Université Marie et Louis Pasteur

Proton Exchange Membrane (PEM) electrolysis stands as a cornerstone technology in the clean energy sector, driving the production of hydrogen and oxygen from water. A critical aspect of ensuring the efficiency and safety of this process lies in the precise monitoring and control of temperature at the electrolysis outlet. However, accurately characterizing temperature changes within the PEM electrolysis system can be challenging due to the fluctuation of renewable energies. This study introduces an approach integrating data with fundamental physics principles known as Physics-Informed Neural Networks (PINNs). This method solves differential equations and estimates the unknown parameters governing the temperature dynamics within the PEM electrolysis system. We consider two distinct scenarios: a zero-dimensional model and a one-dimensional model. The results demonstrate the PINN’s proficiency in accurately identifying the parameters and solving for temperature fluctuations within the system with different input conditions. Furthermore, we compare the PINN with the Long Short-Term Memory (LSTM) method to predict the outlet temperature of the electrolysis. The PINN outperformed the LSTM method, highlighting its reliability and precision, achieving a Mean Squared Error (MSE) of 0.1596 compared to 1.2132 for LSTM models. The proposed method shows a high performance in dealing with sensor noises and avoids overfitting problems. This synergy of physics knowledge and data-driven learning opens new pathways towards real-time digital twins, enhanced predictive control, and improved reliability for PEM electrolysis and other complex, data-scarce energy systems. • PINNs are applied to identify parameters in 0D/1D PEM electrolyzer models. • High precision in predicting temperature distribution along gas/liquid channels. • PINNs models are compared to classical recurrent neural networks to highlight the model robustness. • PINNs are demonstrated to be robust against sensor noises and prevent overfitting efficiently. • PINNs demonstrate high adaptability to the time-varying parameters and applicability in data-scarce systems.

BACKGROUND: Artificial intelligence (AI) tools are increasingly being used to assist researchers with various research tasks, particularly in the systematic review process. Elicit is one such tool that can generate a summary of the question asked, setting it apart from other AI tools. The aim of this study is to determine whether AI-assisted research using Elicit adds value to the systematic review process compared to traditional screening methods. METHODS: We compare the results from an umbrella review conducted independently of AI with the results of the AI-based searching using the same criteria. Elicit contribution was assessed based on three criteria: repeatability, reliability and accuracy. For repeatability the search process was repeated three times on Elicit (trial 1, trial 2, trial 3). For accuracy, articles obtained with Elicit were reviewed using the same inclusion criteria as the umbrella review. Reliability was assessed by comparing the number of publications with those without AI-based searches. RESULTS: The repeatability test found 246,169 results and 172 results for the trials 1, 2, and 3 respectively. Concerning accuracy, 6 articles were included at the conclusion of the selection process. Regarding, revealed 3 common articles, 3 exclusively identified by Elicit and 17 exclusively identified by the AI-independent umbrella review search. CONCLUSION: Our findings suggest that AI research assistants, like Elicit, can serve as valuable complementary tools for researchers when designing or writing systematic reviews. However, AI tools have several limitations and should be used with caution. When using AI tools, certain principles must be followed to maintain methodological rigour and integrity. Improving the performance of AI tools such as Elicit and contributing to the development of guidelines for their use during the systematic review process will enhance their effectiveness.

Electric Vehicles (EV) significantly contribute to reducing carbon emissions and promoting sustainable transportation. Among EV technologies, hybrid energy storage systems (HESS), which combine fuel cells, power batteries, and supercapacitors, have been widely adopted to enhance energy density, power density, and system efficiency. Bidirectional DC-DC converters are pivotal in HESS, enabling efficient energy management, voltage matching, and bidirectional energy flow between storage devices and vehicle systems. This paper provides a comprehensive review of bidirectional DC-DC converter topologies for EV applications, which focuses on both non-isolated and isolated designs. Non-isolated topologies, such as Buck-Boost, Ćuk, and interleaved converters, are featured for their simplicity, efficiency, and compactness. Isolated topologies, such as dual active bridge (DAB) and push-pull converters, are featured for their high voltage gain and electrical isolation. An evaluation framework is proposed, incorporating key performance metrics such as voltage stress, current stress, power density, and switching frequency. The results highlight the strengths and limitations of various converter topologies, offering insights into their optimization for EV applications. Future research directions include integrating wide-bandgap devices, advanced control strategies, and novel topologies to address challenges such as wide voltage gain, high efficiency, and compact design. This work underscores the critical role of bidirectional DC-DC converters in advancing energy-efficient and sustainable EV technologies.

