Sophisticated Measures Research Articles

A truncated mean-parameterized Conway-Maxwell-Poisson model for the analysis of Test match bowlers

A truncated, mean-parameterized Conway-Maxwell-Poisson model is developed to handle under- and overdispersed count data owing to individual heterogeneity. The truncated nature of the data allows for a more direct implementation of the model than is utilized in previous work without too much computational burden. The model is applied to a large dataset of Test match cricket bowlers, where the data are in the form of small counts and range in time from 1877 to the modern day, leading to the inclusion of temporal effects to account for fundamental changes to the sport and society. Rankings of sportsmen and women based on a statistical model are often handicapped by the popularity of inappropriate traditional metrics, which are found to be flawed measures in this instance. Inferences are made using a Bayesian approach by deploying a Markov Chain Monte Carlo algorithm to obtain parameter estimates and to extract the innate ability of individual players. The model offers a good fit and indicates that there is merit in a more sophisticated measure for ranking and assessing Test match bowlers.

Multivariate longitudinal analysis for the association between brain atrophy and cognitive impairment in prodromal Huntington's disease subjects.

Cognitive impairment has been widely accepted as a disease progression measure prior to the onset of Huntington's disease. We propose a sophisticated measurement error correction method that can handle potentially correlated measurement errors in longitudinally collected exposures and multiple outcomes. The asymptotic theory for the proposed method is developed. A simulation study is conducted to demonstrate the satisfactory performance of the proposed two-stage fitting method and shows that the independent working correlation structure outperforms other alternatives. We conduct a comprehensive longitudinal analysis to assess how brain striatal atrophy affects impairment in various cognitive domains for Huntington's disease.

Enhancing Security in Cloud Computing with Anomaly Detection Using Machine Learning

Cloud computing has become an integral part of modern business operations, offering unprecedented scalability, cost-effectiveness, and agility. However, the widespread adoption of cloud services has also raised significant security concerns. This paper addresses the imperative need for enhancing security in cloud computing environments through the application of anomaly detection techniques powered by machine learning. The ubiquity of cloud computing has ushered in a new era of digital transformation, enabling organizations to streamline operations and achieve unprecedented efficiency. Nevertheless, the dynamic nature of the cloud, coupled with the evolving threat landscape, has exposed organizations to a spectrum of security challenges. These challenges encompass data breaches, insider threats, and vulnerabilities inherent to the shared responsibility model, which necessitates a collaborative approach between cloud service providers (CSPs) and customers. Anomaly detection, a key facet of cloud security, offers a proactive and adaptive defense mechanism against a wide range of security threats. At its core, anomaly detection relies on the establishment of a baseline of normal system behavior. This baseline is constructed by analyzing historical data patterns, allowing machine learning algorithms to distinguish deviations from the expected norm. Such deviations, often indicative of security incidents or vulnerabilities, trigger alerts for timely remediation. This paper delves into the principles of anomaly detection in cloud computing environments. It discusses the shared responsibility model, the evolving threat landscape, and the need for sophisticated security measures beyond traditional tools. Key anomaly detection principles, such as baseline establishment and machine learning model selection, are elucidated. The paper explores various machine learning algorithms suitable for anomaly detection, including k-means clustering, Support Vector Machines (SVMs), and autoencoders, highlighting their unique strengths and applications in cloud security. Enhancing security in cloud computing through anomaly detection powered by machine learning is essential in safeguarding valuable data and maintaining the integrity of cloud environments. By understanding the intricacies of cloud security challenges, embracing anomaly detection principles, and implementing appropriate machine learning algorithms, organizations can proactively protect their cloud assets and fortify their defenses against emerging threats. This paper serves as a comprehensive guide for organizations striving to secure their presence in the cloud while harnessing its transformative potential.

Assessing the Readability of Online Patient Education Materials in Obstetrics and Gynecology Using Traditional Measures: Comparative Analysis and Limitations.

