We demonstrated a high-power, high-energy regenerative amplifier (RA) based on Yb-doped CaGdAlO4 (Yb:CALGO) crystal, which achieves a maximum average power exceeding 50 W at a repetition rate greater than 50 kHz, and a maximum pulse energy of approximately 7 mJ at a repetition rate of up to 5 kHz. After compression, 130 fs pulses with a peak power of nearly 45 GW are achieved. To the best of our knowledge, this represents the highest average power and pulse energy reported for a Yb:CALGO RA. The RA cavity is specifically designed to maintain excellent stability and output beam quality under a pumping power of 380 W, resulting in a continuous-wave output power exceeding 70 W. For the seeder, a fiber laser utilizing a nonlinear amplification process, which yields a broadband spectrum to support approximately 80 fs pulses, is employed for the high-peak-power pulse generation.

An analytical expression for the focal intensity of a laser pulse was obtained for an asymmetric out-of-plane compressor with gratings of arbitrary surface shape. The focal intensity is most strongly affected by the linear angular chirp caused by the spatial shift of different frequencies on the second and third gratings. The chirp can be eliminated by simply rotating the fourth grating by an optimal angle, which significantly reduces the requirements for the grating quality. It is shown that the decrease in the focal intensity depends on the product of the grating surface root mean square and pulse spectrum bandwidth. With low-quality gratings, spectrum narrowing would not reduce focal intensity; contrariwise, it may even slightly increase it.

This paper explores the integration of haptic gloves and virtual reality (VR) environments to enhance industrial training and operational efficiency within the framework of Industry 4.0 and Industry 5.0. It examines the alignment of these technologies with the Sustainable Development Goals (SDGs), mainly focusing on SDG 8 (Decent Work and Economic Growth) and SDG 9 (Industry, Innovation, and Infrastructure). By incorporating a human-centric approach, the study leverages haptic gloves to provide realistic feedback and immersive experiences in virtual training environments. The gloves enable intuitive interaction, enhancing the training efficacy and reducing real-world operational errors. Using the 5S principles—Social, Sustainable, Sensing, Smart, and Safe—this research evaluates the system’s impact across various dimensions. The findings indicate significant improvements in user comfort, productivity, and overall well-being, alongside enhanced sustainability and operational efficiency. However, challenges related to realistic hand-object interactions and algorithmic enhancements were identified. The study underscores the importance of continuous improvement and cross-disciplinary collaboration to advance the usability and effectiveness of these technologies. Future research should focus on customization, AI-driven adaptability, sustainability, real-world scalability, and comprehensive impact assessment to further develop smart interfaces in industrial settings. This integration represents a transformative opportunity to enhance workplace safety, skills development, and contribute to global sustainable development goals.

Experiments in engineering are typically conducted in controlled environments where parameters can be set to any desired value. This assumes that the same applies in a real-world setting, which is often incorrect as many experiments are influenced by uncontrollable environmental conditions such as temperature, humidity, and wind speed. When optimizing such experiments, the focus should be on finding optimal values conditionally on these uncontrollable variables. This article extends Bayesian optimization to the optimization of systems in changing environments that include controllable and uncontrollable parameters. The extension fits a global surrogate model over all controllable and environmental variables but optimizes only the controllable parameters conditional on measurements of the uncontrollable variables. The method is validated on two synthetic test functions, and the effects of the noise level, the number of environmental parameters, the parameter fluctuation, the variability of the uncontrollable parameters, and the effective domain size are investigated. ENVBO, the proposed algorithm from this investigation, is applied to a wind farm simulator with eight controllable and one environmental parameter. ENVBO finds solutions for the entire domain of the environmental variable that outperform results from optimization algorithms that only focus on a fixed environmental value in all but one case while using a fraction of their evaluation budget. This makes the proposed approach very sample-efficient and cost-effective. An off-the-shelf open-source version of ENVBO is available via the NUBO Python package.

