Kernel Processing Research Articles

148 Effects of Kernel Processing of Corn Silage with and without the Brown Midrib Trait on Finishing Performance of Steers.

148 Effects of Kernel Processing of Corn Silage with and without the Brown Midrib Trait on Finishing Performance of Steers.

Quasi-symmetries of determinantal point processes

The main result of this paper is that determinantal point processes on $\mathbb{R}$ corresponding to projection operators with integrable kernels are quasi-invariant, in the continuous case, under the group of diffeomorphisms with compact support (Theorem 1.4); in the discrete case, under the group of all finite permutations of the phase space (Theorem 1.6). The Radon–Nikodym derivative is computed explicitly and is given by a regularized multiplicative functional. Theorem 1.4 applies, in particular, to the sine-process, as well as to determinantal point processes with the Bessel and the Airy kernels; Theorem 1.6 to the discrete sine-process and the Gamma kernel process. The paper answers a question of Grigori Olshanski.

New cost-effective bioconversion process of palm kernel cake into bioinsecticides based on Beauveria bassiana and Isaria javanica.

The present study aimed to add value to palm oil by-products as substrates to efficiently produce conidia of Beauveria bassiana and Isaria javanica (Hypocreales: Cordycipitaceae) for biological control of the whitefly Bemisia tabaci (Hemiptera: Aleyrodidae), through a solid-state fermentation process using palm kernel cake and palm fiber as nutrient source and solid matrix, respectively. The optimum culture conditions yielded high concentrations of viable conidia after air-drying, when the fungi were grown on palm kernel cake (B. bassiana 7.65 × 109 and I. javanica 2.91 × 109conidiag-1 dry substrate) after 6days under optimal growth conditions set to 60% substrate moisture and 32°C. Both fungal strains exhibited high efficacy against third-instar whitefly nymphs, inducing mortality up to 62.9 and 56.6% by B. bassiana and I. javanica, respectively, assessed after 9days post-application in a screenhouse. Furthermore, we noted that insect mortality was strongly correlated with high atmospheric moisture, while B. bassiana appeared to require shorter accumulative hours under high moisture to kill whitefly nymphs compared to I. javanica. Our results underpin a feasible and cost-effective mass production method for aerial conidia, using palm kernel as the main substrate in order to produce efficacious fungal bioinsecticides against an invasive whitefly species in Brazil. Finally, our fermentation process may offer a sustainable and cost-effective means to produce eco-friendly mycoinsecticides, using an abundant agro-industrial by-product from Brazil that will ultimately assist in the integrated management of agricultural insect pests.

Study on Energy Productivity Ratio (EPR) at palm kernel oil processing factory: case study on PT-X at Sumatera Utara Plantation

The purpose of this study was to determine the performance, productivity and feasibility of the operation of palm kernel processing plant based on Energy Productivity Ratio (EPR). EPR is expressed as the ratio of output to input energy and by-product. Palm Kernel plan is process in palm kernel to become palm kernel oil. The procedure started from collecting data needed as energy input such as: palm kernel prices, energy demand and depreciation of the factory. The energy output and its by-product comprise the whole production price such as: palm kernel oil price and the remaining products such as shells and pulp price. Calculation the equality of energy of palm kernel oil is to analyze the value of Energy Productivity Ratio (EPR) bases on processing capacity per year. The investigation has been done in Kernel Oil Processing Plant PT-X at Sumatera Utara plantation. The value of EPR was 1.54 (EPR > 1), which indicated that the processing of palm kernel into palm kernel oil is feasible to be operated based on the energy productivity.

Neuromorphic LIF Row-by-Row Multiconvolution Processor for FPGA.

Deep Learning algorithms have become state-of-the-art methods for multiple fields, including computer vision, speech recognition, natural language processing, and audio recognition, among others. In image vision, convolutional neural networks (CNN) stand out. This kind of network is expensive in terms of computational resources due to the large number of operations required to process a frame. In recent years, several frame-based chip solutions to deploy CNN for real time have been developed. Despite the good results in power and accuracy given by these solutions, the number of operations is still high, due the complexity of the current network models. However, it is possible to reduce the number of operations using different computer vision techniques other than frame-based, e.g., neuromorphic event-based techniques. There exist several neuromorphic vision sensors whose pixels detect changes in luminosity. Inspired in the leaky integrate-and-fire (LIF) neuron, we propose in this manuscript an event-based field-programmable gate array (FPGA) multiconvolution system. Its main novelty is the combination of a memory arbiter for efficient memory access to allow row-by-row kernel processing. This system is able to convolve 64 filters across multiple kernel sizes, from 1×1 to 7×7, with latencies of 1.3 μs and 9.01 μs, respectively, generating a continuous flow of output events. The proposed architecture will easily fit spike-based CNNs.

