Unmanned aerial vehicles (UAVs) are becoming more common in wireless communication networks. Using UAVs can lead to network problems. An issue arises when the UAVs function in a network-access-limited environment with nodes causing interference. This issue could potentially hinder UAV network connectivity. This paper introduces an intelligent packet priority module (IPPM) to minimize network latency. This study analyzed Network Simulator–3 (NS-3) network modules utilizing Manhattan long short-term memory (MaLSTM) for packet classification of critical UAV, ground control station (GCS), or interfering nodes. To minimize network latency and packet delivery ratio (PDR) issues caused by interfering nodes, packets from prioritized nodes are transmitted first. Simulation results and evaluation show that our proposed intelligent packet priority module (IPPM) method outperformed previous approaches. The proposed IPPM based on MaLSTM implementation for the priority packet module led to a lower network delay and a higher packet delivery ratio. The performance of the IPPM averaged 62.2 ms network delay and 0.97 packet delivery ratio (PDR). The MaLSTM peaked at 97.5% accuracy. Upon further evaluation, the stability of LSTM Siamese models was observed to be consistent across diverse similarity functions, including cosine and Euclidean distances. Full article

Luxury cruise ships are high-end passenger ships with facilities on board for the leisure and entertainment of passengers, so the comfort of luxury cruise ships is a matter of great concern. In this paper, a finite element model of a new polar exploration cruise ship is established, and the wet modes of the whole ship are calculated using the virtual mass method and compared with the principal frequencies of the excitation forces to initially verify the rationality of the design of the structural vibration characteristics of the whole ship. The admittance matrix of the vibration velocity to excitation force was calculated by a frequency response analysis, and the vibration velocities at the stern plate and main engine foundations were tested during sailing. Then, the obtained propeller and main engine excitation forces were loaded into the finite element model; the vibration velocities of each compartment were calculated and compared with the compartment vibration velocity test values. The errors were within the engineering allowable range, verifying the accuracy of the excitation forces. The propeller and main engine excitation forces were loaded separately on the finite element model to calculate the vibration velocity of each cabin, and the contribution of the two excitation sources to the vibration velocity of each cabin was analyzed. It was found that the contribution of the excitation source to the cabin response was related to the relative position between the cabin and the excitation source. When the cabin was located in the cabin adjacent to or directly above a certain excitation source, the contribution of the excitation source to the cabin response was greater. When the cabin was farther away from both excitation sources, the contribution of the propeller excitation was greater. This provides a targeted reference for the preliminary vibration assessment and later vibration control of the new polar expedition cruise ship. Full article

This systematic review and meta-analysis aimed to verify the association between the genetic variants of adenosine triphosphate (ATP)-binding cassette subfamily B member 1 (ABCB1) and ATP-binding cassette subfamily G member 2 (ABCG2) genes and the presence and severity of gefitinib-associated adverse reactions. We systematically searched PubMed, Virtual Health Library/Bireme, Scopus, Embase, and Web of Science databases for relevant studies published up to February 2024. In total, five studies were included in the review. Additionally, eight genetic variants related to ABCB1 (rs1045642, rs1128503, rs2032582, and rs1025836) and ABCG2 (rs2231142, rs2231137, rs2622604, and 15622C>T) genes were analyzed. Meta-analysis showed a significant association between the ABCB1 gene rs1045642 TT genotype and presence of diarrhea (OR = 5.41, 95% CI: 1.38–21.14, I² = 0%), the ABCB1 gene rs1128503 TT genotype and CT + TT group and the presence of skin rash (OR = 4.37, 95% CI: 1.51–12.61, I² = 0% and OR = 6.99, 95%CI: 1.61–30.30, I²= 0%, respectively), and the ABCG2 gene rs2231142 CC genotype and presence of diarrhea (OR = 3.87, 95% CI: 1.53–9.84, I² = 39%). No ABCB1 or ABCG2 genes were positively associated with the severity of adverse reactions associated with gefitinib. In conclusion, this study showed that ABCB1 and ABCG2 variants are likely to exhibit clinical implications in predicting the presence of adverse reactions to gefitinib. Full article

