Adaptive Framework Research Articles

GAPS IN THE IMPLEMENTATION OF GOVERNMENT-PRIVATE PARTNERSHIPS (PPP) IN INFRASTRUCTURE DEVELOPMENT

Infrastructure is one of the most important pillars in the development of a civilization, especially for archipelagic countries like Indonesia, which faces unique geographical challenges. The gap in the implementation of Government and Business Entity Cooperation (KPBU) in infrastructure development in Indonesia has become an increasingly pressing issue to address. This research discusses the importance of regulatory renewal in Government-Private Partnerships (PPP) to address existing gaps. The research utilized various data sources, including legislative texts, government regulations, legal documents, and policy papers relevant to PPP in Indonesia. The analytical approach allowed for a deeper exploration of the dynamics and implications of the existing regulations, focusing on their effectiveness and applicability in real-world scenarios. The author was able to articulate the challenges faced in the application of PPP regulations and propose targeted recommendations for regulatory renewal and improvement. This study could involve comparative analyses of best practices in regulatory frameworks from other countries that have successfully addressed similar infrastructure challenges, providing valuable insights for the Indonesian context. Ultimately, this research could contribute to creating a more responsive and adaptive regulatory framework that supports sustainable and equitable infrastructure solutions in Indonesia, ensuring that the needs of society are met while fostering a conducive environment for investment.

Enhancing Interoperability in the Web of Things: A Reference Architecture Approach

The concept of the Internet of Things (IoT) has fundamentally redefined the connectivity landscape by integrating physical devices with the Internet for data exchange. The Web of Things (WoT) takes this integration a step further by embedding IoT devices within the web, thus facilitating their interoperability. However, the inherent complexity of WoT architecture requires a layer of abstraction to simplify development and integration. Patterns and reference architectures (RAs) provide such an abstraction, modeling solutions for recurrent problems within this domain. In this paper, we aim to develop an RA for the WoT ecosystem. We use architectural patterns to build the RA of the WoT. Here, we present the WoT gateway pattern, which provides a clear and adaptable framework for integrating diverse IoT devices, ensuring seamless communication and functionality within the broader web ecosystem. The purpose of this pattern is twofold: to ensure the interoperability of WoT implementations and to serve as a foundational guide for developers navigating the multifaceted challenges of WoT system design. The pattern also paves the way for building an RA for IoT environments. KEYWORDS Architectural patterns, Internet of Things, reference architecture, web development, web science, WoT

Adaptive compensation for multi-axial real-time hybrid simulation via nonlinear parameter estimation

For Real-time hybrid simulation (RTHS) to be stable and accurate, it is essential to address the time desynchronization issue between the numerical and physical substructures. Desynchronization is primarily caused by time delays, inherent dynamics of the control plant, system uncertainties, and noises. While existing adaptive compensators have shown effective tracking performance in single-input single-output (SISO) RTHS, their effectiveness in multi-input multi-output (MIMO) RTHS has not been fully demonstrated. MIMO-RTHS presents additional challenges due to its larger solution space, and significant dynamic coupling between actuators. To address these challenges, this study introduces an adaptive compensation framework for MIMO-RTHS. The proposed framework utilizes a control law based on the inverse dynamics of the control plant, incorporating real-time adaptive parameter updates through Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) methods. Both the transfer function (TF) and discrete-time state-space (SS) models of the plant are employed in distinct parameter estimation cases. The performance of the proposed compensation is validated through a multi-axial RTHS (maRTHS) benchmark problem. Extensive simulations on the maRTHS incorporating various earthquake inputs, sensor noise, and model uncertainties, demonstrated an excellent tracking performance and strong robustness across four parameter estimation cases (EKF-TF, UKF-TF, EKF-SS, and UKF-SS). The use of UKF with SS model (UKF-SS) achieved superior performance, effectively managing nonlinearities and noise without requiring low-pass filtering.

