Slow Response Time Research Articles

An Underwater Methane Sensor Based on Laser Spectroscopy in a Hollow Core Optical Fiber.

Existing sensors for measuring dissolved methane in situ suffer from excessively slow response times or large size and complexity. The technology reported here realizes improvements by utilizing a hollow core optical fiber (HFC) as the detection cell in an underwater infrared laser spectrometer. The sensor operates by using a polymer membrane inlet to continuously extract dissolved gas from water. Once inside the sensor, the gas passes through an HCF, within which tunable diode laser spectroscopy is used to quantify methane. The use of an HCF for the optical cell enables advantages of sensitivity, selectivity, compactness, response time, and ease of integration. A submersible prototype has been developed, characterized in the laboratory, and tested in the ocean to a depth of 2000 m. Initial laboratory environmental testing showed a pCH4 detection range up to 10,000 μatm, an uncertainty of 5.6 μatm or ±1.4% (whichever is greater) and a response time of 4.6 min over a range of controlled operating conditions. Operation at sea demonstrated its utility in generating dissolved methane maps, targeted point sampling, and water column profiling.

AI-Driven Incident Management in Retail : A Case Study

This article examines the implementation of AI-driven incident management in a major U.S. retail corporation (Walmart) with annual revenues exceeding $500 billion. The company faced significant operational challenges, including high cart abandonment rates (69%), frequent system downtimes causing $3.5 million losses per incident, and slow incident response times with MTTR averaging 4 hours. Through the implementation of comprehensive AI solutions, including advanced monitoring systems processing over 1 terabyte of log data daily, anomaly detection with 95% accuracy, and automated incident management resolving 60% of issues without human intervention, the company achieved remarkable improvements. Key results include a 40% reduction in downtime, a 20% decrease in cart abandonment rates (recovering $3.6 billion in potential lost sales), and an 81.25% improvement in Mean Time to Resolution (MTTR). The article also reveals broader economic impacts, including creating 5,000 new jobs in the AI and ML fields, a 15% improvement in sector-wide operational efficiency, and a $180 billion contribution to U.S. GDP over three years. The successful implementation demonstrates the transformative potential of AI in retail operations while providing valuable insights for organizations seeking to enhance their incident management capabilities through technological innovation.

Spectrally Tunable Ultrafast Long Wave Infrared Detection at Room Temperature.

Room-temperature longwave infrared (LWIR) detectors are preferred over cryogenically cooled solutions due to the cost effectiveness and ease of operation. The performance of present uncooled LWIR detectors such as microbolometers, is limited by reduced sensitivity, slow response time, and the lack of dynamic spectral tunability. Here, we present a graphene-based efficient room-temperature LWIR detector with high detectivity and fast response time utilizing its tunable optical and electronic characteristics. The inherent weak light absorption is enhanced by Dirac plasmons on the patterned graphene coupled to an optical cavity. The absorbed energy is converted into photovoltage by the Seebeck effect with an asymmetric carrier generation environment. Further, dynamic spectral tunability in the 8-12 μm LWIR band is achieved by electrostatic gating. The proposed detection platform paves the path to a fresh generation of uncooled graphene-based LWIR photodetectors for wide ranging applications such as molecular sensing, medical diagnostics, military, security and space.

A Study on “E-recruitment and Selection”

The study examines the impact of e-recruitment platforms on Bchemix Chemicals hiring process. It evaluates their efficiency, cost savings, and candidate quality. Data was collected through surveys and statistical tools like the Chi-square test. Results show that e-recruitment platforms significantly improve recruitment by reducing time-to-hire and enhancing user experience. However, technical issues and slow response times present challenges that need further optimization. The study concludes that addressing these limitations is crucial for maximizing e-recruitment's potential in streamlining hiring at Bchemix.

