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With the increasing share of wind power in the energy sector, many countries start to cut back supporting policies for wind power and shift towards market-oriented schemes, challenging the profitability of wind farms. Energy storage offers a flexible solution to enhance their profitability. This work explores different wind-related storage investment modes, including 1) direct ownership, 2) cooperative, and 3) competitive modes in a market-based environment. For the direct ownership mode, a bilevel single-leader-single–follower Stackelberg game model is proposed, where wind farms invest in and operate storage facilities strategically to maximize their profits in the upper level, while the lower-level problem represents the system operator’ s market-clearing process. A cooperative game framework is presented for the cooperative mode, that wind farms and storage investors agree on a profit allocation rule, i.e., Shapley value or Nucleolus to collaborate in investing and bidding as a coalition. The competitive mode is interpreted as a multi-leader-single-follower Stackelberg game, describing an independent investor investing in and operating storage facilities in competition with wind farms. Case studies conducted on a 6-bus and the IEEE 30-bus test systems demonstrate that storage facilities directly invested in by wind farms are the best option for maximizing their profits, resulting in up to an 8.7% increase. The cooperative option provides a suboptimal increase of up to 3.1%, diversifying the costs and risks associated with storage investments. In contrast, the competitive mode can diminish wind farms’ profitability, with up to a 30.6% decrease in profits.

Fabric defect detection is a crucial step of quality control in textile enterprises. The use of computer vision inspection technology in the textile industry is key to achieving intelligent manufacturing. This study sought to determine the progress made and future research directions in intelligent fabric surface defect detection by comprehensively reviewing published literature in terms of algorithms, datasets, and detection systems. Initially, the detection methods are classified as traditional and learning-based methods. The traditional methods are subdivided into model, spectral, statistical, and structural approaches. Learning-based methods are categorized into classical machine learning methods and deep learning methods. The principles, model performance, detection rate, real-time performance, and applicability of deep learning methods are highlighted and compared. In addition, the strengths and weaknesses of all the approaches are elaborated. The use of fabric defect datasets and deep learning frameworks is analyzed. Public datasets and commonly used frameworks are collated and organized. The application of existing fabric inspection systems on the market is outlined. Fabric defect types are systematically named and analyzed. Finally, future research directions are discussed to provide guidance for researchers in related fields.

Background: Starting from 2009, H1N1 has been one of the respiratory diseases that afflict the global population. Concurrently, due to the influence of COVID-19, it has become widely accepted that preventing the virus's spread necessitates personal protection measures and disinfection in public spaces. Experiments: This study conducted two experiments. In the classroom experiment, six UVC dose test points were calibrated to test whether the UVC dose at each testing point met the standards for inactivating IAVs and the time required to meet the standards. In the simulated classroom experiment, seven square slides made of IAVs were placed. After 10 min of robot movement, irradiated sterile square slides were made into suspension and injected into chicken embryos. Cultivate chicken embryos and conduct IAVs testing. Results: Classroom experiment has shown that 5 testing points can meet the standards for inactivating IAVs(3 mJ/cm2), with a required time of 80 min, 40 min, 15 min, 5 min and 10 min. The UVC dose for testing points that do not meet the standards in 80 min is only 0.5 mJ/cm2. The simulation classroom experiment outcomes revealed that 99.99 % of IAVs were deactivated. Furthermore, this study established both a desktop control group and a chair arm control group, both of which yielded identical results, indicating an inactivation logarithm of IAVs≥4log. Conclusion: The study presented that IAVs on the surface of an object can be effectively and rapidly deactivated at an irradiation density of 1.8 mW/cm2. Meanwhile, the study provides evidence of the feasibility of using the GXU robot to inactivate IAVs in a classroom environment.

