Search Results (93)

Agentic artificial intelligence (AI) offers new opportunities for intelligent clinical decision support, but deployment in emergency and crisis settings remains challenging because time-critical recommendations must often be generated under incomplete patient information and system constraints. Conventional clinical decision support systems rely on rule-based workflows that degrade when structured data are absent, while standalone language models lack coordination mechanisms to enforce mandatory safety checks. We present TriAgent, a multi-agent framework that unifies adaptive orchestration, iterative retrieval, embedded safety verification, and end-to-end auditability within a single crisis clinical decision support workflow. An Orchestrator Agent dynamically selects specialist modules for clinical assessment, retrieval, treatment planning, safety verification, and system coordination, with routing determined by model reasoning rather than fixed execution paths. A retrieval sub-agent performs iterative query refinement and relevance grading over 49,000 MIMIC-IV discharge notes, while medication-conflict screening and allergy-risk assessment are invoked in parallel only when clinically indicated. A Critique Agent reviews the full reasoning trace before recommendation finalization. In a retrospective evaluation on 1000 real emergency presentations under synthesized incomplete-information inputs, TriAgent achieved 85.0% critical-case recall and 65.7% overall triage accuracy, versus at most 14.7% and 43.4% for matched single-model and retrieval-only baselines, with safety checks executed on every continuation pathway and adaptive routing invoking only the modules each case required. These results support multi-agent orchestration as a promising design pattern for transparent and auditable AI in healthcare. These gains are internal system properties; clinical-safety benefit remains to be established through prospective, clinician-involved validation. Full article

Rotor imbalance and abnormal vibration are classical operating conditions in rotating machinery and can often be identified by conventional vibration analysis. However, the development of low-power, self-powered, and distributed sensing nodes remains important for long-term condition monitoring, particularly in scenarios where external power supply, wiring, and maintenance are constrained. Existing vibration sensors, including piezoelectric and capacitive types, are constrained by power consumption and degraded performance under low-frequency and weak excitation. To address this issue, a magnetically repulsive cushion triboelectric nanogenerator (MRCT) is proposed to enable self-powered vibration sensing. The magnetic-repulsion cushion allows the upper friction layer to undergo stable contact–separation motion under a non-contact restoring force, while the microstructured strip electrode array (MSEA) enhances the triboelectric output and signal stability. A hybrid convolutional neural network–gated recurrent unit (CNN-GRU) deep-learning model is employed to extract time-domain and frequency-domain features from the collected signals, enabling real-time identification of rotor vibration amplitude, frequency, and imbalance weight. Experimental results show that the MRCT provides stable output, a high signal-to-noise ratio, and an identification accuracy above 98% for predefined rotor imbalance-weight states under laboratory conditions. In addition, a shaft-misalignment-related abnormal vibration condition was examined on the motor platform. The corresponding time-domain and frequency-domain analyses show that the MRCT voltage signal exhibits distinguishable signal variations under normal and misalignment-related conditions, including spectral changes around the 2× rotational frequency. A laboratory-scale AIoT-oriented demonstration further verifies the feasibility of integrating MRCT signal acquisition, CNN-GRU inference, wireless transmission, and GUI-based visualization. It should be noted that the present work mainly focuses on imbalance-state recognition, while the misalignment-related experiment provides an additional sensor-response verification. Broader validation involving mechanical looseness, bearing defects, variable-speed operation, cross-machine testing, and long-term industrial conditions remains necessary. Full article

Grain intake is a key operation in grain storage that directly affects storage efficiency, operational safety, and grain quality. In grain-entry scenarios, single LiDAR sensors are easily limited by blind spots and occlusions, making multi-LiDAR collaborative perception necessary for reliable three-dimensional environment sensing. However, heterogeneous LiDARs differ in scan lines, point density, viewing angle, installation pose, and noise characteristics, which leads to low-overlap and mixed sparse–dense point cloud registration challenges. To address this issue, this paper proposes a GICP-based registration flow improvement method for heterogeneous multi-LiDAR systems used in intelligent grain warehousing robots. The method improves registration stability through overlap-region cropping, voxel downsampling, and a star-topology registration strategy, and further introduces a point-to-plane evaluation metric based on local planar models together with cross-LiDAR planar consistency verification. Experimental results show that the proposed method reduces the point-to-plane error to 0.1487 m in the ${\mathrm{L}}_0-{\mathrm{L}}_1$ registration task and 0.1090 m in the ${\mathrm{L}}_1-{\mathrm{L}}_2$ registration task, outperforming ICP, point-to-plane ICP, and NDT while maintaining acceptable computational efficiency. These results demonstrate that the method can improve structural alignment quality and provide reliable geometric support for multi-sensor perception, mapping, and autonomous operation of grain warehousing robots. Rather than proposing a fundamentally new registration mathematical model, this study proposes a highly engineered GICP-based workflow. It should be noted that the proposed workflow is specifically tailored and optimized for plane-dominated and semi-static grain storage environments, restricting its validated scope to static or low-speed multi-LiDAR registration tasks. Full article

