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Modern advanced photonic integrated circuits require dense integration of high-speed electro-optic functional elements on a compact chip that consumes only moderate power. Energy efficiency, operation speed, and device dimension are thus crucial metrics underlying almost all current developments of photonic signal processing units. Recently, thin-film lithium niobate (LN) emerges as a promising platform for photonic integrated circuits. Here, we make an important step towards miniaturizing functional components on this platform, reporting high-speed LN electro-optic modulators, based upon photonic crystal nanobeam resonators. The devices exhibit a significant tuning efficiency up to 1.98 GHz V −1 , a broad modulation bandwidth of 17.5 GHz, while with a tiny electro-optic modal volume of only 0.58 μ m 3 . The modulators enable efficient electro-optic driving of high-Q photonic cavity modes in both adiabatic and non-adiabatic regimes, and allow us to achieve electro-optic switching at 11 Gb s −1 with a bit-switching energy as low as 22 fJ. The demonstration of energy efficient and high-speed electro-optic modulation at the wavelength scale paves a crucial foundation for realizing large-scale LN photonic integrated circuits that are of immense importance for broad applications in data communication, microwave photonics, and quantum photonics. Lithium niobate (LN) devices are promising for future photonic integrated circuits. Here, the authors demonstrate an electro-optic LN modulator with a very small modal volume based on photonic crystal resonator architecture.

Understanding dynamic human mobility changes and spatial interaction patterns at different geographic scales is crucial for assessing the impacts of non-pharmaceutical interventions (such as stay-at-home orders) during the COVID-19 pandemic. In this data descriptor, we introduce a regularly-updated multiscale dynamic human mobility flow dataset across the United States, with data starting from March 1st, 2020. By analysing millions of anonymous mobile phone users’ visits to various places provided by SafeGraph, the daily and weekly dynamic origin-to-destination (O-D) population flows are computed, aggregated, and inferred at three geographic scales: census tract, county, and state. There is high correlation between our mobility flow dataset and openly available data sources, which shows the reliability of the produced data. Such a high spatiotemporal resolution human mobility flow dataset at different geographic scales over time may help monitor epidemic spreading dynamics, inform public health policy, and deepen our understanding of human behaviour changes under the unprecedented public health crisis. This up-to-date O-D flow open data can support many other social sensing and transportation applications.Measurement(s)mobility • InteractionTechnology Type(s)GPS navigation system • machine learningFactor Type(s)geographic scale • temporal interval • geographic location • spatiotemporal regionSample Characteristic - LocationUnited States of AmericaMachine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13135085

Flexible wearable sweat sensors allow continuous, real-time, noninvasive detection of sweat analytes, provide insight into human physiology at the molecular level, and have received significant attention for their promising applications in personalized health monitoring. Electrochemical sensors are the best choice for wearable sweat sensors due to their high performance, low cost, miniaturization, and wide applicability. Recent developments in soft microfluidics, multiplexed biosensing, energy harvesting devices, and materials have advanced the compatibility of wearable electrochemical sweat-sensing platforms. In this review, we summarize the potential of sweat for medical detection and methods for sweat stimulation and collection. This paper provides an overview of the components of wearable sweat sensors and recent developments in materials and power supply technologies and highlights some typical sensing platforms for different types of analytes. Finally, the paper ends with a discussion of the challenges and a view of the prospective development of this exciting field.

The development of integrated semiconductor lasers has miniaturized traditional bulky laser systems, enabling a wide range of photonic applications. A progression from pure III-V based lasers to III-V/external cavity structures has harnessed low-loss waveguides in different material systems, leading to significant improvements in laser coherence and stability. Despite these successes, however, key functions remain absent. In this work, we address a critical missing function by integrating the Pockels effect into a semiconductor laser. Using a hybrid integrated III-V/Lithium Niobate structure, we demonstrate several essential capabilities that have not existed in previous integrated lasers. These include a record-high frequency modulation speed of 2 exahertz/s (2.0 × 10 18 Hz/s) and fast switching at 50 MHz, both of which are made possible by integration of the electro-optic effect. Moreover, the device co-lases at infrared and visible frequencies via the second-harmonic frequency conversion process, the first such integrated multi-color laser. Combined with its narrow linewidth and wide tunability, this new type of integrated laser holds promise for many applications including LiDAR, microwave photonics, atomic physics, and AR/VR. On-Chip integration of laser systems led to impressive development in many field of application like LIDAR or AR/VR to cite a few. Here the authors harness Pockels effect in an integrated semiconductor platform achieving fast on-chip configurability of a narrow linewidth laser.

Soliton microcombs are a promising new approach for photonic-based microwave signal synthesis. To date, however, the tuning rate has been limited in microcombs. Here, we demonstrate the first microwave-rate soliton microcomb whose repetition rate can be tuned at a high speed. By integrating an electro-optic modulation element into a lithium niobate comb microresonator, a modulation bandwidth up to 75 MHz and a continuous frequency modulation rate up to 5.0 × 10 14 Hz/s are achieved, several orders-of-magnitude faster than existing microcomb technology. The device offers a significant bandwidth of up to tens of gigahertz for locking the repetition rate to an external microwave reference, enabling both direct injection locking and feedback locking to the comb resonator itself without involving external modulation. These features are especially useful for disciplining an optical voltage-controlled oscillator to a long-term reference and the demonstrated fast repetition rate control is expected to have a profound impact on all applications of frequency combs. A microwave-rate soliton microcomb whose repetition rate can be modulated at 75 MHz. Moreover, the repetition rate can be locked to an external microwave reference by direct injection locking or feedback locking without external modulation.

