Traffic sign recognition plays a crucial role in enhancing the safety and efficiency of traffic systems. However, in snowy conditions, traffic signs are often obscured by particles, leading to a severe decrease in detection accuracy. To address this challenge, we propose an improved YOLOv8-based model for traffic sign recognition. Initially, we introduce a Multi-Scale Group Convolution (MSGC) module to replace the C2f module in the YOLOv8 backbone. Data indicate that MSGC enhances detection accuracy while maintaining model lightweightness. Subsequently, to improve the recognition ability for small targets, we introduce an enhanced small target detection layer, which enhances small target detection accuracy while reducing parameters. In addition, we replaced the original BCE loss with the improved EfficientSlide loss to improve the sample imbalance problem. Finally, we integrate Deformable Attention into the model to improve the detection efficiency and performance of complex targets. The resulting fused model, named MSGC-YOLOv8, is evaluated on an enhanced dataset of snow-covered traffic signs. Experimental results show that the MSGC-YOLOv8 model is used for snow road traffic sign recognition. Compared with the YOLOv8n model [email protected]:0.95, [email protected]:0.95 is increased by 17.7% and 18.1%, respectively, greatly improving the detection accuracy. Compared with the YOLOv8s model, while the parameters are reduced by 59.6%, [email protected] only loses 1.5%. Considering all aspects of the data, our proposed model shows high detection efficiency and accuracy under snowy conditions. Full article

Most of the deep-learning algorithms on stock price volatility prediction in the existing literature use data such as same-frequency market indicators or technical indicators, and less consider mixed-frequency data, such as macro-data. Compared with the traditional model that only inputs the same-frequency data such as technical indicators and market indicators, this study proposes an improved deep-learning model based on mixed-frequency big data. This paper first introduces the reserve restricted mixed-frequency data sampling (RR-MIDAS) model to deal with the mixed-frequency data and, secondly, extracts the temporal and spatial features of volatility series by using the parallel model of CNN-LSTM and LSTM, and finally utilizes the Optuna framework for hyper-parameter optimization to achieve volatility prediction. For the deep-learning model with mixed-frequency data, its RMSE, MAE, MSLE, MAPE, SMAPE, and QLIKE are reduced by 18.25%, 14.91%, 30.00%, 12.85%, 13.74%, and 23.42%, respectively. This paper provides a more accurate and robust method for forecasting the realized volatility of stock prices under mixed-frequency data. Full article

Under the new development model, the digital economy has become a new engine to promote the green development of the economy and realize the goal of “double carbon”. Based on the panel data of 30 provinces in China from 2010 to 2020, this paper empirically investigates the impact of the development of the digital economy on energy and carbon emissions using a series of econometric models such as baseline regression, a mechanism test, and the spatial Durbin model, etc. Common prosperity plays an intermediary role between digital economy development and carbon emissions; digital economic development optimizes resource allocation, effectively solves the problem of uneven resource distribution, and reduces energy and carbon emissions while achieving common prosperity. In addition, green innovation, industrial structure, urbanization level, R&D intensity, and the degree of marketization also have different degrees of influence on energy and carbon emissions. Therefore, the government should accelerate the construction of new digital infrastructure and implement the digital economy development strategy according to local conditions, so as to promote the digital economy to produce a more significant carbon emission reduction effect. Full article

Despite the recent proliferation of research on internationalization, little attention has been paid to understanding the reasons behind the decrease in firm value accompanying international expansion. By delving into the underlying mechanisms and applying the concept of agency theory to a sample of US firms spanning from 2000 to 2022, we posit that an increased level of information asymmetry in internationally diversified firms incentivizes managers to prioritize their own interests. To protect their careers, CEOs of internationally diversified firms often suppress bad news. This behavior can lead to the accumulation of negative news and heighten the risk of a stock-price crash. Furthermore, we propose that higher levels of international experience, enhanced monitoring effectiveness, and efficient investment practices will negatively moderate the positive relationship between internationalization and stock-price crash risk. Full article