The goal was to model irrigated (IBY) and rainfed (RBY) bean yields in central (Culiacán) and southern (Rosario) Sinaloa state as a function of the essential climate variables soil moisture, temperature, reference evapotranspiration, and precipitation. For Sinaloa, for the period 1982–2013 (October–March), the following were calculated: (a) temperatures, (b) average degree days for the bean, (c) cumulative reference evapotranspiration, and (d) cumulative effective precipitation. For essential climate variables, (e) daily soil moisture obtained from the European Space Agency and (f) IBY and RBY from the Agrifood and Fisheries Information Service were used. Multiple linear regressions were significant for predicting IBY–RBY (dependent variables) as a function of essential climate variables (independent variables). The four models obtained were significantly predictive: IBY–Culiacán (Pearson correlation (PC) = 0.590 > Pearson critical correlation (CPC) = |0.349|), RBY–Culiacán (PC = 0.734 > CPC = |0.349|), IBY–Rosario (PC = 0.621 > CPC = |0.355|), and RBY–Rosario (PC = 0.532 > CPC = |0.349|). Due to the lack of irrigation depth data, many studies only focus on modeling RBY; this study is the first in Sinaloa to predict IBY and RBY based on essential climate variables, contributing to the production of sustainable food. Full article

This paper discusses the discoveries of early donkey and the earliest mule remains in Central Anatolia from the site Derekutuğun. This site represents the remains of a village dating back to the Early Bronze Age and Assyrian Trade Colonies period, associated with mining. The archaeofaunal assemblage was studied by the author and his team using classical archaeozoological methods. The dental remains of the Equidae found at Derekutuğun have been re-examined and are described in this article. The dental evidence indicates that donkeys, and possibly the earliest mules ever found in Central Anatolia, were kept at this site. Although the paper is based on the archaeozoological remains, written sources from the period also support the faunal identification. Derekutuğun was a small settlement that specialized in processing copper ore, and which was an important hub for a trade network because of its extensive mining and extraction operations. Full article

Machine learning and deep learning technologies are rapidly advancing the capabilities of sensing technologies, bringing about significant improvements in accuracy, sensitivity, and adaptability. These advancements are making a notable impact across a broad spectrum of fields, including industrial automation, robotics, biomedical engineering, and civil infrastructure monitoring. The core of this transformative shift lies in the integration of artificial intelligence (AI) with sensor technology, focusing on the development of efficient algorithms that drive both device performance enhancements and novel applications in various biomedical and engineering fields. This review delves into the fusion of ML/DL algorithms with sensor technologies, shedding light on their profound impact on sensor design, calibration and compensation, object recognition, and behavior prediction. Through a series of exemplary applications, the review showcases the potential of AI algorithms to significantly upgrade sensor functionalities and widen their application range. Moreover, it addresses the challenges encountered in exploiting these technologies for sensing applications and offers insights into future trends and potential advancements. Full article

One of the important parameters in orthodontics is the perimeter of the dental arch. Precise assessment is necessary in cases of maxillary constriction treated with a rapid maxillary expander (RME). The orthodontic software allows customization of the processes from diagnosis to manufacturing of the treatment device. The aim of the present study is to evaluate a relationship between the parameters of the dental arch—perimeter, width, and length—and to follow the changes during treatment. The study is based on the digital measurements of 3D models of 33 patients treated with a digitally planned and printed RME. In the results an increase of 3.99 mm in perimeter was achieved. The rest of the parameters were changed as follows: The width of the dental arch was increased in the premolar area by an average of 3.3 mm; in the area of the first molars, the increase was 4.41 mm; the length of the dental arch in the anterior segment was reduced by an average of 0.54 mm; and the whole length by 0.52 mm. Correlation between the studied variables was described by linear equations. In conclusion, rapid maxillary expansion is a reliable method for gaining predictable space in the dental arch. Full article