In-situ diagnosis represents an urgent need for long-term battery safety and optimized performance. Dynamic electrochemical impedance spectroscopy (DEIS) enables in situ frequency response analysis during battery operations, offering critical insights into evolving electrochemical behaviors and emerging failure mechanisms. DEIS links fundamental electrochemical science and dynamic battery performance by elucidating kinetic pathways across time scales. Furthermore, it enables the precise characterization of analytical dynamics and addresses real-world complexities such as nonequilibrium processes and coupled electrochemical-thermal interactions. Moreover, DEIS provides a deeper understanding of battery aging and failure mechanisms that drive advancements in material innovation and operational optimization. This perspective focuses on the potential of DEIS in battery research, offering real-time insights into the intricate interplay of electrochemical processes and enabling safer and high-performing battery systems.

The evolution of electrical power systems into smart grids has brought about significant advancements in electricity generation, transmission, and utilization. These cutting-edge grids have shown potential as an effective way to maximize energy efficiency, manage resources effectively, and enhance overall reliability and sustainability. However, with the integration of complex technologies and interconnected systems inherent to smart grids comes a new set of safety and security challenges that must be addressed. First, this paper provides an in-depth review of the key considerations surrounding safety and security in smart grid environments, identifying potential risks, vulnerabilities, and challenges associated with deploying smart grid infrastructure within the context of the Internet of Things (IoT). In response, we explore both cryptographic and non-cryptographic countermeasures, emphasizing the need for adaptive, lightweight, and proactive security mechanisms. As a key contribution, we introduce a layered classification framework that maps smart grid attacks to affected components and defense types, providing a clearer structure for analyzing the impact of threats and responses. In addition, we identify current gaps in the literature, particularly in real-time anomaly detection, interoperability, and post-quantum cryptographic protocols, thus offering forward-looking recommendations to guide future research. Finally, we present the Multi-Layer Threat-Defense Alignment Framework, a unique addition that provides a methodical and strategic approach to cybersecurity planning by aligning smart grid threats and defenses across architectural layers.

In this work, an integrated energy system combining a wind plant, a solar plant, an electrolyzer, a compressor, a salt cavern as storage, and a fully-hydrogen-powered gas turbine plant is assessed for net-zero demand load matching based on the Power-to-Hydrogen-to-Power process. A bi-level optimization framework is proposed with an outer layer using a genetic algorithm to find the optimal installed capacities of renewables, electrolysis, storage and turbine plant, to maximize the net present value of the project, and an inner linear programming layer formulated as a cost minimization unit commitment problem for the energy management strategy. The Australian context is chosen to conduct a reliability, techno-economic and environmental performance analysis. Results highlight that demand is met as renewables supply base power, hydrogen turbine completes balance, electrolyzer converts excess renewables, and storage acts as buffer. Selling hydrogen to a refinery brings in additional revenues, but subsidies or other revenues streams such as ancillary services are necessary to reach profitability. • Study of a system coupling a hydrogen turbine, renewables, electrolysis, and storage. • Bi-level optimization with linear programming nested in a genetic algorithm. • Analysis of reliability, techno-economic and environmental performance. • Application to the Australian context for power supply and hydrogen sales. • Demand met as hydrogen gas turbine and renewables do supply, and storage acts as buffer.

