Remote Connectivity Research Articles

B-033 Real World Assessment of the Impact of Infectious Disease Assays on Workflow Capabilities of Siemens Atellica IM Analyzers

Abstract Background Instrument immunoassay throughputs publicized in the industry are often theoretical, performed in relatively smaller scale, and/or in the best case generated only with fast assays, not representing the typical assay mix of routine activity. Infectious disease (ID) serology assays typically have long analytical times and have the potential to influence throughput capabilities and turnaround time (TAT) of other assays with shorter analytical times. The objective of this study is to utilize real world evidence across a large fleet of Atellica® IM 1300 and IM 1600 analyzers representing variously sized laboratories, using variations of ID assay mixes, to assess the impact on TAT on select STAT immunoassays. Methods Assay mix, test volumes and TAT data was mined from the Atellica® Smart Remote Services (SRS), a Siemens Healthineers proprietary remote connectivity platform over 3 distinct 14-day time windows. Real world data from >1800 Atellica IM and >8 million tests were queried per time window for the following ID assays: HIV, Hepatitis B, Hepatitis C, TORCH, Syphilis (long analytical time) and a selection of non-ID immunoassays: hs-troponin I (TNIH), Thyroid Stimulating Hormone (TSH3UL), total HCG (ThCG), and B-Type Natriuretic Peptide (BNP) (short analytical time). Median TAT for short assay was analyzed with 6 variations of ID assay mix (0%, <10%, <20%, <30%, <40%, <50%) in the run representing increasing percentages of assays requiring longer incubation times. TAT was represented as barcode to result and aspiration to result. Results The median TAT for TNIH remained consistent at 10.1 minutes across increasing % of ID assays (N=100961). The median TAT for TNIH for platforms not running ID assays (83237) remained consistent at 10.03 minutes. The median TAT for TSH remained consistent at 14.02 minutes across increasing % of ID assays (N=1183344). The median TAT for TSH for platforms not running ID assays (N=480219) remained consistent at 13.98 minutes. The median TAT for ThCG remained consistent at 10.28 minutes across increasing % of ID assays (N=24677). The median TAT for ThCG for platforms not running ID assays (N=79275) remained consistent at 10.27 minutes. The median TAT for BNP remained consistent at 10.1 minutes across increasing % of ID assays (N=42949). The median TAT for BNP for platforms not running ID assays (N=14990) remained consistent at 10.03 minutes. Conclusions Throughput and TAT on the Atellica IM Analyzer is relatively unaffected by mix of longer-incubation and shorter-incubation assays. The dual incubation rings allow more flexibility in the mix of incubation times with predictable and consistent TAT for all assays including STAT.

Implementasi Sistem Pemberian Pakan Ikan Hias Otomatis Menggunakan Esp32 Berbasis IoT (Internet of Things)

This study aims to design and implement an automatic feeding system for ornamental fish using Internet of Things (IoT) technology, with the ESP32 microcontroller and servo motor. Given Indonesia's extensive water resources and the importance of its fisheries sector, ornamental fish farming has gained significant popularity. Fish owners often struggle with providing timely and consistent feeding. IoT technology offers an effective solution through remote connectivity. The developed system utilizes the Blynk application to schedule and monitor aquarium conditions in real-time. Test results indicate that the integration of ESP32 and the servo motor functions effectively, enabling scheduled automatic feeding. The system was tested and found to operate punctually, supporting a consistent daily feeding cycle. This study contributes to the advancement of innovative solutions in aquaculture, particularly in the application of IoT technology for more efficient and effective aquarium management.

A CNN-based approach for anomaly detection in smart grid systems

Power grids are always a major center of attraction for attackers due to their large coverage area, complex technological infrastructure, remote access facilities, connectivity, and many more. The power grids that have bidirectional power flow, information interchange, and advanced communication technologies to improve the quality of power transmission and distribution are called smart grids. DoS attacks and false data injection attacks are the two major attacks on smart grids. This paper considers a false data injection attack considering noise reduction, state estimation, system parameters, and control system generation. Firstly, we measure the noise and mitigate it using the data matrix of the devices (sensors, generators, substations, and breaker systems). Secondly, state estimation uses the CNN to track the injected attack types. Finally, a control system is designed to provide anomaly patterns to the devices as input to prevent attacks in the future. The experimental results show that the proposed method achieves a higher anomaly detection rate with a lower error rate (approximately . 1.43%). The network performance parameters are providing significant results; throughput (12 Mbps), PDR (0.005%), execution time (1.43 s), and energy consumption are reduced by 12%–15% as compared to the existing research findings.

