Search Results (558)

This work addresses the challenge of realizing broadband, low-loss fiber-to-waveguide coupling in the short-wavelength near-infrared range (700–1050 nm), where the required fine structural dimensions and taper tips approach or even exceed current fabrication limits, resulting in tight fabrication tolerances and degraded coupling efficiency. We propose a broadband trilayer adiabatic edge coupler on a thin-film lithium tantalate platform that requires only two standard lithography and etching steps. The design integrates a crossed bilayer taper and a dual-core mode converter to achieve adiabatic mode transformation from a ridge to a thin strip waveguide, ensuring excellent fabrication tolerance and process simplicity. Simulations predict a minimum coupling loss of 0.57 dB at 850 nm, which includes the transmission through the complete edge-coupler structure, along with a 0.5-dB bandwidth exceeding 140 nm. The proposed structure provides a broadband, low-loss, and fabrication-tolerant interface for short-wavelength photonic systems such as quantum photonics, biosensing, and visible-light communications. Full article

This paper systematically reviews the key spectral information extraction methods in Brillouin microscopy, aiming to address the core challenge of accurately extracting material mechanical parameters from raw spectra. Based on technical principles, the methods are categorized into three types for elaboration: Spontaneous Brillouin Scattering (SpBS) is characterized by low signal-to-noise ratio (SNR) and strong background interference, and its processing relies on high-precision spectrometers and complex preprocessing procedures to mitigate noise and background effects; Stimulated Brillouin Scattering (SBS) operates on the mechanism of optical gain/loss, which achieves significantly improved data SNR and thereby enables more robust and accurate Lorentzian fitting for spectral analysis; Impulsive Stimulated Brillouin Scattering (ISBS) retrieves the frequency spectrum by inverting time-domain oscillating signals, and the core of its processing lies in super-resolution algorithms such as Fast Fourier Transform (FFT) and the Matrix Pencil Method, which are tailored to match its high-speed data acquisition capability. The paper further compares the advantages and disadvantages of various methods, outlines future development trends of intelligent processing technologies such as deep learning and multi-modal data fusion, and provides a clear guide for selecting the optimal data processing strategy in different application scenarios. Full article

Achieving coordinated development among social equity (SE), economic development (ED), and ecosystem health (EH) is central to resolving the sustainability trilemma. This study investigated the spatiotemporal evolution and driving forces of SE–ED–EH coordinated development in Hebei Province, China, from 2005 to 2020 using a 1 km grid dataset. A comprehensive analytical framework integrating the Coupling Coordination Degree (CCD) model, fuzzy C-means clustering, and interpretable machine learning (XGBoost–SHAP) was developed to quantify changes in coupling and coordination (CC) levels and reveal nonlinear threshold effects. Results show pronounced spatial heterogeneity: urban cores exhibit “high coupling degree (C)–high coordination degree (T)–high CC level,” southeastern plains show “high C–low T–medium CC level,” and northwestern mountainous areas present “low C–medium/high T–low CC level.” Six dominant temporal evolution types were identified. XGBoost–SHAP reveals that nighttime lights (NL), population density (POP), and elevation (DEM) are the dominant drivers, with clear threshold ranges (NL 500–1500 nits; POP threshold near 40 persons km⁻² with diminishing returns beyond 100 persons km⁻²; DEM constraint at 1000–1250 m) and strong interaction effects. The results suggest that Hebei is entering a quality- and structure-oriented rebalancing stage, where threshold-based management is critical for avoiding marginal loss of coordinated development. This study demonstrates that interpretable machine learning provides a transferable paradigm for threshold calibration, spatial zoning, and policy optimization aligned with SDGs, particularly applicable for resource-constrained regions undergoing late industrial transition. Full article

Early spring frost is a major meteorological hazard during the Apple Flowering period. To improve frost event prediction, this study proposes a hybrid 1D-CNN-BiLSTM-Attention model, with its core novelty lying in the integrated dual attention mechanism (Self-attention and Cross-variable Attention) and hybrid architecture. The 1D-CNN extracts extreme points and mutation features from meteorological factors, while BiLSTM captures long-term patterns such as cold wave accumulation. The dual attention mechanisms dynamically weight key frost precursors (low temperature, high humidity, calm wind), aiming to enhance the model’s focus on critical information. Using 1997–2016 data from Luochuan (four variables: Ground Surface Temperature (GST), Air Temperature (TEM), Wind Speed (WS), Relative Humidity (RH)), a segmented interpolation method increased temporal resolution to 4 h, and an adaptive Savitzky–Golay Filter reduced noise. For frost classification, Recall, Precision, and F1-score were higher than those of baseline models, and the model showed good agreement with the actual frost events in Luochuan on 6, 9, and 10 April 2013. The 4 h lead time could provide growers with timely guidance to take mitigation measures, alleviating potential losses. This research may offer modest technical references for frost prediction during the Apple Flowering period in similar regions. Full article

