Suzhou City University

With the progress of medical technology, biomedical field ushered in the era of big data, based on which and driven by artificial intelligence technology, computational medicine has emerged. People need to extract the effective information contained in these big biomedical data to promote the development of precision medicine. Traditionally, the machine learning methods are used to dig out biomedical data to find the features from data, which generally rely on feature engineering and domain knowledge of experts, requiring tremendous time and human resources. Different from traditional approaches, deep learning, as a cutting-edge machine learning branch, can automatically learn complex and robust feature from raw data without the need for feature engineering. The applications of deep learning in medical image, electronic health record, genomics, and drug development are studied, where the suggestion is that deep learning has obvious advantage in making full use of biomedical data and improving medical health level. Deep learning plays an increasingly important role in the field of medical health and has a broad prospect of application. However, the problems and challenges of deep learning in computational medical health still exist, including insufficient data, interpretability, data privacy, and heterogeneity. Analysis and discussion on these problems provide a reference to improve the application of deep learning in medical health.

Abstract Metal‐based nanomaterials have attracted broad attention recently due to their unique biological physical and chemical properties after entering tumor cells, namely biological effects. In particular, the abilities of Ca 2+ to modulate T cell receptors activation, K + to regulate stem cell differentiation, Mn 2+ to activate the STING pathway, and Fe 2+/3+ to induce tumor ferroptosis and enhance catalytic therapy, make the metal ions and metal‐based nanomaterials play crucial roles in the cancer treatments. Therefore, due to the superior advantages of metal‐based nanomaterials and the characteristics of the tumor microenvironment, we will summarize the recent progress of the anti‐tumor biological effects of metal‐based nanomaterials. Based on the different effects of metal‐based nanomaterials on tumor cells, this review mainly focuses on the following five aspects: (1) metal‐enhanced radiotherapy sensitization, (2) metal‐enhanced catalytic therapy, (3) metal‐enhanced ferroptosis, (4) metal‐enhanced pyroptosis, and (5) metal‐enhanced immunotherapy. At the same time, the shortcomings of the biological effects of metal‐based nanomaterials on tumor therapy are also discussed, and the future research directions have been prospected. The highlights of promising biosafety, potent efficacy on biological effects for tumor therapy, and the in‐depth various biological effects mechanism studies of metal‐based nanomaterials provide novel ideas for the future biological application of the nanomaterials.

Variational mode decomposition has been widely applied to machinery fault diagnosis during these years. However, it remains difficult to set proper hyperparameters for the variational mode decomposition, including number of decomposed modes, initial center frequencies, and balance parameter. Moreover, the low efficiency of the existing variational mode decomposition methods hinders their applications to practical diagnostic task. This article proposes an adaptive and efficient variational mode decomposition method after thoroughly investigating its convergence property characteristic. A convergent tendency phenomenon is discovered and is explained mathematically for the first time. Motivated by the convergent tendency phenomenon, the proposed method rapidly and adaptively determines the number and the optimal initial center frequencies of signal latent modes with the guidance of the convergent tendencies of the initial center frequencies changing from small to large. In the proposed method, the number of decomposed modes and initial center frequencies are not hyperparameters that require to be set in advance any more, but are parameters learned from the analyzed signals. The determined parameters enable efficient extraction of the main latent modes contained in the analyzed signals. Therefore, the proposed variational mode decomposition method represents a major improvement in parameter adaption and decomposition efficiency over the existing variational mode decomposition methods. In the application for bearing fault diagnosis, the faulty modes are selected adaptively and the corresponding balance parameters are further optimized efficiently. Two experimental cases validate the proposed method and its superiority over the existing variational mode decomposition methods and the classical fast spectral kurtosis in bearing fault diagnosis.