LEGAL UNCERTAINTY IN GOVERNMENT REGULATION NUMBER 51 OF 2020 REGARDING THE TEN-YEAR PASSPORT VALIDITY PERIOD

This study examines the legal uncertainty arising from the implementation of Government Regulation No. 51 of 2020 concerning the change in passport validity from 5 years to 10 years, as well as the need for legal reform to address the issue. Legal uncertainty occurs due to the absence of clear implementing regulations, thus affecting the effectiveness of policy implementation in the field of immigration administration, including tariff adjustments and management of passport forms. The purpose of this study is to analyze the lack of clear implementing regulations and their impact on immigration administration, proposing solutions such as creating comprehensive guidelines, legal harmonization, and system updates to ensure policy execution. This research utilizes the normative method to analyze the legal uncertainty surrounding Government Regulation Number 51 of 2020 by examining the relevant legal standards. This study finds that the enactment of Government Regulation No. 51 of 2020 has created legal uncertainties, particularly regarding the transition for existing passports, fee adjustments, and administrative procedures. These ambiguities may disrupt immigration services and confuse both the public and immigration officers, calling for clear guidelines and implementing regulations to ensure the smooth execution of the policy. This study recommends legal reform by preparing comprehensive implementing regulations, harmonization with related laws, and updating the administrative system to support policy implementation. This study suggests a more adaptive legal framework and increases the capacity of technology and human resources to face future immigration challenges.

Global legal frameworks for an equitable energy transition: Balancing growth and justice in developing economies

The global energy transition towards sustainable, low-carbon sources is crucial in mitigating climate change and achieving long-term energy security. However, for developing economies, this transition presents unique challenges, given the need to balance economic growth, social justice, and environmental sustainability. This paper provides a comprehensive analysis of global legal frameworks guiding the equitable energy transition, focusing on how these frameworks can be adapted to the specific contexts of developing economies. We examine international agreements, national policies, and regulatory mechanisms that address energy access, carbon reduction, and economic equity. Additionally, the study highlights the importance of integrating social justice principles to ensure that vulnerable communities within developing nations benefit from the transition without bearing disproportionate costs. Key issues discussed include policy alignment, financial support mechanisms, technology transfer, capacity building, and stakeholder inclusivity. By evaluating case studies from Africa, Asia, and Latin America, this paper offers insights into best practices and emerging trends for a fair energy transition. Recommendations are provided to policymakers to develop adaptable frameworks that balance growth aspirations with justice imperatives, ensuring a sustainable and inclusive energy future for all. Keywords: Energy Transition, Legal Frameworks, Developing Economies, Sustainable Energy, Social Justice.

Advanced Monocular Outdoor Pose Estimation in Autonomous Systems: Leveraging Optical Flow, Depth Estimation, and Semantic Segmentation with Dynamic Object Removal

Autonomous technologies have revolutionized transportation, military operations, and space exploration, necessitating precise localization in environments where traditional GPS-based systems are unreliable or unavailable. While widespread for outdoor localization, GPS systems face limitations in obstructed environments such as dense urban areas, forests, and indoor spaces. Moreover, GPS reliance introduces vulnerabilities to signal disruptions, which can lead to significant operational failures. Hence, developing alternative localization techniques that do not depend on external signals is essential, showing a critical need for robust, GPS-independent localization solutions adaptable to different applications, ranging from Earth-based autonomous vehicles to robotic missions on Mars. This paper addresses these challenges using Visual odometry (VO) to estimate a camera’s pose by analyzing captured image sequences in GPS-denied areas tailored for autonomous vehicles (AVs), where safety and real-time decision-making are paramount. Extensive research has been dedicated to pose estimation using LiDAR or stereo cameras, which, despite their accuracy, are constrained by weight, cost, and complexity. In contrast, monocular vision is practical and cost-effective, making it a popular choice for drones, cars, and autonomous vehicles. However, robust and reliable monocular pose estimation models remain underexplored. This research aims to fill this gap by developing a novel adaptive framework for outdoor pose estimation and safe navigation using enhanced visual odometry systems with monocular cameras, especially for applications where deploying additional sensors is not feasible due to cost or physical constraints. This framework is designed to be adaptable across different vehicles and platforms, ensuring accurate and reliable pose estimation. We integrate advanced control theory to provide safety guarantees for motion control, ensuring that the AV can react safely to the imminent hazards and unknown trajectories of nearby traffic agents. The focus is on creating an AI-driven model(s) that meets the performance standards of multi-sensor systems while leveraging the inherent advantages of monocular vision. This research uses state-of-the-art machine learning techniques to advance visual odometry’s technical capabilities and ensure its adaptability across different platforms, cameras, and environments. By merging cutting-edge visual odometry techniques with robust control theory, our approach enhances both the safety and performance of AVs in complex traffic situations, directly addressing the challenge of safe and adaptive navigation. Experimental results on the KITTI odometry dataset demonstrate a significant improvement in pose estimation accuracy, offering a cost-effective and robust solution for real-world applications.

