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As a low-cost demand-side management application, non-intrusive load monitoring (NILM) offers feedback on appliance-level electricity usage without extra sensors. NILM is defined as disaggregating loads only from aggregate power measurements through analytical tools. Although low-rate NILM tasks have been conducted by unsupervised approaches based on graph signal processing (GSP) concepts, enhancing feature selection can still contribute to performance improvement. Therefore, a novel unsupervised GSP-based NILM approach with power sequence feature (STS-UGSP) is proposed in this paper. First, state transition sequences (STS) are extracted from power readings and featured in clustering and matching, instead of power changes and steady-state power sequences featured in other GSP-based NILM works. When generating graph in clustering, dynamic time warping distances between STSs are calculated for similarity quantification. After clustering, a forward-backward power STS matching algorithm is proposed for searching each STS pair of an operational cycle, utilizing both power and time information. Finally, load disaggregation results are obtained based on STS clustering and matching results. STS-UGSP is validated on three publicly accessible datasets from various regions, generally outperforming four benchmarks in two evaluation metrics. Besides, STS-UGSP estimates closer energy consumption of appliances to the ground truth than benchmarks.

In the present research, a new dendritic fibrous nanosilica (DFNS) based nanocatalyst (DFNS/Dy 2 Ce 2 O 7 ) that has high surface zone as well as easy availability of active zones has been properly expanded using an easy approach. DFNS that possess high surface zone is functionalized by amino groups working as the anchors such that the nanoparticles of Dy 2 Ce 2 O 7 are fine-dispersed onto the DFNS microspheres fibres, without any aggregation. The nanoparticles of Dy 2 Ce 2 O 7 are produced by a green approach in the attendance of gum of ferula assa-foetida of Dy(NO 3 ) 3 and (NH 4 ) 2 Ce(NO 3 ) 6 as ceric and dysprosium sources. The structural evaluation of the samples proved the synthesis of Dy 2 Ce 2 O 7 NPs. Moreover, a straight way for the production of 3-aryl-2-oxazolidinones by carbon dioxide, olefins, and anilines was obtained utilizing nano catalyst of DFNS/Dy 2 Ce 2 O 7 in the lack of solvent. The reaction works properly at moderate states. This method that is cheap and simple may be used to different olefins that has proper to excellent 3-aryl-2-oxazolidinones productions. The catalyst may be simply recycled for ten times without considerable losing the activity of its catalytic. Graphic Abstract

Ankylosing spondylitis (AS) stands as a persistent inflammatory ailment predominantly impacting the axial skeleton, with the immune system and inflammation intricately entwined in its pathogenesis. This study endeavors to elucidate gender-specific patterns in immune cell infiltration and diverse forms of cell demise within the AS milieu. The aim is to refine the diagnosis and treatment of gender-specific AS patients, thereby advancing patient outcomes. In the pursuit of our investigation, two datasets (GSE25101 and GSE73754) pertinent to ankylosing spondylitis (AS) were meticulously collected and normalized from the GEO database. Employing the CIBERSORT algorithm, we conducted a comprehensive analysis of immune cell infiltration across distinct demographic groups and genders. Subsequently, we discerned differentially expressed genes (DEGs) associated with various cell death modalities in AS patients and their healthy counterparts. Our focus extended specifically to ferroptosis-related DEGs (FRDEGs), cuproptosis-related DEGs (CRDEGs), anoikis-related DEGs (ARDEGs), autophagy-related DEGs (AURDEGs), and pyroptosis-related DEGs (PRDEGs). Further scrutiny involved discerning disparities in these DEGs between AS patients and healthy controls, as well as disparities between male and female patients. Leveraging machine learning (ML) methodologies, we formulated disease prediction models employing cell death-related DEGs (CDRDEGs) and identified biomarkers intertwined with cell death in AS. Relative to healthy controls, a myriad of differentially expressed genes (DEGs) linked to cell death surfaced in AS patients. Among AS patients, 82 FRDEGs, 29 CRDEGs, 54 AURDEGs, 21 ARDEGs, and 74 PRDEGs were identified. In male AS patients, these numbers were 78, 33, 55, 24, and 94, respectively. Female AS patients exhibited 66, 41, 40, 17, and 82 DEGs in the corresponding categories. Additionally, 36 FRDEGs, 14 CRDEGs, 19 AURDEGs, 10 ARDEGs, and 36 PRDEGs exhibited differential expression between male and female AS patients. Employing machine learning techniques, LASSO, RF, and SVM-RFE were employed to discern key DEGs related to cell death (CDRDDEGs). The six pivotal CDRDDEGs in AS patients, healthy controls, were identified as CLIC4, BIRC2, MATK, PKN2, SLC25A5, and EDEM1. For male AS patients, the three crucial CDRDDEGs were EDEM1, MAP3K11, and TRIM21, whereas for female AS patients, COX7B, PEX2, and RHEB took precedence. Furthermore, the trio of DDX3X, CAPNS1, and TMSB4Y emerged as the key CDRDDEGs distinguishing between male and female AS patients. In the realm of immune correlation, the immune infiltration abundance in female patients mirrored that of healthy controls. Notably, key genes exhibited a positive correlation with T-cell CD4 memory activation when comparing male and female patient samples. This study engenders a more profound comprehension of the molecular underpinnings governing immune cell infiltration and cell death in ankylosing spondylitis (AS). Furthermore, the discernment of gender-specific disparities among AS patients underscores the clinical significance of these findings. By identifying DEGs associated with diverse cell death modalities, this study proffers invaluable insights into potential clinical targets for AS patients, taking cognizance of gender-specific nuances. The identification of gender-specific biological targets lays the groundwork for the development of tailored diagnostic and therapeutic strategies, heralding a pivotal step toward personalized care for AS patients.

