Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of Control Room Operators’ Competence using Cognitive Engineering Approaches to Improve Process Safety

Operator competence is critical to ensure safe and profitable operation in hazard intensive chemical process industries. Human errors account for the majority of the accidents in these industries. Therefore, it is imperative to develop methodologies to assess the competence of operators to minimize human errors. Traditional approaches to elicit operator competence are based on subjective measures and/or measures derived primarily from the process and operator actions. These approaches ignore the cognitive aspects of operators such as perception, decision-making strategy, and workload, which are crucial for improving performance. Recent development in sensor technology has enabled the researchers to measure human cognitive behavior objectively. Sensors such as eye-tracking, electroencephalography (EEG) and galvanic skin resistance (GSR) are found to provide intrinsic human characteristics that cannot be measured otherwise. In this paper, we discuss how eye-tracking can be used to capture control room operators’ cognition and help infer their competence. Eye-tracking provides information about the location of a person’s gaze, which serves as a trace of their attention allocation. Eye gaze based metrics show a strong correlation with operator cognitive behavior such as situation awareness and cognitive workload during process monitoring tasks.
A stacked ensemble learning method for traffic speed forecasting using empirical mode decomposition

ABSTRACT Traffic speed is an important traffic parameter whose accurate prediction is crucial in traffic management. Different machine learning and ensemble methods have been used for predicting traffic parameters. Proper feature selection, the type of learners, ensemble methods, and appropriate aggregation technique have a significant impact on the prediction accuracy of ensemble methods. ‎To achieve higher prediction accuracy, using the nonlinear learning ability of Support Vector Regression (SVR) model, we propose a stacking ensemble learning model. ‎‏‎ We train the base learners using different training subsets based on random subspace selection. In the analysis of empirical and complex data, Empirical Mode Decomposition (EMD) is used for decomposing time series data into seasonal and trend components. We use EMD to create new features. In order to reduce the computation time, we also propose a sample selection method. The results of our experiments show that the proposed method outperforms the existing hybrid EMD-ARIMA and non-ensemble models. In comparison with the baseline models, using the EMD technique leads to a 0.32 to 4% decrease in RMSE for different links. The results show that the proposed model outperforms the ARIMA and ARIMA-EMD models in terms of long-term traffic speed forecasting performance.
Upper Body Physical Rehabilitation for Children with Ataxia through IMU-Based Exergame

Background: Children with ataxia experience balance and movement coordination difficulties and needs intensive physical intervention to maintain functional abilities and counteract the disorder. Exergaming represents a valuable strategy to provide engaging physical intervention to children with ataxia, sustaining their motivation to perform the intervention. This paper aims to describe the effect of a home-conducted exergame-based exercise training for upper body movements control of children with ataxia on their ataxic symptoms, walking ability, and hand dexterity. Methods: Eighteen children with ataxia were randomly divided into intervention and control groups. Participants in the intervention group were asked to follow a 12-week motor activity program at home using the Niurion® exergame. Blind assessments of participants’ ataxic symptoms, dominant and non-dominant hand dexterity, and walking ability were conducted. Results: On average, the participants performed the intervention for 61.5% of the expected time. At the end of the training, participants in the intervention group showed improved hand dexterity that worsened in the control group. Conclusion: The presented exergame enhanced the participants’ hand dexterity. However, there is a need for exergames capable of maintaining a high level of players’ motivation in playing. It is advisable to plan a mixed intervention to take care of the multiple aspects of the disorder.
Influence of Guided Tissue Regeneration Techniques on the Success Rate of Healing of Surgical Endodontic Treatment: A Systematic Review and Network Meta-Analysis

