Miracle-dz/UniFAR-FTUs huggingface dataset:
FSDA: Flexible Subset Data Aggregation for Smart Grid

Data aggregation is a practical method to protect individual privacy while using data utility, which aggregates the real-time electricity consumption data in an area into a single value in smart grids. Based on traditional data aggregation, multisubset data aggregation can obtain data distribution characteristics rather than just the sum of data. However, almost all existing multisubset aggregation schemes do not pay attention to adjusting the size of subsets according to the data distribution, which limits the data utility. In this article, a flexible subset data aggregation (FSDA) scheme based on Paillier homomorphic encryption for smart grids is proposed. The proposed FSDA scheme can dynamically adjust the size of each subset and aggregate the electricity consumption data into the corresponding subsets while protecting individual privacy. It is proved that FSDA scheme can obtain the distribution of consumption data and resist various internal attacks aiming to expose the individual or aggregated data. Experimental results show that FSDA scheme has lower overhead compared with related schemes, which is practical for resource-constrained smart meter devices.
Green Marketing: Drivers in the Process of Buying Green Products—The Role of Green Satisfaction, Green Trust, Green WOM and Green Perceived Value

Green marketing is currently one of the most powerful strategies in the corporate world as it responds to a growing demand for green products. Therefore, this study aims to analyse the influence of green perceived value on green trust and green satisfaction, study how both variables impact green word of mouth in turn and, at the same time, how the three variables influence green purchase intention. Data collection was carried out through a digital survey of buyers of green products. The hypotheses posed were solved with a PLS-SEM model through the Smart-PLS software. The results showed that green perceived value positively affects green trust and green satisfaction. Furthermore, green satisfaction was found to influence green trust and green WOM. Green trust also influences green WOM. Regarding the antecedents of green purchase intention, only green satisfaction and green trust showed a positive relationship. In contrast, green WOM did not show a relationship with green purchase intention. The article shows the importance of green satisfaction for green companies in achieving green purchase intention, green WOM and green trust. Green perceived value is also an important variable, as it is the trigger for the process that leads to green purchase intention.
Design and single-parameter adaptive fuzzy control of pneumatic lower limb exoskeleton with full state constraints

Abstract With the excellent characteristic of intrinsic compliance, pneumatic artificial muscle can improve the interaction comfort of wearable robotic devices. This paper resolves the safety tracking control problem of a pneumatically actuated lower limb exoskeleton system. A single-parameter adaptive fuzzy control strategy is proposed with high control precision and full state constraints for the safe gait training tasks. Based on the barrier Lyapunov function, all signals in the closed-loop system can be bounded in finite time, which guarantees the deviation of the exoskeleton’s moving trajectory within a bounded range. Furthermore, with the proposed single-parameter adaptive law, the computational burden and the complexity of the control are reduced significantly. Finally, numerical simulations, no-load tracking experiments, and passive and active gait training experiments with healthy subjects validate the effectiveness of the proposed method.
The Blazar 3C 66A in 2003‐2004: hadronic versus leptonic model fits

The low‐frequency peaked BL Lac object 3C 66A was the subject of an extensive multi‐wavelength campaign from July 2003 till April 2004, which included quasi‐simultaneous observations at optical, X‐rays and very high energy gamma‐rays. Here we apply the hadronic Synchrotron‐Proton Blazar (SPB) model to the observed spectral energy distribution time‐averaged over a flaring state, and compare the resulting model fits to those obtained from the application of the leptonic Synchrotron‐Self‐Compton (SSC) model. The results are used to identify diagnostic key predictions of the two blazar models for future multi‐wavelength observations.
Analysis of News Dissemination Path and Impact of Big Data Technology in Public Health Emergencies

