Average Response Research Articles

Exploring the role of AI-driven chatbots in patient care: a critical evaluation amidst healthcare staff shortages

Abstract Background Amidst the backdrop of staff shortages in healthcare systems, patients and their families are increasingly turning to chatbots, powered by Large Language Models (LLMs), for information about their medical conditions. These AI-driven chatbots, capable of generating human-like responses across a broad range of topics, have become a prevalent tool in the healthcare landscape. Given the proliferation of these chatbots, it is crucial to evaluate the quality and accuracy of the responses they provide. Methods We selected five freely accessible chatbots (Bard, Microsoft Copilot, PiAI, ChatGPT, and ChatSpot) for our study. These chatbots were posed questions spanning three medical fields: cardiology, cardio-oncology, and cardio-rheumatology. The responses generated by the chatbots were then compared against established guidelines from the European Society of Cardiology, American Academy of Dermatology, and American Society of Clinical Oncology. In addition to the content, the readability of the responses was evaluated using four different readability scales: the Flesch Reading Scale, Gunning Fog Scale Level, Flesch-Kincaid Grade Level, and Dale-Chall Score. To assess the accuracy of the responses in accordance with the medical guidelines, two independent medical professionals rated them on a 3-point Likert scale (0 - incorrect, 1 - partially correct or incomplete, 2 - correct). This allowed us to gauge the compliance of the chatbot responses with the medical guidelines. Results Results: In our study, we posed a total of 45 questions to each of the chatbots. Out of the five chatbots, Microsoft Copilot, PiAI, and ChatGPT were able to respond to all the questions. The length of the responses varied, with PiAI providing the shortest average response length of 7.26 words, and Bard providing the longest at 18.9 words. In terms of readability, the Flesch Reading Ease Scale scores ranged from 17.67 (ChatGPT) to 39.34 (Bard), indicating the relative complexity of the responses. The Flesch-Kincaid Grade Level, which reflects the academic grade level required to comprehend the text, ranged from 14.02 (PiAI) to 15.97 (ChatGPT). The Gunning Fog Scale Level, another measure of readability, varied from 15.77 (Bard) to 19.73 (ChatGPT). Lastly, the Dale-Chall Score, which assesses the understandability of the text, ranged from 10.24 (Bard) to 11.87 (ChatGPT). These results highlight the variability in the readability and complexity of responses generated by different chatbots. Readability analysis is presented in table 1. Conclusion This study indicates that chatbots vary in length, quality and readability. They answer each question in their way, based on data they have pulled from the network. Our data suggests that people who want information from a chatbot need to be careful and verify the answers they get.

Operational Performance Evaluation Model for Food Processing Machinery Industry Chain

This study aims to create a performance evaluation model for the food processing machinery industry. The goal is to help food processing plants improve both process quality and competitiveness. Additionally, component failures may disrupt the continuous operation of the food processing machine, potentially resulting in insufficient production and delays in delivery, which in turn leads to cost losses. For the sold food processing machinery, decreases in the average number of failures within a unit of time, the average repair response time when a failure occurs, and the average repair duration are three crucial factors in minimizing the total expected loss due to machine failures. Based on these three important factors, this study established the following evaluation indices: (1) the processing performance index, (2) the repair reporting performance index, and (3) the maintenance performance index. These indices serve as tools for assessing the performance of the three key operational aspects. This study employed a radar chart to construct the evaluation model, which can directly compare the critical values with the point estimates of three indices. Consequently, this approach can judge whether the operational performance has achieved the required level. This can maintain the simplicity and usability of point estimates while reducing the risk of misjudgment due to sampling errors.