In environments where GNSS signals are unavailable—such as indoor spaces, underground facilities, and forested areas—autonomous UAV navigation faces challenges related to localization uncertainty and limited onboard sensing capability. This study proposes a lightweight navigation framework using a single Intel RealSense D435i depth camera, integrating RTAB-Map SLAM, DWA-based local planning, and a direction-oriented frontier exploration strategy. The proposed exploration strategy introduces heading consistency into frontier target selection to support navigation in directionally constrained environments. The system is implemented within the ROS framework and evaluated in Gazebo/ArduPilot SITL simulation environments under low-, medium-, and high-density obstacle configurations. The results show that the system successfully completed autonomous traversal and return-to-home missions across all scenarios, with traversal RMSE values of 0.195 m, 0.197 m, and 0.420 m and return RMSE values of 0.295 m, 0.474 m, and 1.084 m, respectively. Qualitative dynamic-obstacle tests further demonstrate the system’s capability for local map updating and replanning. It should be noted that the current evaluation is primarily simulation-based and conducted in simplified environments. Therefore, the results are interpreted as initial system-level validation rather than full real-world deployment verification. The proposed system should not be directly interpreted as a ready-to-deploy real-world UAV navigation solution. Future work will focus on physical UAV experiments and more realistic GNSS-denied environments. Full article

Against the backdrop of the coordinated development of the digital economy and green transformation, the mechanism through which enterprises’ digital transformation affects new quality productive forces deserves systematic examination. Using panel data of Chinese A-share manufacturing listed firms from 2015 to 2023, this paper constructs a two-way fixed effects model and employs an instrumental variable approach to empirically examine the nonlinear impact of digital transformation on new quality productive forces and its underlying mechanisms. From the perspectives of three aspects—nonlinear effects, dual mediation mechanisms, and heterogeneity analysis—this paper systematically uncovers the internal logic through which digital transformation drives new quality productive forces, providing theoretical foundations and policy implications for promoting the coordinated digital and green transformation of the manufacturing sector. It should be noted that this paper has certain limitations. First, the sample is confined to Chinese A-share manufacturing listed firms, and the applicability of the findings to small and medium-sized enterprises and other industries requires further verification. Second, this paper does not fully capture the dynamic evolution of the impact of digital transformation on new quality productive forces. Future research may further deepen the analysis by expanding the sample scope, refining variable measurement, and incorporating dynamic modeling approaches. Full article

To address the limitations of conventional LC resonant battery equalization circuits, including slow balancing speed under small voltage differences, limited scalability in multi-cell configurations, and the risk of over-equalization, this paper proposes a dual-layer LC resonant equalization topology integrated with a Boost-assisted mechanism and a state-of-charge (SOC)-based dual closed-loop current control strategy. In the proposed topology, a Boost converter is introduced to actively enhance the effective voltage difference between cells, thereby improving the equalization current amplitude and accelerating the balancing process. A switched-inductor structure is further adopted to enable scalable inter-group energy transfer in multi-cell battery systems. To improve control accuracy, SOC is selected as the balancing variable, and a dual closed-loop control framework is designed, where the outer loop regulates SOC deviation, and the inner loop controls the equalization current via proportional–integral (PI) controllers. A MATLAB/Simulink model is established to evaluate the proposed method under multiple operating conditions, including idle, charging, and discharging states. The results show that the proposed topology significantly reduces the equalization time compared with conventional LC resonant circuits and improves balancing speed by approximately 49% under the dual closed-loop control strategy. In addition, the system maintains stable performance across different operating conditions. It should be noted that this study focuses on topology design and control strategy validation through simulation. Due to the focus on topology validation and control mechanism analysis, this study is limited to simulation-based verification. Experimental implementation will be conducted in future work. Full article