Adapting an early autism caregiver coaching intervention for telehealth delivery in low-resource settings: A South African study of the 'what' and the 'why'.

We were busy with an early autism caregiver-coaching programme in South Africa, when COVID-19 stopped all in-person work. We changed the programme so it could be done using computers and/or phones. Here, we describe programme changes (which we call the 'what') and the reasons for those changes (which we call the 'why'). We used a tool called the Framework for Modification and Adaptations (FRAME) to describe the 'what', and the Exploration, Preparation, Implementation, Sustainment (EPIS) framework to describe the 'why' of our programme changes. The team members who helped make these changes checked that the changes described were correct. We made 10 changes in total: we used WhatsApp to deliver the programme, made simple pictures with words as visual tools for the programme, changed some session activities, changed a self-reflection checklist, provided all activities online, changed the way assessment and consent was done, made a session recording guide, sent things needed for sessions by email and WhatsApp, and made a caregiver-child play recording guide. The reasons for changes (the 'why') were about factors outside schools (the types of phones and data people had, WhatsApp security rules, COVID-19 rules), things inside schools/workplace (about the caregivers and nonspecialists themselves, ethics boards, things about the school itself), and support from people who developed the programme. Changes were made by working with things inside schools/workplace that could change. Identifying what could change helped focus and guide which changes were made to a programme.

Mind the Gap: Heritage, Evolution, and the Human Quest for Certainty in an Uncertain World

This article investigates the informational completion mechanism of the human mind, through which the brain fills cognitive gaps to create a coherent narrative continuum and eliminate uncertainties. The study aims to analyze whether this process relies solely on immediate experiences and direct learning or if it also includes deeper influences passed down from a transgenerational past and even from the evolutionary stages of species. The central question of the research is: Do we use only recently accumulated information to fill knowledge gaps, or does our brain access a broader fund, imbued with subconscious legacies and evolutionary predispositions? This exploration seeks to reveal how cognitive biases, such as the anchoring effect and confirmation bias, contribute to building an adaptive informational framework intended to support psychological stability and perceptual coherence. The paper also examines how these automatic completions can give rise to beliefs and myths that fill knowledge gaps, reinforcing an overarching narrative. Through informational completion, the brain not only stabilizes an individual’s subjective reality but also shapes common worldviews that support social cohesion and collective identity. The findings suggest that the cognitive completion mechanism, far from being merely adaptive, represents a quest for certainty and stability in the face of the unknown. In a rapidly changing world, this capability becomes essential for human adaptability. However, the question remains: To what extent does this informational fund accessed by the mind extend, and what role do the collective subconscious and evolutionary influences play in the cognitive completion process?

A novel smartphone application to prevent postpartum depression adapted from an evidence-based cognitive behavioral therapy program: Individual end-user interviews to optimize the intervention.

Postpartum depression (PPD) is more common among pregnant patients who have unmet social needs, such as financial stress or food insecurity, compared to those who do not. Mothers and Babies (MB) is a cognitive behavioral therapy (CBT)-based program that prevents up to 50% of de novo PPD when provided in-person to low-income Spanish- and English-speaking pregnant people without depression. MB's reach has been limited by the need for trained personnel to support the program. Transforming MB into a smartphone application (app) may mitigate this key barrier to scaling MB. To utilize qualitative data from target end-users to create and optimize MBapp, a novel app centered on the MB program. Draft wireframes of MBapp were created in English and Spanish with CBT-based modules adapted from MB. These wireframes included several features shown previously to sustain app engagement: 1) push notifications delivered at participant-preferred times; 2) text-, graphic-, and video-based content; and 3) gamification with digital rewards for app engagement. English- or Spanish-speaking individuals with public health insurance who were between 32 weeks' gestation and six months postpartum and owned smartphones were eligible to consent for individual in-depth interviews. Individuals with prior or current depression were excluded. Interviews were recorded, transcribed, and analyzed using deductive and inductive codes to characterize opinions about MBapp and perceptions of challenges and facilitators of use of MBapp or other perinatal or mental health apps. End-user feedback led to major modifications to the wireframes. Each of these changes was categorized according to the Framework for Modification and Adaptation (FRAME), an established method of systematically reporting adaptations and modifications to evidence-based interventions via end-user feedback. Recruitment ceased with content saturation, defined as three successive participants providing only positive feedback on MBapp's wireframe, without further suggestions for improvement. 25 interviews were completed. Participants were racially and ethnically diverse, generally representing our target end-user population, and 48% of interviews were conducted in Spanish. Participants' suggestions to improve MBapp were categorized within the FRAME as adaptations that improved either content or context to optimize reach, retention, engagement, and fit for end users. Specifically, the following features were added to MBapp secondary to end-user feedback: 1) audio narration; 2) "ask a clinician" non-urgent questions; 3) on-demand module summaries accessible upon module completion; and 4) choice to defer assessments and start the next module. Participants also provided insights into features of perinatal or mental health apps they found appealing or unappealing to understand preferences, challenges, and (non)negotiables for MBapp. Adapting MBapp to incorporate end users' perspectives optimized our digital PPD prevention intervention, ideally increasing its appeal to future users. Our team's next steps will confirm that MBapp is a feasible, acceptable intervention among English- and Spanish-speaking perinatal people at risk of PPD.

