Cost-based hybrid flow shop scheduling with uniform machine optimization using an improved tiki-taka algorithm

Cost is the foremost factor in decision-making for profit-driven organizations. However, hybrid flow shop scheduling (HFSS) research rarely prioritizes cost as its optimization objective. Existing studies primarily focus on electricity costs linked to machine utilization. This paper introduces a comprehensive cost-based HFSS model, encompassing electricity, labor, maintenance, and penalty costs. Next, the Tiki-Taka Algorithm (TTA) is improved by increasing the exploration capability to optimize the problem. The cost-based HFSS model and TTA algorithm have been tested using benchmark and case study problems. The results indicated that the TTA consistently outperforms other algorithms. It delivers the best mean fitness and better solution distribution. In industrial contexts, the TTA able to reduces costs by 2.8% to 12.0% compared to other approaches. This holistic cost-based HFSS model empowers production planners to make more informed decisions. Furthermore, the improved TTA shows promise for broader applicability in various combinatorial optimization domains

Kelab Youth of Strength Happiness YOSH UMPSA anjur rewang dan edaran Bubur Lambuk

GAMBANG, 30 Mac 2024 – Mengambil peluang berkongsi kebaikan di bulan Ramadan, Kelab Youth of Strength Happiness (YOSH), Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) dengan kerjasama YOSH MyFundaction telah mengadakan rewang dan edaran Bubur Lambuk bertempat di Masjid Sri Fajar, Gambang dengan kehadiran lebih 40 orang mahasiswa

In-vitro drug release studies of chitosan-alginate nanoparticles loaded with non-volatile extracts of cymbopogon species

The conventional topical dosage forms have plenty of drawbacks which cause patient inconveniences as well as treatment failure. To overcome these complications, drug design engineering is emphasizing on nanoparticle technology for its better drug carrier activity and targeted delivery in desired site of body. The objective of this study was to develop and evaluate topical formulation containing chitosan-alginate nanoparticles loaded with non-volatile extracts of Cymbopogon species. Among all of the test samples, nanoparticles loaded with 6 mg of extraction exhibited best entrapment capacity. This formulation successfully captured around 36.56% drug in nanoparticles. Thermo Gravimetric Analysis also showed thermal stability of the drug loaded nanoparticles. Furthermore, the formulation of nano cream that contained 6 mg of Cymbopogon sp. nanoparticles yielded the smallest nano-cream, with a particle size of 157.3 ± 20.80 nm. Using the basket dialysis approach, nanoparticle formulations loaded with 12 mg and 24 mg extracts showed a release rate of roughly 25%. On the other hand, the dialysis bag approach revealed that the drug released from the 24 mg of Cymbopogon sp. loaded in nanoparticles was about 37%. Lastly, the antioxidant study's findings indicate that cream-loaded nanoparticles containing 6 mg might be capable of scavenging radicals, with an IC50 value of 19.411g/ml. Chitosan-alginate nanoparticles loaded with Cymbopogon species exhibited significant drug release and antioxidant activity, indicating that this method could be useful for the formulation development of nanoparticles for skincare products

Archimedes screw pump efficiency based on three design parameters using computational fluid dynamics software – Ansys cfx

