Energy Schedule Setting Based on Clustering Algorithm and Pattern Recognition for Non-Residential Buildings Electricity Energy Consumption

Building energy modelling (BEM) is crucial for achieving energy conservation in buildings, but occupant energy-related behaviour is often oversimplified in traditional engineering simulation methods and thus causes a significant deviation between energy prediction and actual consumption. Moreover, the conventional fixed schedule-setting method is not applicable to the recently developed data-driven BEM which requires a more flexible and data-related multi-timescales schedule-setting method to boost its performance. In this paper, a data-based schedule setting method is developed by applying K-medoid clustering with Principal Component Analysis (PCA) dimensional reduction and Dynamic Time Warping (DTW) distance measurement to a comprehensive building energy historical dataset, partitioning the data into three different time scales to explore energy usage profile patterns. The Year–Month data were partitioned into two clusters; the Week–Day data were partitioned into three clusters; the Day–Hour data were partitioned into two clusters, and the schedule-setting matrix was developed based on the clustering result. We have compared the performance of the proposed data-driven schedule-setting matrix with default settings and calendar data using a single-layer neural network (NN) model. The findings show that for the data-driven predictive BEM, the clustering results-based data-driven schedule setting performs significantly better than the conventional fixed schedule setting (with a 25.7% improvement) and is more advantageous than the calendar data (with a 9.2% improvement). In conclusion, this study demonstrates that a data-related multi-timescales schedule matrix setting method based on cluster results of building energy profiles can be more suitable for data-driven BEM establishment and can improve the data-driven BEMs performance

Down-Regulation of MiR-150 Alleviates Inflammatory Injury Induced by Interleukin 1 via Targeting Kruppel-Like Factor 2 in Human Chondrogenic Cells

Background/Aims: Interleukin-1 (IL-1) is known to be involved in cartilage degeneration following joint injury or due to osteoarthritis. In the present study, we explored the effects of miR-150 on IL-1-stimulated human chondrogenic cells ATDC5. Methods: ATDC5 cells were transfected with the mimic, inhibitor or negative controls specific for miR-150, and subsequently treated by IL-1. CCK8 assay, PI and FITC-conjugated Annexin V double-staining, Western blot, qRT-PCR and ELISA assay were performed to determine the changes of cell viability, apoptosis, and the release of pro-inflammatory cytokines. Targeting relationship between miR-150 and KLF2 was detected by dual luciferase activity assay. Results: IL-1 reduced cell viability, induced apoptosis, and enhanced the expression and release of pro-inflammatory cytokines (IL-6, IL-8 and TNF-α) in ATDC5 cells. IL-1 also increased the expression of miR-150. Suppression of miR-150 alleviated IL-1-induced cell damage in ATDC5 cells, while overexpression of miR-150 resulted in an opposite impact. KLF2 was negatively regulated by miR-150, and it was proved as a target gene of miR-150. KLF2 overexpression exhibited protective actions in IL-1-injured ATDC5 cells, even if miR-150 was suppressed in cell. Moreover, IL-1-induced activation of NF-kB and Notch pathways was alleviated by KLF2 overexpression. Conclusions: Suppression of miR-150 led to up-regulation of KLF2, which in turn protected ATDC5 cells against IL-1-induced injury

Effect of Helicobacter pylori enrichment in adenoma on clinical and pathological features of colorectal adenoma

Abstract Objective To observe the enrichment of Helicobacter pylori in adenoma tissues of patients with colorectal adenoma and analyze its effect on the clinical and pathological features of colorectal adenoma. Methods The data of 1622 cases of gastrointestinal endoscopy in the Endoscopy Center of Zhejiang Chinese Medical University Affiliated Wenzhou Hospital of Integrated Traditional Chinese and Western Medicine from January 2019 to June 2021 were retrospectively collected. The general data, gastric Hp infection, clinical and pathological features of colorectal adenoma, special staining of Hp methylblue in adenoma, and immunohistochemical staining of Hp in adenoma were compared between colorectal adenoma group (743 cases) and control group (879 cases). Results There were 361 cases (48.59%) of gastric Hp positive in colorectal adenoma group and 331 cases (37.66%) of gastric Hp positive in control group. The difference was significant (P &lt; 0.001). Found that gastric Hp infection significantly increased the risk of Hp enrichment in colorectal adenoma tissues (OR: 28.449; 95% CI: 18.188 – 44.500; P &lt; 0.001). Hp enrichment in colorectal adenomas was also found to be a contributing factor for positive events in terms of adenoma diameter, adenoma pathological type, and adenoma malignancy (OR: 3.536; 3.652; 2.833; P &lt; 0.001 for all). Conclusion There is a positive correlation between gastric Hp infection and intestinal Hp enrichment. Intestinal Hp enrichment has a statistically significant effect on the clinical and pathological features of colorectal adenoma.</jats:p

