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The aim of this study is to develop the theory of prime ideals in ordered semigroups. First, to ensure the existence of prime ideals, we study a class of ordered semigroups which will be denoted by SIP{{\mathbb{S}}}_{IP}. And then we introduce the hull-kernel topology for the prime ideals P(S){\mathcal{P}}\left(S) and the topological properties like separation axioms, compactness and connectedness are studied. Finally, we focus on the subspace ℳ(S,I){\mathcal{ {\mathcal M} }}\left(S,I), minimal prime ideals containing the ideal II in an ordered semigroup SS. We investigate topological properties of this subspace and connections between this subspace and the ordered semigroup SS.

The establishment of the Free Trade (Pilot) Zone (FTZ) is a new attempt by China to embark on modern sustainable urban planning and governmental territorial management at the development stage of a high-quality economy. As urban lands serve as the limited resource foundation for civic production and livelihoods, enhancing land use efficiency becomes a key measure to facilitate metropolitan economies of high quality. Utilizing panel data from prefecture-level cities spanning from 2005 to 2021, this study constructs a multi-period difference-in-differences model to explore the impact of FTZs on urban land use efficiency (ULUE). The results indicate that FTZs can significantly enhance urban land use efficiency, with a more pronounced policy effect observed in central cities, inland cities, and cities with higher urbanization rates. Furthermore, the FTZs also demonstrate spatial spillover effects on urban land use efficiency. This study holds great significance for relevant government departments in formulating policies to optimize land resource allocation, promote FTZ development, and foster high-quality urban planning and territorial management. [ABSTRACT FROM AUTHOR]

Background: Macrotrabecular‐massive hepatocellular carcinoma (MTM‐HCC) is highly aggressive. Comparing the diagnosis ability of CT and gadoxetate disodium (Gd‐EOB‐DTPA) MRI for MTM‐HCC are lacking. Purpose: To compare the performance of Gd‐EOB‐DTPA MRI and CT for differentiating MTM‐HCC from non‐MTM‐HCC, and determine the prognostic indicator. Study Type: Retrospective. Subjects: Post‐surgery HCC patients, divided into the training (N = 272) and external validation (N = 44) cohorts. Field Strength/Sequence: 3.0 T, T1‐weighted imaging, in‐opp phase, and T1‐weighted volumetric interpolated breath‐hold examination/liver acquisition with volume acceleration; enhanced CT. Assessment: Three radiologists evaluated clinical characteristics (sex, age, liver disease, liver function, blood routine, alpha‐fetoprotein [AFP] and prothrombin time international normalization ratio [PT‐INR]) and imaging features (tumor length, intratumor fat, hemorrhage, arterial phase peritumoral enhancement, intratumor necrosis or ischemia, capsule, and peritumoral hepatobiliary phase [HBP] hypointensity). Compared the performance of CT and MRI for diagnosing MTM‐HCC. Follow‐up occurred every 3–6 months, and nomogram demonstrated the probability of MTM‐HCC. Statistical Tests: Fisher test, t‐test or Wilcoxon rank‐sum test, area under the curve (AUC), 95% confidence interval (CI), multivariable logistic regression, Kaplan–Meier curve, and Cox proportional hazards. Significance level: P < 0.05. Results: Gd‐EOB‐DTPA MRI (AUC: 0.793; 95% CI, 0.740–0.839) outperformed CT (AUC: 0.747; 95% CI, 0.691–0.797) in the training cohort. The nomogram, incorporating AFP, PT‐INR, and MRI features (non‐intratumor fat, incomplete capsule, intratumor necrosis or ischemia, and peritumoral HBP hypointensity) demonstrated powerful performance for diagnosing MTM‐HCC with an AUC of 0.826 (95% CI, 0.631–1.000) in the external validation cohort. Median follow‐up was 347 days (interquartile range [IQR], 606 days) for the training cohort and 222 days (IQR, 441 days) for external validation cohort. Intratumor necrosis or ischemia was an independent indicator for poor prognosis. Data Conclusion: Gd‐EOB‐DTPA MRI might assist in preoperative diagnosis of MTM‐HCC, and intratumor necrosis or ischemia was associated with poor prognosis. Evidence Level: 4 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]

