Software Platform Research Articles

Balancing Innovation and Profitability: Technological Diversification in Iran’s Insurance Industry

Abstract This study explores how technological diversification affects financial performance in Iran’s insurance industry. Using semi-structured interviews with six experts, including CEOs and faculty members, key indicators of technological diversification were identified, such as new underwriting software and digital advertising platforms. A 33-item questionnaire was developed based on these insights and distributed to top managers of Iranian insurance companies, with secondary financial data sourced from the Central Insurance of Iran. The study employes regression analysis and Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess the impact of technological diversification on financial performance, controlling for firm size, debt ratio, and company age. Findings indicate a significant negative relationship between technological diversification and both Return on Equity (ROE) and Return on Assets (ROA), suggesting that the costs of adopting new technologies may outweigh their short-term financial benefits. Additionally, high debt ratios were found to adversely affect ROA, highlighting the financial risks of excessive borrowing.

Epigenetic modifications in transdifferentiation of adipose tissue-derived stromal cells into spontaneously beating cardiomyocyte-like cells

Abstract Background Adipose tissue-derived stromal vascular fraction (adipose SVF) contains pluripotent mesenchymal stem cells and rarely transdifferentiate into beating cardiomyocytes. We developed a simple culture protocol under which the adult murine inguinal adipose SVF reproductively transdifferentiates into beating cardiomyocyte-like cells (beating CM) without inductions in the primary culture. We also reported that Mef2c is a crucial factor in this conversion. However, the characteristics of beating CM and the factors that regulate the differentiation of adipose SVF toward the cardiac lineage are unknown. Purpose To investigate sequential changes in global gene expression profiles of adipose SVF in primary culture and determine the mechanism of differentiation into beating CM. Methods Adipose SVF cells were isolated from adult mice's inguinal subcutaneous fat pad and cultured using the previously established beating CM induction method (Sci Rep. 2021). At 6 time points (days 0, 3, 7, 14, 21, and 28) in primary culture, total RNA was extracted, and RNA-sequencing was performed (n = 3). The data was analyzed using Subio Platform and MetaCore software. To assess the candidate gene and proteins, adipose SVF cells were transduced with the gene using a lentivirus vector and supplemented with the specific inhibitors. The cardiac differentiation was evaluated by quantitative PCR on day 28. Results The beating CM was confirmed on days 14, 21, and 28 in the primary culture. Among prefiltered 14,574 genes, the expression level of 749 genes were significantly changed (153 genes upregulated vs. 596 genes downregulated) between days 7 and 14 (2-fold, P < 0.05), in which beating CM appeared. The GO molecular functions analysis showed that O-GluNAC transferase and histone deacetylase (HDAC)-associated genes were expressed dominantly before day 7, and KLF4-associated genes were expressed prominently after day 14 in primary culture. The expression of cardiac troponin T (Tnnt2) and myosin light chain ventricular type (Myl2) increased on day 3 and day 14, respectively. The HDAC7, an epigenetic regulator, decreased the expression on day 14 or later. Treatment with the HDAC inhibitor (150 nmol/L) increased the expression of Tnnt2 (5-fold vs. control) on day 28 in adipose SVF. Furthermore, treatment of Mef2c-transduced adipose SVFs with HDAC inhibitors increased expression of cardiac troponin T (29-fold vs. control, P < 0.05). Conclusion The time course analysis demonstrated that a remarkable change occurred in the regulation of transcription on day 14 in adipose SVF primary culture. Alterations in epigenetic modifications increased beating CM in adipose SVFs. Adipose SVF could be applied to new cardiac regeneration therapies by increasing the efficiency of introduction into the beating CM.

Evaluating Enterprise Resource Planning Adoption Using the EDAS Method: A Strategic Approach

