Time Monitoring Research Articles

The study of psychosocial characteristics of youth with internet addiction

Objective: To explore the mental health status, coping styles, personality traits, and time management tendencies of adolescents with internet addiction, providing a theoretical basis for the mental and physical health education of these youth. Methods: A total of 2,784 students were screened for internet addiction using an internet addiction test questionnaire. Of the 80 identified internet addicts, 77 students were designated as the study group, and 80 students without a tendency towards internet addiction were selected as the control group. Both groups were evaluated using the Symptom Checklist-90 (SCL-90), the Simplified Coping Style Questionnaire (SCSQ), the Eysenck Personality Questionnaire (EPQ), and the Adolescent Time Management Disposition Scale (ATMD). Results: The rate of internet addiction among adolescents was 3.1%. Except for the paranoid and psychotic factor scores, the study group's SCL-90 scores in the remaining eight factors were significantly higher than those of the control group (P<0.01). The active coping dimension score of the SCSQ was significantly lower in the study group than in the control group (P<0.01), while the passive coping dimension score was significantly higher (P<0.01). The introversion and neuroticism dimension scores of the EPQ were significantly lower in the study group than in the control group (P<0.01), while the psychoticism dimension score was significantly higher (P<0.01). The ATMD scores for time value, time monitoring, and time efficacy were all significantly lower in the study group than in the control group (P<0.01). Conclusion: Adolescents with internet addiction have poor mental health status, a more negative problem-solving approach, introverted and reclusive personality types, and unreasonable time management.

Cybersecurity Challenges in Net Energy Metering: Legal Frameworks and Practical Solutions

Net Energy Metering (NEM) has emerged as a key factor in Modern Energy Management, empowering consumers to produce their own electricity and return Net Excess Generation (NEG) to the grid. However, the increased usage of NEM has introduced significant network security challenges. As millions of electronic devices are interconnected through communication networks across critical power facilities cybersecurity has become a concerning factor, directly affecting the reliability of such extensive infrastructure. This paper gives an insight into the rising cyber offences associated with NEM systems, stipulates the legal framework, e-governance, and liabilities under the Information Technology (IT) Act, 2000. Categorization of potential cyber threats like Access control, database security and encryption, intrusion detection, malicious software, operating system vulnerabilities and security, application security has been highlighted. Evaluation of Time of Day (ToD) pricing mechanism focusing on real time monitoring and energy efficiency during peak and off-peak consumption. Incorporating Virtual Private Networks (VPNs) and other cybersecurity measures to safeguard the integrity and reliability of NEM systems. The paper illustrates the impact of cybercrimes on NEM, by exploring existing applications and case studies. It underscores the necessity of implementing robust cybersecurity practices to protect these systems. The objective of the paper is to raise awareness about the cybersecurity challenges encountered in NEM systems and to propose practical solutions to mitigate these risks, ensuring a safe and regulated environment for the functioning of smart grids in a progressively growing digital world.

Gas Pipe Leakage Inspection Robot with Real Time Monitoring System

In this Review paper the study focusses on Pipe inspection robots,robots are advanced devices engineered to traverse the interiors of pipelines, assessing conditions and identifying issues like leaks, corrosion, or blockages in industries such as oil and gas, water supply, and sewage. Equipped with high-resolution cameras, sensors, and diagnostic tools, these robots offer detailed, real-time data to support preventive maintenance and reduce manual inspection challenges. Recent developments focus on improving robot agility, compactness, and durability, enabling them to handle varied pipe diameters and complex pathways. Enhanced with wireless connectivity and artificial intelligence, some models can now perform autonomous navigation and defect detection, significantly boosting efficiency and safety. This review examines current innovations, operational hurdles, and future advancements in pipe inspection robotics, with a particular emphasis on smarter navigation systems and sensor technology to enhance inspection accuracy and dependability.