Obstructive sleep apnea (OSA) is associated with metabolic disorders such as insulin resistance and liver fat accumulation. However, the specific mediating role of liver-related metabolic indicators in this association has not been fully studied. The purpose of this study was to investigate the relationship between Metabolic Score for Insulin Resistance (METS-IR) and OSA, focusing on the mediating effects of liver fat percentage (PLF) and hepatic steatosis index (HSI). Understanding these mechanisms may provide insights into targeted interventions for OSA. A total of 12,655 participants from the National Health and Nutrition Examination Survey (NHANES) were included in this analysis. Obstructive sleep apnea (OSA) was assessed using the NHANES questionnaire. Weighted multivariate logistic regression was employed to assess the relationship between METS-IR and OSA, with a mediation model constructed to explore the mediating roles of key liver and metabolic markers, including PLF, HSI, SII and OBS. Among 12,655 subjects, 31.04% had OSA. METS-IR was closely related to the increased risk of OSA, and the highest quartile group of METS-IR had a significantly increased risk of OSA (OR = 2.36, 95% CI 1.73–3.23). Mediating effect analysis showed that PLF and HSI mediated 6.95% and 17.87% of the effects, respectively, while systemic immunity-inflammation index (SII) and oxidative balance score (OBS) had no significant mediating effect. METS-IR is an important predictor of OSA risk, primarily mediated by hepatic lipid accumulation. Addressing insulin resistance and hepatic metabolic health is crucial for the effective management of OSA and provides valuable guidance for clinical risk assessment in susceptible populations.

Six-axis force/torque sensors are widely installed in manipulators to help researchers achieve closed-loop control. When manipulators work in comic space and deep sea, the adverse ambient environment will cause various degrees of damage to F/T sensors. If the disability of one or two dimensions is restored by self-restoration methods, the robustness and practicality of F/T sensors can be considerably enhanced. The coupling effect is an important characteristic of multi-axis F/T sensors, which implies that all dimensions of F/T sensors will influence each other. We can use this phenomenon to speculate the broken dimension by other regular dimensions. Back propagation neural network (BPNN) is a classical feedforward neural network, which consists of several layers and adopts the back-propagation algorithm to train networks. Hyperparameters of BPNN cannot be updated by training, but they impact the network performance directly. Hence, the particle swarm optimization (PSO) algorithm is adopted to tune the hyperparameters of BPNN. In this work, each dimension of a six-axis F/T sensor is regarded as an element in the input vector, and the relationships among six dimensions can be obtained using optimized BPNN. The average MSE of restoring one dimension and two dimensions over the testing data is 1.1693×10−5 and 3.4205×10−5, respectively. Furthermore, the average quote error of one restored dimension and two restored dimensions are 8.800×10−3 and 8.200×10−3, respectively. The analysis of experimental results illustrates that the proposed fault restoration method based on PSO-BPNN is viable and practical. The F/T sensor restored using the proposed method can reach the original measurement precision.