Analyzing India's National Civil Aviation Policy and Global Trends in Contemporary Civil Aviation Policies

The expansion of the Indian economy is expected to extend to remote regions, and while market-driven processes are ideal for regional air connectivity, there is recognition of the valuable role of public policy in this endeavor. The UDAN Scheme follows a market-oriented approach where operators assess demand, propose routes, and navigate variables like traffic rights, slot allocation, regulatory restrictions, and infrastructure limitations. Global airline industry deregulation has introduced healthy competition, enhancing efficiency and service quality, leading to increased passenger traffic. Studies have highlighted the constructive impact of improved air connectivity on the economic development of regions. Efficient airports are vital economic catalysts, but research suggests that some regional airports operate inefficiently, missing opportunities for profitability. Government subsidies and policies for enhancing regional and remote air connectivity are common practices worldwide, with schemes like EAS in the United State and PSO in Europe bridging the accessibility gap. Research is crucial to understand policy effects on air connectivity and identify factors that improve outcomes. Route dispersal guidelines, such as those in India, can create financial redistribution between route categories, affecting airlines' profitability and serving state capitals over remote regions. Policymakers must find effective ways to enhance air transport to underserved areas where market dynamics might not naturally establish viable transportation links. In summary, this study emphasizes the importance of government-supported policies like UDAN and the role of market-oriented approaches in promoting regional air connectivity. It underlines the need for research to assess policy impacts and identifies the challenges and opportunities in enhancing air connectivity to remote and underserved regions.

Design and Implementation of Remote Lab PID Controller Experiment Based on IoT

The education sector has increasingly embraced distance education to ensure a safe and uninterrupted learning process, especially in wars or epidemics. This paper focuses on designing and implementing a PID (Proportional-Integral-Derivative) controller experiment in remote laboratories utilizing the Internet of Things (IoT). Also, a laboratory experiment was developed using a naturally unstable system, specifically a cart-inverted pendulum. The experiments aim to enhance students' understanding of PID controller tuning within a real-world context. This system was chosen due to its dual motion characteristics, with a linear motion for the cart and a circular motion for the pendulum. Two controllers were designed and implemented for the system to enable control and feedback. Additionally, the Blynk platform was integrated into the experiment setup, allowing for real-time visualization of the system's response, control of PID parameters, and the ability to view the video stream via platforms like Skype. For remote connectivity, NodeMCU, an IoT development board, controlled the pendulum, collected system parameters, and transmitted them to the cloud through the Internet. Moreover, the sensors and the system were mathematically modeled, and their transfer functions were extracted. That allows the students to do the PID experiment theoretically and practically and compare the results. This complete setup enables local and remote access to the experiment, ensuring students can experiment regardless of their physical location. As a result of the study, designing and implementing a remote laboratory PID controller experiment utilizing IoT technology provides students with an innovative and immersive learning experience.

Distinguishable neural circuit mechanisms associated with the clinical efficacy of rTMS in aMCI patients.

Repetitive transcranial magnetic stimulation is used in early-stage Alzheimer's disease to slow progression, but heterogeneity in response results in different treatment outcomes. The mechanisms underlying this heterogeneity are unclear. This study used resting-state neuroimaging to investigate the variability in episodic memory improvement from angular gyrus repetitive transcranial magnetic stimulation and tracked the neural circuits involved. Thirty-four amnestic mild cognitive impairment patients underwent angular gyrus repetitive transcranial magnetic stimulation (4weeks, 20Hz, 100% resting motor threshold) and were divided into high-response and low-response groups based on minimal clinically important differences in auditory verbal learning test scores. Baseline and pre/post-treatment neural circuit activities were compared. Results indicated that the orbital middle frontal gyrus in the orbitofrontal cortex network and the precuneus in the default mode network had higher local activity in the low-response group. After treatment, changes in local and remote connectivity within brain regions of the orbitofrontal cortex, default mode network, visual network, and sensorimotor network showed opposite trends and were related to treatment effects. This suggests that the activity states of brain regions within the orbitofrontal cortex and default mode network could serve as imaging markers for early cognitive compensation in amnestic mild cognitive impairment patients and predict the aftereffects of repetitive transcranial magnetic stimulation response.

