Search Results (2,063)

Investments in urban water supply should be informed by resilience management frameworks that consider traditional reliability requirements, community preparedness during system disruptions, and sustainability goals in long-term planning. Grounded in a framework (WARATA) that integrates these aspects, this paper presents a stepwise, performance-based theoretical approach to resilience quantification, supported by explanations and practical guidance. For instance, in addition to the piped infrastructure components, emergency supply options and human resources should be incorporated within the system boundaries (Step 1), and water supplied to users is recommended as a single performance measure (Step 2). During disruptions, performance at user nodes is influenced by operational rules for resource allocation (Step 3), which must be implemented in the required computer model for simulating performance (Step 4). Equations for computing withstanding, absorptive, restorative, adaptive, and transformative capabilities as time-based metrics are proposed (Step 5), enabling the analysis of results from the bottom up (Step 6) to inform resilience management. Using illustrations of performance curves at individual system nodes, this paper advocates for extended system boundaries that bridge the gap between infrastructure and community resilience; discusses challenges with the modeling of dynamic, adaptive performances; and emphasizes the importance of assessing temporal distances to fail-safe and safe-fail thresholds during disturbances. Pending case study validation and integration into tools for predictive and real-time analyses of options, the quantification approach could support infrastructure and emergency response planning and management, ultimately ensuring sustainable system designs with equitable resilience outcomes. Full article

Passively mode-locked lasers, as essential tools for generating ultrashort pulses, have found widespread applications in industrial manufacturing, optical communications, biomedical imaging, and fundamental scientific research. Saturable absorbers serve as the key components governing the performance of such laser systems. Conventional saturable absorber materials, including semiconductor saturable absorber mirrors, carbon nanotubes, and graphene, however, suffer from inherent limitations in operational wavelength range, damage threshold, and environmental stability. In recent years, two-dimensional transition metal carbides and nitrides, known as MXenes, have emerged as a promising class of materials to address these challenges. Their unique metallic conductivity, broadband saturable absorption, ultrafast carrier dynamics, excellent thermal management capability, and versatile chemical tunability offer unprecedented opportunities for advanced saturable absorber applications. This review systematically summarizes the recent progress of MXene-based saturable absorbers, with an emphasis on their distinctive advantages in extending the mode-locked wavelength range, enhancing output pulse stability, and increasing the optical damage threshold. Furthermore, strategies for performance optimization through surface terminal group engineering, defect modulation, and heterostructure design are discussed in depth. Finally, the future prospects and key challenges toward industrial implementation of MXenes in ultrafast photonics are outlined, aiming to stimulate further advancements in high-performance ultrafast laser technology. Full article

Against the backdrop of accelerating digitalization and intelligent transformation, intelligent manufacturing has emerged as a key driver of green transition in manufacturing. However, evidence on its effects and the mechanisms underlying corporate green innovation remains limited. Using panel data of Chinese A-share manufacturing firms from 2011 to 2023, this study exploits the pilot policy of intelligent manufacturing as a quasi-natural experiment and employs a difference-in-differences (DID) approach. Results indicate that intelligent manufacturing significantly enhances firms’ green innovation, with robust evidence across multiple checks. Mechanism analysis shows that this effect operates through an integrated dynamic capability channel, whereby firms strengthen their adaptive capability, absorptive capability for green knowledge and digital technologies, and innovation capability through technological integration, thereby improving green innovation. Moreover, intellectual property protection strengthens this mechanism by increasing innovation returns and enhancing the capability-to-innovation conversion efficiency. Heterogeneity results suggest stronger effects in non-high-tech firms, non–heavily polluting industries, and technology-intensive firms, reflecting differences in digital readiness and resource reconfiguration capacity. Overall, this study provides causal evidence on the green effects of intelligent manufacturing, clarifies internal mechanisms, and highlights institutional and firm-level heterogeneity, with implications for digital-driven green transformation and policy design. Full article

