Metric Measure Research Articles

Classification of tea leaves diseases by developed CNN, feature fusion, and classifier based model

Due to the increase in the world population day by day, the amount of food needed is also increasing day by day. Diseases that occur in plants reduce the amount and quality of the product obtained. In this study, a computer-aided model was developed to detect diseases in tea leaves. Because plant diseases can be difficult and misleading to detect with the naked eye by farmers or experts. It is very important to detect diseases in tea leaves using artificial intelligence methods. Three Convolutional Neural Network (CNN) architectures accepted in the literature were used as the basis for the classification of diseases in tea leaves. With these three CNN architectures, feature maps of the images in the data set were obtained. After combining the feature maps obtained in each architecture, they were classified in the Linear Discriminant classifier. In addition, the performance of the proposed model was compared with seven CNN architectures accepted in the literature. The performance of the models used in the study was evaluated using different performance measurement metrics. The obtained results showed that the proposed model can be used to classify diseases in tea leaves.

Extreme flooding controlled by stream network organization and flow regime

River floods are among the most common natural disasters worldwide, with substantial economic and humanitarian costs. Despite enormous efforts, gauging the risk of extreme floods with unprecedented magnitude is an outstanding challenge. Limited observational data from very high-magnitude flood events hinders prediction efforts and the identification of discharge thresholds marking the rise of progressively larger floods, termed flood divides. Combining long hydroclimatic records and a process-based model for flood hazard assessment, here we demonstrate that the spatial organization of stream networks and the river flow regime control the appearance of flood divides and extreme floods. In contrast with their ubiquitous attribution to extreme rainfall and anomalous antecedent conditions, we show that the propensity to generate extreme floods is well predicted by intrinsic properties of river basins. Most importantly, it can be assessed prior to the occurrence of catastrophes through measurable metrics of these properties derived from commonly available discharge data, namely the hydrograph recession exponent and the coefficient of variation of daily flows. These results highlight the propensity of certain rivers for generating extreme floods and demonstrate the importance of using hazard mapping tools that, rather than solely relying on past flood records, identify regions susceptible to the occurrence of extreme floods from ordinary discharge dynamics.

Bio-medical imaging (X-ray, CT, ultrasound, ECG), genome sequences applications of deep neural network and machine learning in diagnosis, detection, classification, and segmentation of COVID-19: a Meta-analysis & systematic review.

This review investigates how Deep Machine Learning (DML) has dealt with the Covid-19 epidemic and provides recommendations for future Covid-19 research. Despite the fact that vaccines for this epidemic have been developed, DL methods have proven to be a valuable asset in radiologists' arsenals for the automated assessment of Covid-19. This detailed review debates the techniques and applications developed for Covid-19 findings using DL systems. It also provides insights into notable datasets used to train neural networks, data partitioning, and various performance measurement metrics. The PRISMA taxonomy has been formed based on pretrained(45 systems) and hybrid/custom(17 systems) models with radiography modalities. A total of 62 systems with respect to X-ray(32), CT(19), ultrasound(7), ECG(2), and genome sequence(2) based modalities as taxonomy are selected from the studied articles. We originate by valuing the present phase of DL and conclude with significant limitations. The restrictions contain incomprehensibility, simplification measures, learning from incomplete labeled data, and data secrecy. Moreover, DML can be utilized to detect and classify Covid-19 from other COPD illnesses. The proposed literature review has found many DL-based systems to fight against Covid19. We expect this article will assist in speeding up the procedure of DL for Covid-19 researchers, including medical, radiology technicians, and data engineers.

Skill-level classification and performance evaluation for endoscopic sleeve gastroplasty.

