Cost-effectiveness Data Research Articles

Design and Implementation of Educonnect: A Cloud Native Application for Educational Institutions

Abstract: In modern software environments, a paradigm known as the MERN stack has emerged as an important force due to its breadth of development and management capabilities. Leveraging native MERN (MongoDB, Express.js, React.js, Node.js) software development, deployment, and management techniques with Cloud services, "Educonnect" emerges as an innovative solution for educational institutions seeking efficient and cost-effective data management. This paper deals with the implementation of a cloud native web application Educonnect after a dexterous development strategy to create a cloud native web application. The main focus of this cloud native application is to provide a solution for data organization and collection along with an effective way of sending communication to the students by using cloud services

Examine Heuristic Data Lake Management Using AWS: A Big Data Handling Approach

In this era of technology, the most valued asset can be ‘Data’. With the increasing number of data, the value of it keeps increasing. Data storage and data manipulate for to achieve some particular goals or business requirements increasing in number and storing it has become a complex and tedious task. With the use of some advanced technologies like hadoop, it simplified the data storing process, but due to rapid development and excessive use of AI and ML, tons of data is collected. The quintessence is to ascertain an extra cost effective storage alternative. This paper provides with an effective solution to store data over the cloud with numerous benefits over traditional data storage methods by developing a data lake using AWS a Cost Effective Data Lake Management algorithm (CEDLMA). Furthermore, the functionalities of data lake include managing and storing sorted as well as unsorted data, gathering various analytics from the data lake as per business requirements. Proposed work is evaluated with AWS’s IAM and S3 services.

Automating External Flat File Integration in SAP BW/4HANA: A Cost-Effective ABAP Approach

With SAP BW/4HANA as their enterprise data warehouse, organizations face challenges loading external data like CSV files into the system efficiently. Manual methods using GUIs are slow, error-prone, and lack scalability. This paper presents an automation solution using custom ABAP programming and standard BW/4HANA capabilities like process chains and advanced datastore objects (ADSOs) to orchestrate and load files. An ABAP program handles file discovery, logging, triggering process chains, monitoring status, and error handling. Benefits seen in production deployments include reduced load times and manual efforts, and improved data quality. While requiring upfront development effort, this approach provides a flexible, cost-effective data integration solution leveraging SAP's native tools. Key technical aspects include modular program design, use of SAP standard APIs, logging/status tracking, and BW process chain integration. Limitations are scalability for large volumes and complex transformations. The solution offers organizations an automation template for their BW/4HANA loading needs using in-house resources.

Expanding Education Researchers' Access to Classroom Observation Data With a Remote and Cost-Effective Video Data Collection Protocol.

The onset of the COVID-19 pandemic and associated long-term shifts to virtual instruction among most US schools presented notable challenges among education researchers. Ongoing projects conducted in school settings experienced sudden losses of access to teacher and student participants, in many cases leading to severe interruptions to data collection efforts. Perhaps most notably, upon returns to in-person instruction in the 2021/22 academic year most schools instigated strict policies limiting the number of non-school personnel who could enter school buildings, including researchers conducting in-person data collections. As such, many researchers had to find alternative means to gather data. In this paper, we offer a new protocol that we created in response to these challenges that allows for the secure and fully remote collection of video data in school settings. This new protocol not only addressed the immediate needs of the focal study but also addresses some of the most notable barriers to collecting classroom video data in the field of education research at large. In this paper, we describe the initial development and application of this protocol among a local study of elementary teachers, as well as the scaling of this protocol in a study of elementary teachers in multiple states. It is our hope that this protocol can expand education researchers', practitioners', and policymakers' access to classroom video data.

First Responder's Standard Operating Procedure for Employing Drones to Expedite Insurance Claims

Drones offer cost-effective real-time data collection, payload delivery, and have accelerated various commercial, industrial, and recreational applications. They are commonly used for research, disease transmission monitoring, disaster site assessment, and biohazard monitoring. However, disaster victim insurance claims are hampered by insufficient cooperation between first responders, typically the Civil Defence Department, and the insurance industry. This paper proposes integrating drones into standard operating procedures for disaster victim identification by first responders. Improved collaboration between first responders and insurers can expedite insurance claims, benefiting both parties and disaster victims. Insights are drawn from feedback provided by Civil Defence Department Malaysia's first responders and experienced forensic experts in search and rescue operations