A spacecraft power processing unit (PPU) is utilized to convert power from solar arrays or electric batteries to the payload, including electric propulsion, communication equipment, and scientific instruments. Currently, a high-voltage converter is widely applied to the spacecraft PPU to improve power density and save launch weight. However, the high voltage level poses challenges such as high step-down ratios and high power losses. To achieve less conduction loss, a SiC-based T-type three-level (TL) $LLC$ resonant converter is proposed. To further broaden the gain range and achieve high step-down ratios, a variable frequency and adjustable phase-shift (VFAPS) modulation scheme is proposed. Meanwhile, the steady-state time-domain model is established to elaborate the operation principles and boundary conditions for soft switching. Furthermore, the optimal resonant element design considerations have been elaborated to achieve wider gain range and facilitate easier soft switching. Furthermore, the numerical solutions for switching frequency and phase shift (PS) angle under each specific input could be figured out. Finally, the effectiveness of this theoretical analysis is demonstrated via a 500-W experimental prototype with 650∼950-V input and constant output of 48-V/11-A. Full article

The silicon carbide fiber-reinforced silicon carbide matrix (SiC/SiC), ceramic matrix composite (CMC) and nickel-based superalloy GH4169 can be utilized in high-temperature applications due to their high-temperature performance. The SiC/SiC composites are commonly used in turbine outer rings, where they encounter friction and wear against the turbine blades. This high-speed rubbing occurs frequently in aircraft engines and steam turbines. To investigate the tribological behavior of these materials, rubbing experiments were conducted between the SiC/SiC and the GH4169 superalloy. The experiments involved varying the blade tip speeds ranging from 100 m/s to 350 m/s and incursion rates from 5 μm/s to 50 μm/s at room temperature. Additionally, experiments were conducted at high temperatures to compare the tribological behavior under ambient conditions. The results indicated that the GH4169 superalloy exhibited abrasive furrow wear during rubbing at both room temperature and high temperature. Furthermore, at elevated temperatures, some of the GH4169 superalloy adhered to the surface of the SiC/SiC. The analysis of the experiments conducted at ambient temperatures revealed that the friction coefficient increased with higher blade tip velocities (100~350 m/s). However, the coefficient was lower at high temperatures compared to room temperature. Furthermore, significant temperature increases were observed during rubbing at room temperature, whereas minimal temperature changes were detected on the rubbing surface at high temperatures. Full article

In severe stages of periodontitis, conventional periodontal therapy and maintenance care are usually insufficient due to the viral and bacterial etiology; thus, a mechanical approach alone may not be sufficient to eliminate a substantial portion of subgingival pathogens, especially in deep periodontal sites. Background and Objectives: This single-blind, randomized clinical trial aimed to compare the clinical and microbiological efficacy of a low-cost protocol using povidone–iodine and sodium hypochlorite formulations as adjuncts to non-surgical therapy for patients with stage IV periodontitis when compared with chlorhexidine, the most commonly employed substance to date for antimicrobial regimens in periodontal therapy. Materials and Methods: Forty-five patients were randomly divided into two groups: control (subgingival instrumentation, chlorhexidine-assisted) and test (antiviral medication, subgingival instrumentation with povidone–iodine, sodium hypochlorite rinsing solution, and antibiotics). Clinical measurements and microbiological analyses were performed at baseline and after three months. Results: After three months, notable differences were found in the bacterial detection scores for Porphyromonas gingivalis (a significant reduction in detection frequency was observed in the test compared to the control (p = 0.021)), and there were significant reductions in detection in the test group for Tannerella forsythia and Treponema denticola, showing undetectable levels (p < 0.0001 for both). In the test group, the pocket probing depth median value was reduced significantly (p = 0.0005); similarly, bleeding on probing showed a marked decrease (p < 0.0001). However, changes in clinical attachment loss and full-mouth plaque score were not statistically significant. Conclusions: Using the proposed protocol, substantial improvements in clinical and microbiological parameters were obtained when compared with the current antimicrobial recommendations. Full article

Autonomous drones offer immense potential in dynamic environments, but their navigation systems often struggle with moving obstacles. This paper presents a novel approach for drone trajectory planning in such scenarios, combining the Interactive Multiple Model (IMM) Kalman filter with Proximal Policy Optimization (PPO) reinforcement learning (RL). The IMM Kalman filter addresses state estimation challenges by modeling the potential motion patterns of moving objects. This enables accurate prediction of future object positions, even in uncertain environments. The PPO reinforcement learning algorithm then leverages these predictions to optimize the drone’s real-time trajectory. Additionally, the capability of PPO to work with continuous action spaces makes it ideal for the smooth control adjustments required for safe navigation. Our simulation results demonstrate the effectiveness of this combined approach. The drone successfully navigates complex dynamic environments, achieving collision avoidance and goal-oriented behavior. This work highlights the potential of integrating advanced state estimation and reinforcement learning techniques to enhance autonomous drone capabilities in unpredictable settings. Full article

