The purpose of this study was to explore whether the performance of the green innovation brought positive effect to the competitive advantage. This study found that the performances of the green ...product innovation and green process innovation were positively correlated to the corporate competitive advantage. Therefore, the result meant that the investment in the green product innovation and green process innovation was helpful to the businesses. This study argued that the businesses should cognize the correct value and positioning of the green innovation.
This study examined the long-term risks of heart failure (HF) and coronary heart disease (CHD) following traumatic brain injury (TBI), focusing on gender differences.
Data from Taiwan's National ...Health Insurance Research Database included 29,570 TBI patients and 118,280 matched controls based on propensity scores.
The TBI cohort had higher incidences of CHD and HF (9.76 vs. 9.07 per 1000 person-years; 4.40 vs. 3.88 per 1000 person-years). Adjusted analyses showed a significantly higher risk of HF in the TBI group (adjusted hazard ratio = 1.08, 95% CI = 1.01-1.17, P = 0.031). The increased CHD risk in the TBI cohort became insignificant after adjustment. Subgroup analysis by gender revealed higher HF risk in men (aHR = 1.14, 95% CI = 1.03-1.25, P = 0.010) and higher CHD risk in women under 50 (aHR = 1.32, 95% CI = 1.15-1.52, P < 0.001). TBI patients without beta-blocker therapy may be at increased risk of HF.
Our results suggest that TBI increases the risk of HF and CHD in this nationwide cohort of Taiwanese citizens. Gender influences the risks differently, with men at higher HF risk and younger women at higher CHD risk. Beta-blockers have a neutral effect on HF and CHD risk.
In order to achieve the Sustainable Development Goals (SDG), it is imperative to ensure the safety of drinking water. The characteristics of each drinkable water, encompassing taste, aroma, and ...appearance, are unique. Inadequate water infrastructure and treatment can affect these features and may also threaten public health. This study utilizes the Internet of Things (IoT) in developing a monitoring system, particularly for water quality, to reduce the risk of contracting diseases. Water quality components data, such as water temperature, alkalinity or acidity, and contaminants, were obtained through a series of linked sensors. An Arduino microcontroller board acquired all the data and the Narrow Band-IoT (NB-IoT) transmitted them to the web server. Due to limited human resources to observe the water quality physically, the monitoring was complemented by real-time notifications alerts via a telephone text messaging application. The water quality data were monitored using Grafana in web mode, and the binary classifiers of machine learning techniques were applied to predict whether the water was drinkable or not based on the data collected, which were stored in a database. The non-decision tree, as well as the decision tree, were evaluated based on the improvements of the artificial intelligence framework. With a ratio of 60% for data training: at 20% for data validation, and 10% for data testing, the performance of the decision tree (DT) model was more prominent in comparison with the Gradient Boosting (GB), Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM) modeling approaches. Through the monitoring and prediction of results, the authorities can sample the water sources every two weeks.
This paper focuses on the use of smart manufacturing in lathe-cutting tool machines, which can experience thermal deformation during long-term processing, leading to displacement errors in the ...cutting head and damage to the final product. This study uses time-series thermal compensation to develop a predictive system for thermal displacement in machine tools, which is applicable in the industry using edge computing technology. Two experiments were carried out to optimize the temperature prediction models and predict the displacement of five axes at the temperature points. First, an examination is conducted to determine possible variances in time-series data. This analysis is based on the data obtained for the changes in time, speed, torque, and temperature at various locations of the machine tool. Using the viable machine-learning models determined, the study then examines various cutting settings, temperature points, and machine speeds to forecast the future five-axis displacement. Second, to verify the precision of the models created in the initial phase, other time-series models are examined and trained in the subsequent phase, and their effectiveness is compared to the models acquired in the first phase. This work also included training seven models of WNN, LSTNet, TPA-LSTM, XGBoost, BiLSTM, CNN, and GA-LSTM. The study found that the GA-LSTM model outperforms the other three best models of the LSTM, GRU, and XGBoost models with an average precision greater than 90%. Based on the analysis of training time and model precision, the study concluded that a system using LSTM, GRU, and XGBoost should be designed and applied for thermal compensation using edge devices such as the Raspberry Pi.
Optimization strategies in deep learning models require different techniques for different use cases. Besides, various phases of the model deployment life-cycle specify possible and particular ...optimization strategies. In this paper, an optimized deep learning model on the edge computing environment is proposed for image classification cases. For preparing the dataset, the image preprocessing and data augmentation methods are utilized to prepare the data for the training process. To accelerate the deep learning training process, this system implemented CPU optimization and hyperparameter tuning. Tensorflow is applied as a framework for the training model. InceptionV3, VGG16, and MobileNet are applied as topology implemented in the deep learning training comparison. In this case, InceptionV3 was used for modeling the deep learning applications on edge. To optimize the trained model, a Model Optimizer is used on the edge device. It can be seen in the experiments, MobileNet was the least accurate model (85%) and the longest time to load the model (71s). VGG16 was the most reliable (91%) and the shortest time to load the model (50s). InceptionV3 has median accuracy (87%) and the average time to load the model (52s).
