This study details the optimal characteristics of these systems to design an ideal pumping solution that maximizes agricultural productivity while reducing costs and ecological footprint. The designed system is an off-grid solar pump control device equipped with an MPPT controller for 24V DC photovoltaic panels operating within a 30-48V range. This system operates directly under solar irradiation, eliminating the need for energy storage. A major innovation of this system is its ability to regulate the filling of the water tank based on the measured water flow. When sensors detect low flow, the system automatically activates the water tank recharge and stops its activity when the flow reaches a predetermined threshold, thus optimizing the efficiency of water use for irrigation. The advanced architecture of the system integrates controllers capable of compensating for solar power fluctuations and intelligent sensors to automate the pumping process according to crop water needs. This systemic approach offers a robust and sustainable method to improve water management in agricultural operations, contributing to sustainable development goals and resilience to climate change.

Nowadays, logistics activities are growing annually by 4.4%, reaching annual valuations of up to 10 billion dollars. This area is key for manufacturing companies and commerce in general, which is why the central question arises in this area of knowledge about how to approach proposals or improvement actions in a sustainable way. On the other hand, technological tools such as simulation are an important element to evaluate sustainability alternatives and operational strategies due to their flexibility and high scope. This article addresses a systematic literature review about the use of system dynamics within the management of current supply chains, covering 144 articles in databases such as Scopus and ScienceDirect, for the period between 2000 and 2022 to generate an overview for new sustainability proposals in México.

Earned Value Management (EVM) is a project management tool primarily used in engineering and project management to assess performance in terms of cost and schedule. Earned Value Management/Scheduling systems (EVM/ES) have been fundamental in project control, providing key metrics that measure deviations between planned and actual performance in terms of time and cost. However, its application as a project control technique is not very common in Mexico. In this article, EVM was applied to the intralogistics improvement of a manufacturing process in the automotive sector located in Aguascalientes, Mexico. The case study concluded in February 2024 and includes the project scope, scheduling charts, physical progress reports, and budgeted versus actual cost reports. The aim of this article is to provide practical evidence on how to apply EVM in manufacturing projects in Mexico. This will enable project professionals to more effectively utilize EVM for schedule and cost control in their manufacturing projects, with a specific emphasis on intralogistics.

This paper delves into the rapidly evolving domain of Artificial Intelligence (AI), with a particular focus on Machine Learning (ML), a dynamic and influential subset of AI. It explores how ML empowers computers to learn from data, identify patterns, and make decisions with minimal human intervention. The manuscript examines the broad utility of ML across various real-world scenarios, emphasizing its critical role in enabling organizations to evolve and maintain a competitive edge in the fast-paced technological landscape. It discusses the necessity for organizations to adopt new ways of working and embrace the opportunities presented by AI to remain viable in the global, online marketplace. The paper reviews the evolution of ML, evaluates its advantages and disadvantages, and contemplates the future directions ML could lead organizations willing to integrate this powerful technology. The overarching theme is the transformative potential of ML in reshaping organizational strategies and operations for a more interconnected and intelligent future.

Understanding Oil Formation Volume Factor βo is crucial for effective oil field development, impacting well performance analysis, reservoir simulation, and production engineering calculations. Traditionally, βo is determined through costly and time-consuming laboratory tests, prompting the need for accurate mathematical correlations. Existing correlations such as Vasquez-Beggs, Standing-Glaso, and others have been widely used but show varying degrees of accuracy across different operating conditions. In this study, these correlations were evaluated against 95 datasets of experimental βo data for Sudanese crude oils. Statistical analysis revealed that Vasquez-Beggs and Standing- Glaso models performed best, with average absolute errors of 3.4219 and 3.4477, and correlation coefficients of 0.7563 and 0.7213 respectively. Motivated by the limitations of existing correlations, a new ap- proach using Polynomial Neural Networks (PNN) was developed. This model utilized reservoir temperature, gas gravity, gas oil ratio, and API as input parameters, trained on 70% of the dataset and tested on the remaining 30%. The PNN model exhibited superior predictive performance with a relative average absolute error of 2.8607 and a correlation coefficient of 0.9080. This study contributes a robust predictive tool for estimating βo in Sudanese oil fields, offering enhanced accuracy over traditional correlations and facilitating more reliable reservoir management decisions.

Bakery nutritional products are limited in a global market considering the consumer preferences. Recent years have seen a surge in publications, especially in India and Latin America, reflecting nutritional and development of new products challenge, however, there are project management tools that could help in the development of new products because proactive approaches are necessary to navigate bakery product development complexities. Malnutrition and obesity pose global health challenges, elevating the importance of providing more nutritious bakery options. This systematic literature review explores the intersection of bakery product development with project management methodologies, emphasizing nutritional enhancement and consumer acceptance by the analysis of 69 articles from 2013 through 2024 from prestigious database such as Scopus and Redalyc to generate an overview for new future projects related to enhance the sector in Mexico. The study identifies multiple opportunities in bakery product research. The most researched products in this area are bread, cake, cookies and tortillas compared to pasta, pizza, pudding, waffles, etc. Additionally, only a quarter of articles explore new commercial prototypes, indicating significant potential for further development in this area.