To evaluate differences in the morphology of the frontal sinus in adolescents and adults with different craniofacial patterns, searches up to April 2024 were conducted in six databases and other information sources to identify observational studies. Study selection, data extraction, and quality assessment using the NOS scale were performed independently by two reviewers. Random effects meta-analyses were conducted to estimate the difference in frontal sinus measurements between different craniofacial skeletal patterns (α = 0.05). The certainty of the evidence was evaluated according to GRADE. Fourteen studies were included in the review. All studies had methodological limitations that affected their quality. The syntheses showed that skeletal Class II subjects presented a significantly smaller width of the frontal sinus than skeletal Class I subjects (MD = 0.56; 95% CI: 0.38, 0.74; p < 0.0001; I² = 3%). Skeletal Class III subjects showed a frontal sinus width (MD = −0.91; 95% CI: −1.35, −0.47; p < 0.0001; I² = 36%) and area (MD = −28.13; 95% CI: −49.03, −7.23; p = 0.0084; I2 = 66%) significantly larger than those of the skeletal Class I subjects. The available evidence suggests a positive relationship between mandibular and frontal sinus size. There is limited evidence to make reliable estimates of the association of other craniofacial patterns and frontal sinus characteristics. These reported results are not conclusive and should be evaluated carefully due to the very low certainty of the evidence. The current evidence is scarce and consists of studies with methodological limitations; the results of the studies are often inconsistent, and the pooled estimates are imprecise. New high-quality research is still necessary. Full article

To explore the compensation effect of aeration on tomato vegetative and reproductive growth in arid and semi-arid areas, a two-year field experiment was conducted with four micro-nano aeration ratios (0%, 5%, 10%, and 15%) and three nitrogen topdressing levels (80, 60, and 40 kg·ha⁻¹) during the tomato growth period in Ningxia, China. The results showed that increasing the aeration ratio in the range of 0–15% was conducive to the enhancement of tomato root vigor (the ability of triphenyltetrazolium chloride to be reduced, 3–104%) and the leaf net photosynthetic rate (14–63%), favorable to the facilitation of plant dry matter accumulation (3–59%) and plant nitrogen accumulation (2–70%), and beneficial to the improvement of tomato yield (12–44%) and fruit quality. Interestingly, since the aeration ratio exceeded 10%, the increase in the aeration ratio showed no significant effects on the single-fruit weight, tomato yield, and fruit quality. Moreover, with aerated underground drip irrigation, properly reducing the traditional nitrogen topdressing level (80 kg·ha⁻¹) by 25% was favorable for enhancing tomato root vigor (5–31%), increasing tomato yield (0.5–9%), and improving fruit soluble solid accumulation (2–5%) and soluble sugar formation (4–9%). Importantly, increasing the aeration ratio by 5% could compensate for the adverse effects of reducing the nitrogen topdressing level by 25% by improving the leaf photosynthetic rate, promoting plant dry matter accumulation, increasing tomato yield, and enhancing the soluble solid and soluble sugar accumulation in tomato fruits. Synthetically considering the decrease in the nitrogen topdressing amount, leading to plant growth promotion, a tomato yield increase, and fruit quality improvement, a favorable nitrogen topdressing level of 60 kg·ha⁻¹ and the corresponding proper aeration ratio of 10% were suggested for tomato underground drip irrigation in the Yinbei Irrigation District of Ningxia. Full article

In response to the challenge of low recognition rates for similar phenotypic symptoms of tea diseases in low-light environments and the difficulty in detecting small lesions, a novel adaptive method for tea disease severity detection is proposed. This method integrates an image enhancement algorithm based on an improved EnlightenGAN network and an enhanced version of YOLO v8. The approach involves first enhancing the EnlightenGAN network through non-paired training on low-light-intensity images of various tea diseases, guiding the generation of high-quality disease images. This step aims to expand the dataset and improve lesion characteristics and texture details in low-light conditions. Subsequently, the YOLO v8 network incorporates ResNet50 as its backbone, integrating channel and spatial attention modules to extract key features from disease feature maps effectively. The introduction of adaptive spatial feature fusion in the Neck part of the YOLOv8 module further enhances detection accuracy, particularly for small disease targets in complex backgrounds. Additionally, the model architecture is optimized by replacing traditional Conv blocks with ODConv blocks and introducing a new ODC2f block to reduce parameters, improve performance, and switch the loss function from CIOU to EIOU for a faster and more accurate recognition of small targets. Experimental results demonstrate that YOLOv8-ASFF achieves a tea disease detection accuracy of 87.47% and a mean average precision (mAP) of 95.26%. These results show a 2.47 percentage point improvement over YOLOv8, and a significant lead of 9.11, 9.55, and 7.08 percentage points over CornerNet, SSD, YOLOv5, and other models, respectively. The ability to swiftly and accurately detect tea diseases can offer robust theoretical support for assessing tea disease severity and managing tea growth. Moreover, its compatibility with edge computing devices and practical application in agriculture further enhance its value. Full article