This paper implements deep learning methods of recurrent neural networks and short-term memory models. Two kinds of time-series data were used: air pollutant factors, such as O3, SO2, and CO2 from ...2017 to 2019, and meteorological factors such as temperature, humidity, wind direction, and wind speed. A trained model was used to predict air pollution within an eight-hour period. Correlation analysis was applied using Pearson and Spearman correlation coefficients. The KNN method was used to fill in the missing values to improve the generated model’s accuracy. The average absolute error percentage value was used in the experiments to evaluate the model’s performance. LSTM had the lowest RMSE value at 1.9 than the other models from the experiments. CNN had a significant RMSE value at 3.5, followed by Bi-LSTM at 2.5 and Bi-GRU at 2.7. In comparison, the RNN was slightly higher than LSTM at a 2.4 value.
The metabolic challenges present in tumors attenuate the metabolic fitness and antitumor activity of tumor-infiltrating T lymphocytes (TILs). However, it remains unclear whether persistent metabolic ...insufficiency can imprint permanent T cell dysfunction. We found that TILs accumulated depolarized mitochondria as a result of decreased mitophagy activity and displayed functional, transcriptomic and epigenetic characteristics of terminally exhausted T cells. Mechanistically, reduced mitochondrial fitness in TILs was induced by the coordination of T cell receptor stimulation, microenvironmental stressors and PD-1 signaling. Enforced accumulation of depolarized mitochondria with pharmacological inhibitors induced epigenetic reprogramming toward terminal exhaustion, indicating that mitochondrial deregulation caused T cell exhaustion. Furthermore, supplementation with nicotinamide riboside enhanced T cell mitochondrial fitness and improved responsiveness to anti-PD-1 treatment. Together, our results reveal insights into how mitochondrial dynamics and quality orchestrate T cell antitumor responses and commitment to the exhaustion program.
The elongation of long-chain fatty acids family member 6 (Elovl6) is a key enzyme in lipogenesis that catalyzes the elongation of saturated and monounsaturated fatty acids. Insulin resistance ...involves upregulation of Elovl6, which has been linked to obesity-related malignancies, including hepatocellular carcinoma (HCC). However, the role of Elovl6 in cancer progression remains unknown. In this study, we analyzed the expression of Elovl6 in 61 clinical HCC specimens. Patients with Elovl6 high-expressing tumors were associated with shorter disease-free survival and overall survival compared to those with Elovl6 low-expressing tumors. Knockdown of Elovl6 in HCC cells reduced cell proliferation and Akt activation, as well as sensitivity to fatty acids. Inhibition of Elovl6 reduced tumor growth and prolonged survival in mice bearing tumors. Taken together, our results indicate that Elovl6 enhances oncogenic activity in liver cancer and is associated with poor prognosis in patients with HCC. Elovl6 may be a therapeutic target for HCC; thus, further studies to confirm this strategy are warranted.
The combination of edge and cloud computing is going to make the Internet of Things (IoT) rapid, light, and more reliable. IoT and cloud-edge computing are distinct disciples that have evolved ...separately over time. However, they are increasingly becoming interdependent, and are what the future holds. A crucial aspect is how to design a compound of cloud and edge computing architectures, and implement IoT effectively. In this paper, we proposed a combination of Cloud and Edge Computing architecture and built a set of an intelligent air-quality monitoring system in Tunghai University as a case study. In this case, we implemented container-based virtualization which constructs Kubernetes Minion (Nodes) in the Docker container service independently for each service on the Edge side. Finally, to monitor the high-performance computing systems, clusters, and networks, we used Ganglia Monitoring System. Ganglia collects relevant information such as Central Processing Unit (CPU), memory, network and usage of Protocol Data Unit (PDU) to monitor the power consumption and makes a measurement and evaluation for Kubernetes Pods.
Electricity data could generate a large number of records from smart meter day by day. The traditional architecture might not properly handle the increasingly dynamic data that need flexibility. For ...effective storing and analytics, efficient architecture is needed to provide much greater data volumes and varieties. In this paper, we proposed the architecture of data storage and analytic in the big data lake of electricity usage using Spark. Apache Sqoop was used to migrate historical data to Apache Hive for processing from an existing system. Apache Kafka was used as the input source for Spark to stream data to Apache HBase to ensure the integrity of the streaming data. In order to integrate the data, we use the Hive and HBase principle of Data Lake as search engines for Hive and HBase. Apache Impala and Apache Phoenix are used separately. This work also analyzes electricity usage and power failure with Apache Spark. All of the visualizations of this project are presented in Apache Superset. Moreover, the usage prediction comparison is presented using HoltWinters algorithm.
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