Improve Response Time Research Articles

Applications of Alpha Neurofeedback Processes for Enhanced Mental Manipulation of Unfamiliar Molecular and Spatial Structures.

This study explores a novel approach to enhancing cognitive proficiency by targeting neural mechanisms that facilitate science and math learning, especially mental rotation. The study specifically examines the relationship between upper alpha intensity and mental rotation skills. Although prior neurofeedback research for increasing upper alpha highlights this correlation, mostly with familiar objects, novel chemistry and math learning prompts envisioning unfamiliar objects which question the persistence of this correlation. This study revisits the upper alpha and mental rotation relationship in the context of unfamiliar objects with a single neurofeedback session and examines the efficiency of manual and automatic neurofeedback protocols. Results will provide a basis for integrating neurofeedback protocols into learning applications for enhanced learning. Our study encompassed three cohorts: Group 1 experienced an automatic neurofeedback protocol, Group 2 received a manual neurofeedback protocol, and the control group had no neurofeedback intervention. The experimental phases involved EEG measurement of individual upper alpha (frequency of maximal power + 2Hz) intensity, mental rotation tasks featuring geometric and unfamiliar molecular stimuli, one neurofeedback session for applicable groups, post-treatment upper alpha level assessments, and a mental rotation retest. The neurofeedback groups exhibited increased levels of upper alpha power, which was correlated with improved response time in mental rotation, regardless of stimulus type, compared to the control group. Both neurofeedback protocols achieved comparable results. This study advocates integrating neurofeedback into learning software for optimal learning experiences, highlighting a single session's efficacy and the substantial neurofeedback protocol's impact in enhancing upper alpha oscillations.

Seasonality of respiratory, enteric, and urinary viruses revealed by wastewater genomic surveillance.

Wastewater surveillance can reveal population-level infectious disease burden and emergent public health threats can be reliably assessed through wastewater surveillance. While molecular methods for wastewater monitoring of microorganisms have traditionally relied on PCR-based approaches, next-generation sequencing (NGS) can provide deeper insights via genomic analyses of multiple diverse pathogens. We conducted a year-long sequencing surveillance of 1,408 composite wastewater samples collected from 12 neighborhood-level access points in the greater Tempe area, Arizona, USA, and show that variation in wastewater viruses is driven by seasonal time and location. The temporal dynamics of viruses in wastewater were influenced cyclically, with the most dissimilarity between samples 23 weeks apart (i.e., winter vs summer, spring vs fall). We identified diverse urinary and enteric viruses including polyomaviruses, astroviruses, and noroviruses, and showed that their genotypes/subtypes shifted across seasons. We show that while wastewater data of certain respiratory viruses like severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) strongly correlate with clinical case rates, laboratory-reported case incidences were discordant with surges of high viral load in wastewater for other viruses like human coronavirus 229E. These results demonstrate the utility of wastewater sequencing for informing decision-making in public health.IMPORTANCEWastewater surveillance can provide insights into the spread of pathogens in communities. Advances in next-generation sequencing (NGS) methodologies allow for more precise detection of viruses in wastewater. Long-term wastewater surveillance of viruses is an important tool for public health preparedness. This system can act as a public health observatory that gives real-time early warning for infectious disease outbreaks and improved response times.

Qualitative performance improvement of a hybrid power supply at the DC common coupling point using a neuro-fuzzy method

