Arturo Fajardo

Accurate current-consumption estimation in IEEE 802.15.4 Wireless Sensor Networks requires linking network operation to the hardware states that determine node energy use. This paper proposes a cross-layer energy-modeling methodology that combines experimentally characterized Current Consumption Profiles (CCPs), protocol-derived state-duration models, topology-dependent workload, and a two-stage probabilistic model for retransmissions. The methodology distinguishes structurally constrained states, empirically modeled state durations, and discrete stochastic communication events. In the evaluated CC2538–ContikiMAC implementation, transmission duration is modeled from payload size, ContikiMAC strobe activity, and Clear Channel Assessment (CCA) timing, while sleep duration is obtained from the residual communication-cycle constraint. Retransmission occurrence is represented by a Ridge-regularized logistic model using measured CCA duration, whereas the positive retry count is represented by its calibrated conditional mean. The resulting probabilistic component is integrated with the deterministic CCP formulation to estimate the additional charge associated with retry-enabled communication events. Full-network verification across 30 node–condition observations yielded an overall MAPE of 10.81%, WMAPE of 10.51%, and condition-specific MAPEs ranging from 4.60% to 15.78%.

In cellular communications markets, various stakeholders interact while considering regulatory policies enacted by governments to maximize social welfare. However, there remains an opportunity to develop new tools that support countries in making informed decisions regarding the adoption of strategies aimed at enhancing societal well-being. Accordingly, network economics and game theory offer a relevant framework for analyzing such policies. In this regard, the state-of-the-art lacks a market model for cellular communications that incorporates stakeholder interactions under a subsidization scheme designed to maximize the system’s social welfare. This paper introduces a novel subsidization scheme, formulated as a constrained optimization problem involving three stakeholders: the central planner, the cellular network operator, and the aggregate cellular data users. In this context, the central planner and the cellular network operator, acting as strategic players, optimize their respective payoff functions by incorporating benefit factors that represent their interactions, accounting for both attributes and behavioral aspects. Their decision variables include the subsidization factor, bandwidth allocation across the spectrum license, perceived service quality, and data plan pricing. Meanwhile, the aggregate cellular data users consume cellular data based on optimal pricing and perceived service quality. To capture the interactions among these players, a two-stage dynamic game is proposed, employing the backward induction method in combination with an algorithm to derive the subgame perfect equilibrium from the Nash equilibrium. A case study demonstrates a 46.35% increase in social welfare and a 24.10% in cellular network operator’s profit margin by comparing the results associated with the subsidization factor at its optimal value and when it is not optimal. Furthermore, the proposed model incorporates additional elements beyond those typically considered in the literature such as network infrastructure costs, the number of base stations, and the users-to-base-station ratio resulting in a more realistic and practically relevant model for decision-making in cellular communications markets.

Hybrid power converters that cascade switched-capacitor stages with conventional LC-based DC/DC switching converters have recently emerged as promising solutions for applications requiring a bipolar DC bus, such as photovoltaic systems and in-chip OLED drivers. However, despite clear advantages in efficiency, compactness, and implementation simplicity, their adoption has been limited due to the lack of accurate modeling and experimental validation. Traditional modeling techniques often fail to capture the static and dynamic behavior of these hybrid topologies. Some previous works either neglected capacitive losses or relied on empirical equivalent models without a clearly defined methodology. The Perilla SIBO (Single-Input Bipolar-Output) Boost Converter is a representative example of this new class of hybrid converters, integrating a conventional Single-Inductor Single-Output (SISO) boost stage with a switched-capacitor inverter. In earlier work, a static model for the Perilla Boost converter was derived using the small-ripple approximation and charge balance principles, assuming matched output capacitors and balanced load conditions—but without considering efficiency estimation. This paper extends the previous model to account for unbalanced loading and mismatched output capacitances and introduces a novel analytical expression to estimate converter efficiency. A comprehensive static modeling and experimental evaluation is presented under realistic operating conditions. The model is validated using a prototype operating across an input voltage range of 3 V to 10 V and delivering output power between 1 W and 20 W. The proposed model achieves a mean absolute percentage error of 5.76% and a standard deviation of 5.68%, offering a reliable tool for steady-state performance prediction. The dynamic modeling of the converter, as well as the development of a systematic design methodology, are beyond the scope of this work.

Decentralized desalination systems driven by renewable energy sources have surfaced as a feasible way to alleviate water scarcity in arid and rural areas. This bibliometric study aims to clarify the research trends, conceptual frameworks, and cooperative dynamics in the scientific literature on decentralized renewable-powered desalination techniques. Using a thorough search approach, 1354 papers were found. Duplicates, thematically unrelated works, and entries with poor information were removed using the PRISMA 2020 framework. A selected 832 relevant papers from a filtered dataset were chosen for in-depth analysis. Quantitative measures were obtained by means of Bibliometrix; network visualisation was obtained by means of VOSviewer (version 1.6.19) and covered co-authorship, keyword co-occurrence, and citation structures. Over the previous 20 years, the data show a steady rise in academic production, especially in the fields of environmental science, renewable energy engineering, and water treatment technologies. Author keyword co-occurrence mapping revealed strong theme clusters centred on solar stills, thermoelectric modules, reverse osmosis, and off-grid systems. Emphasizing current research paths and emerging subject borders, this paper clarifies the intellectual and social structure of the field. The outcomes are expected to help policy creation, cooperative projects, and strategic planning meant to hasten innovation in sustainable and decentralized water desalination.