Adoption of enterprise resource planning (ERP) systems is the process of putting ERP systems into use inside an organisation. It entails the fusion of diverse corporate operations into a centralised software platform, including finance, human resources, and supply chain management. By enabling real-time utilisation of integrated data and automated processes, ERP adoption attempts to improve decision-making, increase efficiency, and streamline operations. To achieve successful installation and user acceptance, rigorous planning, arrangement, and instruction are needed. For businesses looking to streamline operations and obtain a competitive edge in today's digital environment, ERP adoption is essential. The implementation of enterprise resource planning (ERP) is significant for research since it can help to better understand and manage organisational operations. Scientists can learn more about the elements that contribute to successful implementation, pinpoint best practises, and address issues that organisations have throughout the adoption process by researching ERP adoption. By optimising resource allocation, increasing operational efficiency, and implementing ERP, this research will assist organisations in making knowledgeable judgements. Additionally, research on ERP adoption aids in the creation of frameworks and theories in the discipline of information technology and offers advice to practitioners and decision-makers working on ERP projects. A technique for making decisions that takes into account an alternative's distance from the mean answer is called evaluation based on distance from the average solution (EDAS). It gauges the performance of the alternatives using the distance between them and the typical solution. When evaluating options, EDAS takes both positive and negative distances into account. To rank and choose the best alternative in multi-criteria decision-making issues, this method is frequently utilised. Decision-makers can use EDAS to make well-informed decisions by taking into account how far away various options are from the typical solution. The Shipping took the first place while Tracking took the Last place in the Enterprise resource planning adoption. In this paper Enterprise resource planning adoption, The Shipping took the first place while Tracking took the Last plac

Inverse dynamics analysis of 4-DOF stacking robot with closed chain structure

A four-degree-of-freedom PR1300 stacking robot with two parallel quadrilateral structures connected in series at the shoulder was designed. The hybrid robot with local closed chains has the characteristics of compact structure, high stiffness, and high modularity. To solve the dynamic problem of the hybrid robot. Firstly, by analyzing the induced relationship between joints, the stacking robot with a local closed chain structure is transformed into two branch chain mechanisms, and the kinematic model of the robot is established using the D-H parameter method. Secondly, the Kane method is applied to model the dynamics of the two branch chains separately, and the rigid body dynamics Kane equation of the entire 4-DOF stacking robot with a closed chain structure is obtained through the augmented matrix. Finally, the SolidWorks Motion mechanical simulation platform and MATLAB software were used to program dynamic simulations and numerical calculations, and the results were compared to verify the correctness of the dynamic theory derivation. This theory provides a necessary theoretical basis for the subsequent synthesis of robot dynamic scales.

MIRD Pamphlet No. 31: MIRDcell V4-Artificial Intelligence Tools to Formulate Optimized Radiopharmaceutical Cocktails for Therapy.

Radiopharmaceutical cocktails have been developed over the years to treat cancer. Cocktails of agents are attractive because 1 radiopharmaceutical is unlikely to have the desired therapeutic effect because of nonuniform uptake by the targeted cells. Therefore, multiple radiopharmaceuticals targeting different receptors on a cell is warranted. However, past implementations invivo have not met with convincing results because of the absence of optimization strategies. Here we present artificial intelligence (AI) tools housed in a new version of our software platform, MIRDcell V4, that optimize a cocktail of radiopharmaceuticals by minimizing the total disintegrations needed to achieve a given surviving fraction (SF) of tumor cells. Methods: AI tools are developed within MIRDcell V4 using an optimizer based on the sequential least-squares programming algorithm. The algorithm determines the molar activities for each drug in the cocktail that minimize the total disintegrations required to achieve a specified SF. Tools are provided for populations of cells that do not cross-irradiate (e.g., circulating or disseminated tumor cells) and for multicellular clusters (e.g., micrometastases). The tools were tested using model data, flow cytometry data for suspensions of single cells labeled with fluorochrome-labeled antibodies, and 3-dimensional spatiotemporal kinetics in spheroids for fluorochrome-loaded liposomes. Results: Experimental binding distributions of 4 211At-antibodies were considered for treating suspensions of MDA-MB-231 human breast cancer cells. A 2-drug combination reduced the number of 211At decays required by a factor of 1.6 relative to the best single antibody. In another study, 2 radiopharmaceuticals radiolabeled with 195mPt were each distributed lognormally in a hypothetical multicellular cluster. Here, the 2-drug combination required 1.7-fold fewer decays than did either drug alone. Finally, 2 225Ac-labeled drugs that provide different radial distributions within a spheroid require about one half of the disintegrations required by the best single agent. Conclusion: The MIRDcell AI tools determine optimized drug combinations and corresponding molar activities needed to achieve a given SF. This approach could be used to analyze a sample of cells obtained from cell culture, animal, or patient to predict the best combination of drugs for maximum therapeutic effect with the least total disintegrations.