Development and Testing of a Fast-Acting, 8-Bit, Digital Throttle for Hybrid Rocket Motors

The potential for throttle control of hybrid rocket systems has long been known as a potential advantage for a variety of applications. Because only a single flow path is controlled, theoretically, hybrids should be significantly easier to throttle than bipropellant systems. Unfortunately, the slow response times and nonlinearity of traditional position-control valves have limited practical applications of hybrid throttling. This paper presents an alternative throttling system where the oxidizer flow path is broken into multiple streams, with each flow path controlled by a solenoid-operated on/off valve. The parallel paths allow significantly faster and more precise control than can be achieved using a single position-control valve. The achievable thrust levels are limited only by the size and number of components in the valve cascade. The 8-bit digital throttle system, developed by Utah State University’s Propulsion Research Lab, uses commercial, off-the-shelf components. The throttle system was tested using a 200-N hybrid rocket motor, burning gaseous oxygen, and ABS plastic as propellants. The testing campaign of more than 50 hot fires has demonstrated multiple profiles, including deep throttle ramps, multistep boxcars, and sine waves at frequencies varying from 0.25 to 1-Hz. Comparisons to analytical models are also presented, showing good agreement. Fourier-transform spectra demonstrating the total-system, frequency response are also presented.

Planetary risk society: from ignored problems to fast solutions

ABSTRACT This article compares the slow response time to the dangers posed by greenhouse gases to the swift move to phase-out ozone-depleting substances after the 1985 discovery of the ‘ozone hole’. I suggest both commercial and psychological factors account for the disparity in reactions, in particular the perceived ability to distance ourselves from the risks and uncertainties that our economic activities create. To prevent irreversible damage to the Earth System, I further argue it is crucial to minimise the time that elapses between recognising emerging problems and taking concrete action to address them. Finally, I outline the implications of these case studies for the global chemical industry, where the rapid introduction of novel entities requires a bold regulatory response to safeguard our planet.

Ultrafast, Fano resonant colorimetric sensor with high chromaticity beyond standard RGB

Fast-responsive colorimetric sensors with a wide color gamut have garnered significant attention for real-time atmospheric monitoring observable to the naked eye. Although swelling medium-based Fabry–Perot cavities, which enable linear resonance shifts with high Q-factors, have been widely suggested, they face limitations such as a restricted color gamut within standard RGB due to subtractive colors and slow response times caused by the top layer blocking, delaying the swelling medium’s equilibrium time. Here, we present two-dimensionally nanostructured Fano resonant colorimetric sensors using a swelling medium with significantly improved responsiveness and color representation beyond standard RGB. The nanostructured Fano resonator is elaborately designed to transform the spectral line shape into a Lorentz state in terms of reflectance, resulting in additive color through controlled coupling parameters of the resonator systems. In addition, the nanostructuring of the surface provides direct channels to water vapors, ensuring fast and strong interaction with the swelling medium. Consequently, the fabricated sensor exhibits a wide color gamut, covering 141% of standard RGB and 105% of Adobe RGB, and demonstrates rapid responsiveness with response and recovery times of 287 ms and 87 ms, respectively.

Polarization-selective dual-band infrared metamaterial perfect absorber

Abstract Metamaterial absorbers have significant effects and great potential for applications in a variety of important sciences and technologies, including infrared imaging, thermal emitters, electromagnetic shielding, and photodetectors. However, conventional absorbers with a single function are difficult to meet the growing demand of devices, especially for the situations where the absorption and polarization are simultaneously required in infrared detection. If the polarization absorption function is realized by integrating absorption and polarization elements, it will lead to a large overall system size and slow response time. To solve this problem, we propose a dual-band polarization-selective metamaterial perfect absorber, whose maximum absorptions at resonant wavelengths of λ1=2.824μm and λ2=7.622μm are 99.99% and 99.88%, respectively. The absorber exhibits strong polarization sensitivity, absorbing TM-polarized incident light and reflecting TE-polarized incident light at the two resonant wavelengths. The extinction ratios of the absorber at the two referred resonant wavelengths are 90.9 and 142.7, respectively. The proposed absorber would be beneficial for infrared polarization detection and imaging.