Utilization of Archimedes screw pumps as water lifting pumps has become widespread in past decade due to frequent occurrences of floods in Malaysia. The problem of insufficient drainage in various urban areas exacerbates the impact of heavy rainfall, prompting efforts to mitigate this issue with minimal maintenance cost and low impact to the environment. Thus, this study is aiming to study the design parameters of screw pump to obtain the optimal efficiency of the Archimedes screw pump specifically for flood mitigation in Malaysia. The main design parameters affecting pump's efficiency are rotor profile, pitch length, length of the pump, rotational speed, inclination angle, and material selection. However, only three design parameters were considered in the study, that are the angle of inclination, the number of blades, and the angular velocity of the rotating pump. These three design parameters are selected as many previous findings focusing on varying angle of inclination with number of blades with constant rotational speed. Thus, this study will find the highest efficiency when these three design parameters are integrated with variation of rotational speeds at 25, 30 and 40 RPM. Basically, screw pump is designed using SOLIDWORKS and simulations with specific boundary conditions are conducted using the ANSYS-CFX software, which utilizes computational fluid dynamics (CFD) techniques. These boundary conditions are based on previous study by Rosly et al in 2016. The inlet flow rate of 0.002 m3/s and diameter of the screw pump are constant while the other three main parameters are varying within the acceptable ranges which are reported from prior studies. The outcomes found that the highest torque is generated by a single rotating blade at 5.65 Nm which rotates at 30 RPM at 30° angle of inclination. Meanwhile, the highest efficiency of 24.04% is obtained with a single rotating blade at 40 RPM with 20° angle of inclination. Based on the findings, it is concluded that these three main design parameters of screw pump may not be sufficient to obtain the optimal efficiency for the specific boundary conditions used in the simulation study. Thus, several combinations of design parameters should be considered in the future to increase the screw pump's efficiency

Flood prediction using artificial neural networks: A case study in Temerloh, Pahang

Flood is one of the natural disasters that causes damage to properties, and sometimes loss of lives. Floods in Malaysia happen every year, especially in East Coast Peninsular Malaysia, due to the Northeast Monsoon and climate change, which may lead to heavy rainfall throughout the end of the year. Temerloh is one of the districts in Pahang that frequently encounters flood events, especially between November and January every year. Even though there are multiple efforts in flood mitigation and preparation, the damage to citizens and properties every year has cost thousands of Ringgits and the time taken to clean the damages caused by floods. Despite this, research on flood prediction in the state needs to be done using machine learning techniques. Due to this, this research explored the hydrological and meteorological factors that caused the flood in Temerloh and developed a machine-learning model capable of predicting the flood occurrence. The study used a dataset from the National Hydrological Network Management System (SPRHiN), which consists of hydrological data, and weather underground for the meteorological data in the location. The correlation analysis found that stream flow and water level are highly correlated to floods, with correlation coefficients (r values) of 0.83 and 0.76, respectively, while the temperature is inversely related to floods with a -0.28 correlation value. A lower temperature has a higher chance of rain and subsequent flooding. The results show that the model, by using an artificial neural network (ANN), has produced an accuracy of 0.9909 and a good performance of the area under the receiver operating characteristic curve (ROC) curve (AUC) at 0.888. The model also shows a low error with the mean squared error (MSE) of 0.009 and the root-mean-squared error (RMSE) of 0.096. The R2 value of 0.768 and F1 value of 0.875 indicate that the model has high precision and recall. Afterwards, a flood monitoring dashboard was created to visualize the data interactively. This research is vital in understanding the flood factors in Pahang and would offer academic insight for future research in floods. In addition, the flood monitoring dashboard will significantly assist governments and authorities in focusing the flood management efforts in areas at high risk of flood and be used to aid the state's future development

UMPSA Raikan Media Berbuka Puasa

Naib Canselor UMPSA, Profesor Dato’ Dr. Yuserrie Zainuddin dalam ucapannya berkata, hubungan silaturahim yang telah terjalin sejak 22 tahun lalu dapat diteruskan lagi khususnya dalam menyalurkan berita dan maklumat demi kepentingan masyarakat, khususnya dalam meningkatkan tahap pendidikan tinggi rakyat di negara ini

Yayasan UMPSA, PUNB dan Maxis hulur bantuan keperluan memasak kepada pelajar luar kampus

PEKAN, 22 Mac 2024 - Yayasan Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA), Perbadanan Usahawan Nasional Berhad (PUNB) dan Maxis Berhad tampil menghulurkan bantuan barang keperluan memasak kepada pelajar UMPSA yang tinggal di luar kampus menerusi Program Salam Cinta Ramadan

Investigating white matter changes in auditory cortex and association fibres related to speech processing in noise-induced hearing loss : A diffusion tensor imaging study