In this paper, the solid-state shear milling (S3M) strategy featuring a very strong three-dimensional shear stress field was adopted to prepare the high-performance polyoxymethylene (POM)/molybdenum disulfide (MoS2) functional nanocomposite. The transmission electron microscope and Raman measurement results confirmed that the bulk MoS2 particle was successfully exfoliated into few-layer MoS2 nanoplatelets by the above simple S3M physical method. The polarized optical microscope (PLM) observation indicated the pan-milled nanoscale MoS2 particles presented a better dispersion performance in the POM matrix. The results of the tribological test indicated that the incorporation of MoS2 could substantially improve the wear resistance performance of POM. Moreover, the pan-milled exfoliated MoS2 nanosheets could further substantially decrease the friction coefficient of POM. Scanning electron microscope observations on the worn scar revealed the tribological mechanism of the POM/MoS2 nanocomposite prepared by solid-state shear milling. The tensile test results showed that the pan-milled POM/MoS2 nanocomposite has much higher elongation at break than the conventionally melt-compounded material. The solid-state shear milling strategy shows a promising prospect in the preparation of functional nanocomposite with excellent comprehensive performance at a large scale. [ABSTRACT FROM AUTHOR]

Background & Aims: Accumulating evidences suggest tumour microenvironment (TME) profoundly influence clinical outcome in hepatocellular carcinoma (HCC). Existing immune subtypes are susceptible to batch effects, and integrative analysis of bulk and single‐cell transcriptome is helpful to recognize immune subtypes and TME in HCC. Methods: Based on the relative expression ordering (REO) of 1259 immune‐related genes, an immuno‐prognostic signature was developed and validated in 907 HCC samples from five bulk transcriptomic cohorts, including 72 in‐house samples. The machine learning models based on subtype‐specific gene pairs with stable REOs were constructed to jointly predict immuno‐prognostic subtypes in single‐cell RNA‐seq data and validated in another single‐cell data. Then, cancer characteristics, immune landscape, underlying mechanism and therapeutic benefits between subtypes were analysed. Results: An immune‐related signature with 29 gene pairs stratified HCC samples individually into two risk subgroups (C1 and C2), which was an independent prognostic factor for overall survival. The machine learning models verified the immune subtypes from five bulk cohorts to two single‐cell transcriptomic data. Integrative analysis revealed that C1 had poorer outcomes, higher CNV burden and malignant scores, higher sensitivity to sorafenib, and exhibited an immunosuppressive phenotype with more regulators, e.g., myeloid‐derived suppressor cells (MDSCs), Mø_SPP1, while C2 was characterized with better outcomes, higher metabolism, more benefit from immunotherapy, and displayed active immune with more effectors, e.g., tumour infiltrating lymphocyte and dendritic cell. Moreover, both two single‐cell data revealed the crosstalk of SPP1‐related L–R pairs between cancer and immune cells, especially SPP1‐CD44, might lead to immunosuppression in C1. Conclusions: The REO‐based immuno‐prognostic subtypes were conducive to individualized prognosis prediction and treatment options for HCC. This study paved the way for understanding TME heterogeneity between immuno‐prognostic subtypes of HCC. [ABSTRACT FROM AUTHOR]

Aims: Adamantinomatous craniopharyngioma (ACP) is a rare benign intracranial tumour that occurs in the sellar region and likely originates from the embryonic craniopharyngeal epithelium (a remnant of the ectoderm, also known as Rathke's pouch). However, progress in ACP research has been slow due to the lack of ACP cell lines. Therefore, in this study, we established an immortalised ACP cell line. Methods: Immortalised ACP cells were established from cultured primary ACP cells by the ectopic expression of human telomerase reverse transcriptase (hTERT). Primary and immortalised cells were compared and characterised by morphological examination, short tandem repeat (STR) profiling, whole exome sequencing (WES) and transcriptome sequencing. The ability of immortalised cells to form tumours was examined using an intracranial tumour formation assay in mice. Immunofluorescence staining was performed to compare the characteristics of human ACP and intracranial xenografts derived from immortalised cells. Results: A novel immortalised ACP cell line, STAM4, was successfully established in a 4‐year‐old male ACP patient. When STAM4 cells were compared with primary ACP cells in terms of morphological characteristics, genetic variants, STR profiles, biological behaviours and transcriptome differences, STAM4 cells were similar to primary ACP cells. Additionally, the STAM4 cells were able to form intracranial tumours that resemble human ACP. Conclusions: This ACP cell line may provide a cell model for further studies on the molecular mechanisms underlying the occurrence and progression of ACP and for the development of new anticancer drugs for ACP. [ABSTRACT FROM AUTHOR]

