Intel (Malaysia)

Application of silica nanoparticles as fillers in the preparation of nanocomposite of polymers has drawn much attention, due to the increased demand for new materials with improved thermal, mechanical, physical, and chemical properties. Recent developments in the synthesis of monodispersed, narrow‐size distribution of nanoparticles by sol‐gel method provide significant boost to development of silica‐polymer nanocomposites. This paper is written by emphasizing on the synthesis of silica nanoparticles, characterization on size‐dependent properties, and surface modification for the preparation of homogeneous nanocomposites, generally by sol‐gel technique. The effect of nanosilica on the properties of various types of silica‐polymer composites is also summarized.

Abstract Green human resource management (HRM) practices can help organizations align their business strategies with the environment. Anchored in the resource‐based view of the firm, this study examines the influence of green HRM practices on sustainability using cross‐sectional data obtained from 112 large manufacturing firms in Malaysia. The results show that green recruitment and green training have positive effects on sustainability. However, green analysis and job description, green selection, green performance assessment, and green reward were not found to have any significant influence on sustainability. The model presented in this paper offers useful insights into the positive role of green HRM in the sustainability of manufacturing firms, and as previous studies exploring the link between green HRM and sustainability using empirical data from Malaysian manufacturing firms are scarce, this research is of significant importance for scholars and practitioners. The scope of this study focuses on emerging economies with a limited number of variables that are contextual and specific to the Malaysian economy. Future research could explore the relationship between green HRM and other variables that may contribute to the present framework in other contexts. Future studies may also consider each dimension of green HRM, or indeed other elements of green HRM, in relation to the different aspects of sustainability.

Brightness preserving methods are very high demand to the consumer electronic products. Numerous histogram equalization (HE)-based brightness preserving methods tend to produce unwanted artifacts. Thus, we propose two methods to overcome the drawbacks. The first proposed method divides the histogram based on the median, and iteratively divides into the lower and upper sub-histograms, to produce a totally four sub-histograms. The separating points in the lower and upper sub-histograms are assigned to a new dynamic range and clipping process is implemented to each sub-histogram. Finally, the conventional HE is implemented. The second method is the extension of the bi-histogram equalization plateau limit (BHEPL). This method segments the histogram of input image based on its mean value. Then, clipping process is implemented to each sub-histogram based on their median value. The proposed methods are compared with several conventional methods. The experiment results show that, both of the proposed methods outperform those conventional methods by producing clearer enhanced image with brightness and details preserving ability <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .

In this paper, we introduce a histogram equalization (HE)-based technique, called quadrant dynamic histogram equalization (QDHE), for digital images captured from consumer electronic devices. Initially, the proposed QDHE algorithm separates the histogram into four (quadrant) sub-histograms based on the median of the input image. Then, the resultant sub-histograms are clipped according to the mean of intensity occurrence of input image before new dynamic range is assigned to each sub-histogram. Finally, each sub-histogram is equalized. Based on extensive simulation results, the QDHE method outperforms some methods existing in literature, which can be considered as state-of-the-arts, by producing clearer enhanced images without any intensity saturation, noise amplification, and over-enhancement. Furthermore, image details of the processed image are well preserved and highlighted. For this reason, the proposed QDHE algorithm is suitable for images captured in low-light environments - an unavoidable situation by many consumer electronics products such as camera devices in cell phone <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .

PURPOSE OF REVIEW: Many studies have reported that individuals with autism spectrum disorder (ASD) have different brain connectivity patterns compared with typically developing individuals. However, the results of more recent studies do not unanimously support the traditional view in which individuals with ASD have lower connectivity between distant brain regions and increased connectivity within local brain regions. In this review, we discuss different methods for measuring brain connectivity and how the use of different metrics may contribute to the lack of convergence of investigations of connectivity in ASD. RECENT FINDINGS: The discrepancy in brain connectivity results across studies may be due to important methodological factors, such as the connectivity measure applied, the age of patients studied, the brain region(s) examined, and the time interval and frequency band(s) in which connectivity was analyzed. SUMMARY: We conclude that more sophisticated electroencephalography analytic approaches should be utilized to more accurately infer causation and directionality of information transfer between brain regions, which may show dynamic changes of functional connectivity in the brain. Moreover, further investigations of connectivity with respect to behavior and clinical phenotype are needed to probe underlying brain networks implicated in core deficits of ASD.