Thyme remains an indispensable herb today, finding its place in gastronomy, medicine, cosmetics, and gardens worldwide. It is highly valued in herbal remedies and pharmaceutical formulations for its antibacterial, antifungal, and antioxidant properties derived from the richness of its essential oil, which comprises various volatile components. However, climate change poses a significant challenge today, potentially affecting the quality of thyme, particularly the extracted essential oil, along with other factors such as biotic influences and the plant’s geographical distribution. Consequently, complex diversity in essential oil composition was observed, also influenced by genetic diversity within the same species, resulting in distinct chemotypes. Other factors contributing to this chemodiversity include the chosen agrotechnology and processing methods of thyme, the extraction of the essential oil, and storage conditions. In this review, we provide the latest findings on the factors contributing to the chemovariability of thyme essential oil. Full article

To cultivate excellent lily germplasms, an interspecific hybrid (LC×SQ-01) was successfully obtained by using a cut-style pollination method in which the rare wild lily Lilium callosum was used as the female parent and the cut flower L. longiflorum ‘Snow Queen’ was used as the male parent. The morphological features of LC×SQ-01 included height, leaf length, and width, which were observed to be between those of the parents in the tissue-cultured seedlings. The height and leaf length of LC×SQ-01 were more similar to those of the male parent, and the width was between the widths of the parents for field-generated plants. The epidermal cell length and the guard cell and stoma sizes were between those of both parents in tissue-cultured and field-generated plants. In addition, the shapes of the epidermal cells and anticlinal wall in LC×SQ-01 were more analogous to those in the male parent, while the stoma morphology was different from that of both parents. Fourteen pairs of polymorphic SSR primers were identified in both parents, and the validity of LC×SQ-01 was demonstrated by PCR amplification using five pairs of SSR primers. Flow cytometry and root tip squashing assays revealed that LC×SQ-01 was a diploid plant, similar to its parents. Furthermore, the LC×SQ-01 hybrid was more resistant to B. cinerea than its parents, and it also showed much greater peroxidase (POD) and catalase (CAT) activity than the parents. These results lay a foundation for breeding a new high-resistance and ornamental lily variety. Full article

The estimation of galactic component masses can be carried out through various approaches that involve a host of assumptions about baryon dynamics or the dark matter model. In contrast, this work introduces an alternative method for predicting the masses of the disk, bulge, stellar, and total mass using the k-nearest neighbours, linear regression, random forest, and neural network (NN) algorithms, reducing the dependence on any particular hypothesis. The ugriz photometric system was selected as the set of input features, and the training was performed using spiral galaxies in Guo’s mock catalogue from the Millennium simulation. In general, all of the algorithms provide good predictions for the galaxy’s mass from ${10}^9 M_{\odot }$ to ${10}^{11} M_{\odot }$, corresponding to the central region of the training domain. The NN algorithm showed the best performance. To validate the algorithm, we used the SDSS survey and found that the predictions of disk-dominant galaxies’ masses lie within a $99\%$ confidence level, while galaxies with larger bulges are predicted at a $95\%$ confidence level. The NN also reveals scaling relations between mass components and magnitudes. However, predictions for less luminous galaxies are biased due to observational limitations. Our study demonstrates the efficacy of these methods with the potential for further enhancement through the addition of observational data or galactic dynamics. Full article