Adaptive Dictionary Identification Framework and its Application to Sparsity-Optimized Harmonic Noise Separation

Coherent noise separation stands as a critical step in seismic data processing. The Morphological Component Analysis (MCA)-based separation method, treating coherent noise and signal as distinct components and representing them sparsely with dictionaries, has found widespread application in the effective suppression of coherent noise. In practice, when constructing effective fixed dictionaries for MCA-based separation methods, a common approach involves conducting numerous experiments to meticulously select appropriate transform basis functions from an extensive dictionary library and tune their parameters. To mitigate time consumption and ensure optimal dictionary construction, we introduce a framework for adaptive identification of the optimal dictionaries used in MCA-based coherent noise separation. Specifically, we initially characterize the notion of a fixed dictionary library, which encompasses dictionaries constructed using various transform basis functions and their corresponding parameters. Then, we present a Relative Sparsity Minimization Problem (RSMP), formulated to identify the optimal fixed dictionaries that lead to minimum relative sparsity within the predefined library. Finally, we design a genetic algorithm to solve RSMP. The optimal dictionaries obtained for representing signal and coherent noise, respectively, are applied to MCA-based coherent noise separation. Synthetic and field data examples demonstrate the effectiveness of the proposed method.

Abstract A001: Navigating the future of therapeutic development: innovations in safety and efficacy through advanced research methodologies and nanotechnology

Abstract In recent years, the field of therapeutic development has experienced a significant paradigm shift driven by strategic scientific innovations that prioritize safety and efficacy in therapeutic agents. This paper explores the intersection of advanced research methodologies, novel material sciences, and biotechnological advancements that are revolutionizing the approach to drug formulation and delivery systems. The integration of precision medicine principles alongside cutting-edge technologies allows for the design of therapeutics that are not only effective in targeting specific diseases but are also engineered to minimize adverse effects associated with conventional therapies. One focal point of this exploration is the application of nanotechnology in drug delivery systems. By utilizing nanoparticles, researchers can enhance the bioavailability of therapeutic agents while reducing systemic toxicity. This targeted approach enables the delivery of higher concentrations of drugs to the affected areas, thereby maximizing therapeutic efficacy and minimizing side effects. Additionally, the paper highlights the role of computational modeling and simulations in predicting the interaction of therapeutic agents at the molecular level, facilitating the identification of safer compounds through virtual screening methods. Another significant aspect addressed in this paper is the development of biologics and biosimilars as safer alternatives to traditional synthetic drugs. The advancement of biomanufacturing processes has made it feasible to produce complex molecules with high specificity and reduced immunogenicity, paving the way for innovative therapies that cater to individual patient profiles. Furthermore, the strategic incorporation of pharmacogenomics in drug development allows for the customization of therapeutic agents, ensuring that treatment regimens are tailored to the genetic makeup of patients, which can drastically improve safety and treatment outcomes. The paper also discusses the regulatory landscape surrounding the introduction of these innovative therapeutic agents. It emphasizes the need for adaptive regulatory frameworks that keep pace with scientific advancements while ensuring patient safety and efficacy. Collaborative efforts between researchers, regulatory bodies, and pharmaceutical companies are essential to facilitate the translation of innovative therapies from the laboratory to clinical practice. In conclusion, this paper underscores the critical importance of strategic scientific innovations in the quest for safer therapeutic agents. By harnessing the potential of advanced technologies, researchers can develop therapeutics that not only meet the growing demand for efficacy but also uphold the highest standards of safety, ultimately improving patient care and health outcomes. The findings presented advocate for continued investment in research and collaboration across disciplines to drive future advancements in therapeutic development. Citation Format: Peter O. David. Navigating the future of therapeutic development: innovations in safety and efficacy through advanced research methodologies and nanotechnology. [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Optimizing Therapeutic Efficacy and Tolerability through Cancer Chemistry; 2024 Dec 9-11; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(12_Suppl):Abstract nr A001