Resources for Teaching Operating Systems: A Survey of Instructors and a Literature Review

Objectives : Faculty new to teaching operating systems or those looking to refresh their course need to understand the state of the art in operating system education. Toward this goal, we conducted a survey of operating system instructors to understand how they approach teaching the course and what textbook and software platforms they use in their classrooms. We also conducted a literature review examining two decades of papers focused on teaching operating systems to undergraduates. Survey : We surveyed people who teach operating systems at the undergraduate level to determine which textbook they use, on which software they base projects, and how they approach teaching the course. Literature Review : We searched the ACM Digital Library for publications focused on undergraduate education about operating systems. We selected a total of 51 papers. For each selected paper, we identified which approach (i.e., concrete vs. abstract) and perspective (i.e., internal or external) on teaching operating systems the authors used, which Curriculum 2023 topics they covered, and on what type of system (e.g., educational, production, research) their projects were based. We also looked at the evaluation of the methods and the impact these papers have had on the field. Findings: Instructors teaching operating systems tend to balance both their approach and their perspective, with a slight leaning toward an internal perspective, whereas authors supporting operating system education overwhelmingly support an internal, concrete approach to the course. In addition, authors also tend to focus on a few key topics, including System Calls and Processes, Concurrency, Scheduling, Virtual Memory, File Systems API and Implementation, and Performance Evaluation. Conclusions : This work will help faculty teaching operating systems to consider which approach and perspective they wish to take in their course and to find the resources most relevant to their preferred approach and perspective. It can also help focus future research in the area of operating system education, to align it better with current practices.

Contactless printing of food micro-particles controlled by ultrasound

Traditional food 3D printing technology, which primarily relies on mechanical control, often faces challenges such as low resolution, stringent material requirements, limited flexibility, and cumbersome equipment. To overcome these limitations, we developed an innovative food 3D printing method utilizing ultrasonic phased arrays and subsequently designed a contactless printing device employing this advanced technology. The device effectively manipulates the phase and amplitude of the ultrasonic phased array to focus the sound field on a specific location. We have successfully demonstrated preliminary control over the suspension of small food particles, including popcorn, chocolate, honey, and marshmallows, using this apparatus. Following this, popcorn particles were selected for detailed practical printing and further analysis. Additionally, we established a software platform, Ultr_printing, based on Ultraino, for intelligent simulation and control of the printing process. Leveraging FPGA and Arduino controllers, this setup facilitates rapid signal transmission from personal computers to sensors, ensuring quick and adaptable movement of suspended particles. Experimental results confirm that the device consistently controls the food particles, irrespective of their motion state. Notably, it achieves a printing precision of 0.16 mm when handling popcorn particles measuring 1.5 mm in diameter. The process, entirely contactless and free from contamination, preserves the original flavor of the food particles. This technology is particularly effective for high-precision printing of micro-scale shapes and offers considerable benefits in assembly and precision manufacturing.

Automatic animal monitoring systems based on AutoML technologies

In contemporary agricultural contexts, the sector is experiencing active processes of automation, underscoring the need for effective tools to develop such automated systems. The utilization of automation tools enhances the efficiency of numerous processes, including those in the domain of animal monitoring. This article examines the application of the AutoML approach as a means for automating the process of generating deep learning models employed in automatic monitoring systems. The VGG19 architecture has been chosen as a testbed for demonstrating the capabilities of the developed technologies. This well-established architecture for deep learning models is designed for object recognition in images.The present study implements a technology for automated structural-parametric synthesis of VGG19 models and the optimization of their hyperparameters. Such an approach allows for the automated creation of models tailored to specific applied problems, even for users lacking specialized knowledge in deep learning.The system delineated in this work is developed on the AutoGenNet software platform, which embodies the No-Code development concept. This concept conceals complex aspects of model creation and training processes from users, significantly lowering the entry barrier for newcomers. Additionally, the AutoGenNet platform incorporates a mechanism for the automatic generation of software wrappers, facilitating efficient interaction with trained models.All aforementioned aspects have contributed to the effective implementation of the AutoML approach for automating the generation and training processes of the VGG19 model. Consequently, the processes associated with solving automatic monitoring tasks reliant on deep learning models have been significantly simplified and expedited.The developed system has been tested on the task of recognizing individual cows. Test results indicated that the system possesses a high degree of scalability and can be adapted for the automated generation of other object recognition models, thereby opening avenues for addressing a diverse array of applied challenges related to the monitoring of various animal species.

