Search Results (316)

The accelerating energy demands from artificial intelligence (AI) deployment introduce systemic challenges for achieving carbon neutrality. Large language models (LLMs) represent a dominant driver of AI energy consumption, with inference operations constituting 80–90% of total energy usage. Current energy benchmarks report aggregate metrics without domain-level breakdowns, preventing accurate carbon footprint estimation for workloadspecific operations. This study addresses this critical gap by introducing a carbon-aware framework centered on the carbon cost of intelligence (CCI), a novel metric enabling workload-specific energy and carbon calculation that balances accuracy and efficiency across heterogeneous domains. This paper presents a comprehensive cross-domain energy benchmark using the massive multitask language understanding (MMLU) dataset, measuring accuracy and energy consumption in five representative domains: clinical knowledge (medicine), professional accounting (finance), professional law (legal), college computer science (technology), and general knowledge. Empirical analysis of GPT-4 across 100 MMLU questions, 20 per domain, reveals substantive variations: legal queries consume 4.3× more energy than general knowledge queries (222 J vs. 52 J per query), while energy consumption varies by domain due to input length differences. Our analysis demonstrates the evolution from simple ratio-based approaches (weighted accuracy divided by weighted energy) to harmonic mean aggregation, showing that the harmonic mean, by preventing bias from extreme values, provides more accurate carbon usage estimates. The CCI metric, calculated using weighted harmonic mean (analogous to P/E ratios in finance, where A/E represents accuracy-to-energy ratio), enables practitioners to accurately estimate energy and carbon emissions for specific workload mixes (e.g., 80% medicine + 15% general + 5% law). Results demonstrate that the domain workload mix significantly impacts carbon footprint: a law firm workload (60% law) consumes 96% more energy per query than a hospital workload (80% medicine), representing 49% potential savings through workload optimization. Carbon footprint analysis using US Northeast grid intensity (320 gCO₂e/kWh) shows domain-specific emissions ranging from 0.0046–0.0197 gCO₂ per query. CCI is validated through comparison with simple weighted average, demonstrating differences up to 12.1%, confirming that the harmonic mean provides more accurate and conservative carbon estimates essential for carbon reporting and neutrality planning. Our findings provide a novel cross-domain energy benchmark for GPT-4 and establish a practical carbon calculator framework for sustainable AI deployment aligned with carbon neutrality goals. Full article

This paper proposes an event-triggered extension of duty-ratio-based model predictive direct speed control (DR-MPDSC) for permanent magnet synchronous motor (PMSM) drives in electric vehicle (EV) applications. The main contribution is the development of an event-triggered execution framework specifically tailored to DR-MPDSC, in which control updates are performed only when the speed tracking error violates a prescribed condition, rather than at every sampling instant. Unlike conventional MPDSC and time-triggered DR-MPDSC schemes, the proposed strategy achieves a significant reduction in control execution frequency while preserving fast dynamic response and closed-loop stability. An optimized duty-ratio formulation is employed to regulate the effective application duration of the selected voltage vector within each sampling interval, resulting in reduced electromagnetic torque ripple and improved stator current quality. An extended Kalman filter (EKF) is integrated to estimate rotor speed and load torque, enabling disturbance-aware predictive speed control without mechanical torque sensing. Furthermore, a unified field-weakening strategy is incorporated to ensure wide-speed-range operation under constant power constraints, which is essential for EV traction systems. Simulation and experimental results demonstrate that the proposed event-triggered DR-MPDSC achieves steady-state speed errors below 0.5%, limits electromagnetic torque ripple to approximately 2.5%, and reduces stator current total harmonic distortion (THD) to 3.84%, compared with 5.8% obtained using conventional MPDSC. Moreover, the event-triggered mechanism reduces control update executions by up to 87.73% without degrading transient performance or field-weakening capability. These results confirm the effectiveness and practical viability of the proposed control strategy for high-performance PMSM drives in EV applications. Full article