To address the problem of wellbore instability in the development of deep coalbed methane reservoirs in Daniudi gas field, this study takes the coal seam cores from Member 1 of the Taiyuan Formation at a depth of approximately 2880 m as the research object. Through CT scanning, scanning electron microscopy (SEM), mineralogical analysis, laboratory mechanical tests, and drilling fluid interaction experiments, the study investigated the coal seam fabric characteristics, mechanical response, anisotropy, and the interaction between drilling fluids and the formation. Based on the double-weak-plane criterion, a wellbore collapse prediction model was established, and instability risk assessment under multi-factor coupling conditions was carried out. Experimental and computational results indicate that the deep coal seam exhibits significant heterogeneity in fabric structure, the clay minerals show low swelling potential, and the bright coal and semi-bright coal are prone to instability due to their dual pore structures. The average uniaxial compressive strength (UCS) of the coal cores is 16.3 MPa, which is weaker than that of the roof, floor, and dirt band. The coal also exhibits anisotropy, with the lowest strength occurring when the loading direction forms an angle of 30–60° with the weak planes, corresponding to 67.5% of the intrinsic compressive strength. Immersion in drilling fluid causes the coal rock strength to decay in a pattern of “rapid decline in the initial stage—gradual decrease in the middle stage—stabilization in the later stage.” After 24 h, the strength is only 55–65% of that in the dry state. Due to its excellent plugging and inhibition performance, HX-Coalmud drilling fluid delays strength loss more effectively than the strongly inhibitive composite salt drilling fluid. The wellbore instability risk assessment indicates that as the drilling time is extended, the collapse pressure rises significantly. After 7 and 20 days of contact between the wellbore and drilling fluid, the equivalent collapse pressure density increases by 0.08–0.15 g/cm³ and 0.13–0.20 g/cm³, respectively. Therefore, homogeneous isotropic models tend to underestimate the risk of wellbore collapse. The findings can provide theoretical and technical support for the safe drilling of deep coalbed methane in Daniudi gas field. Full article

Against the backdrop of the carbon peaking and neutrality (“dual-carbon”) goals and evolving new-type power system dispatch, the share of pumped-storage hydropower (PSH) in power systems continues to increase, imposing stricter requirements on units for higher cycling frequency, greater operational flexibility, and rapid, stable startup and shutdown. Focusing on the entire hot-standby-to-generation transition of a PSH plant, a full-flow-path three-dimensional transient numerical model encompassing kilometer-scale headrace/tailrace systems, meter-scale runner and casing passages, and millimeter-scale inter-component clearances is developed. Three-dimensional unsteady computational fluid dynamics are determined, while the surge tank free surface and gaseous phase are captured using a volume-of-fluid (VOF) two-phase formula. Grid independence is demonstrated, and time-resolved validation is performed against the experimental model–test operating data. Internal instability structures are diagnosed via pressure fluctuation spectral analysis and characteristic mode identification, complemented by entropy production analysis to quantify dissipative losses. The results indicate that hydraulic instabilities concentrate in the acceleration phase at small guide vane openings, where misalignment between inflow incidence and blade setting induces separation and vortical structures. Concurrently, an intensified adverse pressure gradient in the draft tube generates an axial recirculation core and a vortex rope, driving upstream propagation of low-frequency pressure pulsations. These findings deepen our mechanistic understanding of hydraulic transients during the hot-standby-to-generation transition of PSH units and provide a theoretical basis for improving transitional stability and optimizing control strategies. Full article

Urban rivers are critical ecological and cultural assets facing accelerating biodiversity loss. This study examines the integrated redevelopment of the Jadro River spring in Solin, Croatia, where a protected ichthyological reserve intersects layered heritage and urban edges to enhance conservation and public value. Using a single-case study design that combines archival project documentation, participant observation by the architect–authors, and a post-occupancy review three years after completion, the analysis synthesizes ecological, social, and design evidence across planning, delivery, and operation phases. The project delivered phased visitor and interpretation centers, accessible paths and bridges, habitat-compatible materials, and formalized access management that relocated parking from riverbanks, reduced episodic pollution sources, and prioritized inclusive, low-impact use. Governance and programming established a municipal management plan, curriculum-ready interpretation, and carrying capacity monitoring, transforming an underused picnic area into an educational, recreational, and conservation-oriented public landscape while safeguarding sensitive habitats. A transferable design protocol emerged, aligning blue green infrastructure, heritage conservation, adaptive reuse, and social–ecological system (SES)-informed placemaking to protect the endemic soft-mouth trout and strengthen a sense of place and community stewardship. The case supports SES-based riverpark renewal in which conservative interventions within protected cores are coupled with consolidated services on resilient ground, offering a replicable framework for ecologically constrained urban headwaters. Full article

