Free interchange for better transit? Assessing the multi-dimensional impacts on metro to bus interchange behavior - insights from an explainable machine learning method

This study investigates the impact of a newly implemented public transport interchange discount policy in Suzhou, China, focusing on its effects on metro-to-bus interchange behaviors across various spatial and temporal dimensions. Utilizing two distinct datasets spanning periods before and after the policy's implementation, a comprehensive spatial-temporal analysis was conducted, covering weekdays, weekends, and holidays. A novel, real-time, distance-weighted methodology was developed to more accurately identify metro-to-bus interchange catchments, thereby refining the modeling scope. The study examines the interplay between land use, sociodemographic factors, and bus-related attributes-including a newly proposed operation-opportunity combined bus accessibility metric-using an explainable machine learning approach. Results indicate that the interchange discount policy has had an overall positive, though varied, impact on interchange behaviors, with the most pronounced effects observed during weekdays in central urban areas and at metro line bends. Specifically, 76.1 % of metro stations saw an increase in metro-to-bus interchange ratios on weekdays following the policy's implementation, with increases observed at 66.4 % and 67.3 % of stations during weekends and holidays, respectively. Overall, the interchange ratio increased by 12.49 %, with a 17.45 % increase on weekdays. The analysis also reveals that factors such as bus accessibility, bus-to-bus interchange, and population density exhibit different effects depending on the time of week, with non-linear patterns emerging. The policy's introduction shifted the impact thresholds for certain factors, initially triggering competition between bus and metro services but eventually leading to a synergistic rise in metro-to-bus transfers as bus-to-bus interchange ratios increased. Additionally, the policy altered the significance of population density, enhancing the attractiveness of multimodal interchange for users who previously favored other modes of transport.

Deep learning-based gamma spectroscopic analysis considering multiple variables for in situ applications

Gamma spectroscopy in environments with fluctuating temperatures, magnetic fields, and radiation doses can lead to inaccurate material analysis and increased radiation exposure for workers. In this manuscript, we propose a deep learning model that reduces human intervention and remains robust against variables in uncontrolled environments, thereby enhancing the applicability of gamma spectroscopy in the field. The process of establishing a dataset to train, validate, and test the deep learning model involved simultaneous consideration of multiple variables, including gain shifts, detector energy resolutions, the number of mixed radioisotopes (RIs), mixed RI ratios, and statistical fluctuations of the spectrum. The model was trained using multi-label learning to perform RI identification and quantification simultaneously. Specifically, the RI quantification output of our model structure was designed to predict full-energy peak areas and mixed ratios for each RI. Highlighting the importance of a high-performance training dataset, the training and validation results of the gamma spectroscopy model were analyzed in depth. In the test results, organized by RI type and number of mixed RIs, the model demonstrated that following: even under complex conditions with multiple variables, all outcomes for RI identification and quantification were predicted with high accuracy and precision, exceeding 98%.

Relaxed conditions for parameterized linear matrix inequality in the form of nested fuzzy summations

The aim of this study is to investigate less conservative conditions for parameterized linear matrix inequalities (PLMIs) that are formulated as nested fuzzy summations. Such PLMIs are commonly encountered in stability analysis and control design problems for Takagi-Sugeno (TS) fuzzy systems. Utilizing the weighted inequality of arithmetic and geometric means (AM-GM inequality), we develop new, less conservative linear matrix inequalities for the PLMIs. This methodology enables us to efficiently handle the product of membership functions that have intersecting indices. Through empirical case studies, we demonstrate that our proposed conditions produce less conservative results compared to existing approaches in the literature.