Design and Implementation of Virtualization Cloud Computing System Intelligent Terminal Application Layer

Cloud task scheduling has become a trend, and the shortcomings of traditional scheduling algorithms can be optimized through mathematical models of other cloud task scheduling scenarios. In order to improve the virtualization data processing effect of intelligent terminal application layer, this paper proposes an improved krill swarm optimization algorithm based on adaptive weight. The optimization of cluster load balancing and task average response time ratio are used to improve the convergence and accuracy of task scheduling algorithm. Moreover, this paper uses CloudSim simulation tool to conduct experiments to verify the effectiveness of the proposed model. In addition, this paper proposes an application-based virtualization method, which virtualizes the application programs inside the host machine into the virtualization software inside the virtual machine, so that the virtual machine can access it. Finally, this paper verifies the reliability of the proposed method with experiments, thus providing a theoretical reference for the subsequent design of intelligent terminal application layer virtualization cloud computing system. Compared with the traditional way of using physical hardware, using virtual machine hardware is more flexible, efficient and safe, which brings great convenience to the development and deployment of applications.

Seismic behavior of base isolated precast frame

The performance of the base-isolated precast frame is examined for both near-field and far-field earthquakes. For comparison, the corresponding base isolated monolithic frame is analyzed. The frames are isolated using a friction pendulum system (FPS), which is appropriately designed to take up the vertical load coming on the frames. The same isolators are provided for the monolithic and precast frames. Two types of precast frames are considered, namely frames with emulated and non-emulated connections. An ensemble of 7 different earthquakes is used for each type of earthquake to obtain the average responses, which include maximum base shear, maximum acceleration, maximum top story displacement, and maximum inter-story drift ratio (MIDR). SAP2000 is used for the non-linear time history analysis of the frames for different earthquakes. The studies are made for three levels of PGA, namely 0.4 g, 0.6 g, and 0.8 g. The study shows that a significant reduction in base shear, top story displacement and MIDR is achieved by base isolating the precast frames. However, the corresponding base isolated monolithic frame offers more reductions in response especially for MIDR. Of the two types of precast frames, the one with emulated connection shows marginally higher reductions of response as compared to the non-emulated connection. Further, the percentage reductions in responses in main shock and after shock remain almost the same for all cases.

Accuracy of natural language processors for patients seeking inguinal hernia information.

NLPs such as ChatGPT are novel sources of online healthcare information that are readily accessible and integrated into internet search tools. The accuracy of NLP-generated responses to health information questions is unknown. We queried four NLPs (ChatGPT 3.5 and 4, Bard, and Claude 2.0) for responses to simulated patient questions about inguinal hernias and their management. Responses were graded on a Likert scale (1 poor to 5 excellent) for relevance, completeness, and accuracy. Responses were compiled and scored collectively for readability using the Flesch-Kincaid score and for educational quality using the DISCERN instrument, a validated tool for evaluating patient information materials. Responses were also compared to two gold-standard educational materials provided by SAGES and the ACS. Evaluations were performed by six hernia surgeons. The average NLP response scores for relevance, completeness, and accuracy were 4.76 (95% CI 4.70-4.80), 4.11 (95% CI 4.02-4.20), and 4.14 (95% CI 4.03-4.24), respectively. ChatGPT4 received higher accuracy scores (mean 4.43 [95% CI 4.37-4.50]) than Bard (mean 4.06 [95% CI 3.88-4.26]) and Claude 2.0 (mean 3.85 [95% CI 3.63-4.08]). The ACS document received the best scores for reading ease (55.2) and grade level (9.2); however, none of the documents achieved the readibility thresholds recommended by the American Medical Association. The ACS document also received the highest DISCERN score of 63.5 (57.0-70.1), and this was significantly higher compared to ChatGPT 4 (50.8 [95% CI 46.2-55.4]) and Claude 2.0 (48 [95% CI 41.6-54.4]). The evaluated NLPs provided relevant responses of reasonable accuracy to questions about inguinal hernia. Compiled NLP responses received relatively low readability and DISCERN scores, although results may improve as NLPs evolve or with adjustments in question wording. As surgical patients expand their use of NLPs for healthcare information, surgeons should be aware of the benefits and limitations of NLPs as patient education tools.

