Miracle-dz/UniFAR-FTUs huggingface dataset:
A Conceptual Framework for Quantifying the Robustness of a Regression-Based Causal Inference in Observational Study

The internal validity of a causal inference made based on an observational study is often subject to debate. The potential outcomes framework of causal inference stipulates that causal inference is essentially a missing data problem, and we follow this spirit to define the ideal sample as the combination of the observed data and the missing/counterfactual data for regression models. The robustness of a causal inference can be quantified by the probability of a robust inference for internal validity in regression, i.e., the PIVR, which is the probability of rejecting the null hypothesis again for the ideal sample provided the same null hypothesis has been already rejected for the observed sample. Drawing on the relationship between the PIVR and the mean counterfactual outcomes, we formalize a conceptual framework of quantifying the robustness of a regression-based causal inference based on a joint distribution about the mean counterfactual outcomes, holding the observed sample fixed. Interpretatively, the PIVR is the statistical power of the null hypothesis significance testing that is thought to be built on the ideal sample. We demonstrate the conceptual framework of quantifying the robustness of a regression-based causal inference with an empirical example.
Deep vein diameters and perivascular space scores are associated with deep medullary vein hypo-visibility in patients with white matter hyperintensity

Background Deep medullary vein (DMV) hypo-visibility is correlated with white matter hyperintensity (WMH), but the underlying causes remain unclear. This study aimed to explore the relationship between deep vein diameters and perivascular space (PVS) scores, and DMV hypo-visibility in the presence of WMH. Methods This cross-sectional study prospectively analyzed the clinical and imaging data of 190 cerebral small vessel disease patients with WMH and 40 healthy controls from the Lishui Hospital of Traditional Chinese Medicine affiliated with Zhejiang Chinese Medical University. PVS scores ranging from 0 to 4 were determined according to the PVS counts in the basal ganglia area on T2-weighted magnetic resonance images; high-grade PVS was defined as a PVS score >1. The diameters of the deep cerebral veins, including the bilateral septal veins (SVs), thalamostriate veins (TSVs), lateral ventricular veins (LVVs), and internal cerebral veins, were measured using susceptibility weighted imaging (SWI). Left and right DMV scores, ranging from 0 to 9, were calculated based on the visibility of the DMV on SWI in the ipsilateral frontal, parietal, and occipital lobes. Results The deep cerebral vein diameters, left and right DMV scores, and high-grade PVS differed between the healthy controls and WMH patients (P<0.05). Left DMV scores were independently associated with age {β [95% confidence interval (CI)]: 0.050 (0.018, 0.082)}, high-grade PVS [β (95% CI): 0.998 (0.262, 1.737)], and the diameters of the ipsilateral SVs [β (95% CI): –1.114 (–1.754, –0.475)], SVs [β (95% CI): –0.734 (–1.191, –0.277)], and LVVs [β (95% CI): –0.921 (–1.567, –0.275)] [all false discovery rate (FDR)-corrected P<0.05]. Right DMV scores were independently associated with age [β (95% CI): 0.071 (0.037, 0.105)], high-grade PVS [β (95% CI): 0.873 (0.111, 1.635)], and the diameters of the ipsilateral SVs [β (95% CI): –0.837 (–1.386, –0.289)], TSVs [β (95% CI): –0.875 (–1.331, –0.419)], and LVVs [β (95% CI): –1.813 (–2.484, –1.142)] (all FDR-corrected P<0.05). Conclusions Decreased hypo-visibility of DMVs on SWI was associated with a higher age, the presence of high-grade PVS, and smaller diameters of the ipsilateral deep cerebral veins in individuals with WMH. Our findings provide novel insights into the probable mechanisms leading to high DMV scores.
Volcanic lavas of the Yixian Formation in western Liaoning province, China: Products of lower crust delamination or magma mixing?