This paper empirically examines whether integrating entrepreneurial abilities with the theory of perceived behaviour positively influences Sustainable-Development-Goal-8-driven sustainable entrepreneurial intentions (SDG-8 SEIs). The data used in this study were gathered from 540 students from top-ranked Indian engineering colleges that offer entrepreneurship courses and have access to company incubators. According to the theory of planned behaviour (TPB), perceived behavioural control, subjective norms, and entrepreneurial drive are the three elements of perceived entrepreneurial behaviour. The TPB’s dimensions in this study have entrepreneurial competencies as their antecedents. Cognitive competency, risk propensity, and social competency and resilience are antecedents of the TPB’s dimensions. One entrepreneurial viewpoint uses sustainable UNDP-SDG-8 as a metric for assessing intentions; its objectives are the promotion of inclusive and sustainable economic growth, full and productive employment, and decent work for all. This study used partial least squares structural equation modelling (PLS-SEM). According to the findings, engineering students in India are more likely to have entrepreneurial-focused intentions based on sustainability if they adhere to the TPB’s dimensions along with additional constructs. Using an expanded TPB model, we show that the TPB has learnable and stimulating antecedents, with these having a positive effect on SDG-8 SEIs, thus extending entrepreneurial activity in India. Policymakers, universities, and students will find these results very intriguing. The TPB’s dimensions and three additional dimensions are proposed as antecedents in a new conceptual model aimed at sustainable entrepreneurship in this study. Full article

Background/Objectives: A superinfection occurs when a new, secondary organism colonizes an existing infection. Spine infections are associated with high patient morbidity and sometimes require multiple irrigations and debridements (I&Ds). When multiple I&Ds are required, the risk of complications increases. The purpose of this study was to report our experience with spine superinfections and determine which patients are typically affected. Methods: A retrospective case series of spine superinfections and a retrospective case–control analysis were conducted. Data were collected manually from electronic medical records. Spine I&Ds were identified. Groups were created for patients who had multiple I&Ds for (1) a recurrence of the same causative organism or (2) a superinfection with a novel organism. Preoperative demographic, clinical, and microbiologic data were compared between these two outcomes. A case series of superinfections with descriptive data was constructed. Lastly, two illustrative cases were provided in a narrative format. Results: A total of 92 patients were included in this analysis. Superinfections occurred after 6 out of the 92 (7%) initial I&Ds and were responsible for 6 out of the 24 (25%) repeat I&Ds. The preoperative erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) of the patients with a superinfection were significantly lower than those in the control group (p = 0.022 and p = 0.032). Otherwise, the observed differences in the preoperative variables were not statistically different. In the six cases of superinfection, the presence of high-risk comorbidities, a history of substance abuse, or a lack of social support were commonly observed. The superinfecting organisms included Candida, Pseudomonas, Serratia, Klebsiella, Enterobacter, and Staphylococcus species. Conclusions: Superinfections are a devastating complication requiring reoperation after initial spine I&D. Awareness of the possibility of superinfection and common patient archetypes can be helpful for clinicians and care teams. Future work is needed to examine how to identify, help predict, and prevent spine superinfections. Full article

Knowledge graphs have been extensively studied and applied, but most of these studies assume that the relationship facts in the knowledge graph are correct and deterministic. However, in the objective world, there inevitably exist uncertain relationship facts. The existing research lacks effective representation of such uncertain information. In this regard, we propose a novel representation learning framework called CosUKG, which is specifically designed for uncertain knowledge graphs. This framework models uncertain information by measuring the cosine similarity between transformed vectors and actual target vectors, effectively integrating uncertainty into the embedding process of the knowledge graph while preserving its structural information. Through multiple experiments on three public datasets, the superiority of the CosUKG framework in representing uncertain knowledge graphs is demonstrated. It achieves improved representation accuracy of uncertain information without increasing model complexity or weakening structural information. Full article

Photosynthesis, as the primary source of energy for all life forms, plays a crucial role in maintaining the global balance of energy, entropy, and enthalpy in living organisms. Among its various building blocks, photosystem I (PSI) is responsible for light-driven electron transfer, crucial for generating cellular reducing power. PSI acts as a light-driven plastocyanin-ferredoxin oxidoreductase and is situated in the thylakoid membranes of cyanobacteria and the chloroplasts of eukaryotic photosynthetic organisms. Comprehending the structure and function of the photosynthetic machinery is essential for understanding its mode of action. New insights are offered into the structure and function of PSI and its associated light-harvesting proteins, with a specific focus on the remarkable structural conservation of the core complex and high plasticity of the peripheral light-harvesting complexes. Full article