A Learning Model for Explanation Text Literacy Using Flipbook Applications in SMP/MTS

This research aims to develop an explanatory text literacy learning model using the flipbook application as media in SMP/MTS. The background to this problem 1) How is the planning for an explanatory text literacy learning model using the flipbook application as a medium in class VIII SMP/MTS? 2) How to implement an explanatory text literacy learning model using the flipbook application as a medium in class VIII SMP/MTS? 3) What is the effectiveness of the explanatory text literacy learning model using the flipbook application as a medium in class VIII SMP/MTS? This research uses the Research and Development (R&D) research and development method with the ADDIE (Analyze, Design, Development, Implementation, and Evaluation) model. The steps of this research consist of, needs analysis, learning model design, learning model development, implementation of learning model products, evaluation of learning model products. Data collection in this research was by collecting data from the results of expert validation questionnaires and questionnaires from student assessments. The data obtained was analyzed descriptively using a Likert scale. The results of the validation test by media experts obtained a score of 77.94%, the validation test by language experts obtained a score of 81.25%, the Indonesian language teacher's response score obtained a score of 97.33%, and the average student response obtained a score of 82.13%. The conclusion of this research is that the explanatory text literacy learning model using the flipbook application media in SMP/MTS meets the appropriate criteria to be used as a learning model by students or teachers in Indonesian language subjects with explanatory text material in class VIII SMP/ MTS students.

Electronic Health Record User Dashboard for Optimization of Surgical Resident Procedural Reporting.

Background While necessary and educationally beneficial, administrative tasks such as case and patient tracking may carry additional burden for surgical trainees. Automated systems targeting these tasks are scarce, leading to manual and inefficient workflows. Methods We created an electronic health record (EHR)-based, user-specific dashboard for surgical residents to compile resident-specific data: bedside procedures performed, operative cases performed or participated in, and notes written by the resident as a surrogate for patients cared for. Usability testing was performed with resident volunteers, and residents were also surveyed post-implementation to assess for efficacy and satisfaction. Access log data from the EHR was used to assess dashboard usage over time. Descriptive statistics were calculated. Results The dashboard was implemented on a population of approximately 175 surgical residents in 5 different departments (General Surgery, Obstetrics and Gynecology, Neurosurgery, Orthopedics, and Otolaryngology) at a single academic medical center. 6 resident volunteers participating in usability testing completed an average of 96% of preset tasks independently. Average responses to five questions extracted from the System Usability Scale (SUS) questions ranged from 4.0 to 4.67 out of 5. Post-implementation surveys indicated high resident satisfaction (4.39 out of 5) and moderate rates of use, with 46.4% of residents using the dashboard at least monthly. Daily use of the dashboard has increased over time, especially after making the dashboard a default for surgical residents. Conclusion An EHR-based dashboard compiling resident-specific data can improve the efficiency of administrative tasks and supplement longitudinal education.

Validation of On-Head OPM MEG for Language Laterality Assessment.

Pre-surgical localization of language function in the brain is critical for patients with medically intractable epilepsy. MEG has emerged as a valuable clinical tool for localizing language areas in clinical populations, however, it is limited for widespread application due to the low availability of the system. Recent advances in optically pumped magnetometer (OPM) systems account for some of the limitations of traditional MEG and have been shown to have a similar signal-to-noise ratio. However, the novelty of these systems means that they have only been tested for limited sensory and motor applications. In this work, we aim to validate a novel on-head OPM MEG procedure for lateralizing language processes. OPM recordings, using a soft cap with flexible sensor placement, were collected from 19 healthy, right-handed controls during an auditory word recognition task. The resulting evoked fields were assessed for hemispheric laterality of the response. Principal component analysis (PCA) of the grand average language response indicated that the first two principal components were lateralized to the left hemisphere. The PCA also revealed that all participants had evoked topographies that closely resembled the average left-lateralized response. Left-lateralized responses were consistent with what is expected for a group of healthy right-handed individuals. These findings demonstrate that language-related evoked fields can be elucidated from on-head OPM MEG recordings in a group of healthy adult participants. In the future, on-head OPM MEG and the associated lateralization methods should be validated in patient populations as they may have utility in the pre-surgical mapping of language functions in patients with epilepsy.

