Data Access Research Articles

Leveraging natural capital accounting to support businesses with nature-related risk assessments and disclosures.

Nature loss threatens businesses, the global economy and financial stability. Understanding and addressing these risks for business will require credible measurement approaches and data. This paper explores how natural capital accounting (NCA) can support business data and information needs related to nature, including disclosures aligned with the Taskforce on Nature-related Financial Disclosures recommendations. As businesses seek to measure, manage and disclose their nature-related risks and opportunities, they will need well-organized, consistent and high-quality information regarding their dependencies and impacts on nature, which few businesses currently collect or track in-house. NCA may be useful for these purposes but has not been widely used or applied by businesses. National NCA guided by the U.N. System of Environmental-Economic Accounting may provide: (i) a useful framework for businesses in conceptualizing, organizing and managing nature-related data and statistics; and (ii) data and information that can directly support business disclosures, corporate NCA and other business applications. This paper explores these opportunities as well as synergies between national and corporate natural capital accounts. In addition, the paper discusses key barriers to advancing the wider use and benefits of NCA for business, including: awareness of NCA, data access, business capabilities related to NCA, spatial and temporal scales of data, audit and assurance considerations, potential risks, and costs and incentives. This article is part of the theme issue 'Bringing nature into decision-making'.

From Barriers to Boosters: Optimizing Open Government Data through Publishing Guidelines and Data Protection Strategies

Open Government Data (OGD) is a global endeavor, a collaborative effort between governments worldwide to share datasets that encapsulate a wide spectrum of government activities, from environmental issues like pollution and climate to social aspects like education and childcare, and urban concerns like traffic and congestion, and healthcare statistics. As governments, being among the largest producers and collectors of data, are making OGD available online in diverse formats, primarily Word, PDF, or Excel, they are contributing significantly to this global initiative. The OGD initiative holds immense potential to revolutionize the way we access and use government data. Its primary objective is to enhance the discoverability, accessibility, and availability of data in alternative and preferably machine-readable formats. This, in turn, empowers a diverse set of stakeholders to develop innovative data applications under licensing schemes that permit unrestricted reuse. Despite these promising aspects, challenges such as data heterogeneity, data protection, data quality, and data provenance issues persist. This study aims to analyze and categorize these challenges and obstacles that hinder the OGD initiative from realizing its full potential, with a particular emphasis on data protection and security concerns for data providers.

Deciphering inter-catchment groundwater flow: A water balance perspective in the Choshui River Basin, Taiwan

Study regionFive catchments in upstream Choshui River Basin, Taiwan. Study focusInter-catchment groundwater flow (IGF)is commonly assumed negligible role in a catchment water balance due to its obscure nature. Many studies required detailed on-site geological information to analyze and confirm the significance of IGF. In contrast to these studies, this study investigated IGF using the rainfall-runoff model combined with the deterministic optimization algorithm and the stochastic generalized likelihood uncertainty estimation method, relying on simple and easily accessible water balance data. New hydrological insights for the regionThe results showed that the hydrological environment was non-stationary, and IGF inclusion in the model improved its simulation performance. Though IGF direction varied among the catchments, at least three were found with significant IGF potential, with one of the catchments accounting for 30 % (505 mm) of the yearly precipitation. Ignoring IGF may lead to larger errors, especially in smaller catchments and during the dry season. Overall, this study highlights the importance of considering IGF in a catchment water balance and provides insights into the hydrological cycle.