Quality Service of The Online Integrated Referral Information System in The Emergency Department of RSUD Bahteramas Southeast Sulawesi Province

The Integrated Referral System (Sisrute) is important in the referral system for patients who need emergency services. This study aims to determine the quality of the Sisrute IGD service at Bahteramas Hospital through qualitative research with a phenomenological approach. The primary informants were 12, and the supporting informants were 6—determination of informants using non-probability and purposive sampling techniques. The results showed that the effectiveness of the Sisrute program has not been maximized because there are obstacles such as internet network problems, slow referral response time, and a lack of resources to manage the program. The security aspect of the Sisrute application is considered quite good because it is supervised by the State Cyber Agency (BSSN), which uses one password for one user. For Sisrute’s user admin personnel, there may still be insecurity flaws, such as accounts needing to be signed out after use or users giving their access accounts to other unauthorized parties. Regarding focusing on patients, Sisrute services are considered to have been implemented based on the conditions and needs of patients. The timeliness of Sisrute services is considered a waste of time when answering Sisrute.

Silver Telluride Colloidal Quantum Dot Solid for Fast Extended Shortwave Infrared Photodetector.

Extended shortwave infrared (eSWIR) photodetectors that employ solution-processable semiconductors have attracted attention for use in applications such as ranging, night vision, and gas detection. Colloidal quantum dots (CQDs) are promising materials with facile bandgap tunability across the visible-to-mid-infrared wavelengths. However, toxic elements, such as Hg and Pb, and the slow response time of CQD-based IR photodetectors, limit their commercial viability. This article presents a novel eSWIR photodetector that is fabricated using silver telluride (Ag2Te) CQDs. Effective thiolate ligand exchange enables a lower trap density and improved carrier mobility in CQD solids. Furthermore, a vertical p-n photodiode architecture with a favorable energy-level landscape is utilized to facilitate charge extraction, resulting in a fast, room-temperature-operable, and toxic-element-free CQD photodetector. The best eSWIR Ag2Te CQD photodetector exhibits a fall time of 72ns, representing the fastest response time among all prior CQD-based eSWIR photodetectors, including those containing toxic elements, such as Pb and Hg.

Overview of Path Planning Algorithms

Background: Path-planning algorithms are widely used in robotics, vehicles, UAVs, carrier-based aircraft towing vehicles, etc. Using these algorithms, an optimal path with safe obstacle avoidance and high efficiency can be planned. In recent years, path-planning algorithms have received more and more attention from scholars at home and abroad. Objective: In order to promote the application and development of path-planning algorithms, an in-depth analysis of the current development status of path-planning algorithms was presented. The most widely used and representative algorithms in several representative results were extracted, and the characteristics and advantages and disadvantages of each algorithm were analyzed and elaborated in detail for the readers' reference. Methods: The existing path-planning algorithms were classified, and a brief overview of each traditional algorithm was given, followed by an in-depth study of the improved algorithms to summarize the advantages and disadvantages of each type of algorithm. Finally, based on these research results, the future development trend of path-planning algorithms was projected. Results: Through the research and analysis of the path-planning algorithm, it was found that the path- planning algorithm before the improvement had the problems of an unsmooth path, low computational efficiency, a slow response time, the inability to safely avoid obstacles, etc. After the improvement and optimization of the path-planning algorithm, the performance was greatly improved. Conclusion: After research and analysis, it was found that multi-algorithm fusion had great potential for development in path-planning compared to the application and optimization of a single algorithm. In addition, further research is needed in the areas of algorithm adaptability, fusion, intelligent algorithms, and extending the range of algorithmic applications.