In recent years, tourism revenue and visitor numbers in Northwest China have increased steadily. However, many tourists still have limited knowledge of scenic destinations across the five northwestern provinces. When travelers intend to visit the region but have not yet decided on specific destinations, an intelligent recommendation system is urgently needed to assist their decision-making. Based on collaborative filtering, content matching, or knowledge graphs existing systems primarily face three major challenges: Due to reliance on historical data, the recommendation performance for new users and new attractions is weak; limited ability to capture tourists’ current intentions and personalized needs; insufficient utilization of multimodal information. To address these challenges, We propose a novel deep learning-based multimodal recommendation model, T-ECBM. A dataset comprising 23,488 user reviews and 4160 images of 52 attractions was collected. BERT was employed to extract semantic features from reviews, capturing subjective preferences and sentiment, while an improved EfficientNet-CA model extracted visual features from images to identify key scenic elements. The two feature sets were fused and fed into a multilayer perceptron, formulating the recommendation task as a multi-class classification problem. Experimental results demonstrate that text-only BERT achieved a Top-1 accuracy of 82.67%, while image-only EfficientNet-CA reached 83.68%. In contrast, the proposed T-ECBM achieved 96.71% Top-1 accuracy, 99.82% Top-5 accuracy, and an F1-score of 96.70%, proving its significant superiority over unimodal approaches. By integrating textual and visual modalities, T-ECBM effectively reduces information asymmetry, enriches decision-making support, and delivers intelligent, efficient, and personalized recommendations for tourists exploring northwestern China.

Neutrophil chemotaxis plays a vital role in human immune system. Compared with traditional cell migration assays, the emergence of microfluidics provides a new research platform of cell chemotaxis study due to the advantages of visualization, precise control of chemical gradient, and small consumption of reagents. A series of microfluidic devices have been fabricated to study the behavior of neutrophils exposed on controlled, stable, and complex profiles of chemical concentration gradients. In addition, microfluidic technology offers a promising way to integrate the other functions, such as cell culture, separation and analysis into a single chip. Therefore, an overview of recent developments in microfluidic-based neutrophil chemotaxis studies is presented. Meanwhile, the strength and drawbacks of these devices are compared.

Optical microcomb underpins a wide range of applications from communication, metrology, to sensing. Although extensively explored in recent years, challenges remain in key aspects of microcomb such as complex soliton initialization, low power efficiency, and limited comb reconfigurability. Here we present an on-chip microcomb laser to address these key challenges. Realized with integration between III and V gain chip and a thin-film lithium niobate (TFLN) photonic integrated circuit (PIC), the laser directly emits mode-locked microcomb on demand with robust turnkey operation inherently built in, with individual comb linewidth down to 600 Hz, whole-comb frequency tuning rate exceeding 2.4 × 10 17  Hz/s, and 100% utilization of optical power fully contributing to comb generation. The demonstrated approach unifies architecture and operation simplicity, electro-optic reconfigurability, high-speed tunability, and multifunctional capability enabled by TFLN PIC, opening up a great avenue towards on-demand generation of mode-locked microcomb that is of great potential for broad applications. Here the authors demonstrate a laser system that can directly output soliton microcombs, with high power efficiency and reconfigurability, paving the way for communication, computing, and metrology based on integrated photonics.

Sensing the nighttime economy–housing imbalance is of great importance for urban planning and commerce. As an efficient tool of social sensing and human observation, mobile phone data provides an effective way to address this issue. In this paper, an indicator, mobile phone data-based nighttime economy–housing imbalance intensity, is proposed to measure the degree of the nighttime economy–housing imbalance. This indicator can distinguish vitality variations between sleep periods and nighttime activity periods, which are highly related to the nighttime economy–housing imbalance. The spatial pattern of the nighttime economy–housing imbalance was explored, and its association with the built environment was investigated through city-scale geographical regression analysis in Shanghai, China. The results showed that the sub-districts of Shanghai with high-positive-imbalance intensities displayed structures with superimposed rings and striped shapes, and the sub-districts with negative imbalance intensities were distributed around high positive-intensity areas. There were significant linear correlations between imbalance intensity and the built environment. The multiple influences of built environment factors and related mechanisms were explored from a geographical perspective. Our study utilized the social sensing data to provide a more comprehensive understanding of the nighttime economy–housing imbalance. These findings will be useful for fostering the nighttime economy and supporting urban renewal.

Tissues are highly complicated with spatial heterogeneity in gene expression. However, the cutting-edge single-cell RNA-seq technology eliminates the spatial information of individual cells, which contributes to the characterization of cell identities. Herein, we propose s ingle- c ell s patial p osition a ssociated c o- e mbeddings (scSpace), an integrative method to identify spatially variable cell subpopulations by reconstructing cells onto a pseudo-space with spatial transcriptome references (Visium, STARmap, Slide-seq, etc.). We benchmark scSpace with both simulated and biological datasets, and demonstrate that scSpace can accurately and robustly identify spatially variated cell subpopulations. When employed to reconstruct the spatial architectures of complex tissue such as the brain cortex, the small intestinal villus, the liver lobule, the kidney, the embryonic heart, and others, scSpace shows promising performance on revealing the pairwise cellular spatial association within single-cell data. The application of scSpace in melanoma and COVID-19 exhibits a broad prospect in the discovery of spatial therapeutic markers. Methods to reanalyze scRNA-seq data in a spatial perspective are vital but lacking. Here, the authors develop scSpace, an integrative method that uses ST data as spatial reference to reconstruct the pseudo-space of scRNA-seq data and identify spatially variable cell subpopulations, providing insights into spatial heterogeneity from scRNA-seq data.