Reliable Dataset Research Articles

Using Spectral Flow Cytometry for CAR T-Cell Clinical Trials: Game Changing Technologies Enabling Novel Therapies

Monitoring chimeric antigen redirected (CAR) T-cells post-infusion in clinical trials is a specialized application of flow cytometry. Unlike the CAR T-cell monitoring for individual patients conducted in clinical laboratories, the data generated during a clinical trial will be used not only to monitor the therapeutic response of a single patient, but determine the success of the therapy itself, or even of an entire class of therapeutic compounds. The data, typically acquired at multiple testing laboratories, will be compiled into a single database. The data may also be used for mathematical modeling of cellular kinetics or to identify predictive biomarkers. With the expanded context of use, a robust, standardized assay is mandatory in order to generate a valuable and reliable data set. Hence, the requirements for assay validation, traceable calibration, technology transfer, cross-instrument standardization and regulatory compliance are high.

Soundscape Design in an Urban Natural Park

Urban natural parks represent a remarkable concept that evokes the coexistence of human habitation with a wild environment, and the associated interactions between human and natural territories. In this context, urban noise infringes upon the natural soundscape, leading to various consequences for both realms. This study seeks to characterize the impact of anthropic noise levels on biodiversity in the urban natural Văcărești Park (Bucharest, Romania), utilizing on-site measurements and software simulation techniques. The study seeks to develop a method for evaluating integrative strategies to mitigate the impact of traffic noise on wildlife in an urban wild park, without addressing the specific effects of noise on the perception and communication of individual species. By calibrating field measurements with laboratory results, a more reliable data set will be used to identify areas where the biophonic environment is impacted by anthropogenic noise. Since human-generated noise in an urban natural park predominantly originates from road traffic and industrial sites, managing traffic noise and its propagation pathways could substantially improve the park’s soundscape. Additionally, this study will apply software simulations for noise reduction strategies, such as vegetation planting and earthen embankments, to obtain suitable solutions and propose plausible and effective actions to authorities for improving the biophonic environment. This research could also serve as the basis for long-term monitoring, allowing for the assessment of the evolution and impact of implemented measures over time.

Automated geotechnical logging of core box images using machine learning

ABSTRACT Geotechnical characterization is contingent on reliable data to reduce uncertainty in the conditions of slope walls or underground workings. However, geotechnical data for greenfield and brownfield sites are often limited. The data that are available are often inconsistent and difficult to reliably use to inform the geotechnical domain model. SRK has developed machine learning workflows using deep learning for geotechnical classification of existing core box photographs to provide a reliable and objective data set that can be used to inform rock mass characterization and structural modeling. Conventional geotechnical parameters are often unsuitable for automation due to the reliance on subjective decisions by the logger for interval lengths or defining naturally open versus closed breaks. Three classification systems have been developed: the Core Damage Index (CDI), the Pseudo-RQD, and the Break Frequency. The CDI classifies discrete intervals within the photographs according to the average clast sizes relative to the core diameter to provide a high-resolution view of the variability of damage down a drill hole. Pseudo-RQD and Break Frequency are calculated by mapping the breaks and rubble zones observed on drill core images.

Extended benchmark set for lattice parameters of inorganic solids.

The development of novel methods in solid-state quantum chemistry necessitates reliable reference data sets for their assessment. The most fundamental solid-state property of interest is the crystal structure, quantified by the lattice parameters. In the last decade, several studies were conducted to assess theoretical approaches based on the agreement of calculated lattice parameters with respect to experiment as a measure. However, most of these studies used a limited number of reference systems with high symmetry. The present work offers a more comprehensive reference benchmark denoted as Sol337LC, which consists of 337 inorganic compounds with 553 symmetry-inequivalent lattice parameters, representing every element of the periodic table for atomic numbers between 1 and 86, except noble gases, the radioactive elements and lanthanoids. The reference values were taken from earlier benchmarks and from measurements at very low temperature or extrapolation to 0 K. The experimental low-temperature lattice parameters were then corrected for zero-point energy effects via the quasi-harmonic approximation for direct comparison with quantum-chemical optimized structures. A selection of standard density functional approximations was assessed for their deviations from the experimental reference data. The calculations were performed with the crystal orbital program CRYSTAL23, applying optimized atom-centered basis sets of triple-zeta plus polarization quality. The SCAN functional family and the global hybrid functional PW1PW, augmented with the D3 dispersion correction, were found to provide closest agreement with the Sol337LC reference data.

