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Integrating various reinforcements into 3D concrete printing (3DCP) is an efficient method to satisfy critical requirements for structural applications. This paper explores an explainable ensemble machine learning (EML) method to predict the bond failure mode and strength of various reinforcements in 3DCP. To overcome the problem of insufficient experimental data for the 3DCP technology, a deep generative adversarial network (DGAN) is integrated for data augmentation. The trained EML models augmented by the DGAN accurately and reliably evaluated the bond performance. The relative embedded length, concrete cover, and compressive strength of 3D printed concrete were identified as the three most important features based on the explanatory analysis. The effect of the amount of training data and integration of the proposed method in the additive manufacturing process were also investigated. The proposed method combining EML, data augmentation, and model interpretation can be extended for other aspects of digital fabrication with concrete. • Bond performance of reinforcements in 3D concrete printing is explored by explainable ensemble earning. • Deep generative adversarial network is proposed for data augmentation. • Proposed method shows excellent accuracy and robustness compared with traditional method. • Important features regarding printing materials and configurations are identified by explanatory analysis. • Proposed method can be extended to other performance aspects of digital fabrication with concrete. [ABSTRACT FROM AUTHOR]

The interfacial bond behavior of corroded fibers in cracked steel fiber reinforced concrete (SFRC) is critical for understanding its structural deterioration performance, while available research focused on aligned straight or hooked-end fibers. This study investigated the combined effects of corrosion and inclination on the pullout performance of various steel fibers bridging artificial cracks exposed to wet-dry cycles of chlorides. Three deformed steel fibers, i.e., hooked-end (H), crimped (C), and milled-cut (M) fibers, with three angles of 0°, 30°, and 45° were examined under both uncorroded and corroded conditions. After corrosion, the average bond strength of H fibers was enhanced due to increased roughness and compact rust layer if the high inclination-induced fracture was avoided, while that of C and M fibers was decreased at various angles because of loose and porous rusts. The highest bond strengths of corroded H, C, and M fibers were discovered at 30°, 30°, and 0°, as a result of the combined mechanisms of snubbing effect, matrix spalling, or corrosion-induced fracture. The ultimate displacement of all fibers increased with angles due to increased matrix spalling area and fiber plastic deformation. The inclination effect of corroded fibers was more significant on energy absorption capacity than average bond strength, regardless of fiber type. A degradation model that describes the snubbing and matrix spalling effects, as well as the corrosion-induced deterioration and fracture was developed, based on which the critical inclination angle was found to decrease with increasing embedded length and decreasing tensile strength. • Fracture risk of corroded fibers increases with inclination angles regardless of fiber type. • The highest average bond strengths of corroded H, C, and M fibers are obtained at 30°, 30°, and 0°. • Inclination effect is more significant on energy absorption capacity than average bond strength. • Influential mechanisms of corrosion and inclination on fiber-matrix bond are discussed. • Degradation model that describes corrosion and inclination effects is developed. [ABSTRACT FROM AUTHOR]

This paper proposes a new electrical discharge machining (EDM) process evaluation method, which is a combination of decision-making trial and evaluation laboratory (DEMATEL) and analytic network process (ANP). The green manufacturing theory is integrated with the traditional EDM evaluation system to establish a new comprehensive evaluation system based on five perspectives of green manufacturing theory. This method offers a new approach for EDM process evaluation and the promotion of environment and economic aspects. An exact numerical example is given to demonstrate the validity and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Over the past decade, while strengthening intellectual property rights (IPR) protection, China has also explored means to regulate the abuse of IPRs. IPR protection in China is mainly based on the Anti-Monopoly Law (AML), in particular Article 55. Recent years have seen more and more judicial and administrative enforcement practices related to China's antimonopoly regulation on the abuse of IPRs. Provisions on the Prohibition of the Abuse of Intellectual Property Rights to Eliminate or Restrict Competition was released on April 7, 2015, and the draft of Antimonopoly Guidelines in Relation to the Abuse of Intellectual Property Rights was released for public comment in early 2017. It can be expected that China's antimonopoly guidelines on the regulation of abuses of IPRs will come out in the near future. [ABSTRACT FROM AUTHOR]

The bond performance of reinforcing bars in steel fiber-reinforced concrete (SFRC) remains a topic of discussion in the community. The effect of deformed steel fiber types is not fully understood, and a rational mechanics-based model is required to consider this behavior. This study experimentally investigated the bond performance of steel rebars in SFRC containing milled-cut (M) fibers with proven high bridging capacity, namely MCSFRC. The results indicated that M fibers yielded a higher bond strength of rebars compared to hook-end (H), crimped (C), and straight (S) fibers from previous studies. This was attributed to the superior bond resistance of M fibers in the matrix, provided by the rough inner surface, stiff end hook, and longitudinal twist. A meso-mechanical model (rib and fiber scale) was then developed to effectively assess the bond strength of rebars in SFRC, accounting for rib-concrete interaction, as well as the fiber reinforcement differences including fiber–matrix interaction, fiber spatial distribution, content, and dimension. This model was applicable to rebars in SFRC with H, C, S, and M fibers, and the results showed good agreement with 301 tests in the established database. On that basis, a unified local bond-slip model for rebars in SFRC was proposed, which was able to suitably reproduce the entire bond-slip response under pull-out and splitting failures. [ABSTRACT FROM AUTHOR]

This study investigates the chloride-induced corrosion behavior of various steel fibers and associated effects on their interfacial bond performance in cracked steel fiber reinforced concrete (SFRC). Three types of deformed fibers, including hooked-end (H), crimped (C), and milled-cut (M) fibers, were embedded in high strength matrix and then exposed to wet-dry cycles of chlorides under cracking conditions. The scanning electron microscope (SEM) and energy-dispersive X-ray (EDX) technique were employed to gain insights into the surface morphologies and corrosion products at the micro-scale. The bond behaviors of corroded fibers were further evaluated through pull-out tests, where the influences of fiber type, corrosion duration, crack width, and embedded length were discussed. Three distinct corrosion morphologies were identified on the surfaces of three types of fibers owing to the difference in corrosion products. After 40 days of corrosion, the average bond strength of H fibers increased by 37.9% because of dense and rough rust layer, while those of C and M fibers deteriorated by 30.5% and 18.8% due to surficial groove loss and loose rust layer. The average bond strength of H, C, and M fibers improved by 12.4%, 11.7%, and 58.0% as the crack width increased from 0.1 to 0.3 mm, whereas significant reductions in pull-out energy and equivalent bond strength were obtained once the crack width exceeded 0.2 mm owing to fiber fracture. The corrosion-induced bond deterioration of M fibers became less severe as the embedded length increased up to 12 mm and remained unaffected thereafter. [ABSTRACT FROM AUTHOR]