Continuity of power supply service in production industries is of crucial importance, given the severity imposed in these sectors. A poor power supply leads to underproduction or degradation of equipment sensitive to electrical variations. A direct consequence of this is loss of earnings for industry. To ensure continuity of service for industrial power supplies, at least one secondary power source must be available. However, the switching system between sources may not be spontaneous due to the presence of electromechanical switches. The solution to this problem is to use electronic converters for control. The aim of this work is to enhance the parameters of an industrial photovoltaic-grid hybrid power supply to ensure uninterrupted power supply and balance between energy supply and demand. The sources are coupled to the DC bus via electronic converters. The photovoltaic system and the inverter are assumed to be unknown, and only the load terminal voltage is accessible and measurable. To address the lack of mathematical model of the system, we propose a mechanism that combines a fuzzy logic (FLC) centred mechanism with a radial basis function (RBF) neural network for the studied case. The FLC self-determines the image of the compensation power to be extracted through the current to the secondary energy source, ensuring continuity of service and balance between supply and demand. The FLC mechanism self-adjust the parameters of the RBF neural network and coordinates the energy at the DC link. The results obtained in the Matlab/Simulink environment are compared with the fuzzy logic and proportional integral (PI) control method. The quality of the results show that the proposed method is excellent compared with FLC and PI control. It improves response time between 24.223% and 82.9% for PI and between 22.292% and 63.694% for FLC. It guarantees an improvement in the mean square error on PI of between 5.865% and 57.994%, and on FLC of between 1.582% and 29.683%. The proposed method guarantees a high rationing coefficient, helping to maintain the balance between supply and demand, ensure continuity of service for the power supply, and improve the accuracy and self-compensation of the energy to be supplied, both qualitatively and quantitatively.

Tailorable ITO thin films for tunable microwave photonic applications

Tunability is a fundamental prerequisite for functional devices and forms the backbone of reconfigurable microwave photonic (MWP) signal processors. In this paper, we explore the use of indium tin oxide (ITO) thin films, notable for their combination of optical transparency and electrical conductivity, to provide tunability for integrated MWP devices. We study the impacts of post-thermal annealing on the structural, electrical, and optical properties of ITO films. The annealed ITO microheater maintains a low total insertion loss of just 0.1 dB while facilitating the tunability of the microring across the entire free spectral range (FSR) using less than half the voltage required by its non-annealed counterpart. Furthermore, the post-annealed ITO film exhibits a 30% improvement in response time, enhancing its performance as an active voltage-controlled microheater. Leveraging this advantage, we employed the post-annealed device to demonstrate continuous tunable radio frequency (RF) phase shifts from 0–330° across a frequency range spanning 15 GHz to 40 GHz with only 5.58 mW of power. The flexibility in modifying the ITO thin film properties effectively bridges the gap between achieving low-loss and high-speed thermo-optic based microheaters.

Continuously reconfigurable 100 GHz band phased array antenna with improved response time using a microfiber/liquid crystal composite

A 100 GHz band phased array antenna (PAA) based on a microfiber/liquid crystal composite is reported. This antenna features a simple and cost-effective design while delivering outstanding performance. Experimental results demonstrate that our PAA structure exhibits a high gain of nearly 11 dBi, continuous steering across a broad range of approximately 82.5°, and rapid switching times ranging from 80 to 260 ms.

Improvement of blood gas response time in the emergency of a university hospital

Improvement of blood gas response time in the emergency of a university hospital

Next-Gen Weather Resilience: A Holistic Approach to Disaster Mitigation Through Interactive Maps, Flood Detection, and Drought Forecasting

Next-Gen Weather Resilience presents a transformative framework for disaster mitigation by utilizing interactive maps, flood detection technologies, and advanced drought forecasting methods. This holistic approach combines innovative strategies to enhance preparedness and response measures in the face of increasingly unpredictable weather patterns and climate change impacts. Through the integration of real-time data and analytics, interactive maps offer situational awareness and decision support for stakeholders at all levels. Flood detection systems enable early warning mechanisms, improving response times and reducing damages caused by flooding events. Furthermore, cutting-edge drought forecasting techniques provide valuable insights into water availability and resource management, aiding in long-term planning and adaptation strategies. By leveraging these tools in a coordinated manner, Next-Gen Weather Resilience empowers communities to build greater resilience against natural disasters while fostering sustainable development initiatives. Key Words: Weather Resilience, Disaster Mitigation, Interactive Maps, Flood Detection, Drought Forecasting.