Community attitudes toward tourism and quality of life: a case study of Palangan village, Iran

Community perception is essential for influencing the physical, social, and environmental growth of a region and is key to achieving a high Quality of Life (QOL). Tourism serves as a common means of introducing outsiders to the area where the community lives. Embracing tourism in remote locations like small towns or rural areas can be difficult, and the success of tourism hinges on community attitudes towards it. The community's engagement and approval of tourism are dependent on the benefits such as how tourism improves their QOL in both the short. Therefore, this study aims to explore the local community's perceptions on how tourism influences various aspects of QOL. A survey of 160 households in Palangan Village was conducted to collect primary data, using a conceptual model with 16 indices to evaluate QOL. The findings indicate that the local community generally has a positive perception of the effects of tourism, especially on key QOL components. Civic and generative well-being are considered more significant than other aspects of QOL. Analysis of the data reveals differences in perception across the 16 QOL indices. The highest average ratings were for institutional participation (3.476), quality of employment (3.456), and individual capability (3.203), while the lowest ratings were for standard of living (1.883), social solidarity (2.289), and environmental resource quality (2.725). For instance, the institutional participation index received an average response rating of 3.476. Additionally, the dispersion coefficient index highlighted notable variations in perception, particularly for satisfaction with living conditions (0.488) and standard of living (0.488). Conversely, employment quality (0.360), institutional cooperation (0.385), and freedom of choice had the lowest dispersion coefficients (0.386). Other indices showed moderate levels of dispersion and distribution of perception. In conclusion, these results underscore the significance of community perception in tourism development and its affect on QOL. They also underscore the importance of strategic planning and balanced development to ensure that all aspects of QOL benefit from the expanding tourism sector, leading to a more sustainable and harmonious community.

Use of Specific T Lymphocytes in Treating Cytomegalovirus Infection in Hematopoietic Cell Transplant Recipients: A Systematic Review

Cytomegalovirus (CMV) poses a significant threat to post-hematopoietic cell transplantation (HCT). Control strategies include letermovir prophylaxis or ganciclovir pre-emptive therapy (PET). Without prophylaxis, 65–90% of seropositive recipients develop a clinically significant CMV infection. Due to PET drawbacks, letermovir prophylaxis is preferable, as it reduces CMV-related events and improves overall survival. However, refractory or resistant CMV-CS remains a challenge, with maribavir showing limited efficacy. This systematic review followed the Cochrane Manual and PRISMA guidelines and was registered in PROSPERO. Searches were conducted in PubMed, Scopus, Embase, and Web of Science. Out of 1895 identified records, 614 duplicates were removed, and subsequent screening excluded 1153 studies. Eleven included studies (2012–2024) involved 255 HCT recipients receiving adoptive immunotherapy (AI), primarily CMV-specific T-cell therapy. GvHD occurred in 1.82% of cases. Adverse events occurred in 4.4% of cases, while mild CRS was observed in 1.3% of patients. Efficacy, evaluated in 299 patients across eleven studies, showed an average response rate of 78.2%. CMV-CS recurrence was observed in 24.4% of 213 patients, and death due to CMV was reported in 9.7% of 307 patients across nine studies. Adoptive hCMV-specific T-cell immunotherapy appears to be a safe, effective alternative for refractory CMV-CS in HCT.

Adherence to high-frequency ecological momentary assessment in persons with moderate-to-severe traumatic brain injury.

Ecological momentary assessment (EMA) involves repeated collection of real-time self-report data, often multiple times per day, nearly always delivered electronically by smartphone. While EMA has shown promise for researching internal states, behaviors, and experiences in multiple populations, concerns remain regarding its feasibility in samples with cognitive impairments, like those associated with chronic moderate-to-severe traumatic brain injury (TBI). This study examines adherence to a 7-week high-frequency (5x daily) EMA protocol in individuals with moderate-to-severe TBI, considering changes in response rate over time, as well as individual participant characteristics (memory function, education, injury severity, and age). In the sample of 39 participants, the average overall response rate was 65% (range: 5%-100%). Linear mixed-effects modeling revealed a small but statistically significant linear decay in response rate over 7 weeks of participation. Individual trajectories were variable, as evidenced by the significant effect of random slope. A better response rate was positively associated with greater educational attainment and better episodic memory function (statistical trend), whereas the effects of age and injury severity were not significant. These findings shed light on the potential of EMA in TBI studies but underscore the need for tailored strategies to address individual barriers to adherence.

