Shorter Delay Research Articles

Deep reinforcement learning based resource allocation for electric vehicle charging stations with priority service

The demand for public fast charging stations is increasing with the number of electric vehicles on roads. The charging queues and waiting times get longer, especially during the winter season and on holidays. Priority based service at charging stations can provide shorter delay times to vehicles willing to pay more and lower charging prices for vehicles accepting to wait more. Existing studies use classical feedback control and simulation based control methods to maintain the ratio of high and low priority vehicles’ delay times at the station’s target level. Reinforcement learning has been used successfully for real time control in environments with uncertainties. This study proposes a deep Q-Learning based real time resource allocation model for priority service in fast charging stations (DRL-EXP). Results show that the deep learning approach enables DRL-EXP to provide a more stable and faster response than the existing models. DRL-EXP is also applicable to other priority based service systems that act under uncertainties and require real time control.

Динаміка електрокерованих паливних клапанів реактивних двигунів малої тяги на «зеленому» паливі

Recently, the leading space countries have paid much attention to searching for and using new types of untoxic “green” propellants. Developments of 0.1 N to 220 N “green”-propellant thrusters have been reported, and some of them have been successfully tested in flight conditions. Liquid-propellant spacecraft thrusters are actuators of a spacecraft in-orbit motion control system. They are actuated from command signals of the spacecraft control system by applying a voltage to the electromagnet coils of the propellant valves of each of the engines that are to feed the propellant during a specified time interval to produce a required thrust impulse. A problem common to liquid-propellant thrusters irrespective of the propellant type is a thruster start-up delay with respect to the command signal and a thruster shut-down delay with respect to the electromagnet coil de-energizing. In other words, the thruster response is shifted with respect to the command signal. Because of this, the valve parameters must be chosen in such a way as to minimize the delay effect. The goal of this work is to determine design parameters of electrically controlled propellant valves that would provide an efficient in-orbit operation of “green”-propellant thrusters with a minimum of electric energy consumption. To determine these parameters, use was made of a branch standard, the procedure of work with which was based on empirical data and verified by numerical simulation. The key factor in shortening the thruster start-up delay is to reduce the seat-to-tip valve plate travel on condition that a required propellant amount is fed to the reaction chamber. However, this extends the shut-down delay, which has a harmful effect in the short-impulse operation mode. It is shown by calculation that after the electromagnetic parameters have reached their steady-state values, the voltage can be reduced by 30 to 40 per cent of its nominal value with the valve kept in an open state, after which the valve closes with a shorter delay. In addition, this way of valve energizing reduces the total electric energy consumption for valve operation. The mathematical model of valve dynamics allows one to determine the valve operation parameters in the case of short command signals probable in flight conditions. In such a case, a valve does not open to a full extent, which results in a low specific impulse. The results of this study may be used in the development of “green”-propellant thrusters.

Increased risk of cardiovascular events in the weeks following hospitalised COPD exacerbations: the EXACOS-CV French nationwide case-crossover study

