Search Results (102)

China’s roof-cutting and pressure relief gob-side entry retaining (RCPR-GER) technology provides an efficient non-pillar mining solution that significantly enhances coal recovery. This paper presents a systematic review of the technological progress in Chinese coal mines from 2011 to 2023, based on an analysis of 1038 publications from CNKI, EI, and Web of Science using VOS viewer and Origin software. Four main technical approaches are examined: gob-side entry retaining without roadside filling, with roadside filling, with roof-cutting and pressure relief, and hybrid methods. Five key roof-cutting techniques are evaluated: dense drilling, high-pressure water-jet slotting, hydraulic fracturing, blasting, presplitting, and roof water injection softening. Successful applications have been documented in coal seams with thicknesses of 1.6–6.15 m and burial depths of 92–1037 m, demonstrating wide adaptability. The roof-cutting short-beam theory underpins the mechanism, which reduces roadway deformation, shortens the cantilever beam length, and alters stress transfer paths. Compared to previous reviews on general gob-side entry retaining, this study offers a dedicated synthesis and comparative analysis of RCPR-GER technologies, establishing a selection framework grounded in geological compatibility and engineering practice. Future research should focus on adaptive parameter design for deep hard composite roofs, quantitative modeling of passive roof-cutting effects, optimization of cutting timing and orientation, and floor-heave control technologies to extend applications under complex geological conditions. Full article

The article presents a blockchain-based architecture for decentralized electricity trading that tokenizes energy delivery rights and cash-flows. Energy Attribute Certificates (EACs) are implemented as NFTs, while buy/sell orders are encoded as ERC-1155 tokens whose tokenId packs a time slot and price, enabling precise matching across hours. A clearing smart contract (Matcher) burns filled orders, mints an NFT option, and issues two ERC-20 assets: PT, the right to consume kWh within a specified interval, and YT, the producer’s claim on revenue. We propose a simple, linearly increasing discounted buyback for YT within the slot and introduce an aggregating token, IndexYT, which accumulates YTs across slots, redeems them at par at maturity, and gradually builds on-chain reserves—turning IndexYT into a liquid, yield-bearing instrument. We outline the PT/YY lifecycle, oracle-driven policy controls for DSO (e.g., transfer/splitting constraints), and discuss transparency, resilience, and capital efficiency. The contribution is a Pendle-inspired split of electricity into Principal/Yield tokens combined with a time-stamped on-chain order book and IndexYT, forming a programmable market for short-term delivery rights and yield derivatives with deterministic settlement. Full article

The stability and safety of the vertical shaft during construction is an important problem for deep mining engineering because of the high in situ stresses. This paper conducts experimental studies on the difficulty of shaft support during the construction of No. 6 deep shaft at the Huize Mine, Yunnan Province, China. Based on the rule of similarity test, a similar material formula was developed, and standard model samples of the vertical shaft were prepared. Three different support methods were set up, including steel fiber-reinforced concrete support, drilling pressure relief support, and slot filling support. The experiments were conducted by using a true triaxial test system, and the testing process was monitored by a static stress–strain gauge and an acoustic emission system. The experimental results show that the integrity of the borehole pressure relief support shaft is optimal under the in situ stress. As the maximum principal stress increases to the instability and failure of the shaft, the peak load, cumulative number, and energy of acoustic emission events were the highest using the steel fiber concrete support method, and the peak load was the lowest using the borehole pressure relief. The borehole pressure relief transfers the stress around the shaft to the deep part. Although it ensures the integrity of the shaft, it causes internal damage to the shaft, reduces the energy storage of the shaft, and results in the lowest cumulative number and energy of acoustic emission events. After the instability and failure of the shaft, the average block size of the shaft debris is the highest under the borehole pressure relief support along the direction of the maximum principal stress. On the other hand, the mechanical properties of samples with different support methods under dynamic load conditions are studied by applying external low-frequency disturbances, and the test conclusions have been verified through numerical simulation. Field tests have verified that the steel fiber-reinforced concrete lining support can maintain the integrity of the deep shaft wall and ensure safety during mining production. Full article

Intent detection and slot filling are fundamental components for constructing intelligent question-answering systems in agricultural domains. Existing approaches show notable limitations in semantic feature extraction and achieve relatively low accuracy when processing domain-specific agricultural queries with complex terminology and contextual dependencies. To address these challenges, this paper proposes an agricultural knowledge-enhanced deep learning approach that integrates agricultural domain knowledge and terminology with advanced neural architectures. The method integrates HanLP-based agricultural terminology processing with BERT contextual encoding, TextCNN feature extraction, and attention-based fusion. Experimental validation on a curated domain-specific agricultural dataset of 8041 melon cultivation queries demonstrates that the proposed model achieves an accuracy of 79.6%, recall of 80.1%, and F1-score of 79.8%, demonstrating significant improvements (7–22% performance gains) over baseline methods including TextRNN, TextRCNN, TextCNN, and BERT-TextCNN models. The results demonstrate significant potential for advancing intelligent agricultural advisory systems and domain-specific natural language understanding applications, particularly for precision agriculture applications. Full article

