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.

Multiple input-multiple output (MIMO) is a key enabling technology for the next generation of wireless communication systems. However, one of the main challenges in the implementation of MIMO system is the complexity of the detectors when the number of antennas increases. This aspect will be crucial in the implementation of future massive MIMO systems. A flexible design can offer a convenient tradeoff between detection complexity and bit error rate (BER). Deep learning (DL) has emerged as an efficient method for solving optimization problems in different areas. In MIMO communication systems, neural networks can provide efficient and innovative solutions. This paper presents an efficient DL-based signal detection strategy for MIMO communication systems. More specifically, a preprocessing stage is added to label the input signals. The labeling scheme provides more information about the transmitted symbols for better training. Based on this strategy, two novel schemes are proposed and evaluated considering BER performance and detection complexity. The performance of the proposed schemes is compared with the conventional one-hot (OH) scheme and the optimal maximum likelihood (ML) criterion. The results show that the proposed OH per antenna (OHA) and direct symbol encoding (DSE) schemes reach a classification performance F1-score of 0.97. Both schemes present a lower complexity compared with the conventional OH and the ML schemes, used as references. On the other hand, the OHA and DSE schemes have losses of less than 1 dB and 2 dB in BER performance, respectively, compared to the OH scheme. The proposed strategy can be applied to adaptive systems where computational resources are limited.

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.