Input Units Research Articles

Optimizing traceability scheme in a fresh product supply chain considering product competition in blockchain era

Fresh product traceability plays an integral role in ensuring food safety. Recently, many firms have introduced blockchain technology (BCT) for product traceability to guarantee product authenticity and consumer rights. However, introduction of the new technology has also intensified the competition among fresh products. Meanwhile, previous studies on traceability strategies only considered the impact of traceability cost and ignored the issue of increased product competition in blockchain era. Driven by these problems, we build a game-theoretical model to analyze the optimization of traceability schemes for both low-end fresh products (LEFP) and high-end fresh products (HEFP) in a fresh product supply chain (FPSC) in a competitive environment. Moreover, this paper investigates the impact of power structure on traceability schemes and identifies the conditions under which one scheme outperforms the others. We find that the cost per unit of product input has no effect on the traceability decision of the FPSC. Nonetheless, the introduction of BCT increases the number of consumers who choose LEFP and raises the selling price and wholesale price of corresponding products. Finally, from the supply chain perspective, it is best to adopt the same traceability scheme for both products. This study sheds light on the use of BCT in traceability can provide enhanced product authenticity and consumer rights while increasing product competition and demonstrates the significance of traceability schemes in the FPSC.

Sparse compressed deep echo state network with improved arithmetic optimization algorithm for chaotic time series prediction

A sparse compressed deep echo state network (SCDESN) incorporating sparse input units, compressed sampling and principal component analysis (PCA) units is proposed in this paper, and theoretically proved the sufficient and necessary conditions to ensure the echo state characteristics of the proposed scheme. In addition, a hybrid arithmetic optimization algorithm based on matrix design strategy and boundary selection strategy (SVD-HAOA) was proposed to optimize the hyper-parameters of the SCDESN model, taking into account the characteristics of the SCDESN model. The steps and process for optimizing the hyper parameters of the SCDESN model using SVD-HAOA were presented. Finally, the SCDESN model optimized by SVD-HAOA was experimentally and summarized on a classic benchmark dataset and two real-world chaotic time series. The outcomes of the simulation indicated that the suggested model surpassed alternative models in performance and enhanced computational efficiency while maintaining prediction accuracy. This model may serve as a viable alternative for real-world applications. Noteworthy, the source code of SCDESN-SVD-HAOA is publicly available at https://github.com/qiuqiuyaaa/SCDESN-SVD-HAOA.

PEPT: Expert Finding Meets Personalized Pre-training

Finding experts is essential in Community Question Answering (CQA) platforms as it enables the effective routing of questions to potential users who can provide relevant answers. The key is to personalized learning expert representations based on their historical answered questions, and accurately matching them with target questions. Recently, the application of Pre-trained Language Models (PLMs) have gained significant attraction due to their impressive capability to comprehend textual data, and are widespread used across various domains. There have been some preliminary works exploring the usability of PLMs in expert finding, such as pre-training expert or question representations. However, these models usually learn pure text representations of experts from histories, disregarding personalized and fine-grained expert modeling. For alleviating this, we present a personalized pre-training and fine-tuning paradigm, which could effectively learn expert interest and expertise simultaneously. Specifically, in our pre-training framework, we integrate historical answered questions of one expert with one target question, and regard it as a candidate aware expert-level input unit. Then, we fuse expert IDs into the pre-training for guiding the model to model personalized expert representations, which can help capture the unique characteristics and expertise of each individual expert. Additionally, in our pre-training task, we design: 1) a question-level masked language model task to learn the relatedness between histories, enabling the modeling of question-level expert interest; 2) a vote-oriented task to capture question-level expert expertise by predicting the vote score the expert would receive. Through our pre-training framework and tasks, our approach could holistically learn expert representations including interests and expertise. Our method has been extensively evaluated on six real-world CQA datasets, and the experimental results consistently demonstrate the superiority of our approach over competitive baseline methods. 1

Modeling and simulation of a multi source microwave heating of soil based on PSO-BPNN

