Conventional Framework Research Articles

Bridging realms: Western client perspectives on psychotherapy inspired by Indigenous healing

ContextThe decolonial turn in psychology criticizes conventional Western psychotherapeutic frameworks and seeks to decolonize therapeutic practices by considering diverse cultural perspectives. Indigenous healing has been increasingly used in the psychotherapy of ethnic communities, but also in the psychotherapy of Western clients. The research questions of the present study were focused on how Western clients experience the therapies inspired by Indigenous healing. ObjectiveThe study aimed to explore the motivations, experiences, and mental health outcomes of participants of therapies inspired by Indigenous healing with a focus on issues related to intercultural transfer of therapeutic practice. DesignSemi-structured qualitative interviews were used to gather narratives from 28 participants. Thematic analysis was used for data analysis. ResultsParticipants reported the following long-lasting outcomes of Indigenous therapies: increased well-being, calm, satisfaction with life, better emotion regulation, coping with stress, behavioral control, problem solving, decision making as well as a more sensitive and deeper experience of reality. Conventional Western psychotherapy was perceived as conversation-based, while Indigenous therapy was perceived as experience-based and more complex. The integration of experiences from Indigenous therapies was more difficult in the Western sociocultural environment compared to participation in the place of their origin, within the community of Indigenous people. ConclusionIndigenous healing has beneficial outcomes for Western clients, but the integration of these experiences is quite difficult and requires systematic psychotherapeutic support in the Western cultural environment.

How Clean Can We Get? Technological Change and Renewable Energy Use in Mexico, A General Equilibrium Perspective

This study explores the impact of technological change and renewable energy adoption on Mexico's economy using a Computable General Equilibrium (CGE) model. As global concerns about climate change intensify, reducing carbon emissions has become a priority, but traditional CGE models often predict significant economic costs associated with fossil fuel restrictions. In this analysis, we modify the conventional CGE framework to incorporate increasing returns to scale and endogenously modeled technological change, reflecting the real-world diffusion of renewable energy technologies in developing countries like Mexico. By accounting for these factors, our model demonstrates that a combined policy of carbon taxation and renewable energy subsidies can achieve significant CO2 reductions without negatively impacting economic welfare. However, while the environmental benefits—such as reduced emissions of particulates, SO2, and NOx—are substantial, the overall welfare gains remain modest, and the policy's success depends on effective government implementation and potential trade impacts. These findings offer valuable insights for policymakers in Mexico and other developing nations seeking to balance economic growth with environmental sustainability through strategic carbon emission reduction policies.

Framework-Induced Electrochemiluminescence Enhancement of an AIEgen-Based MOF Coupled with Heterostructured TiO2@Ag NPs as an Efficient Coreaction Accelerator for Sensitive Biosensing.

In conventional metal-organic framework (MOF) luminophore-involved electrochemiluminescence (ECL) systems, the aggregation-caused quenching commonly exists for the organic luminescent ligands, limiting the ECL efficiency and detection sensitivity. Herein, by employing the aggregation-induced emission luminogen (AIEgen) 1,1,2,2-tetra(4-carboxylbiphenyl)ethylene (H4TCBPE) as a ligand, one high-efficiency ECL emitter (Zr-MOF) was synthesized through a simple hydrothermal reaction. Compared with H4TCBPE monomers and their aggregates, the resultant Zr-MOF possesses the strongest ECL emission, which is mainly attributed to the framework-induced ECL enhancement. Specifically, the heterostructure was prepared by the deposition of silver nanoparticles on TiO2 microflowers and utilized as an efficient coreaction accelerator. Remarkably, the formative heterojunction can increase the interfacial charge transfer efficiency and promote the carrier separation, facilitating the oxidation of coreactant tripropylamine. In this way, a novel aptamer-mediated ECL sensing platform is constructed, achieving the sensitive analysis of adenosine triphosphate with a low detection limit of 0.17 nM. As a proof-of-concept study, this work may enlighten the rational design of new-type MOF-based ECL materials and expand the application scope of the ECL technology.

