Current Limitations Research Articles

3D Printing Hierarchical Porous Nanofibrous Scaffold for Bone Regeneration.

Current limitations in 3D printing pose significant challenges for the fabrication of hierarchical 3D scaffolds with nanofibrous structures that simulate the natural bone extracellular matrix (ECM) for enhanced bone regeneration. This study presents an innovative approach to 3D printing customized hierarchical porous scaffolds with nanofiber structures using biodegradable nanofibrous microspheres as the bio-ink. In vitro investigations demonstrate that the hierarchical porous architecture substantially enhances cell infiltration and proliferation rates, while the nanofiber topology provides physical cues to guide osteogenic differentiation and ECM deposition. When serving as a cell carrier, the 3D-printed nanofibrous scaffold promotes bone tissue regeneration and integration in vivo. Additionally, the facile and versatile chemical modification facilitates the precise tailoring of the scaffold's functionality. Using nanofibrous microspheres with highly biomimetic and versatile modification properties as the foundational constituent in this universal 3D printing methodology enables comprehensive manipulation of scaffolding biological properties, spanning from macroscopic external morphology to molecular-scale biochemical kinetics, thereby addressing a diverse spectrum of clinical requisites.

Predicting cell type-specific epigenomic profiles accounting for distal genetic effects.

Understanding how genetic variants affect the epigenome is key to interpreting GWAS, yet profiling these effects across the non-coding genome remains challenging due to experimental scalability. This necessitates accurate computational models. Existing machine learning approaches, while progressively improving, are confined to the cell types they were trained on, limiting their applicability. Here, we introduce Enformer Celltyping, a deep learning model which incorporates distal effects of DNA interactions, up to 100,000 base-pairs away, to predict epigenetic signals in previously unseen cell types. Using DNA and chromatin accessibility data for epigenetic imputation, Enformer Celltyping outperforms current best-in-class approaches and generalises across cell types and biological regions. Moreover, we propose a framework for evaluating models on genetic variant effect prediction using regulatory quantitative trait loci mapping studies, highlighting current limitations in genomic deep learning models. Despite this, Enformer Celltyping can also be used to study cell type-specific genetic enrichment of complex traits.

Biocomposite Scaffolds for Tissue Engineering: Materials, Fabrication Techniques and Future Directions

Tissue engineering is an interdisciplinary field that combines materials, methods, and biological molecules to engineer newly formed tissues to replace or restore functional organs. Biomaterials-based scaffolds play a crucial role in developing new tissue by interacting with human cells. Tissue engineering scaffolds with ideal characteristics, namely, nontoxicity, biodegradability, and appropriate mechanical and surface properties, are vital for tissue regeneration applications. However, current biocomposite scaffolds face significant limitations, particularly in achieving structural durability, controlled degradation rates, and effective cellular integration. These qualities are essential for maintaining long-term functionality in vivo. Although commonly utilized biomaterials can provide physical and chemical properties needed for tissue regeneration, inadequate biomimetic properties, as well as insufficient interactions of cells-scaffolds interaction, still need to be improved for the application of tissue engineering in vivo. It is impossible to achieve some essential features using a single material, so combining two or more materials may accomplish the requirements. In order to achieve a proper scaffold design, a suitable fabrication technique and combination of biomaterials with controlled micro or nanostructures are needed to achieve the proper biological responses. This review emphasizes advancements in scaffold durability, biocompatibility, and cellular responsiveness. It focuses on natural and synthetic polymer combinations and innovative fabrication techniques. Developing stimulus-responsive 3D scaffolds is critical, as these scaffolds enhance cell adhesion and promote functional tissue formation while maintaining structural integrity over time. This review also highlights the natural polymers, smart materials, and recent advanced techniques currently used to create emerging scaffolds for tissue regeneration applications.