Uncoordinated electric vehicle (EV) charging can significantly complicate power system operations. In this paper, we develop a hybrid EV charging method that seamlessly integrates centralized EV charging and distributed control schemes to address EV energy demand challenges. The proposed method includes (1) a centralized day-ahead optimal scheduling mechanism and EV shifting process based on mixed-integer linear programming (MILP) and (2) a distributed control strategy based on a genetic algorithm (GA) that dynamically adjusts the charging rate in real-time grid scenarios. The MILP minimizes energy imbalance at overloaded slots by reallocating EVs based on supply–demand mismatch. By combining full and minimum charging strategies with MILP-based shifting, the method significantly reduces network stress due to EV charging. The centralized model schedules time slots using valley-filling and EV-specific constraints, and the local GA-based distributed control adjusts charging currents based on minimum energy, system availability, waiting time, and a priority index (PI). This PI enables user prioritization in both the EV shifting process and power allocation decisions. The method is validated using demand data on a radial feeder with residential and commercial load profiles. Simulation results demonstrate that the proposed hybrid EV charging framework significantly improves grid-level efficiency and user satisfaction. Compared to the baseline without EV integration, the average-to-peak demand ratio is improved from 61% to 74% at Station-A, from 64% to 80% at Station-B, and from 51% to 63% at Station-C, highlighting enhanced load balancing. The framework also ensures that all EVs receive energy above their minimum needs, achieving user satisfaction scores of 88.0% at Stations A and B and 81.6% at Station C. This study underscores the potential of hybrid charging schemes in optimizing energy utilization while maintaining system reliability and user convenience. Full article

The disadvantages of traditional strip fertilization technology for corn planting in China include low fertilizer utilization rates, unstable operation quality, and environmental pollution. Therefore, in this study, a synchronous hole fertilization device for corn planting based on real-time intelligent control is designed, aiming to reduce fertilizer application and increase efficiency through the precise alignment technology of the seed and fertilizer. This device integrates an electric drive precision seeding unit, a slot wheel hole fertilization unit, and a multi-sensor coordinated closed-loop control system. An STM32 single-chip micro-computer is used to dynamically analyze the seed–fertilizer timing signal, and a double closed-loop control strategy (the position loop priority is higher than the speed loop) is used to correct the spatial phase difference between the seed and fertilizer in real time to ensure the precise control of the longitudinal distance (40~70 mm) and the lateral distance (50~80 mm) of the seed and fertilizer. Through the Box–Behnken response surface method, a field multi-factor test was carried out to analyze the mechanism of influence of the implemented forward speed (A), per-hole target fertilizing amount (B), and plant spacing (fertilizer hole interval) (C) on the seed–fertilizer alignment qualification rate (Y₁) and the coefficient of variation in the hole fertilizing amount (Y₂). The results showed that the order of primary and secondary factors affecting Y₁ was A > C > B, and that the order affecting Y₂ was C > B > A; the comprehensive performance of the device was best with the optimal parameter combination of A = 4.2 km/h, B = 4.4 g, and C = 30 cm, with Y₁ as high as 94.024 ± 0.694% and Y₂ as low as 3.147 ± 0.058%, which is significantly better than the traditional strip application method. The device realizes the precise regulation of 2~6 g/hole by optimizing the structural parameters of the outer groove wheel (arc center distance of 25 mm, cross-sectional area of 201.02 mm², effective filling length of 2.73~8.19 mm), which can meet the differentiated agronomic needs of ordinary corn, silage corn, and popcorn. Field verification shows that the device significantly improves the spatial distribution of the concentration of fertilizer, effectively reduces the amount of fertilizer applied, and improves operational stability and reliability in multiple environments. This provides technical support for the regional application of precision agricultural equipment. Full article

Current research on intelligent question answering mainly focuses on high-resource languages such as Chinese and English, with limited studies on question understanding and reasoning in low-resource languages. In addition, during the joint modeling of question understanding tasks, the interdependence among subtasks can lead to error accumulation during the interaction phase, thereby affecting the prediction performance of the individual subtasks. To address the issue of error propagation caused by sentence-level intent encoding in the joint modeling of intent recognition and slot filling, this paper proposes a Cross-lingual Token-level Bi-Interactive Model (Bi-XTM). The model introduces a novel subtask interaction method that leverages the token-level intent output distribution as additional information for slot vector representation, effectively reducing error propagation and enhancing the information exchange between intent and slot vectors. Meanwhile, to address the scarcity of Kazakh (Arabic alphabet) language corpora, this paper constructs a cross-lingual joint question understanding dataset for the Xinjiang tourism domain, named JISD, which includes 16,548 Chinese samples and 1399 Kazakh samples. This dataset provides a new resource for cross-lingual intent recognition and slot filling joint tasks. Experimental results on the publicly available multi-lingual question understanding dataset MTOD and the newly constructed dataset demonstrate that the proposed Bi-XTM achieves state-of-the-art performance in both monolingual and cross-lingual settings. Full article