SBAC-SDN: A Scalable Blockchain-based Access Control in Northbound Interface for Multi-Controller SDN with Load Balancing Mechanism

Abstract The rapid adoption of Software-Defined Networking (SDN) has not only revolutionized network management but also presented challenges in scalability and security, particularly in multi-controller environments. This paper introduces a load balancing technique aimed at enhancing the scalability of the Northbound interface, a critical component often susceptible to performance bottlenecks. The experimental setup utilized Mininet and Ryu to create a simulated multi-controller SDN environment, where SBAC-SDN was thoroughly evaluated based on CPU and memory usage, response times, and error rates. The results showed substantial improvements in critical performance indicators, confirming the effectiveness of the load balancing technique in addressing scalability challenges. The findings indicate that the system can be scaled without excessive resource consumption, as CPU usage peaked at 60.3% and memory usage remained below 8 GB even with eight controllers. The stable average response times were consistently below 1 s, and the high throughput, ranging from 4.34 to 4.67 requests per second, further demonstrates the efficiency of the system. The error rate remained 0.0% across all configurations, highlighting the robustness of the implemented security measures. This study contributes to ongoing efforts to improve the scalability and overall robustness of SDNs, aligning with the broader goals of reliability and efficiency in network management.

Revision of Sabine's and Eyring's reverberation theories

Sabine's and Eyring's reverberation theories are still the most important theoretical frameworks for room acoustics. The important physical quantities derived from these theories are the average sound pressure level (sound energy density) and the reverberation time (sound energy decay). Eyring revised Sabine's theory to become the reverberation time zero in the perfect absorption condition. However, Eyring's theory has contradictions regarding the sound energy density in a steady state and the energy decay from the steady state. In this study, in order to resolve these contradictions, the theoretical framework for the consistent system is presented introducing the new concept of the average impulse response in a room, and the contradictions in the current room acoustic theory are clarified. Based on the theoretical framework, Sabine's reverberation theory is revised by a different way of Eyring's theory. The reverberation time of the revised theory is shorter than that of Sabine's theory and longer than that of Eyring's theory. Finally, ray-tracing computer simulations were performed to verify the revised theory. Results of sound ray tracing simulations were in better agreement with the values calculated using the revised theory rather than those calculated using Sabine's and Eyring's theories.

Optimizing Cloud Computing Performance by Integrating the Novel PSBR Service Broker Policy and Load Balancing Algorithms

As cloud computing advances, organizations' IT infrastructure and application deployment processes are moving to the cloud because cloud computing provides everything as a service over the Internet. The performance of a cloud-based application is based on proper datacenter selection and workload distribution within the selected datacenter. Service broker policies are used for suitable datacenter selection, and load balancing algorithms(LBA) are applied to distribute workloads. This paper is to evaluate the effect of a proposed service broker policy (PSBR) on the performance of cloud-based applications with LBA. To achieve the objective, the behavior of the TikTok application was modeled using the worldwide users’ statistics on the cloud simulation framework, namely CloudAnalyst. As a result, the average response time and data center processing time are measured. Next, the PSBR provides better results than the existing service proximity-based policy. This paper supports cloud service providers' benefits, from coordination between data center configuration, data center selection, and workload distribution to cloud users' identification of the appropriate procedures for their organization or application. PSBR with Active Monitoring had the best average response time of 75.1 ms, while SPR consistently exhibited higher average times across all algorithms, with the highest being 84.5 ms for Round Robin. Under the PSBR policy, Throttled had the lowest average processing time (4.67), while Round Robin had the highest (5.72). Similarly, under the SPR policy, Throttled maintained its efficiency with the lowest average (4.8), while Round Robin showed the highest (5.79).

