Trends In Data Research Articles

Exploring the Quotation Inertia in International Currency Markets

The authors suggest a methodology that involves conducting a preliminary analysis of inertia in financial time series. Inertia here means the manifestation of some kind of long-term memory. Such effects may take place in complex processes of a stochastic kind. If the decision is negative, they do not recommend using predictive management strategies based on trend analysis. The study uses computational schemes to detect and confirm trends in financial market data. The effectiveness of these schemes is evaluated by analyzing the frequency of trend confirmation over different time intervals and with different levels of trend confirmation. Furthermore, the study highlights the limitations of using smoothed curves for trend analysis due to the lag in the dynamics of the curve, emphasizing the importance of considering real-time data in trend analysis for more accurate predictions.

Global Trends of Educational Data Mining in Online Learning

Educational data mining (EDM) in online learning involves data mining techniques to analyze data from online environments to gain insights into student behavior, performance, and engagement. This study explored EDM in online learning publication trends and focuses. It involved a bibliometric analysis of 615 scholarly works related to EDM in online learning as recorded in Scopus, the largest peer-reviewed citation database, on February 1, 2023. The study examined EDM in online learning publications regarding its evolution and distribution, key focus areas, impact and performance, and prominent authors and collaborations in the last decade, in which the timespan is the period from 2012 to 2022. This bibliometric analysis shows that EDM in online learning is a dynamic area of scientific research as related publications grow steadily throughout the years and involve worldwide collaborations. The study reveals current research trends, offering valuable insights for future researchers to guide their investigations in this field.

Assessing Phase-Change Materials as Effective Long-Term Biosensors in Limb Prosthetics.

Monitoring and controlling the microclimate at the skin-socket interface of limb prostheses is an important, yet unresolved, clinical problem. Phase-change materials (PCMs) represent a promising biosensor technology that holds the potential to both detect and alter (i.e., stabilize) changes in the temperature of a hybrid biological/mechanical system, such as a prosthesis. The biologically inspired sensor capabilities of PCMs can enhance the internal socket conditions and offer improved comfort and suspension while minimizing skin injuries for prosthesis users. This study investigated how prosthetic liners equipped with PCM biosensors affected the long-term outcomes for prosthesis users. In this double-blinded longitudinal crossover study, a cohort of transtibial prosthesis users wore regular conventional liners for six months and PCM liners for another six months. Prosthesis utilization, physical performance, and gait symmetry were studied using Modus StepWatch, the 2-minute walk test, and the TekScan F-Scan gait test, respectively. Measured parameters from these various tests, acquired at multiple timepoints during the study, were compared pairwise between the two liners per individual. While the obtained quantitative data trends, such as the gait symmetry, favored the PCM liners, no statistically significant differences were found between the PCM and conventional gel liners in any of the study parameters.

The State of the Field of Pediatric Multimodality Neuromonitoring.

The use of multimodal neuromonitoring in pediatrics is in its infancy relative to adult neurocritical care. Multimodal neuromonitoring encompasses the amalgamation of information from multiple individual neuromonitoring devices to gain a more comprehensive understanding of the condition of the brain. It allows for adaptation to the changing state of the brain throughout various stages of injury with potential to individualize and optimize therapies. Here we provide an overview of multimodal neuromonitoring in pediatric neurocritical care and its potential application in the future. Multimodal neuromonitoring devices are key to the process of multimodal neuromonitoring, allowing for visualization of data trends over time and ideally improving the ability of clinicians to identify patterns and find meaning in the immense volume of data now encountered in the care of critically ill patients at the bedside. Clinical use in pediatrics requires more study to determine best practices and impact on patient outcomes. Potential uses include guidance for targets of physiological parameters in the setting of acute brain injury, neuroprotection for patients at high risk for brain injury, and neuroprognostication. Implementing multimodal neuromonitoring in pediatric patients involves interprofessional collaboration with the development of a simultaneous comprehensive program to support the use of multimodal neuromonitoring while maintaining the fundamental principles of the delivery of neurocritical care at the bedside. The possible benefits of multimodal neuromonitoring are immense and have great potential to advance the field of pediatric neurocritical care and the health of critically ill children.