Abstract Introduction Previous studies indicate that in people living with chronic obstructive pulmonary disease (COPD), the risk of myocardial infarction (MI), heart failure (HF) decompensation, arrhythmias and ischemic stroke increases following a COPD exacerbation. This association has not been explored for detailed types of cardiovascular events (CVE), namely for different types of myocardial infarction. Objective To quantify the risk of CVE following a COPD exacerbation overall, and by detailed type of CVE. Methods A case-crossover study was conducted using the French national hospital discharge database (Programme de Médicalisation des Systèmes d'Information, PMSI). Case-patients: (i) were aged≥40 years with a COPD diagnosis; (ii) hospitalised in 2018-2019 for CVE; (iii) experienced the CVE within 24 weeks of a hospitalised exacerbation of COPD. Hospitalisation date for CVE was index date. Conditional logistic regression models estimated the association between a hospitalised exacerbation and CVE (overall and by type of CVE) – expressed as odds ratios (OR) – comparing the odds of a hospitalised COPD exacerbation during the risk period (4 weeks prior to index date) versus control periods (9-24 weeks prior to index date). Results Among 122,172 patients with COPD hospitalised for a severe CVE in 2018-2019, 9,840 (8%) had a hospitalised exacerbation of COPD in the prior 24 weeks and qualified as case-patients. Mean age was 76.8 years (SD 10.7); 6,496 (66%) were male. The most frequent CVE were decompensated heart failure (n=5,888, 60%) and acute coronary syndrome (n=1,070, 11%); in-hospital death in the 24 weeks following an exacerbation of COPD occurred in 950 (10%) patients. Median time elapsed between hospitalised exacerbation and CVE (any type) was 43 days (Figure 1); the shortest delays were observed for non-STEMI (24 days) and resuscitated cardiac arrest (28 days). Risk of CVE in the 4 weeks following a hospitalised COPD exacerbation was 3-times the risk in the 9-24 weeks post-exacerbation (OR 3.0; 95%CI 2.9-3.2 – Figure 2). This increase in risk was observed for each individual type of CVE. The highest effect size was observed for non-STEMI (OR 5.3; 95%CI 4.5-6.3). Conclusion The risk of CVE substantially increases following a hospitalised exacerbation of COPD overall and for all types of CVE (acute ischemic events, arrhythmias, and heart failure decompensation). The first month following a hospitalised exacerbation is a period of vulnerability for people living with COPD, with some variability in the magnitude and temporality of risk increase according to the type of CVE. These results stress the need for immediate and sustained monitoring of exacerbating COPD patients to prevent a subsequent severe cardiac event. In addition, exacerbations prevention is an essential target of COPD treatment.

Enhancing UAV Swarm Tactics with Edge AI: Adaptive Decision Making in Changing Environments

This paper presents a drone system that uses an improved network topology and MultiAgent Reinforcement Learning (MARL) to enhance mission performance in Unmanned Aerial Vehicle (UAV) swarms across various scenarios. We propose a UAV swarm system that allows drones to efficiently perform tasks with limited information sharing and optimal action selection through our Efficient Self UAV Swarm Network (ESUSN) and reinforcement learning (RL). The system reduces communication delay by 53% and energy consumption by 63% compared with traditional MESH networks with five drones and achieves a 64% shorter delay and 78% lower energy consumption with ten drones. Compared with nonreinforcement learning-based systems, mission performance and collision prevention improved significantly, with the proposed system achieving zero collisions in scenarios involving up to ten drones. These results demonstrate that training drone swarms through MARL and optimized information sharing significantly increases mission efficiency and reliability, allowing for the simultaneous operation of multiple drones.

Remembering as an operant: Effects of instructional control and reinforcement on remembering behavior.

With the general aim of providing more evidence for considering certain behaviors involved in the act of remembering as operant, two experiments were carried out to verify its sensitivity to differential reinforcement, and to some of the variables upon which it depends. In the first experiment, three children participated, and two variables were manipulated in a "free recall" task: the accuracy of the instructions and the magnitude of the reinforcer applied to the emission of the target words. In the second experiment, 60 was changed to one of comparison between groups. In this case, the response-reinforcer interval (immediate vs. delayed) was manipulated using a "recognition" task. In both experiments, a greater number of remembered items were found in the presence of reinforcement compared to its absence, as well as with a greater magnitude of the reinforcer and with a shorter delay. These results are discussed considering the peculiarities of verbal behavior in humans and provide evidence that operant processes have an important role in traditional human memory tasks.

Clinical Impact of 3- versus 5-Minute Delay and 30- versus 60-Second Intervals on Unattended Automated Office Blood Pressure Measurements.

Guidelines advise automated office blood pressure (AOBP) with an initial 5-minute delay and multiple measurements at least 60 seconds apart. Recent studies suggest that AOBP may be accurate with shorter delays or intervals, but evidence in clinical settings is limited. Patients referred to one hypertension (HTN) center underwent 24-hour ambulatory blood pressure monitoring (ABPM) and one of four non-randomized, unattended AOBP protocols: a 3- or 5-minute delay with a 30 or 60-second interval, i.e., 3min/30sec/30sec, 3/60/60, 5/30/30 and 5/60/60 protocols. HTN was defined as systolic blood pressure ≥140 or diastolic blood pressure ≥90 mmHg. We compared differences in mean blood pressure and HTN classification between average AOBP and awake-time ABPM by t-tests and Fisher's exact test. Among 212 participants (mean 58.9 years, 61% women, 25% Black), there was substantial overlap in the probability distributions of awake-time ABPM and each of the three AOBP measures. Systolic blood pressure means were similar between the 5/60/60 and 3/30/30 protocols and 5/30/30 and 3/60/60 protocols. The 5/30/30 was associated with a higher proportion of systolic HTN, while the 3/60/60 protocol was associated with a higher proportion of diastolic HTN. There were no significant differences in systolic or diastolic HTN between 5/60/60 and 3/30/30 protocols with respect to awake-time ABPM. In this quality improvement study, the shortest AOBP protocol did not differ significantly from the longest protocol. The time savings of shorter protocols may improve AOBP adoption in clinical practice without meaningfully compromising accuracy.

