Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence Nodes Identifying Method via Community-Based Backward Generating Network Framework

Traditional methods for influential node identification usually require time consuming network traversal to select the candidate node set. In this article we propose a new influence nodes identification method, called Community-based Backward Generating Network (CBGN). First, the influence maximization framework is built by integrating community detection and Backward Generation Network (BGN); then, nodes in each community are selected using a new method, called imp_BGN, that uses graph traversal to assist the construction of BGN. The ultimate goal of the network generation method is to find a sequence of nodes that can minimize the cost function, and to select high influential nodes without restoring the original network during network construction. finally, an improved submodular CELF (Cost Effective Lazy Forward) algorithm is proposed to hunt for the final seed node from the candidate node pool considering the location relation and structural similarity among nodes. Experimental results show that: in the SIR (susceptible-infected-recovered) model experiment, compared with the benchmark methods, the infection scale of the proposed CBGN method in 6 real networks is improved by 0.45%, 0.59%, 0.84%, 1.05%, 0.71% and 0.14%, respectively.
Agricultural Wastewater Treatment Using Oil Palm Waste Activated Hydrochar for Reuse in Plant Irrigation: Synthesis, Characterization, and Process Optimization

The best possible use of natural resources and the large amounts of trash produced by industrial and human activity is necessary for sustainable development. Due to the threat of global climate change and other environmental challenges, waste management systems are changing, leading to more instances of water resource management. The waste generated must be controlled from a sustainability point of view. Typically, the conventional disposal of Agricultural Wastewater (AW) and biomass can be achieved by recycling, reusing, and converting them into a variety of green products. To improve the AW quality for the purposes of environmental sustainability, Sustainable Development Goals (SDGs) 6 and 14, dealing with clean water, sanitation, and life below water, are very important goals. Therefore, the present investigation evaluates the effectiveness of a Bench-scale Activated Sludge Reactor (BASR) system for AW treatment. The BASR was designed to focus on getting the maximum possible utilization out of a biosorbent derived from oil palm waste activated hydrochar (OPAH). This is in accordance with SDG 9, which targets inorganic and organic waste utilization for added value. An experiment was developed using the Response Surface Methodology (RSM). A Hydraulic Retention Time (HRT) of 1–3 days was used in the bioreactor’s setup and operation, and Mixed Liquor Suspended Solids (MLSS) concentrations of 4000–6000 mg/L were used. BASR was fed with AW with initial mean concentrations of 4486 ± 5.63 mg/L and 6649 ± 3.48 for the five-day Biochemical Oxygen Demand (BOD5) and Chemical Oxygen Demand (COD) experiments, respectively. The results obtained showed that maximum reductions of 84.66% and 72.07% were recorded for BOD5 and COD, respectively. Through RSM optimization, the greatest reductions in the amounts of organic materials were achieved with a 2-day HRT and an MLSS dosage of 5000 mg/L. Substrate elimination thresholds were assessed using the first-order, the Grau second-order, and the modified Stover–Kincannon models. The reported observations were found to be perfectly fit by the modified Stover–Kincannon model, with high R2 values of 0.9908 and 0.9931 for BOD5 and COD, respectively. As a result, the model may be used to design the BASR system and forecast how the reactor would behave. The findings from this study suggest that the developed OPAH has promising potential to be applied as eco-friendly material for the removal of BOD5 and COD from AW. Consequently, the study findings additionally possess the ability to address SDGs 6, 9, and 14, in order to fulfil the United Nations (UN) goals through 2030.
How artificial intelligence impacts the competitive position of healthcare organizations