Trunk borers are among the most destructive forest pests. The larvae of some species living and feeding in the trunk, relying solely on the tree's appearance to judge infestation is challenging. Currently, one of the most effective methods to detect the larvae of some trunk-boring beetles is by analyzing the vibration signals generated by the larvae while they feed inside the tree trunk. However, this method faces a problem: the field environment is filled with various noises that get collected alongside the vibration signals, thus affecting the accuracy of pest detection. To address this issue, vibration signal enhancement is necessary. Moreover, deploying sophisticated technology in the wild is restricted due to limited hardware resources. In this study, a lightweight vibration signal enhancement was developed using EAB (Emerald Ash Borer) and SCM (Small Carpenter Moth) as insect example. Our model combines CRN (Convolutional Recurrent Network) and Transformer. We use a multi-head mechanism instead of RNN (Recurrent Neural Network) for intra-block processing and retain inter-block RNN. Furthermore, we utilize a dynamic pruning algorithm based on sparsity to further compress the model. As a result, our model achieves excellent enhancement with just 0.34M parameters. We significantly improve the accuracy rate by utilizing the vibration signals enhanced by our model for pest detection. Our results demonstrate that our method achieves superior enhancement performance using fewer computing and storage resources, facilitating more effective use of vibration signals for pest detection. [ABSTRACT FROM AUTHOR]

Simple Summary: Tree trunk damage can be influenced by multiple factors, among which trunk-boring insect infestation plays a significant role. Early external detection of such damage poses challenges. Manual observation remains the prevailing method for controlling tree trunk pests, but it demands a substantial workforce and yields limited outcomes. To address these limitations, acoustic technology has gained popularity, using vibration probes embedded in tree trunks to capture vibrations produced by insect larvae feeding, thereby facilitating the detection of pest larvae. However, traditional methods primarily rely on single-channel vibration signal acquisition, often assuming the proximity of the vibration probe to the sound source. Nevertheless, when the probe's position exceeds a certain distance from the original, capturing effective drilling vibration signals becomes difficult due to noise interference and other factors. To overcome this constraint, we have developed a novel multi-channel drilling vibration signal acquisition board that enables the distribution of multiple vibration probes at different locations on the tree trunk, allowing simultaneous collection of vibration signals from diverse probes. Additionally, we have devised a multi-channel signal separation model based on attention mechanisms, which effectively denoises and recovers clean target signals from noisy recordings. Experimental results demonstrate that this approach significantly enhances the detection efficiency of trunk-boring insects. The larvae of certain wood-boring beetles typically inhabit the interior of trees and feed on the wood, leaving almost no external traces during the early stages of infestation. Acoustic techniques are commonly employed to detect the vibrations produced by these larvae while they feed on wood, significantly increasing detection efficiency compared to traditional methods. However, this method's accuracy is greatly affected by environmental noise interference. To address the impact of environmental noise, this paper introduces a signal separation system based on a multi-channel attention mechanism. The system utilizes multiple sensors to receive wood-boring vibration signals and employs the attention mechanism to adjust the weights of relevant channels. By utilizing beamforming techniques, the system successfully removes noise from the wood-boring vibration signals and separates the clean wood-boring vibration signals from the noisy ones. The data used in this study were collected from both field and laboratory environments, ensuring the authenticity of the dataset. Experimental results demonstrate that this system can efficiently separate the wood-boring vibration signals from the mixed noisy signals. [ABSTRACT FROM AUTHOR]

Simple Summary: Trunk-boring insects have emerged as one of the most threatening forest pests globally, causing significant damage to forests. Certain groups of larvae reside within tree trunks without any observable external signs indicating their presence. This poses a significant challenge for pest detection. To tackle this issue, acoustic techniques are frequently utilized, which involve embedding a vibration probe into the tree trunk to capture the vibrations produced by larvae and using a model to distinguish whether the tree is infested. However, this approach requires the acquisition of the purest possible vibrations signal. Thus, to ensure accurate analysis, a noise suppression process is crucial, since the signals collected in real-world environments are often subjected to varying degrees of environmental noise interference. In this study, we employed artificial intelligence techniques to develop a boring vibration enhancement model. The training data utilized in this study comprise boring vibrations captured from trunk sections and typical environmental noise present in the trees' habitat. The experimental results demonstrate that the enhancement method proposed by our model significantly boosts the performance of an established classification model. Overall, this study promotes the development of sustainable and efficient forestry protection approaches by improving the accuracy of pest detection. Recording vibration signals induced by larvae activity in the trunk has proven to be an efficient method for detecting trunk-boring insects. However, the accuracy of the detection is often limited because the signals collected in real-world environments are heavily disrupted by environmental noises. To deal with this problem, we propose a deep-learning-based model that enhances trunk-boring vibration signals, incorporating an attention mechanism to optimize its performance. The training data utilized in this research consist of the boring vibrations of Agrilus planipennis larvae recorded within trunk sections, as well as various environmental noises that are typical of the natural habitats of trees. We mixed them at different signal-to-noise ratios (SNRs) to simulate the realistically collected sounds. The SNR of the enhanced boring vibrations can reach up to 17.84 dB after being enhanced by our model, and this model can restore the details of the vibration signals remarkably. Consequently, our model's enhancement procedure led to a significant increase in accuracy for VGG16, a commonly used classification model. All results demonstrate the effectiveness of our approach for enhancing the detection of larvae using boring vibration signals. [ABSTRACT FROM AUTHOR]

