting strategies that are more responsive to the challenges of the modern era. It also offers practical guidance for parents, educators, and policymakers to create a resilient, ethical generation capable of navigating future socio-technological dynamics. Keywords: Children; Modern Era; Parents; Risk Mitigation; Societal Risk Theory

Nitrate toxicity and health hazards: Problems and mitigation challenges

Abstract Nitrogenous fertilizers are considered an essential input for productive agriculture and the factor highly responsible for higher biomass production. Unfortunately, intensive mineral fertilization brings tremendous changes in soil fertility and productivity, but productivity decreases rapidly in cereal-based cropping systems or without legumes. The excessive use of mineral nitrogenous fertilizers reported an increase in potential health hazards and environmental problems such as groundwater contamination, eutrophication, acid rain, the greenhouse effect, and methemoglobinemia in humans. With future applications’ elevated use and prognosis, this problem may expand to several folds in the approaching decades. Large consumption of defiled water or food with higher concentrations of nitrates (according to the US Environmental Protection Agency (EPA), the permissible limit is 7 mg.kg −1 body weight per day) causes severe infant diseases such as Blue Baby Syndrome, respiratory ailments, gastric cancer, birth malformation and other health problems which earlier were rarely explained or explored worldwide. The unsustainable agronomic practices resulted in our soil being stripped of natural health and blind dependency on mineral fertilizers, ultimately leading to poor human and environmental health. There is an immediate need for new technologies related to farming systems to meet Sustainable Development Goals (SDGs) in view of changing climate. Various sustainable practices such as applying organic manures, precision N management, Soil Test Crop Response (STCR) approach, and nitrogen (N) inhibitors hold tremendous potential to reduce the ill effects of nitrates and secure the food chain if adopted on a large scale.

Intensification of hourly and Small watershed flooding with rising temperatures

Global warming has increased intensity of extreme precipitation events, and it is expected that the intensity of resulting floods will also rise, especially as intensified short-duration extreme precipitation may exacerbate dangerous flash floods. However, quantitative evidence remains limited, and existing research tends to focus on large river basins. Here we analyzed and compared how hourly extreme precipitation and streamflow in the Yangtze River Basin and its tributaries respond to temperature changes using scaling analysis. Our findings reveal a consistent temperature response across different spatial scales in the basins: hourly extreme precipitation increases at approximately 1.75 times the rate of daily precipitation. Furthermore, floods show a positive response to rising temperatures, but the degree of this response is more consistent in the mainstem than it is in the tributaries. Both daily and hourly extreme streamflow in the mainstem increases by about 7 % (7.47 % and 6.18 % respectively) for every 1 °C increase in temperature. Notably, the increase in hourly extreme streamflow is less pronounced than that in hourly precipitation intensity, likely moderated by reservoirs and the various factors influencing flood formation. In contrast, hourly extreme streamflow in most tributaries increases by 14 % or more. These findings highlight that hourly extreme precipitation and flood responses in smaller watersheds significantly exceed thermodynamic expectations, increasing flash flood risks and posing challenges for flood mitigation.

SDN-Driven Security Framework for DDOS Attack Detection and Mitigation

The rising complexity and persistence of Distributed Denial of Service (DDoS) attacks, particularly low-rate variants, present significant challenges in detection and mitigation within Software-Defined Networking (SDN) environments. Existing detection systems often flood networks with alerts, burdening security personnel and delaying timely mitigation. Furthermore, many solutions are designed and tested in simulated conditions, limiting their real-world applicability. To address these challenges, we propose an SDN-based security framework enhanced with automated monitoring, detection, and mitigation capabilities, optimized for slow-rate DDoS attacks. Our framework was rigorously evaluated on a physical testbed, achieving mitigation efficiencies between 91.66% and 100% under varied attack conditions, thus proving its robustness in practical settings. Additionally, we introduce the SDN-SlowRate-DDoS dataset, designed to assist researchers and industry professionals in developing and testing intrusion detection solutions in more realistic scenarios. To further improve DDoS defense in SDN, we incorporate an ensemble online machine learning model that dynamically adapts to evolving attack patterns, enhancing accuracy across attack types and outperforming traditional models with a detection rate of 99.2% on benchmark datasets. This dual approach leverages both real-world testing and adaptive machine learning, advancing proactive DDoS threat management in SDN environments

