Programming Environment Research Articles

Sexual maturation of the chub mackerel <I>Scomber colias</I> (Scombridae) in the East-Central Atlantic

Scientific monitoring of pelagic fish stocks in 2004–2005 and 2014–2017 provided information on characteristics of sexual maturation of Atlantic chub mackerel Scomber colias in the coastal waters of the Central-Eastern Atlantic. Based on these results we describe ecological and geographical variability of some biological characteristics of the species. Features of sexual maturation of females and males of the mackerel can be described by a logistic equation obtained in the R programming environment. Fork length, at which 50% of individuals aged 1+ are mature, is 18.4 cm for females and 20.0 cm for males; 100% of individuals are mature at age 3+ with a fork length of 32.3 and 30.8 cm for females and males, respectively. A high proportion of spawning chub mackerel individuals in the Central-Eastern Atlantic have been recorded from February to March and from June to July. Chub mackerel in the coastal waters of this region are similar in life cycle characteristics to the populations of the waters off the Canary Islands and the Madeira Archipelago, but differ from the populations of the waters off mainland Portugal, the Azores, South Africa, and the Bay of Biscay in faster sexual maturation and growth rates.

New watershed methods for isolating and characterizing discrete objects in 3D data sets

This paper introduces new algorithms for conducting and improving watershed analysis, implemented with the particular goal of improving the ability to measure the shapes of mineral grains to be subsequently be analyzed by mass spectrometry. This application requires a high degree of accuracy and fidelity in terms of both separating all touching grains and preserving their shapes. The algorithms are designed to take advantage of a vector-based programming environment. A new implementation of the Euclidean distance transform utilizes the fact that the distance from any adjacent pair of voxels to the nearest boundary must be within one voxel of each other. In practice, however, this algorithm is outperformed by a smoothed approximate distance transform that is faster to compute and results in less irregular watershed boundaries. A one-pass rainfall-based watershed algorithm is introduced that runs in linear time with the number of segmented voxels, and requires no priority queue. Unlike marker-based watershed algorithms based on the basin-filling approach, the rainfall approach finds watersheds associated with all local maxima in the distance map, even if a marking algorithm is used. A post-watershed smoothing algorithm improves watershed boundaries and eliminates small spurious watersheds. The one-pass watershed and post-watershed smoothing algorithms run in times superior or comparable to basin-fill watershed algorithms implemented in other environments, and offers excellent ability to separate touching objects efficiently while placing watershed boundaries that maximize the preservation of details of particle shape. Further time improvement could come from implementing them in a vector-based environment that allows explicit multi-threading.

STEED: A data mining tool for automated extraction of experimental parameters and risk of bias items from in vivo publications.

Systematic reviews, i.e., research summaries that address focused questions in a structured and reproducible manner, are a cornerstone of evidence-based medicine and research. However, certain steps in systematic reviews, such as data extraction, are labour-intensive, which hampers their feasibility, especially with the rapidly expanding body of biomedical literature. To bridge this gap, we aimed to develop a data mining tool in the R programming environment to automate data extraction from neuroscience in vivo publications. The function was trained on a literature corpus (n = 45 publications) of animal motor neuron disease studies and tested in two validation corpora (motor neuron diseases, n = 31 publications; multiple sclerosis, n = 244 publications). Our data mining tool, STEED (STructured Extraction of Experimental Data), successfully extracted key experimental parameters such as animal models and species, as well as risk of bias items like randomization or blinding, from in vivo studies. Sensitivity and specificity were over 85% and 80%, respectively, for most items in both validation corpora. Accuracy and F1-score were above 90% and 0.9 for most items in the validation corpora, respectively. Time savings were above 99%. Our text mining tool, STEED, can extract key experimental parameters and risk of bias items from the neuroscience in vivo literature. This enables the tool's deployment for probing a field in a research improvement context or replacing one human reader during data extraction, resulting in substantial time savings and contributing towards the automation of systematic reviews.

