Miracle-dz/UniFAR-FTUs huggingface dataset:
Ultrasound Radiomics-Guided Iliac Fascia Block on Postoperative Cognitive Dysfunction in Elderly Patients Undergoing Hip Surgery

Objective Elderly patients with hip surgery are prone to postoperative cognitive dysfunction (POCD), leading to health management difficulties. This study is aimed at investigating the effect of ultrasound radiomics-guided iliac fascia block on POCD. Methods A total of 67 cases of patients who had undergone hip joint surgery were divided into a training set (n = 47) and a validation set (radiomics-guided group, n = 20). The patients were intervened with ultrasound radiomics-guided iliac fascia block, and the maximum relevance minimum redundancy sifts out the image omics features obtained from 2D ultrasound images of patients. Another 20 patients undergone general anesthesia served as control. The incidence of POCD, the total amount of fentanyl, the visual analogue score (VAS) at different time points, and the levels of CRP and NSE in plasma were compared between the two groups. Results The AUC on the training and validation sets were higher than 0.940. The incidence of POCD in the radiomics-guided and general anesthesia group was 5% and 30%, respectively (P = 0.037). Compared with the general anesthesia group, the dosage of fentanyl in the radiomics-guided was lower, the VAS score at 6 h, 1 d, and 2 d after operation was smaller, and the levels of CRP and NSE were lower (all P < 0.05). Conclusions For elderly patients with hip surgery, the ultrasound radiomics-guided iliac fascia block can reduce the incidence of POCD and improve the effect of nerve block.
Environmental, Social, Governance & Financial Performance Disclosure for Large Firms: Is This Different for SME Firms?

This study examines the association between a firm’s environmental, social and governance (ESG) performance and financial performance by examining the extent to which stakeholder and legitimacy theory help explain the effects on explanatory variables used in the study. Moreover, the study makes a novel contribution to existing ESG and performance-based studies by exploring the explanatory effects of ESG and firm performance over ten years. In addition, the study discusses the ESG-performance link of SMEs, thus advancing existing knowledge related to ESG in respect of SME performance. The study uses an extensive Australian sample from Bloomberg’s database from 2007 to 2017, while panel regression analysis is applied to investigate the relationship between a firm’s ESG performance and profitability. The robustness of the results is evaluated after incorporating several robustness checks to address methodological, endogeneity and causality issues related to a firm’s ESG performance disclosure. The empirical findings of this study suggest that improving a firm’s ESG performance is beneficial to all stakeholders of large firms in the long run but not for SME companies. The theoretical model suggests that listed SMEs do not disclose their ESG activities for various reasons, such as a lack of necessary resources. Specifically, the study extends scholarly understanding of existing theory and discusses the significance of the findings for future research.
A Novel Deep Learning Framework Based Mask-Guided Attention Mechanism for Distant Metastasis Prediction of Lung Cancer

Distant metastasis (DM) is the leading cause of death in advanced lung cancer, which is diagnosed by positron emission tomography (PET) scanning. Compared with the expensive price and nocuous contrast medium in PET, using computed tomography (CT) for DM diagnosis is more economical and convenient in clinical practice. However, most existing methods only analyze tumor regions to extract local features for DM prediction, which neglects the rich whole-lung information. To alleviate this problem, we propose a novel deep learning framework based mask-guided attention mechanism called Mask-Guided Two-stream Attention network (MGTA) for DM prediction, including a 3D pseudo-siamese feature pyramid network (PSFPN) to learn both global features in the whole lung and local features in tumor; and a deep cascaded attention module (DCAM) for further feature fusion. The proposed MGTA enjoys several merits. First, to the best of our knowledge, this is the first work to simultaneously mine tumor and whole-lung information through a mask-guided mechanism for DM prediction. Second, the proposed deep cascade attention module can effectively leverage the complementary multi-level features in PSFPN to enhance the feature recognition capacity of small tumors. Extensive experiments on a large-scale DM dataset including 2814 lung cancer patients show that the MGTA achieves good performance with area under the curve (AUC) of 0.822; sensitivity of 0.753; and specificity of 0.743, which outperforms state-of-the-art lung cancer diagnosis methods and the commonly used tumor-based methods. Furthermore, the MGTA shows large improvement especially when the training data size is small.
An Examination of the Utilization of Audit Technology in Influencing Audit Job Performance