Data Modeling and Synchronization Method to Align Power Trading Rules for Integrated Energy Management Systems

Integrated energy systems (IESs) couple multiple energy sources to promote clean energy and reduce emissions. IESs need to participate in business activities, such as power trading, aided by automated data-driven systems to achieve optimal and economical operation. However, challenges arise due to the lack of unified data model standards and the semantic ambiguity of rules, on top of the difficulty of synchronizing data across heterogeneous subsystems of integrated energy management systems (IEMSs). Previous research on power trading data models was limited to certain application scenarios and heterogeneities. This study pivots from proposing model standards to a standard neutral way to align power trading rules across IEMS subsystems. The method features a framework for power trading rules and a software platform called the power trading rule synchronizer. The proposed approach can minimize semantic ambiguity and ensure the automated rule synchronization across subsystems of IEMSs. A case study demonstrated the application of the proposed method, saving an estimated 672 man-days in implementing the rules in 16 subsystems of the IEMS, contributing directly and indirectly to reducing emissions. This study provides a foundation for aligning other rules in IEMSs, such as carbon and gas trading rules.

Spaceborne particle identification platform and its application based on convolutional neural network

The identification of particle types in space radiation particle detection is a problem of great concern in scientific research and engineering applications. At present, the particle types are mainly identified by the difference in energy deposition of particles in the sensor, the difference in waveform in the sensor, the flight time and energy of particles, and the difference in the deflection path of particles in the electric field, such as traditional telescope detectors, particle pulse waveform analysis, flight time TOF system and electrostatic analyzer. However, the current on-orbit particle identification logic is relatively simple and the identification accuracy is limited. Convolutional neural networks have powerful target classification capabilities and are good at capturing and extracting target feature details, which can improve the accuracy of particle energy measurement and identification. Based on the environment commonly used in space environment detection payloads, this paper proposes a method for building an on-orbit convolutional neural network particle identification platform to achieve particle type identification. The platform first constructs a multi-dimensional input data set, completes the model training and weight derivation with the help of the software platform, and completes the waveform inference and data set expansion through the hardware platform. The established identification platform is used to train and test the neutron and gamma waveform data obtained in actual tests, and the accuracy of the identification of the software and hardware platforms is analyzed, completing the verification of the platform. The establishment and application of this platform provides a new idea and method for particle measurement and identification in future space environment detection, and has strong engineering practice significance.

Integrating technical and business aspects in Software as a Service: Systematic literature review, trends, and research directions

One of the most salient problems currently confronting Software as a Service (SaaS) realization and scientific research is understanding how to efficiently connect software platforms using SaaS distribution models integrating the business functions. The extant empirical SaaS literature holds research gaps regarding the commercial and technical aspects of SaaS business models. To address these gaps, this article systematically reviews current empirical studies to understand how SaaS integration projects could be more efficiently managed in organizations by integrating technical and business aspects. Among other findings, we discover significant knowledge gaps in the field, mainly regarding the empirical validation of a coherent business strategy regarding the SaaS model and an integrated view of customer-perceived performance concerning SaaS architecture. Based on the analysis and synthesis of the findings, this study provides a framework for SaaS realization, including the principal dimensions to be considered in SaaS provision. Futhermore, a novel conceptual model proposed complements the framework by organizing 14 principal variables affecting SaaS integration on three decision-making levels, i.e., strategic, tactical, and operational. This study theoretically contributes to solidifying the SaaS integration literature by covering the three levels of decision-making and offering actionable insights into business practice and SaaS implementation. Regarding the practical contributions, this research guides managers in managing SaaS projects by emphasizing a holistic approach that incorporates all decision-making levels and perspectives, as well as interconnected technological and business aspects. Lastly, promising future research opportunities have been identified to move SaaS research forward.

Motion Control System for USV Target Point Convergence.