Human-in-the-loop active learning for goal-oriented molecule generation

Machine learning (ML) systems have enabled the modelling of quantitative structure–property relationships (QSPR) and structure-activity relationships (QSAR) using existing experimental data to predict target properties for new molecules. These property predictors hold significant potential in accelerating drug discovery by guiding generative artificial intelligence (AI) agents to explore desired chemical spaces. However, they often struggle to generalize due to the limited scope of the training data. When optimized by generative agents, this limitation can result in the generation of molecules with artificially high predicted probabilities of satisfying target properties, which subsequently fail experimental validation. To address this challenge, we propose an adaptive approach that integrates active learning (AL) and iterative feedback to refine property predictors, thereby improving the outcomes of their optimization by generative AI agents. Our method leverages the Expected Predictive Information Gain (EPIG) criterion to select additional molecules for evaluation by an oracle. This process aims to provide the greatest reduction in predictive uncertainty, enabling more accurate model evaluations of subsequently generated molecules. Recognizing the impracticality of immediate wet-lab or physics-based experiments due to time and logistical constraints, we propose leveraging human experts for their cost-effectiveness and domain knowledge to effectively augment property predictors, bridging gaps in the limited training data. Empirical evaluations through both simulated and real human-in-the-loop experiments demonstrate that our approach refines property predictors to better align with oracle assessments. Additionally, we observe improved accuracy of predicted properties as well as improved drug-likeness among the top-ranking generated molecules.Scientific contributionWe present an adaptable framework that integrates AL and human expertise to refine property predictors for goal-oriented molecule generation. This approach is robust to noise in human feedback and ensures that navigating chemical space with human-refined predictors leverages human insights to identify molecules that not only satisfy predicted property profiles but also score highly on oracle models. Additionally, it prioritizes practical characteristics such as drug-likeness, synthetic accessibility, and a favorable balance between exploring diverse chemical space and exploiting similarity to existing training data.

Commentary: Expanding the horizons of RE-AIM framework in public health research

Objective: This commentary critically examines Yuliawati et al.’s 2024 review, “Analysis of research public health trends with the RE-AIM model and VOSviewer: A literature review,” which investigates the application of the “Reach, Effectiveness, Adoption, Implementation, Maintenance (RE-AIM)” framework in public health research. The review highlights trends in 279 studies conducted between 2017 and 2022 and draws attention to the insufficient focus on environmental health and sanitation interventions within the RE-AIM framework. The objective is to address these gaps and propose future directions for broadening the framework's application. Methods: The commentary evaluates the methodology used by Yuliawati et al., with particular attention to their utilization of the VOSviewer tool to map trends in RE-AIM-based research. It also explores the identified research gaps and incorporates insights from existing literature to examine the barriers that limit RE-AIM’s application in environmental health and sanitation. Results: Yuliawati et al.’s review demonstrates growing interest in the RE-AIM framework, but points out a notable deficiency in its application to environmental health and sanitation interventions. This commentary identifies potential reasons for this gap, such as limited interdisciplinary collaboration and resource constraints, and suggests that addressing these challenges could improve RE-AIM’s effectiveness in these fields. Conclusion: This commentary advocates for future research to overcome the barriers to RE-AIM’s application in environmental health and sanitation. It emphasizes the importance of interdisciplinary approaches and the need for a more adaptable framework to evaluate complex, multi-faceted public health challenges.