Throughout the past public health early warning work, there is still a lack of early warning. Therefore, starting from the reform of the public health early warning mechanism, this paper sorts out the design and development of our country's public health early warning mechanism and its legal framework and further analyzes the unresolved problems that exist in early warning work. At this stage, our country's public health departments have put forward targeted suggestions for improving the early warning mechanism for emergencies and stabilizing public order. In this context, the following data are obtained: (1) The timeliness under the big data model is 73.81%, the prudence is 84.25%, the sustainability is 96.28%; the timeliness under the data mining model is 61.98%, prudence is 77.58%, sustainability is 83.24%; and the timeliness of the intelligent computing model is 53.44%, prudence is 65.58%, and sustainability is 96.28%. (2) During public health emergencies, the path of news dissemination includes 54 people for radio and television communication, accounting for 13%; 98 people for digital communication, accounting for 24%; 73 people for digital TV communication, accounting for 19%; the number of public relations companies was 63 people, accounting for 16%; the number of people who reprinted and spread was 52 people, accounting for 12%; the number of people who searched and spread was 73 people, accounting for 19%; the number of people who spread mobile phone messages was 32 people, accounting for 9%. At this stage, according to the characteristics of public health emergencies, the problems existing in the early warning work are analyzed, and specific suggestions for improving the early warning mechanism of public health emergencies in our country are put forward to stabilize the “public order. ” Research in this area is ongoing.
MAVIS: preliminary design of the adaptive optics module

MAVIS will be part of the next generation of VLT instrumentation and it will include a visible imager and a spectrograph, both fed by a common Adaptive Optics Module. The AOM consists in a MCAO system, whose challenge is to provide a 30” AO-corrected FoV in the visible domain, with good performance in a 50% sky coverage at the Galactic Pole. To reach the required performance, the current AOM scheme includes the use of up to 11 reference sources at the same time (8 LGSs + 3 NGSs) to drive more than 5000 actuators, divided into 3 deformable mirrors (one of them being UT4 secondary mirror). The system also includes some auxiliary loops, that are meant to compensate for internal instabilities (including WFSs focus signal, LGS tip-tilt signal and pupil position) so to push the stability of the main AO loop and the overall performance. Here we present the Preliminary Design of the AOM, which evolved, since the previous phase, as the result of further trade-offs and optimizations. We also introduce the main calibration strategy for the loops and sub-systems, including NCPA calibration approach. Finally, we present a summary of the main results of the performance and stability analyses performed for the current design phase, in order to show compliance to the performance requirements.
Working with artificial intelligence surveillance during the COVID-19 pandemic: a mixed investigation of the influence mechanism on job engagement in hospitality industry

ABSTRACT This paper examines the interaction effects of social support (supervisor support and coworker support) and AI surveillance on employee job engagement drawing on the social exchange theory. Mixed research methods were applied. Researchers collected 358 valid time-lagged designed questionnaires and tested the model using a path analysis approach. The results revealed that there is a moderated mediation mechanism in relationships between supervisor support/coworker support and job engagement, in which both self-efficacy and self-esteem are mediating variables and AI surveillance plays a moderating role. Specifically, when the degree of job control with AI surveillance is at a low level, the effect of supervisor support/coworker support on employees’ self-efficacy/self-esteem is stronger. The indirect relationship between supervisor support/coworker support and job engagement through self-esteem is moderated by job control with AI surveillance as well, and the indirect relationship becomes stronger when job control with AI surveillance is lower. Findings from a series of semi-structured post-hoc interviews with 18 hotel employees interpretatively support the survey results. This research fills this gap by analyzing relationships among social support, AI surveillance, and job engagement and provides positive suggestions for hotel operation and employee management with AI surveillance during pandemic.
Motivation to Grow Women Entrepreneurs' Home-Based Businesses

Entrepreneurs play an important role in the economy's development. Compared to men, a higher number of women are becoming entrepreneurs. There is a commensurate interest in examining who these women are, what motivates them to become business owners, and what influences their economic success. This paper explores the motivational factors faced by female entrepreneurs as they grow their home-based business (HBB). The research has been conducted via interviews with women entrepreneurs that are involved in HBB, which is located in the Klang Valley area in Malaysia. Results show that women owning and managing HBBs are motivated to grow their business through intrinsic motivations, including a need for independence and a need for achievement. They are also motivated by extrinsic motivations, which are financial issues, fame, and positive feedback from others. The result of this study will help all women entrepreneurs in HBB to always grow and to educate every family, society, and institutions to always support them. This paper also adds to the theory of what motivates people to start their own businesses and to research on what motivates people to grow.
An enhanced CNN-LSTM remaining useful life prediction model for aircraft engine with attention mechanism