Transforming Farming: A Review of AI-Powered UAV Technologies in Precision Agriculture

The integration of unmanned aerial vehicles (UAVs) with artificial intelligence (AI) and machine learning (ML) has fundamentally transformed precision agriculture by enhancing efficiency, sustainability, and data-driven decision making. In this paper, we present a comprehensive overview of the integration of multispectral, hyperspectral, and thermal sensors mounted on drones with AI-driven algorithms to transform modern farms. Such technologies support crop health monitoring in real time, resource management, and automated decision making, thus improving productivity with considerably reduced resource consumption. However, limitations include high costs of operation, limited UAV battery life, and the need for highly trained operators. The novelty of this study lies in the thorough analysis and comparison of all UAV-AI integration research, along with an overview of existing related works and an analysis of the gaps. Furthermore, practical solutions to technological challenges are summarized to provide insights into precision agriculture. This paper also discusses the barriers to UAV adoption and suggests practical solutions to overcome existing limitations. Finally, this paper outlines future research directions, which will discuss advances in sensor technology, energy-efficient AI models, and how these aspects influence ethical considerations regarding the use of UAVs in agricultural research.

Digital twinning of vertical centrifugal casting

This paper explores the transformative potential of digital twin technology in vertical centrifugal casting (VCC), a cornerstone manufacturing process for high-integrity cylindrical components. By integrating real-time data, physical models, and machine learning algorithms, digital twins unlock a new paradigm of process optimization, predictive maintenance, and quality control. Integration of digital twinning enhances the performance in different domains of work like enhancing the quality of research, production etc. Howbeit, digital twinning is nascent in the domain of manufacturing specially in the casting sub domain. A physical set up of VCC is integrated with Internet of Things (IoT) and data acquisition system to stream the collected data to the cloud-based server. Transformation of Internet of Things (IoT) enabled VCC integrated with different sensors into the digital twin helps in quality prognosis for future applications. The data is further fetched from the cloud and interconnection is established between the digital twin. Real time monitoring, controlling and operating can be done easily with the help of a digital twin to predict the quality and tentative defect locations. Further amplifying these benefits, emerging technologies like Virtual Reality (VR), Augmented Reality (AR), and the Metaverse hold immense promise for revolutionising VCC training, collaboration, and visualisation.

Smart Waste Management System

Internet of Things (IoT) plays an important role to make the cities greener, safer, and more efficient. Waste collection management solution based on providing intelligence to wastebins, using an IoT prototype with sensors is been presented in this paper. Planning the waste management for all of the garbage produced in the country is an enormous task which involves both logistical planning and scientific knowledge in order to balance the impact on the environment and the people. With speedy increase in population, the issues related to sanitation with respect to waste operation are demeaning immensely. It creates unhealthy conditions for the citizens in the near surroundings, leading to the spread of contagious conditions and illness. In the proposed system, embedded device helps in real time monitoring of checking the status of the waste in waste dustbins. The data regarding the waste level will be used to deliver optimized route for waste collecting vans, which will reduce cost associated with fuels. The load sensor will increase effectiveness of data related to waste status and moistness sensor will be used to give data of waste segregation in a dust bins. Keywords: Internet-of-Things, smart waste management, Arduino, Wi-Fi, Internet.

Classification of faults in friction stir processed composites using a machine learning and ensemble learning approach

Abstract Aluminium alloy based surface composites with hard reinforcement particles have wide scope in aerospace and automobile manufacturing industries. In this paper, the aluminium composites, manufactured by friction stir processing (FSP) with varying parameters are investigated for the faults occurred during fabrication process. It explores a machine-learning approach to detect defects of surface hybrid composites with an Al6061 alloy matrix, reinforced with copper and graphene nano-powders, using friction stir processing and a tungsten carbide tool on a milling machine. Multi-sensor time series data (vibration, force, and current) collected during fabrication, is preprocessed and labelled with normal and defective categories (e.g., pin break, brazing break, rough surface, no composite) using visual inspection. The important time domain and frequency domain features are extracted using different libraries in python. Thenafter, various types of feature selection techniques, viz filter, wrapper and embedded methods are implemented to select most relevant features. The selected subset of features from all selection methods used, are applied to different machine learning and ensemble learning classifiers and their performances are evaluated. The optimal combinations of the type of feature selection method and classifier used, are obtained for efficient classification of surface defects in composited formed by FSP. The real time monitoring and defect detection system can be developed in future for the composites developed by FSP using the developed models.

