Features Of Platform Research Articles

Risky business: How food-delivery platform riders understand and manage safety at work

This study explores the issue of workplace safety among food-delivery workers who use platforms like UberEATS and Deliveroo to secure work. Despite the high exposure to hazardous traffic, extreme weather conditions, and unsafe work hours and locations that these workers face daily, safety remains a low priority for both platforms and governments. This study utilizes in-depth qualitative interviews with 14 platform food delivery workers in Melbourne, Australia, to examine how they understand and manage safety risks at work, drawing on a theoretical framework of necropolitics and liminal precarity. The riders are predominantly migrant workers on temporary visas who face corporeal risks influenced by factors such as road conditions, time pressures, and weather. Despite their awareness of these dangers, the study reveals that platform-induced necropower, driven by economic incentives, significantly impacts those heavily dependent on gig economy earnings, ultimately turning safety into a trade-off between making a living and surviving. However, riders also demonstrate agency by mediating risks through experience, knowledge-sharing, and strategic use of the platform's features to resist potentially hazardous conditions.

On the Use of an Online Polling Platform for Enhancing Student Engagement in an Engineering Module

Students’ engagement is a fundamental challenge in large classrooms in higher education. In recent years, innovative technologies such as electronic learning and online polling platforms have made learning more engaging, effective, and interactive. By using these platforms, educators can create more inclusive and enriching learning environments. This paper presents a novel approach in which an online technology is employed to enhance students’ learning experience. In this approach, features of an online polling platform, i.e., Slido, are employed to enhance students’ engagement in an engineering module, i.e., ‘Mechanics of Solids’, which is recognised as a fundamentally challenging module with difficult subjects. This study investigates how the interactive features of such technologies, such as real-time polls, question and answer (Q&A) sessions, and quizzes, can provide a more active and practical learning environment by improving student engagement in the classroom. In total, six online polls were designed: one for ice-breaking, two on the topics of shear forces and bending moment, two on stresses, and one on deflection. Each poll was presented to the students, and they participated in them by scanning a QR code or typing the poll’s code online. The rate of students’ participation in polls is extensively discussed to show the effectiveness of the proposed method. The findings of this study show a significant increase in student participation in classroom activities compared to traditional methods. Student feedback also indicates a positive learning experience with the use of the proposed approach. It is shown that the proposed approach has the potential to transform the way engineering students engage with challenging subjects, leading to enhanced learning outcomes and a more positive learning experience.

Body Part Implant Dashboard for Real-Time Monitoring and Reporting

Our innovative platform is revolutionizing the field of medical implants, harnessing the power of 3D printing to deliver unparalleled levels of customization, accuracy, and accessibility. It functions as an all-in-one online ecosystem that simplifies the process of obtaining custom 3D-printed implants for patients and healthcare providers. At its core, the platform features a user-friendly interface, allowing users to explore a diverse selection of medical implants tailored to individual needs, including orthopedic devices and dental prosthetics. The ordering process is straightforward—users can select implant specifications, upload medical data, and place orders through a secure payment gateway for safe transactions. The platform’s integrated tracking system provides real-time updates, allowing users to monitor the progress of their orders from production to delivery. Healthcare professionals’ benefit from a dedicated portal where they can submit custom designs, track orders, and communicate with experts. The platform also offers comprehensive customer support and post-implantation care resources to ensure a seamless experience. By embracing 3D printing technology, our platform is transforming healthcare delivery, paving the way for personalized medicine, and reshaping the future of medical implants. Join us in this journey towards a more accessible and patient-focused healthcare system

Frictions and flows in Twitch’s platform economy: viewer spending, platform features and user behaviours

ABSTRACT In this article we introduce ‘capital flow’ and ‘capital friction’ as terms that characterize features and user behaviours on internet platforms which encourage and discourage spending, respectively. We explore these via leading live streaming platform Twitch, where numerous platform features and normative behaviours both promote and downplay spending. This makes it an ideal site for understanding conflicting economic and social platform dynamics, and the meta-friction between flows and frictions of capital on a platform. We examine these phenomena through three case studies: Channel Points, a non-monetary ‘currency’ exchangeable for in-stream rewards; Bits, a real-money platform currency for donating to streamers; and streamers’ performative and verbal responses to financial support. Through capital flow and capital friction we present Twitch’s platform economy as an ongoing negotiation both among users and between users and platform features, and one where capital friction does not necessarily lead to a reduction of income, but generates new streamer-spectator-platform relations and more circuitous routes for the flow of capital. Our theorization of capital flow and capital friction thus provides new insights into platform features, their uptake or resistance, and the frictions within platform designs and platform cultures.