Several regeneration techniques and materials have been proposed for the healing of bone defects after surgical endodontic treatment; however, the existing literature does not provide evidence on the most recommended techniques or materials. The aim of the present systematic review and network meta-analysis (NMA) is to summarize the clinical evidence on the efficacy of guided tissue regeneration techniques (GRTs). The PRISMA recommendations were followed. Four databases were searched up to December 2021. Randomized clinical trials (RCTs) with a minimum follow-up of 6 months were included. The risk of bias was assessed using the Cochrane Collaboration tool. A fixed effects model and frequentist approach were used in the NMA. Direct GRT technique comparisons were combined to estimate indirect comparisons, and the estimated effect size of the comparisons was analyzed using the odds ratio (OR). Inconsistency was assessed with the Q test, with a significance level of p < 0.01, and a net heat plot. A total of 274 articles was identified, and 11 RCTs (6 direct comparisons of 15 techniques) were included in the NMA, which examined 6 GRT techniques: control, Os, PL, MB, MB + Os, and MB + PL. The MB + Os group compared to the control (OR = 3.67, 95% CI: 1.36–9.90) and to the MB group (OR = 3.47, 95% CI: 1.07–11.3) showed statistically significant ORs (p ˂ 0.05). The MB + Os group presented the highest degree of certainly (P-score = 0.93).
Coronavirus and Carbon Nanotubes: Seeking Immunological Relationships to Discover Immunotherapeutic Possibilities

Abstract Since December 2019, the world has faced an unprecedented pandemic crisis due to a new coronavirus disease, coronavirus disease-2019 (COVID-19), which has instigated intensive studies on prevention and treatment possibilities. Here, we investigate the relationships between the immune activation induced by three coronaviruses associated with recent outbreaks, with special attention to SARS-CoV-2, the causative agent of COVID-19, and the immune activation induced by carbon nanotubes (CNTs) to understand the points of convergence in immune induction and modulation. Evidence suggests that CNTs are among the most promising materials for use as immunotherapeutic agents. Therefore, this investigation explores new possibilities of effective immunotherapies for COVID-19. This study aimed to raise interest and knowledge about the use of CNTs as immunotherapeutic agents in coronavirus treatment. Thus, we summarize the most important immunological aspects of various coronavirus infections and describe key advances and challenges in using CNTs as immunotherapeutic agents against viral infections and the activation of the immune response induced by CNTs, which can shed light on the immunotherapeutic possibilities of CNTs.
Study on self-intersection detection algorithm based on OBJ model

In order to solve the problem of “Clipping” in the animation production process, that is, the self-intersection of the model, this paper proposes a model self-intersection detection algorithm based on the ray method. This method is aimed at the OBJ file format and can render and display the self-intersection detection results. The algorithm has high computational efficiency and accurate results, which can realize rapid evaluation of complex models. This research not only has practical value for common 3D animation production, but also has important significance for the application of deep learning to realize the related research in the field of complex 3D model reconstruction.
Optimal Loop Tiling for Minimizing Write Operations on NVMs with Complete Memory Latency Hiding

Non-volatile memory (NVM) is expected to be the second level memory (named remote memory) in two-level memory hierarchy in the future. However, NVM has the limited write endurance, thus it is vital to reduce the number of write operations on NVM. Meanwhile, in two-level memory hierarchy, prefetch is widely used for fetching certain data before it is actually required, to hide the remote memory access latency. In general, large-scale nested loop is the performance bottleneck in one program due to the write operations on NVM caused by the first level memory (named local memory) miss and data reuse. Loop tiling is the key technique for grouping iterations so as to reduce the communication with remote memory used in compiler. In this paper, we propose a new loop tiling approach for minimizing the write operations on NVMs and completely hiding the NVM access latency. Specifically, we introduce a series of theorems to help loop tiling. Then, a legal tile shape and an optimal tile size selection strategy is proposed according to data dependency and local memory capacity. Furthermore, we propose a pipeline scheduling policy to completely hide the remote memory latency. Extensive experiments show that the proposed techniques can reduce write operations on NVMs by 95.1% on average, and NVM latency can be completely hidden.
Impaired Lung Function and Lung Cancer Incidence: A Nationwide Population-Based Cohort Study