Electricity Consumption (kW) Forecast for a Building of Interest Based on a Time Series Nonlinear Regression Model

This paper investigates the relationship between a building's past energy consumption and the outdoor temperature and predicts the next day's energy consumption using a refined time series model. Maintaining optimal indoor temperatures relative to outdoor temperatures determines a building's HVAC demand and, thus, energy consumption. We want to determine how outdoor temperature and other factors determine this consumption. With increasing urbanization and energy demand, it is important to understand building energy consumption, especially in terms of its impact on the environment. Previous research has shown the link between electricity consumption and external environmental factors and highlighted energy optimization's importance in urban structures. As cities become large energy consumers, studies point to the need to understand energy use patterns on a regional and temporal scale.
 For accurate energy forecasts, data becomes the linchpin. Time series—data points arranged in chronological intervals—are foundational in predictive modeling. Due to buildings' intricate electricity consumption patterns, traditional linear forecasting often falls short. Enter nonlinear regression models: These complex models are apt for mapping and predicting nonlinear data trends. Notwithstanding their advantages, they come with challenges, primarily the high-frequency data influx from smart meters and IoT devices. But their potential benefits - from cost savings to efficient energy management - are significant. In a world caught between urban expansion and ecological preservation, efficient energy management is crucial. Accurate energy forecasting, especially for buildings, combines technological advances, statistical acumen and environmental imperatives. Understanding building energy consumption using sophisticated nonlinear regression models is evolving from an academic goal to a global necessity.

Australian vaccine preventable disease epidemiological review series: tetanus 2003-2019.

Background We examined trends in tetanus notification, hospitalisation and death data from 2003-2019 to assess the impact of adult tetanus booster recommendations in Australia. Methods Tetanus notifications and deaths from the National Notifiable Diseases Surveillance System; hospitalisations from the Australian Institute of Health and Welfare National Hospital Morbidity Database; and deaths from the Australian Coordinating Registry were analysed by age group, sex, Aboriginal and Torres Strait Islander status and state/territory. Annual rates were calculated using Australian Bureau of Statistics mid-year estimated resident populations from 2003-2019 as denominators. To assess the impact of a recommended booster dose of reduced antigen content diphtheria-tetanus-acellular pertussis (dTpa) vaccine for adults aged ≥ 65 years, notification, hospitalisation and death rates of tetanus per 100,000 population were compared pre (2003-2012) and post (2013-2019) the recommendation's introduction. Results There were 63 notifications of tetanus from 2003-2019 with an average annual incidence rate of 0.02/100,000. Similar to previous studies, the burden of tetanus in the Australian population continues to disproportionately affect the elderly, with those aged ≥ 65 years encompassing 63% (40/63) of notifications and 100% (11/11) of the deaths observed in this timeframe. Following the introduction of a recommendation for those aged ≥ 65 years to receive a dTpa booster, average annual notification and hospitalisation rates in those aged ≥ 65 years were significantly lower (notifications: 0.11/100,000 in 2003-2012 and 0.05/100,000 in 2013-2019, p = 0.01; hospitalisations: 0.24/100,000 in 2003-2012 and 0.10/100,000 in 2013-2019, p = 0.01]). The average annual death rate was similar in the two periods (0.002/100,000), although based on small numbers. Conclusions The findings of this analysis suggest a positive impact from the 2013 recommendation. However, the burden is still disproportionately higher in those aged ≥ 65 years and strategies to improve vaccination coverage in older Australians are recommended.

Evaluation of slake durability for carbonaceous rocks exposed to water soaking using electrical resistivity

The present study investigated the electrical conduction and slake durability behaviors of carbonaceous rocks subjected to water soaking. Several series of electrical resistivity and slake durability tests were conducted on carbonaceous rocks with different mineralogical characteristics to explore the intrinsic connection between these two property indexes. Based on the experimental data, a new empirical model was developed to estimate slake durability index of carbonaceous rocks at varied soaking times. The results showed that the clay minerals content of investigated carbonaceous rocks ranged from approximately 20 % to 50 %. The clay minerals are beneficial in enhancing the electrical conduction of carbonaceous rocks, but impose a negative impact on the slake durability, attributing to their superior performance of hydrophilicity and swelling. Electrical resistivity decreased sharply with increasing soaking time within the first 8 days and then it achieved a stable state as soaking time further increased, which was mainly due to the completion of pore saturation and salts/minerals dissolution. The slake durability index exhibited similar evolution characteristics to those of electrical resistivity, which deteriorated sharply to a low or very low level after soaking for 16 days. The clay minerals experienced water absorption and mineral expansion during the first 8 days soaking, leading to the development of micro-cracks that facilitates water intrusion and accelerates the slake durability deterioration. These two processes were essentially complete as the soaking time further increased, and the clay minerals played an insignificant role at this stage. Liner increasing trends were observed in the plot of electrical resistivity decrease versus slake durability index decrease, and the slope values presented a power function relation with the clay minerals content of carbonaceous rocks. The proposed empirical model well captured the trends in the experimental data, with an absolute difference in percentage δ below30 % and a root mean square error RMSE value of 5.613 %. The outcomes of this study provided a viable answer to assess slake durability of carbonaceous rocks exposed to adverse external environments using non-destructive testing techniques.