Abstract With large‐scale integration of renewable generation, energy storage is expected to play an important role in providing flexibility to energy systems. In this paper, the authors construct a trilevel Stackelberg game model to study the co‐investment of merchant and regulated storage in energy and reserve markets. The upper‐level problem is a profit‐maximizing storage investment problem with a desired rate‐of‐return solved by a merchant investor. In the middle‐level problem, the system operator (SO) makes regulated storage investment decisions to minimize system cost. In the lower‐level problem, the SO clears energy and reserve markets. The proposed model captures interactions of regulated and merchant storage investment. Also, it clarifies how different ownership structures of storage influence merchant storage profitability and system cost structures in different capital cost of storage investment and wind penetration level scenarios. The numerical results conducted on a 6‐bus illustrative example and the IEEE 24‐bus Reliability test case validate the proposed model. The results show that both regulated and merchant storage can increase social welfare, and social welfare remains almost the same under different ownership structures of storage.

Objectives: Evidence on cardiovascular-related and all-cause mortality risks in a wide range of cancer survivors is scarce but needed to inform prevention and management.Methods: We performed a nationwide prospective cohort study using information from the Continuous National Health and Nutrition Examination Survey (NHANES) in the United States and the linked mortality follow-up files, available for public access. A propensity score-matched analysis with a 1:1 ratio was conducted to reduce the baseline differences between participants with and without cancer. The relationship between cancer status and the cardiovascular-related and all-cause mortality risk was examined using weighted Cox proportional hazards regression. Independent stratification analysis and cancer-specific analyses were also performed.Results: The study sample included 44,342 participants, aged 20–85, interviewed between 1999 and 2018. Of these, 4,149 participants had cancer. All-cause death occurred in 6,655 participants, of whom 2,053 died from cardiovascular causes. Propensity-score matching identified 4,149 matched pairs of patients. A fully adjusted Cox proportional hazards regression showed that cancer was linked to an elevated risk of cardiovascular-related and all-cause mortality both before and after propensity score matching. Stratification analysis and cancer-specific analyses confirmed robustness of results.Conclusion: Our study confirmed that cancer was strongly linked to cardiovascular-related and all-cause mortality, even after adjusting for other factors that could impact a risk, including the American Heart Association (AHA)’s Life’s Simple 7 cardiovascular health score, age, sex, ethnicity, marital status, income, and education level.

Ultraviolet (UV) radiation has been widely utilized as a disinfection strategy to effectively eliminate various pathogens. The disinfection task achieves complete coverage of object surfaces by planning the motion trajectory of autonomous mobile robots and the UVC irradiation strategy. This introduces an additional layer of complexity to path planning, as every point on the surface of the object must receive a certain dose of irradiation. Nevertheless, the considerable dosage required for virus inactivation often leads to substantial energy consumption and dose redundancy in disinfection tasks, presenting challenges for the implementation of robots in large-scale environments. Optimizing energy consumption of light sources has become a primary concern in disinfection planning, particularly in large-scale settings. Addressing the inefficiencies associated with dosage redundancy, this study proposes a dose coverage planning framework, utilizing MOPSO to solve the multi-objective optimization model for planning UVC dose coverage. Diverging from conventional path planning methodologies, our approach prioritizes the intrinsic characteristics of dose accumulation, integrating a UVC light efficiency factor to mitigate dose redundancy with the aim of reducing energy expenditure and enhancing the efficiency of robotic disinfection. Empirical trials conducted with autonomous disinfecting robots in real-world settings have corroborated the efficacy of this model in deactivating viruses.

BackgroundPrior research on women who had hysterectomies has shown mixed results on whether or not hysterectomies increased the incidence of stroke and cause-specific or all-cause mortality.MethodsUsing information from the Continuous National Health and Nutrition Examination Survey (NHANES) in the United States, including linked mortality follow-up files available for public access, a multicycle cross-sectional design mortality linkage study was performed.ResultsConducted during the years 2005–2018, the study sample included 14,214 female participants ranging in age from 20 to 85 years. The relationship between the hysterectomy status and the risk of stroke and cause-specific and all-cause mortality was examined using a series of weighted logistic regressions and Cox proportional hazards regressions, respectively. The presence of a hysterectomy was consistently linked to an elevated risk of stroke using weighted logistic regression models. The hysterectomy status, however, consistently showed no effect on survival by adjusted weighted Cox regression analysis.ConclusionOur study found a significant association between hysterectomy and stroke, even after adjusting for other factors that could impact risk, such as the American Heart Association (AHA)'s Life's Simple 7 cardiovascular health score and variables of age, ethnicity, marital status, income, education, and depression severity.