Towards a Remote Patient Monitoring Platform for Comprehensive Risk Evaluations for People with Diabetic Foot Ulcers.

Diabetic foot ulcers (DFUs) significantly affect the lives of patients and increase the risk of hospital stays and amputation. We suggest a remote monitoring platform for better DFU care. This system uses digital health metrics (scaled from 0 to 10, where higher scores indicate a greater risk of slow healing) to provide a comprehensive overview through a visual interface. The platform features smart offloading devices that capture behavioral metrics such as offloading adherence, daily steps, and cadence. Coupled with remotely measurable frailty and phenotypic metrics, it offers an in-depth patient profile. Additional demographic data, characteristics of the wound, and clinical parameters, such as cognitive function, were integrated, contributing to a comprehensive risk factor profile. We evaluated the feasibility of this platform with 124 DFU patients over 12 weeks; 39% experienced unfavorable outcomes such as dropout, adverse events, or non-healing. Digital biomarkers were benchmarked (0-10); categorized as low, medium, and high risk for unfavorable outcomes; and visually represented using color-coded radar plots. The initial results of the case reports illustrate the value of this holistic visualization to pinpoint the underlying risk factors for unfavorable outcomes, including a high number of steps, poor adherence, and cognitive impairment. Although future studies are needed to validate the effectiveness of this visualization in personalizing care and improving wound outcomes, early results in identifying risk factors for unfavorable outcomes are promising.

Social Media and Me: How Community Identity Influences Click Speech

ABSTRACT Social media has become an integral part of our society. So did participation in social media communities. Click-speech, the simple expression of opinions via a click in the form of likes and shares, emerged as a dominant feature of various social media platforms. In this research, we investigate how social media users use click-speech within social media communities through the lens of community identity and how it shapes click-speech behavior. Using a scenario-based study, we show that community identity strongly influences click-speech to the point where content is liked and shared even when the community member disagrees with the post or content. We contextualize the construct of fear of isolation from the spiral of silence theory within identity theory. In this context, the fear of being isolated from the community is salient, and click-speech evolves from a function of endorsing content to a socially driven behavior of fitting in.

Abstract PO1-17-06: Driving diversity in research participation with ShareForCures™, a patient-centered, nationwide breast cancer research registry to improve outcomes

Abstract Background The lack of diversity in research and small and siloed datasets have led to a lack of real-world evidence, limiting our ability to practice precision medicine and optimize treatments and care for all breast cancer patients. Susan G. Komen,® a breast cancer advocacy organization and the largest non-profit breast cancer funder outside the U.S. government, launched ShareForCures™ (SFC), a secure, patient-centered research registry. The registry aims to engage breast cancer survivors from diverse backgrounds as research partners to facilitate sharing of their data to drive innovative breast cancer research and improve outcomes. SFC is IRB-approved and open to any adult living in the U.S. and diagnosed with breast cancer. SFC is designed to be easy to join and accessible online. Participants provide survey information about their diagnosis, quality of life, lifestyle, family history, etc. Some of them will be asked to provide a saliva sample, and their medical records will be retrieved. Methods Patient advocates have driven every aspect of the development of SFC, from serving on its governance council to participating in focus groups and beta-testing the platform build. Most important to the success of the registry was to ensure we worked with community partners to conduct breast cancer survivor focus groups and to solicit views and expectations about patient-centered research engagement. Those focus groups were conducted in 2020 and 2021 in-person (N=4) and virtually (N=8) across the U.S., with three conducted in Spanish. The findings that emerged from the focus groups informed the establishment of SFC, from governance and technology implementation to platform features and processes. For feedback on pre-enrollment information, we invited 432 diverse breast cancer survivors to participate in an audience testing of two versions of a SFC landing page, a flyer, and a social media post. One version was designed to be more technical and the other more inspiring/motivational. Finally, we pilot tested SFC with 56 breast cancer survivors. Those who joined provided feedback through individual debriefing sessions about the onboarding process, consent, the questionnaire (“About You”) and the SFC platform. Results Four main themes emerged from the focus groups (N=64 participants): tailor communications to specific groups of participants, create an environment of trust and transparency, address concerns about data privacy and security, and share news and results. Audience testing on SFC materials revealed the majority liked positive personal messaging, although technical messaging highlighting the science, research, and data was also favorably reviewed. Participants ranked privacy and data security as priority concerns. Of the 56 breast cancer survivors invited to participate in the pilot testing of SFC, 31 fully onboarded, three started but did not complete onboarding, and six declined. To fully onboard, participants had to accept the informed consent and medical records release forms and complete the “About You” questionnaire. They indicated the process and online platform were easy, and the dashboard was an important feature for participants to stay engaged. They also confirmed focus group emphasis on clarity regarding SFC research goals and importance of sharing results. Conclusions All individuals who provided feedback expressed interest in sharing their experiences, inspiring others’ experiences, and accelerating research. They understood that this research may not benefit them directly but that it could benefit others. The results indicate that people are willing to participate in research if asked. Komen’s patient-centered approach of involving patients at all steps of developing SFC fosters trust, transparency and engagement that will enhance SFC’s impact. Citation Format: Jerome Jourquin, Stephanie McCoy, Barbara Segarra, Emily Marks, Carlita Proctor McIlwain, Brady Kazar, Melissa Troester, Kimberly Sabelko, Melissa Bondy. Driving diversity in research participation with ShareForCures™, a patient-centered, nationwide breast cancer research registry to improve outcomes [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-17-06.