The bond deterioration behavior of reinforcements in steel fiber-reinforced concrete (SFRC) subjected to chloride-induced corrosion has not yet been fully elucidated. This study investigates the corrosion characterization, resistivity, corrosion-induced cracking, and associated effects on the bond performance of reinforcing bars in SFRC with milled-cut steel fibers (M fibers), referred to as MCSFRC. Pull-out tests were performed following galvanostatic corrosion, with the M fiber, concrete cover, and corrosion level being the main variables. A closer look at the bar corrosion was conducted using 3D laser scanning technology, which allowed for estimating the spatial-domain corrosion pattern and bond index degradation that characterize the rib-concrete interaction. The results indicated that, with the increase in corrosion levels, the bond index decreased exponentially as a result of pits propagating from the rib root to the rib peak. The bond strength of the MCSFRC specimens deteriorated less than that of the control counterparts and hook-end fiber-reinforced specimens in the literature. It is further revealed that the crack opening and bond index reduction are the bond deterioration mechanisms under corrosion, reducing the contact area between the bar and concrete through the ribs. On that basis, a mechanics-based model for bond strength deterioration was developed. The proposed model considered the two aforementioned effects, as well as the fiber reinforcement effect on crack suppression, showing good agreement with the experimental results. • Milled-cut steel fibers mitigate the corrosion-induced bond deterioration. • A closer look at corrosion variation of bars in MCSFRC using 3D laser scanning. • Bond index decreases in an exponential law with increasing corrosion level. • A mechanics-based bond strength deterioration model is presented. [ABSTRACT FROM AUTHOR]

This paper adopts a method of researching the process of energy consumption in manufacturing/remanufacturing systems to achieve lean energy. This method can be used in typical machining systems, especially including the machining process of manufacturing and remanufacturing. To validate this approach, we model and simulate the system with the DEVS theory and input-output model. This model establishes the machining process of a product. On the basis of work station, which is used for receiving work pieces and energy, exporting products and information of different kinds of energy consumption. We can simulate manufacturing/remanufacturing of integration machining process and material flow, information flow, and energy flow of the coupling process. After that, classify the energy consumption according to the quality of the products. The solutions, which are the margin of error between simulation and measurement, show that this method is effective. The output results help manage energy resource and provide train of thought for saving energy and reducing consumption. [ABSTRACT FROM AUTHOR]

[Display omitted] • Unified ML framework precisely predicts bond strength for SRC structure. • Effect of six concrete types NC, SFRC, RAC, LAC, ECC, HSC is considered. • Explainable ensemble LightGBM-SHAP exhibits superior performance and interpretability. • A web app with graphical interface on LightGBM-SHAP is created for design practice. This study evaluates the efficiency of an explainable ensemble learning framework in precisely predicting the bond strength between steel sections with different surface treatments and various concrete types. Besides seven numerical features, two categorical features, surface treatment and concrete type, that significantly affect bond strength but were omitted in existing equations were considered as inputs for such framework. A database comprising 302 push-out test results was carefully constructed. Four standalone machine learning models, multiple linear regression (MLR), multilayer perceptron artificial neural network (MLP-ANN), support vector machine (SVM), classification and regression tree (CART), as well as two latest ensemble models, adaptive boosting (Adaboost), light gradient boosting machine (LightGBM) were developed. Their performance was thoroughly compared in terms of learning ability, computational cost, predictive accuracy, and residuals. Comparative study show that the ensemble model LightGBM outperforms other models and their predictive performance can be ranked as LightGBM>AdaBoost>CART>SVM>MLP-ANN>MLR. The superior reliability of the ensemble model is further confirmed through comparisons with empirical equations. The SHapley Additive exPlanations method (SHAP) is also employed to explain the contributions of essential features to the individual prediction by LightGBM. Finally, a LightGBM-SHAP-based web application with a user-friendly interface was created, which enables convenient and efficient bond performance estimation without cumbersome programming. [ABSTRACT FROM AUTHOR]

Background: Life-limited patients may lose decision-making abilities during disease progression. Advance care planning can be used as a discussion method for healthcare professionals to understand patients' future care preferences. However, due to many difficulties, the participation rate of healthcare professionals in advance care planning is not high. Aim: To explore the facilitators of and barriers to healthcare professionals' provision of advance care planning to life-limited patients to better implement it for this population. Methods: We followed ENTREQ and PRISMA to guide this study. We conducted a systematic search of PubMed, Web of Science, Embase, CINAHL, PsycINFO, CNKI, and SinoMed to include qualitative data on the experiences and perspectives of healthcare professionals in different professional fields in providing advance care planning for life-limited patients. The Joanna Briggs Institute Critical Appraisal Checklist for Qualitative Research was used to assess the quality of the included studies. Results: A total of 11 studies were included. Two themes were identified: unsupported conditions and facilitative actions. Healthcare professionals regarded cultural concepts, limited time, and fragmented record services as obstacles to implementation. They had low confidence and were overly concerned about negative effects. They needed to possess multiple abilities, learn to flexibly initiate topics, and facilitate effective communication based on multidisciplinary collaboration. Conclusion: Healthcare professionals need an accepting cultural environment to implement advance care planning, a sound legal system, financial support, and a coordinated and shared system to support them. Healthcare systems need to develop educational training programs to increase the knowledge and skills of healthcare professionals and to promote multidisciplinary collaboration to facilitate effective communication. Future research should compare the differences in the needs of healthcare professionals in different cultures when implementing advance care planning to develop systematic implementation guidelines in different cultures. [ABSTRACT FROM AUTHOR]