Rapid and quantitative CEST-MRI sequence using water presaturation.

Despite the significant potential for in vivo metabolic imaging in preclinical and clinical applications, CEST MRI suffers from long scan time and inaccurate quantification. This study aims to suppress the contaminations among signals under different frequencies, which could shorten the TR and thereby facilitate CEST imaging acceleration and quantification. A novel sequence is proposed by applying a water-presaturation (WPS) module at the beginning of each TR. WPS CEST quickly knocks down the residual signal from previous TRs so that the magnetization of all TRs recovers from zero, which aligns well with the formula of quasi-steady-state theorem and enables accurate quantification within shorter TR. WPS CEST was assessed by simulations, creatine phantom, and healthy human brain scans at 3 T. In simulation and phantom experiment, WPS CEST allows accurate estimation of exchange rate (ksw) using omega plot and using shorter delay time (Td) and saturation time (Ts) (e.g., 1 s/1 s) compared with the conventional CEST. Simulations further showed that WPS CEST could obtain consistent spin-lock relaxation (R1ρ) values over varied Tds and Tss. Six human scans indicated that R1ρ collected from conventional sequences showed significant differences between two groups with Td and Ts of (1 s/1 s) and (2 s/2 s) (amide: 1.721 ± 0.051 s-1 vs. 1.622 ± 0.050 s-1, p = 0.001; nuclear Overhauser enhancement: 1.792 ± 0.046 s-1 vs. 1.687 ± 0.053 s-1, p = 0.004), whereas WPS CEST scans using these 2 Td/Ts values obtained the same mean R1ρ (amide: 1.616 ± 0.053 s-1 vs. 1.616 ± 0.048 s-1, p = 0.862; nuclear Overhauser enhancement: 1.688 ± 0.064 s-1 vs. 1.684 ± 0.054 s-1, p = 0.544). WPS CEST demonstrated accurate quantitation within shorter TR compared with conventional sequences, and thereby may allow rapid quantitative CEST scans in various situations.

Use of a Liaison-Mediated Referral Strategy and Participation in Cardiac Rehabilitation After Percutaneous Coronary Intervention.

Cardiac rehabilitation (CR) improves outcomes following percutaneous coronary intervention (PCI) but remains underutilized. A liaison-mediated referral (LMR), where a health care professional explains CR's benefits, addresses barriers to participation, and places a referral before discharge, may promote CR use. Our objective was to assess the impact of an LMR on CR participation after PCI. This was a retrospective study of patients who underwent PCI across 48 hospitals in Michigan between January 2021 and April 2022 and referred to CR before discharge. Clinical registry data were linked to administrative claims to identify the primary outcome, CR participation, defined as ≥1 CR session within 90 days of discharge. Bayesian hierarchical logistic regression was used to compare CR participation between patients with and without an LMR. For the secondary outcome, frailty proportional hazard modeling compared days elapsed between discharge and first CR session between liaison cohorts. Among 9023 patients referred to CR after PCI, 4323 (47.9%) underwent an LMR (mean age, 69.3 [SD=11] years; 68.3% male) and 3390 (36.7%) attended ≥1 CR session within 90 days of discharge. The LMR cohort had a higher unadjusted CR participation rate (43.1% [95% CI, 41.5%-44.6%] versus 32.4% [95% CI, 31.1%-33.8%]; P<0.001), a higher adjusted odds ratio of attending ≥1 CR session (adjusted odds ratio, 1.21; 95% credible interval, 1.07-1.38), and a shorter delay in attending the first CR session compared with the non-LMR cohort (28 [interquartile range, 19-42] versus 33 [interquartile range, 21-47] days; P<0.001). An LMR was associated with higher odds of CR participation and may mitigate delays in CR enrollment. This referral strategy may improve CR participation and patient outcomes after PCI.

