Correct Decisions Research Articles

An Incongruent 2-Minute Stroop Task Alters the Response Time Performance in Semi-Elite Soccer Players: A Pilot Study

Introduction: Mental fatigue is a key factor in soccer that can affect physical performance. Mental fatigue protocols based on the Stroop test used in soccer involve extensive time, ranging from 10 to 30 min. Offering a protocol with a shorter duration that is more applicable in ecological situations seems to be an exciting challenge. Objective: To analyze the effects of a mental fatigue induction protocol (2 min Stroop test in its incongruent mode) on decision-making, response time, and technical execution accuracy in soccer players. Methods: A total of 18 semi-elite soccer male players, 25.83 (4.71) years from a Spanish soccer team, participated in this cross-sectional study. The 2 min incongruent Stroop test was used like mental fatigue protocol, followed by a specific analytical soccer task. The Wilcoxon signed-rank and Chi-squared tests were used to analyze the statistical differences. Results: A significant increase in participants’ response time after the fatigue protocol (p < 0.001; r = 0.878) was observed. Regarding the variables of correct decision (p = 0.457; r = 0.031) and technical execution accuracy (p = 1; r < 0.001), no significant differences were found. Additionally, no differences were observed between the first and second attempts of the 2 min incongruent Stroop test, in terms of execution accuracy, errors, or total words. Conclusions: The 2 min Stroop incongruent task may induce some degree of mental fatigue, leading to slower response time during certain technical tasks in semi-elite soccer players. However, this Stroop task was insufficient to promote significant changes in the accuracy of technical execution and the effectiveness of decision-making.

Justification of the approach to identifying promising managers for project management

The article deals with problems of project management in the context of a shortage of human resources at enterprises in the real sector of the economy as well as with promising areas of development to improve the efficiency of project management. The necessity of conducting work on preliminary analysis and selection of candidates is noted, with consideration to individual characteristics and expected goals. Within the framework of the tasks set for the study, the authors have shown the possibility of a three-factor evaluation of candidates to achieve results sufficient from the practical point of view in terms of choosing the most promising candidate from the available staff, and the possibility of obtaining a reasoned decision on the need to involve an external manager for the project implementation in the absence of the required competencies in the company’s staff of the. A brief overview of the methodological framework of management is given, which allows solving the problem of a reasonable approach to the recruitment for appointment to situational positions. It is shown that based on the developed approach, reasonable and strategically correct management decisions can be made. It can be used at enterprises in the real sector of the economy when making decisions on the appointment of project managers to improve the efficiency of work to achieve the goals stated in the project as well as for educational purposes to form decision-making skills and evaluate candidates for leadership positions.

Petri net modeling and analysis of an IoT‐enabled system for real‐time monitoring of eggplants

AbstractAs the current agricultural industry is facing several challenges such as climate changes and lack of qualified farmers, it is extremely necessary to ensure sustainable agriculture and food supply by smart farming (SF). The SF can assist farmers and the associated stakeholders in making correct decisions on improving the yield and quality of agricultural products. In this study, an SF system based on low‐cost Raspberry Pi and Internet of Things (IoT) technologies has been successfully implemented. Through the data items detected by sensors to deeply manage the planting process, the IoT‐enabled communication protocol under ISO standards of Message Queuing Telemetry Transport (MQTT) was used. The bar charts of real‐time environmental parameters are presented on ThingsBoard to achieve the goal of data visualization. Meanwhile, a web server is built for the customized requirements, and the historical datasets are stored in the SQLite database system. Furthermore, a Petri net (PN) model was employed to detect all possible abnormal processes and to verify the feasibility and soundness of an IoT‐enabled system by using a software tool, WoPeD. Finally, the experimental results show that the plants cultivated by the proposed IoT‐enabled system are superior to those cultivated by the existing systems in many perspectives, including the monitoring distance, power consumption, precision, deadlock detection, and running time.