Molecular mechanisms impact on fluoroquinolone resistance among E.coli from enteric carriage monitoring before prostate biopsy and earliest description of qnrB81

Fluoroquinolone-resistant (FQs-R) microorganisms causing infectious complications after ultrasound-guided needle biopsy of the prostate (TRUS-BP) have become an important challenge in healthcare settings globally, questioning the continued utility of FQ as the preferred prophylactic agent. This study aimed to characterize molecular mechanisms of resistance on FQs-R E. coli isolated from the enteric microbiota of patients undergoing (TRUS-BP) and to highlight their impact on Minimum Inhibitory Concentrations (MICs). From February 2016 to December 2018, the incidence of rectal carriage of Qs-FQs resistant Enterobacterales detected from rectal swabs of patients before undergoing (TRUS-BP) was 61.06% (80/131) all related to E. coli species. Based on the MICs range of Qs (24–256 mg/L) and FQs (0.24–128 mg/L) breakpoint by EUCAST, we categorized these E. coli isolates into three resistance profiles (I, II, and III) associated with the patterns of chromosomal mutations in the quinolone resistance-determining regions (QRDRs) of gyrA and parC and the plasmid-mediated quinolone resistance encoding genes (PMQRs) detected by PCR-based assay and sequencing; MICs increase in an escalation step according to the co-occurrence of multiple molecular mechanisms. The mutation of the gyrA gene was the most frequent on codons (Ser83Leu/Thr/Tyr/Trp and Asp87Asn); mutation on the parC gene was the least on codons (Ser80Iso/Leu and Glu84 Val/Gly/Lys). PMQRs genes (4 qnrB ,7 qnrS, and one aac(6’)-Ib-cr) were determined within 15% of the isolates. Allelic variation allows us to report earliest the qnrB81 determinant in an E. coli isolate. Among isolates (35%) belonged to the notorious ST131 lineage. The phylogenetic group showed a predominance of B2 group (51, 25%), however (PFGE) revealed a high level of clonal variability. Worrying incidence of FQs-R E. coli isolates in the rectal flora of our local population showed the potential to cause post-infection. FQ resistance is a complex interplay between mutations in the QRDRs and PMQR determinants that impact MICs. The importance of intestinal microbiota as a reservoir of resistant strains and pandemic clones encourages driving mitigation challenges to characterize molecular mechanisms of antimicrobial resistance to adapt prophylactic therapy, control infection, and ensure epidemiological monitoring.

Understanding Generative AI: From Basic Principles to Real-World Applications

This comprehensive article examines the foundational principles, technical implementations, and societal implications of generative artificial intelligence (GenAI), a transformative technology that has revolutionized the creation of synthetic content across multiple domains. The article explores the architectural frameworks underpinning GenAI, focusing on Generative Adversarial Networks (GANs) and Transformer-based models, while detailing the sophisticated training methodologies and evaluation metrics that enable their functionality. Through an in-depth analysis of real-world applications in healthcare, creative industries, and software development, this article illuminates the technology's potential to enhance human capabilities and drive innovation. The investigation extends to critical ethical considerations, addressing security concerns surrounding deepfakes, challenges in bias mitigation, and complex intellectual property issues. Furthermore, the article presents a forward-looking perspective on research opportunities, policy implications, and industry best practices, emphasizing the importance of responsible development and deployment of generative AI systems. This article contributes to the growing body of knowledge on GenAI by providing a holistic understanding of its current state, challenges, and future directions, while highlighting the crucial balance between technological advancement and ethical considerations in shaping its evolution.

Healthcare workers' experiences protecting themselves and their families during the COVID-19 pandemic in 2020-2021.