Probing stochastic ultralight dark matter with space-based gravitational-wave interferometers

Ultralight particles are theoretically well-motivated dark matter candidates. In the vicinity of the Solar System, these ultralight particles can be described as a superposition of plane waves, resulting in a stochastic field with sizable amplitude fluctuations on scales determined by the velocity dispersion of dark matter. In this work, we systematically investigate the sensitivity of space-based gravitational-wave interferometers to the stochastic ultralight dark matter field within the frequentist framework. We derive the projected sensitivity of a single detector using the time-delay interferometry. Our results show that space-based gravitational-wave interferometers have the potential to probe unconstrained regions in parameter space and improve the current limit on coupling strengths. Furthermore, we explore the sensitivity of a detector network and investigate the optimal configuration for ultralight dark matter detection. We introduce the overlap reduction function for ultralight dark matter, which quantifies the degree of correlation between the signals observed by different detectors. We find that the configuration, where the signals observed by two detectors are uncorrelated, is the optimal choice for ultralight dark matter detection due to a smaller chance of missing signals. This contrasts with the detection of stochastic gravitational-wave background, where the correlated configuration is preferred. Our results may provide useful insights for potential joint observations involving space-based gravitational-wave detectors like LISA and Taiji, as well as other ultralight dark matter detection networks operating in the coherence limit. Published by the American Physical Society 2024

Exploring the Role of Transformer Models in Vision, Multi-Modal, and Orbital Data for Autonomous Driving

Abstract. Autonomous driving aims to reduce human error in driving, improve traffic efficiency, and provide a more comfortable driving experience. The integration of computer vision, advanced sensors, and machine learning has been pivotal in this advancement. The introduction of Transformer models has particularly revolutionized the field by offering a novel approach to processing data through attention mechanisms, which is crucial for tasks involving complex relationships between data elements. The paper categorizes research into three main approaches based on input data types: camera-based perception, multi-modal data fusion, and orbital data integration. As autonomous driving technology progresses towards higher levels of autonomy, with L2+ systems becoming standard, challenges remain in accurately interpreting complex environments, handling edge cases, and navigating legal and regulatory landscapes. The paper concludes that while Artificial Intelligence (AI) and deep learning advancements have brought autonomous driving closer to full realization, further research is necessary to address current limitations and ensure safe and reliable autonomous vehicle operation.

Medical Image Segmentation

Medical image segmentation is a critical component in the development of computer-aided diagnosis and treatment planning systems. This paper provides a comprehensive survey of recent advances in segmentation techniques applied to various imaging modalities, including Magnetic Resonance Imaging (MRI). Traditional methods such as thresholding, region-growing, and active contours are reviewed alongside contemporary machine learning-based approaches, particularly deep learning models. The survey emphasizes the growing dominance of convolutional neural networks (CNNs) and their variants, including U-Net and Fully Convolutional Networks (FCNs), which have shown remarkable success in handling complex medical imaging challenges. Additionally, the paper discusses hybrid methods that combine classical techniques with artificial intelligence to improve accuracy and robustness in segmentation tasks. Key challenges such as class imbalance, boundary delineation, and computational efficiency are also highlighted. Future directions, including the integration of multi-modal data and advancements in self-supervised learning, are explored as potential solutions to overcome current limitations in medical image segmentation.

Inflammasome-Activating Nanovaccine for Cancer Immunotherapy.

A range of advanced nanovaccines (NV) combined with immunotherapies has recently emerged for treating malignant tumors and has demonstrated promising tumor-suppressive effects. Nevertheless, their effectiveness is often limited by immunosuppression within the tumor microenvironment. To overcome this challenge, new approaches for NV development are required to improve antigen cross-presentation and to remodel the tumor microenvironment. In this issue of Cancer Research, Zhou and colleagues have developed a photo-enhanceable inflammasome-activating nanovaccine (PIN) designed for precise, in situ delivery of a tumor antigen and a hydrophobic small molecule that activates the NLRP3 inflammasome pathway. Near infrared light exposure enables the accumulation of PINs at tumor sites by inducing a photo-triggered charge reversal in the BODIPY-modified PAMAM nanocarrier. Systemic administration of PINs resulted in effective intratumoral activation of the NLRP3 inflammasome and antigen cross-presentation in antigen-presenting cells upon light exposure, leading to enhanced immune responses through increased proinflammatory cytokine production without significant systemic toxicity. Importantly, PINs also enhanced the efficacy of immune checkpoint blockade and promoted the development of long-term immune memory in mouse models of melanoma and hepatocellular carcinoma. Overall, inflammasome-activating NVs represent a cancer immunotherapy strategy by harnessing the innate immune system to achieve robust responses against tumors. Ongoing research and development are crucial to addressing current limitations and advancing this innovative technology toward clinical application. See related article by Zhou et al., p. 3834.