An Innovative Clinical Evaluation Protocol after Total Ankle Arthroplasty: A Pilot Study Using Inertial Sensors and Baropodometric Platforms

Background: Total ankle arthroplasty (TAA) has grown in popularity and indications, with encouraging results over time. Today, preoperative and postoperative evaluations are mainly performed using clinical test and diagnostic imaging, but there is a deficiency in objectively evaluating the biomechanics of the foot and ankle, which serve as the functional markers for monitoring the effectiveness and outcomes of surgery. Inertial measurement units associated with plantar pressure measurements may provide an accurate and reliable method of evaluating function through the analysis of gait and ankle joint mobility. The aim of this study was to introduce an innovative technology, to assess its accuracy and feasibility compared to standard clinical assessment methods and to objectify kinematic outcomes in patients with end-stage ankle OA before and after TAA surgery. Methods: A consecutive series of eight patients with symptomatic end-stage osteoarthritis and treated with TAA was prospectively evaluated using clinical scores (AOFAS, MOxFQ, VAS, SF-36, 17-IFFI), physical tests (FPI, ALT), plantar pressure measurements with FLEX EPS/R2 Letsense® baropodometric platform, gait analysis and wearable sensors-based ankle motion and kinematic outcomes using Wiva Science inertial sensors by Letsense®. Data were collected preoperatively and 4 months after surgery. Results: All PROMs exhibited statistical significance in improvement from pre- to postoperative periods, except for one. Physical examinations showed no significant changes of the foot shape and alignment. Plantar pressure analyses revealed no significant changes in static and dynamic evaluations, but a more uniform distribution of plantar pressure was observed between the two periods. Inertial sensor parameters demonstrated no significant differences, except for a significant reduction in stride length and step length for the operated foot after surgery. Conclusions: Gait analysis using inertial sensors and plantar pressure measurements offer ease of handling, cost effectiveness, portability and swift data reading, making them highly appealing for widespread clinical use. Integrating these tools into the routine assessments of patients with TAA holds promise for advancing precision of treating this condition and our depth of its understanding, contributing to more comprehensive and insightful patient care.

A 3D nanoscale optical disk memory with petabit capacity.

High-capacity storage technologies are needed to meet our ever-growing data demands1,2. However, data centres based on major storage technologies such as semiconductor flash devices and hard disk drives have high energy burdens, high operation costs and short lifespans2,3. Optical data storage (ODS) presents a promising solution for cost-effective long-term archival data storage. Nonetheless, ODS has been limited by its low capacity and the challenge of increasing its areal density4,5. Here, to address these issues, we increase the capacity of ODS to the petabit level by extending the planar recording architecture to three dimensions with hundreds of layers, meanwhile breaking the optical diffraction limit barrier of the recorded spots. We develop an optical recording medium based on a photoresist film doped with aggregation-induced emission dye, which can be optically stimulated by femtosecond laser beams. This film is highly transparent and uniform, and the aggregation-induced emission phenomenon provides the storage mechanism. It can also be inhibited by another deactivating beam, resulting in a recording spot with a super-resolution scale. This technology makes it possible to achieve exabit-level storage by stacking nanoscale disks into arrays, which is essential in big data centres with limited space.

Normalizing Large Scale Sensor-Based MWD Data: An Automated Method toward A Unified Database.

In the context of geo-infrastructures and specifically tunneling projects, analyzing the large-scale sensor-based measurement-while-drilling (MWD) data plays a pivotal role in assessing rock engineering conditions. However, handling the big MWD data due to multiform stacking is a time-consuming and challenging task. Extracting valuable insights and improving the accuracy of geoengineering interpretations from MWD data necessitates a combination of domain expertise and data science skills in an iterative process. To address these challenges and efficiently normalize and filter out noisy data, an automated processing approach integrating the stepwise technique, mode, and percentile gate bands for both single and peer group-based holes was developed. Subsequently, the mathematical concept of a novel normalizing index for classifying such big datasets was also presented. The visualized results from different geo-infrastructure datasets in Sweden indicated that outliers and noisy data can more efficiently be eliminated using single hole-based normalizing. Additionally, a relational unified PostgreSQL database was created to store and automatically transfer the processed and raw MWD as well as real time grouting data that offers a cost effective and efficient data extraction tool. The generated database is expected to facilitate in-depth investigations and enable application of the artificial intelligence (AI) techniques to predict rock quality conditions and design appropriate support systems based on MWD data.

