Tecnologico Nacional de Mexico

In rough set theory, a construct is defined as a subset of attributes possessing the same capacity as the complete set of attributes to discern objects from different classes, while preserving similarity between objects from the same class. In the literature, it has been shown that algorithms designed for computing reducts or typical testors can be modified to calculate constructs. However, in practice, there are scenarios where even the fastest algorithms in the current state-of-the-art struggle to compute all constructs within a reasonable time-frame. This paper presents a novel algorithm to compute all constructs within a decision table to reduce this gap. Our proposed algorithm, RCC-MAS, works on the binary discernibility-similarity matrix and employs a recursive approach to reduce the search space systematically by analyzing minimum attribute subsets whose attributes, when excluded, lead to rows with zeros in those attributes in the matrix, violating the construct definition. This strategy reduces the number of subsets generated, focusing on attributes essential for constructs; additionally, we demonstrate theoretically that all constructs are computed. Experimental evaluations spanning several synthetic and real-world decision tables reveal that RCC-MAS is the best option to compute constructs regardless of the density of the SBDSM.

This study presents the design of a decentralized terminal sliding‐mode (TSM) controller to solve the trajectory tracking problem of a composite robotic device made up of two‐dimensional Cartesian and multiple‐degree‐of‐freedom robotic manipulators. The dynamics of the proposed composite robotic device satisfy a standard Lagrangian structure affected by the modeling uncertainties related to the internal interconnection between joint motion and external perturbations. The set of adaptive gains included in the controller implies enforcing the finite‐time convergence of the tracking error (TE) to an invariant region considering the state bounds describing the restricted motion of all joints. The application of the barrier Lyapunov stability analysis theory addresses the previously known state constraints for both devices, considering the inclusion of a time‐varying gain that guarantees the ultimate boundedness of the TE even with the presence of the effect of external perturbations. The suggested controller was evaluated using a virtual representation of the composite robotic device, which showed better tracking performance (while the restrictions were satisfied) than the performances obtained with the traditional linear state feedback and first‐order sliding‐mode controllers with restrictions. Analyzing the mean square error and its integral confirmed the benefits of using the adaptive barrier control to satisfy the TSM form.

Research background The literature of Polish authors points to the study of the financial performance of companies in the process of internationalisation by means of classic profitability indicators. Foreign literature goes beyond this area of studying the performance of companies in the internationalisation process. The authors propose less standard indicators for assessing company performance: market capitalisation, asset value, EBITDA (not ROA, ROE, ROS). Purpose The purpose of this paper is to assess the impact of internationalisation on selected financial ratios of Asian companies in the food sector. Research methodology Purposive sampling was used in the study and the selection of units was based on the availability of financial data in the EMIS database. The target population comprised joint-stock companies in the food sector in Asia that had export operations between 2000 and 2022. Results On the basis of the results of internationalisation and its impact on selected financial results and the relationships between financial indicators (EBITDA, asset size, market capitalisation) examined, it was decided to reject the main hypothesis.: There are links between internationalisation and factors indicative of a company’s economic performance and the interrelationship between these factors (total assets, EBITDA, market capitalisation). Novelty This research presents the impact of internationalisation on less commonly used indicators in performance evaluation, which can be considered a novel contribution to prior studies.

The southeast Asian thrips, Thrips parvispinus (Karny) (Thysanoptera Thripidae), one of the most invasive pests worldwide, is here recorded from Mexico for the first time damaging chilli pepper plants. Native to Asia, this species has spread throughout more than 30 countries across all continents (except South America). In the Americas, this thysanopteran had been accidentally introduced to the US, Canada and Puerto Rico, only. The discovery of this noxious organism in Mexico is unfortunate for national agriculture, as the insect feeds on around 45 species of plants, reducing agricultural yields and causing severe economic losses.