Purpose Digital transformation affected modern society influencing how businesses cooperate and produce value. In this context, Artificial Intelligence plays a critical role. This study aims to explore the role of Artificial Intelligence in organisational positioning within the market, influencing firms' competitiveness. In this vein, this research seeks to respond to the following research question: How does AI impact the competitive advantage of healthcare organizations? Design/methodology/approach To tackle the research question, an explorative analysis using the case study method to investigate an international healthcare center in Dubai was conducted. Nine semi-structured interviews were conducted with the head and the members of the robotic surgery team in CMC Dubai to thoroughly understand what the components of the robotic approach are and how the arrangement before the introduction of this innovative technique while shedding light on the added value and the advantages of adopting such technique on both patient safety and patient satisfaction. Additionally, archival data and online documentation (e.g. industry reports, newspaper articles and internal documents) were analyzed to obtain data triangulation.Findings The results highlight three primary outcomes influenced by implementing AI in organizational processes: clinical, financial and technological outcomes. The study will offer interesting non-studied insights about the implementation of Artificial Intelligence tools in the healthcare sector and specifically robotic surgeries, and to which extent this will contribute and represent a competitive advantage. Results will hopefully insert a brick in the wall of the impact of AI tools on the quality and the results of surgical operations while emphasizing the benefits of integrating AI in surgical practice.Originality/value This study offers interesting theoretical and practical implications. It opens a new perspective to understand and manage AI tools in service. This research is not without limits providing valuable insights for future research.
DeepCAN: Hybrid Method for Road Type Classification Using Vehicle Sensor Data for Smart Autonomous Mobility

Usually, image- and radar-based data are used to perform environmental characteristics related tasks in autonomous cars, while the use of valuable sensor data from the Controller Area Network (CAN) bus has been limited. The vehicle’s CAN bus data consist of multivariate time series data, such as velocity, RPM, and acceleration, which contain meaningful information about the vehicle dynamics and environmental characteristics. The ability to use these data to sense the environment, along with the sight sense (from image-based data), can prevent a single point of failure when image- or radar-based data are missing or incomplete, and contribute to increased understanding of the vehicle’s environment. Moreover, a solution that does not rely on image- or radar-based data also addresses concerns about privacy and the use of location-based data. We present DeepCAN, a novel hybrid method for road type classification that utilizes solely vehicle dynamics data and combines two main approaches for time series classification. In the end-to-end approach, a long short-term memory autoencoder (LSTM AE) is trained, and the learned embedding serves as the input to a fully convolutional network autoencoder (FCN AE), while the feature-based approach utilizes an XGBoost classifier with aggregated time series feature representation. In our comprehensive evaluation on two real-world datasets, we assessed the performance of each model component as an independent solution, as well as a model integrating all of the components in a hybrid solution. The results demonstrate DeepCAN’s efficiency and accuracy and provide a solid basis for its future use by the automobile industry.
Differences in the prevalence and clinical correlates of comorbid suicide attempts in patients with early- and late-onset major depressive disorder

Objective There are many studies on differences in the onset age of major depressive disorder (MDD) patients. However, study on differences in clinical correlates of suicide attempts between early- and late-onset MDD patients is limited. The aim of this study was to investigate the differences in the prevalence and clinical correlates of suicide attempts in patients with early- and late-onset MDD in China. Methods A total of 1718 adult outpatients with MDD were recruited. Demographic and clinical data were collected. The 17-item Hamilton Rating Scale for Depression (HAMD-17), Hamilton Anxiety Rating Scale (HAMA), Positive and Negative Syndrome Scale (PANSS) positive subscale, and Clinical Global Impression-Severity (CGI-S) Scales were used to assess their depressive, anxiety, psychotic symptoms, and the severity of the clinical symptoms, respectively. Results The prevalence of suicide attempts was higher in late-onset MDD patients (291/1369, 21.3%) than in early-onset MDD patients (55/349, 15.8%) (p = 0.023). However after Bonferroni correction no significant difference was found in the prevalence of suicide attempts in late-onset and late-onset MDD patients (p > 0.05). In both early- and late-onset groups, univariate analysis showed that the following characteristics were significantly associated with suicide attempts: HAMA, HAMD and PANSS positive subscale scores, thyroid stimulating hormone (TSH) levels, blood glucose levels, systolic blood pressure (SBP), and diastolic blood pressure (DBP). In both the early- and late-onset groups, the prevalence rates of severe anxiety disorder and psychotic symptoms were significantly higher in the suicide attempt group than in the non-suicide attempt group. In regression analysis, disease duration, TSH levels and HAMA score were independently associated with suicide attempts in the early-onset group, while TSH levels, HAMA and HAMD score were independently associated with suicide attempts in the late-onset group. Conclusion This study suggests that suicide attempts are not frequent in early-onset outpatients with MDD compared with late-onset, and some clinical correlates are associated with suicide attempt in early- and late-onset MDD.
Effects of Controlled Ozone Exposure on Circulating microRNAs and Vascular and Coagulation Biomarkers: A Mediation Analysis

