Small Process Research Articles

Environmental Monitoring of Nebraska Ready-to-eat Meat Processing Establishments Resulted in the Isolation of Listeria Alongside Pseudomonas Highly Resistant to Quaternary Ammonia Sanitizer

Robust environmental monitoring for Listeria monocytogenes often may not be feasible for small and very small meat processors in the United States due to the limitations in finances, staffing, or expertise. Three small/very small processors in Nebraska were sampled using sponge applicators in nonfood contact surface areas to determine if biofilm and sanitizer resistance behaviors of Pseudomonas could relate to the prevalence of L. monocytogenes and Listeria spp. in ready-to-eat meat processing environments. Samples were 3.3% (3/90) positive for L. monocytogenes, and 12.2% (11/90) of samples were positive for Listeria spp. Pseudomonas spp. were also isolated. When Listeria spp. and Pseudomonas spp. were assayed for biofilm production and resistance to a quaternary ammonia sanitizer, multiple isolates belonging to both genera capable of forming biofilms were identified. Four Pseudomonas spp. isolates resisted the 200 ppm manufacturer-recommended sanitizer concentration for food contact surface sanitation, and one Pseudomonas spp. isolated from a drain sample that was also positive for L. monocytogenes demonstrated a sanitizer minimum bactericidal concentration of 1000 ppm. These findings further support the need for monitoring of small and very small meat processors for L. monocytogenes as well as highlight the need to identify other bacteria in these processing environments, like Pseudomonas, that are resistant to environmental stressors.

Understanding the Food Safety Needs of Small and Very Small Processors in the Northeast United States: Food Safety Communicator and Regulator Perspectives

Understanding the Food Safety Needs of Small and Very Small Processors in the Northeast United States: Food Safety Communicator and Regulator Perspectives

Enabling unstructured-mesh computation on massively tiled AI processors: An example of accelerating in silico cardiac simulation

A new trend in processor architecture design is the packaging of thousands of small processor cores into a single device, where there is no device-level shared memory but each core has its own local memory. Thus, both the work and data of an application code need to be carefully distributed among the small cores, also termed as tiles. In this paper, we investigate how numerical computations that involve unstructured meshes can be efficiently parallelized and executed on a massively tiled architecture. Graphcore IPUs are chosen as the target hardware platform, to which we port an existing monodomain solver that simulates cardiac electrophysiology over realistic 3D irregular heart geometries. There are two computational kernels in this simulator, where a 3D diffusion equation is discretized over an unstructured mesh and numerically approximated by repeatedly executing sparse matrix-vector multiplications (SpMVs), whereas an individual system of ordinary differential equations (ODEs) is explicitly integrated per mesh cell. We demonstrate how a new style of programming that uses Poplar/C++ can be used to port these commonly encountered computational tasks to Graphcore IPUs. In particular, we describe a per-tile data structure that is adapted to facilitate the inter-tile data exchange needed for parallelizing the SpMVs. We also study the achievable performance of the ODE solver that heavily depends on special mathematical functions, as well as their accuracy on Graphcore IPUs. Moreover, topics related to using multiple IPUs and performance analysis are addressed. In addition to demonstrating an impressive level of performance that can be achieved by IPUs for monodomain simulation, we also provide a discussion on the generic theme of parallelizing and executing unstructured-mesh multiphysics computations on massively tiled hardware.

A Lightweight YOLOv5 Optimization of Coordinate Attention

As Machine Learning technologies evolve, there is a desire to add vision capabilities to all devices within the IoT in order to enable a wider range of artificial intelligence. However, for most mobile devices, their computing power and storage space are affected by factors such as cost and the tight supply of relevant chips, making it impossible to effectively deploy complex network models to small processors with limited resources and to perform efficient real-time detection. In this paper, YOLOv5 is studied to achieve the goal of lightweight devices by reducing the number of original network channels. Then detection accuracy is guaranteed by adding a detection head and CA attention mechanism. The YOLOv5-RC model proposed in this paper is 30% smaller and lighter than YOLOv5s, but still maintains good detection accuracy. YOLOv5-RC network models can achieve a good balance between detection accuracy and detection speed, with potential for its widespread use in industry.