Patient education materials (PEMs) can be vital sources of information for the general population. However, despite American Medical Association (AMA) and National Institutes of Health (NIH) recommendations to make PEMs easier to read for patients with low health literacy, they often do not adhere to these recommendations. The readability of online PEMs in the obstetrics and gynecology (OB/GYN) field, in particular, has not been thoroughly investigated. The study sampled online OB/GYN PEMs and aimed to examine (1) agreeability across traditional readability measures (TRMs), (2) adherence of online PEMs to AMA and NIH recommendations, and (3) whether the readability level of online PEMs varied by web-based source and medical topic. This study is not a scoping review, rather, it focused on scoring the readability of OB/GYN PEMs using the traditional measures to add empirical evidence to the literature. A total of 1576 online OB/GYN PEMs were collected via 3 major search engines. In total 93 were excluded due to shorter content (less than 100 words), yielding 1483 PEMs for analysis. Each PEM was scored by 4 TRMs, including Flesch-Kincaid grade level, Gunning fog index, Simple Measure of Gobbledygook, and the Dale-Chall. The PEMs were categorized based on publication source and medical topic by 2 research team members. The readability scores of the categories were compared statistically. Results indicated that the 4 TRMs did not agree with each other, leading to the use of an averaged readability (composite) score for comparison. The composite scores across all online PEMs were not normally distributed and had a median at the 11th grade. Governmental PEMs were the easiest to read amongst source categorizations and PEMs about menstruation were the most difficult to read. However, the differences in the readability scores among the sources and the topics were small. This study found that online OB/GYN PEMs did not meet the AMA and NIH readability recommendations and would be difficult to read and comprehend for patients with low health literacy. Both findings connected well to the literature. This study highlights the need to improve the readability of OB/GYN PEMs to help patients make informed decisions. Research has been done to create more sophisticated readability measures for medical and health documents. Once validated, these tools need to be used by web-based content creators of health education materials.

Characteristics of short-term acute care hospitals that experienced a ransomware attack from 2016 to 2021.

As cybercrime increasingly targets the health care sector, hospitals face the growing threat of ransomware attacks. Ransomware is a type of malicious software that prevents users from accessing their electronic systems-demanding payment to restore access. In response, momentum is gathering to enact policy that will help hospitals strengthen their cybersecurity defenses. However, to design effective policy, it is crucial to understand the characteristics of hospitals associated with the risk of ransomware attack. In this paper, we compare the characteristics of ransomware-attacked and non-attacked short-term acute care hospitals in the United States. Using data from the American Hospital Association's Annual Survey and the Healthcare Cost Report Information System, we found that ransomware-attacked hospitals were larger, had higher net operating revenue, were more likely to be financially profitable, and more likely to provide trauma, emergency, and obstetric care than non-attacked hospitals. Measures of information technology sophistication did not vary between ransomware-attacked and non-attacked hospitals. These results can be used to tailor policy interventions in order to most effectively respond to and prevent cybercrime in health care.

Recent trends in agri-food Made in Italy exports

The agri-food sector is a key component of the Italian economy and the Made in Italy products are altogether the core of it with a strong export propensity. This paper analyses the recent export performance of the Made in Italy as a whole and at product level. The analysis is based on the conjoint measure of export specialization (RCA) and sophistication (Prody). Results show that the Made in Italy aggregate is performing well but, at the same time, which single products face significant difficulties. On the one hand, this is the case for those items for which Italy is improving its specialization, but international competition is increasingly based on price, thus reducing profitability (e. g. prepared vegetables, chocolate products, blue cheeses). On the other hand, there are products whose sophistication is growing, and remuneration perspectives rise with it, but for which Italy is eroding its comparative advantage (e. g. ice creams, pasta, grape and wine > 2lt).