Under the influence of the dual policies of sustainable economic development and the national dual-carbon target, the establishment of an environmental protection department for the treatment of heavily polluting industries is imminent, and the country has launched pollution control policies and regulations to restrict the emission rights of heavily polluting industries. Therefore, this paper focuses on whether the restriction of emission rights in key industries has reduced carbon emissions. To achieve this, this paper uses panel data of prefecture-level cities in China from 2006 to 2019 and adopts a two-way fixed-effects DID model to systematically analyze the impact of the key pollution industry governance policies launched by the Ministry of Environmental Protection on CO2 emissions in 2017. And further analyze the role of variables such as green technology innovation patents and energy efficiency using this model, while parallel trend tests and placebo tests, and related policies are used to ensure the robustness of the regression results. This paper reveals that: (1) The heavy pollution industry governance policy implemented in 2017 can effectively reduce CO2 emissions in the cities of the treated group, and the effect is more significant in the year of policy implementation; (2) Green utility patents and energy-use efficiency are the effective mediating mechanisms to reduce CO2 emissions; (3) Over time, the effect of heavy pollution industry governance policy on CO2 emissions gradually decreases; (4) The reliability of the baseline regression results of this paper is proved by the use of parallel trend tests, placebo tests, and tests excluding the influence factors such as relevant policies in the same period. Therefore, the key polluting industries treatment policy launched by China's Ministry of Environmental Protection in 2017 under the recent dual-carbon policy development goals formulated by China, can effectively reduce carbon emissions; however, in the future economic development process, the government should give more consideration to the continuity of the policy impact and its coherence on economic development when implementing the policy. [ABSTRACT FROM AUTHOR]

One third of chondrichthyan species (sharks, rays, and chimeras) are threatened with extinction, mainly due to unsustainable fishing. Large accessible international markets for meat and luxury products like dried fins can help drive overfishing by encouraging targeted capture or retention of high‐value export species. If this is common, then species in international trade could have heightened extinction risk. Here, we examined the species composition of the Hong Kong shark fin market from 2014 to 2018, finding that traded species disproportionately occur in threatened categories (70.9%) and all premium value species are threatened. A small number of cosmopolitan species dominate the trade, but noncosmopolitan coastal species are still traded at concerning levels given their limited distribution. These coastal species are not generally subject to retention prohibitions, fisheries management, or international trade regulations and without management many could become extinct. The conservation potential of international trade regulations alone for coastal chondrichthyans depends on the extent to which overfishing is driven by export markets; socioeconomic studies of coastal fishing communities are needed to make this determination. Nonetheless, adding international trade regulations for more coastal shark species that are in the fin trade could prompt broad engagement with overfishing in nations lacking effective management. [ABSTRACT FROM AUTHOR]

Introduction: Insulin-like growth factor type 1 receptor (IGF1R) is overexpressed in various malignant tumors, which relates to their transformation and recurrence. Craniopharyngioma is a benign tumor with malignant results, often accompanied by a severe inflammatory reaction. However, the relationship between IGF1R expression and the inflammatory response of craniopharyngioma is unclear. Methods: We enrolled 85 patients with adamantinomatous craniopharyngioma (ACP) in a study to explore the relationship between IGF1R expression and clinical features of this disease. Results: Patients in the IGF1R high-expression group had a significantly higher incidence of hypopituitarism, higher recurrence rate, and lower progression-free survival. β-Catenin can further regulate expression of the stem cell marker, CD44, by regulating IGF1R. Using immunofluorescence, we found that tumor stem cell-like cells did not express phosphorylated (p)-ERK, although p-ERK activation was evident in the surrounding cells. Picropodophyllin, a specific inhibitor of IGF1R, increased the expression of p-ERK protein and decreased the transcription level of interleukin-6. Conclusions: High expression of IGF1R might promote inflammation of ACP, which might be an unfavorable factor for pituitary function and prognosis. The high expression of IGF1R in tumor stem cell-like cells might inhibit the expression of p-ERK and promote the generation of inflammatory factors. IGF1R plays a stemness maintenance role in ACP and regulates the production of inflammatory factors through a p-ERK pathway, which suggests that targeting IGF1R and p-ERK might provide a new direction for alleviating tumor inflammation. [ABSTRACT FROM AUTHOR]