Current computing platforms encourage the integration of thousands of processing cores, and their interconnections, into a single chip. Mobile smartphones, IoT, embedded devices, desktops, and data centers use Many-Core Systems-on-Chip (SoCs) to exploit their compute power and parallelism to meet the dynamic workload requirements. Networks-on-Chip (NoCs) lead to scalable connectivity for diverse applications with distinct traffic patterns and data dependencies. However, when the system executes various applications in traditional NoCs—optimized and fixed at synthesis time—the interconnection nonconformity with the different applications’ requirements generates limitations in the performance. In the literature, NoC designs embraced the Software-Defined Networking (SDN) strategy to evolve into an adaptable interconnection solution for future chips. However, the works surveyed implement a partial Software-Defined Network-on-Chip (SDNoC) approach, leaving aside the SDN layered architecture that brings interoperability in conventional networking. This paper explores the SDNoC literature and classifies it regarding the desired SDN features that each work presents. Then, we described the challenges and opportunities detected from the literature survey. Moreover, we explain the motivation for an SDNoC approach, and we expose both SDN and SDNoC concepts and architectures. We observe that works in the literature employed an uncomplete layered SDNoC approach. This fact creates various fertile areas in the SDNoC architecture where researchers may contribute to Many-Core SoCs designs.

Remote laboratories are essential in addressing access and quality challenges in technical education. They enable students from various locations to engage with real equipment, overcome geographic and economic constraints, and provide solutions during crises, such as pandemics, when in-person learning is limited. As a key element of Education 4.0, remote labs promote technical skill development, enhance engineering education, and support diverse learning approaches. This study presents a remote laboratory based on Field Programmable Gate Arrays (FPGAs), developed using a waterfall methodology integrating IoT and Cloud Computing technologies to facilitate close interaction between hardware and software. The lab focuses on controlling DC, servo, and stepper motors, allowing students to apply theoretical concepts such as digital signals, pulse-width modulation (PWM), and data representation in bits in a practical setting. The testing phase involved 50 robotics and mechatronics engineering students who participated in hands-on sessions for one month, followed by a structured survey evaluating their experience, interaction, and the educational relevance of the platform. The survey shows high student satisfaction, highlighting the platform’s strengths and identifying areas for improvement. The results also underscore the system’s potential to significantly enhance the educational experience in remote environments, aligning with the United Nations Sustainable Development Goals (SDGs).

Managing Multi-Processor Systems-on-Chip (MPSoCs) is becoming increasingly complex as demands for advanced capabilities rise. This complexity is due to the involvement of more processing elements and resources, leading to a higher degree of heterogeneity throughout the system. Over time, management schemes have evolved from simple to autonomous systems with continuous control and monitoring of various parameters such as power distribution, thermal events, fault tolerance, and system security. Autonomous management integrates self-awareness into the system, making it aware of its environment, behavior, and objectives. Self-Aware Cyber-Physical Systems-on-Chip (SA-CPSoCs) have emerged as a concept to achieve highly autonomous management. Communication infrastructure is also vital to SoCs, and Software-Defined Networks-on-Chip (SDNoCs) can serve as a base structure for self-aware systems-on-chip. This paper presents a survey of the evolution of MPSoC management over the last two decades, categorizing research works according to their objectives and improvements. It also discusses the characteristics and properties of SA-CPSoCs and explains why SDNoCs are crucial for these systems.

Heavy metal contamination of foods remains a persistent global challenge for food safety and public health, driven by industrialization, mining activities, intensive agriculture, and ongoing environmental degradation. This scoping review synthesizes peer-reviewed literature on the occurrence of priority toxic metals—arsenic, cadmium, lead, mercury, and nickel—in food matrices, with emphasis on contamination pathways, analytical detection strategies, and documented human health effects. The reviewed studies reveal widespread accumulation of heavy metals in staple foods, including cereals, vegetables, seafood, and processed products, with concentrations frequently approaching or exceeding international regulatory limits, particularly in regions exposed to strong anthropogenic pressure. Conventional laboratory-based techniques, such as atomic absorption spectrometry and inductively coupled plasma methods, remain the reference standards for quantitative determination and regulatory compliance; however, their application to large-scale or continuous monitoring is often constrained by cost, infrastructure, and operational complexity. Consequently, increasing attention has been directed toward emerging detection approaches, including portable X-Ray fluorescence, Raman/SERS spectroscopy, electrochemical biosensors, electronic tongues, and in situ magnetic measurements, as complementary tools for rapid screening and field-based surveillance. Among these, environmental magnetism and in situ magnetic techniques stand out as non-destructive, low-cost proxies capable of identifying metal-associated particulate contamination linked to food production systems. Chronic dietary exposure to heavy metals is consistently associated with neurotoxicity, nephrotoxicity, carcinogenicity, and oxidative stress, underscoring the need for integrated, multi-tiered monitoring frameworks to support early detection, risk assessment, and prevention.

Mining activities are conducted to extract valuable minerals from the Earth, which are used to manufacture many objects. However, these operations generate landform alterations, such as deep excavations, artificial embankments, and landscape reshaping. In this study, landform changes were monitored in a mining area in Mazapil, Zacatecas, Mexico, using geomatic techniques. Multitemporal Landsat satellite images and digital elevation models (DEMs) from different years were used to detect and quantify landform alterations and estimate the volumes of removed material. The results show ground depressions greater than −333 m and waste material accumulations greater than +152 m, with an average standard deviation of ±3.6 m. A total excavation volume of 413.524 million m³ and a total fill volume of 431.194 million m³ were quantified, with an estimated standard deviation of ±810 m³. The proposed methodology proved effective for the remote quantification of large-scale relief disturbances in open-pit mining areas. It can also be used for environmental monitoring and hydrological risk assessment in active and inactive mining areas.

This study assessed the spatial distribution and composition of airborne particulate matter within a 10 km long urban green corridor in Zacatecas, Mexico, using magnetic biomonitoring with Tillandsia recurvata and SEM-EDS particle characterization. A total of 44 samples were collected from distinct urban park contexts (e.g., commercial zones, malls, bus stops), revealing mass-specific magnetic susceptibility χ values ranging from −6.71 to 61.1 × 10⁻⁸ m³ kg⁻¹. Three compositional groups were identified based on a PCA performed using elemental concentrations from SEM-EDS and magnetic data, which are associated with traffic emissions and industrial inputs. SEM-EDS images confirmed abundant magnetite-like particles (1–8 μm) with hazardous metals including Pb (up to 5.6 wt.%), Ba (up to 67.6 wt.%), and Cr (up to 31.5 wt.%). Wind direction data indicated predominant SSW–NNE transport, correlating with hotspots in central and northeastern park areas. Overall, vegetated zones exhibited markedly lower magnetic loads (mean χ = 8.84 × 10⁻⁸ m³ kg⁻¹) than traffic-exposed sites (mean χ = 17.27 × 10⁻⁸ m³ kg⁻¹), representing an approximate 50% reduction in magnetic particle accumulation, which highlights the effective role of continuous vegetation cover as a functional green barrier that attenuates the lateral transport and deposition of airborne particulate matter within the park. This research highlights the applicability of combined magnetic and microscopic techniques for evaluating the dynamics of airborne pollution in urban parks and supports their use for identifying both pollution hotspots and mitigation zones, reinforcing the role of urban green spaces as biofunctional filters in cities facing vehicular air pollution.