Background: It is unclear whether the presence of minimal lung function impairment is an independent risk factor for the development of lung cancer in general populations. Methods: We conducted a population-based cohort study using nationally representative data from the Korean National Health and Nutrition Examination Survey and the Korean National Health Insurance Service. Results: Of 20,553 participants, 169 were diagnosed with lung cancer during follow-up (median, 6.5 years). Participants with obstructive lung function impairment had increased risk of lung cancer (aHR: 2.51; 95% CI: 1.729–3.629) compared with those with normal lung function. The lower was the quartile or decile of forced expiratory volume in one second (FEV1) or the FEV1/forced vital capacity (FVC) ratio, the significantly higher was the incidence rate of lung cancer (p for trend < 0.0001). With FEV1 values in the lowest quartile (Q4), the incidence of lung cancer was significantly increased regardless of FVC (FEV1 Q4 and FVC values in the higher three quartiles Q1–3: aHR 1.754; 95% CI 1.084–2.847, FEV1 Q4 and FVC Q4: aHR 1.889; 95% CI 1.331–2.681). Conclusion: Our findings suggest that minimal lung function impairment, as expressed by lower FEV1 or FEV1/FVC value, may be associated with increased risk of lung cancer
Clinical Evaluation of Avelumab in the Treatment of Advanced Urothelial Carcinoma: Focus on Patient Selection and Outcomes

Background First-line therapy for treatment of advanced urothelial carcinoma includes combination platinum-based chemotherapies, though resistance and long-term toxicity concerns to these regimens cause limitations in progression-free survival and overall survival. Maintenance treatment with an alternative agent such as the PD-L1 inhibitor, avelumab (Bavencio®), after initial chemotherapy has been shown to prolong overall survival. The aim of this review is to provide a landscape clinical use of avelumab in the treatment of advanced urothelial carcinoma with a focus on patient selection and outcomes. Methods This review includes the most up to date phases and results from clinical trials published in peer-reviewed journals. Results Three studies were included, one phase 1B trial, one phase 1B trial with 2 year follow-up, and one phase 3 trial. Patients receiving avelumab maintenance therapy at 10 mg/kg IV every two weeks had an overall better performance status, though those with an increased ECOG-PS, increased Bellmunt risk score, or failure of ≥3 chemotherapies had poorer responses. Patients over the age of 65 had a higher ORR (18–25%) compared to younger patients (13–14%). Patients with PD-L1 positive tumors had a significantly increased CR median ORR (13.8%), median PFS (5.7 months), and median 12-month OS rate (79.1%) compared to control subjects receiving best supportive care (1.2%, 2.1 months, 60.4%, respectively). TRAEs were seen in 86.7% of patients, with 32.4% of patients experiencing a ≥grade 3 AE. The most common AE was IRR (32.4%, ≥grade 3 1.01%) and irAE 25.6% of any grade, including various rashes and pruritus AEs, immune-related thyroid disorders, and immune related hepatitis. There were 3 reported treatment-related deaths (0.05%). Ongoing phases of one of the trials is investigating the use of docetaxel and avelumab together after failure of one chemotherapy. Conclusion Avelumab as a maintenance therapy after platinum-based chemotherapy failure or in platinum-ineligible patients with advanced or metastatic urothelial carcinoma is an effective option with increased ORR, PFS, and OS with a similar safety profile to other chemotherapies. Ongoing studies currently in recruitment and active clinical trials will yield valuable insights into optimizing avelumab therapy in conjunction with chemotherapies and/or immunotherapies, better characterization of response for PD-L1 positive tumors, and a clearer insight into clinically validated prognostic factors to improve patient outcomes.
Chinese Aviation Network Analysis under Actual Node Attack

