At present, sustainability and emerging technology are the main issues in any supply chain management (SCM) sector. At the same time, the ongoing pandemic is increasing consumers’ concerns about food safety, processing, and distribution, which should meet sustainability requirements. Thus, supervision and monitoring of product quality with symmetric information traceability are important in fresh food and fishery SCM. Food safety and traceability systems based on blockchain, Internet of Things (IoT), wireless sensor networks (WSN), and radio frequency identification (RFID) provide reliability from production to consumption. This review focuses on RFID-based traceability systems in fisheries’ SCM, which have been employed globally to ensure fish quality and security, and summarizes their advantages in real-time applications. The results of this study will help future researchers to improve consumers’ trust in fisheries SCM. Thus, this review aims to provide guidelines and solutions for enhancing the reliability of RFID-based traceability in food SCM systems so to ensure the integrity and transparency of product information.

Analyzing Electroencephalogram (EEG) signal is a challenge due to the various artifacts used by Electromyogram, eye blink and Electrooculogram. The present de-noising techniques that are based on the frequency selective filtering suffers from a substantial loss of the EEG data. Noise removal using wavelet has the characteristic of preserving signal uniqueness even if noise is going to be minimized. To remove noise from EEG signal, this research employed discrete wavelet transform. Root mean square difference has been used to find the usefulness of the noise elimination. In this research, four different discrete wavelet functions have been used to remove noise from the Electroencephalogram signal gotten from two different types of patients (healthy and epileptic) to show the effectiveness of DWT on EEG noise removal. The result shows that the WF orthogonal meyer is the best one for noise elimination from the EEG signal of epileptic subjects and the WF Daubechies 8 (db8) is the best one for noise elimination from the EEG signal on healthy subjects

Malaysia, being a tropical country located near the equatorial doldrums, experiences the annual occurrence of flood hazards due to monsoon rainfalls and urban development. In recent years, environmental policies in the country have shifted towards sustainable flood risk management. As part of the development of flood forecasting and warning systems, this study presented the urban flood simulation using InfoWorks ICM hydrological−hydraulic modeling of the Damansara catchment as a case study. The response of catchments to the rainfall was modeled using the Probability Distributed Moisture (PDM) model due to its capability for large catchments with long-term runoff prediction. The interferometric synthetic aperture radar (IFSAR) technique was used to obtain high-resolution digital terrain model (DTM) data. The calibrated and validated model was first applied to investigate the effectiveness of the existing regional ponds on flood mitigation. For a 100-year flood, the extent of flooded areas decreased from 12.41 km² to 3.61 km² as a result of 64-ha ponds in the catchment, which is equivalent to a 71% reduction. The flood hazard maps were then generated based on several average recurrence intervals (ARIs) and uniform rainfall depths, and the results showed that both parameters had significant influences on the magnitude of flooding in terms of flood depth and extent. These findings are important for understanding urban flood vulnerability and resilience, which could help in sustainable management planning to deal with urban flooding issues.

The fishing industry is identified as a strategic sector to raise domestic protein production and supply in Malaysia. Global changes in climatic variables have impacted and continue to impact marine fish and aquaculture production, where machine learning (ML) methods are yet to be extensively used to study aquatic systems in Malaysia. ML-based algorithms could be paired with feature importance, i.e., (features that have the most predictive power) to achieve better prediction accuracy and can provide new insights on fish production. This research aims to develop an ML-based prediction of marine fish and aquaculture production. Based on the feature importance scores, we select the group of climatic variables for three different ML models: linear, gradient boosting, and random forest regression. The past 20 years (2000–2019) of climatic variables and fish production data were used to train and test the ML models. Finally, an ensemble approach named voting regression combines those three ML models. Performance matrices are generated and the results showed that the ensembled ML model obtains R² values of 0.75, 0.81, and 0.55 for marine water, freshwater, and brackish water, respectively, which outperforms the single ML model in predicting all three types of fish production (in tons) in Malaysia.

Field Oriented Control (FOC) Permanent Magnet Synchronous Motor (PMSM) is mostly used as it provides superior torque to inertia ratio, high controllability and greater power density amongst all the servo drives. However, expensive computing elements are required for the driver circuits which are the key factor of determining the overall performance of FOC-PMSM drives. In addition, an exact direct and quadrature (dq) current is desired for a highly precise driver which can be ensured by an intelligent PI controller. This research proposes an FPGA implementation of current dq PI controller as a part of real-time hardware motion intelligent driver. The outcome will be a single chip current dq PI controller which was previously solved using software and firmware dependent modules. This hardware implementation system can get a high-speed level as well as an exciting precision. Thus it will be a key element for designing a fully automated real time hardware implementation of FOC-PMSM drive.

Applications such as non-volatile memories (NVM), radio frequency identification (RFID), high voltage generators, switched capacitor circuits, operational amplifiers, voltage regulators, and DC–DC converters employ charge pump (CP) circuits as they can generate a higher output voltage from the very low supply voltage. Besides, continuous power supply reduction, low implementation cost, and high efficiency can be managed using CP circuits in low-power applications in the complementary metal-oxide-semiconductor (CMOS) process. This study aims to figure out the most widely used CP design topologies for embedded systems on the chip (SoC). Design methods have evolved from diode-connected structures to dynamic clock voltage scaling charge pumps have been discussed in this research. Based on the different architecture, operating principles and optimization techniques with their advantages and disadvantages have compared with the final output. Researchers mainly focused on designing the charge pump topologies based on input/output voltage, pumping efficiency, power dissipation, charge transfer capability, design complexity, pumping capacitor, clock frequencies with a minimum load balance, etc. Finally, this review study summarizes with the discussion on the outline of appropriate schemes and recommendations to future researchers in selecting the most suitable CP design methods for low power applications.