Background: Standard single-cell RNA sequencing (scRNA-seq) analysis workflows face significant limitations, particularly the rigidity of clustering-dependent methods that can obscure subtle cellular heterogeneity and the potential loss of biologically meaningful cells during stringent quality control (QC) filtering. This study aims to develop scSelector (v1.0), an interactive software toolkit designed to empower researchers to flexibly select and analyze cell populations directly from low-dimensional embeddings, guided by their expert biological knowledge. Methods: scSelector was developed using Python, relying on core dependencies such as Scanpy (v1.9.0), Matplotlib (v3.4.0), and NumPy (v1.20.0). It integrates an intuitive lasso selection tool with backend analytical modules for differential expression and functional enrichment analysis. Furthermore, it incorporates Large Language Model (LLM) assistance via API integration (DeepSeek/Gemini) to provide automated, contextually informed cell-type and state prediction reports. Results: Validation across multiple public datasets demonstrated that scSelector effectively resolves functional heterogeneity within broader cell types, such as identifying distinct alpha-cell subpopulations with unique remodeling capabilities in pancreatic tissue. It successfully characterized rare populations, including platelets in PBMCs and extremely low-abundance endothelial cells in liver tissue (as few as 53 cells). Additionally, scSelector revealed that cells discarded by standard QC can represent biologically functional subpopulations, and it accurately dissected the states of outlier cells, such as proliferative NK cells. Conclusions: scSelector provides a flexible, researcher-centric platform that moves beyond the constraints of automated pipelines. By combining interactive selection with AI-assisted interpretation, it enhances the precision of scRNA-seq analysis and facilitates the discovery of novel cell types and complex cellular behaviors. Full article

Inter-basin water transfer projects are core infrastructure for achieving sustainable water resource allocation and addressing regional water scarcity, and pumping station units, as their critical energy-consuming and operation-controlling components, are vital to the projects’ sustainable performance. With the growing complexity and scale of these projects, pumping station units have become more intricate, leading to a gradual rise in failure rates. However, existing fault diagnosis methods are relatively backward, failing to promptly detect potential faults—this not only threatens operational safety but also undermines sustainable development goals: equipment failures cause excessive energy consumption (violating energy efficiency requirements for sustainability), unplanned downtime disrupts stable water supply (impairing reliable water resource access), and even leads to water waste or environmental risks. To address this sustainability-oriented challenge, this paper focuses on the fault characteristics of pumping station units and proposes a comprehensive and accurate fault diagnosis model, aiming to enhance the sustainability of water transfer projects through technical optimization. The model utilizes advanced algorithms and data processing technologies to accurately identify fault types, thereby laying a technical foundation for the low-energy, reliable, and sustainable operation of pumping stations. Firstly, continuous wavelet transform (CWT) converts one-dimensional time-domain signals into two-dimensional time-frequency graphs, visually displaying dynamic signal characteristics to capture early fault features that may cause energy waste. Next, the multi-head attention mechanism (MHA) segments the time-frequency graphs and correlates feature-location information via independent self-attention layers, accurately capturing the temporal correlation of fault evolution—this enables early fault warning to avoid prolonged inefficient operation and energy loss. Finally, the improved convolutional neural network (CNN) layer integrates feature information and temporal correlation, outputting predefined fault probabilities for accurate fault determination. Experimental results show the model effectively solves the difficulty of feature extraction in pumping station fault diagnosis, considers fault evolution timeliness, and significantly improves prediction accuracy and anti-noise performance. Comparative experiments with three existing methods verify its superiority. Critically, this model strengthens sustainability in three key ways: (1) early fault detection reduces unplanned downtime, ensuring stable water supply (a core sustainable water resource goal); (2) accurate fault localization cuts unnecessary maintenance energy consumption, aligning with energy-saving requirements; (3) reduced equipment failure risks minimize water waste and environmental impacts. Thus, it not only provides a new method for pumping station fault diagnosis but also offers technical support for the sustainable operation of water conservancy infrastructure, contributing to global sustainable development goals (SDGs) related to water and energy. Full article