Predictive Analytics Technique based on hybrid Sampling to manage Unbalanced Data in Smart Cities

A smart city is deemed smart enough because it has the capability to make decisions on its own. Artificial intelligence needs a lot of data from the physical world to make correct decisions. IoT sensor devices collect data from the surroundings, which is further used for predictive analytics. Collected data may be balanced or imbalanced. Unbalanced data used for decision-making without any pre-processing may lead to ravaging results. This paper proposes a novel predictive analytical technique to manage unbalanced data. A pipeline is designed using Principal Component Analysis (PCA), a hybrid sampling method, and a Machine Learning (ML) prediction method. SMOTE + ENN, a hybrid data balancing method, is used to specify imbalanced data to a balanced state. ML method is applied to form clusters and make predictions over the dataset. A large Smart City IoT dataset having 4,05,184 records has been used in this study. The proposed technique is used to predict the presence of a person in the vicinity of IoT devices. Evaluation parameters such as accuracy, precision, recall, F1-score, and Area Under Curve (AUC)/Receiver Operating Characteristic (ROC) curve are used to evaluate the proposed approach. Accuracy, Precision, Recall, F1-score, and AUC obtained using the proposed technique for cluster 0 are 0.79, 1.0, 0.79, 0.87, and 0.88 and for cluster 1 are 0.86 0.99, 0.86, 0.92, and 0.92, respectively. In view of the encouraging results, the proposed technique may prove to be a good choice to help in decision-making in different application domains in real life.

An empirical evaluation of a formal approach versus ad hoc implementations in robot behavior planning

As autonomous robotic systems integrate into various domains, ensuring their safe operation becomes increasingly crucial. A key challenge is guaranteeing safe decision making for cyber-physical systems, given the inherent complexity and uncertainty of real-world environments.Tools like Gwendolen, vGOAL, and Tumato enable the use of formal methods to provide guarantees for correct and safe decision making. This paper concerns Tumato, a formal planning framework that generates complete behavior from a declarative specification. Tumato ensures safety by avoiding unsafe actions and states while achieving robustness by considering nondeterministic outcomes of actions. While formal methods claim to manage complexity, provide safety guarantees, and ensure robustness, empirical evaluation is necessary to validate these claims.This work presents an empirical study comparing the characteristics of various ad hoc behavior planning implementations (developed by participants with diverse levels of experience in computer science), with implementations using Tumato. We investigate the usability of the different approaches and evaluate i) their effectiveness, ii) the achieved safety (guarantees), iii) their robustness in handling uncertainties, and iv) their adaptability, extensibility, and scalability. To our knowledge, this is the first participant-based empirical study of a formal approach for (safe and robust) autonomous behavior.Our analysis confirms that while ad hoc methods offer some development flexibility, they lack the rigorous safety guarantees provided by formal methods. The study supports the hypothesis that formal methods, as implemented in Tumato, are effective tools for developing safe autonomous systems, particularly in managing complexity and ensuring robust decision making and planning.

Knowledge, attitude, and practice toward nonsteroidal anti-inflammatory drugs among osteoarthritis patients: a cross-sectional study

Background Nonsteroidal anti-inflammatory drugs (NSAIDs) are common drugs in patients with osteoarthritis (OA). NSAIDs are generally used at home by patients, without supervision, requiring proper knowledge and attitudes for correct practice. This study investigated the knowledge, attitude, and practice (KAP) of patients with OA toward NSAIDs. Methods This cross-sectional study enrolled patients with OA at the Qingpu Branch of Zhongshan Hospital of Fudan University between January and March 2024. The KAP scores and demographic information of respondents were collected through a self-designed questionnaire. Results There were 645 participants, with 579 (89.8%) over 45 years old and 394 (61.1%) females. The average scores for knowledge, attitude, and practice were 16.26 ± 3.79 (possible range: 0–24), 18.12 ± 1.99 (possible range: 5–35), and 29.20 ± 5.52 (possible range: 10–50), respectively. The structural equation model (SEM) found that for individuals currently using NSAIDs, the attitude had a direct effect on practice (β = 0.978, P < 0.001). For individuals not using NSAIDs, the attitude had a direct effect on practice (β = 0.936, P < 0.001). Conclusion This study suggested that adequate NSAID knowledge is the prerequisite for correct NSAID-related medical decisions, while attitude has a crucial intermediary effect. Healthcare professionals and society should strengthen education regarding the relevant knowledge of NSAIDs and guide the cultivation of positive attitudes toward NSAIDs.