Dongting Lake, a vital freshwater lake in China with substantial ecological, economic, and social significance, has fractional vegetation coverage (FVC) as a core indicator of regional ecological balance. To characterize the ecosystem’s health and support targeted protection, this study analyzed FVC’s spatio-temporal evolution and associated spatial factors in the Dongting Lake ecological restoration area using 2005–2020 MODIS imagery, integrating the dimidiate pixel model, slope trend analysis, and geographic detector model (noting the latter quantifies spatial explanatory power but not direct ecological causality). Results revealed distinct FVC heterogeneity: 2011 had the poorest vegetation (mean FVC = 0.60), while 2005, 2010, and 2012 showed higher FVC (mean = 0.65); summer exhibited the most vigorous growth due to favorable hydrothermal conditions. Slope was the dominant single factor with the highest spatial explanatory power for FVC (q = 0.50), its distribution strongly associated with soil moisture and erosion. The slope–soil moisture interaction had the strongest joint spatial explanatory power (q = 0.625), reflecting topographic–hydrological synergistic spatial association, implying slope may indirectly modulate vegetation water availability (inferred from spatial correlation, not causality). The slope–DEM interaction (q = 0.534) confirmed combined topographic explanatory effects. Overall, 70.3% of the region saw significant FVC improvement (notably in spring) from 2005 to 2020, with degradation in February, March, and December. Slope emerged as a key factor consistent with interannual and seasonal FVC variations. These findings provide a reliable scientific basis for targeted wetland restoration, emphasizing enhanced vegetation management in summer, autumn, and the growing season. Limitations include: MODIS’s 250 m resolution leading to mixed-pixel effects in fragmented wetlands, limited validation coverage of extreme habitats and single-year verification, and the Geodetector model’s reliance on spatial stratification and factor independence assumptions (deviating from wetland’s continuous factor variation) that preclude causal inference. Full article

Developing electric motors with higher efficiencies for energy savings and environmental protection is crucial. The efficiency of grid-connected induction motors can be measured using various approaches; the preferred method is the indirect approach, which evaluates the separate losses from the additional losses due to residual losses. This approach follows the traditional approach to efficiency determination, introducing experimental procedures to assess additional losses by measuring the torque delivered by the motors. As noted in previous articles, the procedure is complex and requires numerous direct measurements. One area of interest is the determination of measurement uncertainty. This work aims to quantify the sensitivity of the combined uncertainties of losses and efficiency to variations in directly measured input variables: power frequency, rotational speed, torque, power, current, voltage, resistance, coolant temperature, and cold frame temperature. The results presented here help select measurement instrumentation, depending on whether the tests are aimed solely at determining efficiency or whether it is necessary to analyze the trend of the various types of loss, as occurs in optimization and experimental verification processes with high-performance materials, based on a comprehensive analysis of all standard and combined uncertainties, and with experimental data to assign a realistic value to the uncertainties themselves. Full article

Automated legacy code modernization using Large Language Models lacks rigorous evaluation frameworks and multi-objective quality assessment methodologies. Existing research suffers from three critical deficiencies: single-metric evaluation paradigms creating pathological optimization incentives, statistical validation limited to p-values without effect size analysis, and absence of systematic transformation pattern taxonomies explaining what works and why. We present a novel multi-objective statistical framework that jointly assesses Cyclomatic Complexity (CC) and Maintainability Index (MI) while providing comprehensive effect size analysis addressing software engineering research gaps. Applied to 47 legacy Java samples from Apache Ant (version 1.10.x, commit rel/1.10.14), our framework achieves 97.9% metric-level improvement with very large practical effects (Cohen’s $d=1.86$, 95% CI [1.36, 2.35], $p<0.0001$) for maintainability—substantially exceeding prior work and conventional significance thresholds. We note that this success rate reflects quality metric improvement; functional equivalence was verified through syntactic validation and manual inspection of a 20% random sample, while comprehensive automated test-based verification remains a limitation addressed in future work. We contribute: (1) first multi-objective quality assessment framework for code modernization with weighted composite scoring and sensitivity analysis, (2) rigorous statistical methodology with effect size analysis beyond p-values, (3) systematic transformation pattern taxonomy identifying four successful patterns and three failure modes with predictive value (inter-rater agreement $\kappa =0.82$), and (4) negative result showing iterative refinement provides no benefit ($d=0.08$, $p=0.179$), saving community resources. Our transformation taxonomy enables practitioners to predict success likelihood from code characteristics, while our statistical framework provides replicable methodology for evaluating LLM-based software engineering tools. The very large effect size indicates metric-level improvements are materially meaningful for real-world software maintenance, not merely statistically detectable. Full article