Fiber Optic Assisted Optofluidic Viscometer for Biomedical Applications

Fiber Optic Assisted Optofluidic Viscometer for Biomedical Applications

Why We Should See YouTube From Digital Ecology Perspective

This study is a critical perspective in exploring changes in social, cultural and political forms related to the development of contemporary communication technology. The main theme of this research is YouTube's digital space with the topic of utilizing the main features of geo-tagging, hashtags and live streaming. Researchers observed three content creators in utilizing the features of the digital platform to carry out social interactions and actions. The purpose of this study is to explain that the character of the users and the current use of digital platforms is a representation of the interaction and socio-cultural situation of contemporary society as a part of the digital ecosystem. This research is qualitative in nature with specific observations on micro phenomena, so the results are not generalizations, but in the form of data findings to support the hypothesis, namely the importance of a critical perspective of digital ecology to explain contemporary digital ecosystem phenomena. This article is supported by comparative descriptions and descriptions of the development of several theoretical models as well as social-technological changes that have occurred before. This research contributes to providing an important perspective that media and technology studies must move from discussions about human and technology interactions, to discussions about human interactions in technology. As a study of ongoing phenomena, the researcher places this research by opening up to the potential for the latest changes that can occur along with technological developments.

Риски внедрения цифровых платформ как отраслевой аспект стратегирования

Even under the current foreign sanctions and external restrictions, Russia’s economic policy is aimed at industrial innovations. However, the ongoing digital transformation remains a serious challenge for timber enterprises: in fact, this industry demonstrates one of the lowest rates of digitalization. Digital transformation has both strategic advantages and strategic risks for such enterprises. If major business fails to consider these risks, the outcome can have long-term negative consequences. Effective strategizing of industrial enterprises needs special methods to identify, describe, and analyze strategic risks. The authors identified the strategic risks of digital transformation and summarized tools that could help timber and forestry enterprises to overcome them. The research relied on the theory and methodology of strategizing developed by Professor V.L. Kvint. According to this approach, a strategy does not identify problems: its main purpose is to identify opportunities, and opportunities always come with risks. The analysis systematized the general and specific features of ERP-systems and digital platforms as key elements of digital transformation. The article introduces opportunities and risks of digital transformation for timber industry enterprises, which must be taken into account by strategists. It also describes an automated process management model as a means of overcoming the risks.