Robotic warehouse systems considering dynamic priority

The research proposes a new methodological framework based on dynamic priority to handle different order classes in robotic warehouse systems. Traditional static priority methods in facility logistics may cause low-priority orders to experience excessive delays and fail to ensure fairness. Our dynamic priority approach addresses this fairness issue by adjusting priorities over time to fulfill orders within promised times, ensuring both high-priority orders and long-waiting low-priority orders receive timely attention. We present stochastic models of dynamic priority queueing networks to describe warehouse systems and estimate throughput times. Experiments validate the analytical stochastic models, and experimental results indicate that the dynamic priority model achieves shorter delay times than the static priority model and the FCFS model. We propose design insights based on experimental results and provide an approach to select the optimal robot number. Furthermore, by employing a fairness index, we develop a new decision support tool for determining warehouse configurations with requested performance objectives. Experimental results demonstrate that dynamic priority can ensure fairness across a wider range of scenarios. Additionally, with insufficient pickers, the system performs better with the put wall than without it.

Combination of high cetane diesel fuel and post injection mode in a diesel engine: A path towards lower exhaust emissions

The major objective of the presented work is to explore the effect of post injection (PI) strategy using neat diesel fuel (D100) blended with cetane number enhancer (CNE). The motive behind adding CNE in diesel fuel was to lower its self-ignition temperature and hence delay period subsequently. This can lead to more efficient combustion of post injected fuel which leads to lower incomplete combustion losses. The CNE used was Di-Tert-Butyl Peroxide (DTBP) which was blended with D100 in proportion of 1 % by volume. Initially experiments were conducted with D100 under various PI modes where variation of post injection timing (PIT) as well as post quantity (PQ) was considered. The experimental results showed that implementing PI resulted in lower smoke, unburned hydrocarbon (UHC), carbon monoxide (CO) and nitrogen oxides (NOx) emissions compared to single injection while affecting fuel consumption negatively. Then after similar experiments under various PI modes were repeated using blends of D100 and DTBP (D/DTBP) and results were compared with D100 case. It was found that addition of CNE in D100 resulted in more complete combustion of post fuel which further reduced CO and UHC emissions. The shorter delay period in case of D/DTBP blend compared to D100 case led to lower NOx emissions as well as higher smoke emissions. The obtained results revealed that PI mode with 3 mg PQ and 20° after top dead center (ATDC) PIT with D/DTBP blend offered 41.18 %, 17.55 %, 64 % and 62.5 % reduction in smoke, NOx, UHC and CO emissions respectively compared to single injection mode using D100 fuel.

Disparities in Amblyopia Treatment Outcomes: The Impact of Sociodemographic Factors, Treatment Compliance, and Age of Diagnosis

ObjectiveTo identify clinical and sociodemographic factors associated with disparities in amblyopia treatment outcomes. DesignRetrospective chart review. SubjectsChildren ≤8 years old diagnosed and treated for unilateral refractive or strabismic amblyopia at our institution from 2012-2022. MethodsChildren with amblyopia were categorized by outcome: resolved amblyopia (<0.2 logMAR interocular difference [IOD] in visual acuity, or no fixation preference in non-verbal patients) or persistent amblyopia. Demographic and clinical data were recorded from the medical record. Zip codes were used to calculate Childhood Opportunity Index (COI) scores, estimated annual household income, and distance to hospital. Main Outcome MeasuresSociodemographic and clinical factors were compared between children with resolved and persistent amblyopia. Factors significant at p<0.10 on univariate analysis were included in a multivariable regression model. Results168 patients met inclusion criteria, and 131 (78%) had resolved amblyopia. Demographic factors associated with resolution of amblyopia were younger age at diagnosis (3.3±1.7 vs. 4.5±1.9 years, p=0.0009), English as the primary language (79.4% vs. 62.2%, p=0.04), higher estimated annual income ($83,315.93±29,276.64 vs. $71,623.00±26,842.56, p=0.03), higher COI scores (50.9±27.3 vs. 40.0±26.4, p=0.03), and living further from our institution (28.6±37.6 vs. 14.9±12.7 miles, p=0.003). Patients with resolved amblyopia also had higher rates of treatment compliance (83.2±25.0% vs. 75.6±24.4%, p=0.009) and shorter delays in follow-up (40.1±76.8 vs. 61.1±90.4 days, p=0.02). Amblyopia persistence was borderline associated with governmental insurance and higher presenting IOD in visual acuity (both p=0.06). On multivariate analysis, only younger age at amblyopia diagnosis (p=0.0010) remained significantly associated with amblyopia resolution. ConclusionsOur findings suggest that disparities in amblyopia outcomes are related to differences in age at diagnosis. Interventions to lower the age at which amblyopia is diagnosed, such as programs to improve vision screening rates and access to pediatric eye care in at-risk groups, may directly address inequities in rates of amblyopia resolution.