The goal of this paper is to establish a motion control system for unmanned surface vehicles (USVs) that enables point-to-point tracking and dynamic positioning. This includes the heading control and path following control of USVs. A hardware and software platform for USVs using microcontrollers is designed. This paper presents the construction of a kinematics and dynamics model for an unmanned catamaran. The motion process is divided into two segments. In the target point tracking segment, the heading coordinate system and the ship coordinate system are established. Based on these, a control method using differential steering to track the desired yaw angle is designed to improve the tracking efficiency. And the accuracy of heading keeping and path following is improved by combining the cascade PID controller. In the dynamic positioning segment, a self-adjusting mechanism is designed, thereby enhancing the flexibility of thrust distribution and improving the accuracy of the USV's positioning retention in wind and wave environments. Finally, experimental validation is carried out to verify the effectiveness of the design proposed in this paper by issuing control commands and saving the return data through the upper computer, and then analyzing the return data with MATLAB (R2022b, MathWorks, Natick, MA, USA).

Hyperspectral acquisition with ScanImage at the single pixel level: application to time domain coherent Raman imaging

We present a comprehensive strategy and its practical implementation using the commercial ScanImage software platform to perform hyperspectral point scanning microscopy when a fast time-dependent signal varies at each pixel level. In the proposed acquisition scheme, the scan along the X-axis is slowed down while the data acquisition is maintained at a high pace to enable the rapid acquisition of the time-dependent signal at each pixel level. The ScanImage generated raw 2D images have a very asymmetric aspect ratio between X and Y, the X axis encoding both for space and time acquisition. The results are X-axis macro-pixel where the associated time-dependent signal is sampled to provide hyperspectral information. We exemplified the proposed hyperspectral scheme in the context of time-domain coherent Raman imaging, where a pump pulse impulsively excites molecular vibrations that are subsequently probed by a time-delayed probe pulse. In this case, the time-dependent signal is a fast acousto-optics delay line that can scan a delay of 4.5ps in 25μs at each pixel level. With this acquisition scheme, we demonstrate ultra-fast hyperspectral vibrational imaging in the low frequency range [10cm−1, 150 cm−1] over a 500 μm field of view (64 x 64 pixels) in 130ms (∼ 7.5 frames/s). The proposed acquisition scheme can be readily extended to other applications requiring the acquisition of a fast-evolving signal at each pixel level.

БІЛІМ БЕРУ ЖҮЙЕСІНДЕ ГЕЙМИФИКАЦИЯ ҚҰРАЛДАРЫН ҚОЛДАНУДЫҢ МҮМКІНДІКТЕРІН ТАЛДАУ

In the modern information educational environment, there is a growing interest in the use of computer games to improve the quality of education, which determines the relevance of the proposed study for the analysis of software with the possibility of gamification of the educational process in the context of the requirements of the state, society and education. The research methodology is the study of information sources of psychological, pedagogical, methodological and technical orientation, the analysis of computer educational games of foreign and domestic authors. The article considers the need for gamification of the educational process, the principles of gamification, its impact on student motivation and participation, as well as the main aspects of the optimal implementation of game elements in training. Modern software resources and platforms offering gamification in the educational process of personal development are analyzed. All software tools with the possibility of gamification of training were divided into groups: gaming platforms, educational quests, learning management services and game development sites. Particular attention is paid to methods for evaluating the effectiveness of gamified programs and platforms, which will allow creating the best didactic tasks and recommendations for teachers. Gamification can increase student motivation and their participation in various educational tasks. Intrinsic and extrinsic motivation have a special impact on students' cognitive abilities regarding learning outcomes and improve overall academic performance. Motivation, which is an important means of achieving learning goals, motivates students according to their interests. Since students perceive information differently, they can choose their activities according to their learning preferences. According to the research topic, the positive situation of gamified learning is analyzed and the relevance of digital gamification in education is determined.

Effects of Diesel Pre-Injection Time on Combustion Process and Emission Characteristics of Diesel/Methanol Dual Fuel Engine