Revisiting Rural and Remote Connectivity Challenges in B5G and 6G Networks

Revisiting Rural and Remote Connectivity Challenges in B5G and 6G Networks

Augmented Reality-Based Real-Time Visualization for Structural Modal Identification.

In the era of aging civil infrastructure and growing concerns about rapid structural deterioration due to climate change, the demand for real-time structural health monitoring (SHM) techniques has been predominant worldwide. Traditional SHM methods face challenges, including delays in processing acquired data from large structures, time-intensive dense instrumentation, and visualization of real-time structural information. To address these issues, this paper develops a novel real-time visualization method using Augmented Reality (AR) to enhance vibration-based onsite structural inspections. The proposed approach presents a visualization system designed for real-time fieldwork, enabling detailed multi-sensor analyses within the immersive environment of AR. Leveraging the remote connectivity of the AR device, real-time communication is established with an external database and Python library through a web server, expanding the analytical capabilities of data acquisition, and data processing, such as modal identification, and the resulting visualization of SHM information. The proposed system allows live visualization of time-domain, frequency-domain, and system identification information through AR. This paper provides an overview of the proposed technology and presents the results of a lab-scale experimental model. It is concluded that the proposed approach yields accurate processing of real-time data and visualization of system identification information by highlighting its potential to enhance efficiency and safety in SHM by integrating AR technology with real-world fieldwork.

Reducing the Environmental Impact of Health Care Conferences: A Study of Emissions and Practical Solutions.

We aimed to examine the impact of different conference formats (in-person, virtual, and hybrid) of the ASCO conference on greenhouse gas (GHG) emissions and to recommend sustainable options for future conferences. This study used data on the number of attendees, their departure locations, and the type of attendance (in-person v virtual) provided by ASCO between 2019 and 2022. The GHG emissions resulting from air and ground travel, remote connectivity, conference space utilization, hotel stays, distributed conference materials, and electricity use were estimated for each year. Emissions were stratified by attendee country of origin, type of attendance, and year. Simulations were conducted to evaluate how changes in conference size, location, and format impact emissions, as well as estimate the resulting mitigations from adopting the proposed changes. The highest estimated GHG emissions, calculated in carbon dioxide equivalents (CO2e), were associated with the 2019 in-person conference (37,251 metric tons of CO2e). Although international attendees had the largest contribution to emissions in all years (>50%), location optimization models, which selected conference locations that most minimized GHG emissions, yielded only minimal reductions (approximately 3%). Simulations examining changes to the conference format, location, and attendance percentage suggested that hub-and-spoke, where multiple conference locations are selected by global region, or hybrid models, with both in-person and virtual components, are likely to cause the largest drops in emissions (up to 86%). Using historical conference data, this study identifies key aspects that can be modified to reduce emissions and consequently promote more sustainable and equitable conference attendance. Hybrid conferences may be the best solution to maintain the networking opportunities provided by conferences while balancing out their environmental footprint.

Altered local and remote functional connectivity in mild Alzheimer's disease patients with sleep disturbances.

This study aimed to investigate local and remote functional connectivity in mild Alzheimer's disease patients with sleep disturbances (ADSD) and those without sleep disturbances (ADNSD). Thirty eight mild AD patients with sleep disturbances and 21 mild AD patients without sleep disturbances participated in this study. All subjects underwent neuropsychological assessments and 3.0 Tesla magnetic resonance scanning. Static and dynamic regional homogeneity (ReHo) were used to represent the local functional connectivity. Seed-based whole-brain functional connectivity was used to represent the remote functional connectivity. The seed was chosen based on the results of ReHo. Compared to ADNSD, ADSD showed decreased static ReHo in the left posterior central gyrus and the right cuneus and increased dynamic ReHo in the left posterior central gyrus. As for the remote functional connectivity, comparing ADSD to ADNSD, it was found that there was a decreased functional connection between the left posterior central gyrus and the left cuneus as well as the left calcarine. The current study demonstrated that, compared with ADNSD, ADSD is impaired in both local and remote functional connectivity, manifested as reduced functional connectivity involving the primary sensory network and the primary visual network. The abnormality of the above functional connectivity is one of the reasons why sleep disorders promote cognitive impairment in AD. Moreover, sleep disorders change the temporal sequence of AD pathological damage to brain functional networks, but more evidence is needed to support this conclusion.