Advancing sustainable implementation of an evidence-based mental health intervention in Sierra Leone’s schools: protocol for a hybrid type 3 implementation-effectiveness trial

BackgroundMental health disorders among youth contribute substantially to the global burden of disease, which is exacerbated in low- and middle-income countries (LMICs) due to large mental health treatment gaps. In Sierra Leone, a West African country with a long history of complex adversity, the mental health treatment gap is estimated at 98%. Implementing innovative mental health interventions that can be sustained at scale is a priority. The Youth Readiness Intervention (YRI) is an evidence-based mental health intervention for youth that can be delivered feasibly by lay health workers/nonspecialists. Using mobile-based technologies to assist implementation could improve the reach and sustainability of the YRI in Sierra Leone. This study aims to train teachers to deliver the YRI in Sierra Leone’s secondary schools and test the feasibility, acceptability, cost, and fidelity to the YRI of a mobile-based supervision model compared with standard, in-person supervision.MethodsWe will conduct a hybrid type 3 implementation-effectiveness cluster randomized trial to assess the feasibility, acceptability, costs and fidelity to the YRI implemented by teachers receiving mobile-based supervision vs. standard supervision. Enrolled schools (N = 50) will be randomized to YRI + mobile supervision (N = 20), YRI + standard supervision (N = 20) or waitlist control (N = 10). We will recruit and enroll four teachers per intervention-condition school (N = 160) and 1200 youth. We will collect data on implementation outcomes among teachers, principals and youth via a mixed methods approach at baseline and post-intervention. We will also collect quantitative data on youth mental health and functioning as secondary outcomes at baseline and post-intervention, as well as cost-effectiveness data at 12-month follow-up.DiscussionStudy findings have the potential to expand the reach of mental health services among youth in low-resource settings via a teacher workforce. The use of mobile tools, if successful, could support further scale out and sustainment of the YRI to other regions of Sierra Leone and West Africa more broadly, which could help address the mental health treatment gap.Trial registrationClinical Trial Network: NCT05737667.

Costs and benefits in patients with NYHA class III heart failure treated with CardioMEMS in Italy.

CardioMEMS is a device suitable for telemedicine that is currently being evaluated by the Regional Health Technology Assessment (HTA) Committee of Tuscany. Two detailed HTA reports are available in the specialized literature, the results of which need to be transferred to our regional setting. These decisions in Tuscany are made by the so-called Centro Operativo HTA. To validate, with local cost-effectiveness data, the decision on CardioMEMS that will be made in the Tuscany region. Two detailed international HTA reports were rearranged and adapted to our regional setting to generate a simplified analysis that could form the basis of our decision. Two willingness-to-pay (WTP) thresholds of €20,000/quality-adjusted life year (QALY) and €50,000/QALY were considered. Based on epidemiological and regulatory information, the target population in Tuscany for this device is 166 cases. The value-based price of CardioMEMS is estimated to be €4,332 and €16,662 at WTP thresholds of 20,000/QALY and 50,000/QALY, respectively. Its current price in Italy is €12,000. In our region, the introduction of CardioMEMS is likely to be gradual, around 50 patients/year (or €0.60 million/year at current price). This example highlights the need to adapt the information published in the international literature to the local context in which the approval decision is made. In this context, simplified analyses are easier to apply than complex Markov models.

Text Semantics-Driven Data Classification Storage Optimization

Data classification storage has emerged as an effective strategy, harnessing the diverse performance attributes of storage devices to orchestrate a harmonious equilibrium between energy consumption, cost considerations, and user accessibility. The traditional strategy of solely relying on access frequency for data classification is no longer suitable for today’s complex storage environment. Diverging from conventional methods, we explore from the perspective of text semantics to address this issue and propose an effective data classification storage method using text semantic similarity to extract seasonal features. First, we adopt a dual-layer strategy based on semantic similarity to extract seasonal features. Second, we put forward a cost-effective data classification storage framework based on text seasonal features. We compare our work with the data classification approach AS-H, which runs at full high performance. In addition, we also compare it with K-ear, which adopts K-means as the classification algorithm. The experimental results show that compared with AS-H and K-ear, our method reduces energy consumption by 9.51–13.35% and operating costs by 13.20–22.17%.

Scaling-up an evidence-based intervention for family carers of people with dementia: Current and future costs and outcomes.