Cortex-wide characterization of decision-making neural dynamics during spatial navigation.

Decision-making during freely moving behaviors involves complex interactions among many cortical and subcortical regions. However, the spatiotemporal coordination across regions to generate a decision is less understood. Using a head-mounted widefield microscope, cortex-wide calcium dynamics were recorded in mice expressing GCaMP7f as they navigated an 8-maze using two paradigms. The first was an alternating pattern that required short term memory of the previous trial to make the correct decision and the second after a rule change to a fixed path in which rewards were delivered only on the left side. Identification of cortex-wide activation states revealed differences between the two paradigms. There was a higher probability for a visual/retrosplenial cortical state during the alternating paradigm and higher probability of a secondary motor and posterior parietal state during left-only. Three state sequences (motifs) illustrated both anterior and posterior activity propagations across the cortex. The anterior propagating motifs had the highest probability around the decision and propagating motifs peaked following the decision. The latter, likely reflecting internal feedback to influence future actions, were more common in the left-only paradigm. Therefore, the probabilities and sequences of cortical states differ when working memory is required versus a fixed trajectory reward paradigm.

Assessment on Maritime Students' Awareness, Perceptions and Needs in Career Planning in Tertiary Education

Although seafaring is seen as an attractive profession for prospective students in terms of having the opportunity to see different parts of the world while working and earning more money in less time compared to other professions, the harsh conditions of duties onboard sometimes overshadow this attractiveness, which may lead students to question their choices and seek different career alternatives. This study aims at assessing maritime students' knowledge, perceptions, and needs regarding career awareness and career planning at the tertiary level. The study also seeks to understand students' awareness of career-related activities and the role and responsibilities of the career centre. The research component of the study involves administering a questionnaire to 381 first-year students at a maritime-focused university. The questionnaire comprises twenty questions based on a five-point Likert scale and has been electronically distributed. The data collected has been analysed using parametric methods based on four variables: career anchor, career awareness, career centre awareness, and career planning awareness. The study has revealed that most of the students are aware of their own characteristics and the sacrifices required by their future profession, but they had not yet decided which career path to follow after graduation. The study has also revealed that students are interested in developing their skills through extracurricular activities and consider entrepreneurship as a future career option. The results suggest that career offices in maritime universities should create more opportunities to provide students with adequate information about their future careers and to enable them to make correct and realistic decisions about their career paths. The next study on this topic can focus on how to raise awareness of the functions of career officers and how to develop maritime-oriented career planning courses so that the students can make more realistic and satisfying career choices.

Should voice navigation prompts be earlier and more complicated? A study of drivers’ visual attention, physiology and operating performance

Voice navigation systems can help drivers access information about road conditions and make correct decisions, but inappropriate information level and information prompt timing may lead to untimely processing of information, distraction, or even misjudgment of drivers, which in turn may cause safety hazards. The purpose of this study is to investigate the effects of voice information level and information prompt timing on drivers’ driving behavior and physiological characteristics. Twenty-seven participants were recruited to conduct driving simulator experiments. The results showed that the driver’s visual attention is more focused and the driving performance is better when the voice navigation prompts contain four information units (driving directions, traffic facility information, distance information, and road condition information at the same time). Moreover, when the voice navigation prompts with four information units were played 300 meters from the intersection, the driver’s overall driving performance was the highest. The results offer additional theoretical insights for the design of voice navigation systems, aiding drivers in accurately understanding driving conditions and providing them with ample reaction time to enhance their driving decisions.