Transfer Learning in the Transformer Model for Thermal Comfort Prediction: A Case of Limited Data

The HVAC (Heating, Ventilation, and Air Conditioning) system is an important component of a building’s energy consumption, and its primary function is to provide a comfortable thermal environment for occupants. Accurate prediction of occupant thermal comfort is essential for improving building energy utilization as well as health and work efficiency. Therefore, the development of accurate thermal comfort prediction models is of great value. Deep learning based on data-driven techniques has excellent potential for predicting thermal comfort due to the development of artificial intelligence. However, the inability to obtain large quantities of detailed thermal comfort labeling data from residents presents a substantial challenge to the modeling endeavor. This paper proposes a building-to-building transfer learning framework to make deep learning models applicable in data-limited interior building environments, thereby resolving the issue and enhancing model predictive performance. The transfer learning method (TL) is applied to a novel technology dubbed the Transformer model, which has demonstrated outstanding performance in data trend prediction. The model exploits the spatiotemporal relationship of data regarding thermal comfort. Experiments are conducted using the source dataset (Scales project dataset and ASHRAE RP-884 dataset) and the target dataset (Medium US office dataset), and the results show that the proposed TL-Transformer achieves 62.6% accuracy, 57% precision, and a 59% F1 score, and the prediction performance is better than other existing methods. The model is useful for predicting indoor thermal comfort in buildings with limited data, and its validity is verified by experimental results.

Mapping the stability of febrile illness hotspots in Punjab from 2012 to 2019- a spatial clustering and regression analysis

IntroductionFebrile illnesses (FI) represent a typical spectrum of diseases in low-resource settings, either in isolation or with other common symptoms. They contribute substantially to morbidity and mortality in India. The primary objective was to study the burden of FI based on Integrated Disease Surveillance Programme (IDSP) data in Punjab, analyze geospatial and temporal trends and patterns, and identify the potential hotspots for effective intervention.MethodsA retrospective ecological study used the district-level IDSP reports between 2012 and 2019. Diseases responsible for FI on a large scale, like Dengue, Chikungunya, Malaria (Plasmodium Falciparum, P. Vivax), Enteric fever, and Pyrexia of Unknown Origin (PUO), were included in the analysis. The digital map of Punjab was obtained from GitHub. Spatial autocorrelation and cluster analysis were done using Moran’s I and Getis-Ord G* to determine hotspots of FI using the incidence and crude disease numbers reported under IDSP. Further, negative binomial regression was used to determine the association between Spatio-temporal and population variables per the census 2011. Stable hotspots were depicted using heat maps generated from district-wise yearly data.ResultsPUO was the highest reported FI. We observed a rising trend in the incidence of Dengue, Chikungunya, and Enteric fever, which depicted occasional spikes during the study period. FI expressed significant inter-district variations and clustering during the start of the study period, with more dispersion in the latter part of the study period. P.Vivax malaria depicted stable hotspots in southern districts of Punjab. In contrast, P. Falciparum malaria, Chikungunya, and PUO expressed no spatial patterns. Enteric Fever incidence was high in central and northeastern districts but depicted no stable spatial patterns. Certain districts were common incidence hotspots for multiple diseases. The number of cases in each district has shown over-dispersion for each disease and has little dependence on population, gender, or residence as per regression analysis.ConclusionsThe study demonstrates that information obtained through IDSP can describe the spatial epidemiology of FI at crude spatial scales and drive concerted efforts against FI by identifying actionable points.