We previously developed grading metrics for quantitative performance measurement for simulated endoscopic sleeve gastroplasty (ESG) to create a scalar reference to classify subjects into experts and novices. In this work, we used synthetic data generation and expanded our skill level analysis using machine learning techniques. We used the synthetic data generation algorithm SMOTE to expand and balance our dataset of seven actual simulated ESG procedures using synthetic data. We performed optimization to seek optimum metrics to classify experts and novices by identifying the most critical and distinctive sub-tasks. We used support vector machine (SVM), AdaBoost, K-nearest neighbors (KNN) Kernel Fisher discriminant analysis (KFDA), random forest, and decision tree classifiers to classify surgeons as experts or novices after grading. Furthermore, we used an optimization model to create weights for each task and separate the clusters by maximizing the distance between the expert and novice scores. We split our dataset into a training set of 15 samples and a testing dataset of five samples. We put this dataset through six classifiers, SVM, KFDA, AdaBoost, KNN, random forest, and decision tree, resulting in 0.94, 0.94, 1.00, 1.00, 1.00, and 1.00 accuracy, respectively, for training and 1.00 accuracy for the testing results for SVM and AdaBoost. Our optimization model maximized the distance between the expert and novice groups from 2 to 53.72. This paper shows that feature reduction, in combination with classification algorithms such as SVM and KNN, can be used in tandem to classify endoscopists as experts or novices based on their results recorded using our grading metrics. Furthermore, this work introduces a non-linear constraint optimization to separate the two clusters and find the most important tasks using weights.

Efficient medical image retrieval system using Geometric Invariant Point Bilateral Transformation (GIPBT)

Sensor rich environments assist CBIR system to provide granularity in detail for many applications. From sensor rich environments, CBIR can provide the process to exploit images that can integrate both search & retrieval mechanisms. Era of digital technology with Smart devices connected to internet so as to communicate is made possible with Internet of Things (IoT). As IoT deals with huge data in the form of images or videos, to be communicated between smart devices through IoT, deals with complex data processing. Proposed Image Retrieval System reduces the computation overhead for wide range of IoT applications. Medical Images are not only image which holds the pixel also the valuable information of medical diagnosis of every human being. Medical image retrieval has become an inescapable task in every hospital IT system due to the influx of medical pictures. A novel texture fusion technique and a rapid similarity measure metric were used in this study to create an efficient medical CBIR. Proposed feature extraction method of Geometric Invariant Point Bilateral Transformation (GIPBT) algorithm provides the texture information. In this method features are computed as point-to-point geometric information of bilateral filtered image. It automatically inoculates the universal information into the local descriptor and reinforces the feature depiction. To further enhance the descriptor, we used Histogram of Second Order Gradient (HSOG) feature extraction method. By use of these two features, we are performing the feature matching. To analyze and categorize the non-linearity between the features, we propose the kernalized discriminant analysis concept into the score prediction metric. Our proposed similarity measure method is Discriminant Kernalized Disparity (DKD) metric. In DKD, kernel applied Eigen decomposition is utilized for each feature set and disparity metric is evaluated. Finally the proposed system is simulated and evaluated with different medical database images and retrieval performance of Retrieval Efficiency, Normalized Average Rank of Retrieval, Precision, Recall, SSIM and processing Time. Some previous implementations are examined and compared to our results. The proposed method has produced Retrieval efficiency of 98.4%, NARR of 15.18%, Precision and Recall of 98.4%, F1 score of 98.39%, Correlation of 98.2%, SSIM of 97.92% and all at a speed of 30 s 5% more precision as well as less processing time are achieved compared to prior efforts by our proposed solution.

Application of Artificial Intelligence in Measuring Novel pH-Impedance Metrics for Optimal Diagnosis of GERD

Novel metrics extracted from pH-impedance monitoring can augment the diagnosis of gastroesophageal reflux disease (GERD). Artificial intelligence (AI) is being widely used to improve the diagnostic capabilities of various diseases. In this review, we update the current literature regarding applications of artificial intelligence in measuring novel pH-impedance metrics. AI demonstrates high performance in the measurement of impedance metrics, including numbers of reflux episodes and post-reflux swallow-induced peristaltic wave index and, furthermore, extracts baseline impedance from the entire pH-impedance study. AI is expected to play a reliable role in facilitating measuring novel impedance metrics in patients with GERD in the near future.