Due to rapid industrialization, accumulation of organic pollutants in the soil has been an increasing menace, having detrimental effect on crop yields, toxicity to flora and fauna, and environment in total. Microwave soil remediation technology, has garnered a significant attention in the field of thermal degradation of organic-polluted soil. However, the high-temperature remediation often involve the use of microwave devices with multiple sources, leading to competing and coupling effects, significantly impacting heating efficiency. Therefore, an attempt was made to develop a multiple source microwave device with adjustable inputs for soil heating, to design adjustment schemes for multiple input units. A multi-physics field model consistent with the testing apparatus was developed and utilized to generate the temperature for 625 different adjustment schemes. To further refine and to optimize the adjustment schemes a PSO-BPNN model was integrated and utilized. The simulation results were utilized to predict the most effective adjustment scheme, and was tested. The model potentially serves to estimate the optimal adjustment scheme for multiple source microwave inputs for effective heating of soil.

PERANGKAT EKSTRAKSI JAHE TERINTEGRASI BERBASIS ARDUINO UNO DENGAN PENGUJIAN METODE SPEKTROFOTOMETRI VISIBEL

The Automatic Ginger Extractor Based on Arduino Uno consists of three main units: the input unit, the processing unit, and the output unit. The input unit is equipped with Up, Down, and Ok buttons for navigation, as well as a phototransistor sensor that detects the presence of ginger samples (ginger simplicia) to be extracted. The processing unit uses an Arduino Uno microcontroller to process signals from the sensor and control the output unit. The output unit includes a motor driver, a power window motor for operating the grinder to crush the samples, a 12V DC motor for operating the stirrer to mix the samples with the solvent, an electric solenoid valve for dispensing the extract, and an LCD to display the process status. The working principle of this device is that when the phototransistor sensor detects a sample, the sensor signal activates the power window motor to grind the ginger. The user can then manually set the dissolution time using the navigation buttons. The longer the dissolution time, the better the extraction result. The final product is a liquid containing curcumin, which is measured using a curcumin content meter to determine its concentration. The device shows an error rate of 2.74% compared to manual methods. With this design, the Arduino Uno-based automatic ginger extractor offers a more efficient and consistent extraction process. The automation system allows for better control and optimal results with minimal manual intervention.

MAF-Net: A multi-attention fusion network for power transmission line extraction from aerial images

Automatic extraction of power transmission lines from remote sensing images is important for intelligent power inspection. However, this task faces many challenges, such as complex backgrounds and varying illumination, which make accurate segmentation difficult. Additionally, the severe class imbalance issue arising from the smaller pixel proportion of transmission lines compared to background makes segmentation even more challenging. To address these challenges, a multi-attention fusion network, named MAF-Net, is put forward to achieve accurate end-to-end segmentation of remote sensing images of transmission lines. Firstly, aimed at the inadequate process of local features, a multi-scale context fusion (MCF) block is put forward for effective feature aggregation of multi-scale features. Meanwhile, a dense block is also introduced for feature enhancement to acquire multi-scale contexts. Secondly, considering the pooling operation will bring the problem of information loss, a three-branch pooling (TP) block is proposed to take the best of the advantages of different pooling operations. In addition, the multi-scale input unit is also proposed to support deep layers for extracting the discriminative context information. Furthermore, to tackle the class imbalance issue, a multi-attention block (MA) is used to optimize the simple skip connections to fully aggregate spatial attention, pixel attention, and channel attention features, allowing this segmentation model to focus on the key areas containing power transmission lines. Comparison experiments and ablation experiments are carried out on the remote aerial sensing images for performance analysis. Experiments indicate that proposed MAF-Net could achieve a superior segmentation ability.