This work presents a novel $C{O}_2$ gas sensor based on a slotted polymer-phaseshift Bragg grating (SP-PSBG) waveguide filled with polyhexamethylene biguanide (PHMB) as the sensing medium. The transmission resonance, characterized by a narrow peak with a full width at half maximum (FWHM) of 1.6 nm within the Bragg grating bandgap, is highly responsive to refractive index changes in PHMB caused by variations in $C{O}_2$ concentration. Numerical simulations demonstrate a sensitivity of 14.4 pm/ppm, outperforming conventional gas sensors based on functional material coatings. This enhanced performance comes from the direct interaction between the PHMB-filled resonant structure and the cladding that contains $C{O}_2$ molecules, eliminating the need for polymer-coated cladding layers. The optimization approach employed in this design focuses on maximizing the optical confinement factor within the PHMB-filled slot, leading to an effective overlap between the guided optical mode and the sensing material. Full article

Improving the fuel economy of electric buses requires traction motors that provide not only high-power density but also high efficiency under diverse driving conditions. While high slot fill factor (HSFF) coils such as the maximum slot occupation (MSO) coil improve motor torque and power density, they inevitably increase AC copper losses due to elevated AC resistance, especially at high speeds. Unlike conventional motor optimization studies that mainly focus on efficiency at specific operating points, this paper proposes a drive-cycle-aware design optimization method that minimizes AC copper loss to enhance real-world fuel economy. By combining 2D finite element analysis (FEA) with vehicle-level simulations under three representative driving cycles (Manhattan, HWFET, HDUDDS), an optimal motor design was derived. The optimized motor achieved improvements in fuel economy by 0.20%, 0.86%, and 0.36%, respectively, compared to the initial design. Experimental validation through prototype fabrication confirmed the effectiveness of the proposed method. These results demonstrate that the proposed design approach can contribute to improving energy efficiency and reducing operational costs in electric bus applications. Full article

To address the issue of a poor seed-filling performance and seeding quality in air-suction corn seed metering devices during high-speed operation, an energy-efficient precision corn seeder with dual-side-disturbance-facilitated guiding slots is designed. The dual-side-disturbance-promoting method effectively increases the seed agitation, directing the corn seeds toward the suction holes and improving the seed-filling quality. The theoretical design of the dual-side guiding slot angles results in an upper-side guiding slot angle range of 29° to 19° and a lower-side guiding slot angle range of 72° to 90°. Bench tests are conducted to further optimize the parameter range, with the negative suction chamber pressure, upper-side guiding slot angle, and lower-side guiding slot angle as the experimental factors and the seedling qualification rate, missed sowing rate, and double-sowing rate as the evaluation indicators. An orthogonal experiment is carried out to analyze the interaction effects of factors on the evaluation indicators, followed by parameter optimization and verification tests. When the negative suction chamber pressure is 3.7 kPa, the upper-side guiding slot angle is 26.9°, and the lower-side guiding slot angle is 72.9°, the verification test results show a qualification index of 97.7%, a missed sowing index of 1.3%, and a row-spacing variation coefficient of 1%. The operational speed adaptability test results show that when the working speed is ≤14 km/h, the seed metering device achieves a qualification index above 97.1%, a missed sowing index below 1.5%, and a double sowing index below 1.4%, indicating good adaptability to working speeds. The comparison test results show that when the operating speed is between 8 and 12 km/h, the air-suction seed metering device with dual-side-disturbance-facilitated guiding slots achieves a 2.1% increase in the qualification index, significantly improving seeding quality. The energy consumption comparison test results indicate that under the same operational speed and approximately equal seeding quality, the air-suction seed metering device with dual-side guiding slots and dual-side-disturbance-facilitated air suction requires a reduction in negative pressure of more than 0.7 kPa, resulting in lower energy consumption. Full article