During the Corona Virus Disease 2019 (COVID-19) pandemic, many airports were semi-closed to prevent the virus from spreading, which greatly impacted the Chinese aviation network. The current approach to the analysis of the Chinese aviation network completely removes nodes to study node attacks on the Chinese aviation network. However, it is not practical to close the airport completely while ignoring other airlines' adjustments. This paper investigates the changes of Chinese aviation networks under the real node attack and provides useful information and practical suggestions on how to deal with the transmission of infectious diseases by airlines in the future. First, we collect the flight schedules for representative dates from VariFlight, delete useless information, split layover flights and count the number of flights in each airport. Next, we compute the properties of the Chinese aviation network under node attacks such as betweenness centrality and PageRank. Finally, the properties are compared with those of normal dates to investigate the changes of the Chinese aviation network under node attack. The results indicate that the Chinese aviation network is a small-world and scale-free network. Our analyses demonstrate that the decline of airlines in hub airports can influence the properties of network and adjacent airports. Meanwhile, the node attack on a hub airport with many substitutes failed to achieve the expected result. Therefore, traffic restriction should also be applied to the substitutes if there are many large cities around the infectious city.
Research on Management Informatization of Construction Engineering

With the development of computer technology and modern enterprise, the information technology of enterprise management has gradually been taken seriously. The information construction of construction engineering management reflects the goal of construction and building industry development. The articles analysis the problems in information construction of construction engineering management, analyzes the shortcomings in information construction in China's construction project management and then put forward some feasible suggestions
MANUFACTURING PERFORMANCE AND EVOLUTION OF TPM

To improve productivity it is essential to improve the performance of the manufacturing systems. This system consists of various resources like labour, materials, tools, plant and equipment, and others, used for production. The desired production output is achieved through high equipment availability, which is influenced by equipment reliability and maintainability. Maintenance function is therefore vital for sustainable performance of any manufacturing plant, since, a proper maintenance plan improve the equipment availability and reliability. The paper describes the maintenance (Total Productive Maintenance) as strategy to improve manufacturing performance. Further, 5S as the base of Total Productive Maintenance (TPM) and overall equipment effectiveness (OEE) as a measure of effectiveness have also been discussed.
Network Pharmacology-Based Strategy to Investigate the Anti-Breast Cancer Mechanisms of Spatholobus suberectus Dunn

Spatholobus suberectus Dunn (SSD) possesses potential antitumor activity; however, the mechanism underlying its anti-proliferative effect on breast cancer is unclear. In this study, we explored potential SSD targets for breast cancer treatment through a network pharmacology approach. First, by integrating multiple databases, a total of 16 potential bioactive compounds and 252 targets were screened. Differentially expressed genes (DEGs) were screened by analyzing breast cancer gene chip data from The Cancer Genome Atlas and Gene Expression Omnibus databases. By overlapping drug targets and DEGs, 33 common targets were found; their functions were further analyzed with Gene Ontology and KEGG analysis. A network of 16 compounds and 33 common targets was constructed, from which 10 hub targets were identified using CytoHubba. Based on the KEGG result and network analysis, the 33 common targets were mainly enriched in the peroxisome proliferator-activated receptor (PPAR) signaling pathway and PPARγ was identified as the potential target of SSD. Moreover, the 10 hub targets were correlated with prognosis and immune infiltration in breast cancer via bioinformatic analysis. Finally, molecular docking and experiments in vitro further verified the targeting ability and anti-breast cancer activity of SSD. SSD is promising in the treatment of breast cancer; PPARγ may be its potential therapeutic target.
EFFECTS OF AGRO-INDUSTRIAL BY PRODUCTS ON THE GROWTH OF RABBITS IN NORTHERN GHANA

The use of agro-industri al by products as a source of protein could minimize the challenge of obtaining locally available concentrate for rabbit production especially in the dry season. The study was carried out to investigate the effects of four agro-industrial by products and maize as a control on growing rabbits. The agro by-products included Milled Mango Seed Kernel (MMSK), Maize Bran (MB), Brewers Spent Grain (BSG) and Corn Mill Residue (CMR). A total of 20 rabbits were randomly assigned to the five experimental diets for a period of 49 days after one week adaptation period. The OM was in the range of 609- 857g/kg and that of carbohydrate was in the range of 563-768.5g/kg. The crude protein from the experimental diet was in the range of 168 and 330g/kg. The highest ADF fraction was obtained in BSG and the least from maize. There was no significant (p>0.05) difference in final live weight gain and average daily weight gain among the treatments diets. Significant difference was observed in PCV, RBC and Hb with the highest recorded in MB. The total WBC was also significantly different with the highest obtained in CMR and the least in MMSK. There was no significant difference (p>0.05) in dressing percentage, hot carcass and cold carcass weight. Maize bran could be incorporated in the diet of rabbits at 70% without any detrimental effect on grower rabbits.
A Multi-Agent Deep Reinforcement Learning Approach for Enhancement of COVID-19 CT Image Segmentation