In this paper, a novel infrared maritime small target detection method, called local dissimilarity measure with anti interference based on global graph clustering (LDMGGC), is proposed. The Wasserstein distance is introduced to calculate the dissimilarity of gray level distribution between a central region and its neighborhoods. These dissimilarities construct the feature of a region. With this feature, detection for recalling all suspected targets is achieved. As the maritime interferences among suspected targets are able to be clustered, relaxing mutual k nearest neighbor graph is introduced in global graph clustering for filtering interferences. With this method, real targets are detected and maritime interferences are filtered out. Experiments are conducted on three maritime datasets and a non-maritime dataset for comparison. On three datasets, the proposed method achieves the best Receiver Operating Characteristic curves and Area Under Curve (0.99529, 0.99945, 0.99573, and 0.9906) values, meaning that the proposed method has high detection probability and low false-alarm ratio. Target Hit Rate (98.04%, 97.96%, 100%, and 99.24%) and Intersection of Union (0.8170, 0.7542, 0.5824, 0.7707) on four datasets of the proposed method show it has a strong ability to suppress the interferences

The growing uncertainties in power operations due to the integration of renewable generations (RGs) and electric vehicles (EVs) into electricity grids have amplified the significance of ancillary services (AS). These services have become essential to ensure the sustainable functioning of the grid. In light of this, we introduce a two-stage optimization framework to manage competitive energy and AS markets at the interface of the transmission system (TS) and distribution system (DS). Our approach takes into account a comprehensive set of economic, technical, and security factors. This mechanism is structured in two stages: the first stage encompasses the energy market, while the second stage encompasses AS markets. Spinning reserve (SR) is supplied by conventional thermal units (TUs), whereas regulation capacity is provided by energy storage systems (ESSs), fast-response generators, electric vehicles (EVs), and demand response (DR) aggregators. We applied this mechanism to a 30-bus transmission network connected to four 10-node DSs and utilized the GUROBI solver in GAMS for solving. The simulation results demonstrate that the engagement of DSs in the SR market reduces the reliance on costly TUs, thereby decreasing system costs by approximately 10%. Furthermore, involving ESSs, EVs, and DR aggregators in the regulation market enhances technical performance and results in a 6.91% reduction in total system costs. This approach provides a robust solution to the evolving challenges posed by RGs and EVs in modern electricity grids.

The work aims to reduce the energy consumption and carbon emissions generated during the urban logistics transportation and distribution and make the actual path planning flexible. Based on Vehicle Routing Problem (VRP), the routing problem of distribution vehicles is optimized under satisfying customers’ cargo demand and time requirements. Because Non-dominated Sorting Genetic Algorithm (NSGA-II) reduces the complexity of non-inferior sorting genetic algorithm and is characterized by fast running speed and good convergence, it is deeply improved. NSGA-II algorithm based on Multifactorial Evolutionary Algorithm (MFEA) (M-NSGA-II) is proposed. In terms of the solution of the stability of the optimal values of four target functions, including distribution cost, customer satisfaction, fuel conservation, and carbon emission, the lowest distribution costs of M-NSGA-II algorithm in ten experiments were all lower than those of other three standard algorithms. The solution duration of M-NSGA-II algorithm was 85.2s and the corresponding average frontier value amounted to 20. The multi-objective path optimization model designed is of great value for reducing carbon emissions under satisfying customers’ cargo demand and time requirements.

The 2020 COVID-19 pandemic has greatly accelerated the adoption of online learning and teaching in many colleges and universities. Video, as a key integral part of online education, largely influences student learning experiences. Though many guidelines on designing educational videos have been reported, the quantitative data showing the impacts of video length on students' academic performance in a credit-bearing course is limited, particularly for an online-flipped college engineering course. The forced pandemic lockdown enables a suitable environment to address this research gap. In this paper, we present the first step to examine the impact of short videos on students' academic performance in such circumstances. Our results indicate that short videos can greatly improve student engagement by 24.7% in terms of video viewing time, and the final exam score by 9.0%, both compared to the long-video group. The quantitative Likert questionnaire also indicates students' preference for short videos over long videos. We believe this study has important implications for course design for future online-flipped engineering courses.