Comparison of Influenza-Like Illness (ILI) incidence data from the novel LeCellPHIA participatory surveillance system with COVID-19 case count data, Lesotho, July 2020 – July 2021

BackgroundWhile laboratory testing for infectious diseases such as COVID-19 is the surveillance gold standard, it is not always feasible, particularly in settings where resources are scarce. In the small country of Lesotho, located in sub-Saharan Africa, COVID-19 testing has been limited, thus surveillance data available to local authorities are limited. The goal of this study was to compare a participatory influenza-like illness (ILI) surveillance system in Lesotho with COVID-19 case count data, and ultimately to determine whether the participatory surveillance system adequately estimates the case count data.MethodsA nationally-representative sample was called on their mobile phones weekly to create an estimate of incidence of ILI between July 2020 and July 2021. Case counts from the website Our World in Data (OWID) were used as the gold standard to which our participatory surveillance data were compared. We calculated Spearman’s and Pearson’s correlation coefficients to compare the weekly incidence of ILI reports to COVID-19 case count data.ResultsOver course of the study period, an ILI symptom was reported 1,085 times via participatory surveillance for an average annual cumulative incidence of 45.7 per 100 people (95% Confidence Interval [CI]: 40.7 – 51.4). The cumulative incidence of reports of ILI symptoms was similar among males (46.5, 95% CI: 39.6 – 54.4) and females (45.1, 95% CI: 39.8 – 51.1). There was a slightly higher annual cumulative incidence of ILI among persons living in peri-urban (49.5, 95% CI: 31.7 – 77.3) and urban settings compared to rural areas. The January peak of the participatory surveillance system ILI estimates correlated significantly with the January peak of the COVID-19 case count data (Spearman’s correlation coefficient = 0.49; P < 0.001) (Pearson’s correlation coefficient = 0.67; P < 0.0001).ConclusionsThe ILI trends captured by the participatory surveillance system in Lesotho mirrored trends of the COVID-19 case count data from Our World in Data. Public health practitioners in geographies that lack the resources to conduct direct surveillance of infectious diseases may be able to use cell phone-based data collection to monitor trends.

Comparison of Techno-Economic Models for Upgrading PV System without Net-Metering Option and Long Term Impact on Energy Bills

This article presents a techno-economic analysis of upgrading a Photovoltaic (PV) power system at large. A comparative study has been carried out considering various technical and economic factors like per unit cost, annual load, energy demand growth, and PV panel efficiency, annual performance degradation of system, cost of electricity, payback period, initial capital expenditure, and rate of return in the form of saving in energy bills. The PV system is proposed to be upgraded from 1 MW to 2 MW at the National University of Sciences &amp; Technology (NUST), Islamabad. The PV system is subject to be upgraded due to the growth rate of annual load and amount saved by PV contribution, which has been forecasted using data trends of the above-mentioned technical and economic factors. While considering these factors, four economic models have been evaluated and compared, which include Self-Built via Contractor with self-financing, Self-Built with bank financing, Power purchase agreement (PPA), also known as rental agreement and Built, Own, Operate &amp; Transfer (BOOT) model. Merits and demerits of these models have been discussed, and the most feasible model has been proposed based on the mentioned key factors and long-term sustainable development goals (SDG) for affordable clean energy to contribute to climate action, i.e., adaptation of renewable energy resources (RER) and reduction of greenhouse gases (GHG).

An Analytical Data-driven Framework For Advancing Tiger Conservation In India

India is home to a significant population of the world’s wild tigers, but it is disheartening to see that they are still classified as 'Endangered' on the IUCN's Red List. The efforts to protect these magnificent creatures are ongoing, with the Indian government working hand in hand with various Conservation organizations. Though we have seen some promising progress with a notable increase in tiger populations, it is not all smooth sailing. Challenges persist, including the loss of their natural habitats, the ever-present threat of poaching, and unfortunate conflicts between humans and these incredible animals. This paper dives deep into the challenging mission of conserving tigers in India, highlighting the importance of forward-thinking strategies and using data-driven methods. We take a closer look at how data analytics can play a crucial role in conservation, from collecting and managing data to analysing it and making informed decisions. Our main goal is to offer practical recommendations on how to harness the power of data analytics to boost tiger conservation efforts in India, drawing on the most recent data trends and discoveries.

Canadian Surveillance Metrics and History of the COVID-19 Pandemic: Update of Epidemiological Assessment.