An Energy Efficient Load Balancing Tree-Based Data Aggregation Scheme for Grid-Based Wireless Sensor Networks

A wireless sensor network (WSN) consists of a very large number of sensors which are deployed in the specific area of interest. A sensor is an electronic device equipped with a small processor and has a small-capacity memory. The WSN has the functions of low cost, easy deployment, and random reconfiguration. In this paper, an energy-efficient load balancing tree-based data aggregation scheme (LB-TBDAS) for grid-based WSNs is proposed. In this scheme, the sensing area is partitioned into many cells of a grid and then the sensor node with the maximum residual energy is elected to be the cell head in each cell. Then, the tree-like path is established by using the minimum spanning tree algorithm. In the tree construction, it must meet the three constraints, which are the minimum energy consumption spanning tree, the network depth, and the maximum number of child nodes. In the data transmission process, the cell head is responsible for collecting the sensing data in each cell, and the collected data are transmitted along the tree-like path to the base station (BS). Simulation results show that the total energy consumption of LB-TBDAS is significantly less than that of GB-PEDAP and PEDAP. Compared to GB-PEDAP and PEDAP, the proposed LB-TBDAS extends the network lifetime by more than 100%. The proposed LB-TBDAS can avoid excessive energy consumption of sensor nodes during multi-hop data transmission and can also avoid the hotspot problem of WSNs.

Autonomous control for miniaturized mobile robots in unknown pipe networks.

Despite recent advances in robotic technology, sewer pipe inspection is still limited to conventional approaches that use cable-tethered robots. Such commercially available tethered robots lack autonomy, and their operation must be manually controlled via their tethered cables. Consequently, they can only travel to a certain distance in pipe, cannot access small-diameter pipes, and their deployment incurs high costs for highly skilled operators. In this paper, we introduce a miniaturised mobile robot for pipe inspection. We present an autonomous control strategy for this robot that is effective, stable, and requires only low-computational resources. The robots used here can access pipes as small as 75 mm in diameter. Due to their small size, low carrying capacity, and limited battery supply, our robots can only carry simple sensors, a small processor, and miniature wheel-legs for locomotion. Yet, our control method is able to compensate for these limitations. We demonstrate fully autonomous robot mobility in a sewer pipe network, without any visual aid or power-hungry image processing. The control algorithm allows the robot to correctly recognise each local network configuration, and to make appropriate decisions accordingly. The control strategy was tested using the physical micro robot in a laboratory pipe network. In both simulation and experiment, the robot autonomously and exhaustively explored an unknown pipe network without missing any pipe section while avoiding obstacles. This is a significant advance towards fully autonomous inspection robot systems for sewer pipe networks.

Analysis of the most frequent nonconformance aspects related to Good Manufacturing Practices (GMP) among small and medium enterprises (SMEs) in the food industry and their main factors

Despite the increasing evidence that small processors have a low rate of food safety compliance, primarily due to the lack of Good Manufacturing Practice (GMP) implementation, only a limited number of research studies have been conducted to understand their challenges. The aims of this review were twofold, namely 1) to analyse the GMP’s most frequent nonconformance aspects in small food processing and (2) to elucidate the key factors that lead to the nonconformance aspects. The review was carried out based on twenty peer-reviewed publications between the years 2012 to 2021, which collected studies related to GMP and small-scale processors. Inadequate sanitary design and facilities were identified as the most frequent GMP nonconformance aspect (85% of the total number of publications), followed by poor personal hygiene (55%), inadequate or absence of documentation (55%), poor cleaning and maintenance programs (50%), lack of operation control (50%), lack of training (35%), inadequate product information (15%) and lack of workers’ health control (15%). Such nonconformances were found to result in incidences of foodborne diseases, unacceptable microbial growth in products and ambient areas, quality degradation as well as findings of filthy matters. The factors that led to nonconformance were the lack of on-site supervision, financial constraints, lack of knowledge, lack of managerial support, the gap in authority's enforcement and workers' resistance to food safety practices. The review concludes with a suggestion for future work to formulate a cohesive program that integrates multiple strategies in addressing the identified key factors and subsequently, enabling GMP adoption among SMEs.