An advanced machining robot flexible programming methodology supported by verification in a virtual environment

ABSTRACT The solutions for robot offline programming use different programming environments to facilitate the deployment of robots in machining tasks with the development of specific postprocessors. There is no easy exchange of data between programming software to realise robot machining tasks. This paper presents a flexible programming methodology using several interchange file formats that can be easily exchanged between different CAD, CAD/CAM, or specialised robot programming software. Integrating the standard programming based on CAD, CAD/CAM systems, and STEP-NC protocol through different output files enables data interoperability during the realisation of the robot machining tasks. The developed methodology is proposed for executing programming, verification supported by a virtual environment, postprocessing, and machining by industrial robots. It uses the software for programming machine tools and adapts them to the specifics of robots and their programming languages using a developed postprocessor. The presented methodology enables the programming of robots for 2.5 to 5-axis machining tasks depending on the neutral file format used. Programming verification was realised, first, by simulation on configured virtual robots and developed robot native language editor and, second, by machining on the available robots. These experiments include different contours and shapes and file formats, which show the programming’s reliability and accuracy.

Painting the plant body: pigment biosynthetic pathways regulated by small RNAs.

Plant pigments are diverse natural molecules involved in numerous biological functions such as development, growth, and metabolism. As plants age, not only new organs will be formed, but also, they will acquire the necessary pigments in response to the environment and endogenous programming in order to achieve reproductive success. Among the endogenous cues, the small RNAs (sRNAs), an endogenous group of ubiquitous regulatory molecules, may regulate the pigments-associated biosynthetic pathways at posttranscriptional level. Although plant pigments and sRNAs have been comprehensively studied in several processes throughout the entire plant cycle in model and nonmodel species, connections among these central players must be revised. Studying these complex networks allow us not only to know the progress that has been made in this area, but also generate research questions to be explored in order to unravel novel mechanisms for improving plant yield; therefore, in this review we have summarized the emerging roles of sRNAs-regulated nodes in mediating plant pigmentation-associated biosynthetic pathways, focused on chlorophylls, flavonoids, carotenoids, and betalains. In addition, we discuss perspectives related to the manipulation of those genes associated with plant pigments for obtaining genetically improved plants.

Categorizing and Determining of BI-RAD Breast Cancer Score by Using Deep Learning Techniques

Breast cancer is second only to skin cancer in prevalence. Failure to detect and treat breast cancer early could be fatal. Breast cancer is more common in underdeveloped countries due to low health awareness and limited early detection methods. However, detecting equipment and radiography advances have helped solve this difficulty and suggest a drop in mortality. Along with ultrasounds, mammography, Magnetic Resonance Imaging (MRI), Computed tomography (CT) scan, and Positron emission tomography (PET) scans are used for diagnosis. These devices add data to test findings for greater accuracy. Diagnosticians determine breast cancer stage by examining tumor size, shape, and spread. Artificial intelligence users employ these factors as fixed values to classify the illness using region of interest (RIO), Gray Level Co-Occurrence Matrix (GLCAM), clustering, or mass constraints like diameter and volume. Artificial intelligence algorithms have created many techniques to extract features from massive volumes of data through training, such as deep learning. The study investigated DL algorithms (Attention mechanism and efficintNetB7 ) to determine the breast imaging-reporting and data system (BI-RADS) score by categorizing mammography images. The dataset Digital Database for Screening Mammography (DDSM) used in this study, acquired from Kaggle, contains 55,890 training samples, of which 86% are negative, and 14% are positive, categorized into classes C1, C2, C3, C4, and C5. This work utilized deep learning techniques and algorithms to accurately categorize breast cancer at 93%, 92%, 90%, and 93% for each category (C2, C3, C4, and C5). We developed the BI-RADS classifier model using the bycharm framework, an integrated development environment (IDE) for Python programming, and using multiple libraries, namely NumPy, Pandas, TensorFlow, Keras, Matplotlib, and Seaborn. This study used a laptop with the following specifications: an Intel Core i7 CPU, 16GB of RAM, and Intel RTX integrated graphics.