A new sine-inspired probability model: Theoretical features with statistical modeling of the music engineering and reliability scenarios

It is a proven authenticity that probability-schooled methodologies have a cardinal role in making decisions under the vagueness circumstances. Thus, to reach meaningful and fruitful decisions under vagueness circumstances, numerous probability-focused methodologies have been introduced, promoted, and implemented. This paper also contributes to adding another useful probability model to the existing literature for data modeling in engineering-oriented areas. The new model is called the weighted sine exponentiated exponential (WSEE) distribution, which is formed by implementing the exponentiated exponential model together with a sine-adapted method. Certain properties of the WSEE distribution are given. The parameter estimation of the WSEE distribution along with the simulation study is also provided. Last, in order, we apply the WSEE distribution for analyzing three data sets taken from the musical engineering and reliability settings. The optimality of the WSEE distribution is proved by comparing its results with other prominent modifications of the exponentiated exponential distribution. Using 4 statistical criteria as judgment tools, it was shown numerically and pictorially that the WSEE distribution is an optimal and suitable model for the musical engineering reliability data sets.

A wireless, remotely operable and easily customizable robotic flower system

Abstract Understanding the complex interactions between external and internal factors that influence pollinator foraging behaviour is essential to understand ecosystem functioning, design agricultural practices or develop effective conservation strategies. However, it remains challenging to collect large and reliable data sets with reasonable personnel and workload. In this study, we present a wireless and cost‐effective robotic flower equipped with internet of things (IoT) technology that automatically offers nectar to visiting insects while monitoring visitation time and duration. The robotic flower is easy to manipulate and settings such as nectar refill rates can be remotely altered, making it ideal for field settings. The system transmits data completely wirelessly and autonomously, is mobile and easy to clean. The prototype settings allow for approximately 2 weeks of uninterrupted data collection for each battery charge. As a proof‐of‐concept application, a foraging preference dual choice experiment with bumblebees was performed. On average, more than 7000 flower visits per colony were registered daily with a set‐up consisting of 16 robotic flowers. The data show a gradual preference shift away from the pre‐trained low concentration, confirming the hypothesis of favouring sugar water with higher concentration. The robotic flower provides accurate and reliable data on insect behaviour, significantly reducing the price and/or labour costs. Although primarily designed for (bumble)bees, the system could be easily adapted for other flower‐visiting insects. The robotic flower is user‐friendly and can be easily adapted to address a wide range of research questions in pollination ecology, conservation biology, biocontrol and ecotoxicology, and allows for detailed studies on how nectar traits, flower colour and shape or pollutants affect foraging behaviour.

Assessing CMIP6 models in simulating meteo-oceanographic variability on Spanish continental coasts

The ocean is a key player in the Earth's climate, absorbing heat and carbon dioxide from the atmosphere. The ocean's temperature and salinity are influenced by climate change, which can significantly impact marine ecosystems. Reliable data sets of atmospheric and oceanographic parameters are of special interest in coastal productive areas to adequately monitor their variability at regional and local levels. It is therefore essential to continue monitoring and studying the ocean to develop effective mitigation and adaptation strategies. In this context, the main aim of this study is to identify the Earth System Models (ESMs) from the Coupled Model Intercomparison Project 6 (CMIP6) that best capture the variability of water temperature, salinity, and wind speed along the continental Spanish coasts, using them to estimate future impacts in these regions. To achieve this, a multifaceted approach is used, encompassing a historical (2000−2014) assessment comparing ESM outputs to in situ observations from Puertos del Estado (PdE) oceanographic buoys, an examination of the present period (2015−2022) under three IPCC scenarios (SSP1−2.6, SSP2−4.5, and SSP5−8.5), and a projection of future (2023−2100) trends using the same emission scenarios. Results showed that the ESMs from CMIP6 can reproduce the historical patterns of meteo-oceanographic properties, rendering them valuable tools for climate change studies. In the future (2100), considering the most pessimistic scenario (SSP5−8.5), the water temperature may increase by 2.8°C, salinity may decrease by -1.6, and wind speed may decrease by -0.4 m·s−1. These projected changes can significantly impact the Spanish coasts, jeopardizing the growth, reproduction, survival, abundance, and distribution of some marine species.