Under the current power trading model, especially in the context of the large-scale penetration of renewable energy and the rapid integration of renewable energy into the power system, reasonable medium- and long-term decomposition can reduce the fluctuation in the energy price when the integrated energy service provider (IESP) participates in the spot market. It helps to avoid the price risk of the spot market. Additionally, it promotes the optimization of the operation of the regional energy day-ahead scheduling. At the present stage, most of the medium- and long-term contract decomposition methods focus on the decomposition of a single power and take less consideration of the bidding space in the spot market. This limitation makes it challenging to achieve efficient interaction and interconnection among multi-energy resources and smooth integration between the medium- and long-term market and the spot market. To address these issues, this paper proposes an optimal monthly contract decomposition method for IESPs that takes into account the equilibrium of spot bidding. First, the linking process and rolling framework of multi-energy transactions between the medium- and long-term market and the spot market are designed. Second, an optimal decomposition model for monthly contracts is constructed, and a daily decomposition method for monthly medium- and long-term contracts that accounts for the spot bidding equilibrium is proposed. Then, the daily preliminary decomposition result of medium- and long-term multi-energy contracts is used as the boundary condition of the day-ahead scheduling model, and the coupling characteristics of the multi-energy networks of electricity, gas, and heat are taken into account, as well as the operational characteristics. Then, considering the coupling characteristics and operating characteristics of electricity, gas, and heat networks, the optimal scheduling model of a multi-energy network is constructed to minimize the sum of cumulative daily operating costs, and the monthly final contract decomposition value and daily spot bidding space are derived. Finally, examples are calculated to verify the validity of the decomposition model, and the examples show that the proposed method can reduce the variance in spot energy purchase by about 4.64%, and, at the same time, reduce the cost of contract decomposition by about USD 0.33 million. Full article

This paper proposes incorporating linguistic semantic information into discourse relation recognition and constructing a Semantic Augmented Chinese Discourse Corpus (SACA) comprising 9546 adversative complex sentences. In adversative complex sentences, we suggest a quadruple (P, Q, R, $Q_{\beta }$) representing internal semantic elements, where the semantic opposition between Q and $Q_{\beta }$ forms the basis of the adversative relationship. P denotes the premise, and R represents the adversative reason. The overall annotation approach of this corpus follows the Penn Discourse Treebank (PDTB), except for the classification of senses. We combined insights from the Chinese Discourse Treebank (CDTB) and obtained eight sense categories for Chinese adversative complex sentences. Based on this corpus, we explore the relationship between sense classification and internal semantic elements within our newly proposed Chinese Adversative Discourse Relation Recognition (CADRR) task. Leveraging deep learning techniques, we constructed various classification models and the model that utilizes internal semantic element features, demonstrating their effectiveness and the applicability of our SACA corpus. Compared with pre-trained models, our model incorporates internal semantic element information to achieve state-of-the-art performance. Full article

The utilization of wind power all-DC systems with DC collection and transmission is an effective solution for the extensive development of wind power in deep-sea areas. However, in the event of faults occurring in wind power all-DC systems, the fault propagation speed is extremely rapid, with a wide-ranging impact, and to date, there are no complete DC engineering references available. It is crucial to research the topology and fault isolation methods applicable to large-scale offshore wind power all-DC systems in deep-sea areas. This paper proposes a novel series-connected all-DC system topology and presents corresponding fault isolation methods for internal faults in wind turbine units and faults in high-voltage DC transmission lines. The system simulation model was constructed using PSCAD/EMTDC (v4.6.3), and simulations were conducted for internal faults in the wind turbine units and DC transmission line short-circuit faults. The simulation results demonstrate that the proposed system can isolate various DC faults while maintaining stable operation, thereby validating the effectiveness of the control strategies and fault isolation methods proposed in this paper. Full article

Currently, in the blockchain-based distributed microgrid trading system, there are some problems, such as low throughput, high delay, and a high communication overhead. To this end, an improved Practical Byzantine Fault Tolerance (PBFT) algorithm (CE-PBFT) suitable for microgrid power trading is proposed. First, a node credit value calculation model is introduced, and nodes are divided into consensus, supervisory, and propagation nodes according to their credit values, forming a hierarchical network structure to ensure the efficiency and reliability of consensus. Secondly, the consensus process is optimized by adopting a segmented consensus mechanism. This approach calculates the consensus rounds for nodes and selects the methods for node-type switching and consensus based on these calculations, reaching dynamic changes in node states and credit values, effectively reducing the communication overhead of node consensus. Finally, the experiments show that compared with the IMPBFT and PBFT algorithms, the CE-PBFT algorithm has better performance in throughput, delay, and communication overhead, with a 22% higher average throughput and 15% lower average delay than the IMPBFT algorithm and a 118% higher average throughput and 87% lower average delay than the PBFT algorithm. Full article