BACKGROUND: COVID-19 convalescent plasma (CCP) is a treatment option for COVID-19. This study investigated the safety and efficacy of early, very high-titre CCP in immunocompromised individuals with mild COVID-19. METHODS: This randomised, controlled, open-label trial assessed CCP in immunocompromised patients (n = 120) with mild COVID-19 in 10 clinical trial centres across Germany, France, and the Netherlands. Patients were randomised 1:1 to receive either standard of care (SoC) alone (SoC group) or SoC and 2 units of CCP. Most patients (89.7%) had received ≥3 SARS-CoV-2 vaccinations. The primary endpoint was hospitalisation for progressive COVID-19 symptoms or death by day 28 after randomisation, analysed on a modified intention-to-treat basis (117 patients). The safety analysis included the full analysis set. The trial is registered with EudraCT 2021-006621-22, and ClinicalTrials.gov, NCT05271929. FINDINGS: Between April 11, 2022 and November 27, 2023, 120 patients were enrolled. Patients in the CCP group received a median of 559 ml CCP from convalescent, vaccinated donors with very high levels of SARS-CoV-2 antibodies (median 81,810 IU/ml) at a median 4 days after symptom onset. The primary outcome occurred in 5/58 patients (8.6%) in the SoC group and in 0/59 patients (0%) in the CCP group, difference -8.6% (95% confidence interval of difference -19% to -0.80%; p-value 0.027; Fisher's exact test). The course of SARS-CoV-2 antibodies in the patients demonstrated a passive transfer of antibodies by the CCP, in particular neutralising effects against new SARS-CoV-2 variants. Whole genome sequencing of SARS-CoV-2 in patients during follow-up showed significant intra-host viral evolution, but without differences between groups. CCP was well tolerated. INTERPRETATION: Early administration of high-titre CCP can prevent hospitalisation or death in immunocompromised patients with mild COVID-19. FUNDING: Support-e project (European Union's Horizon 2020 Programme), German Federal Ministry of Education and Research, ZonMw, the Netherlands Organisation for Health Research and Development.

4D printing enables the creation of adaptive and reconfigurable devices by combining additive manufacturing with smart materials. This integration introduces challenges in designing printable, responsive materials and structures. Current research focuses on improving the responsiveness and mechanical performance of smart materials, but incremental advances often lack sufficient feedback for achieving specific properties, shapes, and performance targets. Inverse design has emerged as a strategy for determining material compositions and structural configurations to meet desired outputs, but its application remains limited to simple structures. Accelerating material and structural discovery is crucial for advancing 4D printing. Artificial intelligence (AI), especially machine learning (ML), offers promising solutions to address the complexity of 4D printing design. However, conventional AI approaches often lack logical reasoning, explainability, and interpretability. This review paper highlights recent achievements and challenges in 4D printing design and introduces neuro-symbolic AI as a promising approach. By combining ML's learning capabilities with the logical reasoning and semantic understanding of symbolic AI, this approach can enhance the exploration of advanced active materials and structures. The insights provided aim to guide future research toward optimizing 4D printing for broader applications and enhanced performance. • Synthesis of recent achievements and challenges in design for 4D printing. • Relevance of neuro-symbolic artificial intelligence beyond machine learning techniques. • Accelerated development of next-generation of smart materials and structures.

Diesel engines play a pivotal role in transport and industrial operations, but remain a significant source of pollution. Timely fault detection and diagnosis (FDD) in such systems can help mitigate emissions and improve operational safety. This paper proposes a novel, computationally efficient bi-phase framework for diesel engine FDD, leveraging a Mendeley-based dataset and traditional machine learning (ML) techniques. The system is designed in two sequential phases: fault detection, which distinguishes between normal and faulty conditions, and fault diagnosis, which identifies the specific fault type among three predefined categories. A key innovation lies in the feature importance aggregation technique that integrates outputs from six tree-based classifiers, providing robust and interpretable feature selection. To address convergence challenges often encountered in multiclass problems, the proposed framework decomposes the task into two simpler problems, reducing model complexity and enhancing convergence speed to approximately 4.55 × 10 − 4 seconds per sample. Our extensive analysis shows that the system achieves 100% accuracy in both phases across most classifiers, with Random Forest outperforming others in training and convergence speeds. A feature-wise iterative analysis further reveals that only one feature is required for fault detection and nine for accurate diagnosis, underscoring the method's efficiency. Compared to existing approaches, including deep learning and entropy-based models, the proposed solution achieves faster convergence with minimal computational resources, making it suitable for real-world deployment and scalable applications. This is the first study to offer a convergence-optimized and modular tree-based approach for diesel engine fault analysis. • A bi-phase fault detection and diagnosis system improves modularity, accuracy, and convergence speed. • Only 1 feature is needed for fault detection and 9 for diagnosis, with 100% accuracy. • Feature selection combines multiple tree-based models for robust, interpretable ranking. • The framework is fast (under 5 ms/sample), scalable, and suitable for real-time deployment. • Explainable AI is integrated to ensure transparency in diesel engine fault decisions.