Audio-Conversion of Biomedical Signals - A Possible Approach to Improve Remote Monitoring of Elderly and Visually Impaired People.

Long-term remote patients monitoring implies minimal discomfort and reliability throughout the study period. These requirements are fulfilled by portable (wearable) patient devices, with low consumption, which transmit data wirelessly, at a short distance, to a mobile communication device (GSM) and through it, to a remote end recipient - doctor, medical center or a hospital server. The data transfer technology requires the monitored person to perform a sequence of actions, such as: selecting the appropriate application on the mobile phone, establishing a connection between the patient module and the phone, recording the data in the phone's memory, starting the data transfer from the phone to the final receiver. Practice shows that often this sequence of activities is difficult for elderly people and especially for visually impaired people, which as a result compromises the remote monitoring process. In this paper are presented an approach and conceptual implementation of a system for remote monitoring of cardiac activity, using the most popular way of remote connectivity - voice (sound) communication. In addition to the ease of use, this type of communication does not require special data protection, due to the lack of RF interfaces for short-distance data transmission. The presented results of laboratory studies, as well as conducted tests under medical supervision of patients in a cardiology clinic, confirm the workability of the proposed approach for remote monitoring of patients by audio conversion of the ECG signal.

Fronto-temporal dysfunction in appetitive regulation of bulimia nervosa with affective disorders: A regional homogeneity and remote connectivity pattern analysis

ObjectiveThe aim of this study was to assess brain functional alterations in BN patients with affective disorders and their association with maladaptive eating behaviors. MethodsA total of 42 BN patients with affective disorders (anxiety and depression) and 47 healthy controls (HCs) were enrolled in this study. The resting-state fMRI data were analyzed for functional changes as indicated by regional homogeneity based on Kendall's coefficient of concordance (KCC-ReHo) and seed-based functional connectivity (FC). A principal component analysis (PCA) model was used to identify the commonalities within the behavioral questionnaires from the BN group. ResultsPatients in the BN group showed decreased ReHo in the bilateral middle frontal gyrus (MFG) and right supramarginal gyrus (SMG). Additionally, the BN group showed increased FC between the left MFG and the right inferior temporal gyrus (ITG); decreased FC between the right MFG and the bilateral insula and the left middle temporal gyrus (MTG); and decreased FC between the right SMG and the left superior temporal gyrus (STG) and right inferior frontal gyrus (IFG). In the FC-behavior association analysis, the second principal component (PC2) was negatively correlated with FC between the left MFG and the right ITG. ConclusionBased on a brain functional analysis (ReHo and FC), this study revealed significant aberrant changes in the frontal-temporal regions of BN patients with affective disorders. These regions, which serve as fronto-temporal circuitry, are associated with restraint and emotional eating behaviors. Our findings shed new light on the neural mechanisms underlying the condition.

Stratifying ASD and characterizing the functional connectivity of subtypes in resting-state fMRI

Although stratifying autism spectrum disorder (ASD) into different subtypes is a common effort in the research field, few papers have characterized the functional connectivity alterations of ASD subgroups classified by their clinical presentations. This is a case-control rs-fMRI study, based on large samples of open database (Autism Brain Imaging Data Exchange, ABIDE). The rs-MRI data from n=415 ASD patients (males n=357), and n=574 typical development (TD) controls (males n=410) were included. Clinical features of ASD were extracted and classified using data from each patient's Autism Diagnostic Interview-Revised (ADI-R) evaluation. Each subtype of ASD was characterized by local functional connectivity using regional homogeneity (ReHo) for assessment, remote functional connectivity using voxel-mirrored homotopic connectivity (VMHC) for assessment, the whole-brain functional connectivity, and graph theoretical features. These identified imaging properties from each subtype were integrated to create a machine learning model for classifying ASD patients into the subtypes based on their rs-fMRI data, and an independent dataset was used to validate the model. All ASD participants were classified into Cluster-1 (patients with more severe impairment) and Cluster-2 (patients with moderate impairment) according to the dimensional scores of ADI-R. When compared to the TD group, Cluster-1 demonstrated increased local connection and decreased remote connectivity, and widespread hyper- and hypo-connectivity variations in the whole-brain functional connectivity. Cluster-2 was quite similar to the TD group in both local and remote connectivity. But at the level of whole-brain functional connectivity, the MCC-related connections were specifically impaired in Cluster-2. These properties of functional connectivity were fused to build a machine learning model, which achieved ~75% for identifying ASD subtypes (Cluster-1 accuracy = 81.75%; Cluster-2 accuracy = 76.48%). The stratification of ASD by clinical presentations can help to minimize disease heterogeneity and highlight the distinguished properties of brain connectivity in ASD subtypes.