Advancement and Challenges of Field Effect Transistors based on Multi-gate Transistor

The advancement and challenges of field effect transistors are based on multi-gate transistors from the perspective of structure and material. Multi-gate field-effect transistors (Multi-gate FET) have steeper sub-threshold slopes, which can reduce the short channel effect and improve mobility and drive current. A fin field-effect transistor (FinFET) and gate-all-around field-effect transistor (GAAFET) are attractive multi-gate structures most compatible with today’s standard machining technologies. As the future moves towards smaller processes, FinFET and GAAFET processes limit the spacing between n-to-p devices. In order to increase the possibility of transistor miniaturization, innovative structures such as Forksheet FET and Complementary-FET (CFET) have been proposed.

Baseline Survey on Existing Status of Fried Potato Chips From Processing to Marketing in Selected Locations of Bangladesh

The baseline survey was conducted with a view to generate information on existing status of fried potato chips from processing to marketing in selected locations of Bangladesh. The baseline information was collected from seven selected districts namely Dhaka, Gazipur, Tangail, Mymensingh, Bogura, Munshigonj and Jashore. Fifteen hawkers and five producers were randomly selected to collect the information by the pre-tested questionnaire from each location both from producer to consumer. Most of the hawkers of Bogura was above 40 years old (80%) followed by Dhaka who were 25-39 years old (60%). In Bangladesh, eighty percent people were above 40 years old engaged in processing fried potato chips. All aged people in Bogura preferred fried potato chips (100%) whereas maximum child in Jashore (80%) liked fried potato chips. Bogura and Munshigonj people were unknown about healthiness knowledge of fried chips consumption. Bogura, Jashore and Munshigonj processors/producers were hawkers and they prepared the product and distributed the products to the market or direct involved in sale. Fried chips processing people in the survey area used deep frying technique (100%). Soya bean oil used for deep oil frying of potato chips by 80% small processors. Most of the respondents changed their frying oil during frying when frying oil turned into cloudy.
 Bangladesh J. Agril. Res. 45(2): 137-144, June 2020

Efficient and Explainable Deep Neural Networks for Airway Symptom Detection in Support of Wearable Health Technology

Mobile health wearables are often embedded with small processors for signal acquisition and analysis. These embedded wearable systems are, however, limited with low available memory and computational power. Advances in machine learning, especially deep neural networks (DNNs), have been adopted for efficient and intelligent applications to overcome constrained computational environments. Herein, evolutionary algorithms are used to find novel DNNs that are accurate in classifying airway symptoms while allowing wearable deployment. As opposed to typical microphone‐acoustic signals, mechano‐acoustic data signals, which did not contain identifiable speech information for better privacy protection, are acquired from laboratory‐generated and publicly available datasets. The optimized DNNs had a low model file size of less than 150 kB and predicted airway symptoms of interest with 81.49% accuracy on unseen data. By performing explainable AI techniques, namely occlusion experiments and class activation maps, mel‐frequency bands up to 8,000 Hz are found as the most important feature for the classification. It is further found that DNN decisions are consistently relying on these specific features, fostering trust and transparency of the proposed DNNs. The proposed efficient and explainable DNN is expected to support edge computing on mechano‐acoustic sensing wearables for remote, long‐term monitoring of airway symptoms.

UAV system with trinocular vision for external obstacle detection of transmission lines.

External obstacle detection is a significant task in transmission line inspection and is related to the safe operation of the power transmission grid. In recent years, unmanned aerial vehicles (UAVs) equipped with different devices have been widely used for transmission line inspection. However, because of the complex environment of transmission lines and weak power line textures in the obtained images, most existing methods and systems cannot meet the requirements for real-time and high-accuracy external obstacle detection of transmission lines. In this paper, a novel, to the best of our knowledge, UAV system integrated trinocular vision technology with remote sensing is developed to achieve better external obstacle detection of transmission lines in real time, which is composed of a DJ-Innovations (DJI) UAV equipped with a global positioning system (GPS), angle sensors, trinocular vision including three visible cameras with the same parameters, and a small processor with a pre-implanted software algorithm. In this paper, a new method for external obstacle detection of transmission lines is proposed to satisfy the requirements for real-time and high-accuracy practical inspection applications. First, the original trinocular images need to be rectified. Then, the rectified trinocular images are adopted to achieve three-dimensional reconstruction of power lines. Finally, based on trinocular vision, bag of feature, and GPS, the clearance distance measurement, obstacle classification, and obstacle location are realized. Experimental tests on 220 kV transmission lines reveal that our proposed system can be applied in practical inspection environments and has good performance.