BACKGROUND & AIMS: Evidence supporting primary prophylaxis of spontaneous bacterial peritonitis (SBP) is weak and the selection of quinolone-resistant bacteria is a concern. Herein, we present results from a randomized, double-blind, placebo (PBO)-controlled trial to assess whether rifaximin (RFX) has a beneficial effect on 12-month survival in patients with severe cirrhosis and ascites. METHODS: In this trial conducted at 17 French centers, patients with severe cirrhosis and grade 2 or 3 ascites and ascites protein level <15 g/L were randomized 1:1 to receive RFX 550 mg or PBO twice daily for 12 months, as primary prophylaxis for SBP. The primary endpoint was 12-month survival. Secondary endpoints were 3- and 6-month survival, incidence of complications of cirrhosis, and safety of RFX. RESULTS: Between 2018 and 2022, 1,957 patients with cirrhosis and ascites were screened, 159 were randomized, and 152 (80/72 PBO/RFX) were analyzed in the modified intention-to-treat population. RFX did not improve 12-month (PBO vs. RFX: 68.1%, 95% CI 56.2-78.7 vs. 56.6%, 95% CI 43.5-67.8; p = 0.74), 6-month (71.1%, 95% CI 59.5-80.0 vs. 76.4%, 95% CI 64.3-84.8) or 3-month (75.4%, 95% CI 64.1-83.5 vs. 82.6%, 95% CI 71.4-89.7) survival, or the incidence of liver complications (SBP, encephalopathy, gastrointestinal bleeding or hepatorenal syndrome). In the per-protocol population (127 patients adherent to the study drug), a lower 12-month cumulative incidence of liver-related events was observed in the RFX group. RFX was well tolerated throughout the study. CONCLUSIONS: RFX had no beneficial effect in terms of 12-month survival or incidence of complications of cirrhosis in patients with severe cirrhosis and low ascitic fluid protein levels. However, improved adherence may help reduce liver-related complications. IMPACT AND IMPLICATIONS: Selective gut decontamination using norfloxacin is the standard of care for secondary prophylaxis of spontaneous bacterial peritonitis (SBP). Evidence for primary prophylaxis of SBP is weaker, and fluoroquinolones have been associated with an increased risk of antimicrobial resistance. Rifaximin, a well-tolerated broad-spectrum antibiotic associated with a lower risk of antimicrobial resistance emergence, may be an alternative to norfloxacin. Our trial did not demonstrate an improvement in survival or liver complications (SBP, gastrointestinal bleeding, hepatic encephalopathy or hepatorenal syndrome) at 12 months with rifaximin as primary prophylaxis for SBP vs. placebo. However, in the subgroup of patients who adhered to rifaximin, liver complications decreased. Our study underlines the importance of treatment adherence in clinical trials to ensure accurate assessment of outcomes. CLINICAL TRIAL NUMBER: NCT03069131.

As the world shifts toward a low-carbon future, green hydrogen has emerged as a critical pillar of the energy transition. It is produced using renewable energy to power water electrolysis, and it is a clean and flexible alternative to hydrogen made from fossil fuels. However it is still hard to roll out on a large scale because of technological limits, high costs, and the need for infrastructure. This review critically analyzes current electrolysis methods, including established systems like alkaline and PEM electrolyzers, as well as newly developed concepts such as AEMWE and SOWE. It discusses how they can be used in renewable energy systems, important techno-economic and durability problems, system modeling, and grid interaction. This work clarifies both the technological potential and the practical limitations of green-hydrogen electrolyzer systems while highlighting key directions for future research and implementation.