Evaluation of the Ecosystem Services Value of Agricultural Industrial Parks under the Background of Tianfu Granary Construction - A Case Study of the Daoming and Longxing Districts in Chongzhou City

In the new era, China faces two major challenges of food security and ecological protection. The agricultural industrial parks constructed in the process of developing the Tianfu Granary play a significant role in China's food supply and ecological security as a special type of farmland ecosystem. Accurately calculating the value of ecosystem services in agricultural industrial parks is of great importance for achieving sustainable agricultural development and comprehensively realizing rural revitalization. This paper takes the Daoming and Longxing Agricultural Industrial Parks in Chongzhou City as the research subjects, constructs an evaluation index system for ecosystem service value, and assesses and calculates the supply, support, regulation, and cultural services of the agricultural industrial parks. The research findings show that the total value of ecosystem services reaches 260.64 million yuan, which is 4.65 times the value of the agricultural products supplied, and its significant ecosystem service value cannot be ignored. Among the ecosystem service values of the park, the cultural service value is the highest, followed by the social security service value. Based on the above conclusions, this paper discusses how to achieve high-standard construction of agricultural industrial parks, providing a scientific basis for the government and policymakers to make correct decisions in the rational use of resources, reduce and avoid short-term economic behaviors that damage ecosystem service functions, and promote sustainable agricultural development.

A deep learning-based model for stock price prediction under consideration of financial news publishers

Stock price prediction with financial news is beneficial for making correct investment decisions. Recent researches mainly focus on extracting sentiments from news. The publishers as a crucial part of news, which can reflect the authority and reliability, but are usually neglected. In this paper, we propose a novel hybrid deep learning model for stock price prediction that considers the financial news publishers. Specifically, a novel feature about financial news publishers is constructed by classifying financial news publishers. Furthermore, a novel hybrid model named 2d-CNN-AM-LSTM is proposed by combining Convolutional Neural Network (CNN), Attention Mechanism (AM) and Long Short-term Memory, so that it can fuse textual and numerical data in-depth. To verify the superiority of the proposed model, numerous baselines are employed to conduct experiments with representative stock datasets. The experimental analysis demonstrates that the proposed model considering financial news publishers surpasses other benchmarks and achieves outstanding prediction performance.

Transcriptomic Signature of Spatial Navigation in Brains ofDesert Ants.

Navigation is crucial for central-place foragers to locate food and return to the nest. Cataglyphis ants are renowned for their advanced navigation abilities, relying on landmark cues and path integration. This study aims to uncover the transcriptomic basis of exceptional spatial learning in the central nervous system of Cataglyphis niger. Ants navigated a maze with a food reward, and we examined expression changes linked to correct decisions in subsequent runs. Correct decisions correlated with expression changes in the optic lobes, but not the central brain, showing a downregulation of genes associated with sucrose response and Creb3l1. The latter gene is homologous to Drosophila crebA, which is essential for long-term memory formation. To understand how ants use distance information during path integration, we analyzed expression shifts associated with the last distance traveled. We uncovered a transcriptomic footprint in the central brain, but not in the optic lobes, with genes enriched for energy consumption and neurological functions, including neuronal projection development, synaptic target inhibition, and recognition processes. This suggests that transcriptional activity in the central brain is necessary for estimating distance traveled, which is crucial for path integration. Our study supports the distinct roles of different brain parts for navigation in Cataglyphis ants.