Russian Immigrant Families’ Child Care Selection in the United States

AbstractResearch on immigrant populations shows that child care choices are dependent on the population’s region of origin. While the Russian immigrant population in the United States comprises the largest group of immigrants from Eastern Europe and is likely to increase in the future, there is virtually no research on Russian immigrant families’ child care search and selection criteria. This qualitative study applies the theoretical model of Pungello and Kurtz-Costes (1999), which illustrates how factors such as parental demographic characteristics, environmental context, child characteristics, and parental beliefs play into families’ child care selection. The study draws from semi-structured interviews with 11 Russian immigrant families residing in the Mid-Atlantic region of the United States to examine their child care search processes and selection criteria. Results revealed that most parents found child care through friends’ recommendations, Internet search, and online reviews. Nine themes describing child care criteria valued by Russian immigrant families emerged from the analysis. Specifically, parents mentioned culture and personal beliefs, child characteristics, convenience, cost and subsidy, education and learning, program features, facilities and environment, teacher characteristics, and rating and reputation as the major factors in selecting child care. Understanding Russian immigrant parents’ child care information sources and selection criteria will allow for early care and education (ECE) providers to accommodate the needs of Russian immigrant population and for policymakers to facilitate access to ECE programs for these families.

I am, therefore I preserve: Exploring middle-class imagery, biased status perceptions, and pro-environmental behaviour

Extensive research on pro-environmental behaviour (PEB) reveals a significant knowledge gap in understanding the influence of social class, perceived status and the middling tendency on pro-environmental behaviour. Using the International Social Survey Programme Environment dataset, and conducting multilevel mixed-effects linear regressions, we find that the middling tendency and biased status perceptions significantly influences pro-environmental behaviour. Those who deflate their social position have higher pro-environmental behavior and this reinforces the idea that pro-environmental behaviour is driven by a post-materialist effect rather than a status enhancement effect. Moreover, the objective middle class is still a stronger contributor to higher PEB levels compared to subjective middle class. We also find the relation between class, status and PEB vary by country. These findings provide vital insights into the intricate and heterogenous dynamics between class, status and pro-environmental behaviour among different countries and shed light on class and status as driving forces behind pro-environmental behaviour.

An Examination of Career Readiness Among Music Education Doctoral Alumni

The purpose of this research was to examine recent doctoral music education alumni perceptions of how their doctoral programs informed their current work. We placed a specific focus on how the curricular, scholarly, and experiential facets of music education doctoral programs have transferred to graduates’ current positions. Participants ( N = 74) completed a researcher-designed survey based on previously conducted research that consisted of three categories: description of current positions, doctoral experiences, and the significance of doctoral experiences in preparation for their current positions. Results indicated that most participants were employed in full-time tenure track and nontenure track positions and that the most common opportunities during doctoral programs involved writing, presenting, and publishing research. Implications include examining recruitment practices for doctoral programs, degree program environments and expectations, the connection between degree programs and definitions of career success, and ways to prepare alumni for positions both in and outside of R1 institutions.

Menopausal status-dependent alterations in the transcript levels of genes encoding ERα, ERβ, PR and HER2 in breast tumors with different receptor status.

Breast cancer has distinct causes and prognoses in patients with premenopausal and postmenopausal status. The expression status of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) are analyzed by immunohistochemistry (IHC) to classify molecular subtypes of breast cancer among which large differences in prognosis exist. The mRNA expression of ESR1 (encoding ERα), ESR2 (encoding ERβ), PGR (encoding PR), and ERBB2 (encoding HER2) was analyzed based on menopausal status (pre- vs post-menopausal) in tumors from breast cancer patients with different receptor status, in R programming environment, using transcriptomics data from TCGA-BRCA project. In ER-positive or PR-positive or HER2-negative breast tumors, ESR1 transcript levels were found to be higher in tumors from postmenopausal women than those from premenopausal women; in contrast, ESR2 transcript levels were lower in tumors from postmenopausal women than those from premenopausal women. Furthermore, PGR mRNA expression was lower in breast tumors from postmenopausal women than those from premenopausal women, only in those with ER + or PR + status. The expression of these genes between tumors from pre- and post-menopausal patients with breast cancer was also analyzed based on the combination of status of three receptors. Together, the results suggest that mRNA expression of ESR1, ESR2, and PGR might differ depending on menopausal status in breast tumors with certain receptor status. More importantly, the change in the expression of ESR1 and ESR2 following menopause is in the opposite directions in breast cancer patients showing the need to identify particular molecular mechanisms regulating the expression of ER isoforms post-menopause in different directions in breast cancer patients, considering the high clinical importance of these receptors in terms of the prognosis of patients with breast cancer.