A unified model and method for forecasting energy consumption in distributed computing systems based on stationary and mobile devices

The subject of research in this article is the forecasting of energy consumption when computing distributed tasks on computer networks built on the basis of server solutions and distributed systems based on personal smartphones. The goal of this study was to create a universal computing energy cost prediction model that can be applied to both traditional and mobile cloud systems. Tasks: conduct an analysis of energy-saving approaches and technologies used to calculate data; consider computer system models and actions with them, namely: model of distributed job, model of distributed computing system, model of distribution strategy; develop a common and uniform dynamic method of forecasting spent energy with a focus on heterogeneous systems; conduct a study of the proposed approach on stationary and mobile devices. The obtained results include. The results of the experimental measurement of the energy consumption of mobile digital systems and stationary ones are presented. The energy efficiency of computing on GPUs of a stationary device based on CUDA technology and GPUs on mobile devices based on Apple Metal technology was determined. Computation during the calculation of 600 frames on a distributed system from mobile devices with failure settings showed a consumption of 15320 joules of energy. Simulation of computing on a distributed system with stationary devices showed a consumption of 52806 joules of energy. This gives us 3,45 times the consumption benefit from computing on mobile devices. Forecasted consumption is also very accurate. Conclusions. The energy consumption assessment model proved to be quite effective. The results of the experiments show that the energy consumption estimation model takes into account the features of the hardware platform where data processing is performed. Computation of data on the GPU of stationary devices loses energy efficiency to a similar implementation on the GPU of Apple Metal from mobile devices. Therefore, the presented results demonstrate the rationality of using mobile graphics processors for energy-efficient information processing.

Trust building in the sharing economy: proposition and test of an integrated model

PurposeThe purpose of this study is to propose and test an integrated model to explain how trust is built in sharing economy (SE) transactions.Design/methodology/approachInitially, prior literature was systematically selected and synthesized to develop a comprehensive framework applicable to multiple trust-building perspectives and categories of SE platforms. Then, a survey was conducted to validate the constructs and test the model with Airbnb guests. A sample of 351 responses was collected and analyzed using structural equation modeling.FindingsThe results indicate that the cues an individual assesses to infer their counterpart’s trustworthiness and the reasons the individual has for engaging in the SE transaction can explain a large variance in their trust in the counterpart. In addition, the individual’s propensity to trust moderates this relationship.Research limitations/implicationsThe proposed model can help identify the most effective trust-building mechanisms. It can be taken as a common knowledge base for scholars to compare the four trust-building perspectives and different categories of SE platforms, as well as to investigate the subject over time and across cultures.Practical implicationsThis research can also help practitioners understand the complexity of building trust and design platform features to do so.Social implicationsA unified model clarifies trust in the SE, aiding platform growth and community bonding. This insight guides platforms in feature enhancement and policymakers in drafting balanced regulations.Originality/valueTo the best of the authors’ knowledge, for the first time, there is a comprehensive and parsimonious model applicable to the four trust-building perspectives and different categories of SE platforms.

AI for Analyzing Mental Health Disorders Among Social Media Users: Quarter-Century Narrative Review of Progress and Challenges.

Mental health disorders are currently the main contributor to poor quality of life and years lived with disability. Symptoms common to many mental health disorders lead to impairments or changes in the use of language, which are observable in the routine use of social media. Detection of these linguistic cues has been explored throughout the last quarter century, but interest and methodological development have burgeoned following the COVID-19 pandemic. The next decade may see the development of reliable methods for predicting mental health status using social media data. This might have implications for clinical practice and public health policy, particularly in the context of early intervention in mental health care. This study aims to examine the state of the art in methods for predicting mental health statuses of social media users. Our focus is the development of artificial intelligence-driven methods, particularly natural language processing, for analyzing large volumes of written text. This study details constraints affecting research in this area. These include the dearth of high-quality public datasets for methodological benchmarking and the need to adopt ethical and privacy frameworks acknowledging the stigma experienced by those with a mental illness. A Google Scholar search yielded peer-reviewed articles dated between 1999 and 2024. We manually grouped the articles by 4 primary areas of interest: datasets on social media and mental health, methods for predicting mental health status, longitudinal analyses of mental health, and ethical aspects of the data and analysis of mental health. Selected articles from these groups formed our narrative review. Larger datasets with precise dates of participants' diagnoses are needed to support the development of methods for predicting mental health status, particularly in severe disorders such as schizophrenia. Inviting users to donate their social media data for research purposes could help overcome widespread ethical and privacy concerns. In any event, multimodal methods for predicting mental health status appear likely to provide advancements that may not be achievable using natural language processing alone. Multimodal methods for predicting mental health status from voice, image, and video-based social media data need to be further developed before they may be considered for adoption in health care, medical support, or as consumer-facing products. Such methods are likely to garner greater public confidence in their efficacy than those that rely on text alone. To achieve this, more high-quality social media datasets need to be made available and privacy concerns regarding the use of these data must be formally addressed. A social media platform feature that invites users to share their data upon publication is a possible solution. Finally, a review of literature studying the effects of social media use on a user's depression and anxiety is merited.