A framework for focal and connectomic mapping of transiently disrupted brain function

The distributed nature of the neural substrate, and the difficulty of establishing necessity from correlative data, combine to render the mapping of brain function a far harder task than it seems. Methods capable of combining connective anatomical information with focal disruption of function are needed to disambiguate local from global neural dependence, and critical from merely coincidental activity. Here we present a comprehensive framework for focal and connective spatial inference based on sparse disruptive data, and demonstrate its application in the context of transient direct electrical stimulation of the human medial frontal wall during the pre-surgical evaluation of patients with focal epilepsy. Our framework formalizes voxel-wise mass-univariate inference on sparsely sampled data within the statistical parametric mapping framework, encompassing the analysis of distributed maps defined by any criterion of connectivity. Applied to the medial frontal wall, this transient dysconnectome approach reveals marked discrepancies between local and distributed associations of major categories of motor and sensory behaviour, revealing differentiation by remote connectivity to which purely local analysis is blind. Our framework enables disruptive mapping of the human brain based on sparsely sampled data with minimal spatial assumptions, good statistical efficiency, flexible model formulation, and explicit comparison of local and distributed effects.

Smart Automotive Diagnostic and Performance Analysis Using Blockchain Technology

The automotive industry currently is seeking to increase remote connectivity to a vehicle, which creates a high demand to implement a secure way of connecting vehicles, as well as verifying and storing their data in a trusted way. Furthermore, much information must be leaked in order to correctly diagnose the vehicle and determine when or how to remotely update it. In this context, we propose a Blockchain-based, fully automated remote vehicle diagnosis system. The proposed system provides a secure and trusted way of storing and verifying vehicle data and analyzing their performance in different environments. Furthermore, we discuss many aspects of the benefits to different parties, such as the vehicle’s owner and manufacturers. Furthermore, a performance evaluation via simulation was performed on the proposed system using MATLAB Simulink to simulate both the vehicles and Blockchain and give a prototype for the system’s structure. In addition, OMNET++ was used to measure the expected system’s storage and throughput given some fixed parameters, such as sending the periodicity and speed. The simulation results showed that the throughput, end-to-end delay, and power consumption increased as the number of vehicles increased. In general, Original Equipment Manufacturers (OEMs) can implement this system by taking into consideration either increasing the storage to add more vehicles or decreasing the sending frequency to allow more vehicles to join. By and large, the proposed system is fully dynamic, and its configuration can be adjusted to satisfy the OEM’s needs since there are no specific constraints while implementing it.

Development Of a Liquid/Fluid Removing System Module For Medical Hospitals Using IoT

In this paper, the development of an automatic fluid dispensing module for medical applications in the hospital zones using IoT concepts is presented. This project focuses on developing an automatic fluid dispenser designed to offer a seamless and hands-free fluid supply, eliminating the need for manual intervention. This technology is widely used in diverse environments, including households, offices, public spaces, and industrial settings. The dispenser operates through sensors, valves, and pumps to identify the presence of a container, such as a glass or bottle, and dispenses a pre-set amount of fluid. As technology progresses, automatic fluid dispensers are expected to evolve with advanced features like touchless interfaces, fluid purification systems, and remote connectivity for monitoring and control. Key advantages include hands-free operation, fluid conservation, user-friendliness, and the potential for integration with smart systems. The work carried out is the second semester mini-project by the students of Electronics & Communication Engineering under the guidance of the faculties.