Many traditional approaches of accounting and auditing assessment have been transformed by the growth of audit technology. This study aimed to identify key factors driving auditors’ adoption of audit technology in a developing country, Malaysia, through the lens of the Technology to Performance Chain (TPC) framework. The results of this study are based on a survey conducted in Malaysia with audit firms of varying sizes and were analyzed using the Structural Equation Modelling-Partial Least Square (PLS) statistical tools. The direct and interaction effects of audit technology and situational support variables in improving auditor work performance, as well as the mediation effect of audit assessment on the connections, were investigated in this study. According to the findings, audit technology and situational support play a larger role in the efficiency and effectiveness of auditors' work. Additional analysis provides proof of audit assessment as well. This study makes several contributions to the literature including identifying new influential factors in the TPC framework. This framework has not been widely applied in auditing research and it looks beyond the individual perspective to that of the organization as a whole. Keywords: audit performance, auditing, audit technology, situational support, audit assessment
Data cart – designing a tool for the GDPR-compliant handling of personal data by employees

ABSTRACT Employees who process personal data as part of their job play a critical role in protecting privacy. They are expected to follow strict data protection guidelines and protect personal data adequately. However, few studies have addressed the needs of these employees in terms of appropriate tools to assist them in complying with privacy laws. To develop a suitable tool, we used a human-centred design approach and held a series of eight workshops with 19 employees from two German public institutions. Based on the metaphor of a data cart, we developed a concept for a tool that supports employees in data management and data protection compliance. Qualitative usability testing revealed that participants expected the tool to raise their data protection awareness, reduce errors, and increase work efficiency. Our findings also suggest that if Privacy by Design becomes an integral part of digitalisation, employee perceptions of data protection may be positively altered. Employers, IT engineers, and researchers benefit from gaining insights into ways to improve the usability of data protection compliant personal data management tools. Simultaneously, we highlight how they can improve and promote compliance.
Performance estimation of the lower limb exoskeleton for plantarflexion using surface electromyography (sEMG) signals

In this paper, we evaluate the performance of the rigid and soft exoskeleton by measuring electromyography (sEMG) signal of human lower limb muscles. sEMG represents the degree of muscle activation and the higher sEMG level can be measured if the greater muscle force generated. We compared the sEMG activation level whether wearing the rigid exoskeleton or soft exoskeleton. First, we manufactured the rigid inspired by ‘Berkeley Lower Extremity Exoskeleton (BLEEX)’ and soft exoskeleton motivated by ‘Exosuit’ respectively. After developed the systems, sEMG signals on VM, HAM, GAS, and TA with the rigid lower limb exoskeleton were measured during walking. As a result, up to 150 % muscle activation level increased and it implies that the resistance occurred between human and the rigid lower limb exoskeleton and the user should make an effort to generate more force. After validate the limitation of the rigid lower limb exoskeleton, we did isometric experiment with the soft lower limb exoskeleton, there was 3.4 % normalized MAV decrease at GAS muscle. From this result, we concluded that developed soft lower limb exoskeleton assisted the subject with lower muscle activation level. In addition, the density of the sEMG signal was lower when the subject was assisted by the developed system. It implies that lower fatigue human can feel to maintain isometric condition. Therefore, soft lower limb exoskeleton can assist human more effective than the rigid lower limb exoskeleton.
Women’s Empowerment as an Outcome of NGO Projects: Is the Current Approach Sustainable?

The area of women’s empowerment has attracted increasing attention among a wide range of interest groups, from authors to researchers to feminist scholars and non-governmental organizations (NGOs). This paper aims to identify the diverse understandings of women’s empowerment in the literature and to discuss empirical evidence from NGO projects in the field. A systematic literature review is employed that includes an analysis of relevant high-quality articles and research papers published in the Scopus database, as well as those produced by United Nations (UN) bodies and well-published authors. The findings highlighted four common understandings of women’s empowerment including granting women a voice, challenging existing power structures, the radical transformation of lives and livelihoods, and gender mainstreaming. The findings of these empirical studies on the role of NGOs in this field revealed understandings limited to granting women a voice and gender mainstreaming, thus reflecting the fact that limited knowledge of women’s empowerment hampers the ability of NGOs to serve women’s advancement and sustainable development. Furthermore, and given that these approaches and understandings still fall short of achieving social inclusion for women, it is recommended that these NGOs discharge their efforts toward initiating systemic change to actually sustain female empowerment in the communities in which they are active.
SERTAD1 Sensitizes Breast Cancer Cells to Doxorubicin and Promotes Lysosomal Protein Biosynthesis