[Display omitted] • Innovative perforated web connection (PWC) for CES bridges are proposed. • PWC with perforating rebars significantly improves shear-slip response. • Refined FE models considering bond and mechanical anchorage are developed. • Extensive parametric analysis to provide valuable information on PWC. • Artificial Neural Network is developed to predict shear resistance of PWC. The size of the steel section in concrete encased steel (CES) structure of bridge engineering is much larger than that of the building construction, accompanied by great shear demands at the steel–concrete interface. To enhance the interfacial shear performance, perforated web connection (PWC), which perforates the web of steel section with perforating rebars passing through the web openings, is proposed in this research for bridge application. Push-out tests and refined numerical analysis were performed on CES specimens with PWC, aiming to evaluate its effectiveness and to reveal the shear transfer mechanism. The effect of various parameters on the shear resistance was also investigated through a parametric study using the validated modeling method. Eventually, an Artificial Neural Network (ANN) was developed to predict the ultimate shear resistance of PWC. The results indicate that the interfacial shear-slip response especially the post-peak behavior is significantly improved by PWC with perforating rebars, which transforms the failure manner from brittle to ductile. The failure process of the PWC with perforating rebars experiences the loss of bonding, activation of perforating rebars, as well as shear failure of perforating rebars and concrete dowels. The parametric analysis shows that the PWC with large web openings exhibits great ultimate shear resistance and sensitivity to the variation in other parameters owing to good compatibility between the natural bond and reinforced concrete dowels. The well trained ANN model is a powerful tool for efficiently predicting the ultimate shear resistance of such connection. [ABSTRACT FROM AUTHOR]

In this study, pressure visual fibers (PVF) with a core‐shell structure were prepared using a coaxial wet spinning process. The fiber composite shell consists of a luminescent material (as a light source) and a thermochromic pigment (as a filter light source) mixed with a polyacrylonitrile (PAN) matrix. The fiber core layer, on the other hand, was made of gallium‐indium alloy liquid metal. In operation, the filter light source is activated by joule heat drive (resulting in the fading of the thermochromic pigment), and the PVF releases phosphorescence upon exposure to ultraviolet light. When a certain pressure is applied to the fiber shell, the liquid metal in the core layer is squeezed, resulting in a local cross‐sectional area reduction, an increase in local joule thermal power, and thus local discoloration. This localized compression, in combination with a small rated current and the presence of an ultraviolet light source, converts the mechanical force (strain and compression) into an optical response. The excellent electrical conductivity of the liquid metal, combined with its compatibility with the soft matrix, makes it a promising candidate for applications in smart textiles and flexible sensing. Highlights: The technology of visual fiber controlled by current is presented.The bendability and controllability of visual composites are improved.The composite fiber can be visualized when stressed. [ABSTRACT FROM AUTHOR]

• Bond behavior of steel section-concrete interface with studs is investigated. • Studs can improve the interfacial bond resistance and enhance the post-peak bond behavior. • Advanced FE models considering both bond deterioration and mechanical action are established. • The effect of key parameters on the shear transfer is discussed using parametric study. The premature debonding at the steel section-concrete interface is the main cause of performance degradation for SRC structures. It is expected to improve the interfacial bond behavior using headed studs that can provide mechanical interlocking. However, the bond behavior and shear transfer mechanism of the steel section-to-concrete interface with studs has not been fully understood. For this reason, push-out tests on SRC specimens were performed in this research. The effects of the presence and the position of studs on the bond behavior were studied. Further, advanced finite element models considering both bond deterioration and mechanical action were developed to reveal the internal shear transfer mechanism during the entire loading process. Finally, a parametric study was carried out to investigate the influence of other key variables on the overall bond behavior and shear transfer of interface with studs. The results indicated that the headed studs could improve the interfacial bond resistance and shear stiffness, enhance the post-peak bond behavior, and prevent brittle failure, especially for the web-welded type. The evolution of load-slip behavior could be separated into five phases, i.e. the linear ascending, nonlinear ascending, post-peak descending, stable, and stud failure phases, as a result of the progressive deterioration of adhesion and activation of studs and friction. The behaviors and bond stress distribution in these phases were found to be affected by variations in the stud diameter, stirrup ratio, concrete strength, as well as number of stud rows. [ABSTRACT FROM AUTHOR]

Motor-driven equipment (ME) is one of the key components in an air-conditioning system, which contributes to the vast majority of the total energy consumption by air-conditioning systems. Distinguishing variable- and constant-speed equipment is important since the energy simulation models of the two types differ. Traditionally, types of ME are known in advance, and energy consumption data are consequently analyzed. However, in the application scenarios of energy consumption data mining, precedent information on the ME type could be missing. Thus, this study applies this process in reverse, providing new insight into energy consumption data of ME to recognize variable-speed ME in an air-conditioning system. The energy consumption data of ME in an air-conditioning system implemented in a commercial building were collected and numerically analyzed. A proposed simple parameter, coefficient of the median, and several numerical parameters were calculated and used to distinguish variable- from constant-speed ME. Results showed that the energy consumption data distributions of the two types of ME differed. The proposed coefficient of the median could successfully distinguish variable- from constant-speed ME, and it could be applied as an important step in energy consumption data mining of air-conditioning systems. [ABSTRACT FROM AUTHOR]

• Push-out tests on the SRC specimens were conducted. • Failure modes, bond stress-slip curves and steel strain distribution were obtained. • Numerical simulation was carried out to reveal the load transfer mechanism. • Parametric analysis was performed based on the verified FE models. The concrete cover is one of the key factors affecting the bond performance of steel reinforced concrete (SRC) structures. The main objective of this paper was to study the effect of concrete cover on the bond-slip behavior between steel section and concrete in SRC structures. Firstly, push-out tests on SRC specimens were conducted and the failure modes, bond stress-slip curves as well as strain distribution of the specimens were investigated. The experimental results indicated that the bond strength of steel section encased in concrete could be significantly improved by increasing the concrete cover due to the enhancement in the concrete confining effect. As the concrete cover increased from 90 mm to 170 mm, the initial bond stress, ultimate bond stress, and residual bond stress increased by 76.9%, 46.6%, and 97.2%, respectively. The concrete cover also had a considerable impact on the initial and residual slips between the steel section and concrete. Further, nonlinear finite element models of push-out tests were established and verified against the test results. The numerical results demonstrated that there were three shear mechanisms including chemical adhesion, static friction, and sliding friction performing differently at each loading stage. Finally, a parametric study was carried out based on the verified FE models. Four cracking patterns of the concrete surface were observed with the variation of the ratio of concrete cover to the clear distance between the flange tip and the web side surface of concrete. [ABSTRACT FROM AUTHOR]