Creation and evaluation of a three-dimensional-printed synthetic vas deferens for microsurgical training

ABSTRACT Introduction: Individuals choose to undergo vasectomy reversal for a variety of reasons, including remarriage or due to the death of a child. To be proficient in this procedure, the surgeons need to be high volume and the trainees require a safe environment to develop their microsurgical skills. To overcome this challenge, we used three-dimensional (3D) printing to create a synthetic model of the vas deferens with properties similar to the human vas deferens. We distributed this model to experienced microsurgeons for evaluation. Methods: The vas deferens model was created using thermoplastic polyurethane filament. The filament was then infused with a foaming agent to allow for temperature-dependent tuning of the material’s stiffness. The model’s outer diameter was 1 mm and the inner lumen was 0.5 mm. Fellowship-trained male reproductive urologists were recruited from the Society for the Study of Male Reproduction website. They used our model and judged it on several factors by completing a 13-question survey. Results: We received completed evaluations from five microsurgeons. Eighty percent of the surgeons were able to complete a full anastomosis on the model using 9-0 and 10-0 sutures. The majority of the completed anastomoses were performed using the one-layer technique. The average responses for the model’s usefulness as a practice tool, a training tool, and overall assessment ranged from 72 to 79 out of 100. Comments for the improvement included the need for a more flexible and softer model. Conclusions: We created a 3D-printed synthetic vas deferens that serves as a valuable training and practice tool.

Anti-noise weld seam tracking algorithm based on spatio-temporal memory mechanism

In laser vision-based robotic welding, existing seam tracking methods are seriously affected by noise and lack effective utilization of spatio-temporal information during welding process. To address this, this paper proposes an anti-noise weld seam tracking algorithm based on spatio-temporal memory mechanism. First, a memory mechanism with welding history memory information is designed, which can better adapt to seam appearance changes and noise interference during welding. Subsequently, a laser stripe tracker based on spatio-temporal memory is constructed, it enables the current frame to adaptively search historical valid information to eliminate effects of noise and appearance changes. The experimental results show that the proposed method has an average response speed of 43 FPS and an average tracking error of 1.35 pixels for various weld seams. The actual tracking error is less than 0.6 mm. The introduction of memory mechanism improves the weld tracking accuracy by 52.1 % compared to a single tracking template.

The development and characterisation of 3D-printed multi-material thermistor

This paper introduces a multi-material, low-cost, and highly sensitive thermistor concept developed for body temperature monitoring. The designed thermistor employs poly(3,4-ethylenedioxythophene):poly(4-styrenesulfonate) (PEDOT:PSS) for the temperature sensing layer, silver (Ag) for the contact electrodes, and polycarbonate (PC) as the substrate. In contrast to traditional printed electronics substrates used in thermistor development, the utilisation of Material Extrusion via Fused Deposition Modelling (FDM) for the manufacture of PC substrates is the distinct feature of the work. For the manufacture of multi-material thermistors two different Additive Manufacturing (AM) methods: FDM for substrates and micro dispensing for PEDOT:PSS sensing layer and Ag electrodes, were utilised. Two thermistors with varying sensing areas, namely D1 and D2, were designed and fabricated. The thermistors demonstrated a high degree of linearity and repeatability within the temperature range of 25–45°C with a viable hysteresis maximum around 3%. The measurement sensitivity of thermistors was assessed based on Temperature Coefficient of Resistance (TCR) values, which were –0.68 ±0.057% and –0.49 ±0.078% per °C for the D1 design and –0.53 ±0.078% and –0.40 ±0.069% per °C for the D2 design, during heating and cooling, respectively. Comparable average response and recovery times to the literature were also acquired, which were 31.47 ±1.02 and 54.42 ±0.70seconds for D1 and 27.38 ±0.96 and 48.45 ±1.69seconds for D2, during heating and cooling, respectively. It was evident that the sensing area had an impact on thermistor TCR as well as response and recovery times, where higher TCR and faster response and recovery times were recorded with the small sensing area. It was also observed that humidity had a significant impact on thermistor reliability, exhibiting a normalised resistance change of ∼12% and ∼9% of the initial measurement at 50 and 75% RH, respectively. When compared to PEDOT:PSS-based thermistors reported in the literature, the obtained results in this research have demonstrated that our multi-material thermistor concept is a promising candidate for the low-cost and highly sensitive multi-material thermistor that can be manufactured in a fully additive manner using AM technologies.