Feed efficiency of dairy cattle as genetic trait

This review article is devoted to the use of feed efficiency traits in dairy cattle breeding. An efficient cow is defined as the one that produces the same amount of milk and milk solids while consuming less feed and remaining healthy and fertile; thus, allowing to reduce costs without decrease in production. Improving feed efficiency is economically important due to the increasing price of fodder. Feed efficiency is a genetically complex trait that can be described as units of product output (e.g., milk yield) per unit of feed input. Nowadays genetic evaluation of dairy cattle for feed efficiency is routinely conducted in several countries, including Australia, USA, Canada, Netherlands, Denmark, Sweden, Finland, Norway and United Kingdom. Different countries use different measures of feed efficiency of dairy cows. The main feed efficiency traits are dry matter intake, gross feed efficiency, residual feed intake, energy balance and feed saved. Genome-wide association studies demonstrated that feed efficiency in polygenic trait. Nevertheless, several genes with large effects on feed efficiency were identified. Estimates of heritability of these traits vary from 0.07 to 0.49 and show the presence of considerable genetic variation of these traits and therefore, the possibility of their genetic improvement under the conditions of inclusion in breeding programs. Changes in diet and rumen microbiome substantially impact feed efficiency of dairy cows. Feed efficiency is related to methane emissions and excess nitrogen excretion. Genetic improvement of feed efficiency requires recording of individual data on feed intake in cows. Such data are limited. Two options exist to solve this problem: use of indirect predictors and genomic prediction. Accuracy of genomic prediction varies from 0.21 to 0.61 across countries. International cooperative pro­jects such as Efficient Dairy Genome Project in Canada were launched to establish large databases and to increase accuracy of feed efficiency traits genomic prediction. Future directions of research are the use of novel technologies: mid-infrared spectroscopy, artificial intelligence, holo-omics.

Surrogate model-based optimization of methanol synthesis process for multiple objectives: A pathway towards achieving sustainable development goals

This work combines a generalized regression neural network (GRNN) with a non-dominated sorting genetic algorithm (NSGA-II) to optimize a methanol synthesis plant for multiple objectives. The proposed method begins by developing and validating the model for the methanol synthesis involving the recycling of unreacted gas stream into the methanol reactor feed. The data generated using this model is used to develop a GRNN-based surrogate model to expedite the evaluation of fitness values for the optimization algorithm. The objective functions considered for simultaneous optimization are minimizing the total energy cost per unit of energy input (TEC), maximizing overall resource utilization (R), and minimizing CO2-gram equivalent emissions per unit of energy input (EPEI). Pareto optimal solutions are obtained for three bi-and one tri-objective optimization case studies using the GRNN-assisted multi-objective optimization (GRNNAMOO) method. The developed GRNNAMOO approach reduces the computational time from multiple days to a few seconds for all case study scenarios. For the tri-objective optimization case, the operating point selected using multi-criteria decision-making achieves 84.37% of R, 10.45 $/GJ of TEC, and an EPEI value of 81.34 gCO2eq/MJ. These results represent 7.05% higher R, 0.34 $/GJ lower TEC, and 0.82 gCO2eq/MJ lower EPEI than the Aspen plus simulation results. Further, the carbon footprint values obtained are significantly lower than 110 gCO2eq/MJ reported for modern methanol plants for nearly the same R and TEC values. The proposed approach aligns with the United Nations' sustainability development goals by lowering energy costs and promoting sustainable energy through improved environmental sustainability in methanol production plants.

Recent Developments and Challenges in Projecting the Impact of Crop Productivity Growth on Biodiversity Considering Market-Mediated Effects.

The effect of an increase in crop productivity (output per unit of inputs) on biodiversity is hitherto poorly understood. This is because increased productivity of a crop in particular regions leads to increased profit that can encourage expansion of its cultivated area causing land use change and ultimately biodiversity loss, a phenomenon also known as "Jevons paradox" or the "rebound effect". Modeling such consequences in an interconnected and globalized world considering such rebound effects is challenging. Here, we discuss the use of computable general equilibrium (CGE) and other economic models in combination with ecological models to project consequences of crop productivity improvements for biodiversity globally. While these economic models have the advantage of taking into account market-mediated responses, resource constraints, endogenous price responses, and dynamic bilateral patterns of trade, there remain a number of important research and data gaps in these models which must be addressed to improve their performance in assessment of the link between local crop productivity changes and global biodiversity. To this end, we call for breaking the silos and building interdisciplinary networks across the globe to facilitate data sharing and knowledge exchange in order to improve global-to-local-to-global analysis of land, biodiversity, and ecosystem sustainability.