A single exposure to 100% normo-baric oxygen therapy appears to improve sequence learning processes by increasing prefrontal cortex oxygen saturation

Previously, we showed that a normo-baric 100 % oxygen treatment (NbOxTr) enhances motor learning processes, e.g., visuomotor adaptation (VMA) and sequence learning (SL). However, this work was limited to behavioral outcomes and did not identify the physiological mechanistic underpinnings of these improvements. Here, we expand on this research to investigate the effects of a NbOxTr on the oxygen tissue saturation index (TSI) level of the prefrontal cortex (PFC) when performing a SL task and whether potential SL improvements relate to increased TSI levels in the PFC. Twenty four right-handed young, healthy adults were randomly assigned to a NbOxTr group (normo-baric 100 % oxygen, n = 12) or a control group (normal air, n = 12). They received their respective treatments via a nasal cannula during the experiment. Oxygen TSI levels of the right and left PFC were measured via near-infrared spectroscopy (NIRS) throughout different SL task phases (Baseline, Training, Testing). The NbOxTr increased the TSI of the PFC in the Training phase (p < 0.01) and positively affected SL retention in the Testing phase (p < 0.05). We also found a positive correlation between TSI changes in the right PFC during the gas treatment phase (3.4 % increase) and response time (RT) improvements in the SL task training and retention phase (all p < 0.05). Our results suggest that a simple NbOxTr increases the oxygenated hemoglobin availability in the PFC, which appears to mediate the retention of acquired SL improvements in healthy young adults. Future studies should examine treatment-related oxygenation changes in other brain areas involved and their relation to enhanced learning processes. Whether this NbOxTr improves SL in neurologically impaired populations should also be examined.

P006 A new digital front door for existing rheumatology patient queries

Abstract Background/Aims NICE guidelines recommend people with rheumatological conditions need rapid access to specialist care to manage flares and to advise on medication. With growing demand for NHS services, rheumatology advice lines are coming under pressure. We carried out an audit on our advice line queries over four clinics in summer 2021 with a view to developing a triaging system to improve response time. Over four advice line clinics, there were 44 calls averaging 13 minutes per call. The response time was two to three weeks. 34% of calls were deemed to be inappropriate. Inappropriate calls were considered ones that could be better directed elsewhere. Methods In 2022 we started working with DrDoctor, a digital health company to design a digital nurse advice line form to redirect inappropriate calls away from the nurse advice line call backs. This involved designing a digital pathway addressing common inappropriate themes from our audit including redirection of calls to secretaries, patient booking team, prescription management and a digital booking form for phlebotomy appointments. The new digital tool enabled us to expand the service and we used the opportunity to adopt patient assessment tools and educational videos as part of the service. We promoted the digital pathway through patient information leaflets, department website and sent a text with the link to all rheumatology patients. We continued to offer the traditional contact telephone number into the service as well. Results The digital nurse advice line went live at the end of July 2023 and after two months the response time has reduced to 1.5- 2 weeks. Since then there have been 529 form submissions from 417 unique patients. 47% of submissions were inappropriate and redirected. 97 patients called to book a blood test and 76 patients opted for the online self-service clinic. 151 patients had a query about their condition. 50% of patient had RA or inflammatory arthritis. 5% AS. 5% SLE. 17% PsA. 23% selected ‘other’ and a free text box captured conditions such as fibromyalgia, osteoporosis, PMR and OA. 26% of patients with rheumatoid arthritis opted to see the educational video on self-examination and 17% watched the educational video on rheumatoid arthritis. Based on 251 submissions that were inappropriate for the advice line, assuming each inappropriate call takes an average of nine minutes based from our previous audit, 39 hours of phone time was saved from avoiding booking inappropriate nurse advice line appointments in two months. Conclusion The digital rheumatology advice service is an efficient and effective method of responding to patient queries and can be a source of patient education and self assessment. Informal patient and staff feedback has been positive and we will be collecting formal feedback as the service becomes more established. Disclosure K. Nadesalingam: None. C. Short: None. K. Christian: None. P. Helliwell: None.

