Can crop production agglomeration reduce carbon emissions?—empirical evidence from China

Reducing carbon emissions is crucial for environmental protection and the survival of humankind, particularly in agricultural growth, as it ensures the sustainability of the food supply. This study examines the import of the crop production agglomeration on carbon emissions across several areas of China. It employs panel data spanning from 2012 to 2022. The crop production agglomeration was assessed using the average industrial agglomeration rate, whereas the carbon emissions were evaluated using the IPCC carbon emission factors. Empirical analyses were conducted using the panel fixed effects model and the Spatial Durbin Model . The results indicate that crop production agglomeration directly reduces carbon emissions. Moreover, the concentration of crop production has a geographical demonstration effect on carbon emissions, where greater levels of crop production agglomeration result in a more efficient decrease of carbon emissions in nearby regions. An analysis of heterogeneity indicates that the impact of crop production agglomeration on carbon emissions is more pronounced in the eastern and northeastern regions of China compared to the central and western areas. The study advocates for the formulation of tailored carbon reduction methods that align with the distinct attributes of crops in various locations. It promotes variety and low-carbon development in crop production to drive industrial advancement. The study advocates for enhancing cooperation among crop production enterprises across various areas to provide platforms for information exchange and technical innovation. Furthermore, it advocates for governments to design efficient methods and regulations to reduce carbon emissions in crop production

Self-Supervised Random Forest on Transformed Distribution for Anomaly Detection

Anomaly detection, the task of differentiating abnormal data points from normal ones, presents a significant challenge in the realm of machine learning. Numerous strategies have been proposed to tackle this task, with classification-based methods, specifically those utilizing a self-supervised approach via random affine transformations (RATs), demonstrating remarkable performance on both image and non-image data. However, these methods encounter a notable bottleneck, the overlap of constructed labeled datasets across categories, which hampers the subsequent classifiers&amp;#x2019; ability to detect anomalies. Consequently, the creation of an effective data distribution becomes the pivotal factor for success. In this article, we introduce a model called &amp;#x201C;self-supervised forest (sForest)&amp;#x201D;, which leverages the random Fourier transform (RFT) and random orthogonal rotations to craft a controlled data distribution. Our model utilizes the RFT to map input data into a new feature space. With this transformed data, we create a self-labeled training dataset using random orthogonal rotations. We theoretically prove that the data distribution formulated by our methodology is more stable compared to one derived from RATs. We then use the self-labeled dataset in a random forest (RF) classifier to distinguish between normal and anomalous data points. Comprehensive experiments conducted on both real and artificial datasets illustrate that sForest outperforms other anomaly detection methods, including distance-based, kernel-based, forest-based, and network-based benchmarks.</p

Accuracy improvement of fuel cell prognostics based on voltage prediction

Proton exchange membrane fuel cell (PEMFC) is a promising hydrogen technique with various application prospects. However, all the PEMFCs are subject to degradation resulting from mechanical and chemical aging. To tackle this challenge, accurately predicting fuel cell degradation is essential for its durability optimization. In this study, an enhanced data-driven prognostic framework is developed to accurately predict short-term and medium-term degradation using only fuel cell voltage as the input feature. Firstly, a local outlier factor (LOF) algorithm is adopted for automatic detection of outliers in raw data collected from actual sensing environments. Then, an advanced deep learning model, residual–CNN–LSTM-random attention, is proposed to optimize voltage prediction to better indicate future PEMFC degradation trend. The proposed work is validated by the IEEE PHM 2014 Data Challenge. Compared to state-of-the-art methods, the proposed framework provides superior prediction accuracies with high stability. For instance, the framework improves short-term prediction, achieving a root mean square error (RMSE) of 0.0021 and a mean absolute percentage error (MAPE) of 0.0323 at steady state when training stops at 600 h. For medium-term prediction, our method also attains better results with an RMSE of 0.0085 and a MAPE of 0.4237 under same working conditions. Additionally, the comparative analyses demonstrate a lower computational burden and higher suitability of proposed work for practical applications

Online systemic energy management strategy of fuel cell system with efficiency enhancement

Temperature plays a crucial role in efficiency improvement and lifespan extension of the fuel cell system which encourages energy management strategy (EMS) taking thermal into consideration. However, sluggish thermal response prevents the fuel cell performance from tracking the optimal states during scenarios with significant power variations, which was disregarded in the previous works. To solve this issue, an online hydrogen consumption minimization guarantee strategy (HCMG) including thermal management is proposed which is divided into two parts: 1) primary power distribution strategy, where a model predictive control (MPC) based EMS is employed herein to distribute power between fuel cell and battery with the objectives of minimizing hydrogen consumption as well as maintaining the state of charge (SOC), and 2) HCMG, where a modified MPC based method is exploited herein to track the reference power and optimal temperature with minimum hydrogen consumption by adjusting both the duty cycle of fan and fuel cell current. The presented approach ascertains hydrogen consumption reduction for 3.448% even under relatively extensive power changes, during which the temperature cannot reach the optimal value in a brief time. The real-time simulation results show the effectiveness of the proposed technique compared with previous EMS methods under various driving cycles

Emergence of Quantum Confinement in Topological Kagome Superconductor CsV3_3Sb5_5 family