Multi-stage control design for oscillating water column-based ocean wave energy conversion system

Despite the enormous global potential of ocean wave energy, it has yet to achieve a level of maturity and economic competitiveness that would result in a substantial impact. Challenges include direct integration into weak or isolated microgrids, a high proportion of uncertain marine environments, nonlinear dynamics, oscillating water column (OWC) device limitations, slower response times, unplanned power outages, power fluctuations, high capital and operational costs in ocean wave energy conversion (OWEC) systems. To this end, a new independent multi-stage design approach is proposed for the performance enhancement of an OWC-based OWEC system. Firstly, an airflow and rotational speed optimal control stage enhances power capture in the Wells turbine-based OWC plant. Secondly, compared to conventional control, the proposed permanent magnet synchronous generator control incorporates an adaptive nonlinear back-stepping control algorithm based on Lyapunov stability theory. Thirdly, introducing reconfigurable control into the conventional six-leg power converter ensures the uninterrupted operation of an OWEC system. Lastly, a model-predictive control-based energy management system is integrated with a bidirectional DC-DC converter that delivers steady power from the grid-connected OWC OWEC system. Hence, MATLAB simulations ensure the overall performance enhancement and feasibility of the OWEC system application and verify that the proposed multi-stage solution is efficient, robust, and reliable.

Anomaly detection in crowded scenes: technologies, challenges and opportunities

The paper discusses advancements in intelligent video surveillance systems, particularly focused on anomaly detection in crowded environments. These systems aim to enhance public safety by automatically detecting unusual behavior and potential threats in real-time. Traditional video surveillance, relying heavily on human monitoring, faces limitations like reduced concentration and slow response times. In contrast, intelligent surveillance uses machine learning and AI algorithms to process vast amounts of video data, identifying patterns that deviate from normal behavior. Crowd anomaly detection is essential in densely populated areas like transportation hubs, stadiums, and public squares. The diversity of anomalies, ranging from minor disruptions to serious threats such as theft or terrorist attacks, presents a challenge for these systems. Anomalies can be difficult to detect due to their unpredictable nature, and what constitutes an anomaly varies depending on the context. The paper highlights the need for robust systems that can adapt to various environmental conditions and distinguish between normal variations and genuine threats. While supervised machine learning models show promise, they often require large amounts of labeled data, which is difficult to obtain in real-world settings. Unsupervised models and deep learning techniques, such as Convolution Neural Networks, have been effective in analyzing crowd behavior and detecting anomalies. However, these methods still face challenges, including scalability, high false positive rates, and the need for real-time processing in large-scale environments. The paper concludes by addressing the limitations of current crowd anomaly detection methods, such as their computational costs, ethical concerns, and inability to detect novel anomalies. It suggests directions for future research, including the integration of advanced learning techniques to improve system performance and scalability

Comparison of Virtual Reality Exergames and Nature Videos on Attentional Performance: A Single-Session Study.

This study aimed to investigate the acute effects of a single session of a VR exergame (Beat Saber) and a VR nature video (Ireland 4K) on attentional performance, using the Flanker and Attentional Blink (AB) tasks. The objective was to assess whether these VR interventions could enhance attentional control, as measured by improvements in response times and accuracy. A total of 39 psychology students, aged 19-25, were randomly assigned to one of three groups: VR exergame, VR nature video, or control. Participants completed the Flanker and AB tasks before and after the intervention. A repeated measures design was employed to analyze changes in response times and accuracy across pre- and post-test sessions. The study revealed significant improvements in response times and accuracy across all groups in the post-test measures, indicating a strong training effect. In the AB task, shorter stimulus onset asynchrony (SOA) led to decreased accuracy and slower response times, emphasizing the difficulty in processing closely spaced targets. The interaction between Type and Group in response times for target stimuli suggested that the intervention types differentially influenced processing speed in specific conditions. The findings suggest that while brief VR interventions did not produce significant differences between groups, the training effect observed highlights the influence of task-specific factors such as SOA and target presence. Further research is needed to explore whether longer or repeated VR sessions, as well as the optimization of task-specific parameters, might lead to more pronounced cognitive benefits.