Expanded snack extrusion is governed by tightly coupled interactions among raw material composition, moisture, barrel temperature, screw speed, feed rate, screw configuration, die geometry, and energy input. These variables affect not only final responses such as expansion ratio, bulk density, hardness, crispness, and water absorption or solubility indices, but also intermediate state variables including specific mechanical energy (SME), melt temperature, die pressure, melt viscosity, and bubble growth dynamics. As a result, modelling has become essential for product design, process optimisation, and scale-up. This review critically evaluates the major classes of models used to describe process–structure–quality relationships in the extrusion of expanded snacks. The literature shows that empirical regression and response surface methodology (RSM) remain the most widely applied tools because they are experimentally efficient and easy to interpret. However, mixture-process designs are more appropriate when formulation and operating variables are changed simultaneously, while phenomenological and mechanistic approaches provide better physical insight into expansion and structure development. More recently, machine-learning and interpretable artificial intelligence approaches have demonstrated strong predictive capability when large, well-curated datasets are available. Across model families, a consistent theme is that operating variables act on final product quality through intermediate process state variables rather than independently. On that basis, this review proposes a practical hybrid framework for expanded snack extrusion: a mixture-process quadratic model augmented with SME, die pressure, melt temperature and shear-related state variables, and structured in three levels linking (i) controllable inputs to state variables, (ii) state variables to measurable quality attributes, and (iii) quality attributes to a gold-standard product target or sensory-control criterion. Such a model offers a realistic balance between predictive performance, physical interpretability, experimental burden, and industrial usefulness, while also providing a clear pathway toward future digital twin and machine-learning-enabled optimisation. Full article

Mass concrete design presents a significant challenge due to the inherent conflicts among key performance metrics: high compressive strength, low heat of hydration, and low water absorption (a key durability indicator). Traditional trial-and-error methods are inefficient and fail to systematically navigate these complex trade-offs. To address this, this study proposes a data-driven multi-objective optimization framework for mass concrete mix design. A comprehensive experimental dataset of 64 mixtures was established by varying the water-to-binder ratio (0.40–0.55), fly ash content (0–120 kg/m³), and slag content (0–120 kg/m³), with cement content fixed at 400 kg/m³. Kriging surrogate models were developed to accurately map the nonlinear relationships between these design variables and the three performance responses. These models were then integrated with the NSGA-II algorithm to generate a Pareto-optimal front of solutions. The framework’s predictive accuracy and generalization capability were rigorously validated through out-of-sample experiments, demonstrating prediction errors consistently below 10%. The results provide a quantified map of feasible engineering compromises, enabling engineers to select tailored mixtures for specific project priorities, such as low-heat mixes for dams or high-strength mixes for foundations. Full article

Vanadium dioxide (VO₂) is a phase-change material, which changes its properties under thermal or optical stimuli. Thanks to the fact that the material phase transition appears at conditions which are close to environmental ones, VO₂ has been widely used in diverse structures, including metasurfaces, that acquire switching and reconfigurability capabilities. In this paper, we numerically study the functionality-enabling properties of dielectric resonator-based nondiffractive meta-atoms that comprise small VO₂ components, i.e., covers or drops, in switchable coding and wavefront-manipulating scenarios at THz frequencies. The goal is to unveil the potential of these meta-atoms in switching the reflected wave’s phase coverage under temperature variations. The main attention is paid to how the shape and size of the VO₂ components affect the functionality switching that is enabled by the changes in coverage. It is shown that metallic and insulator states of VO₂ can play different roles in diverse switching scenarios. Different resonance regimes exert different influences on the resulting capability of switching, while contributing to multifunctional operating scenarios. Possible roles of state-dependent absorption are clarified. Full article