Performance Characterization of Clusterwise Linear Regression Algorithms

ABSTRACTClusterwise linear regression (CLR) is a powerful extension of the conventional linear regression framework when the data complexity exceeds the capability of a single linear model. This article presents the first examination of CLR algorithms developed over the past two decades through randomized large‐sample testing. Using a unified framework and carefully controlled data characteristics, a comprehensive and systematic assessment of CLR algorithms were performed. The findings of this study provide potential users with a clear understanding of the various benefits and limitations of selecting the appropriate CLR algorithms for their data. Furthermore, this study has disproved past claims which were concluded based on limited samples, and provides insights to better understand the CLR challenges. Finally, this article identifies areas for improvement that could provide crucial performance and reliability improvement of CLR algorithms.

True Crime’s Gothic Realism: Uncanny Masculinities, Haunted Meatscapes, and the Aesthetics of Adjacency in Selva Almada’s Chicas muertas

This article analyses Selva Almada’s Chicas muertas (2014), a nonfiction crime novel that centres on three femicides that occurred in 1980s Argentina. While the novel can be classified as true crime, the article is concerned with uncovering a discernible tension between true crime’s commitment to a rationalist presentation of facts, on the one hand, and the text’s Gothic elements, on the other. Far from eliding true crime’s reality principle, however, these elements are core to Almada’s detective work and can be construed as part of a project whose objective is to cognitively and affectively map femicide. By favouring a logic of synchronicity and adjacency over diachronicity and causality, Almada exposes the limitations of inscribing femicide within a conventional investigative framework, while also unveiling the uncanny aspects to the structures and figurative landscapes that accompany and abet femicide. The article goes on to make some general observations about true crime as Gothic realism.

A data-driven framework for enhancing coastal flood resilience in resource-crunched developing nations

A comprehensive Flood Resilient Scenario Model ‘FReSMo’ employs a data-driven, evidence-based approach for assessing climate-induced flood risk and validating the efficacy of mangroves (as context-specific adaptation measure) in reducing residential building damage. Based on an improvised Source-Pathway-Receptor-Consequence-Evidence concept, FReSMo employs a three-step analysis. First, hazard mapping estimates coastal flood extents for various return period under different Shared Socioeconomic Pathways. Second, the model maps the exposure of residential buildings to these flood extents by projecting built-up area for 2050 using the FUTURES model, based on physiographic, socio-demographic, and economic parameters. Finally, a data-driven probabilistic damage model is applied to estimate the built-up area damage for a 100-year coastal flood event (SSP-2.6). The pre-and post-adaptation damage estimate demonstrates the efficacity of NBS (mangroves) in reducing coastal flood risk. A 100 m mangrove patch on the Sagar coastline reduced the building damage cost by 70% for a 48-hr and 75% for a 24-hr flood. Considering a plantation cost for 6.2 km2, the total benefit, despite persistent losses, was a 222% return on investment. FReSMo transcends conventional risk assessment frameworks by offering a comprehensive approach for evaluating the cost-effectiveness of adaptation investments in developing countries, making it an invaluable tool in the face of climate change.

Country-level evenness measure in assessing progress towards Sustainable Development Goals (SDGs)

Achieving the United Nations’ Sustainable Development Goals (SDGs) requires an overall and well-balanced achievement of all SDGs rather than cherry-picking a few. While it is vital to assess the evenness of SDGs achievement, such analysis, particularly on a global scale, is still evidently absent from the existing literature. To remedy such a scholarly gap, this paper measures the evenness of SDGs achievement for 193 countries from 2010 to 2019 by adapting the evenness assessment method originating from ecology and biodiversity literature. Our analyses regarding the country-level SDG evenness index scores (EIS) indicate a significant heterogeneity across countries. Global South countries usually underperform in the evenness of 17 goals, while a significant portion of Global North countries also suffer from the unbalanced developments in SDGs. By integrating the evenness measures into the conventional global SDG monitoring framework, this study identifies six development paths in pursuing the SDGs and shows that maintaining the overall SDGs improvements while balancing the development of each goal is practical but has not taken place in at least 35% of countries. We estimate that the SDG evenness and average performance could further increase by 3.5 p.p. and 2.1 p.p., or 74 percent and 50 percent of the growth in the baseline continue past paths scenario, respectively, if each country could follow the effective development path of frontier counterparts with similar income levels. Our SDG evenness measures, complemented by inter-temporal cross-country analyses, could inform policies and cooperation strategies to identify the weaknesses in sustainable development, boost effective growth, and protect unevenly developed countries. Future research could further develop these measures to provide deeper insights into achieving rapid and well-balanced development of SDGs at various levels.