Evaluation of the Educational Environment of Orthopedic Residency Training Programs in Saudi Arabia: A PHEEM-Based Study.

This study aimed to assess the quality of the educational environments within which an orthopedic residency training program is being conducted throughout Saudi Arabia and to identify the areas of weaknesses so that the necessary improvements can be made to build a healthy and supportive environment. This cross-sectional study included all orthopedic residents in Saudi Arabia and was surveyed using the validated Postgraduate Health Educational Environment Measure (PHEEM). We used Google Forms to Email the online survey to Saudi trainees in the orthopedic residency program from January 1 through September 30, 2022. The majority of participants were between 26 and 30 years; 15.6% were women. Among the subscales of PHEEM, perception of role autonomy scores were statistically significantly associated with participants' regions and their level of training (p = 0.026 and p = 0.019, respectively). The association of perception of teaching and participants' regions was also statistically significant (p = 0.004). The mean score for the perception of social support subscale was 25.99, which indicates that residents are generally satisfied with the level of support they receive in the training program. However, there is still room for improvement in this area. The overall PHEEM score was positive but highlighted areas for improvement. Role autonomy and instructional subscales were perceived positively, yet still have room for growth. Social support showed more advantages than disadvantages. Despite the generally favorable environment, enhancements are needed in workloads, working hours, clarity of clinical protocols, and teaching supervision quality.

A novel integration of Web 3.0 with hybrid chaotic-hippo-optimized Blockchain framework for healthcare 4.0

HealthCare 4.0 stands as a cutting-edge technology that integrates human-sourced data with communication systems to enable highly accurate clinical diagnostics and treatments. While sensor-based devices have simplified many aspects of daily life, these advancements also face significant security challenges, particularly in safeguarding patient information. In response, this research introduces a novel blockchain-based hybrid chaotic encryption and authentication for maintaining EHR transmissions aimed at robust and secure authentication within HealthCare 4.0. The complete framework incorporates the Infura Web API, using Python 3.19. To strengthen authentication security, modified Hippopotamus-evoked scroll maps are used to generate dynamic hashes in the genesis block formation, securing all EHR data in the proposed architecture. This entire framework was evaluated on the Ethereum Blockchain, leveraging Web 3.0, with Python 3.19 serving as the core programming environment for interface development. Security analysis was performed utilizing Burrows-Abadi-Needham (BAN) logic, while the framework's resilience was rigorously tested against NIST standards. Comparative testing highlights the model's superior defense capabilities against various attacks, surpassing other frameworks in complexity and robustness. The findings ultimately provide a comprehensive view of the optimized encryption scheme on blockchain technology and the future pathways for secure authentication frameworks in HealthCare 4.0.

Tri-band planar antenna for a small robotic vehicle

Abstract This paper describes the design, analysis of properties and fabrication of a tri-band antenna for a small robotic vehicle. The E-shaped tri-band antenna was chosen for its simplicity, practicality and universality. The design of the tri-band antenna and the simulation of its properties were implemented in the Matlab programming environment using the Antenna Toolbox extension. The designed tri-band antenna was fabricated, its properties were measured and the measured results are compared with the simulations for subsequent analysis.

Optimization of the solar cell based on cadmium telluride by adding the CdSeTe absorbing layer (heterostructure simulation)

Cadmium telluride solar cells are among the most common devices for photovoltaic applications. However, the energy conversion efficiency of these elements remains insufficiently high. Using the SCAPS programming environment, research and optimization of a classical thin-film solar cell based on CdTe were carried out. The structure of this element consisted of ITO as a transparent conductive contact, a cadmium sulfide (CdS) layer, and a cadmium telluride (CdTe) absorber layer with a metal contact. To optimize this structure in terms of power conversion efficiency, the influence of the thickness and concentration of acceptor impurities in the CdTe absorbing layer, as well as the influence of the thickness and concentration of donor impurities in the CdS buffer layer, were considered. It was established that the optimal thicknesses for the CdS buffer layer and absorption CdTe layers are 50 nm and 3000 nm, respectively. An additional CdSeTe layer between the CdS and CdTe layers has been proposed as one of the optimization options to improve the device efficiency. The main photovoltaic parameters of such a solar cell were analyzed depending on the thickness of the CdSeTe layer and its selenium content. It has been demonstrated that adding CdSeTe solid solution to the 1500 nm thick CdTe absorber layer increases the efficiency of the solar cell by 6.84 %. The main photovoltaic characteristics of CdS/CdTe and CdS/CdSeTe/CdTe solar cells were compared. The results showed that the simulated CdS/CdSeTe/CdTe structure provides better photoconversion efficiency in the AM1.5G light spectrum compared to the classical CdS/CdTe structure. Such elements can be used to form highly efficient solar panels