Fermentation of pumpkin puree and mature coconut water using water kefir grains is a potential method for producing a novel functional non-dairy-based probiotic drink. In the present study, response surface methodology based on Box–Behnken design (RSM-BBD) was used to optimise fermentation temperature and substrates’ concentrations. The optimised fermentation temperature, pumpkin puree, and brown sugar concentrations of pumpkin-based mature coconut water kefir beverage (PWKC) were 27 °C, 20%, and 10% w/v, respectively. The optimised PWKC (PWKC_opt) obtained an overall acceptability (OA) score of 4.03, with a desirable Lactobacillus count (6.41 Log CFU/mL), 0.68% v/v lactic acid content, 31% of water kefir grains’ biomass growth rate, and fermentation time (to reach pH 4.5) of 4.5 h. The optimized beverage, PWKC_opt, contained 3.26% proteins, 2.75% dietary fibre, 2186.33 mg/L of potassium, 180.67 mg/L phosphorus, and 137.33 mg/L calcium and had a total phenolic content of 89.93 mg GAE/100 mL, flavonoid content of 49.94 mg QE/100 mL, and carotenoid content of 33.24 mg/100 mL, with antioxidant activity (FRAP: 169.17 mM Fe(II)/100 mL, IC₅₀ value of DPPH free radicals scavenging activity: 27.17 mg/mL). Water kefir microorganisms in PWKC_opt remained stable for at least 56 days at 4 °C. Therefore, PWKC_opt might potentially serve as a value-added product, offering a basis for sustainable development within both the coconut and pumpkin industries. Full article

With the rapid development of artificial intelligence technology, data-driven advanced models have provided new ideas and means for airfoil aerodynamic optimization. As the advanced models update and iterate, many useful explorations and attempts have been made by researchers on the integrated application of artificial intelligence and airfoil aerodynamic optimization. In this paper, many critical aerodynamic optimization steps where data-driven advanced models are employed are reviewed. These steps include geometric parameterization, aerodynamic solving and performance evaluation, and model optimization. In this way, the improvements in the airfoil aerodynamic optimization area led by data-driven advanced models are introduced. These improvements involve more accurate global description of airfoil, faster prediction of aerodynamic performance, and more intelligent optimization modeling. Finally, the challenges and prospect of applying data-driven advanced models to aerodynamic optimization are discussed. Full article

This paper investigated the integration of LiDAR technology in cut-to-length (CTL) harvesting machines to enhance tree selection accuracy and efficiency. In the evolution of CTL forest machines towards improving operational efficiency and operator conditions, challenges persist in manual tree selection during thinning operations, especially under unmarked conditions and complex environments. These can be improved due to advances in technology. We studied the potential of LiDAR systems in assisting harvester operators, aiming to mitigate workload, reduce decision errors, and optimize the harvesting workflow. We used both synthetic and real-world 3D point cloud data sets for tree stem defect analysis. The former was crafted using a 3D modelling engine, while the latter originated from forest observations using 3D LiDAR on a CTL harvester. Both data sets contained instances of tree stem defects that should be detected. We demonstrated the potential of LiDAR technology: The analysis of synthetic data yielded a Root Mean Square Error (RMSE) of 0.00229 meters (m) and an RMSE percentage of 0.77%, demonstrating high detection accuracy. The real-world data also showed high accuracy, with an RMSE of 0.000767 m and an RMSE percentage of 1.39%. Given these results, we recommend using on-board LiDAR sensor technologies for collecting and analyzing data on tree/forest quality in real-time. This will help overcome existing barriers and drive forest operations toward enhanced efficiency and sustainability. Full article

The integration between the building information modeling (BIM) methodology and the building energy simulation (BES) can contribute to a thermo-energetic analysis since the model generated and fed into BIM is exported to simulation software. This integration, also called interoperability, is satisfactory when the information flow is carried out without the loss of essential information. Several studies point out interoperability flaws between the methodologies; however, most of them occur in low-geometry-complexity models during quantitative experiments. The purpose of this research was to analyze the BIM/BES integration based on a quantitative and interpretative interoperability analysis of two buildings with complex geometries located on the UFU Campus (library and Building 5T) in Uberlândia, Brazil. To accomplish this, two geometries of each building were modeled, detailed, and simplified to analyze the data import, workflow, and model correction in the BES software. In the case of the library, the integration of Revit with DesignBuilder and IES-VE was analyzed, and in Block 5T, Revit was used with DesignBuilder and eQUEST. The BES software that presented the best integration with Revit for complex geometries was DesignBuilder, with the best performance being in the interpretative criteria. It was concluded that the simplification of complex geometries is essential for better data transfers. To determine the BES software that has better integration with BIM, a comprehensive evaluation is necessary, considering not only data transfers but also ease of working within BES software, the possibility of corrections in these, as well as the availability of tutorials and developer support. Full article

