Musculoskeletal morbidity in adults with spinal cord injuries: A nationwide cohort study.

BACKGROUND: An increase in the demand for quality of life following spinal cord injuries (SCIs) is associated with an increase in musculoskeletal (MSK) pain, highlighting the need for preventive measure research. OBJECTIVE: This study aimed to evaluate the incidence and hazards of MSK morbidities among Korean adults with SCIs, as well as the influence of SCI location on MSK morbidities. METHODS: Patient populations were selected from Korean National Health Insurance Service data (n = 276). The control group included individuals without SCIs (n = 10,000). We compared the incidences and determined the unadjusted and adjusted hazard ratios (HRs) of common MSK morbidities (osteoarthritis, connective tissue disorders, sarcopenia, myalgia, neuralgia, rheumatoid arthritis, myositis, and musculoskeletal infections) based on the location of injury (cervical, thoracic, or lumbar). RESULTS: Adults with SCIs had a higher incidence of MSK morbidity (48.45% vs. 36.6%) and a lower survival probability than those without SCIs. The incidence of MSK morbidity and survival probabilities were not significantly different for cervical cord injuries, whereas both measures were significantly different for thoracic and lumbar injuries. CONCLUSION: SCI increases the risk of MSK morbidity. Lumbar SCI is associated with a higher incidence and risk of MSK morbidity than are cervical or thoracic SCIs.

Characterization of microplastics in the water and sediment of Baram River estuary, Borneo Island.

The Baram River is one of the largest rivers in Sarawak, where many large industries, such as plywood, sawmills, shipyards, interisland ports, and other wood-based industries are located along the river. Microplastic contamination has become a widespread and growing concern worldwide because of the small sizes of microplastics and their presence in seafood such as fish, squid, scallop, crabs, shrimp, and mussels. In this study, microplastics were found in all sampling stations. Out of the 4017 microplastics found in the water and sediment, microplastics fragment accounted for 67.8% of total microplastics, followed by fiber, film, pellet, and foam. Five microplastic polymer types were detected by ATR-FTIR, including polyethylene (PE), polyester (PET) fibers, silicon polymer, nitrile, and polystyrene (PS). The most common microplastics size range in Baram River was 0.3-1 mm, with blue as the highly abundant color.

Pesticides in Drinking Water-A Review.

The ubiquitous problem of pesticide in aquatic environment are receiving worldwide concern as pesticide tends to accumulate in the body of the aquatic organism and sediment soil, posing health risks to the human. Many pesticide formulations had introduced due to the rapid growth in the global pesticide market result from the wide use of pesticides in agricultural and non-agricultural sectors. The occurrence of pesticides in the water body is derived by the runoff from the agricultural field and industrial wastewater. Soluble pesticides were carried away by water molecules especially during the precipitation event by percolating downward into the soil layers and eventually reach surface waters and groundwater. Consequently, it degrades water quality and reduces the supply of clean water for potable water. Long-time exposure to the low concentration of pesticides had resulted in non-carcinogenic health risks. The conventional method of pesticide treatment processes encompasses coagulation-flocculation, adsorption, filtration and sedimentation, which rely on the phase transfer of pollutants. Those methods are often incurred with a relatively high operational cost and may cause secondary pollution such as sludge formation. Advanced oxidation processes (AOPs) are recognized as clean technologies for the treatment of water containing recalcitrant and bio-refractory pollutants such as pesticides. It has been adopted as recent water purification technology because of the thermodynamic viability and broad spectrum of applicability. This work provides a comprehensive review for occurrence of pesticide in the drinking water and its possible treatment.

Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing.

With the increase in the amount of data captured during the manufacturing process, monitoring systems are becoming important factors in decision making for management. Current technologies such as Internet of Things (IoT)-based sensors can be considered a solution to provide efficient monitoring of the manufacturing process. In this study, a real-time monitoring system that utilizes IoT-based sensors, big data processing, and a hybrid prediction model is proposed. Firstly, an IoT-based sensor that collects temperature, humidity, accelerometer, and gyroscope data was developed. The characteristics of IoT-generated sensor data from the manufacturing process are: real-time, large amounts, and unstructured type. The proposed big data processing platform utilizes Apache Kafka as a message queue, Apache Storm as a real-time processing engine and MongoDB to store the sensor data from the manufacturing process. Secondly, for the proposed hybrid prediction model, Density-Based Spatial Clustering of Applications with Noise (DBSCAN)-based outlier detection and Random Forest classification were used to remove outlier sensor data and provide fault detection during the manufacturing process, respectively. The proposed model was evaluated and tested at an automotive manufacturing assembly line in Korea. The results showed that IoT-based sensors and the proposed big data processing system are sufficiently efficient to monitor the manufacturing process. Furthermore, the proposed hybrid prediction model has better fault prediction accuracy than other models given the sensor data as input. The proposed system is expected to support management by improving decision-making and will help prevent unexpected losses caused by faults during the manufacturing process.

A Personalized Healthcare Monitoring System for Diabetic Patients by Utilizing BLE-Based Sensors and Real-Time Data Processing.

Current technology provides an efficient way of monitoring the personal health of individuals. Bluetooth Low Energy (BLE)-based sensors can be considered as a solution for monitoring personal vital signs data. In this study, we propose a personalized healthcare monitoring system by utilizing a BLE-based sensor device, real-time data processing, and machine learning-based algorithms to help diabetic patients to better self-manage their chronic condition. BLEs were used to gather users’ vital signs data such as blood pressure, heart rate, weight, and blood glucose (BG) from sensor nodes to smartphones, while real-time data processing was utilized to manage the large amount of continuously generated sensor data. The proposed real-time data processing utilized Apache Kafka as a streaming platform and MongoDB to store the sensor data from the patient. The results show that commercial versions of the BLE-based sensors and the proposed real-time data processing are sufficiently efficient to monitor the vital signs data of diabetic patients. Furthermore, machine learning⁻based classification methods were tested on a diabetes dataset and showed that a Multilayer Perceptron can provide early prediction of diabetes given the user’s sensor data as input. The results also reveal that Long Short-Term Memory can accurately predict the future BG level based on the current sensor data. In addition, the proposed diabetes classification and BG prediction could be combined with personalized diet and physical activity suggestions in order to improve the health quality of patients and to avoid critical conditions in the future.