This study provides an update to the status of the COVID-19 pandemic in Canada building upon our initial analysis conducted in 2020 by incorporating an additional two years of data. First, we aim to summarize the status of the pandemic in Canada when the World Health Organization (WHO) declared the end of the public health emergency for the COVID-19 pandemic on May 5, 2023. Second, we use dynamic and genomic surveillance methods to describe the history of the pandemic in Canada and situate the window of the WHO declaration within the broader history. Third, we aim to provide historical context for the course of the pandemic in Canada. This longitudinal study analyzed trends in traditional surveillance data and dynamic panel estimates for COVID-19 transmissions and deaths in Canada from June 2020 to May 2023. We also used sequenced SARS-CoV-2 variants from the Global Initiative on Sharing All Influenza Data (GISAID) to identify the appearance and duration of variants of concern. For these sequences, we used Nextclade nomenclature to collect clade designations and Pangolin nomenclature for lineage designations of SARS-CoV-2. We used t-tests of dynamic panel regression coefficients to measure the persistence of COVID-19 transmissions around the WHO declaration. Finally, we conducted a one-sided t-test for whether provincial and territorial weekly speed was greater than an outbreak threshold of ten. We ran the test iteratively with six months of data across the sample period. Canada's speed remained below the outbreak threshold for eight months by the time of the WHO declaration ending the COVID-19 emergency of international concern. Acceleration and jerk were also low and stable. While the 1-day persistence coefficient remained statistically significant and positive (1.074, P-value <.001), the 7-day coefficient was negative and small in magnitude (-0.080, P-value .02). Furthermore, shift parameters for either of the two most recent weeks around May 5, 2023, were negligible (0.003 and 0.018, respectively, with P-values of .75 and .31), meaning the clustering effect of new COVID-19 cases had remained stable in the two weeks around the WHO declaration. From December of 2021 onward, Omicron was the predominant variant of concern in sequenced viral samples. The rolling t-test of speed equal to ten became entirely insignificant from mid-October 2022 onward. While COVID-19 continues to circulate in Canada, the rate of transmission remained well below the threshold of an outbreak for eight months ahead of the WHO declaration. Both standard and enhanced surveillance metrics confirm that the pandemic had largely ended in Canada by the time of the WHO declaration. These results can inform future public health interventions and strategies in Canada, as well as contribute to the global understanding of the trajectory of the COVID-19 pandemic.

Demographic, medical, and financial statistics from the Jaffna Jaipur Centre for Disability Rehabilitation (JJCDR) database, 1987-2018: a prosthetics, orthotics, and mobility clinic in northern Sri Lanka

Background When dealing in global health, it is crucial to have a comprehensive understanding of community demographics and needs. From 1983-2009, a 26-year ethnic civil conflict devastated Sri Lanka, disproportionately affecting people living in the north and creating a large amputee population. Here, we use routinely collected prosthetic and orthotic data to investigate the composition of the amputee community in northern Sri Lanka. Methods The Jaffna Jaipur Centre for Disability Rehabilitation (JJCDR) was established in 1987 and has been the only centre consistently providing prosthetic, orthotic, and non-orthopaedic services in northern Sri Lanka spanning during and after the civil war. In 2004, with the help of the charity Motivation, they established an electronic patient database, which records and maintains information on the JJCDR’s patients. An anonymized copy of the JJCDR database was obtained in November 2018. Summative statistics and temporal trends in patient data were investigated. Results 3,665 unique patients registered with the JJCDR from 1986-2018 (2,605 male, 1,060 female). Individuals ranged from 0-90 years old, with a mean/median age of 34.76/32 years old. Key findings include: (i) 93.0% patients with amputation site recorded were lower limb amputees, with 74.3% being transtibial amputees; (ii) The majority of patients with cause of amputation recorded had war-related amputations (61.2%); (iii) diabetes was the highest cause for non-war-related amputations (18.3%); (iv) war-related amputations have ceased, while diabetic amputations are on the rise; and (v) peak recorded financial costs due to prosthetic and orthotic (P&amp;O) creation/maintenance occurred in 2013, 4 years after the war ended. Conclusions These analyses provide crucial insight into the P&amp;O population in northern Sri Lanka, including distribution and frequency for causes/sites of amputations, temporal patterns in causes of amputations, and costs to a P&amp;O centre due to device creation and maintenance. This database and analysis provide invaluable insight into the P&amp;O cohort in northern Sri Lanka, and a unique insight into the P&amp;O needs of a post-conflict LMIC from the perspective of a P&amp;O centre.