Background Mangrove ecosystems recognized for their biodiversity and ecosystem services that offer unique opportunities for sustainable livelihoods such as honey production. This study characterizes the beekeeping practices associated with mangroves in Sabancuy, Campeche, Mexico, emphasizing their ecological and economic significance. Methods Through 28 semi-structured surveys, we have analyzed the socioeconomic and ecological perspectives of the local beekeepers operating in these coastal environments. The surveyed beekeepers, with an average age of 49 years and 23.6 years of experience, primarily engage in complementary honey production, leveraging both migratory and stationary apiary systems. Hive management practices include queen replacement, artificial feeding, colony division, and adherence to organic certification protocols. Results The study highlights an annual average honey production of 65.37 kg per colony, with peak yields occurring during the transition from dry to rainy seasons (May–June). Integrating floral phenology and phylogenetic frameworks the principal plant resources supporting honeybees, enhancing the sustainability of the mangrove-based beekeeping. Conclusion Apiculture not only produces economic opportunities for the local communities; but also contributes to conservation goals by fostering biodiversity and ecosystem restoration. These findings underscore the potential of mangrove beekeeping as a replicable model for sustainable development in other coastal regions worldwide with similar ecosystems. Furthermore, this research seeks to bridge critical knowledge gaps about Apis mellifera in mangrove ecosystems by addressing socio-ecological factors influencing honey production, evaluating its benefits for local communities, and exploring its role within broader conservation strategies.

The energy requirements of socio-industrial sectors have instigated a global warming issue. Solar energy is a clean and renewable source for electricity generation, with production levels estimated in the range of Peta Watts per second $\left( {{\text{PW}}/{\text{s}}} \right){ }$, and solar panels remain the most prevalent technology for harnessing this energy in the market. Therefore, it becomes necessary to develop alternative systems for utilizing solar energy. Subsequently, this chapter introduces a Deep Learning (DL) solar thermogenerator that aims to design and simulate power generation systems that utilize the Peltier effect on semiconductor materials. An initial contribution is a physical model for the semiconductor material ${\text{Bi}}_{x} {\text{Sb}}_{2 - x} {\text{Te}}_{3}$. As a second contribution, an architecture of an Artificial Neural Network (ANN) is developed based on the physical properties of semiconductors, enabling optimization of the $\alpha \left( T \right)$, $\sigma \left( T \right)$, and $\kappa \left( T \right)$ parameters to match the electrical output values of solar reservoirs within the range of 347 to $423 \,{\text{K}}$. The third contribution involves examining the mean thermal energy storage of H2O, SiO2. The results are divided into two main stages: characterization of ANN tuning with controlled laboratory experiments, focusing on the response of the electric current generated by a difference of 273 to 348–363 K. We compare the result with numerical methods such as those of finite differences and finite volume. Finally, we train the neural network with the average temperature estimates of the reservoirs, which allows us to simulate the behavior in the structure and response of the semiconductor. To summarize, the findings demonstrate that Deep Learning neural networks, when trained with the physical properties of semiconductors, enable the numerical modeling of thermogenerator behavior, thereby pioneering advances in the development of AI-assisted green energy systems.

In this article, we report the synthesis and antibacterial activity of an alternative through organometallic Sn(IV) hybrids with ciprofloxacin dithiocarbamate, of new drugs active against pathogens resistant to traditional antibiotics. The synthesis of triorganotin(IV) ciprofloxacin dithiocarbamates of general formula R3SnCipdtc (R = Ph(1), Cy(2), ⁿBu(3), and Me(4)) and chloro‐diorganotin(IV) ciprofloxacin dithiocarbamates R2SnClCipdtc (R = Ph(5), ⁿBu(6), and tBu(7)) was carried out. To understand the chemical properties and the biological activity, a structural and electron density study was carried out by DFT approximation, and a docking analysis was performed to explain the antibacterial activity of the compounds. The results show that some of the compounds bind as ciprofloxacin and others bind differently, which helps to explain the MIC values obtained in comparison with the drug reference. The compounds were characterized by elemental analysis, IR, TGA, MS (FAB⁺), and ¹H, ¹⁹F, ¹³C, and ¹¹⁹Sn NMR spectroscopy. The solid‐state IR data suggest that the tin atom is coordinated to the ligand in the bidentate coordination mode, and in solution, the ¹¹⁹Sn NMR is consistent with tetracoordination for 2–4 and pentacoordination for 1 and 5–7. The molecular ion [M]⁺ of all compounds, 1–7, was detected (FAB⁺). In compounds 1–7, the Sn(IV) atom binds exclusively to the dithiocarbamate sulfurs, leaving the carboxylic acid oxygens intact. Antibacterial assays showed that all compounds except three were active in ATCC and clinically isolated strains. It is known that normally, the compounds containing triorganotin fragments typically exhibit higher antibacterial activity compared to those containing diorganotin. However, in the case of the compounds reported in this study, the antibacterial effect is contrary to this expectation. The presence of chlorine, instead of a second ciprofloxacin fragment, significantly increases the antibacterial efficiency.