The authors,based on the systematics of all the published major,trace elemental and isotopic data of volcanic lavas from the Yixian Formation,western Liaoning Province,found that the volcanic lavas of the Yixian Formation are composed dominantly of mafic-intermediate components with the increasing intermediate-acidic components in the upper sequence.Their high Mg~# values and adakitic geochemical characteristics are not the primary features of the initial magma,and thus cannot be regarded as the powerful evidence for the existence of lower crust delamination in the region.The mafic components of the Yixian Formation were originally derived from the lithospheric mantle,which were previously modified by the subducted oceanic crust.The mixing of mafic magma with ancient lower crust-derived magma to a certain degree produced the various volcanic lavas of the Yixian Formation.Therefore,volcanic lavas of the Yixian Formation are the products of magma mixing rather than lower crust delamination.Basaltic underplated lower crust was involved in the origin of these volcanic lavas,with little and limited contribution of the asthenosphere.
Biochemical Approach to Poly(Lactide)–Copper Composite—Impact on Blood Coagulation Processes

The paper presents the investigation of the biological properties of Poly(Lactide)—Copper composite material obtained by sputter deposition of copper onto Poly(lactide) melt-blown nonwoven fabrics. The functionalized composite material was subjected to microbial activity tests against colonies of Gram-positive (Staphylococcus aureus), Gram-negative (Escherichia coli, Pseudomonas aeruginosa) bacteria, Chaetomium globosum and Candida albicans fungal mold species and biochemical–hematological tests including the evaluation of the Activated Partial Thromboplastin Time, Prothrombin Time, Thrombin Time and electron microscopy fibrin network imaging. The substantial antimicrobial and antifungal activities of the Poly(Lactide)–Copper composite suggests potential applications as an antibacterial/antifungal material. The unmodified Poly(Lactide) fabric showed accelerated human blood plasma clotting in the intrinsic pathway, while copper plating abolished this effect. Unmodified PLA itself could be used for the preparation of wound dressing materials, accelerating coagulation in the case of hemorrhages, and its modifications with the use of various metals might be applied as new customized materials where blood coagulation process could be well controlled, yielding additional anti-pathogen effects.
The Impact of Independent Directorship on Business Performance in China: Empirical Study on SSE Listed Firms

In an effort to enhance the influence of independent directors within the framework of corporate governance, the China Securities Regulatory Commission (CSRC) has recently introduced revised regulations pertaining to independent directorship in August 2023. This regulatory adjustment signifies a pivotal step towards bolstering Chinese-specific corporate governance. This research endeavors to delve into the potential impact of independent directorship on business performance within mainland China's corporate landscape. This study harnesses data from publicly traded companies on the Shanghai Stock Exchange (SSE) in conjunction with panel data analysis and Ordinary Least Squares (OLS) regression to build empirical models. It is noteworthy that the initial results indicate a favorable connection between independent directorship and business performance. It has been demonstrated that having more independent directors is connected with better corporate profitability. Against the backdrop of the newly implemented regulatory reforms concerning independent directors, this study is poised to make a substantive contribution to the ongoing discourse on corporate governance and its ramifications for business performance in China. As these revised regulations usher in a renewed emphasis on independent directorship, this research's insights hold significant relevance for both practitioners and policymakers navigating the evolving corporate governance landscape.
GCZRec: Generative Collaborative Zero-Shot Framework for Cold Start News Recommendation

The aim of personalized news recommendation is to suggest news stories to the users that are most interesting for them. To improve the user experience, it is important that these news items are not only relevant to the user but also get recommended to them as soon as they are available. The inability of traditional collaborative filtering approach to recommend such cold start items has led to techniques that incorporate latent features of items in order to make cold start recommendations such as content based filtering and deep neural network-based approaches. However, these existing techniques do not make use of any collaborative information between users and items as well as latent features at the same time and thus fail to provide any serendipity which is an important aspect of any recommender system. Moreover, these underlying collaborative signals between users and items are crucial to improving the overall quality of recommender systems and can also be utilized to make cold start recommendations. In this paper, we propose the Generative Collaborative Zero-Shot Recommender System framework (GCZRec) which makes use of both the latent user and item features as well as the underlying collaborative information to generate both warm start and cold start recommendations. We evaluate our framework for news recommendation task given cold start and warm start cases for both users and news items. We also discuss that our model can be plugged in and used as preprocessing to improve the performance of an existing recommender system.
Deep Transformer-Based Asset Price and Direction Prediction