A bi-objective model for location, dispatch and relocation of ambulances with a revision of dispatch policies

PurposeUntimely responses to emergency situations in urban areas contribute to a rising mortality rate and impact society's primary capital. The efficient dispatch and relocation of ambulances pose operational and momentary challenges, necessitating an optimal policy based on the system's real-time status. While previous studies have addressed these concerns, limited attention has been given to the optimal allocation of technicians to respond to emergency situation and minimize overall system costs.Design/methodology/approachIn this paper, a bi-objective mathematical model is proposed to maximize system coverage and enable flexible movement across bases for location, dispatch and relocation of ambulances. Ambulances relocation involves two key decisions: (1) allocating ambulances to bases after completing services and (2) deciding to change the current ambulance location among existing bases to potentially improve response times to future emergencies. The model also considers the varying capabilities of technicians for proper allocation in emergency situations.FindingsThe Augmented Epsilon-Constrained (AEC) method is employed to solve the proposed model for small-sized problem. Due to the NP-Hardness of the model, the NSGA-II and MOPSO metaheuristic algorithms are utilized to obtain efficient solutions for large-sized problems. The findings demonstrate the superiority of the MOPSO algorithm.Practical implicationsThis study can be useful for emergency medical centers and healthcare companies in providing more effective responses to emergency situations by sending technicians and ambulances.Originality/valueIn this study, a two-objective mathematical model is developed for ambulance location and dispatch and solved by using the AEC method as well as the NSGA-II and MOPSO metaheuristic algorithms. The mathematical model encompasses three primary types of decision-making: (1) Allocating ambulances to bases after completing their service, (2) deciding to relocate the current ambulance among existing bases to potentially enhance response times to future emergencies and (3) considering the diverse abilities of technicians for accurate allocation to emergency situations.

Resistance training's impact on blood biomarkers and cognitive function in older adults with low and high risk of mild cognitive impairment: a randomized controlled trial

BackgroundThe aging brain exhibits a neuroinflammatory state, driven partly by peripheral pro-inflammatory stimuli, that accelerates cognitive deterioration. A growing body of evidence clearly indicates that physical exercise partly alleviates neuroinflammation and positively affects the aging process and cognition. In this randomized controlled trial, we aimed to observe the effect of 12 weeks of resistance training (RT) on peripheral biomarker levels, cognitive function changes and their interrelationship, and explore differences in those exercise-induced changes in older adults with high risk of mild cognitive impairment (MCI) compared to older adults with low risk of MCI.MethodsFifty-two participants (aged 60–85 years old, 28 female) were randomly allocated to a 12 week lower limb RT program consisting of two training sessions per week or waiting list control group. The Montreal Cognitive Assessment (MoCA) was used to stratify participants screened as high (< 26/30) or low risk (≥ 26/30) of MCI. We assessed serum Interleukin 6 (IL-6), Insulin-like Growth Factor-1 (IGF-1), and Kynurenine (KYN) levels. Cognitive measurement consisted of and four subtests of Automated Neuropsychological Assessment Metrics (ANAM), the two-choice reaction time, go/no-go, mathematical processing, and memory search test.ResultsTwelve weeks of RT improved Go/No-go test results in older adults with high MCI risk. RT did not significantly affect blood biomarkers. However, IGF-1 level increases were associated with improvements in response time on the mathematical processing test in the exercise group, and IL-6 level increases were associated with improvements in response time on the memory search test in the total group of participants. Finally, KYN levels significantly differed between older adults with low and high MCI risk but no significant associations with performance were found.ConclusionOur study results suggest a different effect of RT on inhibitory control between older adults with low compared to high MCI risk. IGF-1 may play a role in the mechanism behind the cognitive benefit of RT and KYN may be a surrogate biomarker for neurodegeneration and cognitive decline.