Analysis of the parasitic capacitance effects on the layout of latch-based sense amplifiers for improving SRAM performance

Static random-access memory (SRAM) technology is utilized in designing cache memory to enhance the processing performance of computer systems. The sense amplifier (SA) circuit, a crucial component of memory design, significantly impacts data access time and power consumption. In comparison to conventional differential sense amplifiers (DSA) designs, latch-based sense amplifiers (LSA) used in memory-based computing platforms have specific requirements, including robust noise resistance in harsh working environments and low power consumption, particularly for internet of thing (IoT) embedded computing applications. However, the performance can be degraded due to various factors that arise during the layout, such as conductor resistance or the development of parasitic capacitance. Therefore, this study employs low-voltage 22 nm UMC CMOS technology for LSA design layout and analyzes the factors influencing design performance post-layout process. Layout design optimization techniques are applied to mitigate the impact of parasitic capacitance on important signal lines such as data line/data line bar (DLL/DLLB). Based on the performance analysis results, it is possible to achieve a reduction in power consumption of up to 15% and a 5% decrease in read delay time by implementing circuit layout LSA design optimization techniques.

Mobile Tracking System Using SMTP Protocol

Abstract: The objective of this project is to enhance mobile security through a robust Android application capable of tracking, locking, and recovering lost or stolen mobile devices. Utilizing the capabilities of Android devices, the system offers real-time tracking, remote locking, device wiping, and geo fencing features. It supports Android OS and allows users to control their devices remotely via SMS commands or through a web-based dashboard using their account credentials. Users can track the location of their device, remotely lock it, and even manipulate the camera to capture images of the possessor. The rising incidence of mobile device theft, which often results in unauthorized access and personal data breaches, underscores the importance of this sophisticated Mobile Tracking System (MTS). This system addresses these security concerns by providing comprehensive functionalities that enhance user safety and device recovery.

Blockchian Based Hybrid Consensus Algorithm for Data Security in Edge Computing

Abstract: Edge computing has emerged as a paradigm to process data closer to its source, enabling low-latency and real-time applications. However, the distributed and heterogeneous nature of edge environments poses significant challenges for ensuring data security and integrity. Blockchain technology offers a promising solution by providing a decentralized and immutable ledger for recording transactions. This abstract proposes a consensus algorithm tailored for securing data in edge computing environments using blockchain technology. The proposed consensus algorithm leverages a hybrid approach combining Proof of Authority (PoA) and Practical Byzantine Fault Tolerance (PBFT). PoA ensures that only trusted nodes within the edge network are eligible to participate in the consensus process, mitigating the risk of malicious attacks. PBFT enhances the efficiency and scalability of the consensus algorithm by allowing fast decision-making among a subset of nodes, thereby reducing latency in data validation and verification. Furthermore, smart contracts are employed to enforce data access control and execute predefined rules for data processing and sharing. These smart contracts facilitate secure and transparent interactions among edge devices, ensuring that only authorized parties can access and manipulate sensitive data. The integration of blockchain technology with edge computing offers several benefits, including enhanced data security, traceability, and accountability. By employing a tailored consensus algorithm, edge computing environments can effectively mitigate security threats while maintaining low-latency data processing capabilities. Future research directions may explore optimization techniques to further improve the performance and scalability of the proposed solution in large-scale edge networks.

Enhancing Administrative Penalty Enforcement in Market Supervision through Block chain-based Control and Timeliness Assurance

Abstract: This study delves into the complexities of market supervision in China, aiming to explore the integration of blockchain technology into this regulatory landscape. It primarily focuses on tackling challenges related to data access control and timely enforcement of administrative penalties using blockchain. Leveraging its decentralized and immutable ledger system, blockchain offers a promising solution to enhance security and transparency across sectors. By applying blockchain to market supervision, the objective is to establish a robust platform for managing administrative penalties efficiently and accountably. To assess its viability, sophisticated software simulation techniques are employed. Rigorous analysis evaluates security protocols, data availability, and overall system performance. This assessment is vital for identifying vulnerabilities and ensuring compliance with China's regulatory standards. Additionally, the study recognizes the importance of advancing blockchain technology continually. Staying updated on emerging trends is crucial for optimizing system performance, bolstering data privacy, and ensuring legal compliance. By addressing these areas, the study aims to provide actionable insights for designing and implementing blockchain solutions in China's market supervision. Through collaborative efforts and knowledge sharing, it seeks to drive innovation and enhance transparency, efficiency, and accountability in the market environment