Corpus-based comparison of lexical complexity in L1 and L2 postgraduate academic writing

This study adopted a corpus-based, contrastive approach to lexical complexity in the academic writing of first language (L1) and second language (L2) postgraduates. Lexical complexity scores were extracted using the Lexical Complexity Analyzer from the Corpus of Arab Proficient Users of English (CAPUE), consisting of Saudi academics’ dissertations in applied linguistics. To investigate the potential differences between this corpus and native speakers’ corpus, the lexical complexity of writing material from the CAPUE and Corpus of English Native Speakers (CENS) were compared. The computational system employed 25 lexical complexity measures to investigate differences in the two groups’ lexical density, sophistication, and variation. The results revealed similar lexical density in the writing of both groups; however, the texts by L1 researchers were more lexically complex for most measures of sophistication and variation. The results have implications for teaching English for academic purposes and highlights areas with inappropriate lexical choices. These findings call for the design of pedagogical interventions to enhance the lexical complexity development of L2 postgraduates.

Breaking the dilemma of low selectivity in the oxidative coupling of methane over Mn-Na2WO4/SiO2 at low temperatures

Oxidative coupling of methane allows to directly produce higher hydrocarbons (C2-hydrocarbons, mainly C2H6 and C2H4) from cheap and available methane. Although various catalysts have been developed, they operate selectively above 750 °C that is detrimental in terms of reactor construction materials and safe operation. The present study demonstrates that the supported Mn-Na2WO4/SiO2 catalyst achieves the selectivity to C2+-hydrocarbons of 87.3% at methane conversion of 6.6% at 675 °C only. With the aid of sophisticated kinetic measurements, the origins of this unexpectedly high performance have been elucidated. When operating with water-containing feeds, the reaction order of the rate of C2H6 formation with respect to O2 at 675 °C is practically zero in the presence of water, while the corresponding values for CO and CO2 are 0.9 and 0.7. Moreover, O2 partial pressure is also decisive for the strength of the enhancing water effect. The water-induced increase in the rate of CH4 conversion into C2-hydrocarbons can be about 120 times using a feed with the ratio of CH4/O2 of 24. The corresponding enhancement factors for the formation of CO and CO2 are only 20 and 6, respectively. These new fundamentals provide hints for optimizing reactor operation for efficient conversion of methane into C2+-hydrocarbons at low temperatures.

Modeling of secondary organic aerosols (SOA) based on two commonly used air quality models in China: Consistent S/IVOCs contribution but large differences in SOA aging

Secondary organic aerosol (SOA) is an important component of atmospheric fine particulate matter (PM2.5), with contributions from anthropogenic and biogenic volatile organic compounds (AVOC and BVOC) and semi- (SVOC) and intermediate volatility organic compounds (IVOC). Policymakers need to know which SOA precursors are important but accurate simulation of SOA magnitude and contributions remain uncertain. Findings from existing SOA modeling studies have many inconsistencies due to differing emission inventory methodologies/assumptions, air quality model (AQM) algorithms, and other aspects of study methodologies. To address some of the inconsistencies, we investigated the role of different AQM SOA algorithms by applying two commonly used models, CAMx and CMAQ, with consistent emission inventories to simulate SOA concentrations and contributions for July and November 2018 in China. Both models have a volatility basis set (VBS) SOA algorithm but with different parameters and treatments of SOA photochemical aging. SOA generated from BVOC (i.e., BSOA) is found to be more important in southern China. In contrast, SOA generated from anthropogenic precursors is more prevalent in the North China Plain (NCP), Yangtze River Delta (YRD), Sichuan Basin and Central China. Both models indicate negligible SOA formation from SVOC emissions compared to other precursors. In July, when BVOC emissions are abundant, SOA is predominantly contributed by BSOA (except for NCP), followed by IVOC-SOA (i.e., SOA produced from IVOC) and ASOA (i.e., SOA produced from anthropogenic VOC). In contrast, in November, IVOC became the leading SOA contributor for all selected regions except PRD, illustrating the important contribution of IVOC emissions to SOA formation.While both models generally agree in terms of the spatial distributions and seasonal variations of different SOA components, CMAQ tends to predict higher BSOA, while CAMx generates higher ASOA concentrations. As a result, CMAQ results suggest that BSOA concentration is always higher than ASOA in November, while CAMx emphasizes the importance of ASOA. Utilizing a conceptual model, we found that different treatment of SOA aging between the two models is a major cause of differences in simulated ASOA concentrations. The step-wise SOA aging scheme implemented in the CAMx VBS (based on gas-phase reactions with OH radical and similar to other models) exhibits a strong enhancement effect on simulated ASOA concentrations, and this effect increases with the ambient organic aerosol (OA) concentrations. The CMAQ aerosol module implements a different SOA aging scheme that represents particle-phase oligomerization and has smaller impacts on total OA. Different structures and/or parameters of the SOA aging schemes are being used in current models, which could greatly affect model simulations of OA in ways that are difficult to anticipate. Our results indicate that future control policies should aim at reducing IVOC emissions as well as traditional VOC emissions. In addition, aging schemes are the major driver in CMAQ vs. CAMx treatments of ASOA and their resulting predicted mass. More sophisticated measurement data (e.g., with resolved OA components) and/or chamber experiments (e.g., investigating how aging influences SOA yields) are needed to better characterize SOA aging and constrain model parameterizations.