Collaboration to Reduce Sudden Unexpected Infant Death With Child Fatality Review and Outreach.

Sudden unexpected infant death (SUID) is the leading cause of postneonatal infant mortality in the United States, with disproportionately high rates in Baltimore City and Baltimore County in Maryland. This Advocacy Case Study describes the collaboration between the City and County Child Fatality Review teams to decrease infant mortality. B'more for Healthy Babies, formed in 2009 by the Bureau of Maternal Child Health in Baltimore City with the goal of reducing infant mortality through policy change, service improvements, community mobilization, and behavior change has had a sustained effort to respond to SUID. Recognizing that infants born in Baltimore City often reside in Baltimore County (and vice versa), collaboration with Baltimore County has been essential to increasing B'more for Healthy Babies' scope, reach, and effectiveness. Public health messaging campaigns, creation, and dissemination of a "SLEEP SAFE" testimonial video, and Safe Sleep Summits have involved caregivers, healthcare providers, and community partners. Outcomes of this ongoing collaboration demonstrate a decrease in sleep-related infant mortality rates. Our findings also highlight the need for continued, real time monitoring of sleep-related infant mortality trends with a coordinated, multidisciplinary, and crossjurisdictional response. This initiative can serve as a model of cross-sector communication that can be replicated in other similar geographic locations to further reduce SUID.

Emerging Technological Advancement for Chronic Wound Treatment and Their Role in Accelerating Wound Healing.

Chronic wounds are a major healthcare burden and may severely affect the social, mental, and economic status of the patients. Any impairment in wound healing stages due to underlying factors leads to a prolonged healing time and subsequently to chronic wounds. Traditional approaches for the treatment of chronic wounds include dressing free local therapy, dressing therapy, and tissue engineering based scaffold therapies. However, traditional therapies need improvisation and have been advanced through breakthrough technologies. The present review spans traditional therapies and further gives an extensive account of advancements in the treatment of chronic wounds. Cutting edge technologies, such as 3D printing, which includes inkjet printing, fused deposition modeling, digital light processing, extrusion-based printing, microneedle array-based therapies, gene therapy, which includes microRNAs (miRNAs) therapy, and smart wound dressings for real time monitoring of wound conditions through assessment of pH, temperature, oxygen, moisture, metabolites, and their use for planning of better treatment strategies have been discussed in detail. The review further gives the future direction of treatments that will aid in lowering the healthcare burden caused due to chronic wounds.

Can a zero-shot learning Large Language Model code complex interview data?

Abstract Background Psychological autopsy is essential to establish theories, explore trends and identify previously unexplored psychosocial risk factors in suicide research. However, qualitative research has been scrutinized for being prone to interpretation bias, problems with accuracy, challenges to reproducibility, and is very time- and cost intensive. The current study aimed to investigate if a Large Language Model can achieve sufficient agreement with a researcher in the deductive coding of interview data obtained in a psychological autopsy study of suicide, to be integrated with qualitative research procedures. Methods Data from 38 interviews was deductively coded by a researcher and a LLAMA 3 based language model. The model performance was evaluated in four increasingly difficult coding tasks, including binary classification and data summarization. Intercoder agreement scores were calculated using Cohen’s Kappa. Results The preliminary results showed that the LLM achieved substantial agreement with the human coders for the binary classification task (.78). The variability in performance was influenced by code definitions. The results of the quality of LLM interpretation and summarization will also be presented. Conclusions State-of-the-art LLM can be easily integrated into the qualitative analyses of psychological autopsy interviews and may improve real time monitoring of suicides. We recommend a human-AI collaborative model, whereby deductive coding by the LLM is complemented by human inductive coding and further interpretation. Key messages • Integrating an LLM with qualitative research procedures is feasible in a collaborative model. • Integrating an LLM with qualitative research procedures allows near real-time monitoring based on qualitative data, which extends to other public health fields.

Intelligent Human Operator Mental Fatigue Assessment Method Based on Gaze Movement Monitoring.