Privacy-Enhancing Technologies in Biomedical Data Science.

The rapidly growing scale and variety of biomedical data repositories raise important privacy concerns. Conventional frameworks for collecting and sharing human subject data offer limited privacy protection, often necessitating the creation of data silos. Privacy-enhancing technologies (PETs) promise to safeguard these data and broaden their usage by providing means to share and analyze sensitive data while protecting privacy. Here, we review prominent PETs and illustrate their role in advancing biomedicine. We describe key use cases of PETs and their latest technical advances and highlight recent applications of PETs in a range of biomedical domains. We conclude by discussing outstanding challenges and social considerations that need to be addressed to facilitate a broader adoption of PETs in biomedical data science.

Metaversal Real Estate Dynamics: The MRED Framework for Informed Decision-making in Virtual Space

Venturing into the digital frontier, the metaverse challenges our traditional understanding of real estate, beckoning a deep dive into its virtual realms and the complexities they harbour. This research seeks to provide an integrative framework for comprehending the intricacies of virtual real estate within the metaverse, emphasising its valuation, governance and sustainability. Utilising an exploratory design, the study amalgamates insights from existing metaverse literature and stakeholder perspectives to fashion a robust framework, which is further augmented by derived theoretical propositions. The findings shed light on the multifarious challenges inherent to the metaverse’s virtual real estate. There is a spotlight on the substantial energy demands of the metaverse and the consequent ecological imperative for sustainable digital practices. It underscores the need for malleable governance mechanisms in a decentralised metaverse and champions the harmonisation of age-old ownership philosophies with avant-garde technological solutions such as blockchain. The research also postulates a collaborative ethos for virtual space creation and propounds innovative financial paradigms tailored to the unique economic rhythm of the metaverse. Future research vectors could span the cultural and psychological facets of virtual domains and the ripple effects on conventional real estate frameworks.

Ripples of Negligence: Unravelling Iraq’s Descent from Water Abundance to Scarcity

ABSTRACT This study critically assesses security dynamics in developing nations, centering on water scarcity in Iraq. It challenges the reductionist perspective that equates security solely with the absence of military threats, positing instead that the governing body itself can constitute an existential threat to the state. Many developing countries, typified by a myopic conception of security, have engendered internal insecurity. Iraq exemplifies this paradigm, where successive ruling elites, marked by military adventurism and administrative incompetence, directly precipitate water scarcity. The nation’s ominous trajectory results from governmental prioritization of external threats over essential societal well-being. This article elucidates the intricate interplay between governance priorities and societal vulnerabilities, advocating for a more comprehensive understanding of security that transcends conventional militaristic frameworks. The analysis underscores the urgent need for recalibrating security paradigms in developing countries to address internal threats emanating from governmental neglect and skewed priorities.

A new high-level life cycle assessment framework for evaluating environmental performance: An aviation case study

Conventional Life Cycle Assessment (LCA) methods are able to assess environmental impact using significant resources (including time and data). However, due to the challenges associated with data collection these can still suffer from issues including representation accuracy, comparability, data availability, data quality, and uncertainty. This paper describes a new streamlined, high-level framework which seeks to solve these issues through rigorous and iterative application of existing standardised LCA methodologies whilst continually engaging with stakeholders. This new framework has been applied to an aviation case study, which seeks to investigate the potential environmental impact of implementing sustainable aviation fuel (including fuels based on used cooking oil, power to liquid technology, and hydrogen) and digitalisation of training regimes within a UK aircraft manufacturer. These are currently major areas of focus to enable the decarbonisation of the global aviation sector. The proposed framework allowed for efficient joint interpretation of results by different stakeholders, and therefore enabled effective strategic decision making without requiring the granular level of data detail demanded by conventional LCA frameworks. The case study has shown that each scenario offers potential reductions in global warming potential, fine particulate matter formation, and water consumption for an aircraft; but only when the associated supply chain is just as sustainable as the scenario in question. Overall, this research has shown that applying the new framework allows for rapid evaluation of decarbonisation technologies through rigorous environmental assessment to a degree accuracy which still enables strategic decision making, but without the use of unnecessary resources. Although this framework has been developed to work across product, platform, or system, further work should seek to apply it in different contexts as a LCA enabler within technological developments including exploration of other aviation decarbonisation pathways to achieve net zero.