Aplikasi Peramalan Jumlah Pendaftaran Siswa Di SMA IT IQRA’ Kota Bengkulu Menggunakan Metode Weight Moving Average (WMA)

Abstract- Acceptance of new students is a routine activity every academic year that is carried out at school. IT IQRA' High School in Bengkulu City is one of the private schools in Bengkulu City which accepts new students every Academic Year. Based on data obtained from the 2018/2019 Academic Year to the 2022/2023 Academic Year (data attached), it shows that the level of the number of students sometimes decreases and also increases. This fluctuating pattern of student numbers is a problem faced by the school. IT IQRA Senior Middle School' Bengkulu City in determining the strategic steps and policies of the school in dealing with the Acceptance of New Students each Academic Year. The application for forecasting the number of enrollment students at IT IQRA' High School Bengkulu City was built using the Visual Basic .Net programming language and SQL Server 2008r2 Database. the forecasting application for the number of student enrollments at IT IQRA' High School in Bengkulu City is an application that can help schools find out how many new student enrollments will be in the coming school year based on the results of an analysis conducted from previous trend data using the Weight Moving Average Method. From the sample data used in the 2018/2019 Academic Year to 2022/2023, the forecast results obtained for the number of new student enrollments in the 2023/2024 Academic Year were 249 new students.

Evidence of the time-varying impacts of the COVID-19 pandemic on online search activities relating to shopping products in South Korea

The COVID-19 pandemic has changed the level of the received risk of the public and their social behavior patterns since 2020. This study aims to investigate temporal changes of online search activities of the public about shopping products, harnessing the NAVER DataLab Shopping Insight (NDLSI) data (weekly online search activity volumes about +1,800 shopping products) over 2017–2021. This study conducts the singular value decomposition (SVD) analysis of the NDLSI data to extract the major principal components of online search activity volumes about shopping products. Before the pandemic, the NDLSI data shows that the first principal mode (15% of variance explained) is strongly associated with an increasing trend of search activity volumes relating to shopping products. The second principal mode (10%) is strongly associated with the seasonality of monthly temperature, but in advance of four weeks. After removing the increasing trend and seasonality in the NDLSI data, the first major mode (27%) is related to the multiple waves of the new confirm cases of corona virus variants. Generally, life/health, digital/home appliance, food, childbirth/childcare shopping products are associated with the waves of the COVID-19 pandemic. While search activities for 241 shopping products are associated with the new confirmed cases of corona virus variants after the first wave, 124 and 190 shopping products are associated after the second and third waves. These changes of the public interest in online shopping products are strongly associated with changes in the COVID-19 prevention policies and risk of being exposed to the corona virus variants. This study highlights the need to better understand changes in social behavior patterns, including but not limited to e-commerce activities, for the next pandemic preparation.

Water productivity maximization and ecosystem monitoring to estimate tourism economic value

Abstract Water supply from a common pool resource based on productivity indicators for different uses is one of the goals of planning in dry areas. Productivity indicators are defined based on time, geographical location and hydrological conditions in the form of food security, economic benefits and ecosystem restoration. This study was conducted in order to evaluate the contrast between economic criteria and food security in the exploitation of water resources in Lu'an city in Anhui province of China. Probabilistic modeling based on the prediction of uncertain values using the Latin hypercube technique was used for hydrological variables and water resources. The method of data mining and trend analysis of dependent variables was also simulated to estimate economic values in the water cycle. Statistical information of 32 years from 1991 to 2022 has been collected and used as an annual average per population. The results showed that the economic value of water consumption in the tourism industry has increased compared to agriculture. The total water provided for food security is equal to 6.5 m3 per person, the excess of which can be allocated to other uses through weighting indicators based on ecosystem and quality.

Baldur: Bayesian Hierarchical Modeling for Label-Free Proteomics with Gamma Regressing Mean-Variance Trends

Label-free proteomics is a fast-growing methodology to infer abundances in mass spectrometry proteomics. Extensive research has focused on spectral quantification and peptide identification. However, research toward modeling and understanding quantitative proteomics data is scarce. Here we propose a Bayesian hierarchical decision model (Baldur) to test for differences in means between conditions for proteins, peptides, and post-translational modifications. We developed a Bayesian regression model to characterize local mean-variance trends in data, to estimate measurement uncertainty and hyperparameters for the decision model. A key contribution is the development of a new gamma regression model that describes the mean-variance dependency as a mixture of a common and a latent trend—allowing for localized trend estimates. We then evaluate the performance of Baldur, limma-trend, and t test on six benchmark datasets: five total proteomics and one post-translational modification dataset. We find that Baldur drastically improves the decision in noisier post-translational modification data over limma-trend and t test. In addition, we see significant improvements using Baldur over the other methods in the total proteomics datasets. Finally, we analyzed Baldur’s performance when increasing the number of replicates and found that the method always increases precision with sample size, while showing robust control of the false positive rate. We conclude that our model vastly improves over popular data analysis methods (limma-trend and t test) in several spike-in datasets by achieving a high true positive detection rate, while greatly reducing the false-positive rate.