Smart Autonomous Nurse Assistant Robot (SANAR)

Abstract: In the evolving landscape of healthcare, the integration of innovative technologies has become paramount to enhance efficiency, accessibility, and usability across all sectors, particularly in the medical industry. This project proposes the development of a Smart Autonomous Nurse Assistant Robot (SANAR) aimed at revolutionizing medical care delivery, especially during emergencies and pandemics like Covid-19. SANAR utilizes cutting-edge technology to streamline disease diagnosis, monitor patient health in real-time, and facilitate drug delivery. The core of SANAR's functionality lies in its utilization of a health tracker system-based IoT platform, which continuously analyzes vital patient data, including oxygen levels, pulse rate, and temperature. Through simple text commands and a timer-based medicine scheduler, SANAR ensures timely medication administration, leveraging a cloud server for seamless data access and management with utmost security. Equipped with advanced sensors such as oximeter, heartbeat, ultrasonic sensors, and a voice module, SANAR leverages Internet of Things (IoT) technology to enable data storage and retrieval over HTTP and MQTT protocols, facilitating connectivity and scalability. The primary goal of this project is to democratize smart automation in healthcare, making it accessible and affordable to all segments of society, ultimately empowering medical staff to deliver efficient and effective care.

M-fisheries prototype: Innovating towards evidence-based planning and decision-making in marine fisheries management

Kenya’s marine fisheries sector has the potential for increased growth. However, fisheries managers in Kenya currently face the challenge of poor, missing or aggregated catch data. This paper explores Information and Communication Technology (ICT)-based solutions in the fisheries sector. The study adopts exploratory research design. It reports a review and analysis of two ICT-based solutions in the fisheries sector, conducted in the year 2020. From the analysis, the study proposes the development of M-Fisheries Prototype, as an answer to the challenges of the two reviewed solutions. The prototype introduces an SMS/USSD app for data collection. The SMS/USSD app aligns to the mobile-rich but internet-poor context of the fish landing sites, as it addresses the challenges of the other mobile-based systems that only require the use of smartphones. These challenges include infrastructural accessibility, technical usability and internet data costs.

Evolution of publicly available large language models for complex decision-making in breast cancer care.

This study investigated the concordance of five different publicly available Large Language Models (LLM) with the recommendations of a multidisciplinary tumor board regarding treatment recommendations for complex breast cancer patient profiles. Five LLM, including three versions of ChatGPT (version 4 and 3.5, with data access until September 3021 and January 2022), Llama2, and Bard were prompted to produce treatment recommendations for 20 complex breast cancer patient profiles. LLM recommendations were compared to the recommendations of a multidisciplinary tumor board (gold standard), including surgical, endocrine and systemic treatment, radiotherapy, and genetic testing therapy options. GPT4 demonstrated the highest concordance (70.6%) for invasive breast cancer patient profiles, followed by GPT3.5 September 2021 (58.8%), GPT3.5 January 2022 (41.2%), Llama2 (35.3%) and Bard (23.5%). Including precancerous lesions of ductal carcinoma in situ, the identical ranking was reached with lower overall concordance for each LLM (GPT4 60.0%, GPT3.5 September 2021 50.0%, GPT3.5 January 2022 35.0%, Llama2 30.0%, Bard 20.0%). GPT4 achieved full concordance (100%) for radiotherapy. Lowest alignment was reached in recommending genetic testing, demonstrating a varying concordance (55.0% for GPT3.5 January 2022, Llama2 and Bard up to 85.0% for GPT4). This early feasibility study is the first to compare different LLM in breast cancer care with regard to changes in accuracy over time, i.e., with access to more data or through technological upgrades. Methodological advancement, i.e., the optimization of prompting techniques, and technological development, i.e., enabling data input control and secure data processing, are necessary in the preparation of large-scale and multicenter studies to provide evidence on their safe and reliable clinical application. At present, safe and evidenced use of LLM in clinical breast cancer care is not yet feasible.