Background: An important signaling pathway connecting illness and natural immunity is the cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) pathway, but aberrant activation of this pathway is associated with the development of autoimmune and inflammatory diseases. Hence, targeted inhibition of the activation of the cGAS-STING pathway is potentially valuable in the treatment of disease. The primary active component of Salvia miltiorrhiza is total tanshinone (TTN). Research has indicated that TTN possesses noteworthy anti-inflammatory properties. However, the protective mechanism of TTN against acute liver injury (ALI) and autoimmune diseases is unknown. Methods: A model of aberrant activation of the cGAS-STING pathway was established in various cells and treated with TTN, and the expression of cGAS-STING pathway-related proteins, type I interferon, interferon stimulated genes and inflammatory factors was assessed by western blotting, real-time qPCR. Immunofluorescence analysis of the effect of TTN on the entry of associated proteins into the nucleus following aberrant activation of the cGAS-STING pathway. The effect of TTN on STING oligomerisation was investigated using 2'-3'-cyclic GMP-AMP (2',3'-cGAMP) to induce STING oligomerisation. Western blotting was used to examine the impact of TTN on the interactions of STING, tank-binding kinase 1 (TBK1), and interferon regulatory factor 3 (IRF3) after HA or Flag-labelled plasmids were transfected into HEK-293 T cells. A dimethylxanthenone-4-acetic acid (DMXAA) -induced activation model of the cGAS-STING pathway in mice was established to study the effect of TTN on aberrant activation of the cGAS-STING pathway in vivo. On the other hand, an animal model of lipopolysaccharide/D-galactosamine (LPS/D-GaIN)-induced ALI and an autoimmune disease model induced by trex1 knockout were established to study the effects of TTN on inflammatory and autoimmune diseases mediated by the cGAS-STING pathway in vivo. Results: In several models of aberrant activation of the cGAS-STING pathway, TTN significantly inhibited the phosphorylation of STING and IRF3, thereby suppressing the expression of type I interferon, interferon-stimulated genes and inflammatory factors. Additionally, TTN prevented P65 and IRF3 from entering the nucleus after the cGAS-STING signalling pathway was abnormally activated. Subsequent research indicated that TTN was not involved in the oligomerization of STING or the integration of STING-TBK1 and TBK1-IRF3. However, TTN was found to have a substantial effect on the binding process between STING and IRF3. On the other hand, DMXAA-induced STING activation and activation of downstream signalling in vivo are inhibited by TTN. Furthermore, TTN exhibits positive treatment effects on autoimmune diseases caused by deficiency of trex1 and LPS/D-GaIN-induced ALI. Conclusion: Our research indicates that TTN effectively treats ALI and autoimmune illnesses mediated by the cGAS-STING pathway by inhibiting the abnormal activation of this pathway. [ABSTRACT FROM AUTHOR]

Background: Studies have shown that Chinese Clinicians and nurses have positive attitudes toward ACP, but no local tools exist to assess their need for ACP knowledge and skills training. resulting in their inability to initiate ACP conversations as well as poor end-of-life care for patients. Therefore, this study aims to assess the needs of Chinese Clinicians and nurses for ACP knowledge and skills training and assess the validity and reliability of a questionnaire on the Training Needs for Advance Care Planning (TNACP) scale. Methods: From October to November 2021, 170 clinicians and nurses were pre-surveyed using a preliminary draft of the questionnaire. The responses were screened using item analysis, Cronbach's alpha coefficient, and the intraclass correlation coefficient (ICC) to describe the internal consistency and stability of the questionnaire. The Content validity index (CVI), Exploratory factor analysis (EFA) and Confirmatory factor analysis (CFA) were used to test the validity of the questionnaire. Results: After independent samples t-test analysis, Except for the entry "A2", the critical ratio between the two groups of the remaining 23 items was statistically significant (p < 0.05). Based on the above screening methods, the "A2" item was deleted, and the final number of questionnaire items was 23. The I-CVI was 0.79–1.00, and the S-CVI/Ave was 0.90. Three common factors were extracted—the cumulative contribution rate was 69.969%, and the factor loading of all items was 0.506–0.843 (all > 0.40). The results of confirmatory factor analysis showed that the Training Needs for Advance Care Planning (TNACP) scale model fit well(X2/df = 2.504, RMSEA = 0.092, GFI = 0.809, AGFI = 0.745, CFI = 0.931, IFI = 0.932, TLI = 0.916); the Cronbach's α = 0.888 for the total questionnaire, and the three dimensions of Cronbach's α were 0.729 to 0.959; and the ICC for the overall scores between the test-retest evaluations was 0.884 (p < 0.001). Conclusions: The TNACP scale has good reliability and validity and can be used to assess Chinese Clinicians and nurses' training needs for implementing ACP. [ABSTRACT FROM AUTHOR]

Background: Hypertrophic scar (HS) is characterized by the hyperproliferation of fibroblasts and the excessive deposition of extracellular matrix (ECM). Thrombospondin 1 (THBS1) is a component of the ECM, which has been implicated in HS formation. Objectives: This study aimed to explore whether miR-375/miR-5691 could modulate HS formation by targeting THBS1. Methods: The expression levels of miR-375/miR-5691/THBS1 in HS and normal skin tissues were measured by quantitative reverse transcription-polymerase chain reaction. 3-(4,5)-dimethylthiahiazo (-z-y1)-2,5-di-phenytetrazoliumromide and Western blot assays were performed on fibroblasts isolated from HS tissues (HSFBs) to determine cell proliferation and the expression levels of proliferating cell nuclear antigen (PCNA), apoptosis-related proteins (caspase3/9, cleaved caspase3/9, Bax, and Bcl-2), and ECM-related proteins. The binding sites between THBS1 and miR-375/miR-5691 were predicted by the TargetScan. Dual-luciferase reporter and anti-Ago2 immunoprecipitation assays were applied to confirm the interactions between THBS1 and miR-375/miR-5691. Results: The expression levels of both miR-375 and miR-5691 were downregulated in HS tissues and HSFBs, which were negatively correlated with THBS1 expression levels. The overexpression of miR-375/miR-5691 inhibited cell proliferation and ECM production, and promoted apoptosis of HSFBs, while silencing of miR-375/miR-5691 led to an opposite result. In the mechanism analysis, THBS1 was confirmed as the direct target gene of miR-375/miR-5691. Furthermore, rescue experiments showed that the suppressed growth of HSFBs and ECM production induced by silencing of THBS1 was reversed by miR-375/miR-5691 inhibitors. Conclusion: MiR-375/miR-5691 was downregulated in HS tissues, and it could suppress the hyperproliferation and ECM production of HSFBs by targeting THBS1. [ABSTRACT FROM AUTHOR]