The rapid advancement of fifth-generation (5G)-and-beyond networks coupled with unmanned aerial vehicles (UAVs) has opened up exciting possibilities for diverse applications and cutting-edge technologies, revolutionizing the way connections, communications, and innovations unfold in the digital age. This paper presents a comprehensive survey of the deployment scenarios, applications, emerging technologies, regulatory aspects, research trends, and challenges associated with the use of UAVs in 5G-and-beyond networks. It begins with a succinct background and motivation, followed by a systematic UAV classification and a review of relevant works. The survey covers UAV deployment scenarios, including single and multiple UAV configurations. The categorization of UAV applications in 5G is presented, along with investigations into emerging technologies for enhancing UAV communications. Regulatory considerations encompassing flight guidelines, spectrum allocation, privacy, and safety are discussed. Moreover, light is shed on the latest research trends and open challenges in the field, with promising directions for future investigations identified, concluding with a summary of key findings and contributions. This survey serves as a valuable resource for researchers, practitioners, and policymakers in the UAV and communication domains. Additionally, it offers a comprehensive foundation for informed decision-making, fostering collaboration, and driving advancements in UAV and communication technologies to address the evolving needs of our interconnected world.

Sentiment classification is increasingly used to automatically identify a positive or negative sentiment in a text review. In classification, feature selection had always been a critical and challenging problem. Most of the related feature selection for sentiment classification techniques unable to overcome problems of evaluating the significant features that will reduce the classification performance. This paper proposes an enhanced hybrid feature selection technique to improve the sentiment classification based on machine learning approaches. First, two customer review datasets namely Sentiment Labelled and large IMDB are retrieved and pre-processed. Next, the proposed feature selection technique which is the hybridization of Term Frequency-Inverse Document Frequency (TF-IDF) and Supports Vector Machine (SVM-RFE) is developed and tested on these two datasets. TF-IDF aims to measure features importance. The SVM-RFE iteratively evaluates and ranks the features. For sentiment classification, only the ktop features from the ranked features will be used. Finally, the Support Vector Machine (SVM) classifier is deployed to observe the performance of the proposed technique. The performance is measured using accuracy, precision, recall, and F-measure. The experimental results show promising performances with 84.54% to 89.56% in the measurements especially from the large IMDB dataset. The results also outperformed other related techniques in certain datasets. Consequently, the proposed technique able to reduce from 19.25% to 70.5% of the features to be classified. This reduction rate is significant in optimally utilizing the computational resources while maintaining the efficiency of the classification performance.

Gene expression data are expected to be of significant help in the development of efficient cancer diagnoses and classification platforms. In order to select a small subset of informative genes from the data for cancer classification, recently, many researchers are analyzing gene expression data using various computational intelligence methods. However, due to the small number of samples compared to the huge number of genes (high dimension), irrelevant genes, and noisy genes, many of the computational methods face difficulties to select the small subset. Thus, we propose an improved (modified) binary particle swarm optimization to select the small subset of informative genes that is relevant for the cancer classification. In this proposed method, we introduce particles' speed for giving the rate at which a particle changes its position, and we propose a rule for updating particle's positions. By performing experiments on ten different gene expression datasets, we have found that the performance of the proposed method is superior to other previous related works, including the conventional version of binary particle swarm optimization (BPSO) in terms of classification accuracy and the number of selected genes. The proposed method also produces lower running times compared to BPSO.

Wireless technologies are growing unprecedentedly with the advent and increasing popularity of wireless services worldwide. With the advancement in technology, profound techniques can potentially improve the performance of wireless networks. Besides, the advancement of artificial intelligence (AI) enables systems to make intelligent decisions, automation, data analysis, insights, predictive capabilities, learning, and adaptation. A sophisticated AI will be required for next-generation wireless networks to automate information delivery between smart applications simultaneously. AI technologies, such as machines and deep learning techniques, have attained tremendous success in many applications in recent years. Hances, researchers in academia and industry have turned their attention to the advanced development of AI-enabled wireless networks. This paper comprehensively surveys AI technologies for different wireless networks with various applications. Moreover, we present various AI-enabled applications that exploit the power of AI to enable the desired evolution of wireless networks. Besides, the challenges of unsolved research in this area, which represent the future research trends of AI-enabled wireless networks, are discussed in detail. We provide several suggestions and solutions that help wireless networks be more intelligent and sophisticated to handle complicated problems. In summary, this paper can help researchers deeply understand the up-to-the-minute wireless network designs based on AI technologies and identify interesting unsolved issues to be pursued in their research in a fast way.

In this paper we present the HAsim FPGA-accelerated simulator. HAsim is able to model a shared-memory multicore system including detailed core pipelines, cache hierarchy, and on-chip network, using a single FPGA. We describe the scaling techniques that make this possible, including novel uses of time-multiplexing in the core pipeline and on-chip network. We compare our time-multiplexed approach to a direct implementation, and present a case study that motivates why high-detail simulations should continue to play a role in the architectural exploration process.

In this paper we establish a new algorithm based on genetic algorithms (GA) and sequential local search to solve course timetabling problem. Universities are challenged to arise in number of complexity, their resources and events are becoming harder to schedule. Timetabling is a kind of problem in which events (classes, exams, courses, etc) have to be arranged into a number of timeslots such that conflicts in using a given set of resources are avoided. We perform preliminary experiments on standard benchmark course timetable problems and able to produce promising results.