Background/Objectives: Skin cancer is one of the most common diseases in the world, whose early and accurate detection can have a survival rate more than 90% while the chance of mortality is almost 80% in case of late diagnostics. Methods: A modified EfficientNet-B0 is developed based on mobile inverted bottleneck convolution with squeeze and excitation approach. The 3 × 3 convolutional layer is used to capture low-level visual features while the core features are extracted using a sequence of Mobile Inverted Bottleneck Convolution blocks having both 3 × 3 and 5 × 5 kernels. They not only balance fine-grained extraction with broader contextual representation but also increase the network’s learning capacity while maintaining computational cost. The proposed architecture hyperparameters and extracted feature vectors of standard benchmark datasets (HAM10000, ISIC 2019 and MSLD v2.0) of dermoscopic images are optimized with the quantum-behaved particle swarm optimization algorithm (QBPSO). The merit function is formulated by the training loss given in the form of standard classification cross-entropy with label smoothing, mean fitness value (mf^val), average accuracy (mAcc), mean computational time (mCT) and other standard performance indicators. Results: Comprehensive scenario-based simulations were performed using the proposed framework on a publicly available dataset and found an mAcc of 99.62% and 92.5%, mfval of 2.912 × 10⁻¹⁰ and 1.7921 × 10⁻⁸, mCT of 501.431 s and 752.421 s for HAM10000 and ISIC2019 datasets, respectively. The results are compared with state of the art, pre-trained existing models like EfficentNet-B4, RegNetY-320, ResNetXt-101, EfficentNetV2-M, VGG-16, Deep Lab V3 as well as reported techniques based on Mask RCCN, Deep Belief Net, Ensemble CNN, SCDNet and FixMatch-LS techniques having varying accuracies from 85% to 94.8%. The reliability of the proposed architecture and stability of QBPSO is examined through Monte Carlo simulation of 100 independent runs and their statistical soundings. Conclusions: The proposed framework reduces diagnostic errors and assists dermatologists in clinical decisions for an improved patient outcomes despite the challenges like data imbalance and interpretability. Full article

This study presents an optimization-driven design of a wireless communications network to continuously transmit environmental variables—temperature, humidity, weight, and water usage—in poultry farms. The reference site is a four-shed facility in Quito, Ecuador (each shed $120\mathrm{m}\times 12\mathrm{m}$) with a data center located $200\mathrm{m}$ from the sheds. Starting from a calibrated log-distance path-loss model, coverage is declared when the received power exceeds the receiver sensitivity of the selected technology. Gateway placement is cast as a mixed-integer optimization that minimizes deployment cost while meeting target coverage and per-gateway capacity; a capacity-aware greedy heuristic provides a robust fallback when exact solvers stall or instances become too large for interactive use. Sensing instruments are Tekon devices using the Tinymesh protocol (IEEE 802.15.4g), selected for low-power operation and suitability for elongated farm layouts. Model parameters and technology presets inform a pre-optimization sizing step—based on range and coverage probability—that seeds candidate gateway locations. The pipeline integrates MATLAB R2024b and LpSolve 5.5.2.0 for the optimization core, Radio Mobile for network-coverage simulations, and Wireshark for on-air packet analysis and verification. On the four-shed case, the algorithm identifies the number and positions of gateways that maximize coverage probability within capacity limits, reducing infrastructure while enabling continuous monitoring. The final layout derived from simulation was implemented onsite, and end-to-end tests confirmed correct operation and data delivery to the farm’s data center. By combining technology-aware modeling, optimization, and field validation, the work provides a practical blueprint to right-size wireless infrastructure for agricultural monitoring. Quantitatively, the optimization couples coverage with capacity and scales with the number of endpoints M and candidate sites N (binaries $M+N+MN$). On the four-shed case, the planner serves 72 environmental endpoints and 41 physical-variable endpoints while keeping the gateway count fixed and reducing the required link ports from 16 to 4 and from 16 to 6, respectively, corresponding to optimization gains of up to 82% and 70% versus dense baseline plans. Definitions and a measurement plan for packet delivery ratio (PDR), one-way latency, throughput, and energy per delivered sample are included; detailed long-term numerical results for these metrics are left for future work, since the present implementation was validated through short-term acceptance tests. Full article