The STrAtegies for RelaTives (START) intervention is effective and cost-effective in supporting family carers of people with dementia. It is currently not available to all eligible carers in England. What would be the impacts on service costs and carer health-related quality of life if START was provided to all eligible carers in England, currently and in future? Effectiveness and cost-effectiveness data from a previously conducted randomised controlled trial were combined with current and future projections of numbers of people with newly diagnosed dementia to estimate overall and component costs and health-related quality of life outcomes between 2015 (base year for projections) and 2040. Scaling-up START requires investments increasing annually but would lead to significant savings in health and social care costs. Family carers of people with dementia would experience improvements in mental health and quality of life, with clinical effects lasting at least 6years. Scaling up the START intervention to eligible carers was estimated to cost £9.4 million in 2020, but these costs would lead to annual savings of £68 million, and total annual quality-adjusted life year (QALY) gains of 1247. Although the costs of START would increase to £19.8 million in 2040, savings would rise to £142.7 million and Quality adjusted life years gained to 1883. Scaling-up START for family carers of people with dementia in England would improve the lives of family carers and reduce public sector costs. Family carers play a vital part in dementia care; evidence-based interventions that help them to maintain this role, such as START, should be available across the country.

QoE-aware budgeted edge data caching online: A primal–dual approach

Data caching has garnered significant attention in the field of edge computing due to the low-latency services offered by nearby edge servers. To efficiently utilize the data cache budget and ensure minimal data transmission latency, selecting appropriate edge servers for data caching is crucial. While the users’ Quality of Experience (QoE) plays a vital role in the service providers’ benefits, it has not been adequately considered in dynamic edge computing environments. This is primarily due to the non-linear correlation between QoE and Quality-of-Service (QoS), making cost-effective edge data caching challenging within the service provider’s limited budget. In this paper, we formally define the QoE-aware budgeted online edge data caching (QoE-BOEDC) problem, aiming to maximize the average user QoE while adhering to the service provider’s budget in a dynamic edge computing environment. Here, we formulate a formal optimization model for QoE-BOEDC, which can be proven to be NP-complete. Subsequently, we propose an online algorithm, PDOQ (Primal–Dual Optimization for QoE), based on the primal–dual technique, and theoretically establish its comparative ratio. Additionally, we conduct both small-scale and large-scale experimental tests, and the experimental results demonstrate that PDOQ significantly outperforms other representative algorithms.

Evaluation of the Xpert MTB Host Response assay for the triage of patients with presumed pulmonary tuberculosis: a prospective diagnostic accuracy study in Viet Nam, India, the Philippines, Uganda, and South Africa

Non-sputum-based triage tests for tuberculosis are a priority for ending tuberculosis. We aimed to evaluate the diagnostic accuracy of the late-prototype Xpert MTB Host Response (Xpert HR) blood-based assay. We conducted a prospective diagnostic accuracy study among outpatients with presumed tuberculosis in outpatient clinics in Viet Nam, India, the Philippines, Uganda, and South Africa. Eligible participants were aged 18 years or older and reported cough lasting at least 2 weeks. We excluded those receiving tuberculosis treatment in the preceding 12 months and those who were unwilling to consent. Xpert HR was performed on capillary or venous blood. Reference standard testing included sputum Xpert MTB/RIF Ultra and mycobacterial culture. We performed receiver operating characteristic (ROC) analysis to identify the optimal cutoff value for the Xpert HR to achieve the target sensitivity of 90% or more while maximising specificity, then calculated diagnostic accuracy using this cutoff value. This study was prospectively registered with ClinicalTrials.gov, NCT04923958. Between July 13, 2021, and Aug 15, 2022, 2046 adults with at least 2 weeks of cough were identified, of whom 1499 adults (686 [45·8%] females and 813 [54·2%] males) had valid Xpert HR and reference standard results. 329 (21·9%) had microbiologically confirmed tuberculosis. Xpert HR had an area under the ROC curve of 0·89 (95% CI 0·86-0·91). The optimal cutoff value was less than or equal to -1·25, giving a sensitivity of 90·3% (95% CI 86·5-93·3; 297 of 329) and a specificity of 62·6% (95% CI 59·7-65·3; 732 of 1170). Sensitivity was similar across countries, by sex, and by subgroups, although specificity was lower in people living with HIV (45·1%, 95% CI 37·8-52·6) than in those not living with HIV (65·9%, 62·8-68·8; difference of 20·8%, 95% CI 13·0-28·6; p<0·0001). Xpert HR had high negative predictive value (95·8%, 95% CI 94·1-97·1), but positive predictive value was only 40·1% (95% CI 36·8-44·1). Using the Xpert HR as a triage test would have reduced confirmatory sputum testing by 57·3% (95% CI 54·2-60·4). Xpert HR did not meet WHO minimum specificity targets for a non-sputum-based triage test for pulmonary tuberculosis. Despite promise as a rule-out test that could reduce confirmatory sputum testing, further cost-effectiveness modelling and data on acceptability and usability are needed to inform policy recommendations. National Institute of Allergy and Infectious Diseases of the US National Institutes of Health. For the Vietnamese and Tagalog translations of the abstract see Supplementary Materials section.