Chemical compositions and antioxidant capacity of by-products generated during the apricot kernels processing

ABSTRACTThe compositions and antioxidant capacity were investigated about the by-products (blanching water concentrate (BWC) and debitterizing water concentrate DWC) produced during the industrial processing of apricot kernels, to evaluate the by-products’ potential utilization. The results indicate that the by-products contain a great number of compounds like amygdalin, protein, phenolics, and flavonoids. Among them, the amygdalin has the highest content both in BWC and DWC, which is 33.48 ± 0.04 and 23.89 ± 0.03 (g/100 g dry base weight), respectively. Furthermore, the by-products exert good antioxidant functions including the higher reducing power and radical-scavenging capacity on the 2,2-Diphenyl-1-Picrylhydrazyl (DPPH) and hydroxyl radicals, which is mainly attributed to the phenolic compounds, not the highest content of amygdalin by the principal component analysis and verification test of DPPH radical-scavenging capacity. In conclusion, the by-products definitely have great potential for utilization and should be recycled from the wastewater in the apricot kernels processing industry, especially for amygdalin.

Formalization of the Access Control on ARM-Android Platform with the B Method

ARM-Android is a widespread mobile platform with multi-layer access control mechanisms, security-critical in the system. Many access control vulnerabilities still exist due to the course-grained policy and numerous engineering defects, which have been widely studied. However, few researches focus on the mechanism formalization, including the Android permission framework, kernel process management and hardware isolation. This paper first develops a comprehensive formal access control model on the ARM-Android platform using the B method, from the Android middleware to hardware layer. All the model specifications are type checked and proved to be well-defined, with 75%of proof obligations demonstrated automatically. The results show that the proposed B model is feasible to specify and verify access control schemes in the ARM-Android system, and capable of implementing a practical control module.

Compositional inductive biases in function learning

How do people recognize and learn about complex functional structure? Taking inspiration from other areas of cognitive science, we propose that this is achieved by harnessing compositionality: complex structure is decomposed into simpler building blocks. We formalize this idea within the framework of Bayesian regression using a grammar over Gaussian process kernels, and compare this approach with other structure learning approaches. Participants consistently chose compositional (over non-compositional) extrapolations and interpolations of functions. Experiments designed to elicit priors over functional patterns revealed an inductive bias for compositional structure. Compositional functions were perceived as subjectively more predictable than non-compositional functions, and exhibited other signatures of predictability, such as enhanced memorability and reduced numerosity. Taken together, these results support the view that the human intuitive theory of functions is inherently compositional.

Superheated Steam Drying of Cashew Kernels with Testa

Thermal processing of cashew kernels is determined by removal of moisture, and therefore, an indication of the product quality and the entire process economics. This paper reported an improved design of superheated steam drying system for cashew kernels with the objective of determining the drying characteristics and the effect of superheated steam on product quality. The range of drying steam temperature at atmospheric pressure was 120- 140ºC with velocity varied from 1-3 m/s. The total colour difference and browning index were used to examine the change in colour of dried kernels. The results showed that higher temperature and velocity of steam promoted higher drying rate and lower final moisture content of the product. The high temperature of steam was found to be the major cause of colour degradation of dried kernel besides steam velocity.

A Programmable Galois Field Processor for the Internet of Things

This paper investigates the feasibility of a unified processor architecture to enable error coding flexibility and secure communication in low power Internet of Things (IoT) wireless networks. Error coding flexibility for wireless communication allows IoT applications to exploit the large tradeoff space in data rate, link distance and energy-efficiency. As a solution, we present a light-weight Galois Field (GF) processor to enable energy-efficient block coding and symmetric/asymmetric cryptography kernel processing for a wide range of GF sizes (2m, m = 2, 3, ..., 233) and arbitrary irreducible polynomials. Program directed connections among primitive GF arithmetic units enable dynamically configured parallelism to efficiently perform either four-way SIMD 5- to 8-bit GF operations, including multiplicative inverse, or a wide bit-width (e.g., 32-bit) GF product in a single cycle. To illustrate our ideas, we synthesized our GF processor in a 28nm technology. Compared to a baseline software implementation optimized for a general purpose ARM M0+ processor, our processor exhibits a 5-20 x speedup for a range of error correction codes and symmetric/asymmetric cryptography applications. Additionally, our proposed GF processor consumes 431μW at 0.9V and 100MHz, and achieves 35.5pJ/b energy efficiency while executing AES operations at 12.2Mbps. We achieve this within an area of 0.01mm2.