Performance Assessment of Chicken De-Feathering Machine in a Small Scale Slaughtering Processors

Performance Assessment of Chicken De-Feathering Machine in a Small Scale Slaughtering Processors

Iterative multilinear optimization for planar model fitting under geometric constraints.

Planes are the core geometric models present everywhere in the three-dimensional real world. There are many examples of manual constructions based on planar patches: facades, corridors, packages, boxes, etc. In these constructions, planar patches must satisfy orthogonal constraints by design (e.g. walls with a ceiling and floor). The hypothesis is that by exploiting orthogonality constraints when possible in the scene, we can perform a reconstruction from a set of points captured by 3D cameras with high accuracy and a low response time. We introduce a method that can iteratively fit a planar model in the presence of noise according to three main steps: a clustering-based unsupervised step that builds pre-clusters from the set of (noisy) points; a linear regression-based supervised step that optimizes a set of planes from the clusters; a reassignment step that challenges the members of the current clusters in a way that minimizes the residuals of the linear predictors. The main contribution is that the method can simultaneously fit different planes in a point cloud providing a good accuracy/speed trade-off even in the presence of noise and outliers, with a smaller processing time compared with previous methods. An extensive experimental study on synthetic data is conducted to compare our method with the most current and representative methods. The quantitative results provide indisputable evidence that our method can generate very accurate models faster than baseline methods. Moreover, two case studies for reconstructing planar-based objects using a Kinect sensor are presented to provide qualitative evidence of the efficiency of our method in real applications.

Factoring 2048-bit RSA Integers in 177 Days with 13 436 Qubits and a Multimode Memory.

We analyze the performance of a quantum computer architecture combining a small processor and a storage unit. By focusing on integer factorization, we show a reduction by several orders of magnitude of the number of processing qubits compared with a standard architecture using a planar grid of qubits with nearest-neighbor connectivity. This is achieved by taking advantage of a temporally and spatially multiplexed memory to store the qubit states between processing steps. Concretely, for a characteristic physical gate error rate of 10^{-3}, a processor cycle time of 1 microsecond, factoring a 2 048-bit RSA integer is shown to be possible in 177days with 3D gauge color codes assuming a threshold of 0.75% with a processor made with 13 436 physical qubits and a memory that can store 28million spatial modes and 45 temporal modes with 2hours' storage time. By inserting additional error-correction steps, storage times of 1second are shown to be sufficient at the cost of increasing the run-time by about 23%. Shorter run-times (and storage times) are achievable by increasing the number of qubits in the processing unit. We suggest realizing such an architecture using a microwave interface between a processor made with superconducting qubits and a multiplexed memory using the principle of photon echo in solids doped with rare-earth ions.

Assessing the Best Gap-Filling Technique for River Stage Data Suitable for Low Capacity Processors and Real-Time Application Using IoT.

Hydrometeorological data sets are usually incomplete due to different reasons (malfunctioning sensors, collected data storage problems, etc.). Missing data do not only affect the resulting decision-making process, but also the choice of a particular analysis method. Given the increase of extreme events due to climate change, it is necessary to improve the management of water resources. Due to the solution of this problem requires the development of accurate estimations and its application in real time, this work present two contributions. Firstly, different gap-filling techniques have been evaluated in order to select the most adequate one for river stage series: (i) cubic splines (CS), (ii) radial basis function (RBF) and (iii) multilayer perceptron (MLP) suitable for small processors like Arduino or Raspberry Pi. The results obtained confirmed that splines and monolayer perceptrons had the best performances. Secondly, a pre-validating Internet of Things (IoT) device was developed using a dynamic seed non-linear autoregressive neural network (NARNN). This automatic pre-validation in real time was tested satisfactorily, sending the data to the catchment basin process center (CPC) by using remote communication based on 4G technology.