Remaining useful life (RUL) prediction is one of the key technologies of aircraft prognosis and health management (PHM) which could provide better maintenance decisions. In order to improve the accuracy of aircraft engine RUL prediction under real flight conditions and better meet the needs of PHM system, we put forward an improved CNN-LSTM model based on the convolutional block attention module (CBAM). First, the features of aircraft engine operation data are extracted by multi-layer CNN network, and then the attention mechanism is processed by CBAM in channel and spatial dimensions to find key variables related to RUL. Finally, the hidden relationship between features and service time is learned by LSTM and the predicted RUL is output. Experiments were conducted using C-MPASS dataset. Experimental results indicate that our prediction model has feasibility. Compared with other state-of-the-art methods, the RMSE of our method decreased by 17.4%, and the score of the prediction model was improved by 25.9%.
Overexpression of MsRCI2D and MsRCI2E Enhances Salt Tolerance in Alfalfa (Medicago sativa L.) by Stabilizing Antioxidant Activity and Regulating Ion Homeostasis

Rare cold-inducible 2 (RCI2) genes from alfalfa (Medicago sativa L.) are part of a multigene family whose members respond to a variety of abiotic stresses by regulating ion homeostasis and stabilizing membranes. In this study, salt, alkali, and ABA treatments were used to induce MsRCI2D and MsRCI2E expression in alfalfa, but the response time and the expression intensity of the MsRCI2D,-E genes were different under specific treatments. The expression intensity of the MsRCI2D gene was the highest in salt- and alkali-stressed leaves, while the MsRCI2E gene more rapidly responded to salt and ABA treatment. In addition to differences in gene expression, MsRCI2D and MsRCI2E differ in their subcellular localization. Akin to MtRCI2D from Medicago truncatula, MsRCI2D is also localized in the cell membrane, while MsRCI2E is different from MtRCI2E, localized in the cell membrane and the inner membrane. This difference might be related to an extra 20 amino acids in the C-terminal tail of MsRCI2E. We investigated the function of MsRCI2D and MsRCI2E proteins in alfalfa by generating transgenic alfalfa chimeras. Compared with the MsRCI2E-overexpressing chimera, under high-salinity stress (200 mmol·L−1 NaCl), the MsRCI2D-overexpressing chimera exhibited a better phenotype, manifested as a higher chlorophyll content and a lower MDA content. After salt treatment, the enzyme activities of SOD, POD, CAT, and GR in MsRCI2D- and -E-overexpressing roots were significantly higher than those in the control. In addition, after salt stress, the Na+ content in MsRCI2D- and -E-transformed roots was lower than that in the control; K+ was higher than that in the control; and the Na+/K+ ratio was lower than that in the control. Correspondingly, H+-ATPase, SOS1, and NHX1 genes were significantly up-regulated, and the HKT gene was significantly down-regulated after 6 h of salt treatment. MsRCI2D was also found to regulate the expression of the MsRCI2B and MsRCI2E genes, and the MsRCI2E gene could alter the expression of the MsRCI2A, MsRCI2B, and MsRCI2D genes. MsRCI2D- and -E-overexpressing alfalfa was found to have higher salt tolerance, manifested as improved activity of antioxidant enzymes, reduced content of reactive oxygen species, and sustained Na+ and K+ ion balance by regulating the expression of the H+-ATPase, SOS1, NHX1, HKT, and MsRCI2 genes.
Novel αO-conotoxin GeXIVA[1,2] Nonaddictive Analgesic with Pharmacokinetic Modelling-Based Mechanistic Assessment