What are the main findings? * A multi-track InSAR framework identified 530 active landslides in Jishi Mountain, with reliability enhanced by geometric masking and C-Index checks. * The application of a local parallel flow model derived 2D deformation fields for 154 landslides, enabling their classification into five movement patterns. A multi-track InSAR framework identified 530 active landslides in Jishi Mountain, with reliability enhanced by geometric masking and C-Index checks. The application of a local parallel flow model derived 2D deformation fields for 154 landslides, enabling their classification into five movement patterns. What is the implication of the main finding? * The approach offers a transferable, non-contact solution for interpreting landslide mechanisms in complex and remote terrains. * The movement pattern classification supports differentiated risk assessment and informs targeted mitigation strategies. The approach offers a transferable, non-contact solution for interpreting landslide mechanisms in complex and remote terrains. The movement pattern classification supports differentiated risk assessment and informs targeted mitigation strategies. Frequent extreme climate events have intensified landslide hazards in mountainous regions, necessitating efficient identification and classification to understand movement mechanisms and mitigate risks. This study develops a novel, non-contact InSAR framework that seamlessly integrates three key steps—Identification, Inversion, and Classification—to address this challenge. By applying this framework to ascending and descending Sentinel-1 data in the complex terrain of the Jishi Mountain region, we first introduce geometric distortion masking and a C-Index deformation consistency check, which enables the reliable identification of 530 active landslides, with 154 detected in both orbits. Second, we employ a local parallel flow model to invert the landslide movement geometry without relying on DEM-derived prior assumptions, successfully retrieving the two-dimensional (sliding and normal direction) deformation fields for all 154 consistent landslides. Finally, by synthesizing these 2D deformation patterns with geomorphological features, we achieve a systematic classification of movement types, categorizing them into retrogressive translational (31), progressive translational (66), rotational (19), composite (24), and earthflows (14). This integrated methodology provides a validated, transferable solution for deciphering landslide mechanisms and assessing risks in remote, complex mountainous areas.

Cellular senescence-related genes significantly influence the pathophysiology of ischemia–reperfusion injury (IRI). And identifying their shared biomarkers may improve the diagnosis and treatment of IRI. We analyzed three datasets (GSE61592, GSE67308, GSE83472) from the Gene Expression Omnibus database, and intersected them with the cellular senescence-related dataset to obtain 26 significantly altered cellular senescence-related differentially expressed genes in IRI. We used machine learning methods, including logistic regression, LASSO regression for feature screening, and SVM analysis, to construct a model identifying 6 key genes ( CDKN2B , TP53 , ZNF277 , ID1 , STAT3 , TERF2 ). Internal validation shows that the model has high diagnostic accuracy. Immune infiltration analysis revealed a significant increase in 20 immune cell subpopulations in IRI. Among these, CDKN2B showed a strong correlation with central memory CD4 + T cells. Furthermore, regulatory network analysis revealed that TP53 is a high-priority drug target; TERF2 is a hub gene regulated by transcription factors; and ID1 and STAT3 are hub genes regulated by miRNAs. Finally, we validated the differential expression of these 6 genes in a mouse IRI model by qRT-PCR and immunohistochemistry. Overall, this study established a novel diagnostic model containing 6 genes. This model provides new insights into the pathological mechanisms of IRI and offers new directions for improving the early diagnosis and targeted treatment of IRI.

In this paper, we shall give a concise review of recent numerical methods of some generalized fractional models. As is elucidated later, the generalized fractional models may arise from either mathematical point of view or application point of view. We shall focus on the former one and discuss the numerical methods for these models in a concise manner. Finally, some potential research directions are proposed based on existing results as well as some advanced new topics. We hope this review can provide a sketch of current and future studies of generalized fractional models for interested readers.

In the present study, to effectively carbonylate cinnamyl chloride and phenylacetylene with CO 2 , α -cyclodextrin doping dendritic fibrous nanosilica (DFNS) supported nanoparticles of gold was used as a catalyst (DFNS/ α -CD/Au NPs). In the catalyst, the nanoparticles of Au were in situ reduced on the surfaces of DFNS. Transmission electron microscopy (TEM), scanning electron microscope (SEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), and X-ray energy dispersive spectroscopy (EDS) were utilized for characterizing the nanostructures DFNS/ α -CD/Au. It was found that the nanostructures of DFNS/ α -CD/Au can be nominated due to their effective and novel catalytic behaviour during the synthesis of 3a,4-dihydronaphtho[2,3-c] furan-1(3H)-ones from cinnamyl chloride, phenylacetylene, and CO 2 . Graphic Abstract

Constructed wetland systems have been widely used in China due to their advantages of good treatment effect, low cost and environmental friendliness. However, traditional constructed wetlands have challenges in application such as deactivation due to filler clogging, difficulty in filler replacement and low adaptability. To address the above problems, this research proposes a modular filler design constructed wetland based on the concept of assembly construction, which can quickly replace the clogged filler without destroying the overall structure of the wetland. Four commonly used fillers were selected and applied to the pilot system of the assembled constructed wetland in this study, in order to investigate the purification effect of the constructed wetland system with different filler module combinations (CW1, CW2, CW3) on the simulated wastewater. The results showed that the filler combination CW1 was the best for the removal of NH4+-N, and for TP and COD, CW2 has the best removal effect. Therefore, the assembled constructed wetland is adjustable and substantially reduces the maintenance cost, which provides technical guidance for its application in engineering.