Методика сбора данных об активности вредоносного программного обеспечения под ОС Windows на базе MITRE ATT&CK

The digitalization of the modern economy has led to the emergence of information technologies in various areas of human activity. In addition to positive effects, this has enhanced the problem of countering cyber threats. The implementation of cyber threats often impacts serious consequences, especially when it comes to critical information infrastructure. Malware is an important part of the modern landscape of cyber threats; the most high-profile cybercrimes of recent years are associated with the use of malware. In this regard, the problem area of countering malware is actively developing, and one of the promising areas of research in this area is the creation of methods for detecting malware based on machine learning. However, the weak point of many well-known studies is the construction of reliable data sets for machine learning models, when the authors do not disclose the features of the formation, preprocessing and labeling of data on malware. This fact compromises the reproducibility a lot of studies. This paper proposes a methodology for collecting data on malware activity based on the MITRE ATT&CK matrix and Sigma rules and designed for Windows OS. The proposed methodology is aimed at improving the quality of datasets containing malware and legitimate processes behavior’s features, as well as at reducing the time of data label by an expert method. A software stand was prepared and experiments were carried out for testing the methodology. The results of experiments confirmed applicability of our methodology.

Improved forecasting of the compressive strength of ultra‐high‐performance concrete (UHPC) via the CatBoost model optimized with different algorithms

AbstractThis paper focuses on the applicability of CatBoost models constructed using various optimization techniques for improved forecasting the compressive strength of ultra‐high‐performance concrete (UHPC). Phasor particle swarm optimization (PPSO), dwarf mongoose optimization (DMO), and atom search optimization (ASO), which have been very popular recently, are preferred as optimization algorithms. A comprehensive and reliable data set is used to develop the CatBoost models, which include 785 test results with 15 input features. The performance of the CatBoost models (PPSO‐CatBoost, DMO‐CatBoost, and ASO‐CatBoost) optimized with different algorithms is thoroughly assessed by means of various statistical metrics and error analysis to determine the model with the best forecasting capability, and this model is compared with the models obtained from previous studies. In addition, Shapley additive exPlanations (SHAP) analysis is used to ensure the interpretability of the forecasting models and to overcome the “black box” problem of machine learning (ML) models. The obtained results demonstrate that all CatBoost models outstandingly forecast the compressive strength of UHPC. Among these models, the DMO‐CatBoost model stands out compared to the other models in various performance metrics, such as high coefficient of determination (R2) values, low root mean squared error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) values, along with a smaller error ratio. In other words, the RMSE, R2, MAPE, and MAE values of the DMO‐CatBoost model for the training set are 3.67, 0.993, 0.019, and 2.35, respectively, whereas those for the test set are 6.15, 0.978, 0.038, and 4.51. Additionally, the performance ranking of the algorithms used to optimize the hyperparameters of the CatBoost model is as follows: DMO > PPSO > ASO. On the other hand, SHAP analysis showed that age, fiber dosage, and cement dosage significantly influence the compressive strength of UHPC. These findings can guide structural engineers in the design and optimization of UHPC, thus assisting them in developing strategies to improve the strength properties of the material. Finally, based on the best forecasting model developed in this work, a graphical user interface has been developed to easily forecast the compressive strength of UHPC in practical applications without additional tools or software.

Drought deciles index for spatial and temporal assessment of satellite-based precipitation datasets

Obtaining precise and long-term precipitation datasets is vital for addressing hydrological and climatic concerns. Limitation of drought recurrent characterizing and its impact in Iraq have been hampered primarily by unavailability of reliable climate data sets. This work assesses the performance of four gridded precipitation products (GPDs), APHRODITE for the period (1983–2007), CHIRPS for the period (1983–2017), PERSIANN for the period (1983–2017) to evaluate their potential for drought monitoring. This was accomplished by analyzing their ability to estimate the Drought Deciles Index (DDI) at multiple time scales (1-month, 3-month, 6-month, 12-month, and 24-month) across 22 ground stations (GS) in semi-arid Iraq during the period 1970–2018. The study area was divided into three zones based on Köppen's climate classifications. The results confirmed the potential of GPDs for monitoring drought in Iraq. Their performance was stronger for shorter time scales with time scales of 1, 3, and 6 and becomes weaker for time scales of 12 and 24. Furthermore, better performance of GPD found at locations with higher annual precipitation rates and altitudes. Overall, APHRODITE and TRMM showed the greatest potential for accurately representing GS. In addition, it is revealed that the DDI's was capable for effectively analyzing and monitoring drought periods in Iraq.