Refining ambulance clinical response models: The impact on ambulance response and emergency department presentations.

The ambulance service in Victoria, Australia implemented a revised clinical response model (CRM) in 2016 which was designed to increase the diversion of low-acuity Triple Zero (000) calls to secondary telephone triage and reduce emergency ambulance dispatches. The present study evaluates the influence of the revised CRM on emergency ambulance response times and ED presentations. A retrospective study of emergency calls for ambulance between 1 January 2015 and 31 December 2018. Ambulance data were linked with ED presentations occurring up to 48 h after contact. Interrupted time series analyses were used to evaluate the impact of the revised CRM. A total of 2 365 529 calls were included. The proportion allocated a Code 1 (time-critical, lights/sirens) dispatch decreased from 56.6 to 41.0% after implementation of the revised CRM. The proportion of calls not receiving an emergency ambulance increased from 10.4 to 19.6%. Interrupted time series analyses demonstrated an improvement in Code 1 cases attended within 15 min (Key Performance Indicator). However, for patients with out-of-hospital cardiac arrest or requiring lights and sirens transport to hospital, there was no improvement in response time performance. By the end of the study period, there was also no difference in the proportion of callers presenting to ED when compared with the estimated proportion assuming the revised CRM had not been implemented. The revised CRM was associated with improved Code 1 response time performance. However, there was no improvement in response times for high acuity patients, and no change in the proportion of callers presenting to ED.

Deep Learning-Based Intelligent Video Surveillance System for Real-Time Motion Detection

The proliferation of surveillance systems demands advanced solutions to effectively monitor and analyze video feeds for security and safety purposes. In this paper, we propose a Deep Learning-Based Intelligent Video Surveillance System (DIVSS) designed to enhance traditional Closed-Circuit Television (CCTV) setups. DIVSS integrates state-of-the-art deep learning techniques for real-time motion detection and object recognition, enabling proactive threat detection and automated response mechanisms. The system utilizes convolutional neural networks (CNNs) for robust feature extraction and classification, enabling accurate identification of objects of interest amidst complex backgrounds and varying[1] lighting conditions. Furthermore, DIVSS incorporates region of interest (ROI) monitoring to focus attention on specific areas within the surveillance footage, optimizing computational resources and improving response times. We evaluate the performance of DIVSS through comprehensive experiments conducted on diverse surveillance scenarios, demonstrating its effectiveness in enhancing security measures and providing actionable[3] insights for surveillance operators. The proposed system represents a significant advancement in video surveillance technology, offering unparalleled capabilities for proactive threat detection and real-time response in dynamic environments. Keywords— Deep learning has transformed video surveillance, enabling real-time motion detection and object recognition using Convolutional Neural Networks (CNNs). This technology provides proactive threat detection, intelligent systems for ROI monitoring, and enhances overall security measures.

IOT Based Automatic Vehicle Accident Detection and Rescue System

The problem of road accidents causing loss of life is a significant issue, especially in developing nations where there is often a lack of efficient emergency response systems. These accidents result in higher fatalities due to delays in reporting to emergency services and reaching medical facilities. To address this, a new automatic vehicle accident detection and rescue system is proposed. This system utilizes accelerometers to detect vehicle crashes and sends the GPS location of the accident to relevant authorities and contacts through GSM module. This technology aims to improve response times compared to traditional methods, potentially saving more lives through swift intervention. Key Words: RF Transmitter, Accidents, GPS, GSM.