Blockchain Integration in Critical Systems Enhancing Transparency, Efficiency, and Real-Time Data Security in Agile Project Management, Decentralized Finance (DeFi), and Cold Chain Management

Blockchain technology has garnered significant attention for its potential to revolutionize critical systems by enhancing transparency, efficiency, and data security. This review examines the integration of blockchain in three essential domains: Agile Project Management, Decentralized Finance (DeFi), and Cold Chain Management. By leveraging decentralized ledgers and smart contracts, blockchain provides a robust framework for real-time tracking, data integrity, and automated compliance, addressing long-standing challenges across these sectors. In Agile Project Management, blockchain fosters seamless collaboration and transparent decision-making, minimizing bottlenecks and improving accountability. In DeFi, blockchain strengthens security for digital transactions and identity verification while offering financial autonomy and mitigating fraud risks. Within Cold Chain Management, blockchain ensures traceability, reduces data tampering risks, and enhances visibility throughout supply chain processes, safeguarding temperature-sensitive goods. This paper evaluates existing blockchain-based applications and frameworks, identifies current limitations, and discusses future opportunities for optimizing critical systems through blockchain. The findings highlight blockchain's transformative role in driving operational efficiency, security, and data transparency across diverse applications, providing a roadmap for industries to harness its full potential in critical environments.

The relationship between neuroticism as a personality trait and mindfulness skills: a scoping review

IntroductionIn recent decades, researchers have assessed the relationship between mindfulness and personality traits, including neuroticism, a known target in mental health associated with the development of mental health disorders and physical illnesses. The main aim of mindfulness practice is to help individuals develop the ability to regulate and accept their experiences, emotions, and thoughts. Therefore, it could be suggested that mindfulness may be useful in reducing the expression and negative experience of neuroticism. The aim of our review was to assess the relationship between neuroticism and mindfulness.MethodsWe conducted a scoping review of the literature in December 2023, using the databases PubMed and PsycINFO.ResultsForty-nine studies were included in the review, with four common themes identified: (i) mental health, (ii) cognitive outcomes, (iii) physiological symptoms, and (iv) mindfulness-based interventions. Across most of the studies, mindfulness negatively correlated with neuroticism, supporting the idea that mindfulness may be useful in reducing neuroticism and its negative effects on mental and physical health.DiscussionWhile several limitations were identified, the overall results are promising. Future research in this area should focus on overcoming the current limitations to provide a better understanding of the relationship between mindfulness and neuroticism.

Prospects of anti-GD2 immunotherapy for retinoblastoma

Retinoblastoma is the most common type of eye tumor in infants and children. Current treatments for retinoblastoma include intravenous chemotherapy, intra-arterial chemotherapy, intravitreal chemotherapy, cryotherapy, radiotherapy, and surgery. However, these treatments come accompanied by adverse effects such as the toxic side effects of chemotherapeutic drugs, post-operative complications including blindness after surgery, or other complications caused by radiotherapy. Immunotherapy is more promising for its low toxicity on normal cells and effectively improves the quality of life of patients. Disialoganglioside (GD2), a sphingolipid expressed on the surface of retinoblastoma, is a potential therapeutic target for retinoblastoma. We summarized immunotherapeutic approaches for both preclinical studies and clinical trials of GD2. An anti-GD2 monoclonal antibody (Dinutuximab), which has been approved for the treatment of high-risk neuroblastomas, has shown promising efficacy in improving patients’ prognosis. Additionally, chimeric antigen receptors (CAR)-T therapy, GD2 vaccines and nanoparticles are also potential therapeutics. Finally, we discuss the prospects and current limitations of these immunotherapeutic approaches for treating retinoblastoma, as well as how to address these problems.

Functionalization of Emulsion Interfaces: Surface Chemistry made Liquid.

Disperse systems, and emulsions in particular, are currently massively used in fields as varied as food industry, cosmetics, health care and environmentally-friendly materials. To meet increasingly precise needs or targeted applications, these systems need to be endowed with new functionalities at their interfaces, in addition to their composition and structural properties. However, due to the fragility of drops and the low reactivity of their surface, conventional solid surface chemistry cannot be used for such a purpose. Several specific emulsion interface functionalization techniques have thus been developed for targeted systems and applications, but a general framework has yet to be drawn. In this review, we attempt to present these methods in a unified way through the prism of what we may call "liquid surface chemistry". We propose to categorize existing methods into drop-coating strategies, including layer-by-layer techniques and polymer coating, and emulsifier-carrier approaches involving particles and/or amphiphilic molecules. They are discussed in a transversal way, highlighting the underlying physico-chemical principles and providing a comparative analysis of their advantages, current limitations and potential for improvement. We also propose future directions and opportunities, involving for instance DNA-based programmability or artificial intelligence, which could make liquid surface chemistry more versatile and controlled.