Session to Explore the Progress of Precision Psychiatry

Back to table of contents Previous article Next article Annual MeetingFull AccessSession to Explore the Progress of Precision PsychiatryNick ZagorskiNick ZagorskiSearch for more papers by this authorPublished Online:26 Feb 2023https://doi.org/10.1176/appi.pn.2023.03.3.33AbstractHow close is the psychiatric field to matching the right patients with the right treatment? Listen and find out. With innovation being one of the core themes of this year’s APA Annual Meeting, it is fitting that there will be a session focusing on a topic expected to play a major role in future psychiatric care: precision psychiatry.Taking concepts from the broader field of precision medicine, precision psychiatry aims for more individualization in patient care from the moment of diagnosis through treatment. “In psychiatry, we don’t classify disorders using biological systems the way a field like oncology does,” said session chair Daniel R. Karlin, M.D., the chief medical officer at MindMed, a company developing psychedelic and non-psychedelic psychiatric medicines and digital companion devices. Even in other fields like cardiology, physicians can rely on measurable physiological metrics (for example, stroke volume or blood pressure), whereas psychiatrists make a diagnosis based on phenomenology. “It’s how patients tell us they feel, and how we think they feel based on clinical observations,” he said. The result are disorders like depression that are highly likely to be biologically heterogenous.“What precision psychiatry asks is, Can we look at the information which we already have—such as a patient’s medical record and the patient’s behavioral phenomenology—with a finer grain so that we can make more precise diagnoses, ultimately working toward meaningfully incorporating biological information such as genomics?” Karlin said.This session will discuss the evolution of precision psychiatry, which was conceptualized as a field just recently but has a long tradition, Karlin explained. “If you think back to the early years of psychiatry and psychoanalysis, we had a rich system where doctors focused on what patients said and how they acted to develop robust diagnostic formulations and offer what could be called precision treatment in today’s vernacular—highly individualized psychotherapeutic interventions,” he said. Today, busy psychiatrists have to be more efficient with their time, but this is where machine learning programs and other digital tools can be helpful. Karlin and his co-presenters will highlight how technology is being used to collect more detailed patient data and discuss how soon some of these tools will be ready for routine use.“Part of the conversation will involve reigning in the hype,” Karlin said. “We would all love a future where a simple blood test could answer all of our questions, but if not developed with careful attention to clinical meaningfulness, today’s exciting advances in neuroscience or technology ultimately may not improve patient care.” But progress is being made, Karlin noted. The ability to monitor patients in real time with the help of mobile devices and acquire quantitative data on activity, sleep, and other characteristics could be transformative. “Psychiatry entails much observation, and our ability to observe improves daily.” ■ ISSUES NewArchived

Abstract P392: Association Between Blood Pressure Variability and Subclinical Measures of Cardiovascular Disease in Desk Workers

Introduction: Previous research has demonstrated that variability in resting blood pressure (BP) across arms, measurements, or days may be associated with an increased risk for cardiovascular disease (CVD) and mortality. Yet, the extent to which BP variability increases cardiovascular risk and the mechanisms explaining this association are unclear. This study evaluated associations between BP variability with subclinical pathways of CVD development (arterial stiffness (pulse wave velocity [PWV]) and cardiac autonomic function (heart rate variability [HRV]) in desk workers. Hypothesis: Higher BP variability will be associated with higher PWV and lower (worse) HRV Methods: This secondary analysis of baseline data from the Effect of Reducing Sedentary on Blood Pressure randomized controlled trial included 265 sedentary and inactive desk workers (mean age 44.9±11.6 years; 82.0% white; 57.9% female) with elevated-to-stage 2 hypertension and who were not using antihypertensive medication. Sequential BP measurements determined the arm with the higher systolic BP by using an oscillometric device (Omron HEM-907XL). BP was then measured at least twice from the arm with the higher systolic BP at the first (visit-1) and second (visit-2) visits, occurring ≤30 days apart. Systolic BP variability metrics included inter-arm (first measurements between arms at visit-1), intra-arm (first two measurements on the arm with higher systolic BP at visit-1), and inter-visit (average BP from repeated measurements on the same arm across visits-1 and -2). Subclinical measures of CVD included resting carotid- femoral(cfPWV) and carotid-radial(crPWV) PWV (Complier Analyse) and the log transformed standard deviation of normal R-R intervals (SDNN) and the root mean square of the successive differences (RMSSD) HRV measures (Polar Electro). Adjusted linear regression estimated the associations between subclinical measures of CVD and BP variability metrics; Metrics were operationalized into three categories: differences of 0-5 mmHg, 6-10 mmHg, and 11 or more mmHg. Results: The prevalence of having 6 mmHg or more difference for the inter-arm, intra-arm, and inter-visit systolic BP was 43%, 38%, and 53%, respectively. PWV and HRV were not different across categories of inter-arm systolic BP variability or inter-visit systolic BP variability (all p>0.05). However, a same-day intra-arm difference in systolic BP of 6-10 mmHg within visit-1 was associated with higher cfPWV (β=0.42 m/s, (95% CI: 0.07, 0.77) as compared to those with an intra-arm difference of 0-5 mmHg. Conclusion: These data suggest that higher systolic BP variability in the same arm within visit, but not variability across arms or between visits, may be associated with higher aortic stiffness in desk workers with untreated elevated-to-stage 2 hypertension.