ABSTRACT The traditional diesel engine not only causes the reduction of nonrenewable energy but also aggravates environmental pollution. The utilization of renewable fuels in engine can not only effectively reduce the dependence on oil sources but also effectively reduce emissions. As a renewable oxygen-containing fuel with low production cost and widely sourced, methanol holds vast potential for application in engine. The dual-fuel combustion of diesel and methanol has emerged as an important research issue in the internal combustion engine industry. This paper conducted a study on the combustion and emission characteristics of the diesel/methanol dual fuel based on the optical engine test platform and 3D numerical simulation software. The results show that the change in cylinder pressure is relatively small and the heat release rate increases with the advance of diesel pre-injection time. The pre-injection time of diesel mainly affects the mixture distribution of pre-injection diesel, and the mixture is more evenly mixed with the advance of the pre-injection time. The degree of premixed combustion becomes higher, premixed combustion will lead to more rapid combustion, so the peak heat release rate shows an increasing trend. The pre-injection time of diesel is advanced, resulting in a leaner mixture in the early stage, and the fuel is not easy to burn, so CA10 and CA50 are delayed. The soot formation decreases as the mixture becomes more uniform, and the number of pixels of KL factor in each range also decreases. The average temperature in the cylinder is reduced and the NOX shows a decreasing trend with the advance of the pre-injection time. The change in CO generation is relatively small with the advance of pre-injection time.

Technical note: Measuring bicondylar length in computed tomography data.

With the increased use of 3D-generated images in biological research, there is a critical need to adapt classical anatomical measurements, traditionally conducted with calipers, to a virtual environment. We present detailed protocols for measuring bicondylar length, a critical dimension of the femur, using three different imaging software programs-3D Slicer™, Amira™, and Simpleware™. These protocols provide researchers and practitioners in radiology, orthopedics, biomechanics, and biological anthropology with accurate and reproducible measurement techniques. The objective is to standardize and support virtual osteology in biomechanical research, stature estimation, and related medical and anthropological studies. Adhering to standardized protocols, we adapted femoral bicondylar length measurements for computed tomography images from a New Mexican collection (n = 10). The method was designed for applicability and reproducibility across three software platforms. By comparing measurements from the same sample across different observers and different platforms, this study validates the accuracy and consistency of the adapted protocol, demonstrating its utility for research and clinical assessments. We present a step-by-step guide for each program, detailing bone alignment and measurement. We illustrate each step and provide video tutorials via links for an enhanced understanding of the process. Bicondylar length can be measured effectively in each software program following the provided instructions. However, ease of measurement varied among the programs, with some offering a more straightforward process. This variability underscores the importance of choosing appropriate software for the user's needs and proficiency. It also suggests areas for improvement and standardization in software design and instructional clarity.

Surrogate Immunohistochemical Markers of Proliferation and Embryonic Stem Cells in Distinguishing Ameloblastoma from Ameloblastic Carcinoma

PurposeThe current study aimed to investigate the use of surrogate immunohistochemical (IHC) markers of proliferation and stem cells to distinguish ameloblastoma (AB) from ameloblastic carcinoma (AC).MethodsThe study assessed a total of 29 ACs, 6 ABs that transformed into ACs, and a control cohort of 20 ABs. The demographics and clinicopathologic details of the included cases of AC were recorded. The Ki-67 proliferation index was scored through automated methods with the QuPath open-source software platform. For SOX2, OCT4 and Glypican-3 IHC, each case was scored using a proportion of positivity score combined with an intensity score to produce a total score.ResultsAll cases of AC showed a relatively high median proliferation index of 41.7%, with statistically significant higher scores compared to ABs. ABs that transformed into ACs had similar median proliferation scores to the control cohort of ABs. Most cases of AC showed some degree of SOX2 expression, with 58.6% showing high expression. OCT4 expression was not seen in any case of AC. GPC-3 expression in ACs was limited, with high expression in 17.2% of ACs. Primary ACs showed higher median proliferation scores and degrees of SOX2 and GPC-3 expression than secondary cases. Regarding SOX2, OCT4 and GPC-3 IHC expression, no statistically significant differences existed between the cohort of ABs and ACs.ConclusionKi-67 IHC as a proliferation marker, particularly when assessed via automated methods, was helpful in distinguishing AC from AB cases. In contrast to other studies, surrogate IHC markers of embryonic stem cells, SOX2, OCT4 and GPC-3, were unreliable in distinguishing the two entities.