Spatio-temporal features based deep learning model for depression detection using two electrodes

Deep learning has made significant contributions to the medical field and has shown great potential in various applications. Its ability to process vast amounts of data and extraction of patterns has enabled breakthroughs in medical research, diagnosis and treatment. The application of deep learning plays a vital role in depression detection. Depression is a neurological disorder characterized by persistent feelings of sadness, hopelessness and a lack of interest. The prevalence of depression is a significant factor contributing to the rise in suicide cases on a global scale. The electroencephalogram (EEG) is a non-invasive technique used to detect depression. It records brain activity using multiple electrodes. The number of EEG electrodes used for measurement directly affects the instrumentation and measurement complexity of the experiment. The present manuscript proposes a deep learning model for depression detection, focusing on two electrodes named FP1 and FP2. The purpose of employing two electrodes is to enhance the system’s portability while reducing data acquisition time and system cost. EEG is spatio-temporal data and possesses inherent spatial and temporal features. The present manuscript proposes a methodology for extracting temporal and spatial features. The temporal feature extraction module extracts temporal features in the time domain and the spatial module extracts spatial features in the spatial domain. This manuscript presents a study on the applicability of two electrodes for depression detection. This research can enhance accessibility, user-friendliness and easier data collection and analysis. The proposed deep learning model is evaluated on two benchmark datasets. It achieves 93.41% classification accuracy, 92.54% precision, 93.23% recall, 93.06% F1 score and 97.80% area under the curve (AUC) for Hospital University Sains Malaysia dataset and for Multi-modal Open Dataset for Mental-disorder Analysis dataset it achieves 79.40% accuracy, 81.18% precision, 67.73% recall, 73.80% F1 score and 85.66% AUC.

Integrated impact of urban mixed land use on TOD ridership: A multi-radius comparative analysis

The global trend toward urbanization has spurred the widespread adoption of transit-oriented development (TOD). While previous research has extensively explored the relationship between land use and TOD ridership, much of it has focused on linear associations at a singular scale. Leveraging recent advancements in nonlinear modeling and the accessibility of open-source data, this study employs a comprehensive two-step methodology. Firstly, K-means clustering algorithm categorizes TOD sites in Shenzhen into three distinct clusters, providing a site-based understanding of their characteristics. Subsequently, a Light Gradient Boosting Machine (LightGBM) classification model, complemented by SHapley Additive exPlanations (SHAP) values for interpretation, quantitatively evaluates the influence of mixed land use on TOD ridership across various catchment areas. As for the findings, we discover that land-use factors have different effects on TOD site patronage at different buffer radii and delve into the intricacies of these effects. Further results reveal non-linear relationships with varying degrees of positivity and negativity. For instance, residents and health sites positively impact patronage across all buffer radii, while certain commercial land uses exhibit a negative influence. The study demonstrates how the importance of different land-use structures varies across these clusters, shedding light on the nuanced impacts of land use on TOD catchment areas. Our research optimizes land-use mixes based on predominant cluster characteristics by offering actionable recommendations for urban managers.

More is better? The role of strategic data management in a lean manufacturing process