We characterized experiences and strategies used by frontline healthcare workers to prevent severe-acute-respiratory-syndrome-related coronavirus transmission at work and to household members during the coronavirus disease pandemic. Alongside an online questionnaire (n = 234), remote semi-structured interviews (n = 23: 15 clinicians, 8 non-clinicians) were conducted in 2021. Mitigation challenges and facilitators were identified from data to represent experiences as a process considering the before, during, and after work shifts. Journey mapping was utilized to visually describe how healthcare workers experienced the stages of the work environment, leaving work, commuting home, and the home environment, and strategies implemented to stay safe. Major facilitators included the uptake of coronavirus disease vaccines and testing, information regarding virus transmission, and adequate personal protective equipment. The most critical challenges identified included a lack of designated areas for end-of-day disinfection, changing rooms, showers, and lockers in the leaving work stage. Psychosocial and environmental factors must be considered in future hospital pandemic preparations.

Climate Finance: Pioneering Pathways to Sustainability

Climate finance is a progressive financial tool to foster sustainability and address climate change mitigation and adaptation challenges. This study delineates the notion of climate finance and sustainability, illustrating their confluence and its impact on social welfare, ecological quality, and economic well-being. Climate financing involves distributing resources to environmentally sustainable enterprises and projects designed to harmonize economic growth with sustainability. Furthermore, developed nations have committed to offering financial assistance to developing nations to confront the problems presented by climate change. This paper aims to decode the confluence of climate finance and sustainability. Moreover, it underscores the significance of climate financing in tackling environmental issues, reaching carbon neutrality, and eventually realizing sustainable development. The finding indicates that climate finance is essential for addressing climate change and achieving sustainability, highlighting the significant role of developed nations and the private sector in closing the funding gap required for investments in climate-resilient projects and infrastructure. Consequently, this can generate possibilities for people and nations to earn carbon credits, which are tradable in carbon markets, thereby securing financial benefits.

The Role of Machine Learning in Enhancing Corporate Financial Planning

Aim: To examine the role of machine learning in enhancing corporate financial planning. Problem Statement: In the past, corporate financial planning relied on statistical models and expert judgment which are often constrained by predefined assumptions and historical data. By so doing, they usually struggled to handle the non-linear and dynamic nature of financial markets causing limitations in their responsiveness and predictive power. Significance of Study: There is need to incorporate machine learning in corporate financial planning to provide a transformative technique via the introduction of advanced algorithms which are capable of analyzing diverse and vast datasets to uncover sophisticated relationships and patterns. Methodology: Recent relevant published articles in the area of machine learning in enhancing corporate financial planning were consulted. Relevant articles were sourced from the internet using the Google search engine. The abstract of the consulted published articles were thoroughly examined to study their relevance to the subject matter. Discussion: This review article examines the role of machine learning in enhancing corporate financial planning. It was found that machine learning techniques have crucial roles to play in the context of corporate financial planning via decision-making improvement, risk management, operational efficiency enhancement, fraud detection and enabling personalized experiences. Also, Machine Learning technologies have a wide variety of uses in corporate financial planning which enable banks and insurance companies in delivering tailored experiences that give their customers some preferences. Common identified machine learning approaches in corporate financial planning include predictive analytics, natural language processing, computer vision, reinforcement learning and anomaly detection approaches. Major areas of machine learning application include dynamic pricing and offers; regulatory compliance; personalized customer service; predictive analytics for customer retention; personalized product recommendations and so on. Furthermore, the implementation of Machine Learning (ML) in corporate financial planning and financial services industry provides many chances for enhancing customer experiences, improving efficiency and driving innovation. However, ML poses numerous challenges which should be addressed to attain the complete potential of these technologies. Conclusion: As technology continues to advance, financial institutions will need to route the challenges of bias mitigation, transparency and regulatory compliance while the ML full potential is equally leveraged. Also, machine learning has the potential to transform corporate financial planning via the provision of more adaptive, accurate and real-time insights.

Drivers of greenhouse gas emissions in agricultural soils: the effect of residue management and soil type