A Cross-Process Signal Integrity Analysis (CPSIA) Method and Design Optimization for Wafer-on-Wafer Stacked DRAM.

A multi-layer stacked Dynamic Random Access Memory (DRAM) platform is introduced to address the memory wall issue. This platform features high-density vertical interconnects established between DRAM units for high-capacity memory and logic units for computation, utilizing Wafer-on-Wafer (WoW) hybrid bonding and mini Through-Silicon Via (TSV) technologies. This 3DIC architecture includes commercial DRAM, logic, and 3DIC manufacturing processes. Their design documents typically come from different foundries, presenting challenges for signal integrity design and analysis. This paper establishes a lumped circuit based on 3DIC physical structure and calculates all values of the lumped elements in the circuit model with the transmission line model. A Cross-Process Signal Integrity Analysis (CPSIA) method is introduced, which integrates three different manufacturing processes by modeling vertical stacking cells and connecting DRAM and logic netlists in one simulation environment. In combination with the dedicated buffer driving method, the CPSIA method is used to analyze 3DIC impacts. Simulation results show that the timing uncertainty introduced by 3DIC crosstalk ranges from 31 ps to 62 ps. This analysis result explains the stable slight variation in the maximum frequency observed in vertically stacked memory arrays from different DRAM layers in the physical testing results, demonstrating the effectiveness of this CPSIA method.

Analysis of online rubric platforms: advancing toward erubrics

Rubrics play a crucial role in shaping educational assessment, providing clear criteria for both teaching and learning. The advent of online rubric platforms has the potential to significantly enhance the effectiveness of rubrics in educational contexts, offering innovative features for assessment and feedback through the creation of erubrics. This study presents a comprehensive analysis of 19 online rubric platforms structured around five research questions (RQs) examining the general platform features, rubric design features, rubric implementation features, identifying the stronger online rubric platforms available, and investigating if the platforms support the creation and implementation of erubrics. Our analysis of the design features revealed varying levels of customisation and flexibility across platforms, crucial for effective assessment. Regarding implementation features, we found a mix of online and offline capabilities, with a limited number of platforms offering more advanced features (e.g. collaborative options). Through a detailed scoring system, we identify the platforms that lead innovation in design and implementation. Unfortunately, the vast majority of platforms do not support features for the creation of erubrics. We provide a detailed list of implementation recommendations for teachers, researchers, and platform designers (Appendix F).

Stimulating Purchase Intentions in Live Commerce: The Role of Platform Features and Influencer Engagement

Stimulating Purchase Intentions in Live Commerce: The Role of Platform Features and Influencer Engagement

Cooperation in the Gig Economy: Insights from Upwork Freelancers

Existing literature on online labor markets predominantly focuses on how freelancers individually complete tasks and projects. Our study examines freelancers' willingness to work collaboratively. We report results from a survey of 122 freelancers on a leading online labor market platform (Upwork) that examine freelancers' preferences for collaborative work arrangements, and that explore several antecedents of cooperative behaviors. We then test if actual cooperative behavior matches with freelancers' stated preferences through an incentivized social dilemma experiment. We find that respondents cooperate at a higher rate (85%) than reported in previous comparable studies (between 50-75%). This high rate of cooperation may be explained by an ingroup bias. Using a sequential mediation model, we demonstrate the importance of a sense of shared expectations and accountability for cooperation. We contribute to a better understanding of the potential for collaborative work on online labor market platforms by assessing if and what social factors and collective culture exist among freelancers. We discuss the implications of our results for platform designers by highlighting the importance of platform features that promote shared expectations and improve accountability. Overall, contrary to existing literature and predictions, our results suggest that freelancers in our sample display traits that are more consistent with belonging to a coherent group with a shared collective culture, rather than being anonymous actors in a transaction-based market.

