Changzhi University

Objective: Given the global trend of ecological protection and sustainable development, Global Geoparks have become an essential platform for resource conservation and regional growth. Changzhi City in Shanxi Province, China, is actively applying for Global Geopark status, relying on its rich geoheritage sites, cultural history, and natural landscapes. This paper presents a systematic evaluation of the city’s geodiversity and relic value, analyzes the feasibility of establishing a Global Geopark in Changzhi City, and provides scientific support for Changzhi City’s Global Geopark application. Methods: Geodiversity data were collected by region using a 1:25,000 grid for sampling. Four methods were adopted for evaluation, namely, the Shannon diversity index, Simpson diversity index, entropy weight method (EWM), and Pielou evenness index. Upon comprehensive comparison of the four approaches, the most suitable approach was selected to produce the final results. For the value evaluation of the geoheritage, a combination of the analytic hierarchy process and the entropy weight method was employed. Results: (1) According to the results of all four methods, the geodiversity of Changzhi City is higher in the eastern and western regions and lower in the central area. (2) The geoheritage sites are mainly distributed in the eastern part of the city and have relatively high relic value. (3) Changzhi City contains abundant natural reserves and cultural resources, meeting the fundamental requirements for Global Geopark construction. Specifically, 38 townships across eight counties were identified as potential geopark areas, encompassing 54 geoheritage sites, 76 provincial-level or higher cultural-relic protection sites, and 15 provincial-level or higher natural protected areas, with a total area of 4458.51 km2. Conclusions: Our results suggest that the Shannon diversity index is an effective tool for evaluating geodiversity in Changzhi City. Based on the region’s geological and natural conditions, the delineated geopark area is feasible. In summary, our findings provide essential references for the protection and sustainable development of geoheritage sites, geodiversity, and geoparks and offer strong theoretical and data support for Changzhi City’s Global Geopark application.

Converting agricultural solid waste into porous biochar for HCHO adsorption is considered as a “two birds with one stone” strategy, which can achieve the environmental goal of “treating waste with waste”. Unfortunately, the HCHO adsorption performance of pristine biochar is generally unsatisfactory, which is derived from its poor surface activity and insufficient number of pores. In this study, a series of nitrogen-doped porous biochars with adjustable N-containing groups and porosity were synthesized by one-step pyrolysis of melamine and waste jujube pit in different mass ratios (NBC-x, x represented the mass ratio of melamine to waste jujube pit, x = 4–12) for HCHO adsorption. The HCHO adsorption tests indicated that the insertion of nitrogen-containing species improved the adsorption capacity of pristine biochar (BC). However, after the insertion of excessive nitrogen-containing species, the porosity of the samples significantly decreased due to the blockage of pores, which could be disadvantageous for HCHO adsorption. DFT calculation results showed that N doping (especially pyrrolic-N) significantly increased the maxima of absolute ESP values of the carbonaceous models and consequently enhanced the affinity between polar HCHO and carbonaceous models (varied from −20.65 kJ/mol to −33.26 kJ/mol). Thus, the NBC-8 possessing both substantial nitrogen content (19.81 wt. %) and developed porosity (specific surface area of 223 m2/g) exhibited the highest HCHO uptake of 6.30 mg/g. This was approximately 6.4 times larger than that of BC. This work not only deepens the understanding of the HCHO adsorption mechanism at molecular scale, but also concurrently offers a facile and eco-friendly route of N-doped porous biochar preparation, an efficient technology with high-value utilization of waste biomass resources, and a sustainable method of pollution remediation.

A binder- and current-collector-free freestanding SPANI/MWCNT electrode with an ultrahigh sulfur loading of 13 mg cm −2 featuring an internal three-dimensional conductive architecture was fabricated for the first time in lithium–sulfur batteries.

Pixel-level annotation of lung cavities (LCs) in computed tomography (CT) images is challenging due to their morphological diversity and complexity. Weakly supervised semantic segmentation (WSSS) methods, which utilize sparse annotations (e.g., image-level labels), offer a promising solution. However, existing WSSS approaches often generate coarse pseudo-labels and lack sufficient spatial supervision, resulting in under- or over-segmentation of irregular lesions. To address these limitations, we introduce several key innovations. First, we propose a novel Graph-based Affinity Network (GA-Net) that, unlike conventional methods relying on low-level pixel features, models long-range contextual relationships and structural dependencies using a superpixel graph and learned edge inference kernel, enabling structure-aware pseudo-label refinement for complex lesion morphology. Second, we introduce region-wise affinity propagation, which refines segmentation by propagating activations within semantically coherent 3D regions, offering more precise control over under-/over-segmentation compared to global affinity methods. Additionally, we incorporate Exponential Moving Average (EMA) ensembling for training stability and a scribble-based segmentation module that utilizes pseudo-label contours to provide direct boundary supervision. Extensive experiments on three benchmark datasets demonstrate that our method outperforms existing state-of-the-art medical WSSS techniques, achieving precise and reliable segmentation of complex LCs in CT scans.