خوارزمية فصيلة ميركات المعدلة لتعدين القواعد الرابطة

تشكل قواعد جمعيات التعدين (ARM) إحدى تقنيات التنقيب عن البيانات المهمة. أصبحت الأساليب الكلاسيكية التي عمل عليها الباحثون سابقًا غير فعالة للتعامل مع النمو المطرد لقواعد البيانات ، مما دفعنا إلى استخدام عملية التعدين لقواعد الارتباط القائمة على ميتاهيورستك، وفي عملنا سنستخرج جميع القواعد الصحيحة ، و التعدين لا يقتصر على القواعد عالية الجودة. ذكاء السرب يعتبر إحدى هذه الأساليب المعتمدة. في هذه الورقة، تم اقتراح تعديل فصيلة ميركات لتعدين قواعد الجمعية (MCC-ARM). تعتمد الخوارزمية المقترحة بشكل أساسي على خوارزمية فصيلة ميركات (MCA). أكبر فائدة هي تنوع الحلول المرشحة في MCA. سنقوم في عملنا بتمثيل القاعدة باستخدام طريقتين مستعارتين من الخوارزمية الجينية، في المجموعة الأولى، تشير كل مجموعة من القواعد إلى كائن في المجتمع و التي تدعى طريقة بيتسبرغ، بينما تشير القاعدة الثانية إلى كائن في المجتمع و التي تدعى طريقة ميشيغان. تهدف الخوارزمية المقترحة إلى فحص أكبر عدد ممكن من قواعد الارتباط الصحيحة. ما يسمى بخوارزمية تتبع نهج تحديد منطقة البحث الفعالة، والتي تعتمد على آلية عشوائية رئيسية لقيادة الخوارزمية في استخراج القواعد البديلة وتجنب الحلول الكلية من الاسترشاد بنفس القاعدة، وهذا أدى إلى قدر كبير من التنوع. بالإضافة إلى ذلك، يستخدم MCC-ARM طريقة التكثيف في عملية البحث المتجاورة لمنع الخوارزمية من الوقوع في الوضع المحلي. لإثبات كفاءتها، سنطبقها في أربع مجموعات بيانات موثوقة (مثل حديقة الحيوان، والائتمان الألماني، والورم الأساسي والشطرنج). لقد حصل التحسين الذي أحدثته الخوارزمية المقترحة على عاملين حاسمين ، وهما عدد القواعد الصحيحة وقيمة لياقة الجودة.

Medical Question Summarization with Entity-driven Contrastive Learning

By summarizing longer consumer health questions into shorter and essential ones, medical question-answering systems can more accurately understand consumer intentions and retrieve suitable answers. However, medical question summarization is very challenging due to obvious distinctions in health trouble descriptions from patients and doctors. Although deep learning has been applied to successfully address the medical question summarization (MQS) task, two challenges remain: how to correctly capture question focus to model its semantic intention, and how to obtain reliable datasets to fairly evaluate performance. To address these challenges, this article proposes a novel medical question summarization framework based on e ntity-driven c ontrastive l earning (ECL). ECL employs medical entities present in frequently asked questions (FAQs) as focuses and devises an effective mechanism to generate hard negative samples. This approach compels models to focus on essential information and consequently generate more accurate question summaries. Furthermore, we have discovered that some MQS datasets, such as the iCliniq dataset with a 33% duplicate rate, have significant data leakage issues. To ensure an impartial evaluation of the related methods, this article carefully examines leaked samples to reorganize more reasonable datasets. Extensive experiments demonstrate that our ECL method outperforms the existing methods and achieves new state-of-the-art performance, i.e., 52.85, 43.16, 41.31, 43.52 in terms of ROUGE-1 metric on MeQSum, CHQ-Summ, iCliniq, HealthCareMagic dataset, respectively. The code and datasets are available at https://github.com/yrbobo/MQS-ECL

Online feeding behavior monitoring of individual group-housed grow-finish pigs using a low-frequency RFID electronic feeding system.

Early identification of animals in need of management intervention is critical to maximize animal health and welfare and minimize issues with productivity. Feeding behavior, captured by automated feeding systems, can be used to monitor the health and welfare status of individual pigs. Here, we present a framework for monitoring feeding behavior of grow-finish pigs in real time, using a low-frequency radio frequency identification (RFID) system. Using historical data, an autoregressive linear model for predicting daily time at the feeder was developed and utilized to detect anomalous decreases in feeding behavior associated with health status of the pig. A total of 2,826 pigs were individually monitored with our warning system over the entire grow-finish period, and health warnings were compared to caretaker diagnoses. The system detected 55.7% of the caretaker diagnoses, and on average these events were detected 2.8 d earlier than diagnosis by the caretaker. High numbers of potentially spurious health warnings, generated by the system, can be partly explained by the lack of a reliable and repeatable gold standard reference data set. Results from this work provide a solid basis for monitoring individual animals, but further improvements to the system are necessary for practical implementation.