Modern mental fatigue detection methods include many parameters for evaluation. For example, many researchers use human subjective evaluation or driving parameters to assess this human condition. Development of a method for detecting the functional state of mental fatigue is an extremely important task. Despite the fact that human operator support systems are becoming more and more widespread, at the moment there is no open-source solution that can monitor this human state based on eye movement monitoring in real time and with high accuracy. Such a method allows the prevention of a large number of potential hazardous situations and accidents in critical industries (nuclear stations, transport systems, and air traffic control). This paper describes the developed method for mental fatigue detection based on human eye movements. We based our research on a developed earlier dataset that included captured eye-tracking data of human operators that implemented different tasks during the day. In the scope of the developed method, we propose a technique for the determination of the most relevant gaze characteristics for mental fatigue state detection. The developed method includes the following machine learning techniques for human state classification: random forest, decision tree, and multilayered perceptron. The experimental results showed that the most relevant characteristics are as follows: average velocity within the fixation area; average curvature of the gaze trajectory; minimum curvature of the gaze trajectory; minimum saccade length; percentage of fixations shorter than 150 ms; and proportion of time spent in fixations shorter than 150 milliseconds. The processing of eye movement data using the proposed method is performed in real time, with the maximum accuracy (0.85) and F1-score (0.80) reached using the random forest method.

M-SAKHI - Mobile health solutions to help community providers promote maternal and infant nutrition and health: a description of development of the Program Impact Pathway using Theory of Change.

Behaviour Change Communication (BCC) intervention programmes often lack documentation of successful processes. This manuscript aims to describe the development of Program Impact Pathway (PIP) using Theory of Change (ToC) approach for a mHealth BCC intervention titled 'Mobile Solutions Aiding Knowledge for Health Improvement (M-SAKHI)' aimed at reducing stunting in infants at 18 months of age. The PIP was developed using ToC to design the intervention and plan its implementation. Literature review and data from previous pilots helped to identify health service gaps that needed to be addressed by the PIP of this intervention. M-SAKHI was implemented in 244 villages under governance of forty primary health centres of Nagpur and Bhandara districts of eastern Maharashtra in central India. The study investigators and the public health stakeholders participated in developing the PIP. M-SAKHI evaluation study recruited 2501 pregnant women who were followed up through delivery until their infants were 18 months old. The PIP was developed, and it identified the following pathways for the final impact: (1) improving maternal and infant nutrition, (2) early recognition of maternal and infant danger signs, (3) improving access and utilisation to healthcare services, (4) improving hygiene, sanitation and immunisation practices, and (5) improving implementation and service delivery of community health workers through their training, monitoring and supervision in real time. This paper will illustrate the significance of development of PIP for M-SAKHI. It can aid other community-based programmes to design their PIP for nutrition-based BCC interventions.

Magnetic resonance cavitation imaging for the monitoring of ultrasound therapies

Objective.Focused ultrasound (FUS) is a promising non-invasive therapeutic approach that can be used to generate thermal and non-thermal bioeffects. Several non-thermal FUS therapies rely on FUS-induced oscillations of microbubbles (MBs), a phenomenon referred to as cavitation. Cavitation monitoring in real time is essential to ensure both the efficacy and the safety of FUS therapies. This study aims to introduce a new magnetic resonance (MR) method for cavitation monitoring during FUS therapies.Approach.By finely synchronizing the FUS pulse with an accelerated turbo spin-echo MR sequence, the cavitation effect could be quantitatively estimated on the acquired images at 1-Hz refresh rate. The proposed method was assessed in vitro in a water bath. A series of FUS pulses were generated on a silicone tube filled with MBs at different acoustic pressures (0.07-2.07 MPa) and pulse durations (20-2000μs). MR images and passive cavitation detection (PCD) signals were simultaneously acquired for each FUS pulse.Main results.Inertial cavitation was found to induce a quantitatively interpretable signal loss on the MR image. The transition from stable to inertial cavitation was identified on MR cavitation maps with high repeatability. These results were found to be in good agreement with PCD measurements in terms of pressure thresholds between stable and inertial cavitation. MR cavitation imaging was shown to be sensitive to short and even ultrashort FUS pulses, from 2 ms down to 20μs. The presented theoretical model suggests that the signal loss in MR cavitation imaging relies on susceptibility changes related to the diameter of the oscillating MBs.Significance.The proposed MR cavitation imaging method can both locate and characterize cavitation activity. It has therefore the potential to improve the efficacy and safety of FUS therapies, particularly for localized drug delivery applications.