Exposure to ozone (O3) is associated with adverse respiratory and cardiovascular outcomes. Alterations in circulating microRNAs (miRNAs) may contribute to the adverse vascular effects of O3 exposure through inter-cellular communication resulting in post-transcriptional regulation of messenger RNAs by miRNAs. In this study, we investigated whether O3 exposure induces alterations in circulating miRNAs that can mediate effects on downstream vascular and coagulation biomarkers. Twenty-three healthy male adults were exposed on successive days to filtered air and 300 ppb O3 for 2 h. Circulating miRNA and protein biomarkers were quantified after each exposure session. The data were subjected to mixed-effects model and mediation analyses for the statistical analyses. The results showed that the expression level of multiple circulating miRNAs (e.g., miR-19a-3p, miR-34a-5p) was significantly associated with O3 exposure. Pathway analysis showed that these miRNAs were predictive of changing levels of downstream biomarkers [e.g., D-dimer, C-reactive protein, tumor necrosis factor α (TNFα)]. Mediation analysis showed that miR-19a-3p may be a significant mediator of O3-exposure-induced changes in blood TNFα levels [0.08 (0.01, 0.15), p = 0.02]. In conclusion, this preliminary study showed that O3 exposure of healthy male adults resulted in changes in circulating miRNAs, some of which may mediate vascular effects of O3 exposure.
Galectin-4 Is Involved in the Structural Changes of Glycosphingolipid Glycans in Poorly Differentiated Gastric Cancer Cells with High Metastatic Potential

Gastric cancer with peritoneal dissemination is difficult to treat surgically, and frequently recurs and metastasizes. Currently, there is no effective treatment for this disease, and there is an urgent need to elucidate the molecular mechanisms underlying peritoneal dissemination and metastasis. Our previous study demonstrated that galectin-4 participates in the peritoneal dissemination of poorly differentiated gastric cancer cells. In this study, the glycan profiles of cell surface proteins and glycosphingolipids (GSLs) of the original (wild), galectin-4 knockout (KO), and rescue cells were investigated to understand the precise mechanisms involved in the galectin-4-mediated regulation of associated molecules, especially with respect to glycosylation. Glycan analysis of the NUGC4 wild type and galectin-4 KO clones with and without peritoneal metastasis revealed a marked structural change in the glycans of neutral GSLs, but not in N-glycan. Furthermore, mass spectrometry (MS) combined with glycosidase digestion revealed that this structural change was due to the presence of the lacto-type (β1-3Galactosyl) glycan of GSL, in addition to the neolacto-type (β1-4Galactosyl) glycan of GSL. Our results demonstrate that galectin-4 is an important regulator of glycosylation in cancer cells and galectin-4 expression affects the glycan profile of GSLs in malignant cancer cells with a high potential for peritoneal dissemination.
Natural Disasters and Economic Growth in Indonesia

This research examines the relationship between natural disasters and economic growth in Indonesia. The study utilizes secondary data spanning from 1990 to 2021 and employs a dynamic approach utilizing the Fully-Modified Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), and Canonical Co-Integrating Regression (CCR) methods, as well as a causal approach utilizing the Vector Error Correction Model (VECM) method. The findings provide insights into the relationship between natural disasters and economic growth in Indonesia, highlighting the significant long-term impact of natural disasters on economic growth. Moreover, the analysis uncovers a unidirectional causality from natural disasters to economic growth, emphasizing the influence of natural disasters on the country's economic performance. The results highlight the need for strategies from policymakers that focus on investing in upgrading and retrofitting existing infrastructure to withstand natural disasters, especially in key sectors such as transportation, energy, water, and telecommunications to minimize disruptions and enable faster economic recovery, as well as promote small and medium-sized enterprises (SMEs) by providing incentives and support for their growth, as they are often more adaptable and resilient in the face of economic shocks.
Collaborate Global and Local: An Efficient PV Compound Fault Diagnosis Scheme With Multilabel Learning and Model Fusion