The current research study analyzes the influence of short circuit (GMAW-SC) and spray (GMAW-Spray) transfer mode on the microstructure of a 445 ferritic stainless steel. The amount of δ-ferrite in the fusion zone (FZ) was determinant according to the transfer mode in the GMAW welding process. An increase of the heat input (HI) by spray transfer allows to obtain a decrease of δ-ferrite. In contrast, the short-circuit transfer mode shows an increase of 30%. Additionally, the ferritic-austenitic system studied exhibits microstructural differences in the distribution and morphology of the microconstituents depending on the transfer mode employed. The diffusive phenomena of Cr, Ni and Fe are similar regardless of the transfer mode employed. Finally, the short-circuit transfer mode produced a favorable microstructural condition with reduced HI and a homogeneous distribution of δ-ferrite in the weld bead avoiding zones of excessive grain growth in the FZ compromising the integrity of the joint.

Several agro-industrial residues can be utilized for the extraction of sugars through chemical reactions. This study evaluated the extraction of total sugars and reducing sugars from banana (Musa paradisiaca) and orange (Citrus sinensis) peel residues using acid hydrolysis. It also investigated if there is a difference between the concentration of sugars present, and analyzed the effect of different sulfuric acid concentrations and reaction times. A completely randomized 2 × 4 × 2 experimental design was used to evaluate two types of waste, exposed to four concentrations of sulfuric acid (4, 6, 8 and 10% V/V) at reaction times of 2 and 4 hours. This amounted to 16 treatments with three repetitions. The experimental units consisted of borosilicate glass flasks, half submerged in a bath of vegetable oil and assembled in a rapid cooling tower from which the analyzed glucose syrups were obtained. The orange peel residues showed the highest values of reducing sugar extraction with 8% sulfuric acid concentration, presenting mean of 16.46 mg L⁻¹ ± SE = 0.591 and optimal hydrolysis reaction time of 2 hours. With 4 hours reaction time, the extraction of reducing sugars was higher in orange peel residues compared to banana peel residues. Both residues proved to be suitable for sugar extraction, presenting a valuable opportunity for utilization in regions where they are readily available. They can be employed in fermentation processes for bioethanol production. Key words: Polysaccharides; biofuel; biomass; organic matter

This study aims to analyze the implications of green knowledge and technology in organizational green innovation, urban green innovation, and the implementation of green roofs. Green roofs can be an effective strategy for cities to enhance their thermal environment, save energy, and combat climate change. Furthermore, they serve as an efficient energy-saving solution. The analysis is predicated on the assumption that green technology is fundamental to the practices, operations, and activities in the realms of organizational and urban green innovation. The study endeavors to strike a balance between urban development and environmental concerns, considering the principle of sustainable development and its innovative green solutions. The methodology employed in the study draws upon analytical, reflective, and descriptive methods, supplemented by a review of theoretical and empirical literature. The analysis concludes that the sharing of green knowledge plays a pivotal role in creating and developing green technology, with positive implications for organizational green innovation, urban green innovation, and green roofs.

Context In the context of structural interactomics, we generated a 3D model between α and β3 subunits for the hitherto unknown human voltage-gated sodium channel complex (hNa 1.7α/β3). We embedded our 3D model in a membrane lipid bilayer for molecular dynamics (MD) simulations of the sodium cation passage from the outer vestibule through the inner pore segment of our hNa 1.7 complex in presence and absence of auxiliary subunit β3 with remarkable changes close to electrophysiological study results. A complete passage could not be expected due to because the inactivated state of the underlying 3D template. A complete sodium ion passage would require an open state of the channel. The computed observations concerning side chain rearrangements for favorable cooperativity under evolutionary neighborhood conditions, favorable and unfavorable amino acid interactions, proline kink, loop, and helix displacements were all found in excellent keeping with the extant literature without any exception nor contradiction. Complex-stabilizing pairs of interacting amino acids with evolutionary neighborhood complementary were identified. Methods The following tools were used: sequence search and alignment by FASTA and Clustal Omega; 3D model visualization and homology modeling by Vega ZZ, SPDBV, Chimera and Modeller, respectively; missing sections (loops) by Alphafold; geometry optimization prior to MD runs by GROMACS 2021.4 under the CHARMM 36 force field; local healing of bad contacts by SPDBV based on its Ramachandran plots; protein-protein docking by HDOCK 2.4; membrane insertion assisted by OPM; Berendsen V-rescaling for NVT; Parrinello-Rahman and Nose-Hoover for MPT; MD analyses by VMD and XMGRACE