This study explores the impact of noise-induced hearing loss (NIHL) on the microstructural integrity of white matter tracts in the brain, focusing on areas involved in speech processing. While the primary impact of hearing loss occurs in the inner ear, these changes can extend to the central auditory pathways and have broader effects on brain function. Our research aimed to uncover the neural mechanisms underlying hearing loss-related deficits in speech perception and cognition among NIHL patients. Methods: The study included two groups: nine bilateral NIHL patients and nine individuals with normal hearing. Advanced diffusion tensor imaging techniques were employed to assess changes in the white matter tracts. Regions of interest (ROIs), including the auditory cortex, cingulum, arcuate fasciculus, and longitudinal fasciculus, were examined. Fractional anisotropy (FA) values from these ROIs were extracted for analysis. Results: Our findings indicated significant reductions in FA values in NIHL patients, particularly in the left cingulum, right cingulum, and left inferior longitudinal fasciculus. Notably, no significant changes were observed in the auditory cortex, arcuate fasciculus, superior longitudinal fasciculus, middle longitudinal fasciculus, and right inferior longitudinal fasciculus, suggesting differential impacts of NIHL on various white matter tracts. Conclusions: The study's findings highlight the importance of considering association fibres related to speech processing in treating NIHL, as the broader neural network beyond primary auditory structures is significantly impacted. This research contributes to understanding the neurological impact of NIHL and underscores the need for comprehensive approaches in addressing this condition

Parametric investigation of battery thermal management system with phase change material, metal foam, and fins; utilizing CFD and ANN models

The focus on developing an effective battery thermal management system (BTMS) to maintain optimal temperatures for lithium-ion batteries (LIBs), especially in electric vehicle (EV) applications, has grown significantly. The effective BTMS not only enhances the cooling performance of LIBs but also contributes to increased passenger safety and mileage of EVs. This study investigates BTMS configurations with fins, metal foam, and phase change material (PCM) to minimize temperature of battery during 3C discharging in varying conditions. Additionally, the study explores the impact of different BTMS material combinations and various fins lengths on system performance as a parametric investigation. Moreover, to streamline the analysis process and introduce novelty, artificial intelligence is explored as an alternative to computational fluid dynamics for predicting liquid fraction of PCM and temperature of battery, enhancing the innovative aspect of this study. Numerical simulations, using a non-equilibrium thermal model for metal foam modeling, reveal that the fourth case, integrating all three passive approaches, maintains the lowest temperature and enhances LIB cooling. The optimum BTMS shows a reduction of 3 K compared to BTMS utilizing pure PCM. During discharge process, the temperature difference in the battery decreases by approximately 75 % and 66 % in the fourth case compared to the first case (with pure PCM) under normal and harsh environmental conditions, respectively. Applying copper metal foam and copper fins yields the best results in reducing battery temperature. Increasing the length of fins and adding more fins effectively lower the battery temperature. Finally, an artificial neural network model is developed using the backpropagation learning technique coupled with the gradient descent optimization algorithm. The model exhibits excellent predictive capabilities, achieving high R-squared values of 0.98 for PCM liquid fraction and 0.99 for battery temperature

Experimental investigation and finite element analysis of reinforced concrete beams strengthened by fibre reinforced polymer composite materials : A review

As a composite material, fibre reinforced polymer (FRP) has many uses. Incorporating FRP composite material enhances the reinforced concrete beams’ (RCB) performance, properties and behaviour as external reinforcement. A summary of how different FRP influences the RCB properties should be studied. This review paper discusses the use of FRP to reinforce RCB and briefly describes the topic. Previous experimental studies and finite element analysis (FEA) results showed that RCB constructed with FRP significantly improved the axial load, load-deflection, ultimate load, crack propagation, stress-strain distribution, and failure mode of RCB. Since this FRP composite material has superior strength, force, mounting and anchoring properties, it can be used as an alternate exterior reinforcement in RCB. The structural behaviour and performance of RCB can be enhanced by utilising FRP composite material in civil and structural engineering, especially in building construction projects