Microcrystallite dimension and total active surface area of carbon electrode from mixtures of pre-carbonized oil palm empty fruit bunches and green petroleum cokes

Carbon pellets (CP) were prepared from the green pellets (GP) containing mixtures of pre-carbonized oil palm empty fruit bunches (SACG) and Green Petroleum Cokes (GPC), with the weight percentages (x) of SACG in the samples at 10%, 30%, 50%, 70% and 90%. Carbonization process to produce the CP was conducted up to 900oC using a multi steps heating profile. The interlayer spacing (d200 and d100), stack height (Lc), stack width (La,) and effective dimension L of the turbostratic crystallites (microsrystallite) in the CPs were estimated from X-ray diffraction data; d200, d100, La, L increased and Lc decreased with increasing weight percentage of SACG. The total surface area of active material of the CP (Atot) with thickness, t, estimated from Lc was found to follow the equation, Atot = [4.8086 – 0.0083x]1010 t, indicating a significant influence of the SACG content in the mixture of the green body

Analysis study on R-Eclat algorithm in infrequent itemsets mining

There are rising interests in developing techniques for data mining. One of the important subfield in data mining is itemset mining, which consists of discovering appealing and useful patterns in transaction databases. In a big data environment, the problem of mining infrequent itemsets becomes more complicated when dealing with a huge dataset. Infrequent itemsets mining may provide valuable information in the knowledge mining process. The current basic algorithms that widely implemented in infrequent itemset mining are derived from Apriori and FP-Growth. The use of Eclat-based in infrequent itemset mining has not yet been extensively exploited. This paper addresses the discovery of infrequent itemsets mining from the transactional database based on Eclat algorithm. To address this issue, the minimum support measure is defined as a weighted frequency of occurrence of an itemsets in the analysed data. Preliminary experimental results illustrate that Eclat-based algorithm is more efficient in mining dense data as compared to sparse data

Amalan Tidak Berintegriti: Remeh Tapi Salah

Kajian ini merupakan sebuah sorotan kajian mengenai integriti. Integriti merangkumi pelbagai nilai murni termasuk kesopanan, ketepatan, kebolehpercayaan, ketekunan, kemuliaan, kesederhanaan, ketinggian peribadi, jujur, ikhlas, amanah, berpegang pada janji, bercakap benar, dan pelbagai sifat positif lain. Akan tetapi, sesuatu organisasi tidak mampu mencapai kecemerlangan jika berlaku masalah dalaman yang berpunca dari integriti atau jika terdapat pengaruh luar yang tidak diselesaikan berikutan kelemahan institusi itu sendiri. Kebanyakan masalah integriti yang dibangkitkan adalah merujuk kepada hal-hal yang remeh yang sering berkait dengan bidang masing-masing, walau bagaimanapun, ia tetap salah dan perlu diperbetulkan. Oleh itu, terdapat keperluan terhadap mengenal pasti punca kelemahan pembudayaan dan penguatkuasaan integriti dalam sesuatu organisasi, khasnya dalam diri masyarakat. Hasil dari permasalahan yang berbangkit ini, dua kemungkinan telah digariskan yang bertujuan membincangkan faktor isu ini sukar menunjukkan yang ianya akan berakhir. Hasil sorotan kajian mendapati, pengetahuan dan pembudayaan tentang isu integriti dilihat menjadi kelemahan ketara dalam kegagalan pengaplikasian integriti dalam pekerjaan. Oleh itu, kefahaman tentang konsep integriti seorang kakitangan adalah perlu bagi memperlihatkan keselarasan antara kata-kata dengan perlakuan, bertindak bersandarkan kepada prinsip moral, etika dan undang-undang, serta mengutamakan kepentingan umum melebihi kehendak diri dan melakukan kerja dengan komprehensif, holistik dan relevan. Selain itu, budaya organisasi berlandaskan piawai etika yang tinggi harus menjadi penggerak dan pengaruh positif kepada amalan tingkah laku beretika dalam kalangan pekerja