A Study on the Role of Automation and AI in Operational Efficiency and Sustainable Mining

One of the major pillars for world’s economic growth, the mining industry today is facing challenges in maintaining efficiency and productivity while following environmental regulations, at an ever-growing demand. There is limited incorporation of technology in Indian mining practices. To address this gap, the study explores the integration of automation, artificial intelligence (AI), and the Internet of Things (IoT) to enhance operational efficiency and promote sustainable mining. The objective of the research is to evaluate and assess the contribution of advanced technology towards achieving sustainability in mining, benefits derived out of real-time monitoring, and the impact of automation on the safety of work. It is conducted with the qualitative research design approach, which has made an attempt to capture the metamorphosis of mining technologies, benefits out of integration across value chains, and areas that pose challenges through interviews and focus group discussions, carried with industry experts in various parts across India. It also compares the role of technology in mining practices across leading countries and India. These provide evidence that automation and AI are forces in the efficient management of environments and workforce security. IoT devices are quite useful in real- time monitoring and are designed for environmental footprints and resource conservation management. The paper acknowledges, however, some of the methodological limitations, which in turn places additional demands on ongoing innovation. Lastly, the research shows how work can be conducted in the future to integrate state-of-the-art technologies, blockchain, and advanced AI for further value chain enhancement in mining, focusing much on how this industry keeps adapting. Keywords: Sustainable mining, automation and AI, productivity, operational efficiency, integrating technology, real-time monitoring, worker safety, environmental footprint.

Acceptability of dried blood spot collection by primary caregivers of Filipino patients with maple syrup urine disease (MSUD) and phenylketonuria (PKU).

MSUD and PKU require lifetime management hence, regular monitoring of amino acid levels is needed to achieve good metabolic control. Ideally, plasma amino acid analysis (PLAA) is used to monitor concentrations but is expensive and not widely available in local laboratories. The newborn screening program in the Philippines uses dried blood spot (DBS) analysis as an alternative where only trained healthcare providers are allowed to perform the collection at selected facilities. With the increasing number of patients, DBS monitoring has been noted to be delayed due to multiple factors. This issue became even more evident during the COVID-19 pandemic where high-risk patients need to travel outside for blood collection. The study used a cross-sectional study design to determine the primary caregivers' perspective on DBS self-sampling for patients with MSUD and PKU and the acceptability of the samples collected. This was done through a series of collection training, pre-/post- surveys, and 10-item questionnaire, and an in-depth 1-on-1 interview for thematic analysis. The acceptability of samples was processed and evaluated by the newborn screening laboratory. At-home DBS collection by primary caregivers was found to be acceptable. The provision of knowledge and routine collection training by the medical team aids in the increase of sample acceptability as well as a source of empowerment in being equipped to take care of their child. It is highly recommended that DBS samples collected by caregivers be considered acceptable for more time and cost-saving monitoring of the patients' metabolites. This practice also promotes timely and appropriate management which can lead to better patient health outcomes.

Tear-Based Ocular Wearable Biosensors for Human Health Monitoring.

Wearable tear-based biosensors have garnered substantial interest for real time monitoring with an emphasis on personalized health care. These biosensors utilize major tear biomarkers such as proteins, lipids, metabolites, and electrolytes for the detection and recording of stable biological signals in a non-invasive manner. The present comprehensive review delves deep into the tear composition along with potential biomarkers that can identify, monitor, and predict certain ocular diseases such as dry eye disease, conjunctivitis, eye-related infections, as well as diabetes mellitus. Recent technologies in tear-based wearable point-of-care medical devices, specifically the state-of-the-art and prospects of glucose, pH, lactate, protein, lipid, and electrolyte sensing from tear are discussed. Finally, the review addresses the existing challenges associated with the widespread application of tear-based sensors, which will pave the way for advanced scientific research and development of such non-invasive health monitoring devices.