The management of age-related macular degeneration (AMD) presents a significant challenge attributable to high disease heterogeneity. Patient realization of symptoms is poor and it is urgent to treat before permanent anatomic damage results in vision loss. This is true for the initial conversion from non-exudative intermediate AMD (iAMD) to exudative AMD (nAMD), and for the recurrence of nAMD undergoing treatment. Starting from the essential requirements that any practical solution needs to fulfill, we will reflect on how persistent navigation towards innovative solutions during a 25-year journey yielded significant advances towards improvements in personalized care. An early insight was that the acute nature of AMD progression requires frequent monitoring and therefore diagnostic testing should be performed at the patient’s home. Four key requirements were identified: (1) A tele-connected home device with acceptable diagnostic performance and a supportive patient user interface, both hardware and software. (2) Automated analytics capabilities that can process large volumes of data. (3) Efficient remote patient engagement and support through a digital healthcare provider. (4) A low-cost medical system that enables digital healthcare delivery through appropriate compensation for both the monitoring provider and the prescribing physician services. We reviewed the published literature accompanying first the development of Preferential Hyperacuity Perimetry (PHP) for monitoring iAMD, followed by Spectral Domain Optical Coherence Tomography (SD-OCT) for monitoring nAMD. Emphasis was given to the review of the validation of the core technologies, the regulatory process, and real-world studies, and how they led to the release of commercial services that are covered by Medicare in the USA. We concluded that while during the first quarter of the 21st century, the two main pillars of management of AMD were anti-VEGF intravitreal injections and in-office OCT, the addition of home-monitoring-based digital health services can become the third pillar. Full article

A major computational bottleneck in classifying large-scale hyperspectral images (HSI) is the mandatory data decompression prior to processing. Compressed-domain computing offers a solution by enabling deep learning on partially compressed data. However, existing compressed-domain methods are predominantly tailored for the Discrete Cosine Transform (DCT) used in natural images, while HSIs are typically compressed using the Discrete Wavelet Transform (DWT). The fundamental structural mismatch between the block-based DCT and the hierarchical DWT sub-bands presents two core challenges: how to extract features from multiple wavelet sub-bands, and how to fuse these features effectively? To address these issues, we propose a novel framework that extracts and fuses features from different DWT sub-bands directly. We design a multi-branch feature extractor with sub-band feature alignment loss that processes functionally different sub-bands in parallel, preserving the independence of each frequency feature. We then employ a sub-band cross-attention mechanism that inverts the typical attention paradigm by using the sparse, high-frequency detail sub-bands as queries to adaptively select and enhance salient features from the dense, information-rich low-frequency sub-bands. This enables a targeted fusion of global context and fine-grained structural information without data reconstruction. Experiments on three benchmark datasets demonstrate that our method achieves classification accuracy comparable to state-of-the-art spatial-domain approaches while eliminating at least 56% of the decompression overhead. Full article

High brittleness severely restricts the practical use of nanocrystalline cores in low-frequency power electronics. To enhance mechanical strength and facilitate cutting and transportation, curing techniques are commonly employed, yet their influence on the magnetic properties of the cores remains unclear. In this work, three curing techniques, namely, fluid-phase adhesive curing, gel-phase adhesive curing, and vacuum-evacuated gel-phase adhesive curing (VGAC), are applied to prepare cores with varying curing degrees. The magnetic properties of them are quantitatively compared with those of uncured cores within the range of 50–550 Hz. Results show that all three curing techniques demonstrably reduce the eddy current losses of the cores. Specifically, the VGAC-based core exhibits a 50% reduction in eddy current loss compared to the uncured core at 550 Hz and 0.8 T. Meanwhile, high saturation flux density is retained in all cured samples. However, curing also reduces permeability and raises coercivity. Furthermore, cured cores demonstrate increased hysteresis and residual losses, leading to higher total losses. The relationship between core losses and temperature rise is also investigated to provide important guidance for the safe operation of cured cores. In addition, microscopic images under 200× magnification are presented to elucidate the mechanisms underlying these observed influences. Full article

Reactive iron is a key driver of organic carbon preservation in marine sediments, but its participation in organic carbon remineralization complicates efforts to mechanistically constrain its role in preservation. To address this, we investigated the dual role of iron in the Mississippi River-influenced shelf sediment during low discharge (August 2016) and high discharge (May 2017). Duplicate sediment cores (30 cm depth) were collected from two stations; one core served as a natural reference, while the other was used for an incubation experiment. In the natural cores, reactive iron concentrations in the upper 9 cm were lower in August 2016 than in May 2017, whereas iron-bound organic carbon exhibited the opposite temporal pattern. Post incubation, approximately 10% of iron-bound organic carbon was lost at the offshore stations compared to a substantially greater loss (~59%) at the near-shore station. These results suggest that offshore regions may sustain more efficient organic carbon preservation via reactive iron, whereas the mechanism is considerably less effective in near-shore settings. Such spatial heterogeneity introduces significant uncertainty into current assessments of iron-mediated long-term organic carbon preservation on a global scale and underscores the need for more comprehensive investigations of iron–organic carbon interactions in continental shelve sediments. Full article