Background/Objectives: Artificial intelligence (AI) has gained substantial prominence in pediatric dentistry, offering new opportunities to enhance diagnostic precision and clinical decision-making. AI-based systems are increasingly applied in caries detection, early childhood caries (ECC) risk prediction, tooth development assessment, mesiodens identification, and other key diagnostic tasks. This systematic review and meta-analysis aimed to synthesize evidence on the diagnostic performance of AI models developed specifically for pediatric dental applications. Methods: A systematic search was conducted in PubMed, Scopus, Web of Science, and Embase following PRISMA-DTA guidelines. Studies evaluating AI-based diagnostic or predictive models in pediatric populations (≤18 years) were included. Reference screening, data extraction, and quality assessment were performed independently by two reviewers. Pooled sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated using random-effects models. Sources of heterogeneity related to imaging modality, annotation strategy, and dataset characteristics were examined. Results: Thirty-two studies met the inclusion criteria for qualitative synthesis, and fifteen were eligible for quantitative analysis. For radiographic caries detection, pooled sensitivity, specificity, and AUC were 0.91, 0.97, and 0.98, respectively. Prediction models demonstrated good diagnostic performance, with pooled sensitivity of 0.86, specificity of 0.82, and AUC of 0.89. Deep learning architectures, particularly convolutional neural networks, consistently outperformed traditional machine learning approaches. Considerable heterogeneity was identified across studies, primarily driven by differences in imaging protocols, dataset balance, and annotation procedures. Beyond quantitative accuracy estimates, this review critically evaluates whether current evidence supports meaningful clinical translation and identifies pediatric domains that remain underrepresented in AI-driven diagnostic innovation. Conclusions: AI technologies exhibit strong potential to improve diagnostic accuracy in pediatric dentistry. However, limited external validation, methodological variability, and the scarcity of prospective real-world studies restrict immediate clinical implementation. Future research should prioritize the development of multicenter pediatric datasets, harmonized annotation workflows, and transparent, explainable AI (XAI) models to support safe and effective clinical translation. Full article

This paper presents a controller-hardware-in-the-loop simulation (C-HILS) framework for validating models, evaluating control performance, and assessing the thermal lifetime of a tens-of-kilowatt inverter. The real inverter and the C-HILS platform were operated in parallel, and accuracy was quantified using phase-current root mean square error, voltage spectral analysis, and total harmonic distortion (THD). Across a wide range of SVPWM and DPWM cases, deviations remained within 2–5%, confirming close agreement between experiment and simulation. Using the validated C-HILS system, sampling frequency and output power were swept while comparing current tracking, THD, average switching frequency, semiconductor losses, and efficiency. SVPWM achieved lower THD, whereas DPWM reduced average switching frequency and switching losses, improving efficiency. C-HILS waveforms were then applied to a Foster thermal network to reconstruct the junction–temperature trajectory; $T_{\mathrm{j}}\left(t\right)$, and $\mathsf{\Delta }{T}_{\mathrm{j}}$ and $T_{\mathrm{j},\mathrm{m}\mathrm{i}\mathrm{n}}$ were mapped to lifetime using the Bayerer model. For a representative cyclic mission, $\mathsf{\Delta }{T}_{\mathrm{j}}$ decreased from approximately $25.6 °\mathrm{C}$ with SVPWM to about $17.5 °\mathrm{C}$ with DPWM, increasing the estimated lifetime from approximately 1.36 years to 9.14 years. These results demonstrate that the proposed C-HILS framework provides a unified pre-prototype tool for model verification, control strategy comparison, and quantitative thermal reliability assessment of shipboard propulsion inverters. Full article

Variable switching frequency modulation (VSFM) is an easy-to-implement and low-cost method to reduce electromagnetic interference (EMI) of power electronics, yet changes in loss and harmonic behavior make it hard to decide the parameters of the filter and the switching frequency (SF) variation range. In this article, a new VSFM method characterized by evenly distributed SF is proposed, and it is easy to implement. In order to handle the induced variation in loss and current total harmonic distortion (THD) behavior, current dynamics of a full-bridge grid-tied inverter under constant SF modulation (CSFM) are analyzed through multidimensional Fourier decomposition (MFD), and then the results are extended to VSFM. Based on these dynamics, loss of MOSFETs and THD of grid-connected current are estimated through the trapezoidal integral rule, and the analytical expressions of these indexes can be derived. After this, parameters needed for VSFM can be determined while meeting the minimum MOSFET loss and fixed current THD constraint. The performance of EMI, loss, and current harmonic is revealed through simulations and experiments and compared with the CSFM and classical VSFM methods. Full article

Leptospirosis remains a significant public health and economic burden in Southeast Asia, particularly in low- and middle-income countries where environmental, occupational, and socioeconomic factors contribute to its endemicity. Transmission is driven by close interactions between humans and infected animal reservoirs, alongside climatic conditions such as heavy rainfall and flooding. The region’s high but variable seroprevalence reflects inconsistencies in diagnostic methodologies and surveillance systems, complicating disease burden estimation. Major gaps persist in diagnostic capabilities, with current tools often unsuitable for resource-limited settings, leading to underdiagnosis and delayed treatment. Environmental modeling and spatial epidemiology are underutilized due to limited interdisciplinary data integration and predictive capacity. Addressing these challenges requires a One Health approach that integrates human, animal, and environmental health sectors. Key policy recommendations include harmonized surveillance, standardized and validated diagnostics, expanded vaccination programs, improved animal husbandry, and targeted public education. Urban infrastructure improvements and early warning systems are also critical, particularly in disaster-prone areas. Strengthened governance, cross-sectoral collaboration, and investment in research and innovation are essential for sustainable leptospirosis control. Implementing these measures will enhance preparedness, reduce disease transmission, and contribute to improved public health outcomes in all sectors across the region. Full article