Purpose Lean manufacturing has been pivotal in emphasizing the reduction of cycle times, minimizing manufacturing costs and diminishing inventories. This research endeavors to formulate a lean data management paradigm, through the design and execution of a strategic edge-cloud data governance approach. This study aims to discern anomalous or unforeseen patterns within data sets, enabling an efficacious examination of product shortcomings within manufacturing processes, while concurrently minimizing the redundancy associated with the storage, access and processing of nonvalue-adding data. Design/methodology/approach Adopting a lean data management framework within both edge and cloud computing contexts, this study ensures the preservation of significant time series sequences, while ascertaining the optimal quantity of normal time series data to retain. The efficacy of detected anomalous patterns, both at the edge and in the cloud, is assessed. A comparative analysis between traditional data management practices and the strategic edge-cloud data governance approach facilitates an exploration into the equilibrium between anomaly detection and space conservation in cloud environments for aggregated data analysis. Findings Evaluation of the proposed framework through a real-world inspection case study revealed its capability to navigate alternative strategies for harmonizing anomaly detection with data storage efficiency in cloud-based analysis. Contrary to the conventional belief that retaining comprehensive data in the cloud maximizes anomaly detection rates, our findings suggest that a strategic edge-cloud data governance model, which retains a specific subset of normal data, can achieve comparable or superior accuracy with less normal data relative to traditional methods. This approach further demonstrates enhanced space efficiency and mitigates various forms of waste, including temporal delays, storage of noncontributory normal data, costs associated with the analysis of such data and excess data transmission. Originality/value By treating inspected normal data as nonvalue-added, this study probes the intricacies of maintaining an optimal balance of such data in light of anomaly detection performance from aggregated data sets. Our proposed methodology augments existing research by integrating a strategic edge-cloud data governance model within a lean data analytical framework, thereby ensuring the retention of adequate data for effective anomaly detection.

AN EFFICIENT DATA SECURITY IN MEDICAL REPORT USING BLOCKCHAIN TECHNOLOGY

The 'SwiftApply Assistant' addresses the common challenge of time consuming data entry across a multitude of applications. This innovative tool simplifies interactions with forms such as job applications, admissions, claims, scholarships, and healthcare enrollment by automating the process of filling HTML forms. Noteworthy features include its adaptability to various life stages and a user centric design aimed at streamlining the application process while offering control and customization options. With cross platform accessibility ensuring convenience and scalability to adapt to emerging application types, users can expect a significant reduction in time and effort by eliminating the need for repetitive data entry. Key Words: Block chain, Medical Report, Data Accessibility, Security, Encryption, Data Retrieval.

Framework to Create Inventory Dataset for Disaster Behavior Analysis Using Google Earth Engine: A Case Study in Peninsular Malaysia for Historical Forest Fire Behavior Analysis

This study developed a comprehensive framework using Google Earth Engine to efficiently generate a forest fire inventory dataset, which enhanced data accessibility without specialized knowledge or access to private datasets. The framework is applicable globally, and the datasets generated are freely accessible and shareable. By implementing the framework in Peninsular Malaysia, significant forest fire factors were successfully extracted, including the Keetch–Byram Drought Index (KBDI), soil moisture, temperature, windspeed, land surface temperature (LST), Palmer Drought Severity Index (PDSI), Normalized Vegetation Index (NDVI), landcover, and precipitation, among others. Additionally, this study also adopted large language models, specifically GPT-4 with the Noteable plugin, for preliminary data analysis to assess the dataset’s validity. Although the plugin effectively performed basic statistical analyses and visualizations, it demonstrated limitations, such as selectively dropping or choosing only relevant columns for tests and automatically modifying scales. These behaviors underscore the need for users to perform additional checks on the codes generated to ensure that they accurately reflect the intended analyses. The initial findings indicate that factors such as KBDI, LST, climate water deficit, and precipitation significantly impact forest fire occurrences in Peninsular Malaysia. Future research should explore extending the framework’s application to various regions and further refine it to accommodate a broader range of factors. Embracing and rigorously validating large language model technologies, alongside developing new tools and plugins, are essential for advancing the field of data analysis.

Smart Water Pollution Management: IoT for Automatic Detection and Prevention

Water quality monitoring is vital for public health and environmental sustainability, but traditional methods face challenges such as high costs, lack of portability, and limited real-time data access. This paper presents a solution using Arduino-based sensors integrated with IoT technology to enhance water quality monitoring. The system employs various sensors to measure parameters like Total Dissolved Solids (TDS), turbidity, water transparency, methane gas, and alcohol gas. Data from these sensors is transmitted to a NodeMCU microcontroller and uploaded to the Blynk cloud platform, allowing real-time monitoring and remote access via customizable dashboards. The proposed system is cost-effective, portable, and provides immediate data access, addressing the limitations of traditional methods. It enables timely detection of water quality issues, facilitating prompt responses to health and environmental threats. This innovative approach significantly advances water quality monitoring by leveraging IoT technologies to improve environmental management practices