Understanding User Privacy Perceptions in Video Conferencing: Insights from a Feature-Specific User Study

<jats:p>The widespread adoption of video conferencing platforms has raised privacy concerns. Recent studies have shown that users express various concerns, such as reluctance toward mandatory camera-on policies, but these findings remain coarse-grained, lacking details on specific features and social relationships. This paper investigates how users perceive privacy with respect to various features in video conferencing platforms. Using the framework of contextual integrity, we analyze information flows across diverse scenarios, such as business meetings and online classes. Our findings reveal nuanced privacy perceptions regarding features that have been discontinued (e.g., attention tracking) or adjusted (e.g., meeting recording), suggesting that the handling of these features could have aligned better with users’ privacy expectations. Additionally, we identify emerging privacy concerns about the pinning and spotlighting features, as users often feel great discomfort when their video is pinned or spotlighted by others in specific contexts. These insights provide a deeper understanding of privacy in video conferencing, highlighting the need for more refined privacy controls and a proactive approach to feature development.</jats:p&gt

Controlled Mesoscopic Growth of Polymeric Fibers Using Liquid Crystal Template

Orientation-controlled polymeric fiber is one of the most exciting research topics to rationalize the multifunctionality for various applications. In order to realize this goal, the growth of polymeric fibers should be controlled using various techniques like extrusion, molding, drawing, and self-assembly. Among the various candidates to fabricate the orientation-controlled polymeric fibers, the template-assisted assembly guided by a liquid crystal (LC) matrix is the most promising because the template can be manipulated easily with various methods like surface anchoring, rubbing, geometric confinement, and electric field. This review introduces the recent progress toward the directed growth of polymeric fibers using the LC template. Three representative LC-templated polymerization techniques to fabricate fibers include chemical or physical polymerization from the monomers mixed in LC matrix, patterned fibers formed from LC-templated reactive mesogens, and orientation-controlled nanofibers by infiltrating vaporized monomers between LC molecules. The orientation-controlled polymeric fibers will be used in electro-optical switching tools, tunable hydrophilic or hydrophobic surfaces, and control of phosphorescence, which can open a way to design, fabricate, and modulate nano- to micron-scale fibers with various functions on demand.

Integrating flue gas into membrane-based oxygen-enriched gasification of municipal solid wastes for enhancing waste-to-energy conversion

The waste-to-energy (WTE) gasification process converts municipal solid wastes (MSWs) into electrical energy, offering a sustainable solution for solid waste management. Oxygen-enriched gas (OEG) has demonstrated potential to enhance WTE conversion. However, the elevated oxygen content can result in localized overheating, posing damage to the gasifier. The integration of CO2-containing gases, such as through flue gas recirculation (FGR), regulates gasification temperatures and improves operational stability. Despite that, flue gas contains nitrogen, and its impact on OEG gasification efficiency-especially the lower heating value (LHV) of syngas and carbon conversion efficiency (CCE)-remains elusive to date. Hence, this study aims to examine the effects of nitrogen in flue gas and FGR rate on syngas quality and carbon conversion. OEG gasification experiments were conducted using refuse-derived fuel (RDF) as MSW feedstock. As a baseline, membrane-based OEG with 45 % oxygen purity for gasification was employed, owing to the energy-and cost-efficiency of membrane air separation. Flue gas of two different concentrations was then introduced at varying recirculation rates to evaluate its impact on the OEG gasification process. Albeit a dilution effect caused by the non-combustible nitrogen gas, our findings suggest that flue gas with a 35 % CO2 concentration and 10 % recirculation rate is technically viable. Under these conditions, syngas LHV reached 7.74 MJ/m3, while CCE improved by 6 %, as compared to OEG gasification without FGR. These results provide critical insights into the role of flue gas in optimizing OEG gasification for enhanced WTE conversion of MSWs.

Power enhancement of ultraviolet laser beam via spatial beam combining of multiple high-harmonic beams in solid crystal