Cooperative edge-caching based transmission with minimum effective delay in heterogeneous cellular networks

In heterogeneous cellular networks (HCNs), neighboring users often request similar contents asynchronously. Based on the content popularity, base stations (BSs) can download and cache contents when the network is idle, and transmit them locally when the network is busy, which can effectively reduce the backhaul burden and the transmission delay. We consider a two-tier HCN, where macro base stations (MBSs) and small base stations (SBSs) can cooperatively and probabilistically cache contents. Each user is associated to the BS with the maximum average received signal power in any tier. With the cooperative content transfer between MBS tier and SBS tier, users can adaptively obtain contents from BSs or remote content servers. We properly model both wired and wireless delays when a user requests an arbitrary content, and propose the concept of effective delay. Content caching probabilities are optimized using the Marine Predators Algorithm via minimizing the average effective delay. Numerical results show that our proposed cooperative caching scheme achieves much shorter delays than the benchmark caching schemes.

Source Control and Antibiotics in Intra-Abdominal Infections.

Intra-abdominal infections (IAIs) account for a major cause of morbidity and mortality, representing the second most common sepsis-related death with a hospital mortality of 23-38%. Prompt identification of sepsis source, appropriate resuscitation, and early treatment with the shortest delay possible are the cornerstones of management of IAIs and are associated with a more favorable clinical outcome. The aim of source control is to reduce microbial load by removing the infection source and it is achievable by using a wide range of procedures, such as definitive surgical removal of anatomic infectious foci, percutaneous drainage and toilette of infected collections, decompression, and debridement of infected and necrotic tissue or device removal, providing for the restoration of anatomy and function. Damage control surgery may be an option in selected septic patients. Intra-abdominal infections can be classified as uncomplicated or complicated causing localized or diffuse peritonitis. Early clinical evaluation is mandatory in order to optimize diagnostic testing and establish a therapeutic plan. Prognostic scores could serve as helpful tools in medical settings for evaluating both the seriousness and future outlook of a condition. The patient's conditions and the potential progression of the disease determine when to initiate source control. Patients can be classified into three groups based on disease severity, the origin of infection, and the patient's overall physical health, as well as any existing comorbidities. In recent decades, antibiotic resistance has become a global health threat caused by inappropriate antibiotic regimens, inadequate control measures, and infection prevention. The sepsis prevention and infection control protocols combined with optimizing antibiotic administration are crucial to improve outcome and should be encouraged in surgical departments. Antibiotic and antifungal regimens in patients with IAIs should be based on the resistance epidemiology, clinical conditions, and risk for multidrug resistance (MDR) and Candida spp. infections. Several challenges still exist regarding the effectiveness, timing, and patient stratification, as well as the procedures for source control. Antibiotic choice, optimal dosing, and duration of therapy are essential to achieve the best treatment. Promoting standard of care in the management of IAIs improves clinical outcomes worldwide. Further trials and stronger evidence are required to achieve optimal management with the least morbidity in the clinical care of critically ill patients with intra-abdominal sepsis.