Background: Accurate diagnosis of Parkinson's disease (PD) is challenging due to its diverse manifestations. Machine learning (ML) algorithms can improve diagnostic precision, but their generalizability across medical centers in China is underexplored. Objective: To assess the accuracy of an ML algorithm for PD diagnosis, trained and tested on data from different medical centers in China. Methods: A total of 1656 participants were included, with 1028 from Beijing (training set) and 628 from Fuzhou (external validation set). Models were trained using the least absolute shrinkage and selection operator–logistic regression (LASSO-LR), decision tree (DT), random forest (RF), eXtreme gradient boosting (XGboost), support vector machine (SVM), and k-nearest neighbor (KNN) techniques. Hyperparameters were optimized using five-fold cross-validation and grid search techniques. Model performance was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, accuracy, sensitivity (recall), specificity, precision, and F1 score. Variable importance was assessed for all models. Results: SVM demonstrated the best differentiation between healthy controls (HCs) and PD patients (AUC: 0.928, 95% CI: 0.908–0.947; accuracy: 0.844, 95% CI: 0.814–0.871; sensitivity: 0.826, 95% CI: 0.786–0.866; specificity: 0.861, 95% CI: 0.820–0.898; precision: 0.849, 95% CI: 0.807–0.891; F1 score: 0.837, 95% CI: 0.803–0.868) in the validation set. Constipation, olfactory decline, and daytime somnolence significantly influenced predictability. Conclusion: We identified multiple pivotal variables and SVM as a precise and clinician-friendly ML algorithm for prediction of PD in Chinese patients. [ABSTRACT FROM AUTHOR]

• TGF-β1-induced FBs share the characteristics of FBs isolated from hyperplastic scars. • UA abolished the increased proliferation and migration of TGF-β1-induced FBs. • UA attenuates the collagen contraction and fibrotic factors production in TGF-β1-induced FBs. • UA suppressed the Smad2/3 signaling by binding to ALK5 in TGF-β1-induced FBs. Hypertrophic scar (HS) is a skin condition characterized by excessive fibrosis with disordered collagens from skin fibroblasts, which causes abnormal esthetic and even functional symptoms, thereby affecting millions of people. Ursolic acid (UA) is widely used in skincare and exerts anti-fibrotic effects. The present study aimed to delve into the impact of UA on HS and the mechanism. Fibroblasts (FBs) were incubated with TGF-β to investigate physiological characteristics compared with FBs isolated from normal skin (NSFBs) and hyperplastic scars (HSFBs). TGF-β-incubated FBs were subjected to treatment with UA (0–20 μM). The expressions of Vimentin, α-SMA, Collagen I, and Collagen III were examined using immunofluorescence, RT-qPCR, and western blot. Cell viability, proliferation, apoptosis, migration, and contractility were examined by CCK-8, EdU, Annexin V-FITC/PI, Transwell, and collagen gel contraction assays, respectively. The activation of Smad2/3 signaling was also determined by western blot. The binding sites for UA of TGF-βR1 (ALK5) were predicted by the Autodock tool. Compared with NSFBs, the cell proliferation, migration, and contractility of both HSFBs and TGF-β-incubated FBs were all significantly up-regulated. UA markedly impaired the TGF-β-induced increase in cell proliferation, migration, and contractility, α-SMA, collagen I, and Collagen III expression of FBs. UA significantly inhibited the phosphorylation levels of Smad2/3 in TGF-β-incubated FBs with no influence on TGF-βR1 and TGF-βR2 expressions, which might be because of the binding of UA to the catalytic domain of ALK5 protein. UA attenuated TGF-β1-induced hyperproliferation, migration, and collagen deposition in FBs via regulating the Smad2/3 pathway. [ABSTRACT FROM AUTHOR]

Abstract: Fe or N-doped Ag/ZnO nanocomposites were synthesized by the dual-step liquid deposition method and one-step liquid deposition combined with plasma nitriding. The as-synthesized nanocomposites were characterized by transmission electron microcopy, high-resolution transmission electron microcopy, X-ray diffraction and X-ray photoelectron spectroscopy. In addition, the photocatalytic properties of Fe or N-doped Ag/ZnO nanocomposites were evaluated by the degradation of methyl orange (MO) under ultraviolet light irradiation using our self-made photocatalytic apparatus. Results showed that Fe or N-doped Ag/ZnO nanocomposites exhibited superior photocatalytic properties compared to the undoped Ag/ZnO nanocomposites. The concentration of ferric sulfate solution and doping ratio of n(Fe): n(ZnO) played an important role in improving photocatalytic properties of Fe-doped Ag/ZnO nanocomposites and the optimized concentration and doping ratio were determined to be 0.134M and 5%, respectively. Moreover, the plasma nitriding time also played an important role and the optimal plasma nitriding time was 30min. The enhanced photoactivity mechanism for Fe or N-doped Ag/ZnO nanocomposites was adequately discussed. [Copyright &y& Elsevier]