Leveraging Loss Aversion and Self-Efficacy: The Role of Water Pricing and Risk in Driving Individual Innovation for Sustainable Water Consumption

Water scarcity is a growing global challenge, requiring effective management strategies to ensure sustainable water use, particularly in regions like West Dorset, U.K., where rising prices and environmental pressures make sustainable consumption practices essential. This study develops and empirically tests an integrated theoretical framework combining Prospect Theory (PT) and Social Cognitive Theory (SCT) to examine the mechanisms driving household water conservation. Using structural equation modelling on data from 429 residents in West Dorset (response rate 78.3%), we investigated how water pricing (WP) and perceived water risk (PWR) influence sustainable water consumption through individual innovativeness (II) and community engagement (CE), with environmental regulations (ERs) as a moderator. The results revealed significant positive effects of WP and PWR on individual innovativeness, which, in turn, strongly predicted community engagement. The relationship between these factors and sustainable water consumption was significantly moderated by ERs. This study makes three key contributions: Firstly, it empirically validates an integrated PT-SCT framework in water conservation behavior. Secondly, it identifies the crucial mediating role of II in translating price and risk perceptions into community action; and lastly, it demonstrates how regulatory frameworks can enhance or inhibit conservation outcomes. These findings inform policy recommendations for tiered pricing structures, community-based conservation initiatives, and adaptive regulatory frameworks. This study provides a comprehensive model for understanding and promoting sustainable water consumption in water-stressed regions worldwide.

Challenges in Wastewater Treatment Plant

Effective wastewater operation and maintenance are critical for ensuring sustainable water management and protecting public health and the environment. However, these systems face numerous challenges, including equipment durability issues, frequent breakdowns in treatment systems, regulatory compliance pressures, and the impacts of climate change. Financial constraints, such as limited budgets and the high costs of advanced technologies, further exacerbate these issues, while human resource challenges, such as skill gaps and inadequate training, hinder operational efficiency. This research paper explores these challenges in detail, drawing on real-world case studies from leading wastewater treatment companies and municipalities worldwide. Innovative solutions, including the adoption of artificial intelligence (AI), the Internet of Things (IoT), and decentralized wastewater treatment systems, are analyzed as potential pathways to address these hurdles. The findings underscore the need for comprehensive strategies that integrate technology, skilled workforce development, and adaptive policy frameworks to improve wastewater management. This paper also highlights the disparities between developed and developing nations in addressing these challenges and provides actionable insights for future improvements.

Equity Assessment of Groundwater Vulnerability and Risk in Drinking Water Supplies in Arid Regions

Groundwater is a vital drinking water source, especially in arid regions, sustaining both urban and rural populations. Its quality is influenced by natural (hydrogeological) and human-driven (demographic, policy) factors, which may pose significant public health risks, especially for communities relying on unregulated water supplies. This study addresses critical gaps by examining groundwater vulnerability and contamination disparities, emphasizing their implications for public health and equitable resource management. It analyzes the impact of socio-hydrogeological factors on arsenic and nitrate levels in groundwater-supplied systems in Arizona, U.S. Methods include spatial analysis, ANOVA, multivariate regression, and cluster analysis. Significant disparities in arsenic and nitrate contamination, including exceedances of regulatory limits, were observed across supply types, aquifer characteristics, jurisdictional oversights, and groundwater management areas. Domestic wells and community water systems showed distinct contamination risks. Groundwater vulnerability was influenced by geological differences (karst vs. alluvial aquifers) and regulatory oversight, with Tribal and State systems facing unique challenges and resource needs. Socioeconomic disparities were evident, with minority communities, institutional facilities, rural areas, and specific housing types disproportionately exposed to higher contaminant levels. These findings unveil the intersection of race, socioeconomic status, and public health risks, offering an adaptable framework for addressing similar groundwater challenges in arid and semi-arid regions globally. This study is innovative in its focus on policy distinctions between private and regulated wells, karst and alluvial aquifers, and State and Tribal jurisdictions. It emphasizes the need for targeted vulnerability assessments and remediation strategies that integrate geological, hydrological, and regulatory factors to address risk disparities in vulnerable communities. These environmental inequities underscore the urgent need for stronger regulations and strategic resource allocation to support marginalized communities. The study recommends enhancing monitoring protocols, prioritizing resource distribution, and implementing targeted policy interventions to ensure equitable and sustainable access to safe drinking water in arid regions.

An Adaptive Metal-Organic Framework Discriminates Xylene Isomers by Shape-Responsive Deformation.