This study comprehensively investigates the degradation performance of a Mn-doped Zn₂SnO₄ photocatalyst based on time-dependent UV-Vis absorption spectra. Before machine learning modelling, the effects of experimental parameters such as UV–Vis measurement wavelength, reaction time, and Mn doping ratio were statistically validated using One-Way Analysis of Variance (ANOVA) and Multiple Linear Regression (MLR) methods. To overcome the limitations of linear models in representing complex physical systems, an optimized Multi-Layer Perceptron (MLP) architecture was developed to capture the system’s nonlinear dynamics with high accuracy. To validate the model’s out-of-sample prediction capability and prevent data leakage potentially arising from spectral data correlation, the “Leave-One-Doping-Level-Out” (LODLO) cross-validation strategy was applied, during which performance metrics of R²$=0.8889$ and $MSE=0.00238$ were recorded. To make the neural network’s decision-making mechanism transparent, a dual-validation explainability framework comprising Shapley Additive Explanations (SHAP) and Permutation Feature Importance analyses was employed. By quantifying the relative contributions of the experimental parameters to the model predictions, this approach revealed that the UV–Vis measurement wavelength was the dominant predictive variable, followed by the Mn doping ratio and reaction time. This study presents a transparent methodology that offers both strong predictive capability and physically grounded data to shed light on the complex interactions in doped semiconductor photocatalysts. Full article

Polycarbonate is a thermoplastic material well known for its high impact resistance and thermal stability, making it a strong candidate for non-structural aerospace applications. Within the framework of the LIDER project for the techno brick BUS, the behavior of polycarbonate has been experimentally assessed under critical conditions, aiming to validate its potential use in a future tail bumper design for helicopters. The experimental campaign included high strain rate impact tests, ageing tests to evaluate water absorption effects, and high-temperature exposure to assess thermal performance. The results of these tests form the core of this study, demonstrating the material’s capabilities and limitations under operationally relevant conditions. These findings aim to support the development of lightweight and robust non-structural components in aerospace systems. Full article

Smart manufacturing, Industry 4.0, and cyber-physical systems (CPSs) require sensing architectures capable of resolving both spatially distributed asset behavior and highly localized process states. This review examines optical fiber sensors (OFSs) and integrated photonic sensors for industrial monitoring through a deployment-oriented, multi-scale perspective. The discussion covers five major application regimes: continuous infrastructure surveillance, structural health monitoring (SHM) of load-bearing composites, dynamic condition monitoring of machinery, in situ observability in advanced manufacturing, and localized chemical or gas sensing. Extended fiber-optic networks, including distributed fiber-optic sensing (DFOS) based on Rayleigh, Raman, and Brillouin scattering, together with multiplexed fiber Bragg grating (FBG) sensors, provide passive, embeddable, and remotely interrogated monitoring for large-scale assets and harsh environments. Photonic integrated circuits (PICs) shift transduction to compact node-level devices for localized thermal, mechanical, refractive-index, absorption, vibration, and inertial measurements, while plasmonic and dielectric nanophotonic sensors extend optical monitoring toward surface-selective and chemically specific detection. Across these platforms, digital signal processing (DSP), machine learning (ML), sensor fusion, and digital-twin (DT) coupling are treated as artificial-intelligence-enabled (AI-enabled) layers for signal recovery, inverse mapping, uncertainty reduction, and predictive maintenance. The review argues that scalable industrial adoption is less limited by sensing physics than by the complete deployment chain: packaging, fiber–chip interfacing, calibration stability, interrogation robustness, and AI-enabled data interpretation. This manuscript is structured as a deployment-oriented narrative review of optical fiber and integrated photonic sensors for industrial monitoring and smart manufacturing. Full article