Highlights What are the main findings? Low-energy impacts caused matrix cracking and delamination in all specimens, while fiber–matrix debonding and matrix tearing were only observed in flat laminates, not in curved airfoil profiles. Airfoil geometry influenced damage propagation; GOE777-IL showed higher impact resistance while SC(2)-0714 presented larger damage areas. What is the implication of the main finding? Conventional visual inspection may underestimate internal damage, highlighting the importance of advanced non-destructive techniques. Results support the development of geometry-specific impact tolerance criteria for composite structures. Abstract The use of composite materials in aerospace structures has led to significant weight reductions and improved performance. However, their behavior under low-energy impact remains a critical concern due to the potential initiation of barely visible damage. This study investigates the crack initiation mechanisms in composite airfoil profiles subjected to low-energy impact, simulating real-world scenarios such as hail or bird strikes. Two types of airfoil profiles were fabricated using bidirectional carbon fiber reinforced polymer (CFRP) with epoxy resin and tested under ASTM D7136 impact conditions. Tensile tests following ASTM D3039 were conducted to assess post-impact mechanical behavior. The damage patterns were analyzed using high-resolution microscopy and non-destructive inspection techniques. Results revealed that damage severity and propagation depend on impact energy levels and airfoil geometry, with SC(2)-0714 exhibiting better impact resistance than GOE777-IL. Microscopic analysis confirmed that delamination initiated at 45° fiber orientations, expanding along interlaminar regions, while airfoil curvature influenced the impact energy dissipation.

The purpose of this study was to evaluate the functional properties of proteins extracted from Melicoccus bijugatus Jacq. (huaya) seeds and to explore their potential use in the food industry as an alternative to animal-derived proteins, such as lactoglobulin and casein. Protein extraction according to Osborne solubility method was employed to isolate reserve proteins: albumins, globulins, prolamins and glutelins. The functional properties of these protein fractions were evaluated, including emulsifying capacity, foaming capacity, and oil retention. Results indicated that glutelins exhibited high foaming (p < 0.05) capacities to pH 3, 5, 7 and 9; these results suggesting their suitability in the formulation of processed food products such as vegan butter or ice cream. By the other hand, globulins exhibited high emulsifying (p < 0.05) capacities at same pH; albumins and prolamins displayed limited functional properties. Additionally, globulins showed substantial oil retention capacity, making them an attractive option for products requiring stability and a rich fat texture such as salad dressing. This study demonstrated that huaya seed proteins possess functional properties that render them viable as ingredients in the food industry. Moreover, these proteins could be particularly beneficial in the development of vegan and processed products aiming to replace animal proteins with more sustainable alternatives. Potential applications include vegan dairy products, emulsions, and foaming agents, such as ice cream. These findings present a novel option of functional ingredients for the formulation of innovative and sustainable food products. Based on the results obtained, research will continue including huaya glutelins and globulins in the formulation of butter and ice cream to evaluate their physicochemical and sensory properties.

Robust image processing systems require input images that closely resemble real-world scenes. However, external factors, such as adverse environmental conditions or errors in data transmission, can alter the captured image, leading to information loss. These factors may include poor lighting conditions at the time of image capture or the presence of noise, necessitating procedures to restore the data to a representation as close as possible to the real scene. This research project proposes an architecture based on an autoencoder capable of handling both poor lighting conditions and noise in digital images simultaneously, rather than processing them separately. The proposed methodology has been demonstrated to outperform competing techniques specialized in noise reduction or contrast enhancement. This is supported by both objective numerical metrics and visual evaluations using a validation set with varying lighting characteristics. The results indicate that the proposed methodology effectively restores images by improving contrast and reducing noise without requiring separate processing steps.