In order to innovate the planting mode and improve the quality of Chinese chive, we designed an outside-filling Chinese chive adjustable-capacity precision seed-metering device with an adjustable number of sown seeds. The diameter, number of shaped holes, and seed slot parameters of the seeding plate were designed based on the physical characteristics and agronomic planting requirements of the Haoji Chinese chive. A simulation of the seed-metering device’s seeding process was carried out using EDEM software. To carry out the quadratic general rotary combination design experiment, use seed slot diameter and seed slot depth as test factors, longitudinal concentration and transverse concentration as evaluation indexes, and carry out the bench validation test and comparison test under the optimal parameter combination. In the simulation test, the factors affecting the longitudinal concentration in order of priority were seed slot depth and seed slot diameter, and the factors affecting the transverse concentration in order of priority were seed slot diameter and seed slot depth. The optimal parameters were seed slot diameter of 3.075 mm, seed slot depth of 3.323 mm, longitudinal concentration of 0.563, and transverse concentration of 0.634. In the bench test, the relative error of longitudinal concentration was 3.20%, the relative error of transverse concentration was 2.47%, and the number of seeds sown per hole was linearly correlated with the length of the seed slot. The results of the bench test and simulation test are close to each other, which proves that the outside-filling Chinese chive adjustable-capacity precision seed-metering device has a better sowing effect, and the number of sowing grains can be adjusted. Full article

Embedding stacked HTS tapes into twisted slots is one design approach for constructing fusion conductors. This paper adopts a Cable-in-Conduit Conductor (CICC) structure, utilizing commercially REBCO coated conductors. The cable framework is made of copper and features six helically twisted slots filled with 2G HTS tapes. Two 1 m long samples with twist pitches of 200 mm and 300 mm, respectively, were fabricated. In one slot, copper and superconducting tapes were alternated, while the remaining grooves were filled with copper tapes. The 90 µm thick copper-plated bare tapes provided by Shanghai Superconductor were used for testing. By measuring the critical current of tapes positioned at different locations within the grooves at 77 K, the characteristics of each tape in the stacked arrangement were individually characterized. The study obtained the current degradation patterns of tapes located at different positions within the grooves under various bending radii. This paper will present and discuss the preliminary results of the bending measurements conducted at 77 K under a self-field. Full article

Intent detection and slot filling tasks share common semantic features and are interdependent. The abundance of professional terminology in specific domains, which poses difficulties for entity recognition, subsequently impacts the performance of intent detection. To address this issue, this paper proposes a co-interactive model based on a knowledge graph (CIMKG) for intent detection and slot filling. The CIMKG model comprises three key components: (1) a knowledge graph-based shared encoder module that injects domain-specific expertise to enhance its semantic representation and solve the problem of entity recognition difficulties caused by professional terminology and then encodes short utterances; (2) a co-interactive module that explicitly establishes the relationship between intent detection and slot filling to address the inter-dependency of these processes; (3) two decoders that decode the intent detection and slot filling. The proposed CIMKG model has been validated using question–answer corpora from both the medical and architectural safety fields. The experimental results demonstrate that the proposed CIMKG model outperforms benchmark models. Full article

With the development of technology, the popularity of online medical treatment is becoming more and more widespread. However, the accuracy and credibility of online medical treatment are affected by model design and semantic understanding. In particular, there are still some problems in the accurate understanding of complex structured texts, which affects the accuracy of judging users’ intentions and needs. Therefore, this paper proposes a new method for medical text parsing, which realizes core tasks such as named entity recognition, intention recognition, and slot filling through a multi-task learning framework; uses BERT to obtain contextual semantic information; and combines BiGRU and BiLSTM neural networks, and uses CRF to realize sequence annotation and DPCNN to realize classification prediction. Thus, the task of entity recognition and intention recognition can be accomplished. On this basis, this paper builds a multi-task learning model based on BiGRU-BiLSTM, and uses CBLUE and CMID databases to verify the method. The verification results show that the accuracy of named entity recognition and intention recognition reaches 86% and 89%, respectively, which improves the performance of various tasks. The ability of the model to process complex text is enhanced. This method can improve the text generalization ability and improve the accuracy of online medical intelligent dialogue when it is used to analyze medical texts. Full article

Natural language understanding is a foundational task in medical dialogue systems. However, there are still two key problems to be solved: (1) Multiple meanings of a word lead to ambiguity of intent; (2) character errors make slot entity extraction difficult. To solve the above problems, this paper proposes a character-word information interaction framework (CWIIF) for natural language understanding in the Chinese medical dialogue domain. The CWIIF framework contains an intent information adapter to solve the problem of intent ambiguity caused by multiple meanings of words in the intent detection task and a slot label extractor to solve the problem of difficulty in yellowslot entity extraction due to character errors in the slot filling task. The proposed framework is validated on two publicly available datasets, the Intelligent Medical Consultation System (IMCS-21) and Chinese Artificial Intelligence Speakers (CAIS). Experimental results from both datasets demonstrate that the proposed framework outperforms other baseline methods in handling Chinese medical dialogues. Notably, on the IMCS-21 dataset, precision improved by 2.42%, recall by 3.01%, and the F1 score by 2.4%. Full article