Perovskite solar cells (PSCs) are regarded as a marvelous candidate in the revolution of photovoltaic (PV) technology due to the rapid development in the past decade. Flexible perovskite solar cells (FPSCs) are supposed to be an attractive commercialization option with various potential applications, including portable electronics, wearable power sources, and large‐scale industrial roofing. FPSCs have the advantages of low cost, high efficiency, light weight, flexibility, and more importantly, the feasibility of mass production with a roll‐to‐roll industrial process. Recently, many advances in FPSCs have been reported with an efficiency of over 20%. Herein, the critical issues and breakthroughs of FPSCs are elucidated comprehensively, involving materials selection and structure design that are suitable for flexible and durable substrates, transparent electrodes, low‐temperature large‐area fabrication, and mechanical flexible stability in the corresponding part. Finally, challenges and perspectives toward the commercialization of FPSCs are addressed in future developments.

Non-Hermitian photonic systems with loss and gain attract much attention due to their exceptional abilities in molding the flow of light. Introducing asymmetry to the $\mathcal{PT}$-symmetric system with perfectly balanced loss and gain, we reveal the mechanism of transition from the quasibound state in the continuum (quasi-BIC) to the simultaneous coherent perfect absorption (CPA) and lasing in a layered structure comprising epsilon-near-zero (ENZ) media. Two types of asymmetry (geometric and non-Hermitian) are analyzed with the scattering matrix technique. The effect of the CPA-lasing associated with the quasi-BIC is characterized with the unusual linear dependence of the quality factor on the inverse of asymmetry parameter. Moreover, the counter-intuitive loss-induced-lasing-like behavior is found at the CPA-lasing point under the non-Hermitian asymmetry. The reported features of non-Hermitian structures are perspective for sensing and lasing applications.

Radiative cooling technology, which is renowned for its ability to dissipate heat without energy consumption, has garnered immense interest. However, achieving high performance, multifunctionality, and smart integration while addressing challenges such as film thickness and enhancing anisotropic light reflection remains challenging. In this study, a core-shell composite nanofiber, PVDF@PEI, is introduced and designed primarily from a symmetry-breaking perspective to develop highly efficient radiative cooling materials. Using a combination of solvent-induced phase separation (EIPS) inverse spinning and (aggregation) self-assembly methods (EISA or EIAA) and coaxial electrostatic spinning (ES), superconformal surface anisotropic porous nanofiber membranes are fabricated. These membranes exhibit exceptional thermal stability (up to 210 °C), high hydrophobicity (contact angle of 126°), robust UV protection (exceeding 99%), a fluorescence multiplication effect (with a 0.6% increase in fluorescence quantum efficiency), and good breathability. These properties enable the material to excel in a wide range of application scenarios. Moreover, this material achieved a remarkable daytime cooling temperature of 8 °C. The development of this fiber membrane offers significant advancements in the field of wearables and the multifunctionality of materials, paving new paths for future research and innovation.

Exceptional points are spectral singularities of open systems, where several eigenvalues and eigenvectors coalesce. In photonics, they are associated to remarkable phenomena, such as unidirectional scattering, enhanced sensing or chiral mode conversion. In this work, we study scattering of electromagnetic waves by a single dielectric nanoparticle and observe the appearance of exceptional points in its eigenvalue spectrum. Their existence is linked to breaking the mirror symmetry of the particle. Remarkably, they mark the onset from weak to strong coupling of the resonant modes. We discuss in detail the example of the electric and magnetic dipole modes supported by a silicon nanoparticle. We argue that any two modes of a resonant dielectric nanoparticle can merge to create an exceptional point, provided their resonant frequencies cross as functions of a parameter such as, e.g., aspect ratio, and their field distributions have opposite signs after a reflection in the transverse plane of the structure. The strongly coupled modes radiate as a mixture of electric and magnetic dipoles resulting in an intense bianisotropic response, being easily controlled by symmetry-breaking perturbations. We also study the effect of a dielectric substrate and demonstrate that the latter provides an additional mechanism to tune the position of exceptional points in the parameter space. Finally, we discuss applications of bianisotropic EPs, including their use for refractive-index sensing. Published by the American Physical Society 2024