Surface doping effect on the optoelectronic performance of 2D organic crystals based on cyano-substituted perylene diimides

Compared to organic thin films, organic single crystals offer significant potential in organic phototransistors (OPTs) due to their enhanced charge transport, large surface area, and defect-free nature. However, the development of n-type semiconductors has lagged behind p-type semiconductors. To enhance semiconductor device performance, a doping process can be employed, which typically involves the introduction of charged impurities into the crystalline semiconducting material. Its aim is to reduce the Ohmic losses, increase carrier density, improve transport capabilities, and facilitate effective carrier injection, ultimately enhancing the electrical properties of the material. Traditional doping processes, however, often pose a risk of damaging the structure of single crystals. In this study, we have synthesized novel cyano-substituted chiral perylene diimides, which self-assemble into two-dimensional single crystals that can be used for n-type semiconductor devices. We have employed a surface doping strategy using diethylamine vapor without disrupting the crystal structure. The fabricated devices exhibit significantly higher charge transport properties after doping, achieving a maximum electron mobility of 0.14 cm² V−¹ s−¹, representing an improvement of over threefold. Furthermore, the optoelectronic performance of the doped devices has significantly improved, with the external quantum efficiency increased by over 9 times and the significantly improved response time. These results suggest that our surface doping technology is a promising way for enhancing the performance of 2D organic single-crystal OPTs.

Integrating human factors engineering into your pediatric radiology practice.

Human factors engineering involves the study and development of methods aimed at enhancing performance, improving safety, and optimizing user satisfaction. The focus of human factors engineering encompasses the design of work environments and an understanding of human mental processes to prevent errors. In this review, we summarize the history, applications, and impacts of human factors engineering on the healthcare field. To illustrate these applications and impacts, we provide several examples of how successful integration of a human factors engineer in our pediatric radiology department has positively impacted various projects. The successful integration of human factors engineering expertise has contributed to projects including improving response times for portable radiography requests, deploying COVID-19 response resources, informing the redesign of scheduling workflows, and implementation of a virtual ergonomics program for remote workers. In sum, the integration of human factors engineering insight into our department has resulted in tangible benefits and has also positioned us as proactive contributors to broader hospital-wide improvements.

Implicit and Explicit Preferences for Golden Ratio

The golden ratio, also known as Phi (ϕ ≈ 1.618034), attracted the interest of mathematicians, artists, and intellectuals for many centuries, probably from when it was discovered in human anthropometry. Even in recent times, researchers found the presence of the golden ratio in Renaissance paintings and aesthetic preferences. The reasons behind the fascination with the golden ratio remain unclear, but it has been suggested that stimuli containing this proportion are often perceived as beautiful. However, evidence is conflicting, and the literature struggles to establish the existence of individual preferences for the golden ratio. To gain new insights into the nature of these preferences, one hundred participants completed an implicit association task, with either golden ratio or random stimuli presented with positive or negative words. Participants initially categorized the stimuli based on their assigned categories. Then, we assessed their explicit preferences by asking them to rate the stimuli in terms of pleasantness and by completing a line bisection task and the Ultimatum Game. The results revealed the typical effects observed in implicit association tasks, with improved response times and accuracy when golden ratio stimuli were associated with positive word categories. In contrast, explicit ratings yielded mixed results. We discuss our findings in relation to previous studies that have explored this issue, highlighting the ongoing debate surrounding preferences for the golden ratio.

Miniature Optical Fiber Fabry-Perot Interferometer Based on a Single-Crystal Metal-Organic Framework for the Detection and Quantification of Benzene and Ethanol at Low Concentrations in Nitrogen Gas.