Interior permanent magnet synchronous machines (IPMSMs) driven with a square-wave control (i.e., six-step, block, or 120${}^{\circ }$ control), known commonly as brushless direct current (BLDC) drives, are used widely due to their high power density and control simplicity. The advance firing (AF) angle is employed to achieve improved operation characteristics of the drive. The AF angle is, in general, applied to compensate for the commutation effects. In the case of an IPMSM, the AF angle can also be adjusted to exploit reluctance torque. In this paper, a detailed study was performed to understand its effect on the drive’s performance in regard to reluctance torque. Furthermore, a multi-objective optimization of the machine’s cross-section using neural network models was conducted to enhance performance at a constant AF angle. The reference and improved machine designs were evaluated in a system-level simulation, where the impact was considered of the commutation of currents. A significant improvement in the machine performance was achieved after optimizing the geometry and implementing a fixed AF angle of ${10}^{\circ }$. Full article

Industry 4.0 is an industrial paradigm that is causing changes in form and substance in factories, companies and businesses around the world and is impacting work and education in general. In fact, the disruptive technologies that frame the Fourth Industrial Revolution have the potential to improve and optimize manufacturing processes and the entire value chain, which could lead to an exponential evolution in the production and distribution of goods and services. All these changes imply that the fields of engineering knowledge must be oriented towards the concept of Industry 4.0, for example, Mechanical Engineering. The development of various physical assets that are used by cyber-physical systems and digital twins is based on mechanics. However, the specialized literature on Industry 4.0 says little about the importance of mechanics in the new industrial era, and more importance is placed on the evolution of Information and Communication Technologies and artificial intelligence. This article presents a frame of reference for the importance of Mechanical Engineering in Industry 4.0 and proposes an extension to the concept of Mechanics 4.0, recently defined as the relationship between mechanics and artificial intelligence. To analyze Mechanical Engineering in Industry 4.0, the criteria of the four driving forces that defined mechanics in the Third Industrial Revolution were used. An analysis of Mechanical Engineering Education in Industry 4.0 is presented, and the concept of Mechanical Engineering 4.0 Education is improved. Finally, the importance of making changes to the educational models of engineering education is described. Full article

Bone cancer and its related chronic pain are huge clinical problems since the available drugs are often ineffective or cannot be used long term due to a broad range of side effects. The mechanisms, mediators and targets need to be identified to determine potential novel therapies. Here, we characterize a mouse bone cancer model induced by intratibial injection of K7M2 osteosarcoma cells using an integrative approach and investigate the role of capsaicin-sensitive peptidergic sensory nerves. The mechanical pain threshold was assessed by dynamic plantar aesthesiometry, limb loading by dynamic weight bearing, spontaneous pain-related behaviors via observation, knee diameter with a digital caliper, and structural changes by micro-CT and glia cell activation by immunohistochemistry in BALB/c mice of both sexes. Capsaicin-sensitive peptidergic sensory neurons were defunctionalized by systemic pretreatment with a high dose of the transient receptor potential vanilloid 1 (TRPV1) agonist resiniferatoxin (RTX). During the 14- and 28-day experiments, weight bearing on the affected limb and the paw mechanonociceptive thresholds significantly decreased, demonstrating secondary mechanical hyperalgesia. Signs of spontaneous pain and osteoplastic bone remodeling were detected both in male and female mice without any sex differences. Microglia activation was shown by the increased ionized calcium-binding adapter molecule 1 (Iba1) immunopositivity on day 14 and astrocyte activation by the enhanced glial fibrillary acidic protein (GFAP)-positive cell density on day 28 in the ipsilateral spinal dorsal horn. Interestingly, defunctionalization of the capsaicin-sensitive afferents representing approximately 2/3 of the nociceptive fibers did not alter any functional parameters. Here, we provide the first complex functional and morphological characterization of the K7M2 mouse osteosarcoma model. Bone-cancer-related chronic pain and hyperalgesia are likely to be mediated by central sensitization involving neuroinflammation via glial cell activation in the spinal dorsal horn, but not the capsaicin-sensitive sensory neuronal system. Full article