This paper is a focused, standards-aligned survey of where autoregressive (AR) large language models (LLMs) tend to break down when deployed inside industrial informatics workflows that must satisfy long-horizon objectives, hard constraints, traceability, and functional-safety obligations (e.g., IEC 61508/ISO 26262/ISO 21448). Rather than claiming new algorithms or experiments, we synthesize and organize prior work into (i) a control-oriented taxonomy of four AR failure modes that recur in practice (compounding error, myopic objectives, data brittleness/hallucinations, and scaling/latency inefficiencies), (ii) a catalog of standards-compatible deployment patterns that mitigate these issues (human-gated LLM-in-the-loop, retrieval + verification pipelines, planner-of-record architectures, and runtime assurance envelopes), and (iii) an operational decision framework (criteria table with observable proxies, a stepwise decision procedure, and worked examples) for deciding when token-centric mitigations are sufficient versus when state/world-model components become warranted. Joint Embedding Predictive Architectures (JEPA) and Hierarchical JEPA (H-JEPA) JEPA are proposed as representative state-predictive architectures, with discussion explicitly bounded by currently available empirical evidence; we explicitly note that the published evidence base is currently concentrated on vision/multimodal benchmarks and that industrial control validation remains limited. To make evidence boundaries transparent, we introduce (a) a survey method (scope, inclusion/exclusion criteria, and data-extraction fields), (b) a comparison matrix across representative prior systems, and (c) an evidence map that links each deployment pattern to peer-reviewed empirical findings and system reports. Full article

The grading of beef appearance quality is crucial for standardizing market circulation and promoting the upgrading of the beef cattle industry. China’s current beef quality grading system, which relies primarily on human sensory-based visual assessment with marbling and meat color as core parameters, suffers from strong subjectivity, low efficiency, and large errors. This study proposes a rapid grading method for beef rib eye muscle using smartphone imaging combined with ImageJ software. Standardized images were acquired, and ImageJ was employed for grayscale conversion, threshold segmentation, and morphological processing to extract length, width, area, and marbling proportion. The R, G, B color channels were separated to calculate the R/(R + G + B) color ratio. Pearson correlation analysis showed that the ImageJ results were highly consistent with manual measurements (correlation coefficients > 0.97), indicating good reliability. A five-level grading standard (A1–A5) was established, characterized by low cost, simple operation, and objective results. It provides an economical technical solution for beef quality grading and facilitates the intelligent development of the industry. It should be noted that this experimental grading model has only been validated under the specific experimental conditions of this study, and further verification is required for broader application. Full article

Microplastics (MPs) are emerging pollutants with widespread global distribution, continuously accumulating in soils and posing risks of cross-media pollution. Current soil MP detection methods lack unified standards, suffering from high inter-laboratory variability and cost, which become key bottlenecks limiting data comparability and global microplastics pollution control. Here, we systematically reviewed soil MPs studies (2020–2024) and based on stepwise verification, we established a standardized, reproducible detection method: soil samples were dried at 80 °C for 12 h; density separation was performed in Erlenmeyer flasks with decantation, 10 s glass rod stirring, and 12 h settling, repeated five times; digestion was conducted using a 1:2 volume ratio of H₂O₂ to supernatant at 80 °C for 8 h; and MPs were quantified via stereo-microscopy combined with ImageJ. It should be noted that the use of NaCl limits the recovery of high-density polymers (e.g., PVC, PET), and the minimum detectable particle size is approximately 127 µm. The method was validated in sandy, loam, and clay soils, achieving an average recovery rate of 96.4%, with a processing time of 68 h and a cost of USD 9.77 per sample. In contrast to previous fragmented, non-standardized protocols, this workflow synergistically optimizes high recovery efficiency, cost-effectiveness, and broad applicability, offering a low-cost, efficient, and widely applicable approach for soil MPs monitoring, supporting data comparability across studies and contributing to global pollution assessment and the United Nations 2030 Sustainable Development Goals. Full article