The adhesion of marine-fouling organisms to ships significantly increases the hull surface resistance and expedites hull material corrosion. This review delves into the marine biofouling mechanism on marine material surfaces, analyzing the fouling organism adhesion process on hull surfaces and common desorption methods. It highlights the crucial role played by surface energy in antifouling and drag reduction on hulls. The paper primarily concentrates on low-surface-energy antifouling coatings, such as organic silicon and organic fluorine, for ship hull antifouling and drag reduction. Furthermore, it explores the antifouling mechanisms of silicon-based and fluorine-based low-surface-energy antifouling coatings, elucidating their respective advantages and limitations in real-world applications. This review also investigates the antifouling effectiveness of bionic microstructures based on the self-cleaning abilities of natural organisms. It provides a thorough analysis of antifouling and drag reduction theories and preparation methods linked to marine organism surface microstructures, while also clarifying the relationship between microstructure surface antifouling and surface hydrophobicity. Furthermore, it reviews the impact of antibacterial agents, especially antibacterial peptides, on fouling organisms' adhesion to substrate surfaces and compares the differing effects of surface structure and substances on ship surface antifouling. The paper outlines the potential applications and future directions for low-surface-energy antifouling coating technology.

The Chinese urban regeneration movement underscores a “people-oriented” paradigm, aimed at addressing urban challenges stemming from rapid prior urbanization, while striving for high-quality and sustainable urban development. At the community level, fostering quality through a socially sustainable perspective (SSP) is a pivotal strategy for people-oriented urban regeneration. Nonetheless, explorations of community quality assessments grounded in an SSP have been notably scarce in recent scholarly discourse. This study pioneers a multidimensional quantitative model (MQM) for gauging community quality, leveraging diverse geospatial data sources from the SSP framework. The MQM introduces an evaluative framework with “Patency, Convenience, Comfort, and Safety” as primary indicators, integrating multi-sourced data encompassing the area of interest (AOI), Point of Interest (POI), Weibo check-ins, and Dianping data. The model’s efficacy is demonstrated through a case study in the Gusu district, Suzhou. Furthermore, semantic analysis of the Gusu district’s street view photos validates the MQM results. Our findings reveal the following: (1) AI-based semantic analysis accurately verifies the validity of MQM-generated community quality measurements, establishing its robust applicability with multi-sourced geospatial data; (2) the community quality distribution in Gusu district is notably correlated with the urban fabric, exhibiting lower quality within the ancient town area and higher quality outside it; and (3) communities of varying quality coexist spatially, with high- and low-quality communities overlapping in the same regions. This research pioneers a systematic, holistic methodology for quantitatively measuring community quality, laying the groundwork for informed urban regeneration policies, planning, and place making. The MQM, fortified by multi-sourced geospatial data and AI-based semantic analysis, offers a rigorous foundation for assessing community quality, thereby guiding socially sustainable regeneration initiatives and decision making at the community scale.

Abstract Under the background of the transformation of resource-based cities, heritage as the symbolic cultural representation plays a synergistic role in revitalizing urban vibrancy. A majority of contemporary research focuses on specific heritage restoration and renovation. However, scant literature has been concerned with an integrated heritage corridor upgrading framework from the spatial quality perspective, which has limited effects on promoting urban socio-cultural development. This research aims to evaluate the heritage corridor through the GIS-based environmental spatial model (ESM) with multi-source data and verification through AI-based image semantic segmentation analysis, cultivating suggestions for heritage restoration and management to revitalize the holistic urban–rural areas. The research takes a resource-based city, Fengfeng Mining District (FMD) in Handan, China, as a research case. The research found heterogeneity of the heritage evaluation results and their geographical distribution, and image-based spatial quality verification evidenced the suitability and reliability of ESM for heritage assessment. This research proposes a quantitative and holistic evaluation framework for assessing and improving heritage corridors. The restoration and optimization of heritage corridors should combine a comprehensive, precise, and people-oriented spatial quality assessment, and the GIS-based ESM analysis method could be an effective decision-making support system.