An auxiliary correction model implemented by the Correction Property of intermediate layer against adversarial examples

In powerful adversarial attacks against deep neural networks (DNN), the generated adversarial example will mislead the DNN-implemented classifier by destroying the features of the last layer. To enhance the robustness of the classifier, in our paper, a Feature Analysis and Conditional Matching prediction distribution (FACM) model is proposed to utilize the features of intermediate layers to correct the misclassification. Specifically, we first prove that the intermediate layers of the classifier still retain effective features for the original category when the classifier is subjected to adversarial attacks, which is defined as the Correction Property in our paper. According to this, we propose the FACM model consisting of Feature Analysis (FA) correction module, Conditional Matching Prediction Distribution (CMPD) correction module and decision module. Specifically, the FA correction module is comprised of fully connected layers, which takes the features of the intermediate layers as the input to correct the misclassification of the classifier. The CMPD correction module is based on a conditional autoencoder, which not only uses the features of intermediate layers as the condition to accelerate convergence but also mitigates the negative effect of adversarial examples, trained with the Kullback–Leibler loss to match prediction distribution. Through the empirically verified Diversity Property among the individual correction modules, the decision module is proposed to integrate the proposed correction modules to enhance the DNN-implemented classifier’s robustness by reducing the dimensionality of adversarial subspace. That is, the input perturbed in certain directions (i.e., dimensions) that lead to misclassifications for the classifier can be correctly classified by the proposed correction modules. The extended experiments demonstrate our FACM model outperforms the existing methods against adversarial attacks, especially optimization-based white-box attacks and query-based black-box attacks.

Turkish Public Diplomacy in the Minutes of the Turkish Grand National Assembly (1931-1938)

For the young Republic of Turkey, the 1930s were a test of international public diplomacy. Turkey, which based its domestic and foreign policy on the principle of “Peace at home, peace in the world!”, was also concerned about its own future with the dictatorial regimes of Germany, Russia and Italy, which had distanced themselves from democratic governance and entered into an arms race with a sense of revenge, as well as the public diplomacy it would conduct and the measures it would take with the countries in its region. During the First World War, the country had suffered great devastation. The Turkish nation had spent everything it had in the struggle for liberation and was almost exhausted. However, it had managed to build a democratic state based on “National Sovereignty” despite the great economic depression that engulfed the world. However, the political developments in Europe and the possibility of the Republic of Turkey entering into a new chaos with the approaching World War could have destroyed all the gains and the future of the country. The task of protecting Turkey from this impending danger and keeping it safe could only be possible with the rational policies pursued by President Gazi Mustafa Kemal Atatürk and the correct decisions taken by the Turkish Grand National Assembly in line with these policies. The aim of this study is to examine the Turkish public diplomacy as reflected in the minutes of the Turkish Grand National Assembly of Turkey between 1932 and 1938 in order to prevent Turkey's involvement in the upcoming great war and to secure its gains with measures to ensure its security in the region.

Theoretical basics of the self-learning system of intelligent locomotive decision support systems

Analysis of works in the field of artificial intelligence allows to make an assumption that today there is a sufficiently developed theoretical basis for the development of intelligent control systems for locomotive control. This will minimize the risks associated with the human factor on the railways. The paper presents the theoretical rationale for the development of a knowledge base for intelligent locomotive control systems. The approach and structure of the self-learning system of intelligent DSS is proposed, the advantage of which is the presence of a fuzzy classifier that works according to the set criteria and determines a fuzzy image of the current train situation. Learning a fuzzy classifier consists in finding a vector K that minimizes the distance between the results of logical inference and experimental data from the sample. The knowledge base is implemented using linguistic variables formalized by methods of fuzzy logic. The use of linguistic values makes it possible to design the base using the usual language of communication, which greatly simplifies both the design process itself and the analysis of the system's performance. Also, the knowledge base has the possibility of constant self-improvement. This happens in two ways. The first is by adding new rules to the knowledge base in case the current situation does not match the existing ones in the base, in which case an additional rule is created and checked for adequacy. The second way is a mechanism for ranking rules in the knowledge base. If the control action of the locomotive driver coincided with the recommendation of DSS in the current situation, then the rating of this recommendation (rule) increases, and in the future the rule selection algorithm will choose one or another control action for the current situation that has the highest rating (that is, it has already been verified several times person). The experiment has shown that the use of intelligent DSS has positive results. On average, the DSS made the correct train control decisions faster than the locomotive driver.