Diamond coring bits exhibit stable rock-breaking and coring processes as well as a long service life. However, when drilling in complex and challenging formations are characterized by high hardness, strong plasticity, and high abrasiveness, issues such as low rock-breaking efficiency, rapid failure, and shortened service life frequently occur. To prevent premature bit failure and enhance rock-breaking efficiency, this study investigated the effects of drilling pressure and rotational speed on rock-breaking performance through bench-scale experiments using typical rock samples. A total of 15 experimental groups were included in this study, with one independent trial performed for each group. ROP is calculated as the ratio of effective drilling depth to time consumed, and MSE is derived based on axial force, torque, and rock-breaking volume. The experimental results indicated that (1) sandstone is more sensitive to rotational speed, whereas limestone and dolomite are more sensitive to drilling pressure; (2) the minimum mechanical specific energy (MSE) of sandstone was achieved at a drilling pressure of 15 kN and rotational speed of 50 r/min; (3) limestone exhibited the lowest MSE at 10 kN drilling pressure and 50 r/min rotational speed; and (4) dolomite showed the minimum energy consumption at 10 kN drilling pressure and 25 r/min rotational speed. On this basis, this paper establishes a cutting mechanics model for single-crystal diamond and a working load calculation model for the entire bit, respectively. The cutting mechanics model for single-crystal diamond is re-established based on Hertzian contact theory and elastic-plastic deformation theory. The findings of this study are expected to provide a working load calculation method for diamond coring bits in typical complex and challenging drilling formations and offer technical support for the design of coring bit cutting structures and the development of customized new products. It should be noted that the conclusions of this study are limited to the experimental parameter range (drilling pressure: 5–15 kN; rotational speed: 25–80 r/min), and their applicability under higher load conditions requires further verification. Full article

Alcohols and esters are core flavor-active constituents of Qingxiangxing Baijiu (QXB), yet ethanol concentration’s regulatory role in their thresholds and interactions remains unclear. Physicochemical analysis showed reduced-alcohol QXB (L-QX, 42%, v/v) had higher total acid (1.48 g/L) but lower total ester (1.52 g/L) than high-alcohol QXB (H-QX, 53%, v/v; 1.20 g/L total acid, 2.05 g/L total ester). Sensory evaluation (0–5 scale) revealed H-QX had higher fruity (3.6 vs. 2.0), grassy (3.2 vs. 1.8), and grainy (3.0 vs. 1.9) aroma scores, while L-QX showed higher sour (2.1 vs. 1.5) and lees (1.7 vs. 1.1) notes (p < 0.05). The quantification of gas chromatography-flame ionization detection (GC-FID) determined the concentrations of eight alcohols and esters in H-QX samples and identified that most flavor compounds had higher concentrations than L-QX samples. Three alternative forced-choice tests showed 53% ethanol elevated olfactory thresholds (OTs) of five compounds, with ethyl lactate (1.53-fold) and isopentanol (1.89-fold) vs. 42%. For 16 alcohol–ester binary mixtures, 12 pairs had OT ratios (53% vs. 42%) < 1, especially 3 pairs (e.g., n-propanol-ethyl acetate) < 0.5. OAV/S curve analyses indicated all 16 mixtures had masking effects, with 11 pairs stronger at 42%. Verification validated 53% ethanol mitigated masking, enhancing fruity/grassy aromas by 38.1%/25.0%. This study provides support for QXB dealcoholization flavor regulation. Full article

Preventable medication harm in oncology is often driven by drug-related adverse events (AEs) that trigger order changes such as holds, dose reductions, delays, rechallenges, and enhanced monitoring. Much of the evidence needed to make these decisions lives in unstructured clinical texts, where large language models (LLMs), a type of artificial intelligence (AI), now offer extraction and reasoning capabilities. In this narrative review, we synthesize empirical studies evaluating LLMs and related NLP systems applied to clinical text for oncology AEs, focusing on three decision-linked tasks: (i) AE detection from clinical documentation, (ii) Common Terminology Criteria for Adverse Events (CTCAE) grade assignment, and (iii) grade-aligned actions. We also consider how these findings can inform pharmacist-facing recommendations for order-level safety. We conducted a narrative review of English-language studies indexed in PubMed, Ovid MEDLINE, and Embase. Eligible studies used LLMs on clinical narratives and/or authoritative guidance as model inputs or reference standards; non-text modalities and non-empirical articles were excluded. Nineteen studies met inclusion criteria. LLMs showed the potential to detect oncology AEs from routine notes and often outperformed diagnosis codes for surveillance and cohort construction. CTCAE grading was feasible but less stable than detection; performance improved when outputs were constrained to CTCAE terms/grades, temporally anchored, and aggregated at the patient level. Direct evaluation of grade-aligned actions was uncommon; most studies reported proxies (e.g., steroid initiation or drug discontinuation) rather than formal grade-to-action correctness. While prospective, real-world impact reporting remained sparse, several studies quantified scale advantages and time savings, supporting an initial role as high-recall triage with pharmacist adjudication. Overall, the evidence supports near-term, pharmacist-in-the-loop use of AI for AE surveillance and review, with CTCAE-structured, citation-backed outputs delivered into the pharmacist’s electronic health record order-verification workspace as reviewable artifacts. Future work must standardize reporting and CTCAE/version usage, and measure grade-to-action correctness prospectively, to advance toward order-level decision support. Full article