High-power 355 nm ultraviolet (UV) lasers, leveraging their short wavelength, high photon energy, and high absorption across a broad range of materials, have become indispensable light sources for precision manufacturing, semiconductor processing, and laser direct imaging (LDI). In this paper, we demonstrate a high-power 355 nm UV laser system based on a narrow-linewidth polarization-maintaining (PM) Yb-doped fiber laser and cascaded frequency conversion. A single-frequency semiconductor laser is employed as the seed source, with its spectral linewidth broadened to 0.32 nm (full width at half maximum, FWHM) via phase modulation to suppress stimulated Brillouin scattering (SBS). Through a PM master oscillator power amplifier (MOPA) architecture, a maximum average output power of 899 W at 1064 nm is achieved with a beam quality factor of M² = 1.12 (M²_x = 1.11, M²_y = 1.13). By employing lithium triborate (LiB₃O₅, LBO) crystals for extracavity cascaded second-harmonic generation (SHG) and sum-frequency generation (SFG), a maximum green output power of 613.7 W at 532 nm is obtained, corresponding to a SHG conversion efficiency of 68.2%, and a maximum UV output power of 227.1 W at 355 nm is achieved, with a total conversion efficiency of 25.2%. At the maximum output power, the UV beam quality factors are M² = 1.16 (M²_x = 1.24 and M²_y = 1.09), and the power fluctuation is better than ±1.5% root-mean-square (RMS) over 8 h of continuous operation. These results indicate that the cascaded frequency conversion approach based on narrow-linewidth PM fiber lasers possesses the capability for further scaling to higher-power single-path high-brightness UV output and can provide high-brightness UV sources for applications such as flexible printed circuit (FPC) laser cutting, flat-panel display laser direct imaging, and semiconductor wafer scribing. Full article

Global crop production faces serious threats from soil salinization. Microbial resources are often exploited to be used as fertilizers or seed coatings to address this issue. Parametarhizium changbaiense, as a novel beneficial microorganism, has been discovered to be capable of assisting limited crops such as mung bean in resisting salt–alkali stress. To investigate the effects of P. changbaiense on sugar beet under salt–alkali stress, the salt (NaCl:Na₂SO₄, molar ratio 9:1) and alkali (NaHCO₃:Na₂CO₃, molar ratio 9:1) stress were set on sugar beet germplasm 780016B. Results demonstrated that P. changbaiense improved the phenotypic characteristics of sugar beet seedlings under salt–alkali stress. The biomass parameters such as plant height and fresh weight significantly increased by growth-promoting effect. The elevated antioxidant enzyme activity could help protect plants from ROS damage induced by stress. Relative electrical conductivity and MDA content decreased with inoculation, thereby mitigating membrane lipid peroxidation and improving membrane system stability. The higher content of soluble sugar could maintain cell turgor pressure and alleviate osmotic stress. Inoculation with P. changbaiense enhanced chlorophyll content, fluorescence, and photosynthetic capacity. The more superior root vitality and architecture were suitable for the functions of metabolism and absorption. P. changbaiense could promote the growth and physiological characteristics under salt–alkali stress, so it has practical application value in agricultural production. Full article

An optical sensor based on a polymethacrylate matrix (PMM) with immobilized gold nanoparticles (Au⁰ NPs) has been developed for the determination of pollutants in environmental samples. The nanoparticles are synthesized by chemical reduction of Au(III) to Au⁰ using sodium borohydride, which yields conglomerates of spherical particles with an absorption maximum at 530 nm. The time stability of the nanocomposite is demonstrated, as well as the ability to control the nanoparticle loading in the matrix by varying the concentration of the HAuCl₄ solution. The analytical capability of the PMM–Au⁰ system is demonstrated for the direct determination of tetracycline in river water in two linear concentration ranges: 0.001–0.010 mg/L and 0.025–0.100 mg/L, with detection limits of 0.0005 mg/L and 0.012 mg/L, respectively. The determination of tetracycline is based on the enhancement of its intrinsic fluorescence at 520 nm by gold nanoparticles in the solid phase following solid-phase extraction from water in the anionic form H₂TC⁻ using PMM–Au⁰. The colorimetric determination of thiocyanate anions is based on a color change of the PMM–Au⁰ nanocomposite from red to blue, corresponding to a shift in the plasmon absorption maximum from 530 nm to 630 nm. The sensor exhibits a linear response in the thiocyanate concentration range of 0.3–50.0 mg/L, with a detection limit of 0.1 mg/L. Thus, the multifunctional PMM–Au⁰ sensor has been used for the determination of various analytes employing different modes of analytical signal readout after minimal sample preparation. Full article