Habitual physical activity levels of adults with heart failure: systematic review and meta-analysis

ObjectiveTo conduct a systematic review and meta-analysis to quantify habitual physical activity (PA) levels of patients with heart failure (HF) and assess the quality of reporting of device-assessed PA.MethodsEight electronic...

How many days are needed? Measurement reliability of wearable device data to assess physical activity.

Physical activity studies often utilize wearable devices to measure participants' habitual activity levels by averaging values across several valid observation days. These studies face competing demands-available resources and the burden to study participants must be balanced with the goal to obtain reliable measurements of a person's longer-term average. Information about the number of valid observation days required to reliably measure targeted metrics of habitual activity is required to inform study design. To date, the number of days required to achieve a desired level of aggregate long-term reliability (typically 0.80) has often been estimated by applying the Spearman-Brown Prophecy formula to short-term test-retest reliability data from studies with single, relatively brief observation windows. Our work, in contrast, utilizes a resampling-based approach to quantify the long-term test-retest reliability of aggregate measures of activity in a cohort of 79 participants who were asked to wear a FitBit Flex every day for approximately one year. The conventional approach can produce reliability estimates that substantially overestimate the actual test-retest reliability. Six or more valid days of observation for each participant appear necessary to obtain 0.80 reliability for the average amount of time spent in light physical activity; 8 and 10 valid days are needed for sedentary time and moderate/vigorous activity respectively. Protocols that result in 7-10 valid observation days for each participant may be needed to obtain reliable measurements of key physical activity metrics.

Modelling individual head‐related transfer function (HRTF) based on anthropometric parameters and generic HRTF amplitudes

AbstractThe head‐related transfer function (HRTF) plays a vital role in immersive virtual reality and augmented reality technologies, especially in spatial audio synthesis for binaural reproduction. This article proposes a deep learning method with generic HRTF amplitudes and anthropometric parameters as input features for individual HRTF generation. By designing fully convolutional neural networks, the key anthropometric parameters and the generic HRTF amplitudes were used to predict each individual HRTF amplitude spectrum in the full‐space directions, and the interaural time delay (ITD) was predicted by the transformer module. In the amplitude prediction model, the attention mechanism was adopted to better capture the relationship of HRTF amplitude spectra at two distinctive directions with large angle differences in space. Finally, with the minimum phase model, the predicted amplitude spectrum and ITDs were used to obtain a set of individual head‐related impulse responses. Besides the separate training of the HRTF amplitude and ITD generation models, their joint training was also considered and evaluated. The root‐mean‐square error and the log‐spectral distortion were selected as objective measurement metrics to evaluate the performance. Subjective experiments further showed that the auditory source localisation performance of the proposed method was better than other methods in most cases.

How Does Tobin’s Q Respond to Merger and Acquisition Announcements: Evidence of Listed Indian Firms

Purpose: The aim of this study is to examine the linkage between mergers and acquisition announcements and firm’s valuation taking into consideration Tobin’s q. Theoretical framework: The Q theory of investment propounded by James Tobin (1969) is suitable for the present study. According to this theory, there is a significant correlation between a company's market value and its rate of investment. Following this, indicators of the relative potential profitability of investments are the valuations that are placed in financial markets on the securities of companies as ratios to the costs of replacing their assets, which is denoted by q. Design/methodology/approach: The current investigation examines Tobin's q distribution for 140 listed firms on the Bombay Stock Exchange from 2011 to 2022. Tobin’s q is calculated for the financial service sector of the Indian economy. Findings: The results study suggests that Stock markets react significantly to the valuation of Indian firms due to M&A announcements in the category of non-banking financial companies. More specifically, 40 companies have a high Tobin’s q ratio in the full sample of firms. According to the findings, the majority of M&A announcements had no effect on the Tobin's q of the sampling companies. This indicates that the stocks of companies operating in the financial services sector are not affected by news of mergers and acquisitions. It demonstrates that the Indian capital market responded normally to information on mergers and acquisitions. Research, Practical & Social implications: The study helps financial analysts, top-level management, and stakeholders of a company to correctly evaluate the impact of mergers and acquisitions announcements on Tobin’s q of firms as a performance measurement metric. Originality/value: The value of the study can be evaluated from the fact that it investigates the relationship between mergers and acquisitions announcements and Tobin's q as a crucial parameter for corporate valuation, which is the first of its kind for emerging economies.