The field of algorithmic trading, driven by deep learning methodologies, has garnered substantial attention in recent times. Within this domain, transformers, convolutional neural networks, and patch embedding-based techniques have emerged as popular choices within the computer vision community. Here, inspired by the latest cutting-edge computer vision methodologies and the existing work showing the capability of image-like conversion for time-series datasets, we apply more advanced transformer-based and patch-based approaches for predicting asset prices and directional price movements. The employed transformer models include Vision Transformer (ViT), Data Efficient Image Transformers (DeiT), and Swin. We use ConvMixer for a patch embedding-based convolutional neural network architecture without a transformer. Our tested transformer-based and patch-based methodologies aim to predict asset prices and directional movements using historical price data by leveraging the inherent image-like properties within the historical time-series dataset. Before the implementation of attention-based architectures, the historical time series price dataset is transformed into two-dimensional images. This transformation is facilitated through the incorporation of various common technical financial indicators, each contributing to the data for a fixed number of consecutive days. Consequently, a diverse set of two-dimensional images is constructed, reflecting various dimensions of the dataset. Subsequently, the original images depicting market valleys and peaks are annotated with labels such as Hold, Buy, or Sell. According to the experiments, trained attention-based models consistently outperform the baseline convolutional architectures, particularly when applied to a subset of frequently traded Exchange-Traded Funds (ETFs). This better performance of attention-based architectures, especially ViT, is evident in terms of both accuracy and other financial evaluation metrics, particularly during extended testing and holding periods. These findings underscore the potential of transformer-based approaches to enhance predictive capabilities in asset price and directional forecasting. Our code and processed datasets are available at https://github.com/seferlab/price_transformer.
Assessing acceptance of AI nurses for outpatients with chronic diseases: From nurses’ perspective

The primary objective of this article is to investigate and forecast nurses’ attitudes toward using AI nurses for outpatients with chronic diseases. AI technology is used in hospitals in a disease-centric manner. However, it is desired by healthcare regulators to be used in an individual-centric and holistic manner. The research model was developed based on the Unified Theory of Accepting and Using Technology. In determining the causes and consequences of the attitudes, actions, ideas, and beliefs of the nurses, the screening technique of causal comparison was used. Research data was collected from registered nurses who work in research hospitals and use intelligent health technologies for inpatients. Based on 494 responses, this study conducted a dual-phase assessment using Partial Least Squares Structural Equation Modeling as well as the creation of an AI method known as deep learning (artificial neural network). According to the results, nurses are convinced that AI is a suitable tool for their nursing tasks and increases their efficiency and productivity. It has been determined that nurses have intentions to use AI nurses for outpatients with chronic diseases. However, nurses have concerns about the reliability of ambulatory patient data. The policies and strategies of regulators will affect the acceptance of AI technology, not only for nurses but for all healthcare professionals and patients.
Effect of Mineral Powders on the Properties of Foam Concrete Prepared by Cationic and Anionic Surfactants as Foaming Agents