Metadata-enhanced contrastive learning from retinal optical coherence tomography images

Deep learning has potential to automate screening, monitoring and grading of disease in medical images. Pretraining with contrastive learning enables models to extract robust and generalisable features from natural image datasets, facilitating label-efficient downstream image analysis. However, the direct application of conventional contrastive methods to medical datasets introduces two domain-specific issues. Firstly, several image transformations which have been shown to be crucial for effective contrastive learning do not translate from the natural image to the medical image domain. Secondly, the assumption made by conventional methods, that any two images are dissimilar, is systematically misleading in medical datasets depicting the same anatomy and disease. This is exacerbated in longitudinal image datasets that repeatedly image the same patient cohort to monitor their disease progression over time. In this paper we tackle these issues by extending conventional contrastive frameworks with a novel metadata-enhanced strategy. Our approach employs widely available patient metadata to approximate the true set of inter-image contrastive relationships. To this end we employ records for patient identity, eye position (i.e. left or right) and time series information. In experiments using two large longitudinal datasets containing 170,427 retinal optical coherence tomography (OCT) images of 7912 patients with age-related macular degeneration (AMD), we evaluate the utility of using metadata to incorporate the temporal dynamics of disease progression into pretraining. Our metadata-enhanced approach outperforms both standard contrastive methods and a retinal image foundation model in five out of six image-level downstream tasks related to AMD. We find benefits in both a low-data and high-data regime across tasks ranging from AMD stage and type classification to prediction of visual acuity. Due to its modularity, our method can be quickly and cost-effectively tested to establish the potential benefits of including available metadata in contrastive pretraining.

Seating accuracy of removable partial denture frameworks fabricated by different digital workflows in comparison to conventional workflow.

To evaluate the seating accuracy of removable partial denture (RPD) frameworks fabricated by two digital workflows involving selective laser melting (SLM) in comparison to the conventional workflow. A Kennedy class III modification 1 partially edentulous mandibular arch was used as a master model. Three RPD framework groups were included: (1) a conventional workflow group with conventional impression and casting (CC), (2) a partial digital workflow group with conventional impression and digital fabrication (CD), and (3) a complete digital workflow group with digital impression and digital fabrication (DD). A total of 10 frameworks were produced for each group. The marginal gaps at the occlusal rests, retention arms, and reciprocating arms were measured by a traveling microscope. The data were analyzed with the one-way analysis of variance test. At the framework level, the most superior fit was observed for the CD group (79.5µm) followed by DD (85.3µm) and CC (114.2µm) groups. The CD and DD groups were significantly superior to CC (p<0.001). This fit pattern was consistent for the retention and reciprocating arms, while the occlusal rest fit was similar among all the groups. The SLM frameworks had a promising seating accuracy in comparison to conventional frameworks. The type of impression, conventional or digital, did not affect the accuracy of SLM frameworks. The differences observed in the present study are likely to be of minimal clinical significance.

Global Quality of Service (QoX) Management for Wireless Networks

In the fast-changing technological landscape, novel applications are emerging with the potential to reshape the world. These applications, while promising, impose stringent requirements in terms of quality of service (QoS). The advent of wireless networks like 5G, 6G and Wi-Fi 6 brings about resource management solutions to ensure these requirements while meeting the user expectations within the interconnected environment. Nevertheless, user behaviors are also evolving, highlighting the importance of satisfaction and quality of experience (QoE). Furthermore, changes in user behavior trigger shifts in business models, where the quality of business (QoBiz) takes on a pivotal role. This evolving ecosystem, encompassing QoS, QoE, and QoBiz, demands a comprehensive and adaptable approach that conventional QoS management frameworks fail to perform. This paper introduces an implementation methodology for a global QoS management model named QoXphere. The implementation methodology is grounded in machine learning techniques and addresses the multifaceted aspects of quality of service (QoX) and their interconnections within wireless networks. The objective is to facilitate dynamic resource management that not only elevates user satisfaction but also optimizes provider benefits. Real-world examples illustrate the methodology’s applicability in widely deployed networks, complemented by simulated scenarios of modern network environments that further validate the approach.

