Synthetic Sacks as Reinforced Fibers in the Thermosetting Composites

This study was carried out to investigate the preparation of thermosetting polymeric blend consisting of three adhesive types, namely: epoxy, polyvinyl formal (PVF) and unsaturated polyester. Both of epoxy and PVF were used as a matrix-binder at fixed weight. Whilst unsaturated polyester was used at different weights and added to the matrix so as to produce prepared epoxy-PVF-unsaturated polyester blend. Several experiments were performed at different operating conditions, mixing speed and time at room temperature to identify the most favorable operating conditions. The optimum mixing speed and mixing time for the prepared blend were 500rpm and 5 minutes respectively. <br />Solid wastes-synthetic sack fibers from high volume, low cost, renewable fiber sources have been used as environmentally friendly alternatives to reinforcing fibers in composites. Many mechanical and thermal tests were carried out of the prepared blend at different weighted ratios. The optimum weighted ratio of the prepared blend for the untreated samples was characterized by the hardness and bending deflection properties and it was 0.40w/w, while for impact strength and thermal conductivity properties was 0.20w/w respectively. At these optimum weighted ratios of untreated samples with sack fibers, the maximum values of hardness and impact strength properties were 95 shore and 2.25J/cm2 respectively. On the other hand, the minimum bending deflection and thermal conductivity properties values were found to be 4mm and 0.01094W/cm.oC respectively. They showed the best bonding forces and physical interaction between two concentrations of matrix and unsaturated polyester adhesives. <br />Treated samples of sack fibers reinforced composites at their optimum weighted ratio showed better fiber-matrix interaction as observed from the experimental results leading to enhance and improve the mechanical (hardness, impact strength, and bending deflection) and thermal (thermal conductivity) properties when compared to the untreated sample. These improvements in treated samples with two layers of sack fibers were predominant

Post-harvest technology change in cassava processing: a choice paradigm

Open Access Article; Available online: 27 Jan 2020This study employed a choice model to examine the factors influencing the choice of post-harvest technologies in cassava starch processing, using a sample of five hundred and seventy (570) processors in the forest and guinea savanna zones of Nigeria. In addition, the profitability of various post-harvest technologies in the study area was assessed using the budgetary technique while the impact of improved post-harvest technology on processors’ revenue and output was analysed using the average treatment effect model. Sex of the processor, processing experience, income, and cost of post-harvest technology, the capacity of post-harvest technology and access to credit amongst others significantly influence the choice of post-harvest technologies. Although the use of improved post-harvest technology comes with a high cost, the net income from its use was higher than the other types of post-harvest technologies, suggesting that the use of improved techniques was more beneficial and profitable. In addition, using improved post-harvest technology had a positive and significant effect on output and income. These findings shows that investment in improved post-harvest technologies by cassava starch processors and other stakeholders would increase income, thus, improving welfare

Smallholder agroprocessors' willingness to pay for value-added solid-waste management solutions

Open Access JournalThe paper examined the willingness of smallholder cassava processors to pay for value-added solid wastes management solutions in Nigeria. We employed a multistage sampling procedure to obtain primary data from 403 cassava processors from the forest and Guinea savannah zones of Nigeria. Contingent valuation and logistic regression were used to determine the willingness of the processors to pay for improved waste management options and the factors influencing their decision on the type of waste management system adopted and willingness to pay for a value-added solid-waste management system option. Women constituted the largest population of smallholder cassava processors, and the processors generated a lot of solid waste (605–878 kg/processor/season). Waste was usually dumped (59.6%), given to others (58.1%), or sold in wet (27.8%) or dry (35.5%) forms. The factors influencing the processors’ decision on the type of waste management system to adopt included sex of processors, membership of an association, quantity of cassava processed and ownership structure. Whereas the processors were willing to pay for new training on improved waste management technologies, they were not willing to pay more than US$3. However, US$3 may be paid for training in mushroom production. It is expected that public expenditure on training to empower processors to use solid-waste conversion technologies for generating value-added products will lead to such social benefits as lower exposure to environmental toxins from the air, rivers and underground water, among others, and additional income for the smallholder processors. The output of the study can serve as the basis for developing usable and affordable solid-waste management systems for community cassava processing units in African countries involved in cassava production

Parameter Estimation of a Class of Hidden Markov Model with Diagnostics

A stochastic volatility (SV) problem is formulated as a state space form of a Hidden Markov model (HMM). The SV model assumes that the distribution of asset returns conditional on the latent volatility is normal. This article analyzes the SV model with the student-t distribution and the generalized error distribution (GED) and compares these distributions with a mixture of normal distributions from Kim and Stoffer (2008). A Sequential Monte Carlo with Expectation Maximization (SMCEM) algorithm technique was used to estimate parameters for the extended volatility model; the Akaike Information Criteria (AIC) and forecast statistics were calculated to compare distribution fit. Distribution performance was assessed using simulation study and real data. Results show that, although comparable to the normal mixture SV model, the Student-t and GED were empirically more successful

Disposition of Secondary Students towards Charcoal Production in Ibarapa North Local Government Area of Oyo State