Bayesian Calibration for Office-Building Heating and Cooling Energy Prediction Model

Conventional building energy models (BEM) for heating and cooling energy-consumption prediction without calibration are not accurate, and the commonly used manual calibration method requires the high expertise of modelers. Bayesian calibration (BC) is a novel method with great potential in BEM, and there are many successful applications for unknown-parameters calibrating and retrofitting analysis. However, there is still a lack of study on prediction model calibration. There are two main challenges in developing a calibrated prediction model: (1) poor generalization ability; (2) lack of data availability. To tackle these challenges and create an energy prediction model for office buildings in Guangdong, China, this paper characterizes and validates the BC method to calibrate a quasi-dynamic BEM with a comprehensive database including geometry information for various office buildings. Then, a case study analyzes the effectiveness and performance of the calibrated prediction model. The results show that BC effectively and accurately calibrates quasi-dynamic BEM for prediction purposes. The calibrated model accuracy (monthly CV(RMSE) of 0.59% and hourly CV(RMSE) of 19.35%) meets the requirement of ASHRAE Guideline 14. With the calibrated prediction model, this paper provides a new way to improve the data quality and integrity of existing building energy databases and will further benefit usability

Diagenesis and its influence on reservoir quality and oil-water relative permeability: A case study in the Yanchang Formation Chang 8 tight sandstone oil reservoir, Ordos Basin, China

Different from conventional reservoirs, unconventional tight sand oil reservoirs are characterized by low or ultra-low porosity and permeability, small pore-throat size, complex pore structure and strong heterogeneity. For the continuous exploration and enhancement of oil recovery from tight oil, further analysis of the origins of the different reservoir qualities is required. The Upper Triassic Chang 8 sandstone of the Yanchang Formation from the Maling Oilfield is one of the major tight oil bearing reservoirs in the Ordos Basin. Practical exploration demonstrates that this formation is a typical tight sandstone reservoir. Samples taken from the oil layer were divided into 6 diagenetic facies based on porosity, permeability and the diagenesis characteristics identified through thin section and scanning electron microscopy. To compare pore structure and their seepage property, a high pressure mercury intrusion experiments (HPMI), nuclear magnetic resonance (NMR), andwater-oil relative permeability test were performed on the three main facies developed in reservoir. The reservoir quality and seepage property are largely controlled by diagenesis. Intense compaction leads to a dominant loss of porosity in all sandstones, while different degrees of intensity of carbonate cementation and dissolution promote the differentiation of reservoir quality. The complex pore structure formed after diagenesis determines the seepage characteristics, while cementation of chlorite and illite reduce the effective pore radius, limit fluid mobility, and lead to a serious reduction of reservoir permeability

Bayesian Calibration for Office-Building Heating and Cooling Energy Prediction Model

Conventional building energy models (BEM) for heating and cooling energy-consumption prediction without calibration are not accurate, and the commonly used manual calibration method requires the high expertise of modelers. Bayesian calibration (BC) is a novel method with great potential in BEM, and there are many successful applications for unknown-parameters calibrating and retrofitting analysis. However, there is still a lack of study on prediction model calibration. There are two main challenges in developing a calibrated prediction model: (1) poor generalization ability; (2) lack of data availability. To tackle these challenges and create an energy prediction model for office buildings in Guangdong, China, this paper characterizes and validates the BC method to calibrate a quasi-dynamic BEM with a comprehensive database including geometry information for various office buildings. Then, a case study analyzes the effectiveness and performance of the calibrated prediction model. The results show that BC effectively and accurately calibrates quasi-dynamic BEM for prediction purposes. The calibrated model accuracy (monthly CV(RMSE) of 0.59% and hourly CV(RMSE) of 19.35%) meets the requirement of ASHRAE Guideline 14. With the calibrated prediction model, this paper provides a new way to improve the data quality and integrity of existing building energy databases and will further benefit usability