An optical spectrometer system based on 60 channels of fibers has been designed and employed to diagnose light emissions from laser–plasma interactions. The 60 fiber collectors cover an integrated solid angle of $\pi$, enabling the measurement of global energy losses in a symmetrical configuration. A detecting spectral range from ultraviolet to near-infrared, with angular distribution, allows for the understanding of the physical mechanisms involving various plasma modes. Experimental measurements of scattered lights from a conical implosion driven by high-energy nanosecond laser beams at the Shenguang-II Upgrade facility have been demonstrated, serving as reliable diagnostics to characterize laser absorption and energy losses from laser–plasma instabilities. This compact diagnostic system can provide comprehensive insights into laser energy coupling in direct-drive inertial confinement fusion research, which are essential for studying the driving asymmetry and improving the implosion efficiencies.

A biofilm refers to an intricate community of microorganisms firmly attached to surfaces and enveloped within a self-generated extracellular matrix. Machine learning (ML) methodologies have been harnessed across diverse facets of biofilm research, encompassing predictions of biofilm formation, identification of pivotal genes and the formulation of novel therapeutic approaches. This investigation undertook a bibliographic analysis focused on ML applications in biofilm research, aiming to present a comprehensive overview of the field’s current status. Our exploration involved searching the Web of Science database for articles incorporating the term “machine learning biofilm,” leading to the identification and analysis of 126 pertinent articles. Our findings indicate a substantial upswing in the publication count concerning ML in biofilm over the last decade, underscoring an escalating interest in deploying ML techniques for biofilm investigations. The analysis further disclosed prevalent research themes, predominantly revolving around biofilm formation, prediction and control. Notably, artificial neural networks and support vector machines emerged as the most frequently employed ML techniques in biofilm research. Overall, our study furnishes valuable insights into prevailing trends and future trajectories within the realm of ML applied to biofilm research. It underscores the significance of collaborative efforts between biofilm researchers and ML experts, advocating for interdisciplinary synergy to propel innovation in this domain.

With the fast development of modern microscopes and bioimaging techniques, an unprecedentedly large amount of imaging data is being generated, stored, analyzed, and shared through networks. The size of the data poses great challenges for current data infrastructure. One common way to reduce the data size is by image compression. This study analyzes multiple classic and deep-learning-based image compression methods, as well as an empirical study on their impact on downstream deep-learning-based image processing models. We used deep-learning-based label-free prediction models (i.e., predicting fluorescent images from bright-field images) as an example downstream task for the comparison and analysis of the impact of image compression. Different compression techniques are compared in compression ratio, image similarity, and, most importantly, the prediction accuracy of label-free models on original and compressed images. We found that artificial intelligence (AI)-based compression techniques largely outperform the classic ones with minimal influence on the downstream 2D label-free tasks. In the end, we hope this study could shed light on the potential of deep-learning-based image compression and raise the awareness of the potential impacts of image compression on downstream deep-learning models for analysis.

A compact, wideband, and high-gain circularly polarized array antenna is proposed based on substrate integrated gap waveguide (SIGW) using sequential rotational phase (SRP). The array antenna consists of four $2\times2$ corner-cutting corner patches and an SIGW-SRP feeding network. The SIGW-SRP feeding network is achieved by utilizing the spatial vector addition property to compensate for phase, aiming to improve the bandwidth and gain. Unlike the traditional SRP feeding network, the proposed feeding scheme is simpler and easier to fabricate, and each port can achieve more stable phase and bandwidth. In addition, benefiting from the surface wave suppression and in-phase reflection property of SIGW, the proposed array antenna has a stable radiation pattern and low cross-polarization covering wideband frequencies. The measured results indicate that the −10-dB impedance bandwidth ranges from 12.2 to 17.3 GHz (34.6%), the 3-dB axial ratio bandwidth ranges from 13.5 to 16.7 GHz (21.2%), and the peak gain is 16 dBic.

The crystal structure of palbociclib (C24H29N7O2) used as a medication for the treatment of breast cancer has been solved and refined using synchrotron radiation after density functional theory optimization. Palbociclib crystallizes in the monoclinic system (space group P21/c, #14) at room temperature with crystal parameters: a = 11.3133(2), b = 5.62626(9), c = 35.9299(9) Å, β = 101.5071(12), V = 2241.03(8) Å3, and Z = 4. The crystal structure contains infinite N–H⋯N bonded layers. The powder pattern has been submitted to ICDD for inclusion in the Powder Diffraction File™ (PDF®).

