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Study on the driving mechanism of cultivated land change in the urban–rural fringe with Bayesian network modeling
Wideband tilted beam end-fire antenna using double semi-circular rings
Abstract This article presents a novel wideband (WB) tilted-beam end-fire planar antenna for microwave and millimeter-wave (mmWave) applications. The antenna comprises of a microstrip fed double semi-circular rings over a curvilinear slotted ground plane. It is impedance matched over a broad frequency range from 11.5 to 62.5 GHz, covering the 5G New Radio (NR) mmWave bands n257, n258, n260, n261, and partly covering the unlicensed 60 GHz band. Across this entire band, the antenna exhibits a return loss better than 12 dB and a gain exceeding 6.5 dBi, with a peak gain of 11.6 dBi at 40 GHz. The overall electrical size of the antenna is 1.28 $$\times$$ 1 $$\times$$ 0.08 $$\lambda _0^3$$ , where $$\lambda _{0}$$ corresponds to the free-space wavelength at 32 GHz. Within the 24–40 GHz frequency range, corresponding to a 50% fractional bandwidth and covering the four 5G NR bands, end-fire radiation is achieved with a tilted beam angle of $$\textrm{65}^{\circ }$$ ± $$\textrm{10}^{\circ }$$ . A prototype of the antenna is fabricated and experimentally characterized. The measured results show good agreement with full-wave simulations, validating the proposed design. Owing to its compact planar geometry, wide bandwidth, and high gain, the antenna is a strong candidate for future high-data-rate wireless communication systems.
Enhancing the resolution of microseismicity through dense array monitoring in complex extensional settings
Abstract Characterizing geometry and mechanics of structures hosting moderate-to-large earthquakes is essential for seismic hazard assessment, yet remains challenging in extensional environments, where fault systems include multiple segments and bends. In this study, we demonstrate how a short-term array deployment can provide critical insights into seismicity patterns and fault geometry in Southern Apennines, Italy.We integrated data recorded by arrays during a one-year experiment with machine learning methodologies, producing a seismic catalog that enhances the manual catalog for the same period by nearly an order of magnitude, lowering completeness magnitude by one unit. Approximately 65% of the detected events can be accurately relocated, with median uncertainties of ~ 100 m, comparable to those of long-term catalogs. Our results reveal consistent seismicity properties down to decametric earthquake size, with hypocenters and b-value mirroring those from the previous decade. We distinguish a shallow, diffuse seismicity, likely influenced by hydrological loading, from deeper clusters, mostly rupturing patches a few-hundred meters across. Beyond asperity-scale complexity, seismicity follows the boundaries of tomographic anomalies, delineating a 50–60 km-long curving fault, featuring a right-stepping jog several kilometers wide. Dynamic simulations suggest that ruptures nucleating on this fault could propagate through these complexities, potentially generating earthquakes up to magnitude 7.0.
A multi-agent reinforcement learning scheduling algorithm integrating state graph and task graph structural modeling for ride-sharing dispatching
Benchmarking econometric, decomposable additive, and neural network methods for food inflation prediction featuring policy insights
Layer-dependent Raman polarization anisotropy in MoS₂ films grown by 200 mm-scale atomic layer deposition
Combined effects of physical activity and diabetes medications on glycemic control: a real-world data study
Exploration of potential biomarkers in salivary duct carcinoma based on bioinformatics analysis
Uncertainty aware hybrid learning framework for fast and safe charging of lithium-ion batteries using multi-fidelity observers
Evaluating human–machine collaboration through a comparative analysis of experts, machine learning, and hybrid approaches in real estate valuation
Abstract Accurate prediction of real estate prices remains a major challenge due to dynamic market conditions and the limitations of traditional valuation methods. Empirical studies that directly compare human experts, machine learning (ML) models, and hybrid approaches are rare. This study examines the predictive accuracy and efficiency of an XGBoost-based ML model, real estate experts, and a hybrid human–machine approach. A model was trained using 21,736 real estate transactions from Vienna (2018–2022). We then conducted an experimental procedure with 13 experts who evaluated newly built apartments sold in 2023 under three conditions: limited information, state-of-the-art expert methods, and collaboration between experts and ML model. The results show that the ML model achieves accuracy comparable to that of experts while significantly reducing the time required for the task. Within the hybrid approach, experts were able to achieve the highest accuracy in comparison to other methods. These results underscore the potential of human-ML collaboration.
Comprehensive energy audit and conservation strategy for public buildings: enhancing energy efficiency and grid sustainability
Sustainable α-AO@CS composite for effective humic acid elimination from water
CDC25C downregulation suppresses HCC growth via mitochondrial stress-induced autophagy and apoptosis
γ-Radiation induces region-specific subcellular alterations of amyotrophic lateral sclerosis and frontotemporal dementia markers in swine brain
A repurposing Dapagliflozin via polymeric nanogels for colorectal cancer therapy
The construction and optimization of resilient community living sphere driven by digital twins
The Athabasca River regulates methylmercury burdens of waterbirds breeding downstream
Abstract This study characterized the transport pathways and sources of mercury in downstream food webs of the Athabasca River Basin in northern Alberta, Canada. Flowing through a large boreal watershed in the Athabasca Oil Sands Region, the river drains into the ecologically-sensitive Peace-Athabasca Delta and western Lake Athabasca. Mercury stable isotope measurements on abiotic and biotic matrices were combined with spatial and temporal sampling to evaluate the importance of the Athabasca River as a conduit for mercury. Mixing model estimates derived from mercury isotopes indicated the Athabasca River was the source of 62–94% of the bioaccumulated mercury in otter, fish, and tern eggs collected from the delta or lake. A time series from 2009 to 2022 showed mercury loads from the Athabasca River enhanced bioaccumulation in western Lake Athabasca with the doubling of total mercury (THg) concentration in eggs of breeding terns following high flow years compared to low flow years. Mercury isotope data from potential abiotic sources (Athabasca River and Lake sediment, leaf litter, soil, air, rain) suggested a predominately terrestrial origin of mercury in fish of the Athabasca River. The similarity of terrestrial mercury isotope signatures with oil sands-related matrices (natural bitumen seeps, industry samples) precluded an estimation of contributions from oil sands operations to mercury bioaccumulation. Catchment processes or disturbances that increase mercury loads in the Athabasca River will increase mercury concentrations of aquatic biota in downstream receiving waters.