Derivation and dynamics of discrete population models with distributed delay in reproduction

We introduce a class of discrete single species models with distributed delay in the reproductive process and a cohort dependent survival function that accounts for survival pressure during that delay period. These delay recurrences track the mature population for species in which individuals reach maturity after at least τ and at most τ+τM breeding cycles. Under realistic model assumptions, we prove the existence of a critical delay threshold, τ˜c. For given delay kernel length τM, if each individual takes at least τ˜c time units to reach maturity, then the population is predicted to go extinct. We show that the positive equilibrium is decreasing in both τ and τM. In the case of a constant reproductive rate, we provide an equation to determine τ˜c for fixed τM, and similarly, provide a lower bound on the kernel length, τ˜M for fixed τ such that the population goes extinct if τM≥τ˜M. We compare these critical thresholds for different maturation distributions and show that if all else is the same, to avoid extinction it is best if all individuals in the population have the shortest delay possible. We apply the model derivation to a Beverton–Holt model and discuss its global dynamics. For this model with kernels that share the same mean delay, we show that populations with the largest variance in the time required to reach maturity have higher population levels and lower chances of extinction.

Understanding Diagnosis Delay in Children With Cancer: Evidence From a Single Institution in Jordan.

This study aimed to evaluate the factors associated with diagnosis delay in children with cancer who are treated at a single institution, which caters to most children with cancer in Jordan. This was a cross-sectional study with a retrospective chart review of selected patients who were diagnosed from August 2018 to December 2021. Data on patient and household characteristics, medical history, and diagnostic delay were collected through structured interviews. Univariable and multivariable linear and logistic regression models were used to identify predictors of delay. The study included a cohort of 202 patient-caregiver pairs, with a median total delay from symptom onset to treatment initiation of 47 days (interquartile range [IQR], 21 to 114d). Notably, 86% of families pursued medical consultation within a month of recognizing symptoms. A regression model revealed CNS tumors as a significant independent predictor of increased total delay ( P =0.002), with affected patients experiencing a median delay markedly longer than those with other cancer types. In addition, older patient age predicted longer total delay ( P =0.025). Symptomatology played a pivotal role in the timeliness of the diagnosis; specifically, visible symptoms such as pallor, bruises, and jaundice were associated with more expedient medical attention, with significantly shorter delays ( P values: 0.011, <0.001, and 0.045, respectively). Furthermore, our investigation disclosed a notable variance in symptom prevalence across different cancer categories, elucidating the complex relationship between clinical presentation and diagnostic timelines. This study highlights the importance of the diagnosis of CNS tumors, patient age, and symptoms in predicting diagnosis delay in pediatric oncology patients. These findings can inform interventions to reduce delays in diagnosis and improve outcomes for these patients. These insights are crucial for developing targeted educational programs aimed at healthcare professionals and families to accelerate the recognition and referral of pediatric cancer cases.

Strength development and durability requirement in recycled aggregate concrete: effect of drying and post curing

Present research critically investigates drying sensitivity and cement hydration resumption in recycled aggregate concrete (RAC). To determine minimum post-curing time for optimum strength resumption in RAC, a curing ratio (δ) is introduced. Curing ratio is defined as the ratio between post-curing duration and delay time before curing. Drying sensitivity of RAC is observed significantly lower than NAC. The maximum strength loss under drying conditions is 25.9% in RAC (OPC) and 23.2% in RAC (PPC). Strength criteria determine the curing ratios of RAC (OPC) and RAC (PPC) as 2.22 and 0.30. The maximum delay time for RAC (OPC) and RAC (PPC) is determined at 8 days and 21 days with a minimum post-curing for 17.76 and 6.63 days. The longest allowable delay produces ion penetration less than 1000 Coulombs, resistivity greater than 208 ohm-m, superior structural integrity and internal compactness, and sorptivity comparable to RACs cured after a shorter delay.

Investigation on NOx control of a CI engine through the collective effect of hydrogen and biodiesel blend at modified compression ratio

The main challenges with using biodiesel in CI engines are higher NOx emissions and reduced brake thermal efficiency. These biodiesel-related issues can be handled by concurrently introducing gaseous fuels along with primary fuel into CI engines. This study examines the effects of hydrogen in a dual fuel mode operation with diesel and Calophyllum inophyllum biodiesel blend (B20) at different compression ratios on the engine's performance, combustion, and emission characteristics. A single-cylinder, 4-stroke, variable compression ratio engine operating at 1500 rpm was used for the research. A standard compression ratio (SCR) of 17.5 and a modified compression ratio (MCR) of 15.5 at a constant hydrogen flow rate of 6 L per minute (lpm) were used with diesel and B 20. The addition of hydrogen with diesel and B20 resulted in higher cylinder pressure and heat release rate when compared to pure diesel and B20 operation. This resulted in a shorter delay period (by an average of 9.4 % and 7.1 %) with lower peak pressure than pure diesel and B20 operation. During the dual fuel mode of H2 with B 20 at a modified compression ratio, the maximum heat release rate was decreased and its occurrence was retarded. NOx, UBHC, and CO emissions of the engine were decreased significantly by an average of 20.8% 43.7, %, and 22 % respectively with 37.7 % increase in smoke opacity. The increase in NOx emission with the use of H2 with diesel was reduced by operating H2 with B20 and at a reduced compression ratio. This lower emission was achieved with a compromise of a marginal decrease in engine brake thermal efficiency.