Foam concrete is a type of cement mortar in which air bubbles are introduced using an appropriate foaming agent. The complex conditions for the preparation of solid particle stabilized foams limit their wide application in construction. In this study, a method of adding small amounts of calcite (Cal) and muscovite (Mus) to the cement paste matrix is proposed to improve the properties of foam concrete prepared with cationic and anionic surfactants as foaming agents. The effects of mineral powders on the flowability, compressive strength, water absorption, pore characteristics, thermal conductivity and frost resistance of foam concrete were investigated and the enhancement mechanism was revealed by the results of XRD, low-field nuclear magnetic resonance (LF-NMR), Fourier transform infrared spectroscopy (FTIR) and SEM. The results showed that the mineral powders interacted with anionic and cationic surfactants through physical adsorption. Whether anionic or cationic surfactants were used as foaming agents, the addition of mineral powders promoted the formation of shell-like structures around the foam, thus enhancing the performance of foam concrete. As a result, the fluidity, compressive strength and frost resistance of the foam concrete increased, the water absorption and thermal conductivity decreased, and the average size of the pores decreased.
Insights into the Microbiome and Antibiotic Resistance Genes from Hospital Environmental Surfaces: A Prime Source of Antimicrobial Resistance

Hospital environmental surfaces are potential reservoirs for transmitting hospital-associated pathogens. This study aimed to profile microbiomes and antibiotic resistance genes (ARGs) from hospital environmental surfaces using 16S rRNA amplicon and metagenomic sequencing at a tertiary teaching hospital in Malaysia. Samples were collected from patient sinks and healthcare staff counters at surgery and orthopaedic wards. The samples’ DNA were subjected to 16S rRNA amplicon and shotgun sequencing to identify bacterial taxonomic profiles, antibiotic resistance genes, and virulence factor pathways. The bacterial richness was more diverse in the samples collected from patient sinks than those collected from staff counters. Proteobacteria and Verrucomicrobia dominated at the phylum level, while Bacillus, Staphylococcus, Pseudomonas, and Acinetobacter dominated at the genus level. Staphylococcus epidermidis and Staphylococcus aureus were prevalent on sinks while Bacillus cereus dominated the counter samples. The highest counts of ARGs to beta-lactam were detected, followed by ARGs against fosfomycin and cephalosporin. We report the detection of mcr-10.1 that confers resistance to colistin at a hospital setting in Malaysia. The virulence gene pathways that aid in antibiotic resistance gene transfer between bacteria were identified. Environmental surfaces serve as potential reservoirs for nosocomial infections and require mitigation strategies to control the spread of antibiotic resistance bacteria.
Preferensi Konsumen Terhadap Penggunaan Aplikasi E-Wallet OVO (Studi Kasus Pada Generasi Z Bengkulu)

Implementasi financial technology (fintech) di Indonesia akan terus meningkat seiring dengan kemajuan teknologi yang semakin berkembang pesat. Riset ini bermaksud melihat seberapa besar preferensi konsumen terhadap penggunaan aplikasi e-wallet OVO berdasarkan Model UTAUT 1. Populasi pada riset ini ialah Generasi Z dengan jumlah sampel yakni 209 responden. Data riset dikumpulkan dengan menyebarkan kuesioner yang dinilai dengan Skala Likert. Metode riset yang diterapkan ialah kuantitatif, dengan analisis data dilaksanaka SEM (Structural Equation Modeling) pada aplikasi SmartPLS 4.0. 9.7. Temuan riset memperlihatkan variabel ekspektasi usaha, pengaruh sosial, kondisi yang memfasilitasi serta use behavior berkorelasi signifikan. Namun, pada variabel ekspektasi kerja dan behavior intention tidak terdapat pengaruh yang signifikan terhadap preferensi konsumen terhadap penerapan aplikasi e-wallet OVO. Hasil pengaruh secara simultan dari keseluruhan variabel adalah 79,6%. Saran dari penelitian ini untuk menambah variabel yang relevan dan sampel yang beragam dari berbagai kalangan.
Prevention and Potential Treatment Strategies for Respiratory Syncytial Virus