Innovations and innovation capability in palm kernel processing industry in southwestern Nigeria

PurposeThis study aims to examine the innovations available in the small-scale palm kernel processing firms in southwestern Nigeria. The sample population of 265 respondents was obtained through respondent-driven sampling tools. The research tools used were questionnaire, personal observations, interviews and secondary data collection approach. The questionnaire was administered to palm kernel processors and elicited information on innovations available in the firms. Both descriptive and inferential statistical techniques were used for data analysis.Design/methodology/approachThe study area consisted of Oyo, Ogun, Osun and Ondo states in the southwestern Nigeria, because of the abundant supply of palm kernel as well as the presence of small and medium palm kernel oil (PKO) processing firms. The study population consists of all small palm kernel processing enterprises in these states. A total of 265 firms were purposively selected for the study. The sampling procedure involved the initial purposive selection of a palm kernel processing firm in a location, from where other firms within the locality were then identified. The primary data were collected through the use of questionnaire, interview and personal observation.FindingsThe results of the study showed that only process, organisational and market innovations were recorded by the palm kernel processing firms. Apart from the sieving operation where majority of the firms (91.30 per cent) used manual method, other unit operations were done mechanically. There was evidence of one or two innovation(s) available in the unit operations of these firms. Also, improvements were carried out occasionally in the process and marketing operations; the major sources of ideas for innovation were from related firms and customers. The organisations where majority of these firms had linkage with were Nigeria Palm Kernel Processing Association, government agencies and customer/suppliers.Research limitations/implicationsThe findings are limited to the south-western part of Nigeria, there is need to extend the study to other states in the southern part where palm produce is the major cash crop. This will assist in making better generalisation on the innovation and innovation capability of the processors in Nigeria.Practical implicationsThe study showed that the palm kernel processing firms experienced low innovation capability which could be due to their weak interactions with the knowledge institutions. Hence, there is need for these palm kernel processing firms to establish strong linkage with the knowledge institutions where their innovation capability can be enhanced.Social implicationsThe findings in this paper can serve as an input to the design of policies that can enhance the innovation capability of the various actors in the value chain. This will assist in preventing wastages, increasing the quantity and quality of products and creating job opportunities. This is because the quality of PKO depends on the processing method; hence, better process innovation will improve the characteristics of the oil and widen its application.Originality/valueMuch has been written about palm kernel processing in medium and large enterprises, but information is still scanty on the small-scale processing enterprises. This paper contributed to knowledge by examining the innovations existing in the palm kernel processing enterprises in the south-western part of Nigeria and the innovation capability possessed by these enterprises.

Effect of Socio-Economics Characteristics, Production and Innovation Capabilities on the Performance of Palm Kernel Processing Firms in Southwestern Nigeria

Effect of Socio-Economics Characteristics, Production and Innovation Capabilities on the Performance of Palm Kernel Processing Firms in Southwestern Nigeria

Palm Kernel Processing for Income and Employment in the Kwaebibirem District, Ghana

Palm Kernel Processing for Income and Employment in the Kwaebibirem District, Ghana

Data-driven sensitivity inference for Thomson scattering electron density measurement systems.

We developed a method to infer the calibration parameters of multichannel measurement systems, such as channel variations of sensitivity and noise amplitude, from experimental data. We regard such uncertainties of the calibration parameters as dependent noise. The statistical properties of the dependent noise and that of the latent functions were modeled and implemented in the Gaussian process kernel. Based on their statistical difference, both parameters were inferred from the data. We applied this method to the electron density measurement system by Thomson scattering for the Large Helical Device plasma, which is equipped with 141 spatial channels. Based on the 210 sets of experimental data, we evaluated the correction factor of the sensitivity and noise amplitude for each channel. The correction factor varies by ≈10%, and the random noise amplitude is ≈2%, i.e., the measurement accuracy increases by a factor of 5 after this sensitivity correction. The certainty improvement in the spatial derivative inference was demonstrated.

Parameterization of Nonlinear Observer-Based Fault Detection Systems

This note addresses parameterization issues of nonlinear observer-based fault detection (FD) systems which are composed of a residual generator, a residual evaluator and a threshold. Our study consists of two steps. In the first step, with the aid of nonlinear factorization and input-output operator techniques, we prove that any stable residual generator can be parameterized by a cascade connection of the process kernel representation and a post-filter that represents the parameter system. In the second step, based on the state-space representation of the parameterized residual generators, we investigate the so-called ${\mathcal L}_{\infty}$ - and ${\mathcal L}_{2}$ - classes of observer-based FD systems. This leads to the parameterization of the threshold settings for both classes of FD systems and, associated with them, to the characterization of the existence conditions.