Acquired chemoresistance of tumor cells is an unwanted consequence of cancer treatment. Overcoming chemoresistance is particularly important for efficiently improving cancer therapies. Here, using multiple lines of evidence, we report the suppressive role of SERTAD1 in apoptosis/anoikis. Among various breast cancer cell lines, higher SERTAD1 expression was found in MCF7 and MDA-MB-231 in suspension than in adherent cell culture. We revealed an unexpected phenomenon that different types of cell deaths were induced in response to different doses of doxorubicin (Dox) in breast cancer cells, presumably via lysosomal membrane permeabilization. A low dose of Dox highly activated autophagy, while a high dose of the chemotherapy induced apoptosis. Inhibition of SERTAD1 promoted the sensitivity of breast cancer cells to Dox and paclitaxel, leading to a significant reduction in tumor volumes of xenograft mice. Simultaneously targeting cancer cells with Dox and autophagy inhibition successfully induced higher apoptosis/anoikis. The novel role of SERTAD1 in maintaining cellular homeostasis has also been suggested in which lysosomal contents, including LAMP1, LAMP2, CTSB, and CTSD, were reduced in SERTAD1-deficient cells.
Preventive maintenance of a batch production system under time-varying operational condition

This paper proposes a dynamic opportunistic preventive maintenance (PM) strategy for a production system with a time-varying batch production pattern. The operation of such a system is generic in that the operational condition (OC) varies from batch to batch and the information about the next batch can be confirmed only upon the completion of the current batch. To accommodate time-varying OC, a modified imperfect maintenance model is developed to optimise the performance of maintenance actions that can only be performed at batch-shift points. The first study presents a PM policy for a single machine with short-term production plans. Then, a multi-machine system is studied with a goal of developing an optimum dynamic opportunistic PM strategy for a group of machines at batch-shift points. Numerical examples are proceeded to illustrate the proposed maintenance strategy in practice. The result reveals that more cost will be incurred if OC is ignored. Moreover, the proposed opportunistic PM strategy achieves the lowest total cost comparing with other strategies as the system downtime cost and maintenance cost has been jointly minimised.
Relationship between the extent of Implementation of Kiswahili Curriculum in Secondary Schools in Vihiga County and Student Kiswahili Achievement and Attitudes Towards Kiswahili

Effective curriculum implementation is essential for the realisation of set objectives. This study examined the relationship between the extent of implementation of the Kiswahili curriculum in secondary schools in Vihiga County and student Kiswahili achievement and attitudes towards Kiswahili. The study used an Ex-Post Facto research design. The target population included secondary school students. Probability sampling approaches were used. Ninety-eight students from national schools were selected 105 from Extra- County schools, and 140 from County schools. Research instruments included; questionnaires, achievement tests, and an attitude scale for students. The test-retest method was used to determine the reliability of the evaluation results. Data were analysed using frequencies, means and standard deviations. ANOVA was used to test hypotheses. The study concluded that the Kiswahili curriculum was implemented to some extent in Vihiga County. Students’ performance was influenced by the extent of Kiswahili curriculum implementation. Students performed well where the curriculum was implemented to a large extent. Consequently, it was established that there was a relationship between students’ attitudes and the extent of Kiswahili curriculum implementation. Students tended to have positive attitudes towards Kiswahili, where the curriculum was implemented to a large extent. The study recommended that teachers needed to come up with new teaching methods that would focus on improving the performance of students in Kiswahili subjects. This could be achieved through regular in-service training programs that were diversified to accommodate various needs of teachers coming from different schools.
Evaluation of the promotion policy on electric vehicles using a static CGE model

Electric vehicles (EV) are a crucial technology to tackle environmental issues like air pollution and energy depletion. In this paper, we use a static computable equilibrium model to estimate the effects of two different policies: the subsidy and the credit regulation. The results show that both policies have negligible effects on the macro economy. The subsidy on EV creates more emissions as the substitution between conventional vehicles and EV isn’t very effective, leading to more production. The credit regulation, otherwise, suppresses the production of conventional vehicles while encouraging the production of EV, thus, emissions of CO2, NOx, Dust, COD are largely reduced. And the combination of the two policies has a mixed effect. However, the emissions of SO2 and NH3-N increase no matter which policy is implemented, suggesting that different sectors perform diversely under the stimulus. Policy-maker needs to make a trade-off between the benefits and risks of certain policies.
Research on the FREEDM micro-grid and its relay protection