In this study, a set of accurate dispersive nonlinear wave equations is established, using the wave velocity and free surface elevation as variables. These equations improve upon previous equations in which the velocity potential is used as a variable by considering the rotational wave motion and by adding a second-order bottom slope term that applies to general situations, allowing the equations to consider the influence of rapidly changing, horizontal, two-dimensional bottom topographies. The problem of the inaccuracy of the integral calculations used in previous equations in nearshore areas is solved by approximating the integral calculations into differential calculations, and a set of coupled wave equations is established by keeping the free surface elevation and the horizontal velocity constant, thus allowing the calculation of nearshore wave-generated currents. The benefits of the current model are confirmed through comparisons with corresponding laboratory experimental findings and are illustrated through a comparison with the numerical outcomes of other pertinent models. Full article

The finite element numerical simulation results of deep pit deformation are greatly influenced by soil layer parameters, which are crucial in determining the accuracy of deformation prediction results. This study employs the orthogonal experimental design to determine the combinations of various soil layer parameters in deep pits. Displacement values at specific measurement points were calculated using PLAXIS 3D under these varying parameter combinations to generate training samples. The nonlinear mapping ability of the Back Propagation (BP) neural network and Particle Swarm Optimization (PSO) were used for sample global optimization. Combining these with actual onsite measurements, we inversely calculate soil layer parameter values to update the input parameters for PLAXIS 3D. This allows us to conduct dynamic deformation prediction studies throughout the entire excavation process of deep pits. The results indicate that the use of the PSO-BP neural network for inverting soil layer parameters effectively enhances the convergence speed of the BP neural network model and avoids the issue of easily falling into local optimal solutions. The use of PLAXIS 3D to simulate the excavation process of the pit accurately reflects the dynamic changes in the displacement of the retaining structure, and the numerical simulation results show good agreement with the measured values. By updating the model parameters in real-time and calculating the pile displacement under different working conditions, the absolute errors between the measured and simulated values of pile top vertical displacement and pile body maximum horizontal displacement can be effectively reduced. This suggests that inverting soil layer parameters using measured values from working conditions is a feasible method for dynamically predicting the excavation process of the pit. The research results have some reference value for the selection of soil layer parameters in similar areas. Full article

Background: Human-Machine Interaction (HMI) has been an important field of research in recent years, since machines will continue to be embedded in many human actvities in several contexts, such as industry and healthcare. Monitoring in an ecological mannerthe cognitive workload (CW) of users, who interact with machines, is crucial to assess their level of engagement in activities and the required effort, with the goal of preventing stressful circumstances. This study provides a comprehensive analysis of the assessment of CW using wearable sensors in HMI. Methods: this narrative review explores several techniques and procedures for collecting physiological data through wearable sensors with the possibility to integrate these multiple physiological signals, providing a multimodal monitoring of the individuals’CW. Finally, it focuses on the impact of artificial intelligence methods in the physiological signals data analysis to provide models of the CW to be exploited in HMI. Results: the review provided a comprehensive evaluation of the wearables, physiological signals, and methods of data analysis for CW evaluation in HMI. Conclusion: the literature highlighted the feasibility of employing wearable sensors to collect physiological signals for an ecological CW monitoring in HMI scenarios. However, challenges remain in standardizing these measures across different populations and contexts. Full article

As a pivotal node in both urban and rural power grids, the box-type substation not only serves the functions of power conversion and distribution but also need to provide structural support and environmental adaptability. However, deficiencies in strength, stiffness, or vibration characteristics may lead to vibration and noise issues, and extreme environmental changes can pose risks of structural damage. This study aims to verify and optimize the seismic resistance and environmental adaptability of box-type substations through finite element simulation methods. Using SOLIDWORKS, a three-dimensional model of the box-type substation was constructed, and static and dynamic analyses were conducted using Ansys Workbench to comprehensively evaluate the dynamic response of the box-type substation under wind, snow loads, and seismic action. Through iterative simulations and a comparison of multiple design solutions, the structural optimization of the substation was achieved. The optimized structure balances strength and stiffness, significantly reducing the weight of the substation body, with the wall thickness reduced by 60%. Additionally, the phenomenon of stress concentration on the side walls was eliminated, ensuring that the equivalent stress is below the material yield strength. This research provides methods and empirical results for enhancing the performance and reliability of box-type substations under seismic conditions, confirming the feasibility of a lightweight design, while ensuring structural safety. Full article