Quantifying bark-stripping damages to address the relationship between external and internal wood defects

The detachment of tree bark (i.e. bark-stripping) by wild ungulate species can be observed in many European forests and it can lead to significant damages to the wood. In our study, bark-stripping of different intensity and age was recorded for 110 European beech (Fagus sylvatica L.) trees growing in the Harz Mountains, Germany. Our goal was to compare the performance of a conventional and a structure-from-motion approach to quantify the external damage and to evaluate whether bark-stripping damages on the outside may accurately reflect internal wood defects. Also, we wanted to know whether potential relationships between inner and outer damages are affected by the condition of the wound. Surprisingly, despite large difference between the two approaches (mean absolute error: 46%) the wood surface on the sawn boards (internal damage) could not be predicted more accurately using structure-from-motion, for the assessment of the external damage when compared to the conventional measurements based on damage height and damage width (rhombus formula). The internally damaged area was smaller than the externally damaged area across all wound categories. In contrast though, and most notably, the length of the stem section negatively affected by the damage was always greater inside than outside. We conclude that sophisticated measurements of the outside wound area do not allow for a better estimation of the internal wound area. Foresters may continue to rely on simple measures of damage extension on the outside of the tree to estimate internal damages. They should however consider the wound status as an additional information for a better prediction of internal damages.

Strategy To Increase Technological Sophistication To Increase Helmet Productivity With Technometric Methods And 5W1H

Strategy by utilizing technological advances is one way in the process of continuous improvement. UMKM Helmet for Children’s Sidoarjo is an industry engaged in the production of helmets are one of the main equipment that is safe for motorcycle riders. The helmet product produced by the Sidoarjo Children’s Helmet UMKM has been markeed to several cities. The success of the actor’s or personal performance is strongly influenced by aspects of technological sophistication to support businesses in obtaining a accurate data. One strategy that can be used to win the market is to take advantage of technological advances to make continuous improvements. In the production process that has increased, oftentimes market demand has decreased by 40% which usually can deliver 100 addresses every day, down to 60 addresses every day. So it is necessary to conduct a technology assessment in the helmet production process to determine the value of each technology component index. The technometric approach will aassess the level of sophistication of each technology component, namely technoware, humanware, infoware, and orgaware then formulate aproposwd strategy using 5w1h analysis. The results of this study produce a value for the level of sophistication of the technological component, namely technoware is worth 0,645, humanware is worth 0,796, infoware is worth 0,696, orgaware is worth 0,851 and a TCC value is 0,715. From the results of the collection and processing, results are obtained that will become suggestions for improvement and can become the concern and evaluation of UMKM in increasing productivity.
 Highlights :
 
 Importance of Technological Assessment: Evaluating technological components (technoware, humanware, infoware, orgaware) is crucial for understanding strengths and weaknesses.
 Utilizing Technometric Approach: Technometric analysis provides a quantifiable measure of technological sophistication, aiding in strategy formulation.
 Emphasis on Productivity Enhancement: Recommendations from the assessment facilitate targeted improvements, enhancing UMKM productivity and market competitiveness.
 