The abuse of high concentrations of forchlorfenuron can lead to malformed fruits and pose risks to human health, highlighting the need for effective detection methods. In this study, a flexible multi-frequency resonant terahertz metamaterial sensor is designed and validated both theoretically and experimentally. The sensor exhibits magnetic dipole resonances at 0.53 THz and 2.30 THz, and the physical enhancement mechanism of the magnetic dipole resonances is analyzed through the distributions of electric field, magnetic field, and surface current. Simulation results reveal that the metamaterial sensor achieves a maximum detection sensitivity of 440 GHz/RIU, with multi-frequency resonance maintained across a polarization angle range of 0°– 90°. Experimental detection reveals that the designed multi-frequency resonant metamaterial sensor successfully detects different concentrations of forchlorfenuron, with a minimum detectable concentration of 0.01 mg/L. These theoretical and experimental results confirm the feasibility of a multi-frequency resonant terahertz metamaterial sensor for forchlorfenuron detection.

AlGaN and AlN, III-nitride semiconductors distinguished by wide bandgaps, strong polarization, and exceptional chemical stability, hold substantial potential for applications in high power and high frequency electronics, high temperature sensors, and optoelectronic devices. These materials thus serve as a cornerstone for next generation technologies. Metal organic chemical vapor deposition (MOCVD) is the preeminent technique for synthesizing high quality AlGaN and AlN films, with vertical showerhead reactors being widely employed due to their efficiency and stability. This study focuses on optimizing the arrangement of heating wires within an MOCVD reaction chamber. Using a quadratic regression orthogonal rotation combination design, we investigated the effects of heating wire width and spacing on temperature distribution within the reaction cavity. By univariate analysis and quadratic regression modeling, we identified the optimal combination of heating wire width and spacing to achieve uniform and stable temperature distribution. Our results show that the heating wire gap has the most significant impact on the average temperature of the mixing chamber, followed by the heating wire width and their interaction effects. The optimized heating wire arrangement markedly enhances the average temperature of the reaction chamber, thereby providing an improved thermal environment for the growth of high quality AlN films. This research offers both theoretical insights and practical guidance for the design and optimization of MOCVD reactors.

China’s crop production evolution has significantly reshaped spatial and temporal patterns of agricultural carbon emissions. However, a comprehensive understanding of the underlying driving mechanism of agricultural carbon emissions remains limited, particularly across different regions and crop types. This study integrates micro and macro-level data, spanning 14 crop types, 13 emission sources, and 31 provinces, and constructs a full-lifecycle panel database from 2001 to 2021. Using Moran’s I global index analysis and spatial autoregressive models, we comprehensively assessed the impacts and spatial spillover effects of economic features, agricultural production characteristics, supporting policies, and climate conditions on carbon emissions from crop production. Our analysis reveals a nationwide increase in crop production carbon emissions, accompanied by widening disparities across regions and crop types. Additionally, it demonstrates that a 1% increase in urbanization, precipitation, and temperature is associated with a 0.02%, 0.26%, and 0.52% decrease in total agricultural carbon emissions, respectively. Moreover, contrary to previous findings suggesting that economic development exacerbates agricultural emissions, we observed that in major grain-producing regions, a 1% decrease in per capita GDP is linked to a 0.32% increase in carbon emissions, along with a 0.23% increase in emission intensity per unit area and 0.22% increase in per unit yield, which implies that these underdeveloped areas rely more on agricultural production and need more government support, putting more pressure on the environment. This study highlights the importance of integrating agricultural and environmental policies to address economic development, regional disparities, sustainable crop production, and carbon emission management.

Abstract With the quick incorporation of distributed photovoltaic (PV) systems into the contemporary energy grid, guaranteeing consistent power output and system reliability has become critical. These systems are susceptible to a variety of operational anomalies, including dust accumulation, and shading which can reduce energy efficiency without being detected. Traditional anomaly detection methods frequently fail to capture the internal dynamics and latent factors that affect PV system performance over time. There is an urgent need for a robust, real-time, and dynamic monitoring strategy that can accurately detect abnormal behavior in distributed PV systems. The goal of this research is to create and execute a state space model (SSM)-based anomaly detection framework that dynamically estimates system conditions and alerts to deviations from expected power output in real time. A dataset of 5000 records and 10 attributes was used, which contained time-series data from distributed PV systems, such as actual and expected power output, irradiance, temperature, and other operational parameters. Model matrices (A, B, and C) were estimated using linear regression and enhanced by Kalman filtering. The system estimated power output at each time step and calculated the deviation from actual values. A threshold-based approach was employed to classify anomalies. The model was trained on data labeled as normal and validated against both normal and abnormal entries. The proposed SSM-based anomaly detection model had an accuracy of 94.70%, a precision of 93.85%, a recall of 94.30%, an F1-score of 94.07%, and a Matthews Correlation Coefficient (MCC) of 89.32%, indicating strong predictive capability in detecting abnormal performance in PV systems.