Co3O4/CQDs composite for selective ammonia detection in exhaled human breath analysis

Co3O4 was synthesized using electrospinning method and different amounts of carbon quantum dots (CQDs) were subsequently doped into the Co3O4 matrix via grinding, resulting in Co3O4/CQDs composite materials with various ratios. Gas sensing tests revealed that the resulting composite Co3O4/CQDs sensors exhibited excellent performance at a low temperature of 50°C, with outstanding selectivity towards ammonia gas and rapid response and recovery characteristics. Specifically, response and recovery time for 50 ppm ammonia gas were 14 s and 164 s, respectively. The outcomes of the exhalation simulation test utilizing sensors composed of this material demonstrate that the sensor's response to simulated chronic kidney disease breath closely approximates that of pure biological samples (1.08), exceeding the average response value of healthy exhaled breath samples (1.0). This demonstrates the sensor's capability to differentiate between healthy individuals and chronic kidney disease patients despite significant water vapor and carbon dioxide interference. This study provides valuable insights for the development of more efficient ammonia sensors and their role in the early detection of chronic kidney disease.

Highly sensitive and low-temperature triethylamine sensor based on in situ growth of hierarchical α-MoO3 flowers

To challenge high working temperature of present triethylamine (TEA) sensor, well-defined hierarchical α-MoO3 flowers were in situ grown on Al2O3 tubes through a simple solvothermal method. The α-MoO3 flowers were about 3 μm in diameter consisted of overlapping nanosheets with average thickness of ∼30 nm, growing radially from the center of the flowers. The α-MoO3 flowers in situ grew on Al2O3 tubes to constitute the thin film sensors, which exhibited improved gas-sensing performance to TEA compared to the thick film sensors painted with the powder of α-MoO3 produced in the same reaction system. The average response value of the α-MoO3 thin film sensors to 10 ppm TEA was 243.5 at relatively low operating temperature of 133 °C, which was 4.5 times as high as that of the thick film sensors (54.2). And the detection limit of the thin film sensors to TEA is further decreased to 0.001 ppm compared to the α-MoO3 thick film ones. The superior TEA-sensing capability should be attributed to the hierarchical nanostructure grown in situ that supplies more active sites for gas molecule adsorption and electron transfer. The synthesized α-MoO3 flowers with hierarchical nanostructure are promising material for trace TEA detection in practical application.

LEVERAGING DATA-DRIVEN TECHNIQUES FOR EFFICIENT DATA MINING IN CLOUD COMPUTING ENVIRONMENTS

The capacity to efficiently use big data and analytics is becoming a critical differentiator for company growth in today's data-driven environment. Using important trends, obstacles, and best practices as a framework, this article investigates how to promote company growth via the use of big data and analytics. An important issue in cloud computing is deciding on an acceptable amount and location of data. Decisions about resource management are based on data aspects and operations in data-driven infrastructure management (DDIM), a novel solution to this problem. It is critical to have a unified system that can manage various forms of big data and the analysis of that data, as well as common knowledge management functions. The approach stated in this research is DD-DM-CCE, or Data-Driven Methods for Efficient Data Mining in Cloud Computing Environments. Improving data using derived information from maximum frequent correlated pattern mining is the main focus of the work. By considering the centrality factor, the DD-DM-CCE method may help choose the best locations to store data in order to reduce access latency. In order to gain a competitive edge, this study offers a cloud-based conceptual framework that can analyze large data in real time and improve decision making. Efficient big data processing is possible with cloud computing infrastructures that can store and analyze massive amounts of data, as this reduces the upfront cost of the massively parallel computer infrastructure needed for big data analytics. According to simulations run on cloud computing, the DD-DM-CCE approach does better than the status quo regarding hit ratio, effective network utilization, and average response time. According to this study, data mining methods are valuable and successful in predicting how consumers will utilize cloud services.