 Keywords - Technological Sophistication, Technological Components, Technometrics, 5W1H.

Early Experience and Inflammation

In this featured symposium, three researchers explore how adverse exposures during childhood and adolescence relate to in vivo and in vitro measures of inflammatory functioning and cardiovascular health, as well as individual and social factors that help protect youth from these adverse impacts. In the first presentation, Greg Miller presents data on how direct and vicarious violence exposure relate to inflammatory responses to microbial challenge, and the role of the central executive network of the brain in modulating these associations. Hannah Schreier presents data on how family-level factors modify the associations between adversity and youth inflammation, the associations between child maltreatment and inflammation, and on a psychosocial intervention designed to alter CVD risk in youth. In the third presentation, Jonathan Turner examines associations between early life adversity and multiple aspects of inflammatory and immune-system functioning, revealing the role of immunometabolic pathways in immunosenescence. Combined, these talks demonstrate the importance of linking sophisticated measures of early adverse exposures with detailed mechanistic measures of neural, inflammatory, immune system and metabolic functioning to better reveal the biological pathways linking ELA with short and long-term health outcomes.

Lexical complexity changes in 100 years’ academic writing: Evidence from Nature Biology Letters

Previous diachronic studies of lexical changes in academic writing have focused on the evolution of specific sets or classes of words with specific grammatical or discourse functions, with no scholarly attention paid to diachronic changes in the overall lexical complexity of academic writing, a construct that has been shown to be closely related to text readability and writing quality. This study explores diachronic lexical complexity changes in academic writing based on a self-built corpus, the Corpus of Nature Biology Letters, comprised of 421 scientific letters sampled with ten-year intervals for 100 years (1929–2019). The corpus was analyzed for nine measures of lexical density, sophistication, and diversity to ascertain the trend and strength of lexical complexity changes. Our findings show that the patterns of diachronic changes in the lexical complexity of academic writing accord with the overall trends in academic writing towards greater compression and conventionalization. These findings add to our understanding of the evolution of the academic writing practices of the scientific community and may also have implications for academic writing pedagogy.

Out of control? Tracking system technologies and performance measurement

This article explores how sophisticated and intrusive socio-technological surveillance advances, particularly tracking systems, have become normalized in the North American baseball industry. To make sense of the extensive archival and interview data, we draw on a Deleuzo-Guattarian framework. To this end, we argue that baseball has evolved into a society of control, facilitated and maintained by a rhizomatic surveillance network. We find that the introduction of new technologies has disrupted and changed performance measurement and management control practices in three key ways: (i) establishing the prospect of an ‘objective’ information utopia; (ii) transforming performance measurement into something independent of context; and (iii) measuring processes, not outcomes. Next, in the spirit of Deleuze and Guattari, we consider how the surveillance is being resisted. To this end, we find that instead of formal resistance, players voluntarily co-construct the system. We offer explanations for this, but we also turn our gaze on the consequences of the ever-expanding network of surveillance. Specifically, we consider the identity construction challenges for the target; which we refer to as the dark side of surveillance. We discuss the wider implications of our findings and challenge managers, regardless of workplace, to consider the consequences of introducing ever-more sophisticated monitoring and measurement systems, especially for those whom the systems target.