Quantum state over time is unique

The conventional framework of quantum theory treats space and time in vastly different ways by representing temporal correlations via quantum channels and spatial correlations via multipartite quantum states—an imbalance absent in classical probability theory. Since Leifer and Spekkens [] called for a causally neutral formulation of quantum theory in their seminal work, numerous attempts have been made to rectify this asymmetry by proposing a dynamical description of a quantum system encapsulated by a static , without a definite consensus on which one is most appropriate. In this paper, we propose sets of operationally motivated axioms for quantum states over time alternative to the ones proposed by Fullwood and Parzygnat [], which we show is unable to induce a unique quantum state over time. Our proposed axioms are better suited to describe quantum states over any spacetime regions beyond two points. Through this reformulation, we prove that the Fullwood-Parzygnat state over time uniquely satisfies all these operational axioms, unifying the bipartite spacetime correlations of quantum systems. Published by the American Physical Society 2024

Ultrahigh energy harvesting ability of PVDF incorporated with 2D halide perovskite nanosheets via interface effect

Two-dimensional (2D) perovskites have recently emerged as an new family of ferroelectrics and attracted extensive attention for their specific surface interface characteristics benefitting from the unique structure by inserting hydrophobic organic cations into conventional three-dimensional frameworks. In this study, 2D metal–organic perovskite nanosheets, (C4H9NH3)2CsPb2Br7, ((BA)2CsPb2Br7), were successfully synthesized by facile temperature-cooling method and introduced into poly (vinylidene fluoride) (PVDF) polymer fibers to construct flexible piezoelectric energy harvester (PEH). (BA)2CsPb2Br7/PVDF nanofibers reveal a high piezoelectric coefficient (d33) of 63.3 pm/V, which is 2.9 times of the PVDF polymer (21.7 pm/V). Principally, the PVDF composite with 5 wt% (BA)2CsPb2Br7 nanosheet filler assumes an superior electroactive phase amount of about ∼ 93 % profiting from the interfacial interaction between BA+ cations of (BA)2CsPb2Br7 and the dipoles of PVDF. The PEH based on (BA)2CsPb2Br7 nanosheets embedded in PVDF fibers showed a maximum output voltage, 135 V, which is almost 17 times of the PEH based on neat PVDF fibers. Furthermore, the interfacial interaction between (BA)2CsPb2Br7 and PVDF was analyzed by first principle DFT study which reveals electrons migrate and the interfacial polarization between the interface. Simultaneously, (BA)2CsPb2Br7/PVDF composite fibers based PEH reveals exceptional long-term stabilities and durability. More attractively, the exceptional performance of this fabricated PEH evinces a pressure sensitivity of ∼ 7.3 V/N and bio-mechanical harvesting behavior arising from varied human motions, such as beating, pressing, twisting, walking and running. This cost-effective and high performance of (BA)2CsPb2Br7/PVDF based PEH is favorable for the continuation of 2D halide perovskites for further application in new energy and biomechanical filed.

Empowering aged persons care services: Leveraging data-driven BiLSTM-GRU integration with wearable devices

Wearable medical devices (WMDs) have emerged as a promising medical technology to enhance patient care from a variety of medical angles. However, due to inaccurate signal measurement in sensors, the conventional model-driven framework has problems with its limited reconstruction ratio and compression ratio. A growing need for effective and individualized aged care services is created by the world's population's increasing aging. To increase the system quality and overall performance, the collected data is preprocessed to normalized data by using Z score Normalization. This study proposes a novel approach that integrates wearable device data with Elderly people's health and wellbeing may be predicted and monitored using Gated Recurrent Unit (GRU) and Bidirectional Long Short-Term Memory (BiLSTM) models. This data-driven strategy enables caregivers to deliver tailored care plans, timely treatments and early identification of potential health problems, which improve the aged population's overall quality of life. The BiLSTM-GRU evaluates the effectiveness and efficiency of an approach by analyzing the performance of Accuracy (96%), Precision (93%), F1-score (95%), Sensitivity (94%), Specificity (90%) and MSE. By advancing data-driven techniques in aged care services, this research intends to pave the way for a more effective and long-lasting healthcare system that is designed to meet the needs of the senior population.