The problem complexity of multi-criteria decision-making (MCDM) has been raised in the distribution of coronavirus disease 2019 (COVID-19) vaccines, which required solid and robust MCDM methods. Compared with other MCDM methods, the fuzzy-weighted zero-inconsistency (FWZIC) method and fuzzy decision by opinion score method (FDOSM) have demonstrated their solidity in solving different MCDM challenges. However, the fuzzy sets used in these methods have neglected the refusal concept and limited the restrictions on their constants. To end this, considering the advantage of the T-spherical fuzzy sets (T-SFSs) in handling the uncertainty in the data and obtaining information with more degree of freedom, this study has extended FWZIC and FDOSM methods into the T-SFSs environment (called T-SFWZIC and T-SFDOSM) to be used in the distribution of COVID-19 vaccines. The methodology was formulated on the basis of decision matrix adoption and development phases. The first phase described the adopted decision matrix used in the COVID-19 vaccine distribution. The second phase presented the sequential formulation steps of T-SFWZIC used for weighting the distribution criteria followed by T-SFDOSM utilised for prioritising the vaccine recipients. Results revealed the following: (1) T-SFWZIC effectively weighted the vaccine distribution criteria based on several parameters including T = 2, T = 4, T = 6, T = 8, and T = 10. Amongst all parameters, the age criterion received the highest weight, whereas the geographic locations severity criterion has the lowest weight. (2) According to the T parameters, a considerable variance has occurred on the vaccine recipient orders, indicating that the existence of T values affected the vaccine distribution. (3) In the individual context of T-SFDOSM, no unique prioritisation was observed based on the obtained opinions of each expert. (4) The group context of T-SFDOSM used in the prioritisation of vaccine recipients was considered the final distribution result as it unified the differences found in an individual context. The evaluation was performed based on systematic ranking assessment and sensitivity analysis. This evaluation showed that the prioritisation results based on each T parameter were subject to a systematic ranking that is supported by high correlation results over all discussed scenarios of changing criteria weights values.

High-speed field-programmable gate array (FPGA) implementations of an adaptive least mean square (LMS) filter with application in an electronic support measures (ESM) digital receiver, are presented. They employ "fine-grained" pipelining, i.e., pipelining within the processor and result in an increased output latency when used in the LMS recursive system. Therefore, the major challenge is to maintain a low latency output whilst increasing the pipeline stage in the filter for higher speeds. Using the delayed LMS (DLMS) algorithm, fine-grained pipelined FPGA implementations using both the direct form (DF) and the transposed form (TF) are considered and compared. It is shown that the direct form LMS filter utilizes the FPGA resources more efficiently thereby allowing a 120 MHz sampling rate.

= 4). Deep analysis for each category was performed in terms of several aspects, including issues and challenges encountered, contributions, data set, evaluation criteria, MADM techniques, evaluation and validation and bibliography analysis. This study emphasised the current standpoint and opportunities for MADM in the midst of the COVID-19 pandemic and promoted additional efforts towards understanding and providing new potential future directions to fulfil the needs of this study field.

In this work, the performance of polydimethylsiloxane (PDMS) nanocomposites with carbon black (CB) and multi-walled carbon nanotube (MWCNT) fillers was studied. The carbon nanofillers were first introduced in the solvent to promote an adequate dispersion. The silicone rubber was then reinforced with the carbon nanofillers by a mechanical mixing process followed by film casting. It was found that only small amount of MWCNTs is required to reach the percolation threshold that produces high electrical conductivity. Filler size and segregation, as observed by scanning electron microscopy, play important roles in determining the electrical properties of silicone elastomer filled composites. Transmission electron microscopy was also performed to examine the tube–tube interaction of MWCNT in silicone rubber. The MWCNT/PDMS nanocomposites have higher electrical conductivity value compared to the CB/PDMS nanocomposite. The percolation threshold for MWCNT/PDMS nanocomposites was approximately 1.0 vol% of MWCNT loading with a value of –4.06 log σ (S/cm). On the contrary, no obvious percolation threshold of CB/PDMS nanocomposites was observed, as the CB fillers added from 0.5 to 2.0 vol% in the PDMS. The MWCNT/PDMS nanocomposite also showed better thermal stability than the CB/PDMS nanocomposite. The onset temperature for 0.5 vol% of MWCNT/PDMS and CB/PDMS nanocomposites were 528°C and 492°C, respectively.