Using data and analytics to optimise the human resources processes

Introduction: Business development and HR management systems based on modern technologies open up significant prospects for companies to actively promote themselves in the market and achieve positive results in the context of HR management. Currently, many companies are implementing modern HR tools aimed at increasing efficiency and reducing ongoing risks at minimal cost. In this regard, HR analytics has become a necessary tool to help find information about employees and make informed decisions based on it. Objective. Given the relevance of the research topic, it becomes possible to determine its purpose, яка полягає в узагальненні та систематизації підходів до застосування інструментів, програмних продуктів та платформ для аналітики процесів управління персоналом з метою покращення загального економічного стану компанії. Methods. To achieve this goal, the general scientific methods of analysis, synthesis, generalisation, induction and deduction were used. Results. To achieve this goal, the following results were obtained: the essence of HR analytics and the possibilities of its application for personnel management were determined; software products and platforms for analysing personnel management processes were generalised; the main analytical tools used in the field of personnel management were systematised. It is proved that one of the most important areas of application of HR analytics is the recruitment process. Conclusions. With the help of data and new existing analytical methods, HR professionals have the opportunity to optimise recruitment procedures, identify suitable candidates, which will ultimately contribute to improving the company's condition, provided that the labour resources and intellectual capital are used in a rational and balanced manner

Covalent Labeling Automated Data Analysis Platform for High Throughput in R (coADAPTr): A Proteome-Wide Data Analysis Platform for Covalent Labeling Experiments.

Covalent labeling methods coupled to mass spectrometry have emerged in recent years for studying the higher order structure of proteins. Quantifying the extent of modification of proteins in multiple states (i.e., ligand free vs ligand-bound) can provide information on protein interaction sites and regions of conformational change. Though there are several software platforms that are used to quantify the extent of modification, the process can still be time-consuming, particularly for proteome-wide studies. Here, we present an open-source software for quantitation called Covalent labeling Automated Data Analysis Platform for high Throughput in R (coADAPTr). coADAPTr tackles the need for more efficient data analysis in covalent labeling mass spectrometry for techniques such as hydroxyl radical protein footprinting (HRPF). Traditional methods like Excel's Power Pivot (PP) are cumbersome and time-intensive, posing challenges for large-scale analyses. coADAPTr simplifies analysis by mimicking the functions used in the previous quantitation platform using PowerPivot in Microsoft Excel but with fewer steps, offering proteome-wide insights with enhanced graphical interpretations. Several features have been added to improve the fidelity and throughput compared to those of PowerPivot. These include filters to remove any duplicate data and the use of the arithmetic mean rather than the geometric mean for quantitation of the extent of modification. Validation studies confirm coADAPTr's accuracy and efficiency while processing data up to 200 times faster than conventional methods. Its open-source design and user-friendly interface make it accessible for researchers exploring intricate biological phenomena via HRPF and other covalent labeling MS methods. coADAPTr marks a significant leap in structural proteomics, providing a versatile and efficient platform for data interpretation. Its potential to transform the field lies in its seamless handling of proteome-wide data analyses, empowering researchers with a robust tool for deciphering complex structural biology data.

Devastation of the cerrado of mato grosso do sul and the advance of arenization in the pardo river watershed

Despite the significant biodiversity in the Cerrado, the process of occupation, subsequent economic cycles, and intensification of agriculture in a predatory manner have caused profound alterations in this biome. This study analyzed changes in land use and land cover in the Cerrado of Mato Grosso do Sul between 1985 and 2022 by mapping and also investigating the formation and dynamics of sand dunes in the Bauru sedimentary basin, focusing on the Pardo River watershed. MapBiomas Network data, collection 8, were used for land use and land cover analysis. Arenization foci cartography was conducted using brightness index 2 based on Sentinel-2 satellite images in Sentinel Application Platform software. The historical analysis of land use and cover in the Cerrado of Mato Grosso do Sul revealed a rapid transformation of the landscape, with a drastic reduction of native vegetation and an intensification of large-scale monocultures. Between 1985 and 2022, the Cerrado of Mato Grosso Sul lost 4.6 million hectares of native vegetation (forest formations, savannahs, grasslands, and wetlands), representing 24.54% of this territory. Recent changes in land use include pasture-soybean and pasture-eucalyptus. In the Pardo River watershed, arenization foci cover an area of 17,834.34 hectares, with varying dimensions ranging from less than 1 hectare to 376.41 hectares. The arenization process occurs in different slope compartments, such as the base, slope, and interfluves. The physical and chemical characteristics of Quartzipsamments, such as low organic carbon content in soil, are associated with high rates of surface exposure and weakened load-dependent structures, leading to degradation. Arenization occurs where natural vegetation has been suppressed and soil has been depleted by poorly managed production processes.