The study assessed the disposition of secondary school student towards charcoal production. A multistage sampling technique was used to select 225 respondents from six secondary schools in the area. Information was elicited from the respondents with the aid of a questionnaire, information collected was analyzed using descriptive (Frequency, percentages, tables and mean) and inferential (chi-square and T-test) statistical tool. The result of the study shows that there is no gender bias in the involvement in the charcoal activities as 50.7% are males, with mean age of 17 years and household size of 8. The result also revealed that 68% of the students are involved with an average period of 4 years while 35% reported their parents are also involved and make an average of N8, 754 on monthly basis. On the activities they are involved in the findings revealed that they are involved in different activities with transportation being the major one while the need to make more money (49.4%), being common occupation in the community (48%) and the prestige associated with it (41.3%) as the major influencing factors of their involvement. Their disposition was largely indifferent (88%) as revealed by the study as many of them are involved just to get the benefit without considering the effect on them. The result of the T-test (t-value 4.117, p=0.000 at P&lt; 0.005) reveals significant difference in the disposition of those involved and those not involved while the chi-square analysis also showed significant relationship between sex(χ2 = 29.874, p = 0.000), household size(χ2 = 17.472, p = 0.002) and parental involvement (χ2 = 8.855, p = 0.012) at p &lt; 0.05. The study therefore concluded that the students are involved and are indifferent in their disposition to charcoal production and recommend that there is a need for proper orientation of the student in relationship to their involvement while inculcating teaching on environmental sustainability. Keywords: Charcoal, Youth, Disposition, Oyo state, Ibarapa and Involvement

OPTIMISATION OF HIDDEN MARKOV MODEL FOR DISTRIBUTED DENIAL OF SERVICE ATTACK PREDICTION USING VARIATI ONAL BAYESIAN

Distributed Denial of Service (DDoS), is a coordinated attack that is majorly carried out on a massive scale against the availability of services property of a target system or network resources. Due to the continuous evolution of new attacks and ever-increasing number of vulnerable hosts on the Internet, several DDoS attack detection, prevention or prediction techniques have been proposed. Some of these techniques have shortcomings such as high false positive rate, high computational time, low prediction precision and so on. In order to overcome these shortcomings, researches are being carried out to improve on the existing systems. This paper, which is one of such efforts to improve on the performance of existing DDoS attack prediction methods, presents a novel learning method based on Variational Bayesian (VB) algorithms to obtain an Hidden Markov Model (HMM) with optimized number of states in the HMMs and its model parameters for DDoS attack prediction. This method not only&nbsp; overcomes the shortcomings of the slow convergence speed of the HMM approach, but it also avoids the problem of overfitting the model structure by removing excess transition and emission processes. From the experiments with the DARPA 2000 intrusion specific datasets, this method is able to find the optimal topology in every case. The experiments show that the VB-HMM approach has a better average precision rate than the HMM trained by the Baum-Welch method.&nbsp; This shows that the VB-HMM method is better optimized than the HMM trained by the Baum-Welch method

REVIEW ON ENVIRONMENTAL IMPACT AND VALOURIZATION OF WASTE COOKING OIL

&nbsp;There is a large volume of waste cooking oil (WCO) in the world, which has made waste management extremely difficult. The main purpose of the large-scale organized collection of WCO is the synthesis of biodiesel. Although alternative applications are equally important and necessitate attention, the majority of studies focus primarily on the synthesis of biodiesel from WCO. The major objective of this review paper is to draw attention to the potential environmental implications of used cooking oil as well as its potential for reuse in products other than biodiesel. It can be transformed into direct-burn energy, biodiesel, hydrogen gas, pyrolytic oil, or hydrogen. Applications like combined heat and power generation (CHP) are where WCO is most useful. Additionally, it can be chemically processed to create biodegradable polyurethane sheets, soaps, alkyd resins, greases, and lubricants. WCO is a carbon source that can be used in fermentation processes to create polyhydroxybutyrate and rhamnolipid biosurfactant after being completely cleaned and sterilized. Therefore, waste cooking oil can be viewed as a waste that can be converted into energy or used as a catalyst for biological or chemical processes

Selection of Mass Transfer Models for Competitive Adsorption of Antibiotics Mixture from Aqueous Solution on Delonix regia Pod Activated Carbon

The&nbsp;selection&nbsp;of&nbsp;suitable&nbsp;mass&nbsp;transfer&nbsp;models&nbsp;that&nbsp;fit&nbsp;the&nbsp;adsorption&nbsp;of&nbsp;a&nbsp;mixture&nbsp;of&nbsp;antibiotics&nbsp;in&nbsp;aqueous&nbsp;solution&nbsp;onto&nbsp;activated&nbsp;carbon derived&nbsp;from Delonix Regia Pods (DRPs) was examined in this study. The ripe DRPs were cleaned, activated with KOH and then carbonised at 350 °C. The surface chemistry of the raw and the modified DRPs were characterised using Fourier Transform Infrared (FTIR), before being subjected to batch adsorption of a mixture of Amoxicillin (AMO), Tetracycline (TETRA) and Ampicillin (AMP)&nbsp; under the effect of time (0-240 mins), and concentration (20-100 mg/l). The adsorption diffusion mechanisms of the process were analyzed. The spectra of the raw and modified DRP indicate the existence of hydroxyl groups alkanes, unconjugated ketone, carbonyl, and ester groups.&nbsp; McKay has the highest &nbsp;(0.9445) for the mass transfer diffusion model. This indicates that the adsorption rate of the selected antibiotics in the wastewater is regulated and monitored by the internal mass transport processes in accordance with a pore diffusion mechanism

FORECASTING DISTRIBUTED DENIAL OF SERVICE ATTACK USING HIDDEN MARKOV MODEL

Distributed denial of service (DDoS) attack bombards the network with loads of packets and requests that consumes the system resources in terms of time, memory, and processors. This paper presents a proposed method for forecasting DDoS in networks. The proposed model employs hidden Markov model (HMM) to forecast DDoS attacks. The method uses the inherent characteristic features of DDoS to determine the observable states of the system.To avoid intractable computations, Kullback-Leibler divergence algorithm was employed to reduce the number of observable states to three. The proposed model is formulated and trained through experiments using DARPA 2000 data set and the preliminary resultsshows that the characteristic features of the DDoS and the entropy concept can be used to formulate an HMM to predict DDoS