Porous cellulose photonic film via controlled unidirectional interlayer freezing for rapid visual sensing

Structure color, arising from the interaction of light with regularly arranged sub-micrometer-sized structures, has spurred interest in sensor design. However, typical cellulose nanocrystal (CNC) photonic films derived from biomass, known for their sustainability and cost-effectiveness, often suffer from limited sensitivity and slow response times due to their dense structure. To address this challenge, we have utilized a unidirectional interlayer freezing-photopolymerization strategy to introduce porous structures into CNC photonic films without compromising their vibrant structural color. This method harnesses ice crystal-induced lamellar pores while preserving the periodic arrangement of CNCs. The underlying mechanism of ice kinetics and CNC assembly is established, highlighting the transition from non-iridescent aerogels to iridescent, porous photonic films. The resulting porous CNC photonic film exhibits apparent color response and rapid sensing capabilities in response to various solvent stimuli, outperforming its non-porous counterparts. We have validated the film as a portable vapor detector for rapid visualized alcohol detection. This approach provides promising developments in sustainable, highly responsive sensor technologies.

Early insights into SLA with chatGPT: Navigating CS teachers and student perspectives in an opinion-based exploration

This study explores the integration of ChatGPT into Social Learning Analytics (SLA) to support programming education among computer science students at Mustapha Stambouli University, Mascara, Algeria. Utilizing a mixed-methods approach, the research combines quantitative surveys and qualitative analysis of recorded interactions of one example and interviews to examine the effectiveness, challenges, and perceptions of ChatGPT’s use in programming tasks across Arabic, French, and English. The study involved 57 students and five teachers, providing a comprehensive view of ChatGPT’s impact on learning experiences, engagement patterns, and programming performance. Results indicate that ChatGPT is frequently used as a supplementary tool, especially for programming-related queries, debugging, and last-minute assistance before deadlines. The tool’s adaptability to students’ needs, combined with its ease of use, enhances its perceived value in supporting independent learning. However, the limitations of the free version—such as restricted access, slower response times, and occasional inaccuracies—were frequently cited as barriers to consistent, effective use. Teachers acknowledged ChatGPT’s role in easing instructional burdens but emphasized the need for critical oversight to prevent over-reliance on AI-generated content. Ethical concerns regarding data privacy, academic integrity, and the quality of AI feedback were highlighted as key issues requiring attention. Interestingly, a significant portion of students expressed the belief that AI, including ChatGPT, could potentially replace human programmers in the near future, reflecting both optimism and concern about the evolving role of AI in the field. Despite this, educators maintained that while ChatGPT can augment programming education, human intuition, creativity, and contextual understanding remain irreplaceable. The study concludes that ChatGPT’s integration into SLA offers substantial opportunities to enhance educational support and enrich data on student learning behaviors. However, addressing accessibility issues, enhancing multilingual support, and mitigating ethical challenges are critical for maximizing the tool’s effectiveness. The findings underscore the importance of a balanced approach that leverages AI’s strengths while maintaining the essential role of human expertise in education and programming.

A CuO/MoO3-based SO2 gas sensor with moisture resistance and ultra-fast response at 90°C

Sulfur dioxide (SO2) is a toxic and harmful decomposition product generated during arc discharge or partial discharge in sulfur hexafluoride (SF6) electrical equipment, which can corrode internal pipelines. Detecting SO2 is crucial for assessing the insulation condition of equipment and preventing faults. To overcome the challenges faced by traditional SO2 gas sensors, including high operating temperatures, slow response times, and poor moisture resistance, this paper proposes a novel SO2 gas sensor based on CuO/MoO3. The morphology, composition, and chemical states of the materials were analyzed using different characterization techniques. At 90°C, the S2 sensor (CuO: MoO3=1: 1) exhibited a fast response time (10 s) to 10 ppm SO2 and demonstrated good moisture resistance (a 1 % change in relative humidity (RH) has the same effect on the sensor as 25.9 ppb SO2). The response of the S2 sensor to 100 ppm SO2 was 26.5. Additionally, the voltage sensitivity of the S2 sensor at low (1–10 ppm) and high (10–100 ppm) concentrations are 658.74 mV/ppm and 33.43 mV/ppm, respectively. Therefore, the S2 sensor is more suitable for low concentration conditions. These good sensing characteristics of the S2 sensor are owing to the high content of oxygen vacancies (39.2 %) and the heterojunction effect in the composite material. Additionally, this sensor exhibits excellent anti-interference capabilities, including moisture resistance and high selectivity. Therefore, the CuO/MoO3-based SO2 gas sensor can be effectively applied to detect the decomposition of SF6.