Quantum confinement is a restriction on the motion of electrons in a material to specific region, resulting in discrete energy levels rather than continuous energy bands. In certain materials quantum confinement could dramatically reshape the electronic structure and properties of the surface with respect to the bulk. Here, in the recently discovered kagome superconductor CsV$_3$Sb$_5$ (A=K, Rb, Cs) family of materials, we unveil the dominant role of quantum confinement in determining their surface electronic structure. Combining angle-resolved photoemission spectroscopy (ARPES) measurement and density-functional theory simulation, we report the observations of two-dimensional quantum well states due to the confinement of bulk electron pocket and Dirac cone to the nearly isolated surface layer. The theoretical calculations on the slab model also suggest that the ARPES observed spectra are almost entirely contributed by the top two layers. Our results not only explain the disagreement of band structures between the recent experiments and calculations, but also suggest an equally important role played by quantum confinement, together with strong correlation and band topology, in shaping the electronic properties of this family of materials

Identifying Interprofessional Global Health Competencies for 21st-Century Health Professionals

Background: At the 2008 inaugural meeting of the Consortium of Universities for Global Health (CUGH), participants discussed the rapid expansion of global health programs and the lack of standardized competencies and curricula to guide these programs. In 2013, CUGH appointed a Global Health Competency Subcommittee and charged this subcommittee with identifying broad global health core competencies applicable across disciplines. Objectives: The purpose of this paper is to describe the Subcommittee's work and proposed list of interprofessional global health competencies. Methods: After agreeing on a definition of global health to guide the Subcommittee's work, members conducted an extensive literature review to identify existing competencies in all fields relevant to global health. Subcommittee members initially identified 82 competencies in 12 separate domains, and proposed four different competency levels. The proposed competencies and domains were discussed during multiple conference calls, and subcommittee members voted to determine the final competencies to be included in two of the four proposed competency levels (global citizen and basic operational level – program oriented). Findings: The final proposed list included a total of 13 competencies across 8 domains for the Global Citizen Level and 39 competencies across 11 domains for the Basic Operational Program-Oriented Level. Conclusions: There is a need for continued debate and dialog to validate the proposed set of competencies, and a need for further research to identify best strategies for incorporating these competencies into global health educational programs. Future research should focus on implementation and evaluation of these competencies across a range of educational programs, and further delineating the competencies needed across all four proposed competency levels

The association between Toll-like receptor 2 single-nucleotide polymorphisms and hepatocellular carcinoma susceptibility

<p>Abstract</p> <p>Background</p> <p>Toll-like receptors (TLR) are key innate immunity receptors participating in an immune response. Growing evidence suggests that mutations of TLR2/TLR9 gene are associated with the progress of cancers. The present study aimed to investigate the temporal relationship of single nucleotide polymorphisms (SNP) of TLR2/TLR9 and the risk of hepatocellular carcinoma (HCC).</p> <p>Methods</p> <p>In this single center-based case-control study, SNaPshot method was used to genotype sequence variants of TLR2 and TLR9 in 211 patients with HCC and 232 subjects as controls.</p> <p>Results</p> <p>Two synonymous SNPs in the exon of TLR2 were closely associated with risk of HCC. Compared with those carrying wild-type homozygous genotypes (T/T), risk of HCC decreased significantly in individuals carrying the heterozygous genotypes (C/T) of the rs3804099 (adjusted odds ratio (OR), 0.493, 95% CI 0.331 - 0.736, <it>P </it>< 0.01) and rs3804100 (adjusted OR, 0.509, 95% CI 0.342 - 0.759, <it>P </it>< 0.01). There was no significant association found in two TLR9 SNPs concerning the risk of HCC. The haplotype TT for TLR2 was associated significantly with the decreased risk of HCC (OR 0.524, 95% CI 0.394 - 0.697, <it>P </it>= 0.000). Inversely, the risk of HCC increased significantly in patients with the haplotype CC (OR 2.743, 95% CI 1.915 - 3.930, <it>P </it>= 0.000).</p> <p>Conclusions</p> <p>These results suggested that TLR2 rs3804099 C/T and rs3804100 C/T polymorphisms were closely associated with HCC. In addition, the haplotypes composed of these two TLR2 synonymous SNPs have stronger effects on the susceptibility of HCC.</p

Competency, Proficiency, and Mastery: Learning Curves for Robotic Distal Pancreatectomy at 16 International Expert Centers

Objective: The aim of this study was to evaluate the different phases of the learning curve for robotic distal pancreatectomy (RDP) in international expert centers. Summary background data: RDP is an emerging minimally invasive approach; however, only limited, mostly single center data are available on its safe implementation, including the learning curve. Methods: Consecutive patients undergoing elective RDP from 16 expert centers across three continents were included to assess the learning curve. Based on the first 100 RDPs at each center, three cutoffs were used to define the learning curve: operative time for competency, major complications (Clavien-Dindo grade ≥III) for proficiency, and textbook outcome for mastery. Clinical outcomes before and after the cutoffs were compared. Results: The learning curve analysis was conducted on 1109 of 2403 RDPs. Competency, proficiency, and mastery, respectively, were reached after 46, 63, and 73 RDP procedures. After competency, operative time decreased from 245 to 235 minutes (P=0.002). Attaining proficiency was reflected by a reduction in the rate of major complications from 20% to 15% (P=0.012), and mastery was associated with a higher proportion of patients with textbook outcome (71% vs. 63%; P=0.028). The postoperative pancreatic fistula rate remained stable along the learning curve, ranging between 18.5% and 21.5%. Previous laparoscopic experience accelerated the learning process by virtue of reduced operative time and an earlier decrease in major complications. Conclusion: Competency, proficiency, and mastery for RDP were reached after 46, 63, and 73 procedures, respectively, at international expert centers. The findings highlight that the learning curves for intraoperative parameters are completed earlier; however, extensive experience is needed to master RDP