Among various hydrogen production technologies, proton exchange membrane water electrolyzers (PEMWEs) are promising thanks to their ability to operate at high and intermittent loads, high efficiency, and high hydrogen purity. The development and application of PEMWEs rely strongly on performance characterization and estimation techniques. Electrochemical impedance spectroscopy (EIS) is one of the most important non-invasive characterization tools for electrochemical devices such as PEMWEs. Nevertheless, modeling and interpreting the impedance spectrum remain an open challenge that hinders its application in PEMWEs. To bridge the gaps, a model-free distribution of relaxation times (DRT)-based approach is proposed to analyze EIS measured from in-operation PEMWEs. Moreover, the interpretation of the full frequency range including low-frequency inductive loops is investigated. To this end, experiments have been performed to measure the impedance spectra under different temperatures, cathode pressures, water flow rates, and current loads. Then, the DRT-based approach is applied to analyze the measured spectra. Conclusions have been drawn regarding the influence of various operating conditions on the performance of the PEMWE stack. Especially, the low-frequency inductive loops are systematically investigated for the first time to reveal their influencing factors and possible causes. The temperature is identified as the dominant influencing factor, followed by water flow rate and cathode pressure. This work provides useful insights into the PEMWE functionality through interpreting impedance spectra including low-frequency inductive loops and its application to PEMWEs. • Interpreting full frequency range impedance spectra using distribution of relaxation times. • EIS characterization regarding temperature, cathode pressure, water flow rate, and current. • Investigating the influence of varying operating conditions on PEMWEs. • Investigating low-frequency inductive loops in a PEMWE stack.

Optical computing offers potential for ultra high-speed and low-latency computation by leveraging the intrinsic properties of light, such as parallelism and linear as well as nonlinear ultra-high bandwidth signal transformations. Here, we explore the use of highly nonlinear optical fibers (HNLFs) as platforms for optical computing based on the concept of extreme learning machines (ELMs). To evaluate the information processing potential of the system, we consider both task-independent and task-dependent performance metrics. The former focuses on intrinsic properties such as effective dimensionality, quantified via principal component analysis (PCA) on the system response to random inputs. The latter evaluates classification task accuracy on the MNIST digit dataset, highlighting how the system performs under different compression levels and nonlinear propagation regimes. We show that input power and fiber characteristics significantly influence the dimensionality of the computational system, with longer fibers and higher dispersion producing up to 100 principal components (PCs) at input power levels of 30 mW, where the PC corresponds to the linearly independent dimensions of the system. The spectral distribution of the PC's eigenvectors reveals that the high-dimensional dynamics facilitating computing through dimensionality expansion are located within 40 nm of the pump wavelength at 1,560 nm, providing general insight for computing with nonlinear Schrödinger equation systems. Task-dependent results demonstrate the effectiveness of HNLFs in classifying MNIST dataset images. Using input data compression through PC analysis, we inject MNIST images of various input dimensionality into the system and study the impact of input power upon classification accuracy. At optimized power levels, we achieve a classification test accuracy of 87 % ± 1.3 %, significantly surpassing the baseline of 83.7 % from linear systems. Noteworthy, we find that the best performance is not obtained at maximal input power, i.e., maximal system dimensionality, but at more than one order of magnitude lower. The same is confirmed regarding the MNIST image's compression, where accuracy is substantially improved when strongly compressing the image to less than 50 PCs. These are highly relevant findings for the dimensioning of future, ultrafast optical computing systems that can capture and process sequential input information on femtosecond timescales.

Abstract A way to prevent the fast configurational interconversion of tertiary amines is to invoke Tröger's base analogs, which display methano‐ or ethano‐bridged diazocine cores fused to aromatic rings. These derivatives are configurationally stable, even in acidic media when their structures bear ethylene bridges. Here, a two‐ to three‐step synthesis is presented of methano‐ and ethano‐bridged Tröger's base analogs with two peripheral fluorophores, i.e., anthracene, pyrene, and 9,9‐dimethylfluorene units. These compounds, possessing two nitrogen stereogenic centers, exhibit good circularly polarized luminescence (CPL) dissymmetry factors (| g lum | up to 1.2 × 10 −3 ) and brightnesses ( B CPL up to 26.3 M −1 cm −1 ), as well as excellent fluorescence quantum yields, demonstrating the Tröger´s base core to be a convenient scaffold to prepare CPL emitters upon functionalization with simple achiral fluorophores. Furthermore, the configurationally stable ethano‐bridged Tröger's base analogs are employed to modulate their CPL response, generating a CPL switch through their protonation/deprotonation by consecutive additions of acid and base. The reversibility of the switching process is demonstrated for two cycles without altering the CPL performance of the molecule. It is believed that this straightforward and efficient approach to building CPL emitters employing the Tröger's base core could lead to its incorporation in CPL‐based sensors and materials.