β-SiC nanowires (SiC NWs) were modified by H2 and N2 plasma treatment for improving the field-emission (FE) properties. The FE property of SiC NWs treated by H2 and N2 plasma was significantly improved, their turn-on field and threshold field were 3.2 and 6.7Vµm-1 for a 10-min H2 treatment, 3.0 and 6.3Vµm-1 for a 20-min H2 treatment, and 2.8 and 6.0Vµm-1 for a 10-min N2 treatment, respectively, while SiC NWs treated by N2 for 20 min displayed poorer FE properties compared with untreated SiC NWs. The results of scanning electron microscope (FE-SEM), transmission electron microscopy, high-resolution transmission electron microscopy, selected-area electronic diffraction, and X-ray diffraction showed that the surface of the SiC NWs became rough, but their microstructure did not change after the plasma treatments. The point effect was proposed to explain the improvement of FE properties. A new, simple, and effective method for improving SiC NWs FE properties was discovered, and it may serve as a referential work for enhancing FE properties of other one-dimensional nonmaterials. [ABSTRACT FROM AUTHOR]

• By combining automatic image acquisition-transmission system, the image segmentation model trained on U 2− net , and an improved structure from motion algorithm (IVOP & AKAZE-SFM), a 3D reconstruction method is proposed for obtaining high-precision point cloud of complex plant automatically. • A scale restoration method including color filtering, Euclidean clustering, and a size calibration algorithm based on prior information is proposed to obtain a clean and proportional point cloud of complex plant. • A plant phenotyping platform based on the multi-view images to reconstruct high-precision and proportional point clouds for complex plants is constructed, which integrates hardware components such as the Raspberry Pi camera and motorized turntable, as well as algorithms for image acquisition, image segmentation, 3D reconstruction, and point cloud processing. Three-dimensional reconstruction plays a crucial role in quantifying crop phenotypes and exploring crop physiological structures. This paper presents a phenotyping platform designed for the 3D reconstruction of complex plants, utilizing multi-view images and introducing a joint evaluation criterion for both the reconstruction algorithm and the platform. Initially, a device composed of Raspberry Pi camera, SSH protocol, USB-TTL, motorized turntable, and shadow booth is built for automated image acquisition and transmission. Then, a dataset containing carex cabbage and kale is created and trained based on U 2 - n e t to achieve precise image segmentation. After that, an improved structure from motion algorithm, named IVOP & AKAZE-SFM, and multi-view stereo algorithm are utilized for the fine-scale reconstruction of plants. Next, by combing color filtering and Euclidean clustering, a denoising algorithm is proposed to obtain clean point clouds of plants. Finally, a method for calibrating the size of the plant point cloud based on priori condition is proposed to solve the problem of point cloud deformation in reconstruction. The evaluation of image segmentation model resulted in a precision of 0.91, a recall of 0.972, an IOU of 0.943, and a max F β of 0.099. The proposed IVOP&AKAZE-SFM is assessed against mainstream algorithms, the results show that our algorithm has the minimum average track length, minimum average reprojection error and generated the most points. The correlation coefficient (R 2) between the extracted traits and measured phenotype, such as plant height and plant width, are 0.999 and 1.000, while the root means squared errors (RMSE) are 0.298 cm and 0.338 cm. Consequently, the platform offers a cost-effective, automated, and integrated solution for fine-scale plant 3D reconstruction. [ABSTRACT FROM AUTHOR]

Increased data monitoring enables the energy-efficient operation of air-conditioning systems via data-mining. The latter is projected to have lesser consumption but more comprehensive diagnosis than traditional methods. Following the companion paper that proposed a systematic method for energy-saving potential calculations via data-mining, this article presents a detailed case study in an ice-storage air-conditioning system by employing the proposed method. Raw data were preprocessed prior to recognizing the constant- and variable-speed devices in the system. Classification and regression tree algorithms were utilized to identify the operating modes of the system. The regression models between the energy-consumption and operating-state parameters of the nine pumps and two chillers were fitted. Furthermore, the constraints pertaining to system operation were summarized. From the results, the particle swarm optimization method was applied to elucidate the benchmark energy cost and the consequent cost savings potential. The cost savings potential for the chiller plant room during the investigation duration of 59 d reached as high as 24.03%. The case study demonstrates the feasibility, effectiveness, and stability of the systematic approach. Further studies can facilitate the development of corresponding control strategies based on the potential analysis results, to investigate better optimization algorithm, and visualize the analysis process. [ABSTRACT FROM AUTHOR]

Air-conditioning systems contribute the most to energy consumption among building equipment. Hence, energy saving for air-conditioning systems would be the essence of reducing building energy consumption. The conventional energy-saving diagnosis method through observation, test, and identification (OTI) has several drawbacks such as time consumption and narrow focus. To overcome these problems, this study proposed a systematic method for energy-saving diagnosis in air-conditioning systems based on data mining. The method mainly includes seven steps: (1) data collection, (2) data preprocessing, (3) recognition of variable-speed equipment, (4) recognition of system operation mode, (5) regression analysis of energy consumption data, (6) constraints analysis of system running, and (7) energy-saving potential analysis. A case study with a complicated air-conditioning system coupled with an ice storage system demonstrated the effectiveness of the proposed method. Compared with the traditional OTI method, the data-mining-based method can provide a more comprehensive analysis of energy-saving potential with less time cost, although it strongly relies on data quality in all steps and lacks flexibility for diagnosing specific equipment for energy-saving potential analysis. The results can deepen the understanding of the operating data characteristics of air-conditioning systems. [ABSTRACT FROM AUTHOR]

Occupants' behavior has a significant impact on building energy consumption. Various clustering algorithms, represented by the K-means algorithm based on the "Euclidean" distance, are widely used to extract occupants' behavior patterns. However, these commonly used algorithms cannot effectively capture the temporal characteristics of occupants' behavior. Although some studies have started to adopt clustering algorithms considering time series for research, the differences in clustering effects of different algorithms on occupants' behavior are still unclear, especially in the clustering application study of behavior with intermittent characteristics, this intermittent behavior is a behavior that does not always exist, and the timing of its occurrence changes. Therefore, this study takes the heating demand behavior of occupants with obvious intermittency as a case and compares the clustering effect of the common clustering algorithm (K-means) and three clustering algorithms considering time series (K-shape, Dynamic Time Warping (DTW), and Derivative Dynamic Time Warping (DDTW)). The study also explores possible reasons leading to differences and introduces three evaluation methods for clustering results of intermittent heating demand. This study provides a reference for selecting appropriate clustering algorithms to study occupants' intermittent behavior and has practical application value in improving the extraction and prediction of behavior patterns of occupants with intermittent characteristics. • Review the occupant behavior clustering methods in building energy. • Study the clustering algorithm on occupants' intermittent heating demand behavior. • Introduce three metrics to evaluate occupants' intermittent heating demand clustering. • Analyze the difference in clustering results with different algorithms. [ABSTRACT FROM AUTHOR]