RSSI Analysis on CSS Modulation in the 433 MHz Frequency Band Using Lora in Flood Sensor

Long Range (LoRa) low power is a transmission technology capable of connecting multiple sensors to the Internet of Things in the future. LoRa uses Chirp Spread Spectrum (CSS) frequency modulation which promises to achieve remote connectivity at very low power. More specifically, this paper focuses on the analysis of Receive Signal Strength Indicator (RSSI) on CSS modulation on flood sensors. This paper presents a detailed model of using LoRa on flood sensors, showing that LoRa is capable of transmitting sensor data over long distances with perfect RSSI values. The results showed the potential for a close communication range with a narrow band network at a frequency of 433 MHz and an average RSSI of -50 dB at a maximum distance of 600 meters without obstruction. Although LoRa communication uses CSS modulation and has good RSSI values, in long-distance communication with data in the form of video or large sensor data, this toughness is not sufficient due to long distances and large bandwidth usage, while in this study LoRa type Ai Thinker Ra- 02 SX 1278 can only be set up to 125 KHz according to the datasheet. The disadvantages of using large sensor data over long distances and a larger footprint make CSS vulnerable to other, possibly larger, resources.

A software development for medical with a multiple decision taking functionalities

BackgroundSmart devices including sensors and smartphones have grown exponentially during the past few years due to rapid growth in wireless sensor network (WSN) technology under IoT. In addition with smart city, concepts of smart traffic systems and smart health are also being developed. It enables remote connectivity of smart devices (i.e., mobiles, sensors) and medical smart software with the internet for monitoring. The availability of smart devices plays a remarkable role in the new era of mobile health (mHealth) monitoring. mHealth software is mostly used for humans who suffer from any chronic disease and need remote monitoring. In this case, such human needs smart medical software for careful monitoring from a distance due to their continuous movement and distance from any medical support. Existing LimitationsNowadays, due to the frequent use and development of intelligent software for medical still, this domain has several limitations such as (i) not rescuing timely, (ii) no mechanism to assure that medical condition is abnormal or due to any other reason i.e. environmental effects, (iii) not a decision taking medical software for selection of multiple ontologies simultaneously, (iv) no acknowledgment process for enlisting the medical centers which can provide the medical facilities and (v) issues with assessing the environmental factors. These limitations make smart medical field less effective particularly for users who have severe situations. Proposed SolutionTherefore, this work has proposed a framework and developed medical software for humans. For the development of the software, we used the hardware including Arduino UNO board, for pulse values used a pulse sensor and for its connectivity with the mobile software used a Bluetooth module HC-05 module. Arduino and Android based software are linked via a Bluetooth module. ImplementationFor testing the software, case studies are used with different scenarios. This study has revealed more valuable results in respect of human rescue and ensuring the severe condition of the human with current activities, examining the effects of the environmental factors on the health, preventing the system from false alert generation, and timely receiving of medical facilities. ConclusionThe developed software as per the suggested framework's features has overcome the mentioned challenges from remote monitoring. It potentially outperformed with easiness and in a better way towards smart health monitoring services.

Identifying passenger preferences for alternative ferry transport hubs

The demand and preference for alternative ferry transport hubs are discussed using the region of the Aegean Archipelago as a case study. A complex network of connections among the islands and to the Greek peninsula serves the needs of tourism, transportation of goods and services, and islanders traveling to and from the mainland.However, this system has shortcomings; in particular, interconnections for many islands are only possible via the mainland ports of Greece. Interconnectivity among the islands is important for the economic, social, and territorial cohesion of the archipelago as well as to meet the needs of tourism. This study looks into passengers’ acceptance of two alternative minor island connection ports that would increase remote island connectivity. Econometric tools and a stated preferences survey were used to assess the acceptance of minor island hubs versus the existing major mainland maritime hubs. Discrete choice models were estimated in which the attributes of the alternatives include travel times, waiting times at the interchange hubs, fares, and selected socioeconomic parameters of the respondents. The results show a positive trend in favor of the proposed minor hub system. In conclusion, this innovative methodology could be used for policy testing and transferable to similar archipelagic regions.

TELEMEDICINE IN POST-CIED CARE

Previously, all patients post-CIED implant returned to the clinic to have wound assessment, reinforce device knowledge, and confirm remote connectivity. This did not offer any tailored options for patient follow-up.