Developing successful mitigation strategies for greenhouse gases (GHGs) from crop residue returned to the soil can be difficult due to an incomplete understanding of factors controlling their magnitude and direction. Therefore, this study investigates the effects of varying levels of wheat residue (WR) and nutrient management on GHGs emissions (CO2, N2O, and CH4) across three soil types: Alfisol, Vertisol, and Inceptisol. A combination of laboratory-based measurements and a variety of data analysis techniques was used to assess the GHG responses under four levels of WR inputs (0, 5, 10, and 15 Mg/ha; WR0, WR5, WR10, and WR15) and three levels of nutrient (NP0: no nutrient, NP1: nutrients (N and P) were added to balance the residue C/nutrient stoichiometry of C/N/P= 100: 8.3: 2.0 to achieve 30% stabilization of added residue C input at 5 Mg/ha (R5), and NP2: 3 × NP1). The results of this study clearly showed that averaged across residue and nutrient input, Inceptisol showed negative N2O flux, suggesting consumption which was supported by its high legacy phosphorus (19.7 mg kg⁻1), elevated pH (8.49), and lower clay content (13%), which reduced microbial activity, as indicated by lower microbial biomass carbon (MBC) and alkaline phosphatase (Alk-P) levels. N2O emissions were more responsive to nutrient inputs, particularly in Vertisol under high WR (15 Mg/ha) input, while CH4 fluxes were significantly reduced under high residue inputs, especially in Vertisol and Inceptisol. Alfisol exhibited the highest total carbon mineralization and GWP, with cumulative GWP being 1.2 times higher than Vertisol and 1.4 times higher than Inceptisol across residue and nutrient input. The partial least square (PLS) regression revealed that anthropogenic factors significantly influenced CO2 and N2O fluxes more than CH4. The anthropogenic drivers contributed 62% and 44% of the variance explained for N2O and CH4 responses. Our study proves that different biogeochemical mechanisms operate simultaneously depending on the stoichiometry of residue C and nutrients influencing soil GHG responses. Our findings provide insight into the relative contribution of anthropogenic and natural drivers to agricultural GHG emissions, which are relevant for developing process-based models and addressing the broader challenge of climate change mitigation through crop residue management.

High-Temperature-Resistant Dual-Scale Ceramic Nanofiber Films toward Improved Air Filtration.

Currently, air pollution primarily arises from industrial emissions, coal combustion, and automobile exhaust, posing significant challenges for mitigation. This highlights the urgent need for advanced and efficient filtration materials with low pressure drop and high-temperature resistance. Traditionally, improving filtration property has involved increasing the thickness of the filtration materials, which consequently leads to higher costs. Here, dual-scale mullite nanofiber (MNF) films containing interwoven thick nanofibers (606 nm) and thin nanofibers (186 nm) are prepared using solution blow spinning. The dual-scale structure design enables the films to maintain a low pressure drop while achieving high filtration efficiency. At an airflow velocity of 5.3 cm s-1, the films with an areal density of 3.8 mg cm-2, achieve a filtration efficiency of 98.23% and a pressure drop of 141 Pa for PM0.3. In addition, the MNF films exhibit excellent flexibility and high-temperature resistance, making them have great potential for use in high-temperature flue gas filtration.

O IMPACTO DAS ALTERAÇÕES CLIMÁTICAS NO COTIDIANO DA SOCIEDADE MODERNA

Climate change has caused significant impacts on the daily life of modern society. Changes in temperature, rainfall patterns and the increase in extreme weather events are directly affecting agriculture, water resources, urban infrastructure and public health. The increasing frequency of natural disasters, such as hurricanes, floods and droughts, forces society to deal with adaptation and mitigation challenges. Economic sectors such as agriculture and tourism are especially vulnerable, as are the poorest populations, who are less able to face the effects of these changes. Furthermore, rampant urbanization and industrialization contribute to increased greenhouse gas emissions, further aggravating the situation. The psychological, social and economic impact of these changes becomes more evident as inequalities are accentuated, with vulnerable communities being the most harmed. Thus, the urgency of concrete actions, both at the local and global level, to mitigate the impacts of climate change, has become one of humanity's main challenges in the 21st century.

Evaluation of SPARC divertor conditions in H-mode operation using SOLPS-ITER

Abstract The predicted divertor conditions for the SPARC tokamak are calculated using SOLPS-ITER for a range of scrape-off-layer (SOL) heat flux widths $\lamq$, input powers, and particle fuelling locations. Under H-mode scenario conditions with an upstream separatrix density of 1\e20 m$^{-3}$, the most conservative range of $\lamq$ extrapolations ($\approx$ 0.15 mm) results in extremely high unmitigated particle and energy fluxes to the divertor, both under full field (12.2 T) and power ($P_{SOL}$ = 29 MW) conditions, and 2/3 field with $P_{SOL}$ = 10 MW. Increasing the cross-field SOL diffusivities by 2-10x reduces the magnitude of the mitigation challenge, however strategies such as impurity seeding or strike-point-sweeping will likely still be required. 