GRDATFusion: A gradient residual dense and attention transformer infrared and visible image fusion network for smart city security systems in cloud and fog computing

AbstractThe infrared and visible fusion technology holds a pivotal position in smart city for cloud and fog computing, particularly in security system. By fusing infrared and visible image information, this technology enhances target identification, tracking and monitoring precision, bolstering overall system security. However, existing deep learning‐based methods rely heavily on convolutional operations, which excel at extracting local features but have limited receptive fields, hampering global information capture. To overcome this difficulty, we introduce GRDATFusion, a novel end‐to‐end network comprising three key modules: transformer, gradient residual dense and attention residual. The gradient residual dense module extracts local complementary features, leveraging a dense‐shaped network to retain potentially lost information. The attention residual module focuses on crucial input image details, while the transformer module captures global information and models long‐range dependencies. Experiments on public datasets show that GRDATFusion outperforms state‐of‐the‐art algorithms in qualitative and quantitative assessments. Ablation studies validate our approach's advantages, and efficiency comparisons demonstrate its computational efficiency. Therefore, our method makes the security systems in smart city with shorter delay and satisfies the real‐time requirement.

Hybrid deep learning-based traffic congestion control in IoT environment using enhanced arithmetic optimization technique

The Internet of Things (IoT) is essential in several Internet application areas and remains a key technology for communication technologies. Shorter delays in transmission between Roadside Units (RSUs) and vehicles, road safety, and smooth traffic flow are the major difficulties of Intelligent Transportation System (ITS). Machine Learning (ML) was an advanced technique to find hidden insights into ITSs. This article introduces an Improved Arithmetic Optimization with Deep Learning Driven Traffic Congestion Control (IAOADL-TCC) for ITS in Smart Cities. The presented IAOADL-TCC model enables traffic data collection and route traffic on existing routes for avoiding traffic congestion in smart cities. The IAOADL-TCC algorithm exploits a hybrid convolution neural network attention long short-term memory (HCNN-ALSTM) method for traffic congestion control. In addition, an IAOA-based hyperparameter tuning strategy is derived to optimally modify the parameters of the HCNN-ALSTM model. The presented IAOADL-TCC model effectively enhances the flow of traffic and reduces congestion. The experimental validation was performed using the road traffic dataset from the Kaggle repository. The proposed model obtained an average accuracy of 98.03 % with an error rate of 1.97 %. The experimental analysis stated the superior performance of the IAOADL-TCC approach over other DL methods.

Balancing performance and comfort in virtual reality: A study of FPS, latency, and batch values

AbstractThis manuscript investigates the relationships among various performance metrics in a virtual reality (VR), namely frames per second (FPS), latency, batches, and the number of triangles (tris) and vertices (verts). The study aims to uncover correlations and directional associations between these metrics, shedding light on their impact on VR performance. The findings reveal a significant correlation between FPS and latency, albeit in opposite directions. Higher FPS values are associated with reduced latency, indicating that a smoother visual experience is accompanied by shorter delays in the VR. Conversely, lower FPS values are linked to increased latency, suggesting a potential degradation in overall system responsiveness. Additionally, a strong correlation is observed between latency and batches processed. This finding implies that latency has a direct impact on the system's ability to efficiently process and render objects within VR. Furthermore, a positive correlation is identified between the number of batches and the values of tris and verts. This relationship suggests that higher batch counts are associated with larger quantities of triangles and vertices, reflecting a more complex scene rendering process. Consequently, the performance of VR may be influenced by the density and intricacy of the virtual environments, as indicated by these metrics.