The COVID-19 epidemic has created unprecedented challenges for global economies, affecting every industry, including the building construction industry in Nepal as well. This research aims to examine the impact of the COVID-19 epidemic on the building construction industry in Nepal. This study examines institutional, psychological, individual, operational, contractual, and financial factors. To achieve the objective, data were collected from 330 Nepalese construction professionals using a structured Likert scale questionnaire and analyzed with Smart PLS version 3 software for partial least squares structural equation modeling. The reliability and validity of both the measurement and structural models were tested and found satisfactory. All six factors were found to be significant at a 5% level of significance. Among all factors, the institutional factor was found as the most significant factor with a t-value of 7.654 and a beta value of 0.679, emphasizing the crucial role of institutional support in Nepal's building construction industry. The psychological factor also emerged as the second most significant influential factor (t value: 6.087, beta value: 0.463), underscoring the profound effect on the mental well-being of professionals in the field. The finding highlights the critical importance of institutional support and the profound influence of psychological factors on the well-being of construction professionals, necessitating targeted interventions to support the industry's recovery and resilience.

Security monitoring is a crucial aspect of cybersecurity and a prong of organizational cybersecurity policies. It is achieved primarily using SIEM tools supported by logs ingested from intrusion detection tools and other security solutions. SIEM tools generate alerts of varying severities when detection rules identify anomalies or possible security incidents after analysis of ingested logs. These alerts need to be investigated, but due to the volume of alerts generated and the limited monitoring manhours, it is important to prioritize which security alerts are investigated first. This paper presents a sliding window technique for prioritizing security events by computing a priority value using the severity of previous alerts, alert entities, and criticality ratings. Findings from the experiment show that this approach improves the prioritization of security alerts with severe and medium alerts affecting critical systems prioritized over low, high, and critical alerts affecting non-critical systems. This work can potentially streamline and enhance the efficiency of security monitoring operations.

In today's rapidly evolving technological landscape, artificial intelligence (AI) and machine learning (ML) stand out as pivotal elements. Their integration into the financial domain is particularly noteworthy, revolutionizing the sector through continuous advancements. With each passing day, these technologies introduce more sophisticated techniques to bolster financial intelligence. This necessitates a comprehensive evaluation of their influence on finance, examining both the current enhancements and the future possibilities they present. It’s crucial to assess the transformative power of AI and ML in finance and to anticipate the emerging opportunities they may unveil.

Supply chain management (SCM) is crucial for companies looking to enhance their business processes, with information technologies playing a significant role in revolutionizing SCM. This paper analyzes modern technologies used in SCM, focusing on logistics, information technology, and supply chain management. It explores how technology is utilized in various sectors where SCM is prevalent. Previous research demonstrates a wide array of technologies used in logistics and SCM, with this analysis highlighting a few key ones. The study reveals that new technologies greatly improve SCM by enhancing quality, efficiency, effectiveness, productivity, and reducing costs. It also investigates the diverse effects of technologies on SCM and logistics, showcasing real-world case studies of successful technology implementation. Overall, the analysis emphasizes the importance of contemporary technologies in advancing organizational progress, particularly in SCM optimization. It provides valuable insights into the benefits and practical applications of technological advancements in SCM, while also acknowledging their disruptive potential.

The prediction model equation of a draught force of animal-drawn Mouldboard plough has several advantages in improving tillage performance in smallholder farming systems. These include fully utilizing input data for implement designers and extension workers, proper usage of draught animals, and minimizing operator tragedy. A 1 x 3 x 3 factorial experimental design was arranged in a Randomized Complete Design (RCD) on three blocks of test plots each measuring 25 m x 80 m to generate input parameters for predicting the draught of animal-drawn mouldboard plough on the sandy loam soils at Yola. The model input parameters include implement mass, operation speed, operation depth, soil moisture content, and bulk density. A pair of oxen weighing 560 kg was used as a power source. The highest mean draught values of 436.40 N and the lowest of 381.47 N were obtained at a speed and depth combination of 1.25 m/s and 0.183 m and 0.69 m/s and 0.083 m respectively. A mathematical model with a correlation between the measured and predicted r2 value of 0.9683 was developed using the concept of Buckingham's Pi theorem. The model developed effectively predicted draught for animal implements by 96.83 %. A paired t-test revealed no significant difference between the measured and the predicted values at 0.05 significant levels. The result showed that a developed mathematical equation can effectively predict the draught force of mouldboard plough in sandy loam soil.