We propose a physics-constrained convolutional neural network (PC-CNN) to solve two types of inverse problems in partial differential equations (PDEs), which are nonlinear and vary both in space and time. In the first inverse problem, we are given data that is offset by spatially varying systematic error (i.e., the bias, also known as the epistemic uncertainty). The task is to uncover the true state, which is the solution of the PDE, from the biased data. In the second inverse problem, we are given sparse information on the solution of a PDE. The task is to reconstruct the solution in space with high resolution. First, we present the PC-CNN, which constrains the PDE with a time-windowing scheme to handle sequential data. Second, we analyze the performance of the PC-CNN to uncover solutions from biased data. We analyze both linear and nonlinear convection-diffusion equations, and the Navier–Stokes equations, which govern the spatiotemporally chaotic dynamics of turbulent flows. We find that the PC-CNN correctly recovers the true solution for a variety of biases, which are parameterized as non-convex functions. Third, we analyze the performance of the PC-CNN for reconstructing solutions from sparse information for the turbulent flow. We reconstruct the spatiotemporal chaotic solution on a high-resolution grid from only 1% of the information contained in it. For both tasks, we further analyze the Navier–Stokes solutions. We find that the inferred solutions have a physical spectral energy content, whereas traditional methods, such as interpolation, do not. This work opens opportunities for solving inverse problems with partial differential equations.

Urban communities rely on built utility infrastructures as critical lifelines that provide essential services such as water, gas, and power, to sustain modern socioeconomic systems. These infrastructures consist of underground and surface-level assets that are operated and geo-distributed over large regions where continuous monitoring for anomalies is required but challenging to implement. This article addresses the problem of deploying heterogeneous Internet of Things sensors in these networks to support future decision-support tasks, for example, anomaly detection, source identification, and mitigation. We use stormwater as a driving use case; these systems are responsible for drainage and flood control, but act as conduits that can carry contaminants to the receiving waters. Challenges toward effective monitoring include the transient and random nature of the pollution incidents, the scarcity of historical data, the complexity of the system, and technological limitations for real-time monitoring. We design a SemanTics-aware sEnsor Placement framework (STEP) to capture pollution incidents using structural, behavioral, and semantic aspects of the infrastructure. We leverage historical data to inform our system with new, credible instances of potential anomalies. Several key topological and empirical network properties are used in proposing candidate deployments that optimize the balance between multiple objectives. We also explore the quality of anomaly representation in the network through new perspectives, and provide techniques to enhance the realism of the anomalies considered in a network. We evaluate STEP on six real-world stormwater networks in Southern California, USA, which shows its efficacy in monitoring areas of interest over other baseline methods.

Many documents are produced over the years of managing assets, particularly those with long lifespans. However, during this time, the assets may deviate from their original as-designed or as-built state. This presents a significant challenge for tasks that occur in later life phases but require precise knowledge of the asset, such as retrofit, where the assets are equipped with new components. For a third party who is neither the original manufacturer nor the operator, obtaining a comprehensive understanding of the asset can be a tedious process, as this requires going through all available but often fragmented information and documents. While common knowledge regarding the domain or general type of asset can be helpful, it is often based on the experiences of engineers and is, therefore, only implicitly available. This article presents a graph-based information management system that complements traditional PLM systems and helps connect fragments by utilizing generic information about assets. To achieve this, techniques from systems engineering and data science are used. The overarching management platform also includes geometric analyses and operations that can be performed with geometric and product information extracted from STEP files. While the management itself is first described generically, it is also later applied to cabin retrofit in aviation. A mock-up of an Airbus A320 is utilized as the case study to demonstrate further how the platform can provide benefits for retrofitting such long-living assets.

This paper presents a compact broad dual-band rectifier based on a transmission line matching network. This method improves the overall impedance matching performance over two bands, and improves bandwidth of the rectifier’s efficiency. A π-type direct current filter with excellent harmonic suppression performance is proposed. The multi-section transmission line used as the dual-band input impedance matching network is analyzed to achieve an arbitrary frequency ratio. A rectifier is designed and implemented using a three-stage transmission-line matching network. Simulation and experimental results show that a dual-band rectifier is successfully performed with the measured power conversion efficiency (PCE) of 75.7% and 76.3% at 0.915 and 2.45 GHz, respectively. Additionally, the rectifier exhibits bandwidths of 0.48 and 0.25 GHz when the PCE exceeds 70%. Significant enhancement of bandwidth over conventional rectifiers is demonstrated.