Background: Fine particulate matter (PM_2.5) is an established systemic toxicant, yet its association with clinical skin aging remains incompletely characterized. Although pigmentary changes and wrinkles are commonly attributed to ultraviolet exposure, experimental and epidemiologic evidence suggests that long-term PM_2.5 exposure may contribute to extrinsic skin aging through oxidative, inflammatory, and aryl hydrocarbon receptor-mediated pathways. However, human studies specifically quantifying PM_2.5 exposure in relation to validated skin aging outcomes are sparse, and no prior meta-analysis has systematically synthesized this evidence. Objective: To conduct a systematic review and meta-analysis of epidemiologic studies reporting measured or modeled long-term PM_2.5 exposure and extractable quantitative associations with clinical skin aging outcomes. Methods: We performed a comprehensive PRISMA 2020-guided search of PubMed, Embase, Web of Science, and Scopus (inception to 18 November 2025). Eligible studies included human participants, quantified long-term PM_2.5 exposure, validated clinical or imaging-based skin aging outcomes, and extractable effect estimates. Ratio-type effect measures (arithmetic mean ratios, geometric mean ratios, and odds ratios) were transformed to the natural-log scale, standardized to a common exposure contrast of per 10 µg/m³ PM_2.5, and synthesized as generic relative association metrics. Random-effects models with DerSimonian–Laird estimation and Hartung–Knapp adjustment were applied for pigmentary outcomes. VISIA imaging β-coefficients were synthesized narratively. Results: Four epidemiologic cohorts met predefined eligibility criteria. From these, we extracted seven PM_2.5-specific pigmentary effect estimates, one clinically assessed wrinkle estimate, and two VISIA imaging outcomes. The pooled relative association for pigmentary aging corresponded to a ratio of 1.11 per 10 µg/m³ PM_2.5 (95% CI, 0.82–1.50), indicating a directionally positive but statistically imprecise association compatible with both increased and unchanged pigmentary aging. All individual pigmentary estimates were directionally positive. A single cohort reported a 3.2% increase in wrinkle severity per 10 µg/m³ PM_2.5 (ratio 1.032). VISIA imaging showed significant worsening of brown spot severity (+9.5 percentile per 10 µg/m³), while wrinkle percentiles showed a non-significant change. Conclusions: Based on a comprehensive PRISMA-guided search, the available epidemiologic evidence suggests a consistent directionally positive association between long-term PM_2.5 exposure and pigmentary skin aging outcomes, with limited and uncertain evidence for wrinkle-related phenotypes. The current evidence base remains small, heterogeneous, and of low certainty. Accordingly, these findings should be interpreted as hypothesis-generating and underscore the need for larger, longitudinal, and methodologically harmonized studies. (Registration: PROSPERO CRD420251231462) Full article

To address the torque ripple and speed fluctuation issues in high-frequency square-wave injection-based sensorless control of interior permanent magnet synchronous motors (IPMSM) caused by low-order stator current harmonics (primarily the fifth and seventh), this paper proposes a harmonic voltage compensation strategy based on a sixth-order quasi-proportional resonant (QPR) controller, which effectively suppresses these specific harmonic disturbances. The proposed method, building upon conventional high-frequency square-wave injection, introduces a harmonic current extraction technique based on multiple synchronous reference frame transformations to separate the fifth and seventh harmonic components accurately; then, according to the established harmonic voltage compensation equation, generates targeted compensation voltage commands; finally, further precisely suppresses the corresponding harmonic currents through a sixth-order QPR controller connected in parallel with the current proportional-integral (PI) controller. This paper comprehensively establishes the mathematical models for harmonic extraction and voltage compensation, and conducts a detailed analysis of the parameter design of the sixth-order QPR controller. Simulation results demonstrate that the proposed strategy can significantly suppress stator current distortion, effectively reduce torque and speed ripples, and substantially improve rotor position estimation accuracy, thereby verifying the superiority of the novel harmonic-suppression-based sensorless control strategy. Full article