Investigation of Computational Thinking Skills through Instructional Techniques, Games and Programming Tools

Computational thinking (CT) has become a vital approach to problem-solving. This method has been proven successful in various areas, especially science, technology, engineering, mathematics (STEM), and professional development. The integration of CT in education provides a practical approach to solving problems. This concept paper investigated the various uses of instructional techniques and programming tools to enhance CT skills used in previous empirical research. CT consists of four main pillars: decomposition, which refers to breaking down identified problems into smaller parts; pattern recognition, which involves finding occurring patterns that can be seen; abstraction through identification of important details and removing unnecessary information; and algorithm, where a step-by-step procedure is solution is made. By exploring instructional strategies and programming environments, this paper outlines the possible impacts of these instructional strategies, games, and programming environments on improvement in CT skills, thus providing a theoretical basis for future empirical studies. The strength and combination of these approaches provide a comprehensive learning experience. This paper also suggests instructional techniques and programming tools to enhance underexplored CT skills based on research gaps.

Rmacrostrat : An R package for accessing and retrieving data from the Macrostrat geological database

The geological record is a vast archive of information that provides the only empirical data about the evolution of the Earth. In recent years, concentrated efforts have been made to compile macrostratigraphic data into the online centralized database Macrostrat. Macrostrat is a global stratigraphic database containing information regarding surface and subsurface rock units and their respective ages, lithologies, geographic extents, and various other associated metadata. However, these raw data are currently directly accessible only through the Macrostrat application programming interface, which is a barrier to potential users that are less familiar with such services. This data accessibility hurdle currently prevents full capitalization of the value offered by Macrostrat, particularly its potential to improve understanding of the geological and biological evolution of the Earth. Here, we introduce rmacrostrat, an R package that interfaces with the Macrostrat database to access and retrieve a variety of geological, paleontological, and economic data directly into the R programming environment. In this article, we provide details about how the package can be installed, its implementation, and potential use cases. For the latter, we showcase how rmacrostrat can be used to visualize regional stratigraphic columns, produce regional geologic outcrop maps, and investigate temporal trends in macrostratigraphic units. We hope that this package will make geological data more readily accessible and in turn will facilitate new research utilizing Earth system data.

Hubungan antara Gaya Kepemimpinan Partisipatif Vroom-Yetton dan Motivasi Belajar Siswa di Lembaga Pendidikan Indonesia

This literature review research aims to explore the relationship between the Vroom-Yetton participatory leadership style and students' motivation to learn at school. The method used is an analysis of empirical studies and the latest literature relevant to the research topic. The results showed that the Vroom-Yetton participatory leadership style had a positive impact on students' learning motivation at school. This leadership style involves active participation of students in making decisions related to learning programs and the learning environment. Students are given the opportunity to express their opinions, provide feedback, and make decisions together with educational leaders and teachers. This study shows that students who are involved in decision-making and are given an active role in learning feel more valued and have a sense of ownership of their learning. This can increase students' learning motivation and help them to achieve their academic goals. In addition, the Vroom-Yetton participatory leadership style also allows teachers to obtain better feedback from students regarding their learning experiences. Teachers can use this feedback to modify the learning program and learning environment according to the needs of students, thereby increasing their motivation to learn. In conclusion, this study shows that the Vroom-Yetton participatory leadership style can have a positive impact on students' motivation to learn at school. Therefore, educational leaders should consider implementing the Vroom-Yetton participatory leadership style in learning environments to increase students' motivation to learn and the overall quality of education. Further studies can be conducted to explore more deeply the impact of the Vroom-Yetton participatory leadership style on student motivation in different educational environment.