This study reports for the first time, to the best of our knowledge, a real-time detection of ultralow-concentration chemical gases using fiber-optic technology, combining a miniaturized Fabry-Perot interferometer (FPI) with metal-organic frameworks (MOFs). The sensor consists of a short and thick-walled silica capillary segment spliced to a lead-in single-mode fiber (SMF), housing a tiny single crystal of HKUST-1 MOF, imparting chemoselectivity features. Ethanol and benzene gases were tested, resulting in a shift in the FPI interference signal. The sensor demonstrated high sensitivity, detecting ethanol gas concentrations (EGCs) with a sensitivity of 0.428 nm/ppm between 24.9 and 40.11 ppm and benzene gas concentrations (BGCs) with a sensitivity of 0.15 nm/ppm between 99 and 124 ppm. The selectivity study involved a combination of three ultralow concentrations of ethanol, benzene, and toluene gases, revealing an enhancement factor of 436% for benzene and 140% for toluene, attributed to the improved miscibility of these conjugated ring molecules with the alkane chains of the ethanol-modified HKUST-1. Experimental tests confirmed the sensor's viability, demonstrating significantly improved response time and spectral characteristics through crystal polishing, indicating its potential for quantifying and detecting chemical gases at ultralow concentrations. This technology may prevent energy resource losses, and the sensor's small size and robust construction make it applicable in confined and hazardous locations.

Saving ambulance resources: a service evaluation of the identification of non-viable out-of-hospital cardiac arrest in London by advanced paramedic practitioners in critical care.

Advanced paramedic practitioners in critical care (APPCCs) are advanced clinical practitioners focused on the delivery of pre-hospital critical care. While working in an ambulance control room setting, APPCCs seek to identify emergency calls appropriate for operational APPCCs to attend. These would include out-of-hospital cardiac arrest (OHCA). Through interrogation of incoming emergency calls they are also able to identify OHCA calls where resuscitation may be futile. In these cases, and within a governance framework, they stand down multiple ambulance responders, leaving only the closest responding resource to attend, thereby 'saving resources' that can be re-directed to other waiting emergency calls. It is believed that this is the first initiative of this nature in the United Kingdom. A three-year retrospective service evaluation of data was undertaken. The aim was to quantify the number of 'saved resources', including both double crewed ambulances (DCAs) and solo (single-person) responders, and furthermore to equate those savings into potential hours saved, using average known job cycle times (JCTs). Additionally, safety was assessed by searching all mandated incident reports for occasions where, despite cancellation of resources by an APPCC, resuscitation was commenced by the first response to scene. A total of 13,356 ambulance resources were saved. Of these, 6593 (49.4%) were DCAs and 6763 (50.6%) were solo responders. Using the average JCT for deceased patients of 104.8 minutes, the total time saving equated to 23,328.48 hours of work or 1944.04 12-hour shifts. When considering DCAs alone, the average JCT for obviously deceased patients was 110.9 minutes. This equates to 12,186.1 hours of work or 1015.5 12-hour shifts. A total of 15 incident reports were identified. All had been investigated, revealing appropriate decision making in cancelling ambulance resources. No patient harm was identified. APPCCs working within a governance framework safely saved a significant number of ambulance resources over a three-year period. Perceived benefits include 'freeing up' DCA and solo responders, allowing them to be redirected to other emergency calls, leading to potential improvement in response times for patients waiting for an ambulance resource.

DTformer: An Efficient Digital Twin Model for Loss Measurement in UHVDC Transmission Systems

Ultra-high Voltage Direct Current system (UHVDC) can realize long distance transmission, and its loss measurement has great significance for loss reduction and fault diagnosis. Currently, loss measurements can be performed by model-driven and data-driven approaches. However, poor operating conditions can lead to system sensing errors, which can significantly reduce the accuracy and speed of measurement. To deal with this problem, we propose a digital twin architecture and constructs a data-driven learning model. The model, DTformer, is embedded in the digital twin architecture. And it has three remarkable processes: encoder-decoder based random feature extraction, loss measurement transfer learning and knowledge distillation. These processes extract parameter information and topology features from the data of UHVDC. The model has good accuracy, response time and resistance to the missing data. The comparison experiment shows that DTformer outperforms popular feature extraction models with an average decrease of 38.80% in RMSE and 42.82% in MAE, and the best R2 value is closest to 1. The analyses of lightweight and missingness show that the DTformer also has significant response time improvement and missing resistance. Finally, we also implement the visualization of digital twin to enable operators to perform monitoring, computation, analysis and management of UHVDC.