Pengaruh Penggunaan Strategi Pembelajaran Small Group Discussion Terhadap Keaktifan Belajar Siswa Kelas VIII Pada Mata Pelajaran PAI Di SMPN 1 Palupuh

This research is motivated by the low activeness of student learning caused by learning that is still teacher-centered so that students do not actively participate in the learning process such as when the teacher asks questions no one raises their hand to answer the question, students also do not take the initiative to ask when the teacher does not understand what has been conveyed by the teacher, this is also caused by the use of monotonous learning strategies so that students feel bored and bored when learning takes place. The purpose of this research is to find out how much influence the use of Small Group Discussion learning strategies has on activeness study class VIII students at SMPN 1 Palupuh. This research is a Quasi-Experimental Design or quasi-experimental design. The design used is the Nonequivalent Control Group Design using quantitative data trends. The population in this study amounted to 60 people consisting of two classes. The sample for this study was class VIII 1 as the experimental class and class VIII 2 as the control class. The experimental class was given treatment using the Small Group Discussion strategy while the control class was not given treatment. The data analysis technique used was the t test which had previously passed the prerequisite analysis test by carrying out the normality and homogeneity tests. The results showed that there was an effect of using the Small Group Discussion strategy on student learning activity significantly with a significant value of Sig &lt;0.05 (0.000 &lt; 0.05) and t count &gt; t table (7120&gt; 2.001), it can be concluded that is rejected and is accepted.

New Mathematical Technique for Step Rate Test Analysis Aids Determination of Fracture Pressure and Overcomes the Uncertainty of Conventional Analysis.

Experience in the oil industry has shown that it is challenging to sustain successful long-term matrix injection, as injection water quality cannot be maintained rigorously due to facility hiccups and membrane clogging. Most oil field operators have resolved this problem of injectivity decline by increasing the surface injection pressure to part the formation and inject just above the fracture gradient with strict offtake management for zonal conformance. This is not an easy task as injection much above fracture opening pressure can lead to water fingering and poor sweep that results in uneconomical waterflood recovery. The operators, thus, strive to inject at a pressure just above the fracture opening pressure so that the fracture opens near the wellbore but does not extend and then maintain the pressure just above the fracture closing pressure. Therefore, determination of the fracture opening pressure and fracture closing pressure has remained critical data for the success of waterflood projects. The most reliable industry approach to estimating fracture opening and fracture closing pressures comes from the step rate test (SRT). This traditional approach of Cartesian analysis of pressure-rate plot fits straight lines through the data in a plot of injection pressure against injection rate and then estimates the fracture pressure from the intersection of these lines having different slopes. The data received often do not exhibit one clear change in slope, thus resulting in multiple possible solutions, making it difficult for the operator to use the data for high CAPEX facilities design. Most past studies indicate the subjectivity of this Cartesian slope fitting technique. Alternative solutions through multirate superposition analysis found limited application in the analysis of SRT data due to considerable sensitivity to the value of initial pressure used for superposition and lack of stability of rate and pressure data. In this article, a new technique of SRT analysis is presented, which provides a unique solution for fracture opening and fracture closing pressures. It helps to overcome the limitations of the traditional technique of arbitrary fitting of straight lines. It uses the mathematical understanding of cumulative derivatives to recognize that the matrix opens when the cumulative growth of the rate of injectivity shows a change. It estimates the derivative of the injection rate with respect to injection pressure at each step. Then, it estimates the fracture pressure from the plot of the cumulative of this derivative against pressure at each step. It helps to overcome the challenge SRT solutions posed by the nonlinear trend of pressure data at each injection step both before fracture and after fracture is initiated. It also overcomes the limitations of the multirate superposition technique, as it is not sensitive to the value of initial pressure used for superposition.