Electric induction motors are fundamental to industry, where reliability and continuous operation are critical. Though robust, they are prone to faults, particularly in demanding environments such as highway tunnels. Non-invasive diagnostic techniques are widely used for condition monitoring, yet most studies occur under controlled laboratory conditions, limiting their applicability in real-world scenarios. This research investigates the feasibility of applying Motor Current Signature Analysis (MCSA) for monitoring highway tunnel axial fan motors, aiming to determine its effectiveness for real-time diagnostics in industrial environments. Measurements were performed under actual operating conditions, highlighting practical challenges. Data acquisition was implemented remotely from electrical cabins feeding tunnel services, reducing installation complexity and costs compared to in-tunnel measurements. This approach enabled monitoring of all motors in a tunnel using minimal hardware (a single acquisition system equipped with Rogowski sensors) making the solution cost-effective and suitable for periodic measurements. Frequency domain analysis focused on harmonics associated with rotor bar defects and eccentricity, selected for their slow degradation and diagnostic relevance. The magnitude of these harmonics was tracked over time and compared across motors of the same model. Since most of the time the ventilators are de-energized, the periodic measurements can be seen almost as a real-time monitoring, at least for the faults considered, with much lower costs. Results were validated against maintenance reports, confirming bearing faults with eccentricity in two motors, while suspected rotor porosity remained unverified, as expected at low severity. Findings demonstrate that MCSA can provide operational insights for fault detection in tunnel environments, supporting predictive maintenance strategies. A key outcome of this study was selecting and implementing an effective measurement setup for industrial applications, while preparing the base for future machine learning integration to estimate Remaining Useful Life. Full article

The Icelandic Government’s climate action plan proposes climate-neutral beef production, reduced methane emissions, and improved fertilizer management. However, a life cycle assessment (LCA) of cattle production is lacking to determine the current status of its environmental impacts. This study conducts a cradle-to-farm gate LCA of interconnected dairy and beef cattle systems. The functional unit (FU) is “1 kg of edible cattle meat” for the meat and “1 kg of fat and protein corrected milk” (FPCM) for milk produced in Iceland in 2019. The multifunctionality between meat and milk from the dairy system is handled using mass, economic, and biophysical allocations, respectively. The environmental impacts were estimated using the ReCiPe 2016 v1.08 mid-point (H) impact assessment method. Furthermore, this study conducts an uncertainty and global sensitivity analysis to understand the possible range of environmental impacts and identifies key influential parameters in the dairy and beef cattle system. Animal production is a hotspot for global warming, while the feed (hay and concentrate) is a hotspot for other environmental categories. The allocation method choice highly influences the environmental impacts. This study underscores the need to harmonize data collection and access to centralized, reliable data sources to reduce uncertainty and meet climate action plan goals on both the national and global scale. Full article

Background/Objectives: The limited specificity of prostate-specific antigen (PSA) drives unnecessary biopsies in prostate cancer (PCa). Urinary extracellular vesicles (uEVs) provide a non-invasive reservoir of tumor-derived nucleic acids and proteins. Aptamers selected by SELEX enable highly specific capture, and artificial intelligence (AI) can accelerate their optimization. This systematic review evaluated AI-assisted SELEX for urine-derived and exosome-enriched aptamer panels in PCa detection. Methods: Systematic searches of PubMed, Scopus, and Web of Science (1 January 2010–24 August 2025; no language restrictions) followed PRISMA 2020 and PRISMA-S. The protocol is registered on OSF (osf.io/b2y7u). After deduplication, 1348 records were screened; 129 studies met the eligibility criteria, including 34 (26.4%) integrating AI within SELEX or downstream refinement. Inclusion required at least one quantitative metric (dissociation constant K_d, SELEX cycles, limit of detection [LoD], sensitivity, specificity, or AUC). Risk of bias was appraised with QUADAS-2 (diagnostic accuracy studies) and PROBAST (prediction/machine learning models). Results: AI-assisted SELEX workflows reduced laboratory enrichment cycles from conventional 12–15 to 5–7 (≈40–55% relative reduction) and reported K_d values spanning low picomolar to upper nanomolar ranges; heterogeneity and inconsistent comparators precluded pooled estimates. Multiplex urinary panels (e.g., PCA3, TMPRSS2:ERG, miR-21, miR-375, EN2) yielded single-study AUCs between 0.70 and 0.92 with sensitivities up to 95% and specificities up to 88%; incomplete 2 × 2 contingency reporting prevented bivariate meta-analysis. LoD reporting was sparse and non-standardized despite several ultralow claims (attomolar to low femtomolar) on nanomaterial-enhanced platforms. Pre-analytical variability and absent threshold prespecification contributed to high or unclear risk (QUADAS-2). PROBAST frequently indicated high risk in participants and analysis domains. Across the included studies, lower K_d and reduced LoD improved analytical detectability; however, clinical specificity and AUC were predominantly shaped by pre-analytical control (matrix; post-DRE vs. spontaneous urine) and prespecified thresholds, so engineering gains did not consistently translate into higher diagnostic accuracy. Conclusions: AI-assisted SELEX is a promising strategy for accelerating high-affinity aptamer discovery and assembling multiplex urinary panels for PCa, but current evidence is early phase, heterogeneous, and largely single-center. Priorities include standardized uEV processing, complete 2 × 2 diagnostic reporting, multicenter external validation, calibration and decision impact analyses, and harmonized LoD and K_d reporting frameworks. Full article