Digital forensics investigation procedures of smart grid environment

Smart grids have been widely used around the world. The security of this system is debatable among the researchers because this area requires an improvement in order to reassure the grid is secured from cyberattacks. However, many malware were found attacking the smart grid systems such as Stuxnet, Flames, Triton, etc. Some of them are designed to avoid being tracked by a forensic investigator. The perpetrators used the fragility of digital evidence as an advantage to launch an attack on the smart grid without leaving traces. Technology development gives challenges to digital forensic procedures because the data volume is much higher. Thus, the digital forensic procedure needs to be redesigned, modified, and improved to capture traces and handle digital evidence. This paper aims to propose a digital forensic procedure to guide investigators to perform the digital forensic investigation, especially in a smart grid environment. This paper has discussed several suitable tools and techniques in digital forensic investigation to solve the problem or the challenges. This study discussed two cyberattacks examples and simulated the attack using a testbed to guide forensic investigators based on the proposed digital forensic procedure. Examples of cyberattacks are Distributed Denial of Service and False Data Injection attacks. This paper presented an appropriate methodology and relevant forensic tools to ensure the evidence's integrity during collection and analysis as legal evidence in court

Autologous Peripheral Blood Stem Cell Transplantation Among Lymphoproliferative Disease Patients: Factors Influencing Engraftment

Objectives: Autologous peripheral blood stem cells transplantation (APBSCT) is a therapeutic option which can be used in various hematological, neoplastic disorders including lymphoproliferative disease (LPD). Differences in patient populations and treatment modalities in different transplant centers mean it is important to improve the knowledge of the different factors affecting engraftment after APBSCT for the success of this procedure. We sought to determine the factors influencing neutrophil and platelet engraftment after APBSCT in patients with LPD. Methods: We conducted a retrospective review of 70 patients with LPD (35 with lymphoma and 35 with multiple myeloma) who had undergone APBSCT between January 2008 and December 2016. Data obtained included disease type, treatment, and stem cell characteristics. Kaplan-Meier analysis was performed for probabilities of neutrophil and platelet engraftment occurred and was compared by the log-rank test. The multivariate Cox proportional hazards regression model was used for the analysis of potential independent factors influencing engraftment. A p-value < 0.050 was considered statistically significant. Results: Most patients were ethnic Malay, the median age at transplantation was 49.5 years. Neutrophil and platelet engraftment occurred in a median time of 18 (range 4–65) and 17 (range 6–66) days, respectively. The majority of patients showed engraftment with 65 (92.9%) and 63 (90.0%) showing neutrophil and platelet engraftment, respectively. We observed significant differences between neutrophil engraftment and patient’s weight (< 60/≥ 60 kg), stage of disease at diagnosis, number of previous chemotherapy cycles (< 8/≥ 8), and pre-transplant radiotherapy. While for platelet engraftment, we found significant differences with gender, patient’s weight (< 60/≥ 60 kg), pre-transplant radiotherapy, and CD34+ dosage (< 5.0/≥ 5.0 × 106/kg and < 7.0/≥ 7.0 × 106/kg). The stage of disease at diagnosis (p = 0.012) and pre-transplant radiotherapy (p = 0.025) were found to be independent factors for neutrophil engraftment whereas patient’s weight (< 60/≥ 60 kg, p = 0.017), age at transplantation (< 50/≥ 50 years, p = 0.038), and CD34+ dosage (< 7.0/≥ 7.0 × 106/kg, p = 0.002) were found to be independent factors for platelet engraftment. Conclusions: Patients with LPD who presented at an early stage and with no history of radiotherapy had faster neutrophil engraftment after APBSCT, while a younger age at transplantation with a higher dose of CD34+ cells may predict faster platelet engraftment. However, additional studies are necessary for better understanding of engraftment kinetics to improve the success of APBSCT

Correlation between microbial community structure and performances of membrane bioreactor for treatment of palm oil mill effluent