Clean and sustainable hydrogen production can be achieved by using electrolysis when powered with renewable energy sources. Yet, integrating intermittent operation poses a challenge, given that most industrial electrolyzers are currently designed for steady operation. While intermittency significantly influences system operation and performance, there is still a scarcity of comprehensive studies investigating these effects. Moreover, standardized methods or test protocols for thoroughly assessing these impacts are lacking. Addressing this gap, the proposed study introduces an experimental approach to consistently evaluate the short-term performance of both proton exchange membrane (PEM) and alkaline industrial systems operating intermittently. The findings indicated no significant impacts on the key performance indicators of the two industrial PEM and alkaline electrolyzers in the short term when comparing constant and intermittent operation at a same equivalent mean load. • No significant impact of intermittent vs. constant operation at equivalent mean load. • Comparable results across two electrolysis technologies and operational scales. • Hydrogen purity meets expectations in all tested scenarios. • The impact of the mean electrical load is dominant over load fluctuations. • Slight deviation from constant operation attributed to system fluidic response.

ABSTRACT A murine IgG2b monoclonal antibody, named TG11, binding to an extracellular polysaccharide antigen secreted by all Mucorales fungi has been recently developed and integrated into a lateral-flow device (TG11-LFD). The aim of this study was to establish the clinical performance of TG11-LFD on bronchoalveolar lavage (BAL) fluids for the diagnosis of mucormycosis. Thirteen BAL samples from 13 patients with mucormycosis, all of which tested positive for Mucorales qPCR ( Mucor/Rhizopus [ n = 5], Lichtheimia [ n = 2], Rhizomucor [ n = 5], and Cunninghamella [ n = 1]), were used to assess the TG11-LFD. We also selected 49 BAL samples from 25 patients with other invasive fungal infections (IFI) (aspergillosis, Pneumocystis infection, candidiasis, and possible IFI) and from 20 patients without IFI for use as negative controls. The intensities of the test and control lines were recorded using a Cube reader. The diagnostic performance was assessed by analyzing the receiver operating characteristics (ROC) curve with the Jamovi software package (version 2.6.13). The area under the curve of the ROC curve was 0.739. Using a threshold value positivity ≤531 artificial units, the TG11-LFD test has a sensitivity and specificity of 76.92% and 75.51%, respectively, a positive predictive value of 45.45%, and a negative predictive value of 92.5%. In this study, we evaluated the performance of TG11-LFD on clinical samples for the first time and demonstrated its significant potential for enhancing the rapid detection of mucormycosis. Combining antigen detection with qPCR, as successfully applied in the diagnosis of aspergillosis, is likely to yield the most reliable diagnostic approach. IMPORTANCE Mucormycosis is a severe emerging, invasive fungal disease caused by fungi in the order Mucorales . The mortality rate remains high at approximately 50%. Rapid diagnosis and prompt initiation of targeted treatment are associated with an improved prognosis. Gold standard diagnostic procedures have poor sensitivity and long turnaround times. Mucorales polymerase chain reaction in blood and respiratory samples has improved diagnosis, but this technique is not widely available due to high costs and the need for specialist equipment. A prototype lateral-flow device (TG11-LFD) incorporating a mouse monoclonal antibody, which binds to an extracellular polysaccharide antigen specific to Mucorales fungi, has been recently developed. In this study, we evaluated for the first time the performance of the TG11-LFD test on clinical bronchoalveolar lavage fluids for diagnosing mucormycosis. With 76.92% sensitivity and 75.51% specificity, this innovative, simple, and affordable approach shows great potential for improving the rapid diagnosis of mucormycosis.