Currently, most mask extraction techniques are based on convolutional neural networks (CNNs). However, there are still numerous problems that mask extraction techniques need to solve. Thus, the most advanced methods to deploy artificial intelligence (AI) techniques are necessary. The use of cooperative agents in mask extraction increases the efficiency of automatic image segmentation. Hence, we introduce a new mask extraction method that is based on multi-agent deep reinforcement learning (DRL) to minimize the long-term manual mask extraction and to enhance medical image segmentation frameworks. A DRL-based method is introduced to deal with mask extraction issues. This new method utilizes a modified version of the Deep Q-Network to enable the mask detector to select masks from the image studied. Based on COVID-19 computed tomography (CT) images, we used DRL mask extraction-based techniques to extract visual features of COVID-19 infected areas and provide an accurate clinical diagnosis while optimizing the pathogenic diagnostic test and saving time. We collected CT images of different cases (normal chest CT, pneumonia, typical viral cases, and cases of COVID-19). Experimental validation achieved a precision of 97.12% with a Dice of 80.81%, a sensitivity of 79.97%, a specificity of 99.48%, a precision of 85.21%, an F1 score of 83.01%, a structural metric of 84.38%, and a mean absolute error of 0.86%. Additionally, the results of the visual segmentation clearly reflected the ground truth. The results reveal the proof of principle for using DRL to extract CT masks for an effective diagnosis of COVID-19.
Micro-to-Nanometer Scale Patterning of Perovskite Inks via Controlled Self-Assemblies

In the past decade, perovskite materials have gained intensive interest due to their remarkable material properties in optoelectronics and photodetectors. This review highlights recent advances in micro-to-nanometer scale patterning of perovskite inks, placing an undue emphasis on recently developed approaches to harness spatially ordered and crystallographically oriented structures with unprecedented regularity via controlled self-assemblies, including blade coating, inkjet printing, and nanoimprinting. Patterning of the perovskite elements at the micro- or nanometer scale might be a key parameter for their integration in a real system. Nowadays, unconventional approaches based on irreversible solution evaporation hold an important position in the structuring and integration of perovskite materials. Herein, easier type patterning techniques based on evaporations of polymer solutions and the coffee ring effect are systematically reviewed. The recent progress in the potential applications of the patterned perovskite inks is also introduced.
Blood Vessels: The Pathway Used by Schwann Cells to Colonize Nerve Conduits

The repair of severe nerve injuries requires an autograft or conduit to bridge the gap and avoid axon dispersion. Several conduits are used routinely, but their effectiveness is comparable to that of an autograft only for short gaps. Understanding nerve regeneration within short conduits could help improve their efficacy for longer gaps. Since Schwann cells are known to migrate on endothelial cells to colonize the “nerve bridge”, the new tissue spontaneously forming to connect the injured nerve stumps, here we aimed to investigate whether this migratory mechanism drives Schwann cells to also proceed within the nerve conduits used to repair large nerve gaps. Injured median nerves of adult female rats were repaired with 10 mm chitosan conduits and the regenerated nerves within conduits were analyzed at different time points using confocal imaging of sequential thick sections. Our data showed that the endothelial cells formed a dense capillary network used by Schwann cells to migrate from the two nerve stumps into the conduit. We concluded that angiogenesis played a key role in the nerve conduits, not only by supporting cell survival but also by providing a pathway for the migration of newly formed Schwann cells.