Predatory pricing in platform markets: a modified test for firms within the scope of Article 3 of the DMA and super-dominant platform firms under Article 102 TFEU

ABSTRACT The paper examines predatory pricing in the context of two-sided digital platforms, arguing that traditional tests based on Average Variable Cost (AVC) may be inadequate for these markets. While predatory pricing by dominant firms is prohibited in both EU and US competition law, the current standards may not effectively capture predatory behavior in platform markets characterized by strong network effects and low marginal costs. The paper analyses cases where cross-subsidization between platform sides had predatory elements and resulted in findings of abuse of dominant position. Given platforms' unique characteristics, it proposes a modified test under Article 102 TFEU for super-dominant platforms and those within the scope of Article 3 of Digital Markets Act's scope. The proposal extends the Akzo test by presuming prices below Average Total Cost (ATC) to be abusive, rather than using AVC, with LRAIC as a proxy for ATC. This addresses the current test's limitations for low marginal cost businesses while allowing for objective justification.

Organoid-based precision medicine in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) ranks among the leading causes of cancer-related deaths worldwide. Despite advances in precision oncology in other malignancies, treatment of PDAC still largely relies on conventional chemotherapy. Given the dismal prognosis and heterogeneity in PDAC, there is an urgent need for personalized therapeutic strategies to improve treatment response. Organoids, generated from patients' tumor tissue, have emerged as a powerful tool in cancer research. These three-dimensional models faithfully recapitulate the morphological and genetic features of the parental tumor and retain patient-specific heterogeneity. This review summarizes existing precision oncology approaches in PDAC, explores current applications and limitations of organoid cultures in personalized medicine, details preclinical studies correlating in vitro organoid prediction and patient treatment response, and provides an overview of ongoing organoid-based clinical trials.

Association between maternal dietary diversity during pregnancy and birth outcomes: evidence from a systematic review and meta-analysis.

Maternal nutrition is a key factor influencing birth and offspring health outcomes in later life. Dietary diversity (DD) is a proxy for the macro/micronutrient adequacy of an individual's diet. There is inadequate comprehensive evidence regarding maternal nutrition during pregnancy, measured through DD and birth outcomes. This study aimed to provide extensive evidence on maternal DD during pregnancy and birth outcomes. A comprehensive search was performed using PubMed, HINARI, and Google Scholar databases up to January 17, 2024. Studies conducted among pregnant mothers and measuring maternal DD with an evaluation of birth outcomes (low birth weight, small for gestational age, preterm birth), in the global context without design restriction were included. The Newcastle Ottawa Scale and the Cochrane Risk of Bias tool were used to assess the risk of bias. The results are summarized in a table, and odds ratios were pooled where possible. Between-study heterogeneity was evaluated using I2 statistics. Potential publication bias was assessed using a funnel plot and Egger's regression test. To explore the robustness, a leave-one-out sensitivity analysis was conducted. Thirty-three studies were used to synthesize narrative evidence (low birth weight: 31, preterm birth: 9, and small for gestational age: 4). In contrast, 24 records for low birth weight, eight for preterm birth, and four for small for gestational age were used to pool the results quantitatively. Of the 31 studies, 17 reported a positive association between maternal DD and infant birth weight, 13 studies reported a neutral association (not statistically significant), and one study reported a negative association. Overall, inadequate DD increased the risk of low birth weight OR = 1.71, 95% CI; (1.24-2.18), with I2 of 68.7%. No significant association was observed between maternal DD and preterm birth. Inadequate DD was significantly associated with small for gestational age (OR = 1.32, 95% CI; 1.15-1.49, and I2 = 0.0%). Inadequate maternal DD is associated with an increased risk of low birth weight and small for gestational age but not preterm birth, underscoring the importance of promoting adequate DD during pregnancy. To address these issues, it is essential to implement and expand nutritional programs targeted at pregnant women, especially in low-resource settings, to ensure they receive diverse and adequate diets. Further research is needed to address the current limitations and to explore the long-term implications of maternal nutrition on child health. The study was prospectively registered on PROSPERO (registration number CRD42024513197). No funding was received for this study.