We present a high-precision theoretical study of attosecond transient absorption spectroscopy (ATAS) of atomic hydrogen by numerically solving the time-dependent Schrödinger Equation (TDSE). A broadband extreme ultraviolet (XUV) attosecond pulse creates a wave packet of singly-excited bound states, which is subsequently probed by a time-delayed synthesized optical attosecond pulse (SOAP) with varying bandwidths and durations. When the SOAP has a narrow bandwidth (1.3–1.5 eV) and a long duration (~17 fs), the absorption spectrum exhibits conventional features, namely AC Stark shifts, half-cycle modulations (1.48 fs), and light-induced intermediate states, consistent with previous ATAS studies. In contrast, when the SOAP has a broad bandwidth (0.5–5.5 eV) and an attosecond duration (400 as), the dynamics are completely different. The spectrum reveals transverse wavelike modulations along the absorption lines and, remarkably, quantum beats with distinct frequencies, which are different from previous reports in hydrogen ATAS. To interpret these observations, we employ a dipole-control model. The model quantitatively reproduces the dominant modulation frequencies, identifying resonant couplings via two-photon processes (TPPs, 1.89 eV, period 2.18 fs) and three-photon processes (THPPs, 10.2 eV and 12.1 eV), as well as higher-order couplings. The validity of the $\delta$-like pulse approximation is quantitatively assessed. The model remains accurate for pulse durations shorter than 700 as (bandwidth broader than 3.5 eV) but fails for longer pulses (exceeding 4 fs), where energy level splittings emerge. Our results demonstrate that the dipole-control model provides a reliable and intuitive framework for interpreting complex multiphoton interactions in ATAS, and highlight the unique capability of broadband SOAP probes to resolve attosecond-scale quantum beats inaccessible with conventional few-cycle infrared pulses. Full article

CsPbI₃ exhibits multiple crystal phases, and the kinetic barriers for phase transitions are relatively low, facilitating the formation of abundant interphase boundaries (IBs) during phase transitions. These IB structures significantly influence the optoelectronic performance of the material. In this work, based on three types of CsPbI₃ IB structures, we systematically investigate the effects of pressure on the formation feasibility and optoelectronic properties of these IBs by calculating their formation energies, band alignments, optical absorption characteristics, and carrier effective masses. The results show that moderate pressure can increase the formation feasibility of certain IB structures and effectively modulate the band alignment at the CsPbI₃ IBs, thereby enabling the switching of different optoelectronic functions within the same material. Meanwhile, the application of pressure can also improve optical absorption and the spectroscopic-limited maximum efficiency, and reduce carrier effective masses in some IB systems, which is beneficial for enhancing carrier transport capabilities. This study demonstrates that pressure serves as an effective means to regulate the IB structures and optoelectronic properties of CsPbI₃, providing theoretical support for the design of multifunctional optoelectronic materials based on IB engineering and for expanding photovoltaic applications. Full article

Βackround/Objectives: The rising global burden of cardiometabolic disorders and chronic low-grade inflammation underscores the need for therapies capable of modulating multiple interconnected pathways. Methods: In this work, a ligand-based virtual screening campaign centered on a previously reported scaffold (compound 1a) was combined with molecular docking, 200 ns molecular dynamics simulations and ADMET prediction to identify and prioritize small-molecule multitarget candidates against PCSK9, GLP1R, FGFR1, GIPR, NF-κB and NLRP3. Results: Among the screened analogs, D4Z emerged as the most balanced lead, displaying consistently favorable binding profiles, stable interactions within functionally relevant pockets and a drug-like physicochemical and pharmacokinetic profile with high predicted oral absorption. Conclusions: Although these findings remain purely computational, they support D4Z as a prioritized multitarget lead for synthesis and experimental validation and illustrate the potential of rational multitarget design for addressing the cardiometabolic–inflammatory axis. Full article