Optimization of dual-stage controllers in renewable energy sources-based interconnected power systems through refinement of the African Vultures Optimization Algorithm

With an emphasis on a three-region system, this paper explores automaticgeneration control within a conventional framework. Area 2 uses thermal-thermal sources, Area 3 uses gas-thermal sources, and Area 1 uses thermal-biodiesel sources. IPD(1 + I) is a unique cascade controller that combines integral-proportional-derivative (IPD) components with one-plus-integral (1 + I) components. To find the best gains and settings for the IPD(1 + I) controller, a meta-heuristic method called the African Vulture Optimization Algorithm (AVOA) is applied. Reducing the integral squared error (ISE), as determined by the performance index (Pi), is the main goal. The performance of the IPD(1 + I) controller is evaluated by contrasting it with a range of secondary controllers. Hybrid Peak Area-ISE (hPA-ISE), a novel performance metric, is also assessed in addition to the conventional ISE. The incorporation of renewable energy sources, such solar thermal energy and solid oxide fuel cells, significantly improves the system’s performance. Interestingly, under normal conditions, the IPD(1 + I) controller’s parameter settings continue to work for both sinusoidal and random load situations, obviating the need for additional tuning.

On the convergence of Fourier representations and Schwartz distributions

While Fourier theory remains a cornerstone for analyzing and interpreting the spectral content of diverse signals, its limitations often surface in practical applications. Notably, popular signals like sinusoids, Dirac deltas, signums, and unit steps lack convergent Fourier representations within the conventional framework. This discrepancy necessitates distribution theory to build and interpret suitable representations for such signals. However, existing literature in signal processing and communication engineering often glosses over these intricacies, leaving researchers grappling with obscure concepts regarding the very existence of Fourier representations for non-conforming signals. This work bridges this critical gap by offering a comprehensive exploration of the conditions guaranteeing the existence of Fourier representations. We introduce a novel linear space – the Gauss–Schwartz (GS) function space – and its corresponding class of tempered superexponential (TSE) distributions. We demonstrate that the Fourier transform (FT) acts as an isomorphism on the GS space of test functions and, by duality, on TSE distributions. Crucially, the GS space proves to be minimal in the sense that its dual, encompassing TSE distributions, represents the largest possible linear space over which the FT can be defined through duality. This theoretical advancement signifies a twofold contribution. Firstly, it clarifies the existence and interpretation of Fourier representations for prevalent signals that evade conventional analysis. Secondly, the introduction of the GS-TSE framework expands the reach of the FT to encompass a broader spectrum of functions, enabling novel applications in diverse fields. Ultimately, this work paves the way for a more robust and accessible understanding of Fourier analysis, empowering researchers and practitioners to leverage its full potential in exploring and processing a wider range of signals.

Chemical Mapping of Temperate Sub-Neptune Atmospheres: Constraining the Deep Interior H2O/H2 Ratio from the Atmospheric CO2/CH4 Ratio

Understanding the planetary envelope composition of sub-Neptune-type exoplanets is challenging due to the inherent degeneracy in their interior composition scenarios. Particularly, the planetary envelope’s H2O/H2 ratio, which can also be expressed as the O/H ratio, provides crucial insights into its original location relative to the ice line during planetary formation. Using self-consistent radiative transfer modeling and a rate-based automatic chemical network generator combined with 1D photochemical kinetic-transport atmospheric modeling, we investigate various atmospheric scenarios of temperate sub-Neptunes, ranging from H2-dominated to H2O-dominated atmospheres with equilibrium temperatures (T eq) of 250—400 K. This study includes examples such as K2-18 b (T eq = 255 K), LP 791-18 c (T eq = 324 K), and TOI-270 d (T eq = 354 K). Our models indicate that the atmospheric CO2/CH4 ratio can be used to infer the deep interior H2O/H2 ratio. Applying this method to recent JWST observations, our findings suggest that K2-18 b likely has an interior that is 50% highly enriched in water, exceeding the water content in a 100 × Z ⊙ scenario and suggesting a planetary formation mechanism involving substantial accretion of ices. In contrast, our model suggests that approximately 25% of TOI-270 d’s interior is composed of H2O, which aligns with the conventional metallicity framework with a metallicity higher than 100 × Z ⊙. Furthermore, our models identify carbonyl sulfide (OCS) and sulfur dioxide (SO2) as strong indicators for temperate sub-Neptunes with at least 10% of their interior composed of water. These results provide a method to delineate the internal composition and formation mechanisms of temperate sub-Neptunes (T eq < ∼ 500 K) via atmospheric characterization through transmission spectroscopy.