High-harmonic generation (HHG) is a nonlinear optical frequency up-conversion process that can generate a coherent ultraviolet (UV) - extreme ultraviolet (EUV) light source. In particular, solid-state HHG can transfer the wavefront of the driving beam to the ultraviolet (UV) wavelength without distortion based on the insensitive phase-matching condition compared to gaseous HHG, and thus has attracted attention in beam-control-based precision optical measurement. However, material damage of the solid medium has limited the optical output of the generated ultraviolet harmonic waves. In this study, we demonstrate the optical power enhancement of solid-state HHG via multiple driving beams. The wavefront of the driving beam is modulated using a spatial light modulator (SLM) to split the beam into multiple beams, which is then incident on a solid medium. Each multi-beam is focused into a nonlinear medium to produce UV harmonics, which are combined at the back of the medium. The summed optical power of multiple UV harmonics improved in proportion to the number of multiple driving beams. This technology overcomes the output limitation of HHG and can be used as a high-power coherent ultraviolet light source for precision metrology applications

SPECTRAL ANALYSIS OF THE NEUMANN--POINCARE\' OPERATOR FOR THIN DOUBLY CONNECTED DOMAINS

We analyze the spectrum of the Neumann--Poincare'\ (NP) operator for a doubly connected domain lying between two level curves defined by a conformal mapping, where the inner boundary of the domain is of general shape. The analysis relies on an infinite-matrix representation of the NP operator involving the Grunsky coefficients of the conformal mapping and an application of the Gershgorin circle theorem. As the thickness of the domain shrinks to zero, the spectrum of the doubly connected domain approaches the interval [ - 1/2, 1/2] in the Hausdorff distance and the density of eigenvalues approaches that of a thin circular annulus.

Spontaneous formation and oxidation of metal-phenolic networks by zinc oxide: Expanding photoprotection and suppressing photoreactivity

Wide-bandgap metal-oxide nanoparticles are promising candidates for broad-spectrum sunscreens, yet their application is limited by photocatalytic activity and insufficient high-energy visible (HEV) light absorption. Here, we report a simple, scalable one-pot strategy for the spontaneous formation of a metal-phenolic network (MPN) on zinc oxide (ZnO) nanoparticles (ZnO/MPN NPs), utilizing intrinsic Zn2+ ion release from ZnO to initiate tannic acid (TA) complexation and in situ oxidation. This process forms a nanoscale MPN layer on the ZnO surface, while ZnO-mediated TA oxidation and dimerization (inspired by natural fruit browning) enhance electron delocalization, extending light absorption to the HEV region. The resulting browned MPN-coated ZnO nanoparticles (ZnO/MPN-B NPs) exhibited approximately a threefold enhancement in both sun protection factor (SPF) and UVA protection factor (UVAPF) compared to uncoated ZnO NPs. Additionally, the MPN layer effectively suppresses over 99 % of photogenerated reactive oxygen species (ROS) through its intrinsic ROS scavenging properties, significantly improving photostability. Cell viability assays further demonstrate that the MPN layer mitigates photoinduced cytotoxicity, supporting the safety and biocompatibility of these hybrids. This study suggests ZnO/MPN-B NPs as eco-friendly, high-performance candidates for next-generation sunscreen formulations, offering a scalable, efficient route to address the dual challenges of photoprotection and safety in inorganic sunscreen agents.

Enhancing flexibility and reliability in wearable OLEDs through silbione-blended hybrimer-based encapsulation

Numerous studies have aimed to improve the mechanical flexibility of thin-film encapsulation, a key obstacle in commercializing wearable organic light-emitting diodes (OLEDs). This study develops a silbione-blended organic/inorganic hybrid epoxy polymer (hybrimer) with high toughness as an organic barrier to enhance the flexibility of organic-inorganic multi-barriers. The optimal silbione-blended hybrimer (SBH) films exhibit superior mechanical properties, including increased elongation and tensile strength, compared to the hybrimer. The 3.5-dyad SBH-based encapsulation achieves a water vapor transmission rate of 7.83 x 10-6 g/m2/day and 9.45 x 10-5 g/m2/day before and after bending at a strain of 2%, respectively. In addition, the SBH barrier effectively protects the inorganic barrier by forming a robust aluminate phase at the interface between the inorganic and organic barrier, even under harsh conditions of 85 degrees C/85% relative humidity, demonstrating its potential for wearable applications. As a result, SBH-based encapsulations offer mechanical and environmental stability, making them ideal for wearable OLEDs.