Dynamics-based network analysis identifies laci rheostats with high accuracy.

One current challenge in protein engineering involves accurately predicting distal mutations which lead towards a desired function. A more complex problem is identifying and characterizing putative “rheostat” sites which result in progressive, amino acid-dependent biophysical changes upon substitution. The E. coli LacI system contains several rheostat positions with respect to the binding of LacI inducer IPTG, which allosterically inhibits DNA binding. Here we employ in-house computational tools for characterizing network dynamics: Site-specific flexibility via the Dynamic Flexibility Index (DFI), residue pair coupling strength via the Dynamic Coupling Index (DCI), and Vibrational Density of States (VDOS) analysis to generate measures of position-specific dynamic-based metrics. Comparison of bound and unbound LacI shows specific regions of each system exhibit fundamentally different flexibilities where critical DNA binding domain positions show increased flexibility when bound to IPTG. Further, we assess asymmetry in dynamic coupling as a cause-effect measure between ligands/ligand binding positions and all other positions within the protein. Additionally, it is possible to calculate positional vibrational entropy through VDOS; this analysis, again, showed distinctions between bound and unbound simulations in both the DNA binding domain as well as the IPTG binding region. Taken together, the drastic differences of these dynamics-based metrics between bound and unbound conformations indicate their strength in construction unique dynamical fingerprints for both systems. Using a machine learning approach, we construct features based off of these dynamic properties from which we extract probabilities of a given position being “neutral”, “toggle” (i.e., abolishing biophysical activity upon mutation) or “rheostat” based upon experimental IPTG EC50 data of IPTG binding. Using a small subset of dynamic network features, we show it is possible to categorize, with accuracy rates of ∼0.9, a given position's probability of falling into one of the three above categories.

Improvement in Hypertension Control Among Adults Seen in Federally Qualified Health Center Clinics in the Stroke Belt: Implementing a Program with a Dashboard and Process Metrics.

Attain 75% hypertension (HTN) control and improve racial equity in control with the American Medical Association Measure accurately, Act rapidly, Partner with patients blood pressure (AMA MAP BP™) quality improvement program, including a monthly dashboard and practice facilitation. Eight federally qualified health center clinics from the HopeHealth network in South Carolina participated. Clinic staff received monthly practice facilitation guided by a dashboard with process metrics (measure [repeat BP when initial systolic ≥140 or diastolic ≥90 mmHg; Act [number antihypertensive medication classes prescribed at standard dose or greater to adults with uncontrolled BP]; Partner [follow-up within 30 days of uncontrolled BP; systolic BP fall after medication added]) and outcome metric (BP <140/<90). Electronic health record data were obtained on adults ≥18 years at baseline and monthly during MAP BP. Patients with diagnosed HTN, ≥1 encounter at baseline, and ≥2 encounters during 6 months of MAP BP were included in this evaluation. Among 45,498 adults with encounters during the 1-year baseline, 20,963 (46.1%) had diagnosed HTN; 12,370 (59%) met the inclusion criteria (67% black, 29% white; mean (standard deviation) age 59.5 (12.8) years; 16.3% uninsured. HTN control improved (63.6% vs. 75.1%, p<0.0001), reflecting positive changes in Measure, Act, and Partner metrics (all p<0.001), although control remained lower in non-Hispanic black than in non-Hispanic white adults (73.8% vs. 78.4%, p<0.001). With MAP BP, the HTN control goal was attained among adults eligible for analysis. Ongoing efforts aim to improve program access and racial equity in control.