An enhanced broadband piezoelectric energy harvester via elastic amplification structure for multidirectional vibration

Abstract Vibration energy harvesting using a piezoelectric mechanism has significant potential for powering wireless sensors. However, most current vibration energy harvesters face limitations such as bidirectionality, narrow bandwidth, and high operating frequencies. To address these issues, we propose an enhanced broadband piezoelectric energy harvester utilizing an elastic amplification structure for multidirectional vibration (EB-PVEH). By utilizing the multidirectional rotation capacity of the excitation block and the amplified foundation excitation provided by springs, the EB-PVEH effectively captures broadband vibrations in 2D space under low-frequency excitation. Additionally, its design features long-term durability, as the piezoelectric beams are smoothly excited by the pendulum-induced motion of the block without a tip mass. The practical feasibility and the impact of structural parameters on the output behavior of EB-PVEH were investigated through theoretical analysis and experimental testing. The results revealed that the introduction of springs dynamically amplified the harnessed electrical power output. Moreover, EB-PVEH could harvest the multidirectional vibration, and it exhibited different power-generating characteristics in various directions. Furthermore, the resonance frequency could be efficiently tuned by adjusting the flexible arm length and proof mass, with different optimal arm lengths identified for each vibration direction to maximize working bandwidth. The harvester achieved an optimal output power of 3.98 mW. Practical applications, such as charging a capacitor by driving an e-bike or a bike, demonstrate the potential of the proposed harvester to provide power for micro-electrical devices.

The Evolution of Data Visualization Tools and Their Impact on Business Decision-Making : A Historical Analysis

This article examines the transformative evolution of data visualization tools and their profound impact on business decision-making processes. Through a comprehensive analysis of historical developments, theoretical frameworks, and technological advancements, the article explores how visualization tools have progressed from basic manual charts to sophisticated AI-powered analytical platforms. The investigation encompasses the cognitive foundations of visual information processing, the emergence of first-generation Business Intelligence tools, and the current landscape of modern visualization platforms. Particular attention is given to the integration of artificial intelligence in data visualization, including automated generation, pattern recognition, and predictive analytics capabilities. The article presents both quantitative and qualitative assessments of these tools' impact on organizational decision-making, highlighting significant improvements in decision speed, accuracy, and user engagement. The article reveal substantial benefits in terms of ROI, decision-making efficiency, and organizational adoption patterns. The article concludes by examining emerging trends, including the potential of extended reality technologies and edge computing, while acknowledging current limitations and providing recommendations for future research directions in this rapidly evolving field.

Exploring the Origins of Hexaploid Wheats: Typification of Archaeological Triticum vulgare var. antiquorum and Description of Modern Triticum sphaerococcum subsp. antiquorum (Poaceae: Triticeae)

This study addresses a critical issue in plant taxonomy and phylogeny: the relationship between archaeological materials and potentially analogous living populations. Given the current limitations in definitively establishing the identity between archaeological and contemporary materials, we propose an intermediate approach. This approach serves as a useful framework while scientific methods advance towards definitively assessing whether an archaeological wheat sample, approximately 5000 years old from Central Europe, belongs to the same species as a modern wheat currently endemic to Central Asia. This approach consolidates the taxonomic validity of both archaeological and living materials, allowing them to be treated as distinct taxa while preserving the possibility of future identification convergence. Triticum vulgare var. antiquorum, an archaeobotanical small-grained, free-threshing wheat, was originally described in 1865. The 1982 discovery of morphologically similar living wheat in Tajikistan raised questions about their taxonomic relationship. Our study reviews the nomenclature of both taxa, designating an illustration from the original description of T. vulgare var. antiquorum as the lectotype to align with the traditional concept of the name. We address the ambiguity surrounding “Triticum antiquorum” as used by Russian agronomists and botanists, proposing a more precise circumscription within the current systematic framework of the genus based on cytogenetic data. Consequently, we describe a new taxon, Triticum sphaerococcum subsp. antiquorum. The holotype, selected from material with available cytogenetic data and grown from Professor Udachin’s original Pamir (Tajikistan) collection, is preserved in the N.I. Vavilov All-Russian Institute of Plant Genetic Resources (WIR) collection. It is deposited at the I.M. Krasnoborov Herbarium of Central Siberian Botanical Garden SB RAS (NS), with an isotype at the WIR. This taxonomic revision and new subspecies designation provide a robust framework for reconciling archaeological and contemporary wheat diversity, advancing our understanding of wheat evolution and agricultural history.

