This study focussed on composite boards made from Elaeis guineesis empty fruit bunches (EFB). The EFB supplied by a smallholder oil palm planter in Kuala Krai, Kelantan. The fibre cutter and crusher were used in turning the EFB into smaller size particles. They were screened with four-tier sieve shaker used to remove the oversize particles and impurities present. Hardeners and wax added during the mixing process at 1% and 3%. Boards of three (3) different densities were produced using urea-formaldehyde as the bonding agent. The boards produced later conditioned in a chamber set at 20±2°C and 65% relative humidity. The testing procedure set by EN Standards and specifications were followed. The tests results showed the EFB composite boards possessed excellent physical and mechanical properties. The MOR, MOE and internal bonding of the boards were 22.91 N/mm2, 2059.56 N/mm2, and 0.98 N/mm2. The internal bonding for both edge and face screw withdrawal were 467.47 N/mm2, and 512.37 N/mm2 respectively. Boards with 700 g/cm3 density and 14% resin content met all the requirement needed according to standard exercised. Scanning electron microscope images of low-performance boards showed the resin and fibre in the board interacted closely, but voids appeared at the cross-section suggesting moisture penetrated the board via the open spaces and attacked the linkages existed, thus cause the board to have a low property. The thermal stability of the boards manufactured studied using the Thermogravimetric Analysis.
In conjunction with a nationwide motorcycle safety program, the provision of exclusive motorcycle lanes has been implemented to overcome link-motorcycle accidents along trunk roads in Malaysia. However, not much work has been done to address accidents at junctions involving motorcycles. This article presents the development of predictive model for motorcycle accidents at three-legged major-minor priority junctions of urban roads in Malaysia. The generalized linear modeling technique was used to develop the model. The final model reveals that motorcycle accidents are proportional to the power of traffic flow. An increase in nonmotorcycle and motorcycle flows entering the junctions is associated with an increase in motorcycle accidents. Nonmotorcycle flow on major roads had the highest effect on the probability of motorcycle accidents. Approach speed, lane width, number of lanes, shoulder width, and land use were found to be significant in explaining motorcycle accidents at the three-legged major-minor priority junctions. These findings should enable traffic engineers to specifically design appropriate junction treatment criteria for nonexclusive motorcycle lane facilities.
The coronavirus (COVID-19) has arisen as one of the most severe problems due to its ongoing mutations as well as the absence of a suitable cure for this virus. The virus primarily spreads and replicates itself throughout huge groups of individuals through daily touch, which regretfully can happen in several unanticipated way. As a result, the sole viable attempts to constrain the spread of this new virus are to preserve social distance, perform contact tracing, utilize suitable safety gear, and enforce quarantine measures. In order to control the virus's proliferation, scientists and officials are considering using several social distancing models to detect possible diseased individuals as well as extremely risky areas to sustain separation and lockdown procedures. However, models and systems in the existing studies heavily depend on the human factor only and reveal serious privacy vulnerabilities. In addition, no social distancing model/technique was found for monitoring, tracking, and scheduling vehicles for smart buildings as a social distancing approach so far. In this study, a new system design that performs real-time monitoring, tracking, and scheduling of vehicles for smart buildings is proposed for the first time named the social distancing approach for limiting the number of vehicles (SDA-LNV). The proposed model employs LiFi technology as a wireless transmission medium for the first time in the social distance (SD) approach. The proposed work is considered as Vehicle-to-infrastructure (V2I) communication. It might aid authorities in counting the volume of likely affected people. In addition, the proposed system design is expected to help reduce the infection rate inside buildings in areas where traditional social distancing techniques are not used or applicable.
Cognitive radio (CR) enables unlicensed users (or secondary users, SUs) to sense for and exploit underutilized licensed spectrum owned by the licensed users (or primary users, PUs). Reinforcement learning (RL) is an artificial intelligence approach that enables a node to observe, learn, and make appropriate decisions on action selection in order to maximize network performance. Routing enables a source node to search for a least-cost route to its destination node. While there have been increasing efforts to enhance the traditional RL approach for routing in wireless networks, this research area remains largely unexplored in the domain of routing in CR networks. This paper applies RL in routing and investigates the effects of various features of RL (i.e., reward function, exploitation, and exploration, as well as learning rate) through simulation. New approaches and recommendations are proposed to enhance the features in order to improve the network performance brought about by RL to routing. Simulation results show that the RL parameters of the reward function, exploitation, and exploration, as well as learning rate, must be well regulated, and the new approaches proposed in this paper improves SUs' network performance without significantly jeopardizing PUs' network performance, specifically SUs' interference to PUs.
As of late 2019, the COVID19 pandemic has been causing huge concern around the world. Such a pandemic posed serious threats to public safety, the well-being of healthcare workers, and the overall health of the population. If automation can be implemented in healthcare systems, patients could be better cared for and health industries could be less burdened. To combat such challenges, e-health requires apps and intelligent systems. Using WBAN sensors and networks, a doctor or medical professional can advise patients on the best course of action. Patients' fitness could be assessed using WBAN sensors without interfering with their daily activities. When designing a monitoring system, system performance reliability for competent healthcare is critical. Existing research has failed to create a large device capable of handling a large network or to improve WBAN topologies for fast transmitting and receiving patient data. As a result, in this research, we create a multisensor WBAN (MSWBAN) intelligent system for transmitting and receiving critical patient data. To gather information from all cluster nodes and send it to multisensor WBAN, a novel additive distance-threshold routing protocol (ADTRP) is proposed. In small networks where data are managed by the transmitting node and the best data route is determined, this protocol has less redundancy. An edge-cutting-based routing optimization (ES-EC-R ES-EC-RO) is used to find the best route. The Trouped blowfish MD5 (TB-MD5) algorithm is used to encrypt and decrypt data, and the encrypted data are stored in a cloud database for security. The performance metrics of our proposed model are compared to current techniques for the best results. End-to-end latency is 63 ms, packet delivery is 95%, security is 95.7%, and throughput is 9120 bps, according to the results. The purpose of this article is to encourage engineers and front-line workers to develop digital health systems for tracking and controlling virus outbreaks.