The separation of xylene isomers, especially para-xylene, is a crucial but challenging process in the chemical industry due to their similar molecular dimensions. Here, a flexible metal-organic framework, Ni(ina)2, (ina = isonicotinic acid) is employed to effectively discriminate xylene isomers. The adsorbent with adaptive deformation accommodates the shapes of isomer molecules, thereby translating their subtle shape differences into characteristic framework deformation energies. Through a combination of multiple local flexibility behaviors, Ni(ina)2 exhibits guest-specific structural transformations that energetically favor para-xylene over other isomers. Single-crystal X-ray diffraction, in situ powder X-ray diffraction, and theoretical investigations validate this "adaptive fitting" mechanism, where the degree of structural deformation scales with the mismatch between the molecular shape and pore geometry. As a result, Ni(ina)2 achieves exceptional para-xylene selectivity in both liquid and vapor phase separations, maintaining a high para-xylene purity and productivity. This work demonstrates a novel strategy of exploiting framework flexibility to address challenging molecular separations and provides fresh insights into the design of selective adsorbents.

UDA-GS: A cross-center multimodal unsupervised domain adaptation framework for Glioma segmentation

Gliomas are the most common and malignant form of primary brain tumors. Accurate segmentation and measurement from MRI are crucial for diagnosis and treatment. Due to the infiltrative growth pattern of gliomas, their labeling is very difficult. In turn, the already available annotated datasets, such as well-known BraTS, are difficult to generalize to multicenter unannotated datasets due to the variations in imaging machines and parameters. To address this challenge, a novel unsupervised domain adaptation framework for glioma segmentation (UDA-GS) is proposed. UDA-GS uses GliomaMix to mix labeled tumors with unlabeled images and aligns the features of the same tumor, allowing the network to adapt to different backgrounds across different centers. Additionally, the framework leverages tumor information generated by GliomaMix as prior knowledge for self-supervised regression tasks to enhance feature encoding for tumors in different domains. Using Mean-Teacher as the basic framework, UDA-GS also incorporates weighted consistency regularization and mask combining strategy to achieve efficient unsupervised domain adaptation. Quantitative and qualitative evaluations were conducted on 1179 cases across 27 centers, without requiring any local annotations. The results demonstrate that UDA-GS outperforms the second-best method in terms of Dice coefficient segmentation metrics by 18.2 %, 6.9 %, and 4.6 % for the whole tumor, tumor core, and enhanced tumor, respectively, on the internal testing set. Additionally, the evaluations reveal that doctors express greater satisfaction with the segmentation outcomes achieved by UDA-GS in comparison to other methods including the segment anything model (SAM).

Epistimologi Sebagai Landasan Metodologi Ilmiah untuk Pengembangan Teori Baru Bidang Manajemen Pendidikan Islam

This study aims to identify and analyze the relationship between Islamic epistemology and scientific methodology in the context of Islamic education. This study uses a literature review methodology, data collection through in-depth examination of relevant and credible sources, such as scientific journals, academic books, research articles, and other related documents that support the research topic. The data collection technique involves systematically filtering the literature based on specific criteria, including topic relevance, source credibility, and year of publication, to ensure the accuracy and timeliness of the data. This analytical process helps in developing a comprehensive conceptual framework and building strong arguments for interpreting the research findings. The results of the analysis show that epistemology plays an important role in the development of new theories in the field of Islamic education management. Islamic epistemology, which combines revelation and reason, provides a solid foundation for a scientific methodology that is in line with Islamic values. In addition, an epistemological approach can also help to address the challenges in Islamic education management in the modern era by providing an ethical and adaptive framework

Macroeconomic Factors and their Impact on Stock Market Volatility: Trends, Drivers, and Policy Recommendations

The study investigates how key macroeconomic factors contribute to stock market volatility from 2015 to 2024, a period marked by significant economic fluctuations and policy shifts. Focusing on variables such as GDP growth, inflation, interest rates, and economic policy uncertainty, this research examines their impact on market stability across developed and emerging economies. Using quantitative analysis and secondary data, the study highlights periods of heightened volatility in response to economic shocks, particularly around events like the COVID-19 pandemic and subsequent policy responses. Findings reveal that rising inflation and policy uncertainty are primary drivers of volatility, often exacerbating market instability during economic downturns. Additionally, GDP growth and interest rate changes are shown to influence investor sentiment and asset pricing, underscoring the importance of macroeconomic stability for market resilience. While these macroeconomic fundamentals are crucial in understanding market dynamics, this study also addresses challenges in maintaining stability, such as varying policy responses and global economic interdependence, emphasizing the need for adaptive policy frameworks to mitigate volatility risks. This analysis contributes to the broader understanding of how macroeconomic factors shape financial markets, offering valuable insights for policymakers and investors aiming to navigate uncertain economic landscapes.