Enhancing Big Data Security through Comprehensive Data Protection Measures: A Focus on Securing Data at Rest and In-Transit

Purpose: This research paper aims to enhance Big Data security by implementing comprehensive data protection measures, focusing on securing data at rest and in transit. In the era of Big Data, organizations handle vast quantities of data characterized by high velocity, volume, and variety, which complicates management and increases security risks. Methodology: The study examines various data protection strategies, including encryption, access control, data masking, immutable storage, tokenization, and physical security for data at rest. For data in transit, it explores encryption protocols, secure transfer methods like SSH and TLS, VPNs, Zero Trust architecture, and secure APIs. These methods are crucial for safeguarding sensitive information and preventing unauthorized access. Findings: The findings highlight common security challenges in Big Data, such as data breaches, unauthorized access, and integrity issues. The study emphasizes the need for robust protection measures and offers a comprehensive view of the data security landscape. Implementing these strategies helps organizations safeguard sensitive information and ensure compliance with international data protection regulations, enhancing their overall security posture. Unique contribution to theory policy and practice: This paper contributes to theory, policy, and practice by advocating comprehensive data protection strategies. It stresses the importance of continuous monitoring and regulatory compliance, providing practical insights into best practices and technologies that protect Big Data. The research supports developing robust data protection policies and practices, advancing knowledge in Big Data security.

Machine learning for outcome prediction of atrial fibrillation recurrence after catheter ablation

Abstract Background Outcome prediction after catheter ablation for atrial fibrillation (AF) based on electronic health records (EHR) using machine learning (ML) is not yet established. Purpose The aim of the study was to assess the value of ML methods to predict the risk of AF recurrence after catheter ablation based on easily accessible EHR. Methods We analyzed 1362 patients from our prospective registry. Only patients undergoing a first AF ablation were studied. Follow-up was performed at 3, 6 and 12 months after the ablation including 24-hour and 7-day Holter electrocardiogram (ECG). Four different analyses with increasing complexity out of a set of overall 22 features using seven simple and three ensemble method ML algorithms were performed: model 1 (including age, sex, height, weight, and established anamnestic risk factors: AF-type, coronary artery disease, myocardial infarction, stroke, heart failure, hypertension, current smoker, diabetes, renal insufficiency), model 2: model 1 plus additional AF-history features (duration of AF in months, previous typical atrial flutter, number of previous electrical cardioversions, number of failed antiarrhythmic drugs), model 3: model 2 plus simple echocardiographic parameters (left ventricular ejection fraction, LA diameter), model 4: model 3 plus biomarkers (CRP, creatinine, NTproBNP). To evaluate the performance of the prediction, we report C-statistics, accuracy, and recall (sensitivity). The C-statistics of five risk scores for AF recurrence (APPLE, DR-FLASH, BASE-AF2, ATLAS, CHA2DS2-Vasc) and a logistic regression with a forward selection were calculated for comparison. Results Of the 1362 patients, 996 (73%) were male, mean age was 62±10 years and 60% presented with paroxysmal AF. Recurrence of AF during 1-year follow-up was observed in 458 patients (34%). The results for the four models using the eleven different ML algorithms are summarized in the Figure. In detail, the C-statistics were modest with a maximal value of 0.602 for the random forest (RF) classifier for the most complex model 4. The performance for the risk scores was maximal for the APPLE score (C-statistics 0.572) and comparable for the forward-selection logistic regression including five features with a C-statistics of 0.607 (95% CI 0.573-0.641). Conclusion In conclusion, outcome prediction of AF recurrence after catheter ablation with easily accessible EHR data remains challenging and cannot be improved simply by applying machine learning methods. Whether a more comprehensive feature selection, such as more complex ECG parameters instead of the selection of easily available features from the EHR, a combination with deep neural network-based feature extraction, or unsupervised ML might improve the result in a clinically relevant way needs further investigation.Results of the eleven ML algorithms