FREEDM(Future Renewable Electric Energy Delivery and Management)is a brand-new smart micro-grid model established by the FREEDM System Center at NC State University.This article introduces the advanced concept of FREEDM for the first time to our country.In this paper,the basic principle,system configuration and topology of FREEDM are reviewed and researched,the key networking equipments of FREEDM including SSFID and FID are discussed respectively from the aspects of their working principle,topology and development trend.Through comparative analysis by using Simulink,it is pointed out that SST has a strong current limiting effect because it contains a lot of energy storage device and power electronic control loop.The short-circuit current characteristic of FREEDM is summarized.Finally the protection strategy for the application of regional pilot current differential protection in FREEDM is researched.
The force analysis for large-span overhanging the SRC beam sections based on machine learning

Many different machine learning algorithms have been developed and implemented in a variety of programming languages over the past 20 years. Some optimization models (Artificial Neural Network (ANN) and Genetic Algorithm (GA)) for the design of large overhanging steel and concrete beams have been proposed. Their ultimate aim is to optimize the material cost in making the beam satisfy conditions of strength, deformation etc. and to give advice to engineers when first selecting a section for such beams. In this study, this essay aimed at demonstrating of adapting scikit-learn to predict the compressive strength and using a simple genetic algorithm for the optimal selection of large-span overhanging the SRC beam sections.
Core collection of common bean formed from traditional Brazilian germplasm

The aim of this study was to select traditional accessions, compose a core collection of common bean, and assess the representativeness of the collection in relation to the base collection accommodated in the BAG of Embrapa using analysis strategies for multivariate models. We used data characterizing 2903 accessions from collections representing all geographic areas of Brazil regarding three morphologic descriptors (seed color, growth habit type, and seed size) and four ecogeographic descriptors (geographical areas, states, altitudes, and soil classes). A set of 400 accessions were selected using multivariate models applied to the data transformed in multibinary values. The accessions sampled had maximum similarity (100%) to the traditional collection, phenotypic diversity, and representative heterogeneity in relation to the traditional collection. In the core collection, the accessions represented 9.5% of the traditional accessions and were equivalent to 3% of the accessions of the base collection. Thus, it is possible to form a core collection that is representative of the base collection regarding genetic diversity and the conservation of rare alleles.
Sentence classification based on the concept kernel attention mechanism

Sentence classification is important for data mining and information security. Recently, researchers have paid increasing attention to applying conceptual knowledge to assist in sentence classification. Most existing approaches enhance classification by finding word-related concepts in external knowledge bases and incorporating them into sentence representations. However, this approach assumes that all concepts are equally important, which is not helpful for distinguishing the categories of the sentence. In addition, this approach may also introduce noisy concepts, resulting in lower classification performance. To measure the importance of the concepts for the text, we propose the Concept Kernel Attention Network (CKAN). It not only introduces concept information into the deep neural network but also contains two attention mechanisms to assign weights to concepts. The attention mechanisms are the text-to-concept attention mechanism (TCAM) and the entity-to-concept attention mechanism (ECAM). These attention mechanisms limit the importance of noisy concepts as well as contextually irrelevant concepts and assign more weights to concepts that are important for classification. Meanwhile, we combine the relevance of concepts and entities to encode multi-word concepts to reduce the impact of the inaccurate representation of multi-word concepts for classification. We tested our model on five public text classification datasets. Comparison experiments with strong baselines and ablation experiments demonstrate the effectiveness of CKAN.
Predicting wildfire induced changes to runoff: A review and synthesis of modeling approaches

Wildfires elicit a diversity of hydrological changes, impacting processes that drive both water quantity and quality. As wildfires increase in frequency and severity, there is a need to assess the implications for the hydrological response. Wildfire‐related hydrological changes operate at three distinct timescales: the immediate fire aftermath, the recovery phase, and long‐term across multiple cycles of wildfire and regrowth. Different dominant processes operate at each timescale. Consequentially, models used to predict wildfire impacts need an explicit representation of different processes, depending on modeling objectives and wildfire impact timescale. We summarize existing data‐driven, conceptual, and physically based models used to assess wildfire impacts on runoff, identifying the dominant assumptions, process representations, timescales, and key limitations of each model type. Given the substantial observed and projected changes to wildfire regimes and associated hydrological impacts, it is likely that physically based models will become increasingly important. This is due to their capacity both to simulate simultaneous changes to multiple processes, and their use of physical and biological principles to support extrapolation beyond the historical record. Yet benefits of physically based models are moderated by their higher data requirements and lower computational speed. We argue that advances in predicting hydrological impacts from wildfire will come through combining these physically based models with new computationally faster conceptual and reduced‐order models. The aim is to combine the strengths and overcome weaknesses of the different model types, enabling simulations of critical water resources scenarios representing wildfire‐induced changes to runoff.