ESG Maturity: A Software Framework for the Challenges of ESG Data in Investment

Given the rising demand for more transparent, consistent, and comprehensive non-financial information in investment, there is a need to provide more reliable, meaningful, and measurable ESG metrics, in a way that most frameworks cannot. Most established frameworks face difficulties and challenges in providing sustainability information to investors in a significant way, lacking in areas such as transparency, reliability, consistency, materiality, and particularly, their focus on the “S” dimension of ESG. The present article purposes to review the challenges associated with several frameworks and to present a solution to overcome them, by giving an overview of a new and innovative software as a service framework, ESG Maturity. This software presents itself as a solution for both reporting companies and their respective investors, by providing both with an assessment of the ESG maturity index of the companies, and delivering a report containing relevant initiatives, strategies, and action plans tailored to each one of them, within different sectors, dimensions, and geographic areas and consequently, their financial and non-financial implementation impact. ESG Maturity is considered a possible answer to the challenges in ESG reporting, having the potential to revolutionize the way companies report their non-financial information and how investors receive it.

An Effective Fuzzy Clustering of Crime Reports Embedded by a Universal Sentence Encoder Model

Crime reports clustering is crucial for identifying and preventing criminal activities that frequently happened in society. In the proposed work, named entities in a report are recognized to extract the crime-related phrases and subsequently, the phrases are preprocessed by applying stopword removal and lemmatization operations. Next, the module of the universal encoder model, called the transformer, is applied to extract phrases of the report to get a sentence embedding for each associated sentence, aggregation of which finally provides the vector representation of that report. An innovative and efficient graph-based clustering algorithm consisting of splitting and merging operations has been proposed to get the cluster of crime reports. The proposed clustering algorithm generates overlapping clusters, which indicates the existence of reports of multiple crime types. The fuzzy theory has been used to provide a score to the report for expressing its membership into different clusters, and accordingly, the reports are labelled by multiple categories. The efficiency of the proposed method has been assessed by taking into account different datasets and comparing them with other state-of-the-art approaches with the help of various performance measure metrics.

Day-night patterns in heart rate variability and complexity: differences with age and cardiopulmonary disease.

Heart rate variability (HRV) measures provide valuable insights into physiology; however, gaps remain in understanding circadian patterns in heart rate dynamics. We aimed to explore day-night differences in heart rate dynamics in patients with chronic cardiopulmonary disease compared with healthy controls. Using 24-hour heart rate data from patients with chronic obstructive pulmonary disease (COPD) and/or heart failure (n = 16) and healthy adult controls (older group: ≥50 years, n = 42; younger group: 20-49 years, n = 136), we compared day-night differences in conventional time and frequency domain HRV indices and a multiscale-entropy-based complexity index (CI1-20) of HRV among the 3 groups. Twenty-four-hour HRV showed significant day-night differences (marked with "△") among younger healthy (mean age: 34.5 years), older healthy (mean age: 61.6 years), and cardiopulmonary patients (mean age: 68.4 years), including change in percentage of adjacent intervals that differ > 50 ms (△pNN50), high frequency (△HF), normalized low frequency (△nLF), ratio (△LF/HF), and △CI1-20. Among these, △LF/HF (2.13 ± 2.35 vs 1.1 ± 2.47 vs -0.35 ± 1.25; P < .001) and △CI1-20 (0.15 ± 0.24 vs 0.02 ± 0.28 vs -0.21 ± 0.27; P < .001) were significant in each pairwise comparison following analysis of variance tests. Average CI1-20 was highest in younger healthy individuals and lowest in cardiopulmonary patients (1.37 ± 0.12 vs 1.01 ± 0.27; P < .001). Younger healthy patients showed a heart rate complexity dipping pattern (night < day), older healthy patients showed nondipping, and cardiopulmonary patients showed reverse dipping (night > day). As measures of 24-hour variability, traditional and complexity-based metrics of HRV exhibit large day-night differences in healthy individuals; these differences are blunted, or even reversed, in individuals with cardiopulmonary pathology. Measures of diurnal dynamics may be useful indices of reduced adaptive capacity in patients with cardiopulmonary conditions. Ma Y, Chang M-C, Litrownik D, Wayne PM, Yeh GY. Day-night patterns in heart rate variability and complexity: differences with age and cardiopulmonary disease. J Clin Sleep Med. 2023;19(5):873-882.