Intensity- and time-based strategies for micro/nano-sizing via single-particle ICP-mass spectrometry: A comparative assessment using Au and SiO2 as model particles

BackgroundSingle-particle ICP-mass spectrometry (SP-ICP-MS) is a powerful method for micro/nano-particle (MNP) sizing. Despite the outstanding evolution of the technique in the last decade, most studies still rely on traditional approaches based on (1) the use of integrated intensity as the analytical signal and (2) the calculation of the transport efficiency (TE). However, the increasing availability of MNP standards and advancements in hardware and software have unveiled new venues for MNP sizing, including TE-independent and time-based approaches. This work systematically examines these different methodologies to identify and summarize their strengths and weaknesses, thus helping to determine their preferred application areas. ResultsDifferent SP-ICP-MS methods for MNP sizing were assessed using AuNPs (20–70 nm) and SiO2MNPs (100–1000 nm). Among TE-dependent approaches, the particle frequency method was characterized by larger uncertainties than the particle size method. The results of the latter were dependent on the appropriate selection of the reference MNP, making the use of multiple reference MNPs recommended. TE-independent methods were based on external (linear and polynomial) calibrations and a relative approach. These methods exhibited the lowest uncertainties of all the strategies evaluated. External calibrations benefited from simpler calculations, but their application could be hindered by a lack of reference MNPs within the desired size range or by the need for interpolations outside the calibration range. Finally, transit time signals are directly proportional to the MNP size rather than its mass. The time-based method demonstrated adequate performance for sizing AuNPs but failed when sizing the largest SiO2MNPs (1000 nm). Significance and noveltyThis work provides further insights into the application of different SP-ICP-MS methodologies for MNP sizing. Both TE-independent approaches and the monitoring of the transit time as the analytical signal are underused strategies; in this context, a Python script was developed for accurate transit time measurement. After 20 years of development, a quantitative comparison of the different methodologies, including the most novel approaches, is deemed necessary for further growth on solid theoretical ground.

A-348 Portable aptamer-based home monitoring system for blood phenylalanine measurement

Abstract Background Aptatek BioSciences, Inc. has developed a home monitoring tool for blood phenylalanine (phe) levels. The Aptatek system integrates a proprietary aptamer assay approach with a user-friendly handheld, smartphone-enabled test platform. PKU (phenylketonuria), is an inborn error of phenylalanine (Phe) metabolism If undiagnosed or untreated, phe accumulates in all body tissues, including the blood and brain, where it is especially neurotoxic and when untreated it can cause significant cognitive impairment and neurological defects. PKU management targets maintaining blood Phe concentration within the therapeutic range (120-360uM) to avoid severe neurological sequelae, mainly through nutritional therapy. Methods PheCheck™ includes a portable hand-held reader, software accessible as a mobile phone application, and a disposable single-use lateral flow cassette, blood collection, cap, and buffer chamber. PheCheck™ detects phe by binding of the phe in a small blood sample using small molecule-specific DNA aptamers. A patient (or caregiver) collects 5μl of capillary blood using a lancet to lance the fingertip and use the collection cap to collect the specimen. Once collected, the specimen is placed into the buffer chamber where the blood is diluted and processed in preparation for a lateral flow assay. The buffer chamber is placed on top of a lateral flow cassette to release the solution into the cassette. The lateral flow cassette is inserted into a small electronic reader after specimen incubation. The reader will detect a fluorescent signal from the strip and report the quantitative Phe concentration present in the specimen to the integrated mobile phone application. The reader has a simple design with a single operating push button, USB data connection port, and replaceable battery compartment. The reader reads the output of the lateral flow cassette and reports the result to a Smartphone app. The app is designed to either display the quantitative results or store the results without displaying the quantitative value. Results Twelve patients were trained on the PheCheck system and observed using the system for self-testing. Patients also prepared dried blood spot cards for comparative testing using LC-mass spectrometry. Patients were asked to return to the clinic within two weeks to retest. The blood phenylalanine levels measured by the PheCheck system were within targeted standard deviation of the dried blood spots demonstrating the feasibility of portable home-based testing for routine monitoring of blood phenylalanine levels in phenylketonuria patients. Such a portable system would enable real time monitoring and adjustment of diet or therapy for chronic diseases such as PKU. Conclusions Aptatek has developed a DNA aptamer based portable monitor for measuring blood phenylalanine levels. The system quantitatively measures phenylalanine from a 5-microliter finger stick blood sampler. Aptamer-based detection of phenylalanine provides reliable quantitation across a wide clinical concentration range and encouraging performance relative to gold standard testing of venous blood draw or dried blood spots. Analysis of samples from patients on Palynziq indicate that the PheCheck system could provide more reliable testing than venous blood draws. The system shows strong promise for routine home monitoring of blood phe levels by patients.