αO-conotoxin GeXIVA[1,2] was isolated in our laboratory from Conus generalis, a snail native to the South China Sea, and is a novel, nonaddictive, intramuscularly administered analgesic targeting the α9α10 nicotinic acetylcholine receptor (nAChR) with an IC50 of 4.61 nM. However, its pharmacokinetics and related mechanisms underlying the analgesic effect remain unknown. Herein, pharmacokinetics and multiscale pharmacokinetic modelling in animals were subjected systematically to mechanistic assessment for αO-conotoxin GeXIVA[1,2]. The intramuscular bioavailability in rats and dogs was 11.47% and 13.37%, respectively. The plasma exposure of GeXIVA[1,2] increased proportionally with the experimental dose. The plasma protein binding of GeXIVA[1,2] differed between the tested animal species. The one-compartment model with the first-order absorption population pharmacokinetics model predicted doses for humans with bodyweight as the covariant. The pharmacokinetics-pharmacodynamics relationships were characterized using an inhibitory loss indirect response model with an effect compartment. Model simulations have provided potential mechanistic insights into the analgesic effects of GeXIVA[1,2] by inhibiting certain endogenous substances, which may be a key biomarker. This report is the first concerning the pharmacokinetics of GeXIVA[1,2] and its potential analgesic mechanisms based on a top-down modelling approach.
A Centrifugal Pump Fault Diagnosis Framework Based on Supervised Contrastive Learning

A novel intelligent centrifugal pump (CP) fault diagnosis method is proposed in this paper. The method is based on the contrast in vibration data obtained from a centrifugal pump (CP) under several operating conditions. The vibration signals data obtained from a CP are non-stationary because of the impulses caused by different faults; thus, traditional time domain and frequency domain analyses such as fast Fourier transform and Walsh transform are not the best option to pre-process the non-stationary signals. First, to visualize the fault-related impulses in vibration data, we computed the kurtogram images of time series vibration sequences. To extract the discriminant features related to faults from the kurtogram images, we used a deep learning tool convolutional encoder (CE) with a supervised contrastive loss. The supervised contrastive loss pulls together samples belonging to the same class, while pushing apart samples belonging to a different class. The convolutional encoder was pretrained on the kurtograms with the supervised contrastive loss to infer the contrasting features belonging to different CP data classes. After pretraining with the supervised contrastive loss, the learned representations of the convolutional encoder were kept as obtained, and a linear classifier was trained above the frozen convolutional encoder, which completed the fault identification. The proposed model was validated with data collected from a real industrial testbed, yielding a high classification accuracy of 99.1% and an error of less than 1%. Furthermore, to prove the proposed model robust, it was validated on CP data with 3.0 and 3.5 bar inlet pressure.
Socio-spatial differentiation in a Latin American metropolis: urban structure, residential mobility, and real estate in the high-income cone of Santiago de Chile

ABSTRACT The High-Income Cone (HIC) is characteristic of the urban structure of Latin-American metropolises, consisting of a delimited area of the city where inhabitants of high socioeconomic status are located, consolidating the patterns of social segregation and inequality that are typical of those societies. Despite the urban transformations experienced by the metropolises of the continent in the last decades, little study has been done to understand the internal dynamics of HICs, which are usually considered a socially homogeneous space. This article delves into the internal complexities of the HIC of Greater Santiago, investigating its residential mobility processes and distinguishing between traditional and recent inhabitants, or ‘inheritors’ and ‘achievers’. Our results indicate the presence of parallel processes of residential mobility, where ‘achievers’ are concentrated in the apartments located in the pericentral zone of the HIC, while ‘inheritors’ move to the houses located in the extreme east. This process of permeability and filtering is conditioned by the recent trends of neoliberal urban densification and expansion, where the construction of more accessible buildings allows the arrival of certain people to the pericentral areas of the HIC, while the more exclusive houses and gated-communities far east are more accessible for inheritors.
A Potential Four-Gene Signature and Nomogram for Predicting the Overall Survival of Papillary Thyroid Cancer