Verb argument construction complexity indices and L2 written production: effects of task complexity and task repetition

ABSTRACT This study examines the extent to which verb argument construction (VAC) based indices of syntactic complexity could predict four different writing task conditions: a simple task with no repetition (S + NR), a simple task with repetition (S + R), a complex task with no repetition (C + NR), and a complex task with repetition (C + R). Ninety-six high-intermediate second language (L2) learners of English performed two argumentative writing tasks and repeated them at one-week intervals, with the order of the tasks counterbalanced. The 384 essays collected at four different time points were analyzed in terms of VAC sophistication measures using the Tool for the Automatic Analysis of Syntactic Sophistication and Complexity (Kyle 2016). Results indicated that S + R as well as C + NR and C + R conditions led to more lower-frequency VAC-verb combined structures used in the essays. Further analyses revealed a significant average association strength between verbs and VACs for the C + R group, showing the superiority of this condition over the others in developing syntactically complex argumentative essays. The implications for L2 writing pedagogy and assessment will be discussed.

Novel instrumentation for high-impedance electromotive force (EMF) measurements

Instrumentation is reported for measuring electromotive force (EMF) from a high-impedance solid electrolyte-based electrochemical cell. Generally, concentration galvanic cells are used to study the high-temperature thermochemical properties of materials. The thermochemical results are determined by precise measurement of the high-impedance EMF and temperature of the electrochemical cell with suitable user interface software for data acquisition. The data are conditioned and processed using the unique design of the hardware and sent to a computer through the serial port. Online graphical displays of the data, averaging, trend viewing, and storage, are performed using a graphical user interface (GUI). The instrumentation was validated by conducting experiments with different materials using an electrochemical cell and its performance was demonstrated to be on par with commercial instruments. This work has enables low-cost sophisticated EMF measurements for determining the thermochemical properties of materials.

Conceptualizing and Measuring Social Media Use in Health and Well-being Studies: Systematic Review.

Despite an increasing number of studies revealing both the benefits and harms of social media use on well-being, there is heterogeneity and a lack of consensus on how social media use is conceptualized, defined, and measured. Additionally, little is known whether existing literature focuses on ill-being or well-being outcomes and whether studies use theories. The main objective of this review was to examine (1) how social media use has been conceptualized and measured, (2) what health and well-being outcomes have been focused on, and (3) whether studies used theories. Studies were located through a comprehensive search strategy involving 4 steps. First, keyword searches were conducted on 6 major databases: PubMed, Web of Science, PsycINFO, Embase, ProQuest, and Annual Reviews. Second, a search was conducted on Google Scholar using the same sets of search terms, and the first 100 results were examined. Third, the reference sections of reviews identified in the first 2 rounds of searches were examined, and finally, the reference lists of the final set of papers included in the review were searched. Through a multistage screening, papers that met our inclusion criteria were analyzed. The review included a total of 233 papers published between 2007 and 2020 in 51 different countries. While 66 (28%) of the studies investigated the effects of the problematic use or addiction of social media on health and well-being, 167 (72%) studied the effects of social media use as a "normal" behavior. Most of the studies used measures assessing the time users spend using social media. Most of the studies that examined the effects of problematic social media use or addiction used addiction scales. Most studies examined the association of social media use with mental illnesses such as depression, anxiety, self-esteem, and loneliness. While there are a considerable number of studies investigating physical health outcomes such as self-rated health, sleep, and sitting time or lack of physical activity, relatively a small number of studies examined social, psychological, and emotional well-being. Most of the studies 183 (79%) did not use any theory. Most studies conceptualized social media use as a "normal" behavior and mostly used time-spent measures, whereas a considerable number of studies conceptualized social media use as an addiction and used various addiction measures. The studies disproportionately focused on investigating the associations of social media use with negative health and well-being outcomes. The findings suggest the need for going beyond time spent to more sophisticated measurement approaches that consider the multiplicity of activities that users perform on social media platforms and the need for more theory-based studies on the association of social media use with not only negative well-being or "ill-being" but also with positive health and well-being outcomes.