A combination of steady-state and time-dependent SOLPS-ITER simulations are used to map out phase space diagrams of upstream and divertor conditions. At low upstream density the inner and outer divertor conditions are highly asymmetric, with a large temperature difference and significant heat fluxes driven by parallel currents. The solution has sharp bifurcations with a region of hysteresis, depending on whether the initial state is at a low or high density. This behavior is observed even when the fuelling location, cross-field diffusivity, and impurity level is changed, although the density window with asymmetry is reduced.

The addition of neon impurity seeding reduces the divertor heat fluxes, but also causes a drop in the upstream electron density with fixed particle throughput. This drop can be counteracted by increased main ion throughput, however too much neon results in a back transition into the asymmetric divertor regimes suggesting a need for control of both main ion and impurity seeding levels to achieve a desired divertor state.

A Literature-based Overview of Current Narratives on the Capacitation of Community Leaders in Addressing the Challenges of Climate Change Mitigation and Adaptation

In most many countries worldwide, community leaders (CLs) are expected to play an important role in mobilising and soliciting contributions from others for the proactive implementation of climate change adaptation strategies. Due to a lack of capacity at the local leadership level, several local-level climate change mitigation and adaptation initiatives have not been effectively actioned with most of them frequently excluding local decision-makers, and key community stakeholders. Basing assessment on what is available in the literature, it is evident that there is urgent need for the scientific community to develop innovative strategies that facilitate the meaningful implementation of informed climate change policies. To successfully address the challenges of climate change, this paper posits that it is necessary for researchers, policy makers and planistrators must endeavour to explore and implement actionable local-level climate change mitigation and adaptation strategies. The take-home message from this paper is that the time to act is now and refrain from doing so until it is too late to do so for tomorrow may never come. The likely implications for the benefits of proactive and effective community leaders are considered against this backdrop and insights from literature-based multi-level governance, participatory learning, action-oriented transformative leadership initiatives that were sourced from the literature.

Gamification as a tool to identify and address ongoing challenges in space activities: the case of space debris mitigation

This article outlines a project aimed at developing a serious game to address the challenge of space debris mitigation within the broader context of space security. The first section examines the current state of space activity issues and their gamification. The second section introduces the concept of the game utilizing the ARDI model, emphasizing the significance of each element in addressing these challenges. The third section focuses on the practical refinement of the proposed game concept. The article suggests that the game could serve as a tool for identifying and resolving ongoing challenges in space activity.

Enhancing geothermal efficiency: Experimental evaluation of a high-temperature preformed particle gel for controlling preferential fluid flow

Enhanced Geothermal System (EGS) reservoirs represent a vital frontier in clean energy production. However, short-circulation flow within these reservoirs can significantly affect their immediate efficiency and long-term viability. Injected cold fluids moving through wide fractures or direct channels between injection and production wells reduce thermal production temperatures, disrupting energy output. Preformed Particle Gels (PPGs) have proven effective in controlling preferential fluid flow in oil and gas reservoirs, effectively regulating fluid movement. This work explores the potential of a novel High-Temperature Preformed Particle Gel (HT-PPG), designed for geothermal applications, to plug fractures in a simulated geothermal reservoir. Core flooding experiments in coated and uncoated sandstone models were conducted under varying HT-PPG sizes, swelling ratios, and fracture widths to determine gel plugging efficiency. Variations in the HT-PPG injection pressure, breakthrough pressure, and residual resistance factor (Frr) were evaluated. Water breakthrough pressures can reach 464.10 psi/ft. While the stable injection pressure of HT-PPG decreased with increasing swelling ratio and fracture width, it was higher in uncoated cores. The HT-PPG significantly sealed the fractures, drastically reducing conductivities to millidarcy levels. This work validates HT-PPG a robust solution to mitigate fluid diversion challenges in sandstone EGS reservoirs, enhancing performance and advancing sustainable geothermal energy production.