Corrosion of Copper, Cupronickel, Nickel Aluminium Bronze and Super-Duplex Stainless Steel Rotating Cylinder Electrodes in Seawater under Turbulent Flow Conditions

The corrosion of uncoated metals in a marine environment restricts the choice of suitable engineering metals and alloys. Anodic dissolution of metal and cathodic reduction of oxygen are important processes and formation of a surface oxide film can affect both electrode reactions. Charge transfer and mass transfer data can provide information on corrosion rate and mechanism. Several metals and alloys having a history of use in marine engineering are considered, including copper, two copper alloys (cupronickel and nickel aluminium bronze) plus a more recent addition to strong, corrosion resistant alloys (a super duplex stainless steel). The rotating cylinder electrode offers the benefits of a controlled, turbulent flow of electrolyte, facilitating controlled mass transfer in a compact cell geometry which is well-suited to bench-top operation. These features are illustrated using a variety of electrochemical techniques, including open-circuit potential vs time monitoring, linear sweep voltammetry, rotation speed step- or potential step current transients and linear polarisation resistance measurements. Seawater taken from Langstone Harbour, Hampshire, UK, was filtered, air-saturated, pH 8.0 and maintained at 25 °C. Dimensionless group correlations and graphical plots (using an analogous approach to the Koutecky-Levich equation for an RDE) enabled contributions of charge transfer-, mixed- and mass transfer- controlled data to be appreciated, allowing the Tafel slopes of electrode reactions, the diffusion coefficient of dissolved oxygen, the mass transfer coefficient for oxygen reduction and the mean corrosion rate to be estimated.

Impact of an emergency department closure on out-of-hospital cardiac arrest survival and emergency medical service system in Ulsan, South Korea

IntroductionThis study evaluated the impact of an emergency department (ED) closure on out-of-hospital cardiac arrest survival outcomes and the emergency medical service system in Ulsan, South Korea. MethodsWe conducted a retrospective observational cohort study from March 2017 to February 2021 and compared survival to hospital discharge and favorable neurological outcomes. We investigated transport time intervals (TTIs) in the total study population and subgroup covered by the closed ED. The analysis methods included propensity score matching (PSM), inverse propensity weighting (IPW), and logistic regression analysis. ResultsThe study included 692 and 920 patients before and after ED closure groups, respectively. After adjusting for covariates, the adjusted odds ratios and 95 % confidence interval for survival discharge and favorable neurological outcomes were 1.040 (0.749–1.444, p = 0.814) and 2.845 (1.563–5.177, p = 0.001), respectively. PSM showed values of 1.015 (0.727–1.416, p = 0.932) for discharge survival and 1.777 (1.074–2.941, p = 0.025) for neurological outcomes. IPW showed values of 0.980 (0.762–1.261, p = 0.875) for discharge survival and 1.782 (1.196–2.656, p = 0.005) for neurological outcomes. The TTI increased from 5.8 ± 5.0 to 6.7 ± 5.5 min (p < 0.001) in the total population and from 9.2 ± 7.9 to 16.2 ± 6.5 min (p < 0.001) in the subgroup. ConclusionsThe findings suggest that while the overall system may remain stable, subgroups could be disproportionately affected by an ED closure, underscoring the importance of the ongoing monitoring of survival outcomes and transport times.