Introduction: The vaccine distribution for the COVID-19 is a multicriteria decision-making (MCDM) problem based on three issues, namely, identification of different distribution criteria, importance criteria and data variation. Thus, the Pythagorean fuzzy decision by opinion score method (PFDOSM) for prioritising vaccine recipients is the correct approach because it utilises the most powerful MCDM ranking method. However, PFDOSM weighs the criteria values of each alternative implicitly, which is limited to explicitly weighting each criterion. In view of solving this theoretical issue, the fuzzy-weighted zero-inconsistency (FWZIC) can be used as a powerful weighting MCDM method to provide explicit weights for a criteria set with zero inconstancy. However, FWZIC is based on the triangular fuzzy number that is limited in solving the vagueness related to the aforementioned theoretical issues. Objectives: This research presents a novel homogeneous Pythagorean fuzzy framework for distributing the COVID-19 vaccine dose by integrating a new formulation of the PFWZIC and PFDOSM methods. Methods: The methodology is divided into two phases. Firstly, an augmented dataset was generated that included 300 recipients based on five COVID-19 vaccine distribution criteria (i.e., vaccine recipient memberships, chronic disease conditions, age, geographic location severity and disabilities). Then, a decision matrix was constructed on the basis of an intersection of the 'recipients list' and 'COVID-19 distribution criteria'. Then, the MCDM methods were integrated. An extended PFWZIC was developed, followed by the development of PFDOSM. Results: (1) PFWZIC effectively weighted the vaccine distribution criteria. (2) The PFDOSM-based group prioritisation was considered in the final distribution result. (3) The prioritisation ranks of the vaccine recipients were subject to a systematic ranking that is supported by high correlation results over nine scenarios of the changing criteria weights values. Conclusion: The findings of this study are expected to ensuring equitable protection against COVID-19 and thus help accelerate vaccine progress worldwide.

Purpose The purpose of this paper is to investigate supply chain management practices related to flexibility, value chain and capabilities. It describes an exploratory study to examine the interrelated factors to propose a research framework. Design/methodology/approach A comparative case study was conducted on several manufacturing organizations in the electrical and electronic industry, investigating the business drivers and response effect of a flexible value chain. Findings In general, all the organizations enhanced their manufacturing flexibility components with supply and logistic networks in order to be responsive to customers and gain tangible benefits. The core flexibility of the value chain can be defined from operational, supply and logistics perspectives where different levels of integration and implementation strategies offer different levels of flexibility response to volume and product mix. Research limitations/implications Research through case survey requires further empirical investigation to quantify the determinants and the significance of the relationship theorized. However, the findings confirmed the practical aspect of manufacturers to consider flexibility in designing their value chain within the industry. Originality/value The paper highlights the fact that local manufacturers value the flexibility aspect of supply chains to stay competitive during demand uncertainties and being responsive to customers.

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> This paper presents the implementation of a high-performance direct torque control (DTC) of induction machines drive. DTC has two major problems, namely, high torque ripple and variable switching frequency. In order to solve these problems, this paper proposed a pair of torque and flux controllers to replace the hysteresis-based controllers. The design of these controllers is fully discussed and a set of numerical values of the parameters for the proposed controllers is given. The simulation of the proposed controllers applied to the DTC drive is presented. The simulation results are then verified by experimental results. The hardware implementation is mainly constructed by using DSP TMS320C31 and Altera field-programmable gate array devices. The results prove that a significant torque and stator flux ripples reduction is achieved. Likewise, the switching frequency is fixed at 10.4 kHz and a more sinusoidal phase current is obtained. </para>

Similarity or distance measures have been used widely to calculate the similarity or dissimilarity between two samples of dataset. Cheminformatics is known as the domain that dealing with chemical information and both similarity and distance coefficient have been an important role for matching, searching and classification of chemical information. There are various types of similarity/distance coefficient used in molecular structure similarity searching. Generally, the calculation using similarity/distance coefficient is focusing more on 2-dimensional (2D) rather than 3-dimensional (3D) structure. In this paper, the popular similarity/distance coefficients for molecular structure will be reviewed together with the review on 3D molecular structure.

The Operating System scheduler is designed to allocate the CPU resources appropriately to all processes. The Linux Completely Fair Scheduler (CFS) design ensures fairness among tasks using the thread fair scheduling algorithm. This algorithm ensures allocation of resources based on the number of threads in the system and not within executing programs. This can lead to fairness issue in a multi-threaded environment as the Linux scheduler tends to favor programs with higher number of threads. We illustrate the issue of fairness through experimental evaluation thus exposing the weakness of the current allocation scheme where software developers could take advantage by spawning many additional threads in order to obtain more CPU resources. A novel algorithm is proposed as a solution towards achieving better fairness in the Linux scheduler. The algorithm is based on weight readjustment of the threads created in the same process to significantly reduce the unfair allocation of CPU resources in multi-threaded environments. The algorithm was implemented and evaluated. It demonstrated promising results towards solving the raised fairness issue. We conclude this paper highlighting the limitations of the proposed approach and the future work in the stated direction.