DFT + F12 QFFs for Cost-Effective Rovibrational Spectral Data Predictions of Ground and Excited Electronic States.

The quest for faster computation of anharmonic vibrational frequencies of both ground and excited electronic states has led to combining coupled cluster theory harmonic force constants with density functional theory cubic and quartic force constants for defining a quartic force field (QFF) utilized in conjunction with vibrational perturbation theory at second order (VPT2). This work shows that explicitly correlated coupled cluster theory at the singles, doubles, and perturbative triples levels [CCSD(T)-F12] provides accurate anharmonic vibrational frequencies and rotational constants when conjoined with any of B3LYP, CAM-B3LYP, BHandHLYP, PBE0, and ωB97XD for roughly one-quarter of the computational time of the CCSD(T)-F12 QFF alone for our test set. As the number of atoms in the molecule increases, however, the anharmonic terms become a greater portion of the QFF, and the cost comparison improves with HOCO+ and formic acid, requiring less than 15 and 10% of the time, respectively. In electronically excited states, PBE0 produces more consistently accurate results. Additionally, as the size of the molecule and, in turn, QFF increase, the cost savings for utilizing such a hybrid approach for both ground- and excited-state computations grows. As such, these methods are promising for predicting accurate rovibrational spectral properties for electronically excited states. In cases where well-behaved potentials for a small selection of targeted excited states are needed, such an approach should reduce the computational cost compared to that of methods requiring semiglobal potential surfaces or variational treatments of the rovibronic Hamiltonian. Such applications include spectral characterization of comets, exoplanets, or any situation in which gas phase molecules are being excited by UV-vis radiation.

Feature Engineering and Decision Trees for Predicting High Crash-Risk Locations Using Roadway Indicators

Road crashes are a prevalent public health issue across the globe. The objective of this research was to develop a methodology for accurately classifying high-risk crash locations. The hypothesis of this study was that readily obtained roadway indicators can be used along with machine learning techniques to categorize locations as high crash-risk. A database containing 5,383 locations was created during 2012 to 2015 as part of the Hellenic National Road Safety Project and used to develop three binary machine learning models to classify high crash-risk locations based on roadway indicators. The three models were random forest, gradient boosting, and extra trees. This research used features engineering to reduce the number of indicators in the model, and the synthetic minority oversampling technique to address imbalances in the dataset between the minority (high crash-risk locations identified using crash reports) and majority classes (medium to low crash-risk locations identified based on local police testimonies, site inspections, and geometry analysis). Although all three models performed similarly, the extra trees model outperformed the other two on a range of performance metrics, including the area under the precision–recall curve and the F1-score. The findings revealed that design speeds, pavement markings, signage presence, and pavement condition were the most influential factors affecting roadway safety. The contribution of this research is in the development of a transferable methodology for classifying high crash-risk locations in addition to revealing key indicators for crash-risk potential, which in turn can inform cost-effective data collection and maintenance activities.

Views of healthcare professionals regarding barriers and facilitators for a Fracture Liaison Service in Malaysia.