To Sample or Not to Sample? An Analysis of the Need for Salmonella Sampling of Smaller Poultry Processors.

Within the European Union (EU), microbiological criteria (MC) sampling for Salmonella in poultry was introduced in 2005. In particular, processors had to meet a target of fewer than seven positive samples out of 50. However, processors producing small amounts of poultry meat did not have to sample if national authorities determined this was an acceptable risk. The U.K. Food Standards Agency (FSA) thus has a sampling regime based on throughput that allows smaller processors not to sample. In 2011, the limit of 7/50 was reduced to 5/50. Given the current uncertainty regarding U.K. trade relations with the EU, the U.K. FSA decided to conduct a new risk assessment of the risks of Salmonella produced by smaller processors, to determine whether sampling was now necessary. Current evidence suggests that an MC sampling regime in smaller slaughterhouses is not warranted from a national public health perspective. Because of the insensitivities of the MC sampling scheme, the introduction of MC sampling into smaller slaughterhouses would only be necessary if the suspected carcass prevalence was 15% or more. While our analysis is prone to uncertainty, we estimated that the carcass prevalence in smaller processors is below this. Thus, we recommended that the current sampling framework, allowing smaller processors not to sample, was still applicable.

Asynchronous Approximation of a Single Component of the Solution to a Linear System

We present a distributed asynchronous algorithm for approximating a single component of the solution to a system of linear equations $Ax = b$ , where $A$ is a positive definite real matrix and $b \in \mathbb {R}^n$ . This can equivalently be formulated as solving for $x_i$ in $x = Gx + z$ for some $G$ and $z$ such that the spectral radius of $G$ is less than 1. Our algorithm relies on the Neumann series characterization of the component $x_i$ , and is based on residual updates. We analyze our algorithm within the context of a cloud computation model motivated by frameworks, such as Apache Spark, in which the computation is split into small update tasks performed by small processors with shared access to a distributed file system. We prove a robust asymptotic convergence result when the spectral radius $\rho (|G|) , regardless of the precise order and frequency in which the update tasks are performed. We provide convergence rate bounds that depend on the order of update tasks performed, analyzing both deterministic update rules via counting weighted random walks, as well as probabilistic update rules via concentration bounds. The probabilistic analysis requires analyzing the product of random matrices that are drawn from distributions that are time and path dependent. We specifically consider the setting where $n$ is large, yet $G$ is sparse, e.g., each row has at most $d$ nonzero entries. This is motivated by applications in which $G$ is derived from the edge structure of an underlying graph. Our results prove that if the local neighborhood of the graph does not grow too quickly as a function of $n$ , our algorithm can provide significant reduction in computation cost as opposed to any algorithm that computes the global solution vector $x$ . Our algorithm obtains an $\epsilon \Vert x\Vert _2$ additive approximation for $x_i$ in constant time with respect to the size of the matrix when the maximum row sparsity $d = O(1)$ and $1/(1-\Vert G\Vert _2) = O(1)$ , where $\Vert G\Vert _2$ is the induced matrix operator 2-norm.

Reducing the Delay for Decoding Instructions by Predicting Their Source Register Operands

The fetched instructions would have data dependency with in-flight ones in the pipeline execution of a processor, so the dependency prevents the processor from executing the incoming instructions for guaranteeing the program’s correctness. The register and memory dependencies are detected in the decode and memory stages, respectively. In a small embedded processor that supports as many ISAsas possible to reduce code size, the instruction decoding to identify register usage with the dependence check generally results in long delay and sometimes a critical path in its implementation. For reducing the delay, this paper proposes two methods—One method assumes the widely used source register operand bit-fields without fully decoding the instructions. However, this assumption would cause additional stalls due to the incorrect prediction; thus, it would degrade the performance. To solve this problem, as the other method, we adopt a table-based way to store the dependence history and later use this information for more precisely predicting the dependency. We applied our methods to the commercial EISC embedded processor with the Samsung 65nm process; thus, we reduced the critical path delay and increased its maximum operating frequency by 12.5% and achieved an average 11.4% speed-up in the execution time of the EEMBC applications. We also improved the static, dynamic power consumption, and EDP by 7.2%, 8.5%, and 13.6%, respectively, despite the implementation area overhead of 2.5%.