In the context of rapidly evolving industrial ecosystems, the human–machine interaction (HMI) has shifted from basic interface control toward complex, adaptive, and human-centered systems. This review explores the multidisciplinary foundations and technological advancements driving this transformation within Industry 4.0 and the emerging paradigm of Industry 5.0. Through a comprehensive synthesis of the recent literature, we examine the cognitive, physiological, psychological, and organizational factors that shape operator performance, safety, and satisfaction. A particular emphasis is placed on ergonomic interface design, real-time physiological sensing (e.g., EEG, EMG, and eye-tracking), and the integration of collaborative robots, exoskeletons, and extended reality (XR) systems. We further analyze methodological frameworks such as RULA, OWAS, and Human Reliability Analysis (HRA), highlighting their digital extensions and applicability in industrial contexts. This review also discusses challenges related to cognitive overload, trust in automation, and the ethical implications of adaptive systems. Our findings suggest that an effective HMI must go beyond usability and embrace a human-centric philosophy that aligns technological innovation with sustainability, personalization, and resilience. This study provides a roadmap for researchers, designers, and practitioners seeking to enhance interaction quality in smart manufacturing through cognitive ergonomics and intelligent system integration.

We describe the short-term frequency stability characterization of external-cavity diode lasers stabilized onto the 6S1/2−7P1/2 transition of Cs atoms at 459 nm, using a microfabricated vapor cell. The laser beatnote between two nearly identical systems, each using saturated absorption spectroscopy in a simple retroreflected configuration, exhibits an instability of 2.5 × 10−13 at 1 s, consistent with phase noise analysis, and 3 × 10−14 at 200 s. The primary contributors to the stability budget at 1 s are the FM-AM noise conversion and the intermodulation effect, both emerging from laser frequency noise. These results highlight the potential of microcell-based optical references to achieve stability performances comparable to that of an active hydrogen maser in a remarkably simple architecture.

The Power Transmission Line (PTL) is a pivotal infrastructure in the efficient distribution of electrical power, connecting the source generating stations to end-point consumers. This intricate system utilizes various components of diverse sizes and functionalities to transmit power. However, these components are susceptible to failures due to harsh environmental conditions and aging phenomena, posing a risk to the secure transmission and availability of electric power. In this study, data were gathered through the simulation of power transmission lines using MATLAB Simulink. The collected data were meticulously analyzed to discern the relationship between fault occurrences and input parameters. Subsequently, a preprocessing phase involved the introduction of new features as part of feature engineering. Six machine learning classifiers were employed in a bi-phased system: the primary objective was to detect faulty samples within the data, then diagnose these faults and distinguish their types. The results demonstrated the robust performance of LightGBM in detecting faulty samples, achieving an accuracy that exceeds 99.8%. In the second phase, the ExtraTrees classifier dominated, exhibiting a 99.8% accuracy and 100% classification precision in diagnosing four of the five fault types studied. Through machine learning explainability, it was revealed that the added features can significantly enhance prediction performance, particularly in the phase of fault diagnosis. The training time did not exceed 0.5 s for fault detection and 0.3 s for fault diagnosis. This swift convergence, coupled with the minimal training times, underscores the efficiency of the proposed feature engineering process and the selected classifiers. • Proposed a bi-phase tree-based framework for fault detection and diagnosis. • Conducted exhaustive data analysis to extract insights and engineer a novel feature. • Introduced transparency and interpretability using explainable machine learning.

We present a novel light-locked dynamic covalent polymerization methodology to synthesize conjugated polymers based on BisAzaCoroneneDiimides (BACDs). This metal-free process converts reversible poly imines into kinetically locked conjugated polymers using visible light, generating minimal side products. By incorporating aldehyde-functionalized comonomers, the approach enables the creation of diverse n-type semiconducting polymers with tunable optical band gaps and low LUMO levels. The polymers exhibit exceptional thermal, electrochemical, and photostability with strong interchain interactions upon electrochemical reduction observed in solution, attributed to the BACD core. Broad absorption from the visible to the near-infrared range underscores their potential in charge and energy transport applications for organic electronics. This scalable, sustainable strategy unlocks access to a versatile class of n-type diimide polymers.