Long-term machine perfusion of human split livers: a new model for regenerative and translational research.

Recent advances in machine perfusion have revolutionised the field of transplantation by prolonging preservation, permitting evaluation of viability prior to implant and rescue of discarded organs. Long-term perfusion for days-to-weeks provides time to modify these organs prior to transplantation. By using long-term normothermic machine perfusion to facilitate liver splitting and subsequent perfusion of both partial organs, possibilities even outside the clinical arena become possible. This model remains in its infancy but in the future, could allow for detailed study of liver injury and regeneration, and ex-situ treatment strategies such as defatting, genetic modulation and stem-cell therapies. Here we provide insight into this new model for research and highlight its great potential and current limitations.

Abstract 4116141: Von Willebrand Factor Inhibition by BB-031 Prevents and Thrombolyses Clots in a Murine Model of Venous Thrombosis

Introduction: Venous thromboembolism (VTE) manifesting as deep vein thrombosis and pulmonary embolus results in ~300,000 US deaths annually. Risk factors include obesity and current therapy limitations leave ~ 50% of patients with no effective treatment. Circulating von Willebrand Factor (VWF) levels portend poor clinical outcomes. BB-031, an aptamer-based VWF inhibitor with prophylactic and arterial thrombolytic efficacy may translate to VTE. Methods: Jugular vein occlusion (JVO) was induced by FeCl 3 either before or after intravenous treatment in 14 week old male and female C57/BL6 wild type (WT) or male diet-induced obese (DIO) mice. 5.0 mg/kg BB-031 was administered as a bolus, 10.0 mg/kg rtPA with 10% bolus and remaining 90% over 60 minutes, 400 U heparin as a bolus, or vehicle. Doppler jugular vein flow velocity was recorded from baseline to determine patency 30 minutes after injury. Thrombolytic clots were stabilized for 20 minutes. Results: Prophylactic mice occluded ~20 minutes after FeCl 3 application with rtPA=18.32 +/- 6.54 min, heparin 17.14 +/- 8.53 min, and vehicle 17.29 +/- 7.26 min. Thrombosis did not occur with BB-031 treatment (n=4-12/group). Treatment after JVO resulted in no difference compared to vehicle in male WT mice, but BB-031-treated WT females resulted in 76.09% recanalization 35 minutes after occlusion compared to rtPA (9.64%, p<0.0001), heparin (30.95%, p=0.7306), or vehicle (22.07%, p<0.0001), (n=5/group). Male DIO mice significantly reperfused with BB-031 (100%) vs rtPA (10.52%), heparin (4.54%), and vehicle (22.50%), p<0.0001, (n=4-6/group). Conclusion: VWF inhibition by BB-031 maintains patency in a VTE murine model offering future therapeutic alternatives.

Model and Ensemble Indicator-Guided Assessment of Robust, Exploitable Groundwater Resources for Denmark

Groundwater constitutes 99% of the Earth’s liquid freshwater and is crucial for human health, economic development, and ecosystem sustainability. This study assesses groundwater sustainability in Denmark by employing a comprehensive hydrological model and a set of ensemble indicators. The paper describes the methodology and the results based on nine selected indicators. Three indicators focus on recharge capture and aquifer sustainability, one focuses on groundwater level and wetland capture, two focus on baseflow and drainage flow capture, and three focus on eco flow capture. Our findings highlight that while overall exploitable groundwater resources are estimated at 1.1 billion m3/year, significant regional disparities exist, with certain areas, notably Zealand, facing over-exploitation rates exceeding 250% of sustainable limits. The indicators developed not only provide a framework for assessing current groundwater resource limits, but also serve as a basis for future monitoring and adaptive management strategies. This research underscores the need for stakeholder engagement and integrated approaches to ensure the sustainability of groundwater resources in the face of growing anthropogenic pressures and climate change. Our work contributes to the ongoing discourse on sustainable water management and offers a robust methodology for assessing groundwater sustainability.