Which way of learning benefits your career? The role of different forms of work-related learning for different types of perceived employability

ABSTRACT Building and maintaining employability is important for employees and organizations in a changing world of work. Research considered work-related learning as an antecedent of employability but provided ambiguous findings. We attribute this inconsistency to the multidimensionality of the constructs involved. Drawing on Conservation of Resources Theory, we investigated the links of three different forms of work-related learning (i.e., informal, formal, and self-regulated learning) with both internal (i.e., within the organization) versus external (i.e., in the general labour market) and qualitative (i.e., finding a better job) versus quantitative (i.e., finding another job) perceived employability. Our two-wave study (N = 307 employees) showed that informal learning positively relates to qualitative and quantitative internal employability, but not to any type of external employability. Conversely, self-regulated learning is positively linked to qualitative external employability, but not to quantitative external or any type of internal employability. Formal learning (i.e., training and workshops) surprisingly did not contribute to employability at all. Furthermore, we did not find that learning forms conceptually associated with more resource investment regarding effort would show stronger relations with employability. We discuss learning-related strategies to foster employability and address the missing association between formal learning and employability, calling for more sophisticated measures of formal learning.

Comparison between shadow imaging and in-line holography for measuring droplet size distributions

A direct comparison of the droplet size and number measurements using in-line holography and shadow imaging is presented in three dynamically evolving laboratory scale experiments. The two experimental techniques and image processing algorithms used to measure droplet number and radii are described in detail. Droplet radii as low as r = 14 µm are measured using in-line holography and r = 50 µm using shadow imaging. The droplet radius measurement error is estimated using a calibration target (reticle) and it was found that the holographic technique is able to measure droplet radii more accurately than shadow imaging for droplets with r le 625 µm. Using the measurements of droplet number and size we quantitatively cross-validate and assess the accuracy of the two measurement techniques. The droplet size distributions, N(r), are measured in all three experiments and are found to agree well between the two measurement techniques. In one of the laboratory experiments, simultaneous measurements of droplets (r ge 14 µm, using holography) and dry aerosols (0.07 lessapprox r lessapprox 2 µm, using an scanning mobility particle sizer and 0.15 lessapprox r lessapprox 5 µm using an optical particle sizer) are reported, one of the first such comparison to the best of our knowledge. The total number and volume of droplets is found to agree well between both techniques in the three experiments. We demonstrate that a relatively simple shadow imaging technique can be just as reliable when compared to a more sophisticated holographic measurement technique over their common droplet radius measurement range. The agreement in results is shown to be valid over a large range of droplet concentrations, which include experiments with relatively sparse droplet concentrations as low as 0.02 droplets per image. Advantages and disadvantages for the two techniques are discussed in the context of our results. The main advantages to in-line holography are the greater accuracy in droplet radius measurement, greater spatial resolution, larger depth of field, and the high repetition rate and short pulse duration of the laser light source. In comparison, the main advantages to shadow imaging are the simpler experimental setup, image processing algorithm, and fewer computer resources necessary for image processing. Droplet statistics like number and size are found to be very reliable between the two methods for large range of droplet densities, {mathcal {P}}_{r>50}, ranging from 10^{-4} le {mathcal {P}}_{r>50} le 10^{-1} cm^{-3}, when the two techniques are implemented as shown in this paper.

Sistem Notifikasi Token Listrik Menggunakan Metode Fuzzy Tsukamoto Dengan Sms Gateway Berbasis Arduino

Rapidly developing technological advances have encouraged the emergence of various increasingly sophisticated electrical measurement equipment, including the Kilo Watt Hour meter (KWH). The weakness of this KWH meter system is that the warning system is only in the form of indicator lights and buzzer alarms which are often ignored by users and their range is limited. Users who are outside the home also cannot know the condition of electricity at home. So an electricity token notification system is needed to make it easier for users. This tool is designed by utilizing Arduino Uno, SIM 800L, Potentio. SIM 800L is used to send SMS notifications and Potentio to simulate the number of electricity tokens. In this tool Tsukamoto Fuzzy method is applied to determine the output condition based on the input value. The purpose of designing this tool is to improve the KWH meter warning system so that it can reach users outside the home.