Fast and accurate fault diagnosis of photovoltaic (PV) modules has been the focus of intelligent operation and maintenance for PV systems. Considering efficiency and cost, many machine learning (ML)-based methods are widely used in practice. Due to the increasing scale of PV stations, the series–parallel topology of the system becomes more complicated, and multiple faults may inevitably occur together. However, most existing methods only consider the case of a single fault occurrence or the most basic compound faults, which fails to assess the system state comprehensively. For this reason, a novel global–local dual-stream collaborative framework is proposed for multiclass PV compound fault diagnosis. In the proposed method, a local mining algorithm is designed for the characteristics of PV data to extract local details more conveniently and efficiently. Then, we construct a shared convolutional neural network (CNN) network to extract fault features from global and local streams to improve the modeling efficiency. Next, two model fusion mechanisms are proposed for different data conditions to collaboratively use the fault features of the global and local streams. Both simulations and experiments are conducted to verify the effectiveness of the proposed method.
Sparse subspace linear discriminant analysis

ABSTRACT We study high dimensional multigroup classification from a sparse subspace estimation perspective, unifying the linear discriminant analysis (LDA) with other recent developments in high dimensional multivariate analysis using similar tools, such as penalization method. We develop two two-stage sparse LDA models, where in the first stage, convex relaxation is used to convert two classical formulations of LDA to semidefinite programs, and furthermore subspace perspective allows for straightforward regularization and estimation. After the initial convex relaxation, we use a refinement stage to improve the accuracy. For the first model, a penalized quadratic program with group lasso penalty is used for refinement, whereas a sparse version of the power method is used for the second model. We carefully examine the theoretical properties of both methods, alongside with simulations and real data analysis.
Profile of taste-related compounds and bioactivity of split gill mushroom (Schizophyllum commune) as affected by blanching and drying

ABSTRACT Edible mushrooms are important in the human diet as a source of nutritive compounds with an appreciated taste. The split gill mushroom (Schizophyllum commune) is popularly consumed in Asia owing to its umami taste relating to a meaty flavor. Typically, blanching and drying are performed to preserve food quality and extend shelf-life and may affect the taste characteristics and bioactivity of mushrooms. This work investigated the effect of different methods of blanching – hot-water (HW) or microwave-assisted (MW) – and drying – hot air drying (HAD) and freeze-drying (FD) – on the characteristics, taste profile, and bioactivity of S. commune. The results showed that S. commune contained umami-related compounds, and the umami level was influenced by the processing conditions. Compared to HW blanching, MW blanching was more efficient at prohibiting polyphenol oxidase activity and maintaining the umami taste of the mushroom. Drying, particularly FD, successfully improved the umami level of S. commune. Nevertheless, HAD tended to provide the mushroom with higher bioaccessibility to phenolic compounds and antioxidant activity levels. The results suggested that S. commune could be promising as a functional ingredient with an umami taste. Pre-treatment using MW blanching, followed by FD could preserve appropriately the umami taste and quality of the mushroom.
Matrix Completion With Covariate Information

ABSTRACT This article investigates the problem of matrix completion from the corrupted data, when the additional covariates are available. Despite being seldomly considered in the matrix completion literature, these covariates often provide valuable information for completing the unobserved entries of the high-dimensional target matrix A0. Given a covariate matrix X with its rows representing the row covariates of A0, we consider a column-space-decomposition model A0 = Xβ0 + B0, where β0 is a coefficient matrix and B0 is a low-rank matrix orthogonal to X in terms of column space. This model facilitates a clear separation between the interpretable covariate effects (Xβ0) and the flexible hidden factor effects (B0). Besides, our work allows the probabilities of observation to depend on the covariate matrix, and hence a missing-at-random mechanism is permitted. We propose a novel penalized estimator for A0 by utilizing both Frobenius-norm and nuclear-norm regularizations with an efficient and scalable algorithm. Asymptotic convergence rates of the proposed estimators are studied. The empirical performance of the proposed methodology is illustrated via both numerical experiments and a real data application.
Is MIPO as Safe and Effective as Conventional ORIF for Simple Fractures of Humerus Diaphysis?