Novel hybridized infrared-hot air method for roasting of peanut kernels

Abstract Roasting is an important step in the processing of peanut kernels. In this study the combination of infrared (IR) and hot-air was explored for roasting of peanut kernels and the effects of processing conditions including hot air temperature (100 and 120 °C), infrared power (130 W, 165 W and 200 W) and roasting time (10, 20, 30 min) on different characteristics of kernels (moisture content, color, textural characteristics, chemical properties, specific energy consumption and sensory properties) were investigated. The results showed that increasing the roasting air temperature, infrared power and roasting time decreased the moisture content, fracture force, hardness, apparent modulus of elasticity, compressive energy, pH and solid soluble content and increased the browning index, total phenolic compounds, and total acceptance of peanuts kernels. The total color difference (ΔE) varied from 2.87 to 19.33. After roasting, hardness of the peanut kernels reduced from initial 91.31 to 33.86 N and total phenolic compounds value were 2.71 mg GAE/g, all in the acceptable limit for good quality of roasted peanuts. The lowest value of specific energy consumed in roasting peanut kernels was observed at 100 °C and 200 W for 10 min (5.06 kWh/kg) and the highest value was at 120 °C and 130 W for 30 min (23.20 kWh/kg). This study demonstrated that combination of infrared and hot-air roasting can produce high-quality roasted peanuts with lower energy cost and time roasting; therefore it could be considered as a new technology for the peanut roasting industry. Industrial relevance Infrared radiation could be considered as a promising technique for roasting of soybeans due to lower energy cost.

The effect of moisture content on grinding process of wheat and maize single kernel

The mechanical properties and the resistance of grains are key characteristics that enhance grinding behaviour of wheat and maize and are dependent on the moisture content of the grains. These properties were defined in the single-kernel compression test, and it seems that the qualities expressing the relations resulting during mechanical loads like mechanical and rheological properties are significant. The aim of the study reported here is to show the influence of moisture content on grinding process of wheat and maize single kernel. To show this influence it is necessary to study the physical and mechanical properties of wheat and maize single kernel at different moisture content 10%, 12%, 14%, 16%, 18% and 20%. The measurement results showed significant relationship between the cereal type, its resistance characteristics and the moisture content in the grinding process.

Fusion of Gaussian Process Kernel Regressors for Fault Prediction in Intelligent Energy Systems

Integration of energy systems with machine intelligence technologies advances the new generation of intelligent energy systems. One feature of intelligent energy systems is their ability to predict a future fault state (prognosis), and thus support control actions. This paper introduces a prognostic framework based on concepts originating from the machine learning universe and proceeds to assess the performance of the prognostics algorithms with statistical methods aiming to formulate a linear predictor whose coefficients are the solution of a multi-objective optimization problem. Prediction is achieved through independent Gaussian process kernel regressors put together as terms of a linear forecaster. In this novel framework the available data is used to train the regression models whose degradation predictions cover a predetermined time period and have the form of a predictive distribution whose mean and variance values are computed for future moments. Thus, given the observed data points one may search for the most probable values of other quantities of interest, or the values at different points from those measured. The feasibility of the cascading prognostics methodology is demonstrated via a turbine blade degradation example. This implementation is characterized by advantages that include the utilization of an optimization process that simultaneously determines the lowest possible values in a set of different statistical measures and the employment of a set of kernels for modeling various data features and capturing the system dynamics.

Measurement device independent quantum key distribution assisted by hybrid qubit

Measurement device independent Quantum Key Distribution (MDI-QKD), is immune to all attacks on detection and achieve immense improvement with respect to quantum key distribution system security. However, Bell state measurement (BSM), the kernel processing in MDI-QKD, can only identify two of the four Bell states, which limits the efficiency of the protocol. In this paper, a modified MDI-QKD with hybrid qubit is proposed to provide a major step towards answering this question. The hybrid qubits, which are composed of single photon qubit qubits and coherent qubit, are sent to the quantum relay to perform parallel BSMs synchronously and bit flip can be easily operated to complete the whole key distribution process. The secure key rate can be improved with our modified protocol owing to the higher success probability of BSM, which is increased by adding the parity check of coherent qubit. Furthermore, though our protocol requires photon number resolving detectors, the BSM of coherent state could be instead implemented using squeezed state which makes our scheme practical with state-of-the-art devices.

Impact of packaging atmosphere, storage and processing conditions on the generation of phytoprostanes as quality processing compounds in almond kernels

The thermal processing of almond kernels implies the use of techniques that produce chemical changes such as oxidation. Phytoprostanes (PhytoPs) are considered biomarkers of the oxidative stress in plants. We studied the PhytoP profile in kernels of almond cultivars under different conditions, in relation to packaging, temperature and time of storage and processing. The most abundant PhytoP was the F1t series. The PhytoP levels increased significantly with the time of storage (3 and 6months) and the total PhytoP concentration was higher under air than in a vacuum packaging atmosphere. Storage at 24°C raised the concentrations of individual PhytoPs and the total sum of PhytoPs. The frying and roasting processes led to a strong reduction of the original concentration of most PhytoPs and promoted the synthesis of specific PhytoPs that were not detected in raw kernels and thus could be biomarkers of the degree of oxidative degradation of almonds.