Local Climate Zone in Xi’an City: A Novel Classification Approach Employing Spatial Indicators and Supervised Classification

The Local Climate Zone (LCZ), as a foundational element of urban climate zone classification proposed by Oke and Stewart, categorizes urban surface types based on 10 influential parameters affecting the urban heat island effect, such as building density, surface reflectivity, sky view factor, and surface roughness length. This method divides cities into 17 different Local Climate Zones (LCZs) to standardize climate observations and promote global climate research exchange, offering valuable insights for heat island studies. In this study, we enhance the existing local climate zones spatial classification approach by focusing on Xi’an city’s urban layout and architectural features. By using urban spatial indicators and employing a supervised classification approach and a spatial clustering method with land parcels as statistical units, we investigate typical urban areas and classify Xi’an’s land parcels into 17 or 15 distinct local climate zones. Subsequently, through the evaluation of two distinct classification methods, the most suitable urban microclimate zoning method for Xi’an city was selected. This optimization of the local climate zoning representation introduces a spatial classification method tailored to urban climate planning and control, utilizing urban spatial indicators and remote sensing data. The resulting urban climate zoning map not only supports sample selection for urban heat environment parameter observation but also aids urban planners in identifying spatial distribution patterns for climate zoning

Bi-level optimal dispatching of distribution network considering friendly interaction with electric vehicle aggregators

The widespread application of electric vehicles (EVs) is a positive force driving green development. However, their widespread penetration also poses significant challenges and threats to the security and stable operation of the power grid. To address this urgent issue, this article constructs a bi-level optimal dispatching model fostering collaboration between electric vehicle aggregators and the distribution network. The upper-level optimization targets the minimization of peak-valley differences in the distribution network via considerably arranging power outputs of gas turbines, while the lower-level one focuses on reducing the charging expense of EV aggregators via efficient charging transfer. Note that the charging expense is not only composed of electric cost but also a dynamic carbon emission factor-based cost, which contributes to the electricity economy and carbon reduction concurrently. A geometric mean optimizer (GMO) is introduced to solve the mode. Its efficiency is evaluated against three typical algorithms, i.e., genetic algorithm, great-wall construction algorithm, and optimization algorithm based on an extended IEEE 33-bus system with different charging behaviors of EVs on both a typical weekday and weekend. Simulation results demonstrate that the GMO outperforms other competitive algorithms in accuracy and stability. The peak-valley difference between the distribution network and the total cost of EV aggregators can be decreased by over 98% and 76%, respectively

Multi-objective optimal scheduling considering low-carbon operation of air conditioner load with dynamic carbon emission factors

As global temperatures rise and climate change becomes more severely. People realize that air conditioning systems as a controllable resource and play an increasingly important role in reducing carbon emissions. In the past, the operation optimization of air conditioning systems was mainly oriented to user comfort and electricity costs ignoring the long-term impact on the environment. This article aims to establish a multi-objective model of air-conditioning load to ensure user temperature comfort performance and reduce the total cost (i.e., electricity cost and carbon emission cost) simultaneously. Multi Sand Cat Swarm Optimization (MSCSO) algorithm combined with gray target decision-making (GTD) is used to explore optimal solution. Meanwhile four competitive strategies are applied to validate the effectiveness of the proposed method, i.e., genetic algorithm (GA), MSCSO-comfort objective, MSCSO-total electricity cost objective and unoptimization. The simulation results show that the MSCSO-GTD based objective method can significantly reduce total costs while taking into account appropriate indoor temperature comfort

Cerebellum and hippocampus abnormalities in patients with insomnia comorbid depression: a study on cerebral blood perfusion and functional connectivity

Chronic insomnia disorder and major depressive disorder are highly-occurred mental diseases with extensive social harm. The comorbidity of these two diseases is commonly seen in clinical practice, but the mechanism remains unclear. To observe the characteristics of cerebral blood perfusion and functional connectivity in patients, so as to explore the potential pathogenesis and biological imaging markers, thereby improving the understanding of their comorbidity mechanism. 44 patients with chronic insomnia disorder comorbid major depressive disorder and 43 healthy controls were recruited in this study. The severity of insomnia and depression were assessed by questionnaire. The cerebral blood perfusion and functional connectivity values of participants were obtained to, analyze their correlation with questionnaire scores. The cerebral blood flow in cerebellum, vermis, right hippocampus, left parahippocampal gyrus of patients were reduced, which was negatively related to the severity of insomnia or depression. The connectivities of left cerebellum-right putamen and right hippocampus-left inferior frontal gyrus were increased, showing positive correlations with the severity of insomnia and depression. Decreased connectivities of left cerebellum-left fusiform gyrus, left cerebellum-left occipital lobe, right hippocampus-right paracentral lobule, right hippocampus-right precentral gyrus were partially associated with insomnia or depression. The connectivity of right hippocampus-left inferior frontal gyrus may mediate between insomnia and depression. Insomnia and depression can cause changes in cerebral blood flow and brain function. Changes in the cerebellar and hippocampal regions are the result of insomnia and depression. They reflect abnormalities in sleep and emotion regulation. That may be involved in the pathogenesis of comorbidity

Identification of CD8+ T-cell epitope from multiple myeloma-specific antigen AKAP4

Multiple myeloma (MM) is a malignant plasma cell disorder affecting mainly the elderly population. Revolutionary progress in immunotherapy has been made recently, including monoclonal antibodies and chimeric antigen receptor T cell (CAR-T) therapies; however, the high relapse rate remains problematic. Therefore, combination therapies against different targets would be a reasonable strategy. In this study, we present a new X-chromosome encoded testis-cancer antigen (CTA) AKAP4 as a potential target for MM. AKAP4 is expressed in MM cell lines and MM primary malignant plasma cells. HLA-A*0201-restricted cytotoxic T lymphocytes (CTLs) induced by dendritic cells (DCs) transduced with an adenovirus vector encoding the full-length AKAP4 gene were demonstrated to lyse AKAP4+ myeloma cells. Seven of the 12 candidate epitopes predicated by the BIMAS and SYFPEITH algorithms were able to bind HLA-A*0201 in the T2 binding assay, of which only two peptides were able to induce CTL cytotoxicity in the co-culture of peptide-loaded human mature dendritic cells and the autologous peripheral blood mononuclear cells (PBMCs) from the same HLA-A*0201 donor. The AKAP4 630–638 VLMLIQKLL was identified as the strongest CTL epitope by the human IFN-γ ELISPOT assay. Finally, the VLMLIQKLL-specific CTLs can lyse the HLA-A*0201+AKAP4+ myeloma cell line U266 in vitro, and inhibit tumor growth in the mice bearing U266 tumors in vivo. These results suggest that the VLMLIQKLL epitope could be used to develop cancer vaccine or T-cell receptor transgenic T cells (TCR-T) to kill myeloma cells