P.056 Real-world reduction in oral corticosteroid utilization following efgartigimod initiation in patients with generalized myasthenia gravis

Background: Reducing oral corticosteroids (OCS) use can alleviate the risk of many adverse events related to long-term OCS use. Here, we evaluate real-world utilization of OCS among patients with generalized myasthenia gravis (gMG) over the first 6 months following efgartigimod initiation. Methods: Patients with gMG using OCS who initiated efgartigimod treatment were identified retrospectively from an open US medical and pharmacy claims database (IQVIA Longitudinal Access and Adjudication Data [LAAD], April 2016-April 2023). Average daily dose (ADD) of OCS was analyzed during the 3-month period preceding efgartigimod initiation, and at 3 and 6 months post-efgartigimod initiation. Results: Of 231 patients assessed, 17 (7.4%), 109 (47.1%), and 105 (45.5%) had baseline OCS ADD of 0–5 mg, 5–20 mg, or >20 mg, respectively. At 3 and 6 months post-efgartigimod, 82 (35%) and 99 (43%) patients, respectively, reduced ADD by ≥5 mg. Proportion of patients with ADD of 0–5 mg increased >3-fold (7% baseline vs. 26% 6 months post-efgartigimod) and proportion of patients with ADD of >20 mg decreased by 35% (45% baseline vs. 29% 6 months post-efgartigimod) following efgartigimod initiation. Conclusions: Approximately 43% of patients were able to decrease steroid use or achieved steroid-free status within 6 months of efgartigimod treatment initiation.

Challenges to Alzheimer Care in Pakistan

Madam, Alzheimer's, the prime culprit behind Dementia, is a neurodegenerative disorder that gradually erases memory and cognition, affecting daily tasks 1. Symptoms often emerge in later years, and in Pakistan, where a rising aging population and middle-income status prevail, the risk amplifies. By 2030, global dementia cases can soar to 1.3 million from 850,000 in 2015, surging to 3.6 million by 2050 without preventive action 2. Urgent attention beckons. Yet, challenges abound. With Pakistan’s depleting literacy rate currently of 58.9% in 2023, misconceptions are likely 3. Stigma, cultural norms, and language barriers shroud the path to treatment. Moreover, mental health issues like depression, an Alzheimer’s symptom, are seen as taboo, deterring help-seeking. Till date, the lack of national databases stifles research progress, and a shortage of neurologists-just less than 150 for millions of people-worsens with scarce training programs4. Private caretakers in this developing country may be limited. As this chronic Alzheimer's has no cure, high drug costs hinder adherence, while the affordability of the new "Leqembi" drug, if approved, raises concerns. Additionally, access to MRI and CT scanning equipment in Pakistan is not widespread and their procurement along with testing entail significant costs. Furthermore, government policies and funding for Alzheimer's treatment and research might be insufficient with the rising aging population. NGOs in Pakistan related to Alzheimers are very limited- “Alzheimers Pakistan” the one and only centered in just three regions across this vast country. Solutions for these challenges emerge as vibrant threads. How can we aid patients and families in Alzheimer education? Imagine engaging workshops and media partnerships with prominent figures like the UNICEF Goodwill ambassadors thereby igniting a good community-healthcare bond, and envision ‘Alzheimer homes’ for patients lacking caregivers. Besides, a national database involving stakeholders, from NGOs to healthcare providers, can not only pave the way for easy data accessibility but also to proper treatment plans and protocols. Engaging these stakeholders could also lead to initiatives for more affordable medicines. For the shortage of neurologists, medical tourism initiatives that attract foreign neurologists to work temporarily in the country, sharing their expertise and contributing to healthcare delivery can be deployed. Targeted training for healthcare professionals and collaborating with international organizations for increased research, clinical trial participation and even in machinery purchase hold a promise to resolving this issue. Conclusively, emphasizing these concerns holds utmost significance in the pursuit of discovering effective interventions for this incurable ailment.