BackgroundMorbidity and mortality of arteriosclerotic cardiovascular disease (ASCVD) varied according to socioeconomic status (SES), and evidence on the association between SES and ASCVD risk, and cause-specific and all-cause mortality was nevertheless lacking in large-scale or population-based studies.MethodsA multicycle cross-sectional design and mortality linkage study was conducted using data from Continuous National Health and Nutrition Examination Survey (NHANES) in the United States, including public use linked mortality follow-up files through December 31, 2019. Poverty income ratio (PIR) served as a SES index. A series of weighted Logistic regressions and Cox proportional hazards regressions were used to investigate the association between the SES and the risk of ASCVD and mortality, respectively.ResultsThe study sample was comprised of 30,040 participants aged 20–85 years old during the 2005–2018 period. Weighted Logistic regression models consistently indicated significant relationship between people experiencing poverty and increased risk of ASCVD, and linear trend tests were all statistically significant (all P for trend < 0.001). Additionally, weighted Cox regression analysis consistently demonstrated that the hazards of cause-specific and all-cause mortality increased, with the decrease of each additional income level, and trend analyses indicated similar results (all P for trend < 0.001).ConclusionsOur study confirmed that the SES was strongly linked to living with ASCVD, and cause-specific and all-cause mortality, even after adjusting for other factors that could impact risk, such as the American Heart Association (AHA)'s Life's Simple 7 cardiovascular health score and variables of age, sex, marital status, education, and depression severity.

Research on the association between level of depression and coronary heart disease (CHD), stroke risk, and all-cause and cardiovascular mortality is lacking in large-scale or population-based studies incorporating cardiovascular disease (CVD) endpoints. We aim to assess the relationship between the level of a person's depression and their risk of CHD, stroke, and all-cause and cardiovascular mortality. Utilizing data from the United States National Health and Nutrition Examination Survey (NHANES), multicycle cross-sectional design and mortality linkage studies were conducted. The study sample included 30918 participants aged 20–85 years old during the 2005–2018 period. Depression was assessed using the nine-item Patient Health Questionnaire (PHQ-9), with scores of 5, 10, 15, and 20 being the cut-off points for mild, moderate, moderately severe, and severe depression, respectively. A series of weighted logistic regression analyses and Cox proportional hazards models were utilized to examine the relationship between the level of depression with the risk of CHD, stroke, all-cause, and cardiovascular mortality. Trend analyses were conducted by entering the level of depression as a continuous variable and rerunning the corresponding regression models. Weighted logistic regression models consistently indicated a statistically significant association between the level of depression and increased risk of CHD and stroke, and those linear trend tests were statistically significant (P for trend < 0.001). Furthermore, weighted Cox regression analyses consistently indicated that participants who had a more severe degree of depression were at a higher risk of all-cause death, and trend analyses suggested similar results (P for trend < 0.001). Another weighted Cox regression analysis also consistently indicated that except for severe depression, the hazard of cardiovascular death was increased with each additional level increase of depression. Our study confirmed that the level of depression was strongly associated with CHD, stroke, and all-cause and cardiovascular mortality, even after accounting for other factors that could impact risk, including variables of age, gender, ethnicity, income, education, body mass index (BMI), marital, and smoking status.

Three-dimensional point cloud classification tasks have been a hot topic in recent years. Most existing point cloud processing frameworks lack context-aware features due to the deficiency of sufficient local feature extraction information. Therefore, we designed an augmented sampling and grouping module to efficiently obtain fine-grained features from the original point cloud. In particular, this method strengthens the domain near each centroid and makes reasonable use of the local mean and global standard deviation to extract point cloud’s local and global features. In addition to this, inspired by the transformer structure UFO-ViT in 2D vision tasks, we first tried to use a linearly normalized attention mechanism in point cloud processing tasks, investigating a novel transformer-based point cloud classification architecture UFO-Net. An effective local feature learning module was adopted as a bridging technique to connect different feature extraction modules. Importantly, UFO-Net employs multiple stacked blocks to better capture feature representation of the point cloud. Extensive ablation experiments on public datasets show that this method outperforms other state-of-the-art methods. For instance, our network performed with 93.7% overall accuracy on the ModelNet40 dataset, which is 0.5% higher than PCT. Our network also achieved 83.8% overall accuracy on the ScanObjectNN dataset, which is 3.8% better than PCT.