Objective: This study aimed to compare radiological, functional, and clinical results of minimal invasive plate osteosynthesis (MIPO) and conventional open reduction and internal fixation (ORIF) techniques in simple fractures of humerus diaphysis (AO/OTA 12A2b and AO/OTA 12A3b). Materials and Methods: We retrospectively evaluated 51 patients who were operated in our clinic for simple fractures of humerus diaphysis (AO/OTA 12A2b and AO/OTA 12A3b) between 2016 and 2021. Group A included patients treated by MIPO while Group B consisted of patients treated by conventional ORIF. Radio-logical outcomes were determined by fracture union, varus, and valgus malalignment. We evaluated clinical parameters by calculating deep tissue infection, injury to the radial nerve, and implant failure rates. We made the functional assessment by calculating QuickDASH, UCLASS, MEPI, and ROM. Results: We found no statistically significant differences between the two groups regarding radiological outcomes except that varus angulation was determined to be higher in the MIPO group (p<0.001). We followed the obligation of the Helsinki Declaration in our research. The clinical and functional evaluations did not reveal any statistically significant difference between the two surgical techniques. Conclusion: MIPO technique for simple fractures of humerus diaphysis demonstrated similar radiological, functional, and clinical outcomes to conventional ORIF technique. Thus, we believe that MIPO technique may be as safe and effective as conventional ORIF technique.
Mixed Event‐Triggered Mechanism Modeling and Controlling for Networked Control Systems with Time‐Varying Delays and Uncertainties

This paper is concerned with modeling and controlling under the mixed event‐triggered mechanism (ETM) for networked control systems (NCSs) with time‐varying delays and uncertainties. Firstly, an event‐triggered threshold is set by using both state and state‐independent informations in the mixed ETM. Then the event‐triggered NCSs with network‐induced time‐varying delays which exist in both sensor‐to‐controller and controller‐to‐actuator channels are modeled as a general time‐delay system. Based on the piecewise differentiable characteristic of the time‐varying delay and by using the approach of free weighting matrix and reciprocally convex, a less conservative criterion to be globally uniformly ultimately bounded (GUUB) stability and a controller design method are derived. Furthermore, an algorithm is proposed to obtain the desired mixed ETM and state‐feedback controller which can render the network load and control performance to reach an expected level. Compared with the relative and absolute ETMs, the proposed mechanism can effectively improve the transmission efficiency during the whole working time. Finally, a numerical example is given to show the effectiveness of the proposed approach.
A neurofuzzy controller for active front steering system of vehicle under road friction uncertainties

In this study, a neurofuzzy controller is proposed to improve vehicle handling in different road friction coefficients. This controller adapts itself to neutralize the effects of unpredictable changes in road friction coefficient on vehicle handling. This adaptive neurofuzzy controller can improve vehicle handling, manoeuvrability and path tracking. First a proportional-integral-derivative (PID) controller is proposed and tuned by using PSO (particle swarm optimization). Then, this tuned PID controller is applied to the vehicle system and training data is gathered. The next step is to train a fuzzy controller by importing thistraining data tothe ANFIS (adaptive neurofuzzy inference system) toolbox of MATLAB software. Then the trained fuzzy controller is applied to a vehicle that exploits AFS (active front steering system). This controller is able to adapt itself during manoeuvres, by using back propagation of error as a learning algorithm. Results show that neurofuzzy controller can improve handling of the vehicle in different road conditions, because neurofuzzy conteroller can adapt itself in unpredictable situations.