Upland cotton (Gossypium hirsutum L.) accounts most of the natural fiber production worldwide. Lint percentage (LP) and seed index (SI) are important components of cotton fiber yield, which is a constant breeding goal of cotton. So, the loci underpinning LP and SI should be extensively dissected. Here, one single-locus and four multi-locus genome-wide association study (GWAS) models were employed to detect candidate loci for lint percentage and seed index under seven environments with 196 upland cotton accessions and 41,815 single nucleotide polymorphism (SNP) markers. Totally, 39 and 45 significant quantitative trait locus (QTL) were identified in at least two environments or two models, including 24 previously reported QTLs and six pleiotropic QTLs. Referred to the genome and gene expression database of TM-1, 614 candidate genes were detected for lint percentage and seed index, including 103 genes preferentially expressed in fiber or ovule. The gene Gh_A10G0378, functioned in potassium ion transport, was considered to be related to lint percentage. Collectively, the associated markers and promising genes detected herein will help to elucidate the genetic architecture of lint percentage and facilitate fiber yield improvement in cotton. [ABSTRACT FROM AUTHOR]

• Single-locus and multi-locus models were employed for association mapping accuracy. • Fiber quality traits in six environments were used for the stable loci detection. • Promising genes were identified using the gene expression in fiber development. • Compared to previous studies, 23 stable QTL and 71 stable genes were identified in this study. • Common genes associated with both fiber quality and cottonseed nutritional traits were detected. Upland cotton makes an important contribution to natural fiber production worldwide. Given the necessity to high quality fiber required in the textile industry, the genomic variation of diverse accessions and the loci underpinning fiber quality should be extensively investigated. Here, a panel of 196 upland cottons and 41,815 single-nucleotide polymorphism (SNP) markers were examined for detection of candidate quantitative trait loci (QTL) for five fiber quality traits in six environments using both single- and multi-locus genome-wide association studies (GWAS) models. With discriminant analysis of principal components (DAPC), the accessions were divided into four subpopulations, and among these, 40 SNPs and 38 QTL were significant in at least three environments and at least two models. Reference to the TM-1 genome and related gene expression data, 89 candidate genes preferentially expressed in fiber or ovules were identified. In this study, 23 candidate QTL and 71 candidate genes were also identified in previous studies, and of them, five promising QTL and 13 promising genes may have pleiotropic effects. In addition, nine putative genes were identified with quantitative real-time PCR. Furthermore, 17 common genes between fiber quality traits and cottonseed nutritional traits were identified. All of the SNP markers and promising genes detected in this study could be used in future breeding practices and for putative gene functional studies. [ABSTRACT FROM AUTHOR]

This study investigates the impact of steel fiber geometrical properties on the interfacial bond and tensile behavior of ultra-high-performance alkali activated concrete (UHP-AAC). Two types of steel fibers, straight and hooked-end, with aspect ratios of 65, 97.5, and 125 were examined. Results showed that the hooked-end steel fiber exhibited the highest average and equivalent bond strengths in the UHP-AAC matrix, measuring 15.13 and 11.46 MPa, respectively, which were approximately 66% and 94% higher than those of straight steel fibers in the same matrix and bonding area. The best tensile performance of UHP-AAC was achieved using straight steel fibers with an aspect ratio of 97.5, demonstrating the highest tensile strength and strain energy density of 12.28 MPa and 55.24 kJ/m3, respectively, and the second highest strain capacity of 0.533%. Straight steel fibers were more effective in increasing the tensile strength of UHP-AAC than hooked-end steel fibers with the same aspect ratio. Higher fiber aspect ratios resulted in larger microcrack widths, with the straightened end-hooks acting on the crack plane to transmit tensile force more effectively, contributing to increased microcrack width and deformability. [ABSTRACT FROM AUTHOR]

Codon usage bias (CUB) is an important evolutionary feature in a genome which provides important information for studying organism evolution, gene function and exogenous gene expression. The CUB and its shaping factors in the nuclear genomes of four sequenced cotton species, G. arboreum (A2), G. raimondii (D5), G. hirsutum (AD1) and G. barbadense (AD2) were analyzed in the present study. The effective number of codons (ENC) analysis showed the CUB was weak in these four species and the four subgenomes of the two tetraploids. Codon composition analysis revealed these four species preferred to use pyrimidine-rich codons more frequently than purine-rich codons. Correlation analysis indicated that the base content at the third position of codons affect the degree of codon preference. PR2-bias plot and ENC-plot analyses revealed that the CUB patterns in these genomes and subgenomes were influenced by combined effects of translational selection, directional mutation and other factors. The translational selection (P2) analysis results, together with the non-significant correlation between GC12 and GC3, further revealed that translational selection played the dominant role over mutation pressure in the codon usage bias. Through relative synonymous codon usage (RSCU) analysis, we detected 25 high frequency codons preferred to end with T or A, and 31 low frequency codons inclined to end with C or G in these four species and four subgenomes. Finally, 19 to 26 optimal codons with 19 common ones were determined for each species and subgenomes, which preferred to end with A or T. We concluded that the codon usage bias was weak and the translation selection was the main shaping factor in nuclear genes of these four cotton genomes and four subgenomes. [ABSTRACT FROM AUTHOR]