Classifying alkaliphilic proteins using embeddings from protein language model

Alkaliphilic proteins have great potential as biocatalysts in biotechnology, especially for enzyme engineering. Extensive research has focused on exploring the enzymatic potential of alkaliphiles and characterizing alkaliphilic proteins. However, the current method employed for identifying these proteins that requires web lab experiment is time-consuming, labor-intensive, and expensive. Therefore, the development of a computational method for alkaliphilic protein identification would be invaluable for protein engineering and design. In this study, we present a novel approach that uses embeddings from a protein language model called ESM-2(3B) in a deep learning framework to classify alkaliphilic and non-alkaliphilic proteins. To our knowledge, this is the first attempt to employ embeddings from a pre-trained protein language model to classify alkaliphilic protein. A reliable dataset comprising 1,002 alkaliphilic and 1,866 non-alkaliphilic proteins was constructed for training and testing the proposed model. The proposed model, dubbed ALPACA, achieves performance scores of 0.88, 0.84, and 0.75 for accuracy, f1-score, and Matthew correlation coefficient respectively on independent dataset. ALPACA is likely to serve as a valuable resource for exploring protein alkalinity and its role in protein design and engineering.

The German Transplant Registry - An Analysis of Legacy Data 2006-2016

In 2018, medical transplant data from three institutions were merged to create a German transplant registry. Since June 2021, access to data of the registry has been available. It was planned to analyze the registry data in order to compare special allocation rules with regular allocation for heart, liver, lung, and kidney transplantation. Our approach led to a quality analysis of the registry. Upon request, legacy data (2006-2016) of the registry was provided, divided into 61 elements. From these elements, the user had to compile the required dataset. Data checks were performed for completeness, correct allocation of information, and consistency among different sources. Software used for these tasks included R, SQL, and Excel. The initial elements ("waiting list" elements) of the four types of transplantations contained data from a total of 80,259 originally listed patients. However, these patients were only partially present in other elements resulting in complete datasets reflecting waiting time in only 23%, 30%, 50%, and 96%, and for post-transplantation outcomes in 14%, 11%, 38%, and 13% (heart, liver, lung, and kidney transplantation, respectively). The linking of urgency information with clinical data was successful in only a small proportion, with only 6% for heart transplantation. Incorrect and thus implausible allocations in the case of special allocation rules indicated incorrect entries in the registry. Data from different data providers were inconsistent. The incompleteness and incorrect data allocation raise doubts about the reliability of scientific studies based on the transplant registry. The complex structure also hinders the compilation of a reliable dataset, which is uncommon internationally. New data (acquisition since 2017) has only been available since December 2023. The transplant registry urgently needs restructuring. Competent clinical data management, involving transplant medical expertise, and continuous quality controls are essential in this process.

EVALUATING THE QUALITY OF DIGITAL DATASETS IN TERMS OF AVIATION SAFETY DERIVED FROM STEREO SATELLITE IMAGERY IN SARAWAK, MALAYSIA'S AIRPORTS

Abstract. High-resolution satellite imagery offers new possibilities in geospatial data acquisition, particularly in 3D city modeling, topographic mapping, and aviation safety. This study evaluates the quality of digital datasets generated from satellite imagery for five airports in Sarawak, as part of the eTOD (Electronic Terrain and Obstacle Data) project conducted by the Civil Aviation Authority of Malaysia (CAAM). The airports under examination include Miri Airport (WBGR), Bintulu Airport (WBGB), Mukah Airport (WBGK), Sibu Airport (WBGS), and Kuching Airport (WBGG). A comprehensive assessment was conducted to evaluate accuracy and reliability by combining stereo satellite imagery with on-site validation. The study yielded digital datasets of exceptionally high accuracy and reliability. The overall error rate remained below 3%, with a mean error value of 0.96 meters across all five airports. Notably, it has been observed that using satellite images as the only data source creates difficulties in detecting Power Transmission Lines and Poles that require precise detection. A multifaceted approach was adopted to increase the detection accuracy, involving site verification measurements, incorporation of external source data (Sarawak Energy Company), and manual interpretation. In conclusion, this study underscores the significant potential of very high-resolution satellite imagery for creating precise and reliable digital datasets for aviation safety. Additionally, the study's emphasis on detecting Power Transmission Lines highlights the need for advanced methods to improve accuracy in challenging scenarios. These findings not only contribute to enhancing aviation safety but also provide valuable insights into the utilization of satellite imagery for similar applications worldwide.

