Review of Process Industry Accidents Analysis towards Safety System Improvement and Sustainable Process Design

Risk assessment is the tool for maintaining perfect safety management systems and aiding sustainable process design, with hazard identification as the critical step. This step can be executed by past accidents analysis (PAA) to achieve the mentioned objectives. Despite of available analyses, the recurring of accidents identifies the shortcomings in PAA and requires a detailed examination as reported in this review. The intensified exploration of accident information will strengthen both the safety management system at existing facilities and process designing in terms of sustainability.     

Photolysis of vinyl ketone copolymers. 4: Macromonomeric products from photolyses of copolymers of styrene and methyl acrylate with methyl vinyl ketone

Some statistical copolymers of styrene and methyl vinyl ketone, prepared by free radical copolymerization, have been photolyzed in benzene solution using u.v. light of wavelength 300 or 350 nm. It is confirmed by n.m.r. spectroscopy that the oligomeric products so obtained have unsaturated end-groups with structures resembling that of -methyl styrene, as would be expected on the basis of a predominantly Norrish type II photolysis mechanism. The molecular weights of the oligomers slightly exceed those expected from the copolymerization statistics, indicating probably that some slight cross-linking or repolymerization of end-groups accompanies the chain scission. Although not homopolymerizable, the unsaturated styrene oligomers produced by photolysis can be free-radically copolymerized with methyl acrylate, demonstrating that they behave as macromonomers. A copolymer of methyl acrylate with methyl vinyl ketone also gives fragments with unsaturated end-groups on photolysis, but here the accompanying cross-linking or end-group repolymerization is more dominant, leading to oligomeric products with extremely broad molecular weight distributions.     

Chemical and Morphological Characteristics of Malaysian Cultivated Kenaf (Hibiscus cannabinus) Fiber

In this study, an attempt is made to understand the chemical and morphological characteristics of Malaysian cultivated kenaf fractions (bast, core, and whole stem). Results based on morphologyand chemical analysis indicate that bast and core fiber fractions in the kenaf are significantly different, although the chemical composition of kenaf is quite similar to the woody mass. In the case of whole stem fibers, it is concluded that the effective length of fibers from the whole stem is reasonably good for the purpose of making printing papers.     

Experimental Evaluation of Trilateration-Based Outdoor Localization with LoRaWAN

Long Range Wide Area Network (LoRaWAN) in the Internet of Things (IoT) domain has been the subject of interest for researchers. There is an increasing demand to localize these IoT devices using LoRaWAN due to the quickly growing number of IoT devices. LoRaWAN is well suited to support localization applications in IoTs due to its low power consumption and long range. Multiple approaches have been proposed to solve the localization problem using LoRaWAN. The Expected Signal Power (ESP) based trilateration algorithm has the significant potential for localization because ESP can identify the signal’s energy below the noise floor with no additional hardware requirements and ease of implementation. This research article offers the technical evaluation of the trilateration technique, its efficiency, and its limitations for the localization using LoRa ESP in a large outdoor populated campus environment. Additionally, experimental evaluations are conducted to determine the effects of frequency hopping, outlier removal, and increasing the number of gateways on localization accuracy. Results obtained from the experiment show the importance of calculating the path loss exponent for every frequency to circumvent the high localization error because of the frequency hopping, thus improving the localization performance without the need of using only a single frequency.     

Symmetric and asymmetric impacts of the palm oil industry on carbon dioxide emissions in Malaysia based on linear and nonlinear autoregressive distributed lag models

Clean Technologies and Environmental Policy - Palm oil is a major global commodity that is vital for the well-being of many communities. Despite its significant contribution to the attainment of...     

A lightweight deep learning with feature weighting for activity recognition

With the development of deep learning, numerous models have been proposed for human activity recognition to achieve state-of-the-art recognition on wearable sensor data. Despite the improved accuracy achieved by previous deep learning models, activity recognition remains a challenge. This challenge is often attributed to the complexity of some specific activity patterns. Existing deep learning models proposed to address this have often recorded high overall recognition accuracy, while low recall and precision are often recorded on some individual activities due to the complexity of their patterns. Some existing models that have focused on tackling these issues are always bulky and complex. Since most embedded systems have resource constraints in terms of their processor, memory and battery capacity, it is paramount to propose efficient lightweight activity recognition models that require limited resources consumption, and still capable of achieving state-of-the-art recognition of activities, with high individual recall and precision. This research proposes a high performance, low footprint deep learning model with a squeeze and excitation block to address this challenge. The squeeze and excitation block consist of a global average-pooling layer and two fully connected layers, which were placed to extract the flattened features in the model, with best-fit reduction ratios in the squeeze and excitation block. The squeeze and excitation block served as channel-wise attention, which adjusted the weight of each channel to build more robust representations, which enabled our network to become more responsive to essential features while suppressing less important ones. By using the best-fit reduction ratio in the squeeze and excitation block, the parameters of the fully connected layer were reduced, which helped the model increase responsiveness to essential features. Experiments on three publicly available datasets (PAMAP2, WISDM, and UCI-HAR) showed that the proposed model outperformed existing state-of-the-art with fewer parameters and increased the recall and precision of some individual activities compared to the baseline, and the existing models.     

Syngas Production via Bi-Reforming of Methane Over Fibrous KCC-1 Stabilized Ni Catalyst

Topics in Catalysis - Bi-reforming of methane (BRM) technology has the potential to serve as an alternative energy source while also mitigating greenhouse gas emissions. However, the main hurdle in...