• Spatial-Temporal-utilization coefficient was proposed to evaluate heating demand. • The Lorenz curve revealed the distribution of demands in spatial and temporal terms. • The Gini coefficient was used to quantify the nonuniformity of heating demand. • Huge energy saving potential lies in the intermittent heating behavior. The heating demand in actual residential buildings is not equal between different rooms and times. Such non-uniformity in spatial and temporal terms has a direct impact on the applicability, performance, and efficiency of heating systems, and the associated energy consumption. When people participate more in system design and regulation, such as in rural detached houses in northern China, the significance of recognizing the demand characteristics is more evident. In-depth analysis and quantitative evaluation of intermittent and distribution characteristics are essential. This study sought to propose a new approach to identify and evaluate actual demands characteristics, and their distribution in a quantitative and graphical manner. First, comprehensive coefficients, spatial–temporal-utilization factor (STUF), and spatial–temporal-utilization coefficient (STUC) were proposed to quantify heating demand and evaluate the disparity between actual partial heating and "full-time, full-space" heating. Thereafter, the Lorenz curve and Gini coefficient were introduced and modified to visually describe the distribution among rooms and temporal variation, and quantify the distribution non-uniformity. Four houses were used to evaluate this method. By using this method, the heating demand of each room, the overall heating intensity of a house, the proportion of time when heating is not required, and its non-uniformity in spatial and temporal terms can be identified and evaluated. The results verified the non-uniformity of the heating demand and the implication of a noticeable energy-saving potential by the actual intermittent heating behavior. This method could be used to effectively quantify the heating demand and evaluate its non-uniform degree. Based on these quantitative findings, further research can be conducted to promote system design and building energy savings. [ABSTRACT FROM AUTHOR]

Metal-organic frameworks (MOFs) are compounds consisting of metal cations and organic anions organized in porous three-dimensional structures. MOFs exhibit higher selective adsorption performance for CO2-containing gas mixtures than traditional porous materials, making them promising candidates in CO2capture. However, it is important to understand H2O effects on CO2adsorption in MOFs before making them more applicable in the process designed to separate CO2from wet gas mixtures. In this review, we summarize the current state of MOF development for CO2adsorption. The stability of MOFs toward water and the strategies of enhancing the water stability for MOFs were discussed. The different effects of water on the CO2adsorption and separation performance and the mechanism of the effects were analyzed. [ABSTRACT FROM AUTHOR]

Polarized fluorescent polymers can be promisingly used for many applications. In this work, the polarized fluorescent poly[2-methoxy,5-(2′-ethylhexyloxy)-1,4-phenylene vinylene]@MOF (MEH-PPV@MOF) composites were synthesized via the direct chain-introduction method. Firstly, long-range ordered porous metal-organic framework (MOF) crystals were synthesized. Through morphology observation and X-ray diffraction measurement, the water stability, thermal stability and structural stability of synthesized MOF were analyzed, to determine the preservation treatment of the material and the process parameters of introducing polymer chains in the subsequent experiment. Then, the MEH-PPV chains were directly introduced into the MOF channels via a solution-mediated incorporation approach. The measurement results of powder X-ray diffraction, nitrogen adsorption and Fourier transform infrared spectroscopy showed that the MEH-PPV chains were successfully introduced into the MOF channels. Finally, the fluorescent properties of the MEH-PPV@MOF composites were characterized via the photoluminescence spectroscopy. It was found that the accommodation of MEH-PPV chains within 1-D channels of MOF led to a strong blue-shift of the photoluminescence spectrum, due to the quantum confinement effect. Also, the polarization ratio calculated from the polarization luminescence spectra of the composites was 2.215. This indicates that the MEH-PPV@MOF composites prepared by the direct chain-introduction method with the help of the solution-mediated approach have good photoluminescence anisotropy. • Direct introduction of MEH-PPV chains into the MOF channels via solution-mediated approach. • Noticeable polarized luminescence properties of MEH-PPV chains aligned in the MOF channels. • Strong blue-shift of PL spectrum of MEH-PPV chains in 1-D MOF channels due to quantum confinement. [ABSTRACT FROM AUTHOR]

• Occupants on-demand heating control behavior in a rural residential building is investigated. • Independently controlled split-type heating equipment were used to elucidate actual demand patterns. • Data collection over three years under an interference avoidance scheme. • Five representative intermittent heating patterns were obtained from a real-world case study. A better understanding of actual heating demands is crucial for optimizing operational heating systems, because disparities between expected load and actual demand are key factors in inefficient heating system design, implementation, and corresponding energy loss. Additionally, the discrete, energy consumption-based billing systems applied to freestanding rural residential buildings encourages reductions in energy waste but produces spatially and temporally distinct heating patterns. Conventional heating systems have only basic controls and slow response times that cannot reflect actual user demand. Hence, this paper presents an experimental scheme for measuring room-wise heating demands, and thus, the actual heating demand patterns of rural residential buildings. A case study was conducted on a typical rural residential building in Beijing to validate the method's feasibility. Real-world usage patterns were obtained using indoor environment sensors and independently-controlled heating devices that collected the operational parameters of each room over three years, in a long-term non-intervention study design. Clustering analysis and various indicators were used to characterize heating demand for each room: heating patterns, usage intensity, and temporal variation. In most case, the occupants heated only a few rooms, rather than the whole building. These results have potential real-word implications for predicting occupant heating behavior and energy conservation. [ABSTRACT FROM AUTHOR]

• Six LTAAHPs are installed in six rooms as the sole heating equipment for a family. • Usage of remote control for 75 days is considered as occupant control behavior. • Difference in heating control behavior of occupants in six rooms is studied. • Only power on/off, temperature set, and fan speed are frequently used by occupants. • The proposed method helps to further understand real-life heating demand. The traditional design principle of household heating is based on full-space and continuous heating, which does not match the true heating demands of different functional spaces and thus leads to energy wastage. In this study, we used low-temperature air-to-air heat pumps (LTAAHPs) for space heating and studied the heating demands of six different rooms by testing the control behavior of occupants using heating equipment in a rural house in Beijing, China. The diverse heating demands of the six rooms were observed for 75 days (from January 12 to March 27, 2017) and verified using field measurements: (1) the utilization rates of the LTAAHPs differ; the mean rates range from 6.0% to 58.7%, indicating that heating is not required 41.3%–94.0% of the time; (2) the desired temperature settings differ, with average temperatures ranging from 20.3 to 25.7℃; (3) the power inputs per unit heating area differ, with average inputs ranging from 34.2 to 97.4 W/m2; and (4) the energy consumption per unit heating area differs, ranging from 6.7 to 37.3 kWh/m2. These results indicate that different rooms have different heating demands, although they belong to the same household. This study helps to understand actual heating demands while saving energy based on the proper control of individual heating equipment. [ABSTRACT FROM AUTHOR]