The 2023 SDGs report underscores the prolonged disruption of COVID-19 on community living spaces, infrastructure, education, and income equality, exacerbating social and spatial inequality. Against the backdrop of the dual impact of significant events and the emergence of digital technologies, a coherent research trajectory is essential for characterizing social–spatial equity and understanding its influential factors within the urban planning discipline. While prior research emphasized spatial dimensions and mitigated spatial differentiation to ensure urban equity, the complexity of these interconnections necessitates a more comprehensive approach. This study adopts a holistic perspective, focusing on the “social–spatial” dynamics, utilizing social perception (sentiment maps) and spatial differentiation (housing prices index) pre- and post-pandemic to elucidate the interconnected and interactive nature of uneven development at the urban scale. It employs a multi-dimensional methodological framework integrating morphology analysis of housing conditions, GIS analysis of urban amenities, sentiment semantic analysis of public opinion, and multiscale geographically weighted regression (MGWR) analysis of correlation influential factors. Using Suzhou, China, as a pilot study, this research demonstrates how these integrated methods complement each other, exploring how community conditions and resource distribution collectively bolster resilience, thereby maintaining social–spatial equity amidst pandemic disruptions. The findings reveal that uneven resource distribution exacerbates post-pandemic social stratification and spatial differentiation. The proximity of well-maintained ecological environments, such as parks or scenic landmarks, generally exhibits consistency and positive effects on “social–spatial” measurement. Simultaneously, various spatial elements influencing housing prices and social perception show geographic heterogeneity, particularly in areas farther from the central regions of Xiangcheng and Wujiang districts. This study uncovers a bilateral mechanism between social perception and spatial differentiation, aiming to delve into the interdependent relationship between social–spatial equity and built environmental factors. Furthermore, it aspires to provide meaningful references and recommendations for urban planning and regeneration policy formulation in the digital era to sustain social–spatial equity.

Accelerated urbanization has led to regional disruptions and exacerbated imbalances in spatial quality, social cohesion, and inequalities. Urban regeneration, as a mitigating strategy for these disruptions, faces significant social challenges, particularly at the community scale. This study addresses the existing research gap by comprehensively reviewing community regeneration (CR) from a socially sustainable perspective (SSP). Utilizing VOSviewer software, we synthesize and categorize relevant research trends and methods spanning from 2006 to 2023, retrieving 213 coded articles among 5002 relevant documents from Web of Science bibliometric datasets. The study explores the implementation trajectory of CR, considering novel scenario demands, emerging technologies, and new development paradigms and approaches. It delves into human-centric approaches to enhance the quality of life, precision, and diversification of community engagement and cultivate a sense of community equity and belonging. Moreover, the findings highlight densification as a synergistic and adaptive strategy for current regeneration actions. This scientometric review leverages new tools and innovative approaches for regeneration policy and planning decision-making, ultimately contributing to the improvement of livability. The study provides valuable insights into the challenges and opportunities associated with socially sustainable CR, offering a foundation for future research, and guiding practical urban planning and design interventions.

Abstract A disordered crystal structure is an asymmetrical atomic lattice resulting from the missing atoms (vacancies) or the lattice misarrangement in a solid‐state material. It has been widely proven to improve the electrocatalytic hydrogen evolution reaction (HER) process. In the present work, due to the special physical properties (the low evaporation temperature of below 900 °C), Zn is utilized as a sacrificial component to create senary PtIrNiCoFeZn high‐entropy alloy (HEA) with highly disordered lattices. The structure of the lattice‐disordered PtIrNiCoFeZn HEA is characterized by the thermal diffusion scattering (TDS) in transmission electron microscope. Density functional theory calculations reveal that lattice disorder not only accelerates both the Volmer step and Tafel step during the HER process but also optimizes the intensity and distribution of projected density of states near the Fermi energy after the H 2 O and H adsorption. Anomalously high alkaline HER activity and stability are proven by experimental measurements. This work introduces a novel approach to preparing irregular lattices offering highly efficient HEA and a TDS characterization method to reveal the disordered lattice in materials. It provides a new route toward exploring and developing the catalytic activities of materials with asymmetrically disordered lattices.