To address the problems that the traditional permanent magnet synchronous motor airgap flux is difficult to adjust and that the weak magnetic speed expansion ability is poor, a new series-connected hybrid excitation permanent magnet synchronous motor is proposed. A DC excitation winding is added to the rotor, allowing the excitation field generated by this winding to form a series connection with the magnetic field of the permanent magnets. The structure of this paper includes an overview of the novel rotor structure and principle of operation. For the complex rotor structure, a multi-objective genetic algorithm is used for optimization, followed by finite element analysis to compare the performance of the initial motor, the optimized motor, and the conventional motor in terms of no-load air-gap magnetism, reverse electromotive force, as well as output torque and efficiency. The magnetic load of the motor in the demagnetized state is increased from 0.2 to 0.266 compared to the unexcited state, and the magnetization capacity is improved by 33%. The output torque of the optimized motor is 252 N·m at low speed; the output torque of the conventional motor is 220 N·m; and the starting torque of the motor was improved by 14.5%. The maximum speed is increased from 10,000 rpm to 11,500 rpm, and the speed expansion capacity is enhanced by 15%. The effectiveness and feasibility of the series-connected hybrid excitation permanent magnet synchronous motor are verified. [ABSTRACT FROM AUTHOR]

To address the problems of torque ripple, vibration, and noise generated by cogging torque in a dual-stator single-rotor axial magnetic field permanent magnet motor, this article adopts an unequal thickness pole structure to reduce cogging torque. At first, the process of cogging torque generation is analyzed, followed by an examination of the mathematical formulation of cogging torque using the energy technique and Fourier decomposition method. Then, the impacts of several pole optimization approaches on cogging torque reduction are then compared, and the findings are investigated using the finite element method to demonstrate the efficiency of the optimization method. The results show that the optimization effect of unequal thickness pole structure is the best. Lastly, the optimized motor's air-gap ux density, counter-electromotive force, harmonic content, and rotor mechanical strength were compared and studied to demonstrate that the unequal-thickness structure used in this research can increase motor performance. Finally, based on the determined motor parameters, experimental study of the prototype was carried out to verify the correctness of the motor structure and analysis. [ABSTRACT FROM AUTHOR]

Transforming growth factor-ß1 promotes excessive extracellular matrix deposition and epithelial-mesenchymal transition of tubular epithelial cells, thus stimulating the progression of renal fibrosis. Carvacrol has been shown to alleviate cardiac and liver fibrosis and attenuate renal injury. However, the role of carvacrol on renal fibrosis has not been examined. First, measurements using Cell Counting Kit-8 showed that carvacrol reduced cell viability of tubular epithelial cell line HK-2 in a dose-dependent fashion. Second, transforming growth factor-ß1 induced excessive extracellular matrix deposition in HK-2 cells with enhanced collagen I, collagen IV, and fibronectin expression. However, carvacrol decreased the expression of collagen I, collagen IV in a dose-dependent manner and fibronectin to attenuate the extracellular matrix deposition in HK-2. Third, carvacrol attenuated TGF-ß1-induced decrease of E-cadherin and increase of snail, vimentin, and alpha-smooth muscle actin in HK-2 cells. Transforming growth factor-ß1-induced increase in PI3K and AKT phosphorylation in HK-2 were also reversed by carvacrol. Collectively, carvacrol ameliorates renal fibrosis through inhibition of transforming growth factor-ß1-induced extracellular matrix deposition and epithelial-mesenchymal transition of HK-2 cells, providing potential therapy for the treatment of renal fibrosis. [ABSTRACT FROM AUTHOR]