Intercity Travel Solution: Web Application

Amidst the rise of urbanization and environmental consciousness, our research paper presents a pioneering solution to modern transportation challenges. Unlike traditional ride-sharing platforms, our innovative approach focuses on routes as the fundamental units of collaborative travel. Users create route profiles, facilitating the sharing and offering of rides with like-minded commuters along chosen routes. This unique system fosters dynamic networks and promotes community engagement, while prioritizing environmental responsibility. Our platform features route discovery, collaborative travel arrangements, dynamic pricing, and route communities. By prioritizing routes over profiles, we revolutionize the carpooling experience, creating a vibrant ecosystem for users to connect, share, and positively impact their daily commutes. This paper explores the transformative potential of our route-centric model in reshaping urban mobility landscapes, offering a sustainable alternative for transportation needs. Top of Form Keywords— Urban Mobility, Sustainable Transportation, Route-Centric Approach, Ride-Sharing, Community Engagement, Environmental Responsibility, Dynamic Networks, Collaborative Travel, Route Discovery, Dynamic Pricing, Community-Centered Solutions, Transportation Innovation, Social Cohesion, Environmental Stewardship, Mobility Justice.

EL Greco Platform: A novel Python programming learning platform that uses a real robot

AbstractThis paper introduces the El Greco Platform, a Python programming platform for distance learning that employs an educational robot. This website allows prospective learners to remotely control Greco, a social humanoid robot designed to be cost‐effective, simple to construct, and appropriate for use in education. El Greco is capable of performing multiple tasks, including combined movements. These Robot capabilities can be programmed using either Python code or the Blockly library, which adds an editor to an application that visualizes coding concepts as interlocking blocks. Programming a robot appears to be a significantly more effective and creative method for students to learn a programming language. This educational tool was designed primarily for use by students and allows anyone to learn Python while controlling a robot for free. El Greco Platform features gamification elements that increase the enjoyment and engagement of the learning experience while reinforcing the concepts taught. The survey results on students aged 13–18 revealed that the El Greco Platform captivated the study participants and positively affected their attitudes toward programming and robotics. In addition, it significantly impacted their comprehension of programming and motivated them to seek additional opportunities to expand their knowledge of robotics and programming.

Automatic offline-capable smartphone paper-based microfluidic device for efficient biomarker detection of Alzheimer's disease

BackgroundAlzheimer's disease (AD) is a prevalent neurodegenerative disease with no effective treatment. Efficient and rapid detection plays a crucial role in mitigating and managing AD progression. Deep learning-assisted smartphone-based microfluidic paper analysis devices (μPADs) offer the advantages of low cost, good sensitivity, and rapid detection, providing a strategic pathway to address large-scale disease screening in resource-limited areas. However, existing smartphone-based detection platforms usually rely on large devices or cloud servers for data transfer and processing. Additionally, the implementation of automated colorimetric enzyme-linked immunoassay (c-ELISA) on μPADs can further facilitate the realization of smartphone μPADs platforms for efficient disease detection. ResultsThis paper introduces a new deep learning-assisted offline smartphone platform for early AD screening, offering rapid disease detection in low-resource areas. The proposed platform features a simple mechanical rotating structure controlled by a smartphone, enabling fully automated c-ELISA on μPADs. Our platform successfully applied sandwich c-ELISA for detecting the β-amyloid peptide 1–42 (Aβ 1–42, a crucial AD biomarker) and demonstrated its efficacy in 38 artificial plasma samples (healthy: 19, unhealthy: 19, N = 6). Moreover, we employed the YOLOv5 deep learning model and achieved an impressive 97 % accuracy on a dataset of 1824 images, which is 10.16 % higher than the traditional method of curve-fitting results. The trained YOLOv5 model was seamlessly integrated into the smartphone using the NCNN (Tencent's Neural Network Inference Framework), enabling deep learning-assisted offline detection. A user-friendly smartphone application was developed to control the entire process, realizing a streamlined “samples in, answers out” approach. SignificanceThis deep learning-assisted, low-cost, user-friendly, highly stable, and rapid-response automated offline smartphone-based detection platform represents a good advancement in point-of-care testing (POCT). Moreover, our platform provides a feasible approach for efficient AD detection by examining the level of Aβ 1–42, particularly in areas with low resources and limited communication infrastructure.