Vehicle-to-grid (V2G) technology has emerged as a promising solution for enhancing the integration of electric vehicles (EVs) into the electric grid, offering benefits, such as distributed energy resource (DER) integration, grid stability support, and peak demand management, among others, as well as environmental advantages. This study provides a comprehensive review of V2G systems, with a specific focus on the role of the communication, as they have been identified as key enablers, as well as the challenges that V2G must face. It begins by introducing the fundamentals of V2G systems, including their architecture, operation, and a description of the benefits for different sectors. It then delves into the communication technologies and protocols in V2G systems, highlighting the key requirements in achieving reliable and efficient communication between EVs and the different agents involved. A comprehensive review of communication standards is described, as well as the main communication technologies, which are evaluated in terms of their suitability for V2G applications. Furthermore, the study discusses the challenges and environmental implications of V2G technology, emphasizing the importance of addressing strong and reliable communications to maximize its potential benefits. Finally, future research directions and potential solutions for overcoming challenges in V2G systems are outlined, offering useful insights for researchers, policymakers, and administrations as well as related industry stakeholders. Full article

As a crucial national security defense line, the existing risk prevention and screening system of customs falls short in terms of intelligence and diversity for risk identification factors. Hence, the urgent issues to be addressed in the risk identification system include intelligent extraction technology for key information from Customs Unstructured Accompanying Documents (CUADs) and the reliability of the extraction results. In the customs scenario, OCR is employed for M2M interactions, but current models have difficulty adapting to diverse image qualities and complex customs document content. We propose a hybrid mutual learning knowledge distillation (HMLKD) method for optimizing a pre-trained OCR model’s performance against such challenges. Additionally, current models lack effective incorporation of domain-specific knowledge, resulting in insufficient text recognition accuracy for practical customs risk identification. We propose a customs domain knowledge graph (CDKG) developed using CUAD knowledge and propose an integrated CDKG post-OCR correction method (iCDKG-PostOCR) based on CDKG. The results on real data demonstrate that the accuracies improve for code text fields to 97.70%, for character type fields to 96.55%, and for numerical type fields to 96.00%, with a confidence rate exceeding 99% for each. Furthermore, the Customs Health Certificate Extraction System (CHCES) developed using the proposed method has been implemented and verified at Tianjin Customs in China, where it has showcased outstanding operational performance. Full article

The drone-assisted Internet of Vehicles (DIoV) displays great potential in the punctual provision of rescue services without geographical limitations. To ensure data security in accident response and rescue services, authentication schemes with access control are employed. These schemes ensure that only specific rescue vehicle operators acting within a valid period can achieve mutual authentication from a designated processor, while access for mismatched, revoked, or expired users is denied. However, the current alternatives fail to ensure session key forward secrecy, entities’ mutual authentication, and user anonymity, thereby compromising users’ privacy and the security of communications. Moreover, executing too many time-consuming operations on vehicles’ resource-constrained devices inevitably degrades the performance of the authentication protocol. Balancing security and performance in the design of an authentication protocol with access control presents a significant challenge. To address this, a more efficient and robust authentication with access control has been designed. The proposed protocol ensures user anonymity through dynamic pseudonym allocation, achieves forward secrecy by excluding the long-term key from session key generation, and obtains mutual authentication by verifying the integrity of the messages exchanged. According to the security and performance analysis, it is demonstrated that the proposal is a robust, efficient, and cost-effective solution. In particular, the proposal can reduce the computational overhead by 66% compared to recent alternatives. Full article

Induction motors (IMs) must meet high reliability and safety standards in mission-critical applications, such as electric vehicles (EVs), where sensorless control strategies are fundamental. However, sensorless rotor speed estimation demands improvements to overcome filtering distortions, tuning complexities, and sensitivity to IM model mismatch. Algebraic methods offer inherent filtering capabilities and design flexibility to address these challenges without introducing additional dynamics into the control system. The objective of this paper is to provide an algebraic estimation strategy that yields an accurate rotor speed estimate for sensorless IM control. The strategy includes an algebraic estimator with single-parameter tuning and inherent filtering action. We propose an EV case study to experimentally evaluate and compare its performance with a typical drive cycle and a dynamic torque load that emulates a small-scale EV power train. The algebraic estimator exhibited a signal-to-noise ratio (SNR) of 43 dB. The closed-loop experiment for the EV case study showed average tracking errors below 1 rad/s and similar performance compared to a well-known sensorless strategy. Our results show that the proposed algebraic estimation strategy works effectively in a nominal speed range for a practical IM sensorless application. The algebraic estimator only requires single-parameter tuning and potentially facilitates IM model updates using a resetting scheme. Full article