This study aimed to explore the views of healthcare professionals regarding the barriers and facilitators for a Fracture Liaison Service (FLS) in Malaysia. The qualitative study was conducted from February to December 2021 at a tertiary hospital in Malaysia. Doctors, nurses, pharmacists, and policymakers were recruited via purposive sampling. Semi-structured in-depth interviews were conducted until thematic saturation was achieved. Data were transcribed verbatim and analysed using thematic analysis. Thirty participants [doctors (n = 13), nurses (n = 8), pharmacists (n = 8), and policymakers (n = 1)] with 2-28 years of working experience were recruited. Three themes emerged: 1) Current delivery of secondary fracture prevention; 2) Importance of secondary fracture prevention, and 3) FLS sustainability. Some participants reported that the current post-hip fracture care was adequate, whilst some expressed concerns about the lack of coordination and continuity of care, especially in non-hip fragility fracture care. Most participants recognised the importance of secondary fracture prevention as fracture begets fracture, highlighting the need for a FLS to address this care gap. However, some were concerned about competing priorities. To ensure the sustainability of a FLS, cost-effectiveness data, support from relevant stakeholders, increased FLS awareness among patients and healthcare professionals, and a FLS coordinator were required. Training and financial incentives may help address the issue of low confidence and encourage the nurses to take on the FLS coordinator role. Overall, all participants believed that there was a need for a FLS to improve the delivery of secondary fracture prevention. Addressing concerns such as lack of confidence among nurses and lack of awareness can help improve FLS sustainability.

Optical Insights into Fibrotic Livers: Applications of Near-Infrared Spectroscopy and Machine Learning

Background: Liver fibrosis staging is critical for patient selection and management prior to transplantation, but biopsy is invasive and serum biomarkers lack accuracy. Near-infrared spectroscopy (NIRS) is an emerging non-invasive technology that can detect liver fibrosis via changes in tissue composition. Machine learning (ML) enables analysis of NIRS data for diagnostic modeling. Purpose: To review the potential of NIRS-ML approaches for rapid, point-of-care liver fibrosis detection, including technological principles, promising applications, current limitations, and future directions. Main body of the abstract: NIRS leverages unique near-infrared absorbance patterns reflecting collagen accumulation, lipid reduction, and other chemical alterations in fibrotic liver. Handheld/hyperspectral systems acquire tissue spectroscopic data in minutes. Multiple human studies correlate NIRS with histological fibrosis scores. ML techniques like partial least squares regression, neural networks, support vector machines, and random forests analyze spectra to develop optimized diagnostic algorithms. Initial models differentiate mild versus advanced fibrosis and stage cirrhosis with high accuracy, outperforming traditional biomarkers. Recent advances include smartphone-based scanning, cloud computing, and integrated user-friendly platforms. However, further large validation trials, standardization, assessment of confounding factors, improved ML methodology, and cost-effectiveness data are required before widespread clinical implementation. Conclusion: With ongoing research to address remaining barriers, NIRS-ML approaches hold great disruptive potential for rapid, non-invasive point-of-care quantification of liver fibrosis, including optimizing transplant surgery planning and management.

Mocking Bird ( Mimus polyglottos ) calls potentially confound acoustic indices of bird diversity and provide a potential heuristic to distinguish them.

This study explores the increasing use of autonomous recording units (ARUs) in wildlife surveys. While ARUs offer cost-effective and efficient data collection, challenges arise in analyzing large datasets and accurately assessing species abundance. Our research focuses on avian communities, emphasizing the impact of vocal mimicry by Northern Mockingbirds ( Mimus polyglottos ) on survey accuracy. Utilizing the Merlin Bird ID application, we found an average accuracy rate of ~81.3%, with mockingbirds contributing ~31% of false positive identifications. Finding potential solutions for distinguishing mimics in bioacoustic survey data is crucial for enhancing accuracy as researchers increasingly adopt this methodology in the future.

On-receptor computing with classical associative learning in semiconductor oxide memristors.

The increasing demand for energy-efficient data processing leads to a growing interest in neuromorphic computing that aims to emulate cerebral functions. This approach offers cost-effective and rapid parallel data processing, surpassing the limitations of the conventional von Neumann architecture. Key to this emulation is the development of memristors that mimic biological synapses. Recently, research efforts have focused on the incorporation of nociceptors-sensory neurons capable of detecting external stimuli-into memristors for applications in robotics and artificial intelligence. This integration enables memristors to adapt to various circumstances while remaining cost-effective. A nonfilamentary gradual resistive switching memristor is utilized to implement artificial nociceptor and synaptic behaviors. The fabricated Pt/indium gallium zinc oxide (IGZO)/SnOx/TiN device exhibits essential properties of biological nociceptors, including threshold response, no-adaptation, relaxation, sensitization, and recovery. Furthermore, the device leverages short-term memory principles to emulate learning behaviors observed in the brain by showcasing "forgetting" paradigms. Additionally, control of the input spikes yields different synaptic plasticity responses, thus emulating the key functions of our synapse. Computational simulations demonstrate the device's ability to perform both computing and sensing tasks effectively, thus enabling on-receptor computing with associative learning capabilities.