Influence of problem-solving ability and personality variables on the improvement and creativity of tactical decisions in basketball.

Basketball players are increasingly required not only to read the tactical actions that take place in a game but also to make correct tactical decisions. This includes greater creativity in the type of solutions they must provide when faced with various tactical situations. To acquire these skills, young players need training in which personality and general intelligence variables significantly influence the learning process. A Perceptual Tactical Training (PTT) program was implemented, correlated with the Eysenck Personality Questionnaire and the Raven's Progressive Matrices test, to observe improvements in tactical perception and decision-making among young basketball players. The PTT produced significant improvements in tactical solutions and tactical perception for all players. Players who scored high in general intelligence found it easier to perceive tactical situations but struggled to generate original tactical solutions. Conversely, players with high psychoticism and high general intelligence were more capable of creating innovative tactical solutions but did not excel in perceiving tactical game situations. Therefore, it is crucial to be aware of the individual differences in intelligence and personality traits among young players to enhance their tactical decision-making and creative abilities.

Unusual familial cystic kidney disease: combining fine radiologic and genetic evaluation to solve the case

BackgroundAutosomal dominant polycystic kidney disease (ADPKD) is the most prevalent hereditary kidney disease, characterized by enlarged kidneys with numerous cysts, high blood pressure, and a variety of extrarenal complications. This disease is a significant cause of renal failure and requires accurate differentiation from other cystic kidney diseases, especially when family history does not clearly indicate ADPKD. This is crucial due to differences in prognosis, treatment, and familial implications. Advanced molecular genetics and imaging techniques are employed to diagnose and assess the prognosis of patients and their families.Case presentationThe case study revolves around three patients from the same family—two sisters and one daughter—referred to a nephrology department for ADPKD management. The initial proband, a 42-year-old woman, experienced abdominal discomfort leading to an ultrasound that suggested ADPKD. However, MRI findings indicated standard-sized kidneys with bilateral parapelvic cysts, and no genetic markers for ADPKD were found. Her sister, presenting with controlled hypertension and similar ultrasound findings, also had her initial ADPKD diagnosis refuted by MRI and genetic testing, which revealed no significant mutations. The daughter, however, exhibited a different scenario with enlarged kidneys and multiple cysts characteristic of early-stage ADPKD. Genetic testing confirmed a deleterious PKD1 mutation, suggesting a de novo mutation, as her father showed no signs of the disease.ConclusionThis study highlights the complexity and necessity of thorough diagnostic processes in suspected ADPKD cases to prevent misdiagnosis. The initial symptoms and imaging might misleadingly suggest ADPKD, as seen in the cases of the two older patients. Still, further detailed imaging and genetic analyses revealed no ADPKD, preventing inappropriate treatment and stress. In contrast, the younger patient's distinctive clinical and genetic profile confirmed ADPKD, illustrating the variability within even closely related individuals. Such detailed assessments are crucial in guiding correct treatment decisions and providing accurate familial counseling, emphasizing the importance of considering a broader spectrum of renal cystic disorders before confirming a diagnosis of ADPKD.

Robust and highly reliable oil leakage detection with optimal performance in faulty and normal conditions