Waste-minimized continuous flow copper-catalyzed azide-alkyne cycloaddition with low metal contamination

Metal contamination is a waste-generating and serious issue in the synthesis of chemicals, in particular in the case of products with biological activity. The appropriate selection of operating conditions plays a crucial role in the abatement of metal leaching in solution and associated wastes. Herein we report a waste-minimized continuous flow process for the synthesis of 1,4-disubstituted β-keto 1,2,3-triazoles exploiting the use of a copper tube flow reactor (CTFR). The selection of the proper azeotropic mixture allowed an almost quantitative recovery of the reaction medium greatly influencing the E-factor of the protocol. A thorough understanding of the main parameters affecting the waste generation was given by calculation of the E-factor distribution for different work-up tested under batch and flow conditions. Furthermore, the measurement of different green metrics (AE: Atom Economy, SF: Stoichiometric Factor, RME: Reaction Mass Efficiency, and MRP: Mass Recover Parameter) clearly demonstrated the benefits of the flow scale-up that allowed to perform a low environmental footprint CuAAC reaction.

Development of DVS evaluation methods from user perspective

We report measurement methods and metrics for the evaluation of dynamic vision sensor (DVS) pixels. In particular, we developed automated test environments and test metrics which can quantify the sensitivity, latency and background noise of DVS pixels. For sensitivity measurements, response probabilities of pixels were analyzed at various conditions, such as base light intensity and region of interests of a sensor. Pixel latency was measured by varying the duty of light pulse, and noise level were also characterized at different light intensities. We expect the developed methods and metrics can help to clarify the performance of DVS pixels at the user point of view.

COVID-19 KINETICS BASED ON REPORTED DAILY INCIDENCE IN HIGHLY DEVASTATED GEOGRAPHICAL REGION: A UNIQUE ANALYSIS APPROACH OF EPIDEMIC

The recent outbreak of COVID-19 has impacted the whole globe in various facets of life. Most challenges stem from the public health effect with morbidities and mortalities extensively and comprehensively recorded and reported. The present case provides a unique analysis of the database of the observed cases and deaths of the corona virus pandemic. The focus was on one of the most devastating geographical locations in the world. The dataset of the USA was extracted and arranged chronologically over 106 weeks. The kinetics of mortality and morbidity were analyzed using three-dimensional graphs and control charts. Modeling of the cumulative dataset after logarithmic transformation was executed and the model was analyzed for fitting in terms of regression and error. A strong correlation existed between the number of reported cases and deaths over the study period with noticeable signs of six overlapping waves. Logarithmic transformation (to the base ten) showed improvement in data fitting with an appreciable reduction in standard error and residual parameters. The best-fitted model was an exponential association that could be used to evaluate the severity and frequency of the epidemic in terms of cumulative daily cases and deaths. The provided examination highlights the importance of the new statistical techniques for public health assessment and confrontation of the outbreaks by practitioners and professionals for community protection by providing measurable metrics for the evaluation of the health effect of the disease in the community. Peer Review History: Received: 1 November 2022; Revised: 7 December; Accepted: 4 January 2023, Available online: 15 January 2023 Academic Editor: Dr. Jennifer Audu-Peter, University of Jos, Nigeria, drambia44@gmail.com Received file: Reviewer's Comments: Average Peer review marks at initial stage: 6.0/10 Average Peer review marks at publication stage: 7.5/10 Reviewers: Dr. Bilge Ahsen KARA, Ankara Gazi Mustafa Kemal Hospital, Turkey, ahsndkyc@gmail.com Dr. George Zhu, Tehran University of Medical Sciences, Tehran, Iran, sansan4240732@163.com Similar Articles: COVID-19 PANDEMIC AWARENESS AND ITS IMPACT ON BANGLADESHI PEOPLE: A COMMUNITY BASED SURVEY KNOWLEDGE, CHARACTERISTICS, WORK SITES OF HEALTH PRACTITIONERS FOR CHEST PHYSIOTHERAPY IN PATIENTS WITH COVID-19, IN KHARTOUM, SUDAN COVID-19 IN MEXICO: PREPARING FOR FUTURE PANDEMICS