An Analysis of The Constructs Influencing Public and Private Hospitals' Level of Service Quality: A Study in Context of Nepal

This research study examines the significance of the data collected as well as the service quality provided by hospitals in Nepal through a systematic review and analysis of the literature. Given the swift growth of the healthcare sector in Nepal, evaluating the caliber of hospital services is essential. The project's objectives are to compile all of the available data, pinpoint knowledge gaps, and offer wise counsel to academics, policymakers, and medical professionals. Using pre-established keywords linked to Nepali hospital service quality, a comprehensive search was carried out across several electronic databases, including PubMed, Scopus, and Web of Science. Studies released between January 2000 and April 2023 were taken into consideration. The systematic review comprised thirteen studies in all, including both qualitative and quantitative research methods. The systematic study's findings brought to light several essential elements of high-quality services, including tangibles, assurance, responsiveness, empathy, infrastructure, and assurance. An exploratory factor analysis technique was used to identify the contributing factors that are primarily responsible for the disparities in services offered between public and private sector hospitals in Nepal, based on the 600 valid responses to the survey that was conducted among the general public regarding private and public hospitals.
 Methods: To obtain an overall picture, a reliable data set was chosen from the sample data gathered from patient parties and administrative staffs throughout public and private sector hospitals using systematic statistical procedures.
 Results: The study's findings contribute to our understanding of the standard of hospital treatment in Nepal and could eventually raise public and professional awareness of the disparity in quality between hospital care in Nepal.
 Discussions: The impact of hospital services on the people who use them is determined in this study, and it is a major aspect in assessing the standard of healthcare provided by the various sectors.

Advancing crop classification in smallholder agriculture: A multifaceted approach combining frequency-domain image co-registration, transformer-based parcel segmentation, and Bi-LSTM for crop classification.

Agricultural Remote Sensing has the potential to enhance agricultural monitoring in smallholder economies to mitigate losses. However, its widespread adoption faces challenges, such as diminishing farm sizes, lack of reliable data-sets and high cost related to commercial satellite imagery. This research focuses on opportunities, practices and novel approaches for effective utilization of remote sensing in agriculture applications for smallholder economies. The work entails insights from experiments using datasets representative of major crops during different growing seasons. We propose an optimized solution for addressing challenges associated with remote sensing-based crop mapping in smallholder agriculture farms. Open source tools and data are used for inter and intra-sensor image registration, with a root mean square error of 0.3 or less. We also propose and emphasize on the use of delineated vegetation parcels through Segment Anything Model for Geospatial (SAM-GEOs). Furthermore a Bidirectional-Long Short-Term Memory-based (Bi-LSTM) deep learning model is developed and trained for crop classification, achieving results with accuracy of more than 94% and 96% for validation sets of two data sets collected in the field, during 2 growing seasons.

A finite failure software reliability model using extended log-logistic distribution

Software reliability engineering has been developed to examine software systems over the years. Non-homogeneous Poisson process (NHPP) models are widely employed for analyzing software reliability data, as they have the ability to describe the expected number of failures over time using the mean value function. In addition, these models have an intensity function representing the failure rate over time. This article proposes a new non-homogeneous Poisson process model based on extended log-logistic (NHPP ELL) distribution with three parameters. The model properties were derived and represented graphically. Furthermore, the maximum likelihood estimation method was utilized to estimate the model parameters, which were computed numerically using the Newton–Raphson method. Three real software reliability datasets were utilized to evaluate the performance of the new NHPP ELL model based on three distinct criteria: the mean square error, the coefficient of determination (R2), the Theil statistic, the predictive-ratio risk, and the predictive power. Moreover, a comparative study was conducted between the proposed model and several well-known NHPP models regarding goodness-of-fit and predictive performance. All the evaluation measures indicate that the proposed model outperforms the other models across all the considered datasets. In addition, the graphical plots depicting the actual data and the prediction results for all the fitted models show that the proposed model performs well in all three considered software reliability datasets compared to other models.

Temporal Analysis Of COVID-19 Cases And Recovery Trends In Panvel City: A Spline Curve Approach

The spline curve method in mathematical modeling, was assessed across degrees for its efficacy in fitting daily cured and COVID-19 positive patient data of Panvel City. This study highlights the critical role of selecting an appropriate degree in spline interpolation for reliable disease-related dataset modeling.