This paper proposes the design of robust and highly reliable oil leakage detection with optimal performance in faulty and normal conditions. Flow rates and sound are two parameters considered for the design of this project. The purpose of this design is to ensure that faults do not result in malfunctioning and system failure and to achieve the best performance even with minimum number of input and output devices working properly. The fault detection and isolation (FDI) problem is an inherently complex one and for this reason, the immediate aims are to preserve the stability of the process and if it is possible, to control and continue the process in a slightly degraded manner. The role of the FDI algorithms is to ensure that the control equipment must make correct decision. This system has two flow stations that are connected by a pipeline and flow rate difference is used to detect leakage. First flow station has 2 sound alarms and pumps as output devices and 2 flow meters and start button as input devices. Also the second flow station has the same arrangement as the first flow station except that it does not have a pump. Both flow stations use mobile phones as modems to send and receive short message services. The essence of the two flow meters and two sound alarms at each of the flow stations is to ensure optimal performance and robustness even if one or more of the devices failed. Once, the system starts, the first flow station pumps crude oil to the second flow station while the former also sends the flow rate at its end M1 to the later. Then, the second flow station measures its flow rate as M2 and compares it with M1 so as to check whether the difference is still within the allowable threshold. If the difference between M1 and M2 is within the allowable threshold, the system continues working but once the difference is above the recommended threshold, the second flow station sends short message services (sms) to the first flow station and equally alerts its workers using sound alarm. Then the first flow station switches off the pump and equally alerts its worker that leakage has occurred using the sound alarms. The developed system showed that at sensors (input devices) and actuators (output devices) reliabilities of 50% and above, the system efficiency was up to 95%. This system would go a long way to ensure robust oil leakage detection in our oil industries, if implemented.

Utilization of Open Polygon Method for Land Mapping in Mojoroto Kediri with Geospatial Approach

Land mapping is an important research activity to determine the location of points on the earth's surface, describing the physical condition of parts of the surface that resemble the actual condition. The objective of this process is to gain a comprehensive understanding of the land to be built and used through land area measurements, topographic maps, and land volume analysis at various altitudes. In this mapping, the open polygon method is used for calculations which are conventional methods using data point values as data centers to represent the area of influence. During this mapping, there are three main points used with the open polygon method. A total of 26 ground contours were recorded, and 4 road contours recorded. During the mapping process, corrections are required on the open polygon calculations to ensure the accuracy of the results. This correction involves adjusting the data to take into account field variability and inaccuracies of measuring instruments, so that the resulting data can be reliable for further analysis and correct decision-making. The land that has been painted can be used for various fields such as construction, and mining. Land mapping allows partial surface physical state imaging, classification of land, and can be used to plan, build, and maintain infrastructure such as highways and bridges. The result of this practice obtained data of the maximum height of 90,0945 masl and minimum 88,76 masl with the average ground height 90,05059 masl, while the average highway height is at 88,86175 masl.

Incorporation of computational routines in a microservice architecture in AgDataBox platform

Agriculture has been undergoing a digital process that aims to apply digital technologies to make the sector more productive, profitable, and environmentally responsible. This trend has been adopted since applying precision agriculture (PA) techniques and, more recently, with digital agriculture (DA). DA aims to use all available information and knowledge to enable the automation of sustainable processes in agriculture, applying data analysis methods and techniques by specific software and platforms to collect and transform data into meaningful information for agriculture. Platform AgDataBox (ADB) offers tools to allow agriculture specialists to obtain, process, and visualize data for the correct decision-making. However, its structure needed to be readjusted to new software architecture to allow the aggregation of new functionalities and expand the ADB platform. This study aimed to develop a web microservices architecture (ADB-MSA) to incorporate the required functionalities to create thematic maps (TMs) and delineate management zones (MZs). ADB-MSA provided eight microservices, six of which (statistics, spatial, interpolation, clustering, rectification, and lime/nutrient recommendation) execute procedures based on JavaScript, R, and Python programming languages. At the same time, the other two are used to store data. In the case study, the procedures to create TMs and delineate MZs were performed with data from one commercial area. Thus, the services provided in the architecture meet the steps of creating TMs and delineating MZs, as MZs for fertilizer application were generated and evaluated according to phosphorus and potassium requirements. ADB-MSA allows the development of several new client applications (web, mobile, desktop, and embedded systems) to promote solutions in agriculture, streamlining processes, as it abstracts the implementation and execution complexity of available algorithms.