Background Although the prognosis of papillary thyroid cancer (PTC) is relatively good, some patients experience recurrence or distant metastasis after thyroidectomy and progress to radioactive iodine refractory stage. Therefore, accurate prediction of clinical outlook can aid to screen out the minority of patients with poorer prognosis and avoid excessive treatment in low-risk patients. Methods The RNA-seq and clinical data of PTC patients was downloaded from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) databases. Multivariate and Lasso Cox regression analyses were used to construct a prognostic nomogram to predict overall survival (OS). Thereafter, quantitative RT-PCR and Human Protein Atlas (HPA) database were employed to verify the expression of key genes. Results A four-gene risk score comprising ABI3BP, DPT, MRO, and TENM1 was exhibited strong prognostic value. Moreover, an integrated nomogram was established based on the risk score, age, AJCC (American Joint Commission on Cancer) stage, tumor size, extrathyroidal extension, and history of neoadjuvant treatment, which exhibited significantly better predictive performance than TNM stage system (P < 0.05). GSEA (Gene Set Enrichment Analysis) and GSVA (Gene Set Variation Analysis) revealed that the different tumor-associated hallmarks, biological processes, and pathways were substantially enriched in the poor-prognosis group. In addition, a ceRNA network was constructed by including the four genes (ABI3BP, DPT, MRO, and TENM1), 54 lncRNAs, and 10 miRNAs. Finally, both the relative mRNA and protein expression of ABI3BP, DPT, MRO, and TENM1 were validated. Conclusion The present study identified a four-gene risk signature and developed a novel nomogram, which could be regarded as a reliable prognostic model for PTC patients. The findings also revealed preliminary potential mechanisms that may influence the prognosis outcome. These results can be conducive to design personalized treatment and prognosis management in affected patients.
Principals’ Effective Practice of Promoting School Achievement at High Performing Secondary Schools Malaysia

The study aims to examine the effective practice of Promoting School Achievement among principals of High Performing Secondary Schools Malaysia. The framework of the study was adapted from Hallinger and Murphy [1], Latip [2], Surayya and Salleh [3] and Salleh & Hatta [4]. Data was collected using questionnaires survey from 418 respondents of principals and teachers of different genders, academic qualification, ethnicity, and, position of responsibilities at schools. The data gathered from the survey was analyzed in descriptive and inference statistics of the SPSS-Versions 23.0 programme. The findings indicated that the highest rank at ‘very effective’ practiced was function ‘Developing and Enforcing Academic Standards’ achieved mean 4.1097, second highest at ‘very effective’ practiced was function ‘Providing Incentives for Learning’ mean 4.0075 and third rank at ‘Effective’ level was function ‘Promoting Professional Development’ mean 3.6189. It is hoped that the findings of the study would substantively inspire principals and teachers to effectively facilitate students' academic performance in their respective schools and join hands in the collective effort of raising the work force compatible to meet national goals towards realization of Malaysia Development Plan 2030.
Discovery of Quinacrine as a Potent Topo II and Hsp90 Dual-Target Inhibitor, Repurposing for Cancer Therapy

Topo II and Hsp90 are promising targets. In this study, we first verified the structural similarities between Topo IIα ATPase and Hsp90α N−ATPase. Subsequently, 720 compounds from the Food and Drug Administration (FDA) drug library and kinase library were screened using the malachite green phosphate combination with the Topo II-mediated DNA relaxation and MTT assays. Subsequently, the antimalarial drug quinacrine was found to be a potential dual−target inhibitor of Topo II and Hsp90. Mechanistic studies showed that quinacrine could specifically bind to the Topo IIα ATPase domain and inhibit the activity of Topo IIα ATPase without impacting DNA cleavage. Furthermore, our study revealed that quinacrine could bind Hsp90 N−ATPase and inhibit Hsp90 activity. Significantly, quinacrine has broad antiproliferation activity and remains sensitive to the multidrug−resistant cell line MCF−7/ADR and the atypical drug−resistant tumor cell line HL−60/MX2. Our study identified quinacrine as a potential dual−target inhibitor of Topo II and Hsp90, depending on the ATP−binding domain, positioning it as a hit compound for further structural modification.