Respiratory syncytial virus (RSV) is a significant viral pathogen that causes respiratory infections in infants, the elderly, and immunocompromised individuals. RSV-related illnesses impose a substantial economic burden worldwide annually. The molecular structure, function, and in vivo interaction mechanisms of RSV have received more comprehensive attention in recent times, and significant progress has been made in developing inhibitors targeting various stages of the RSV replication cycle. These include fusion inhibitors, RSV polymerase inhibitors, and nucleoprotein inhibitors, as well as FDA-approved RSV prophylactic drugs palivizumab and nirsevimab. The research community is hopeful that these developments might provide easier access to knowledge and might spark new ideas for research programs.
Real Estate Industry Sustainable Solution (Environmental, Social, and Governance) Significance Assessment—AI-Powered Algorithm Implementation

As the global imperative for sustainable development intensifies, the real estate industry stands at the intersection of environmental responsibility and economic viability. This paper presents a comprehensive exploration of the significance of sustainable solutions within the real estate sector, employing advanced artificial intelligence (AI) algorithms to assess their impact. This study focuses on the integration of AI-powered tools in a decision-making process analysis. The research methodology involves the development and implementation of AI algorithms capable of analyzing vast datasets related to real estate attributes. By leveraging machine learning techniques, the algorithm assesses the significance of energy efficiency solutions along with other intrinsic and extrinsic attributes. This paper examines the effectiveness of these solutions in relation to the influence on property prices with a framework based on an AI-driven algorithm. The findings aim to inform real estate professionals and investors about the tangible advantages of integrating AI technologies into sustainable solutions, promoting a more informed and responsible approach to industry practices. This research contributes to the growing interest in the connection of the real estate sector, sustainability, and AI, offering insights that can guide strategic decision making. By implementing the random forest method in the real estate feature significance assessment original methodology, it has been shown that AI-powered algorithms can be a useful tool from the perspective of real estate price prediction. The methodology’s ability to handle non-linear relationships and provide insights into feature importance proved advantageous in comparison to the multiple regression analysis.
A novel dynamic enterprise architecture model: leveraging MAPE-K loop and case-based reasoning for context awareness

Nowadays, enterprises are required to take the uncertainty of the environment as decisive factor of success. For this reason, Enterprises should be prepared up-stream to react dynamically to the turbulent context. Considering that enterprise architecture is a tool drawing a blueprint that gives a holistic view of the enterprise, this blueprint should be able to represent this awareness to context and implements the techniques and mechanisms to react in a dynamic manner depending on the triggers of change. In this paper, the proposed model stipulates a “context-awareness” that monitors the internal and external context, and then adapt its reaction in alignment with the prefixed goals. The operationalization of our conception is realized through the monitor-analyze-plan-execute-knowledge (MAPE-K) loop, the case-based reasoning and machine learning techniques organized and orchestrated through a global algorithm of 6 main functions to monitor, compare, analyze, plan, execute and enrich the knowledge base. The results are verified in the light of a case study that demonstrates the applicability of our proposed model.
Intelligent Real-Time Utilization of Hybrid Energy Resources for Cost Optimization in Smart Microgrids

With ever-depleting fossil fuels and significant increase in the carbon footprint worldwide, efficient utilization of renewable energy sources plays an important role. Installation of solar rooftops and photovoltaic panels is getting a broader response, even in residential and commercial buildings. However, at the same time, it is of paramount importance to manage the appliances in these buildings according to the local energy generated from these renewable sources to obtain a better supply–demand ratio. We address this problem by proposing a coordinated scheduling approach for flexible devices and the optimal usage of heterogeneous energy sources to minimize the energy drawn from the main grid and thereby reduce electricity costs for the users. We present a mixed-integer linear programming formulation for the problem and solve it optimally using Gurobi optimizer. Moreover, we also propose a set of efficient heuristic algorithms that consider the predicted renewable energy and price information to obtain the solutions efficiently for a large dataset. The results obtained show that the proposed algorithms deviate within 2.28%–18.47% from the optimal solutions in various settings, depicting their capability to be implemented in a real-world system.