Saving energy resources during operation of rolling stock of underground electrified transport

Purpose. To analyze the energy saving reserves under the conditions of implementation and integration of the system in order to find rational driving modes in the general system of managing underground electrified transport. Methodology. The work presents the method for processing data arrays obtained experimentally with the help of a measuring system and theoretically with the use of the “Rational Trajectory” software. Findings. Experimental studies were carried out using a testing system created on the basis of a refurbished train with energy recovery system. Theoretical studies were carried out using the “Rational Trajectory” software, which is based on the principle of solving a multi-criteria problem by the method of the main criterion. The minimum amount of electricity consumption from the overhead contact line was chosen as the main criterion. The software was developed in the LabVIEW graphical programming environment in order to determine the rational modes of driving rolling stock and energy indicators in a given area of its operation. The amount of electricity consumed for traction and the amount of electricity generated by the train during regenerative braking were determined based on the results of experimental and theoretical studies, respectively, under typical and rational modes of driving the train for given identical operating conditions. Originality. Further research on the analysis of energy saving reserves on the rolling stock of underground electrified transport was achieved due to the introduction of a system for finding a rational driving mode. Practical value. It has been established that the implementation and incorporation of the “Rational Trajectory” software into the train control system will save up to 14.7 % of the amount of electricity consumed for traction, compared to typical modes operation on a given track section.

Enhancing spatial prediction of groundwater-prone areas through optimization of a boosting algorithm with bio-inspired metaheuristic algorithms

Groundwater resources are essential for ensuring a consistent water supply in many regions. Groundwater potential maps (GPMs) can be utilized in many ways to estimate the quantity, quality, and distribution of subsurface water, supporting the decision-making processes of numerous stakeholders. This study contributes to improving the accuracy of GPMs, focusing on implementing Geospatial Artificial Intelligence (GeoAI) models. For this purpose, the accuracy performance of the Extreme Gradient Boosting (XGBoost) algorithm is improved in this study. To do this, two such popular metaheuristic algorithms, i.e., invasive weed optimization (IWO) and biogeography-based optimization (BBO), are integrated into the XGBoost algorithm for modeling and spatial prediction of the areas prone to groundwater. Three models—XGBoost, XGBoost-IWO, and XGBoost-BBO—are implemented within the Python programming environments to execute spatial modeling and generate predictive maps. The evaluation of results unfolds in two stages: model validation and GPM validation. For the training data, the root mean square error (RMSE) and mean absolute error (MAE) indices were 0.165 and 0.121 for XGBoost, 0.13 and 0.087 for XGBoost-IWO, and 0.114 and 0.082 for XGBoost-BBO, respectively. The test data showed similar trends, with XGBoost yielding RMSE and MAE values of 0.424 and 0.295, XGBoost-IWO at 0.416 and 0.287, and XGBoost-BBO at 0.39 and 0.28. XGBoost-BBO, XGBoost-IWO, and XGBoost had a prediction accuracy higher than other models. The respective area under the curve (AUC) of GMPs using receiver operating characteristic (ROC) curves for XGBoost, XGBoost-IWO, and XGBoost-BBO were 81.8 %, 83.1 %, and 83.7 %. Using bio-inspired metaheuristic algorithms, the GPM accuracy rate has improved further. The study of groundwater resources demonstrated how geological feature extraction by GeoAI may help employ advanced techniques.

A flexible programming and verification methodology for reconfigurable CNC woodworking machine

An advanced flexible programming methodology for CNC woodworking machines was developed. As the research starting base, a three-axis CNC woodworking machine was used. The developed methodology is proposed for programming, simulation, postprocessing, and machining by woodworking machine. This flexible programming method integrates the standard programming based on CAD, CAD/CAM systems, and STEP-NC protocol through different output files, enabling data interoperability during the realization of the machining tasks. The control system for the machine is configured based on the open-architecture software LinuxCNC to verify the flexible programming method and the results obtained. Programming verification was realized by simulation on a configured virtual machine in different programming environments and finally on a virtual machine integrated with the control system. The results obtained from the study were evaluated comparatively.