Fire emergency management of large shopping malls: IoT-based evacuee tracking and dynamic path optimization

As urbanization accelerates and buildings become more complex, fire emergency evacuation has become increasingly challenging. Traditional evacuation plans often struggle with slow response times and suboptimal path planning in real-time dynamic and complex fire scenarios. To address these issues, this study proposes the IoT-based DWM-Evac model for fire emergency evacuation path planning. The model leverages IoT technology by using various sensors placed inside buildings to monitor fire incidents and spread in real-time, collecting critical data such as temperature, smoke concentration, and flame location. It integrates Dynamic Graph Neural Networks (DGNN), Whale Optimization Algorithm (WOA), and Markov Decision Process (MDP) to enhance path efficiency and safety. Experimental results indicate that the DWM-Evac model achieves an average evacuation time of 315 s in a virtual mall environment, 25 s shorter than traditional plans, with an average path length of 255 meters and a path safety score of 0.92, higher than the traditional plan’s 0.88. The application of IoT in fire emergency management not only improves response speed but also optimizes path planning, significantly enhancing personnel safety.

Positive and negative touch differentially modulate metacognitive memory judgements for emotional stimuli.

Touch plays a crucial role in providing humans with information from the external environment and can be perceived by humans as positively or negatively valenced. It is well documented that touch can differentially influence social functions, but very little is known about how touch can modulate (meta)cognition. Utilizing a within-subject design, participants were exposed to (a) positive, (b) negative, and (c) no touch, alongside encoding of emotionally valenced (positive and negative) images. After a 20-minute delay, participants completed a Yes/No recognition task to investigate how touch influenced memory-related decision components (e.g. criterion, confidence). Results showed that, compared to the control condition, both positive and negative touch were associated with overall lower confidence ratings, a less liberal response bias and slower response times. Interestingly, for correct recognitions, only negative touch (vs. no touch) led to inappropriately lower confidence and slower response times while both confidence and response time remained unaltered in positive touch. Our findings provide the first evidence that positive and negative touch differentially interact with metacognitive memory-related decisions. Altered metacognitive judgements after being touched could have significant consequences in applied settings, such as situations of eyewitness testimony.

Carbon nanodots-based interfacial nanofluid for high-performance solar-driven water evaporation

Solar steam generation through volumetric heating using nanofluids is a promising approach for wastewater treatment and desalination. However, low evaporation rate and slow response time to the change in solar intensity seriously affect their cumulative evaporation performance in practice. Here, we propose an interfacial nanofluid structure for high-performance solar-driven water evaporation using carbon nanodot (CDs) nanofluid and airlaid paper. CDs nanofluid transferred down through the paper from a source water tank to form an interfacial evaporation structure and ensure continuous water supply for evaporation. The solar conversion heat was only localized on a small amount of nanofluid on the paper resulting in low heat loss to the bulk nanofluid and a fast response time of within 2 min to reach a steady evaporation rate. In addition, the flowing nanofluid on the paper can absorb environmental energy to achieve high-rate evaporation of 1.93 kg m−2 h−1 under one sun irradiation. This study provides an effective strategy to improve the performance of volumetric heating systems for solar wastewater treatment and desalination.