River pollution from the agriculture industry has become an important cause of unscheduled water disruptions in Malaysia. Thus, rigorous control and treatment of high strength wastewater prior to discharge into the river are needed. This study aims at evaluating the performance of a pilot membrane bioreactor (MBR) for the treatment of palm oil mill effluent (POME). POME was subjected to MBR operating in an aerobic environment for 40 days, followed by 10 days without aeration and water pumping to simulate failure event, and finally resumed to operational condition. Higher proportions of protein (-PN) compared to polysaccharides (-PS) were present in both the mixed liquor and biofilm, indicating that protein was the main constituent of extracellular polymeric substances (EPS). Biological samples from the mixed liquor and biofilm on the membrane surfaces were collected on day 25, 50 and 75 to investigate taxonomic distribution of microbial community. High throughput sequencing of the 16S rRNA gene was used to investigate the composition of microbial communities in the MBR. Microbial groups such as the phyla Proteobacteria (19–23%), OD1 (11–15%), Chloroflexi (11–13%) were the dominant members in biofilm under operational condition while OD1 (20%), Proteobacteria (18%) and Planctomycetes (16%) were found to be dominant in biofilm under static and non-operational conditions. The taxonomic profile of the microbial community on day 75 is more similar to that of day 25, compared to day 50, suggesting the capability of the microbial communities to revert to the operational state despite a period of downtime

Vinyl-functionalized mesoporous carbon for dispersive micro-solid phase extraction of azole antifungal agents from aqueous matrices

A simple, rapid and sensitive vinyl-functionalized mesoporous carbon-based dispersive micro-solid phase extraction method has been developed for the preconcentration and quantification of azole antifungal drugs in aqueous matrices. The effects of type of adsorbent, desorption solvent, amount of adsorbent, pH, desorption time, salt addition and extraction time were investigated. Under the optimized conditions, the method demonstrated good linearity over the range of 1–300 µg L−1 for water sample and 5–400 µg L−1 for biological samples, low limits of detection (0.4 µg L−1 to 1.6 µg L−1), good analyte recoveries (89.8–113.9%) and acceptable RSDs (7.5–13.4%)

Forensic analysis on false data injection attack on IoT environment

False Data Injection Attack (FDIA) is an attack that could compromise Advanced Metering Infrastructure (AMI) devices where an attacker may mislead real power consumption by falsifying meter usage from end-users smart meters. Due to the rapid development of the Internet, cyber attackers are keen on exploiting domains such as finance, metering system, defense, healthcare, governance, etc. Securing IoT networks such as the electric power grid or water supply systems has emerged as a national and global priority because of many vulnerabilities found in this area and the impact of the attack through the internet of things (IoT) components. In this modern era, it is a compulsion for better awareness and improved methods to counter such attacks in these domains. This paper aims to study the impact of FDIA in AMI by performing data analysis from network traffic logs to identify digital forensic traces. An AMI testbed was designed and developed to produce the FDIA logs. Experimental results show that forensic traces can be found from the evidence logs collected through forensic analysis are sufficient to confirm the attack. Moreover, this study has produced a table of attributes for evidence collection when performing forensic investigation on FDIA in the AMI environment

Forensic analysis on distributed denial of service attack on IoT environment

The Distributed Denial of Service (DDoS) attack is a malicious attempt to render the users unable to access a server service, usually by temporarily disabling or suspending its hosting server services. With the increase popularity of IoT devices such as the massive deployment of smart meter in Advance Metering Infrastructure, can create a situation where attacker can launch a DDoS attack in this environment. This work will focus on analyzing the impact of DDoS attack in AMI by performing data analysis from DDoS attacks that performed from IoT testbed. The testbed is use as a platform to perform the testing using multiple variation of DDoS attacks that can be launch from IoT devices. It also helps the system detect any DDoS attacks against IoT devices by tracking any abnormalities in the communication inside the testbed and connected IoT devices