Rational design of facile and low-cost efficient electrocatalysts for oxygen evolution reaction (OER) is crucial to solve the energy crisis. Benefiting from in situ self-reconstruction from metal–organic frameworks (MOFs) to (oxy)hydroxides in alkaline electrolytes, MOFs have become alternative OER catalysts. Thus, Fe-doped Co-MOF nanosheets (Co-MOF/Fe) were prepared and utilized straightforwardly as OER electrocatalysts. CoFe-layered bimetallic hydroxides (CoFe-LDHs) with abundant active sites are obtained from in situ conversion of Co-MOF/Fe after etching by the KOH electrolyte, which are generally actual active species. Meanwhile, the introduction of Fe ions will also produce a synergistic effect that greatly improves the electrocatalytic OER performance. The optimized catalyst (Co-MOF/Fe10) shows exceptional OER activity (η10 = 260 mV) and excellent durability over 50 h. The outstanding OER performance of Co-MOF/Fe10 can also be reflected in the two-electrode hydrolyzer (1.57 V at 10 mA cm–2). This study offers a pathway to probe the catalytic mechanism of MOFs and the rational construction of efficient MOF-derived catalysts.

Abstract A chiral meta‐optics platform that incorporates hologram‐multiplexing with low phase distortion and wide‐incident angle tolerance over the broad spectral range of the ultraviolet‐visible (UV–vis) regimes holds great potential for photonic‐encryption‐based applications, particularly in next‐generation 3D displays, high‐resolution biomedical imaging, holographic anti‐counterfeiting labeling, and multi‐channel optical communication. However, the design incorporating giant chirality from UV–vis wavelengths coupled with wide‐incident‐angle tolerance and cost‐effective fabrication is still challenging. Here, the study introduces a pragmatic multifunctional dielectric chiral meta‐platform designed for simultaneous spin‐ and wavelength‐multiplexing of optical information in the UV–vis spectrum. The unit cell comprises a dimer structure based on wide‐bandgap silicon nitride (SiN x ), ensuring substantial dual‐spectrum chiro‐optical effects. The meticulously engineered chiral meta‐platform offers an incident angle tolerance of up to 40 degrees, coupled with significant chiro‐optical transmission. To demonstrate the concept, two distinct phase profiles are embedded in the meta‐platform, utilizing the spin and wavelength of incident light as keys to unlock the specific holographic information. The chiral meta‐platform is experimentally validated for oblique illumination angles, showcasing its adaptability across the UV–vis spectrum. The demonstrated meta‐device with dual‐spectrum visual encryption can be applied in various anti‐counterfeiting and security applications.

Previous reflectionless metasurfaces based on balanced electric and magnetic responses in engineered resonant meta-atoms become ineffective at oblique incident angles and usually have strong reflection at grazing incidence, where the impedance becomes near-zero or divergent. Here, by introducing the concept of anomalous generalized Brewster effect to metasurfaces, we demonstrate an exceptional resonance-free Brewster metasurface that exhibits ultrabroadband zero reflection at grazing incidence. The anomalous generalized Brewster effect is obtained via combining the mechanisms of the generalized Brewster effect and the anomalous Brewster effect, which are both resonance-free and thus enable ultrabroadband functionalities. As a practical application, Brewster metasurfaces exhibiting ultrabroadband reflectionless perfect absorption at grazing incident angles are constructed and demonstrated by full-wave simulations and microwave experiments. Our work could enable reflectionless wave manipulation at grazing incidence with an ultrawide working bandwidth.