The present work highlights the differences between two simultaneous groups taking the electric circuit theory course. Both groups were given the same non-mandatory homework prior to the evaluation; however, the instructions provided to the groups differed. Historically, this homework has been assigned without any guidelines, and instructors have not been aware of whether students completed the task. In this study, specific instructions were developed to simulate the same stressful conditions regarding time constraints for finding the correct answer that students face during an exam. The results indicate that the group of students who received categorized exercises along with a structured schedule performed better than the non-guided group (2.8/5 versus 2.3/5 on average). The study also reveals that a significant portion of students engaged with this optional activity during the initial weeks (45%), but this engagement decreased substantially over time (to 5%), showing a correlation between evaluation results and commitment to the exercises. Sharing these findings with students may help improve performance in a course that historically has had a pass rate below 50%.

24-pulse diode rectifiers have been extensively studied and adopted in industrial applications due to their low output ripple, high average voltage, and low input current harmonic distortion. Increasing the number of rectified pulses has been proven to be effective for canceling lower-order harmonics. To do so, some proposed topologies use multi-tap Interphase Reactors (IPR) or transformers for cascading rectifiers. However, that approach includes the design and implementation of additional windings, increasing the cost and weight of the equipment. In this paper, a three-phase 24-pulse diode rectifier topology will be proposed, obtained from two 12-pulse diode rectifiers connected in cascade. Our proposed configuration avoids the use of interface transformers or reactors, representing a reduction in cost and design complexity. The proposed topology will find its main application in equipment requiring electrical isolation between input and output, such as HVDC transmission. The results obtained from the measurement of a reduced-scale model show good agreement with the theoretically analyzed output voltage and input current. The final measured output voltage was 547.6 V, and the input current total harmonic distortion (THD) was 5.8%.

Several wireless communication technologies, including Wireless Sensor Networks (WSNs), are essential for Internet of Things (IoT) applications. WSNs employ a layered framework to govern data exchanges between sender and recipient, which facilitates the establishment of rules and standards. However, in this conventional framework, network data sharing is limited to directly stacked layers, allowing manufacturers to develop proprietary protocols while impeding WSN optimization, such as energy consumption minimization, due to non-directly stacked layer effects on network performance. A Cross-Layer (CL) framework addresses implementation, modeling, and design challenges in IoT systems by allowing unrestricted data and parameter sharing between non-stacked layers. This holistic approach captures system dynamics, enabling network design optimization to address IoT network challenges. This paper introduces a novel CL modeling methodology for wireless communication systems, which is applied in two case studies to develop models for estimating energy consumption metrics, including node and network lifetime. Each case study validates the resulting model through experimental tests, demonstrating high accuracy with less than 3% error.

Isolated water and energy microgrids (IWEMGs) serve as vital solutions for enhancing the well-being of remote and rural communities, particularly in areas where water and energy resources are scarce. This has spurred research into the interdependence between the water and energy sectors (water–energy nexus), a field that has grown in response to technological advancements. Through a systematic optimization framework, this review critically evaluates the integration of various technologies within IWEMGs, encompassing infrastructure, management, and strategic planning, while considering economic and social impacts. IWEMGs incorporate diverse technologies for the infrastructure, management, and strategic planning of water and energy resources, integrating economic and social considerations to inform decisions that affect both immediate and long-term sustainability and reliability. This article presents an exhaustive review of the literature on IWEMG management, employing an approach that synthesizes existing studies to enhance the understanding of strategic IWEMG management and planning. It introduces a structured taxonomy for organizing research trends and tackling unresolved challenges within the field. Notably, the review identifies critical gaps, such as the lack of comprehensive data on water demand in isolated locations, and underscores the emerging role of game theory and machine learning in enriching IWEMG management frameworks. Ultimately, this review outlines essential indicators for forthcoming research, focusing on the optimization, management, and strategic planning of IWEMG resources and infrastructure, thereby setting a direction for future technological and methodological advancements in the field.

Aloe Barbadensis Miller, commonly known as Aloe vera, has been widely used in different applications, such as medicinal treatments and cosmetic products. However, its transportation and handling present challenges due to oxidation and property loss caused by direct environmental exposure. A strategy to mitigate these effects is dehydration, where different industrial-scale methods such as freeze-drying, spraying, refractory windows, and convective drying can be applied. Despite their effectiveness, those dehydration techniques are both energetically and economically costly. Solar drying technology offers a cost-effective, lower-energy alternative addressing sustainability, socioeconomic, scientific progress, and integrated sustainable development challenges. Nevertheless, solar drying through direct sunlight exposure has been minimally explored for drying high-water-content products like Aloe vera, potentially due to the inherent challenges of drying under uncontrolled environmental conditions. In response, this paper introduces a methodology for pre-treating and pre-drying Aloe vera gel using a low-cost solar dryer prototype, achieving up to 50% water activity reduction in experimental tests under uncontrolled conditions in Colombia, South America. The proposed prototype features a drying cabinet with energy autonomy and forced convection. The experimental evaluation compares the quality of pre-dried Aloe vera gel with freeze-dried samples, demonstrating comparable attributes under favorable environmental conditions. The results demonstrate the feasibility of pre-drying Aloe vera gel within 13 to 48 h, with a maximum drying rate of 0.38 g/min. During this process, water activity decreased from an initial value of 0.975 to a final value ranging between 0.472 and 0.748. Furthermore, the quality of the dehydrated gel was assessed through color analysis, comparing it with a freeze-dried sample. Subsequent color analysis of the freeze-dried samples revealed minor changes in product quality compared to those dried using the proposed solar drying method. These results demonstrate the effectiveness of the proposed solar dryer in pre-dehydrating Aloe vera gel, yielding characteristics similar to those achieved through conventional methods.