Timely identification of crop conditions is relevant for informed decision-making in precision agriculture. The initial step in determining the conditions that crops require involves isolating the components that constitute them, including the leaves and fruits of the plants. An alternative method for conducting this separation is to utilize intelligent digital image processing, wherein plant elements are labeled for subsequent analysis. The application of Deep Learning algorithms offers an alternative approach for conducting segmentation tasks on images obtained from complex environments with intricate patterns that pose challenges for separation. One such application is semantic segmentation, which involves assigning a label to each pixel in the processed image. This task is accomplished through training various models of Convolutional Neural Networks. This paper presents a comparative analysis of semantic segmentation performance using a convolutional neural network model with different backbone architectures. The task focuses on pixel-wise classification into three categories: leaves, fruits, and background, based on images of semi-hydroponic tomato crops captured in greenhouse settings. The main contribution lies in identifying the most efficient backbone-UNet combination for segmenting tomato plant leaves and fruits under uncontrolled conditions of lighting and background during image acquisition. The Convolutional Neural Network model UNet is is implemented with different backbones to use transfer learning to take advantage of the knowledge acquired by other models such as MobileNet, VanillaNet, MVanillaNet, ResNet, VGGNet trained with the ImageNet dataset, in order to segment the leaves and fruits of tomato plants. Highest percentage performance across five metrics for tomato plant fruit and leaves segmentation is the MVanillaNet-UNet and VGGNet-UNet combination with 0.88089 and 0.89078 respectively. A comparison of the best results of semantic segmentation versus those obtained with a color-dominant segmentation method optimized with a greedy algorithm is presented.

This study proposes an Evolutionary Algorithm (EA) to optimize the workstation layout in multi-product handcrafted furniture workshops with simultaneous manufacturing. The algorithm models the arrangement of workstations within a limited space, enhancing activity coordination and reducing unnecessary worker movement. The optimized solution is obtained through the application of evolutionary operators, including selection, crossover, mutation, and refinement, iterating over successive generations. To evaluate the EA’s performance, a computational simulation is conducted using ProModel®, comparing its efficiency against conventional methodologies such as Systematic Layout Planning (SLP) and the CRAFT algorithm. In a case study involving the simultaneous elaboration of three different products, each by a different artisan, the EA successfully reduces the total worker travel distance by 51.45% and the system’s total processing time by 13.2%. The results indicate that the proposed approach not only enhances operational efficiency in a smaller environment but also lays the groundwork for integrating advanced strategies. These include cellular manufacturing and hybrid production schemes, ultimately enhancing flexibility and sustainability in this sector.

We study a ring of three Duffing oscillators coupled unidirectionally, focusing on the effects of the coupling strength, time-dependent damping and attractor following using time series, phase portrait, Fourier transformations, bifurcation diagrams, potential planes, Lyapunov exponents and hysteresis paths for two cases of damping: constant $(\alpha=0.4)$ and linearly increasing $(\alpha(t)=\frac{t}{\tau})$. For constant damping term the dynamics shows quasiperiodic 2D torus, limit cycles, heteroclinic orbits, fixed points, chaos, and hyperchaos. These behavior shows the existence of hysteresis and an important change in the potential energy. If damping term is dependent of the time $(\alpha = \frac{t}{\tau} )$, the dynamics of the system shows bottleneck behavior influenced by higher frequencies which persists for a small range of values of $(\sigma)$. In resume, the potential energy in this set of three Duffing oscillators depends strongly of three factors: first the attractor tracking (past memories), second the coupling strength and third the damping term.

Quantum computing has become a breakthrough in many different research and applied areas. As various authors have demonstrated, the quantum properties have made some computational processes parallel and impossible to compute or even simulate for classical computers. An area that has been significantly impacted is machine learning. For instance, notable advances have been made in the field of classification and clustering, with computational complexity being reduced or with results being achieved that are, at least, equivalent to those classically obtained. In these problems, metric selection is critical. This paper proposes to compute the Hausdorff metric for the classification problem using quantum-based Euclidean distance. The Hausdorff metric has been shown to offer optimal results in various domains, including pattern recognition and image segmentation, making it a preferred metric in these applications. The presented algorithmic approach was applied to the Iris and Palmer Penguins datasets as input, with the classical k-neighbor nearest algorithm serving as the basis for classification. The quantitative results of this work demonstrate comparable evaluation metrics concerning the pure-classical k-neighbor-nearest algorithm.

This paper proposes a model-based methodology to estimate multiple nodal demands by using only pressure and flow rate measurements, which should be recorded at the inlet of the distribution system. The algorithm is based on an array of multiple extended Kalman filters (EKFs) in a cascade configuration. Each EKF functions as an unknown input observer and focuses on a section of the pipeline. The pipeline model used to design the filters is an adaptation of the well-known rigid water column model. Simulation and experimental tests on standardized pipeline systems are presented to demonstrate the proposed method’s effectiveness. Finally, for the case of the experimental validation, both steady-state and variable input conditions were considered.