Review on Value Chain Analysis Potato in Ethiopia

More than 80% of the Ethiopian population earns their livelihood from crop cultivation and livestock rearing activities. Crop production is a major contributor to Gross Domestic Product accounting for approximately 28% from the sub sectors of agriculture. Among the crops, coffee, pulses, oilseeds, potatoes, sugarcane, vegetables and cereals are principal crops grown in Ethiopia. As a result, potato is the most important vegetable in terms of both production and consumption in Ethiopia. The objective of this study is to review the major potato value chain actors and their roles, determinants of quantity of potato supplied to the market and factors affecting potato value addition at different stages of the value chain in Ethiopia. Finally, from the review literature almost all results the production of potato and the demand of potato increase from year to year.The major actors of potato value chain were channeled from producer to final consumers, such as input supplier, producers (farmer), wholesalers, retailers, small scale processor, and consumers. Although trading of potato is profitable across all samples and Potato is among the major vegetable export products. It is traded in both local market and export market outlets. Among the different literatures the following variables determine the supply of potato such as, distance to the market, Access of market information, price of produce, Access to extension service, size of land, yield, Access of credit and access of non-farm income. In addition to these the factor affecting value addition are not washing off soil from harvested potato, lack of packing material or baskets, as well as lack of transportation, storage, access of credit, access of extension services, small quantity to produce and distance to market. Most of the scholars are not address the value addition issue so this review tries to give recommendation for other researchers to conduct research on this issue. The review suggests that proper method of handling, information, transporting can keep quality and quantity of potato production. Therefore, it is recommended to assign efficient extension system, updating the producer’s knowledge and skill with improved production, storing and marketing system that enables to increase benefits of producers. Keywords: Potato, Value chain, production, value addition DOI: 10.7176/IEL/10-2-02 Publication date: March 31 st 2020

Integrated Prediction Error Filter Analysis Application On Lake Towuti

Every naturally-occurred phenomenon on earth is related to cyclicity. On a larger scale, it can be defined as the occurrence of climate periodicity which is caused by the revolution of earth towards the sun. It can also be examined on a smaller process such as the days and nights cycles, as the effect of earths rotation. This research will specifically discuss about the cyclicity of grain size changes in sediments from lake Towuti, Indonesia. The cyclicity of the sediments is deduced using Integrated Prediction Error Filter Analysis (INPEFA) trends. The INPEFA trends are used to analyse the probability of sediments distribution by simply calculating the cumulative errors between predicted and actual data. Unlike any other implementation of INPEFA that mainly observing lithology controlled by sea level changes, this research is aimed at applying INPEFA to enhance well correlation process across an area that is strongly influenced by rainfall intensity and some climatically-driven processes. By correlating the sediment units, the lateral distribution of the climatically-driven diatom ooze will eventually help a better understanding of paleoclimate events on lake Towuti. This research is aimed at constructing and applying systematic algorithm on INPEFA logs calcultation. Two main cores that construct the INPEFA calculation are trend removal process and autoregressive coefficients calculation using Burgs method. When dealing with real datasets the trend removal process is an imperative process to prevent ambiguous INPEFA trend. Moreover, the use of trend removal process is also favourable in interpreting INPEFA trends for various cyclicity periods, this is achieved by varying the input parameters on the trend removal process. Autoregressive coefficients calculation on the other hand is the keystone that constructs the INPEFA logs calculation process. Well correlations process is successfully achieved through interpreting the INPEFA trends logs. Validation of the INPEFA logs shows good correlation between the result and core sample from lake Towuti with widely-distributed tephra being the main key validator. The changes in INPEFA trends is interpreted to be linked with the change in grain size and also in sediments impedance. Comparing and validating the INPEFA trends with two seismic traces from the lake reveals that the turning point of INPEFA trends are associated with strong reflection on the seismic traces. We approach the building of pseudo-INPEFA section through applying optimum Wiener filter (OWF) during the multi-attribures analysis. The lateral continuation of predicted pseudo-log was improved, overall correlation showed an increase by 15% and a decreased in error value by 25%.