This work presents the application of a hybrid adaptive Kalman filter (HAKF) for the implementation of a shunt active power filter (SAPF), enabling selective harmonic and interharmonic compensation in real time. The hybrid term refers to the combination of a DFT-based harmonic grouping method with the adaptive Kalman estimation framework, which preserves the classical estimation method of the Kalman filter while dynamically defining its state-space model according to the spectral composition of the load current. This allows the accurate identification and parametrization of the HAKF state matrices and ensures precise individual quantification of each frequency component. In this way, the SAPF control system can select individual components to be eliminated, either totally or partially. Selective compensation is particularly useful when the compensating current required for full mitigation of the nonfundamental frequency components exceeds the SAPF current capability. Partial compensation can also be employed to match the compensating current with the maximum allowable SAPF current. Based on this, a simple selective compensation strategy is proposed to demonstrate the effective behavior of the SAPF under such conditions. A single-phase power system consisting of the proposed SAPF and a nonlinear load has been simulated using Matlab/Simulink. The simulation results confirm the effectiveness of the proposed system in mitigating harmonics and interharmonics, thereby enhancing power quality, while validating both the current processing method and the use of the HAKF in the implementation of the selective SAPF. Full article

This paper presents an improved sliding-mode observer (SMO) for estimating mechanical parameters and compensating load torque in permanent magnet synchronous motor (PMSM) drives. Traditional SMOs have limited robustness when the motor model is inaccurate. To solve this, an enhanced sliding-mode observer (ESMO) is proposed. It can estimate both the total inertia and the load torque at the same time. The method is verified using Lyapunov stability analysis and convergence time calculation. Experimental results show that, when combined with a single-vector Model Predictive Current Control (MPCC), the proposed ESMO achieves zero overshoot during no-load startup and keeps the steady-state error below 0.1% under load changes. It also reduces q-axis current ripple and improves harmonic suppression. This control method is suitable for applications that require high precision and strong robustness, such as robots, electric vehicles, and smart manufacturing. Full article

This paper presents a high-performance sensorless control strategy for induction motors using an Adaptive Neuro-Fuzzy Inference System (ANFIS) for rotor speed estimation, eliminating the need for mechanical sensors. The ANFIS approach leverages stator voltages and currents, reducing costs and complexity. The motor is controlled via Indirect Stator Field Orientation Control (ISFOC) with a three-level Neutral–Point–Clamped (NPC) inverter employing Space Vector Modulation (SVM). Symmetry in the motor’s magnetic structure and SVM’s switching patterns enhances control precision, stability, and efficiency while minimizing harmonic distortion. Simulation results validate the proposed ANFIS-based estimator’s superior performance compared to a MRAS-based Luenberger observer under various operating conditions, demonstrating accurate speed tracking and robustness against load disturbances. Full article

This paper describes the status of developing Josephson arbitrary waveform synthesizer (JAWS) chips at NIM (National Institute of Metrology, China). To obtain high junction integration density and fewer data input channels, the chip employs an on-chip Wilkinson power divider and inside/outside dc blocks, enabling both arrays to be driven by a single pulse-generator channel. In addition, the tapered coplanar waveguide structure is used to ensure the microwave uniformity of the long-junction array. Each array consisted of 4000 double-stack Nb/Nb_xSi_1−x/Nb junctions, and 16,000 junctions are integrated in the chip in total. The JAWS chip demonstrates good performance, capable of synthesizing a 300 mV root mean square (rms) voltage with exceptionally low harmonic distortion. Dc and ac voltage-current characteristics measurements indicate that the junctions are with a critical current of 2.5 mA, and a normal-state resistance of 4.5 mΩ per junction. Contact aligners are manually operated to fabricate the chips, and process errors in the fabrication are estimated in this paper. Full article