An Image Histogram Equalization Acceleration Method for Field-Programmable Gate Arrays Based on a Two-Dimensional Configurable Pipeline.

New artificial intelligence scenarios, such as high-precision online industrial detection, unmanned driving, etc., are constantly emerging and have resulted in an increasing demand for real-time image processing with high frame rates and low power consumption. Histogram equalization (HE) is a very effective and commonly used image preprocessing algorithm designed to improve the quality of image processing results. However, most existing HE acceleration methods, whether run on general-purpose CPUs or dedicated embedded systems, require further improvement in their frame rate to meet the needs of more complex scenarios. In this paper, we propose an HE acceleration method for FPGAs based on a two-dimensional configurable pipeline architecture. We first optimize the parallelizability of HE with a fully configurable two-dimensional pipeline architecture according to the principle of adapting the algorithm to the hardware, where one dimension can compute the cumulative histogram in parallel and the other dimension can process multiple inputs simultaneously. This optimization also helps in the construction of a simple architecture that achieves a higher frequency when implementing HE on FPGAs, which consist of configurable input units, calculation units, and output units. Finally, we optimize the pipeline and critical path of the calculation units. In the experiments, we deploy the optimized HE on a VCU118 test board and achieve a maximum frequency of 891 MHz (which is up to 22.6 times more acceleration than CPU implementations), as well as a frame rate of 1899 frames per second for 1080p images.

Detailed analysis of a deep learning energy forecast model considering different input units and dimensions

The use of smart buildings technologies in residential houses, commonly referred to as smart homes, can benefit the operation and management of the buildings as well and its users. The application of energy related models in smart buildings can improve building sustainability and efficiency, but, usually, requires the existence of energy forecasting models capable of providing accurate and precise information. In this paper, it was studied how different input configurations of an energy forecasting model impact its performance by considering multiple error metrics. The conducted study allowed a detailed analysis of each input configuration to see which one is better suitable to be integrated with the smart grid management platform developed by the authors, which is also presented in this paper. The results demonstrate that the inputs’ units and dimensions have impact on the forecast error, indicating that accumulative Wh values are potentially better for the energy forecast model considered.

Masked Face Recognition Using FaceNet Algorithm

In the past three years, the entire world has been exposed to a new virus, COVID-19. It has become very difficult to deal with the fingerprint system, as the exchange of things and contact with devices by more than one person is a good way to transmit the virus. Accordingly, many institutions and organizations have resorted to using face prints as an alternative to handprints to identify people and record the attendance of employees in general. Since the virus is a virus that infects the respiratory system, people are forced to wear face masks or the so-called masks to avoid transmission of the virus during coughing by the infected person. Since the wearing of masks greatly affects the facial features, it was necessary to come up with a technology that allows the identification of masked faces, and this is the subject of our study. Since the face loses many of its features while wearing masks, an algorithm was proposed to recognize the face and train the convolutional neural networks (CNN) through some of the main facial features, including 3D imaging, as it was concluded that the triple loss does not apply to our data sets, as 3D selection has less loss compared to 2D image, Due to its ability to select all feature samples from feature spaces with larger distances between layers and reduced distances between regions, a large cosine loss was utilized as the training loss function. In order to reach a model that deals more with areas not covered by the mask, the input unit was designed, as its function is to combine the Inception- Resent unit and the Convolutional Mass unit, and so, any portion of the face that isn't covered gains weight, thus increasing the importance of those areas in the recognition of masked faces, and experiments that were conducted on several sets of masked faces data showed that the algorithm works to significantly increase the accuracy of masking face recognition, and it can accurately recognize the face using masks.

Smart home appliances control system using mobile phone

Smart home automation refers to the interconnection of home and all the appliances in the building. There are some challenges encounters through the manual operation of Home appliances. These challenges such as stress, electric shock, difficulty for old aged people and handicap. These problems stated above as called for the development of Home automation system. This home automation system permits user to operate their home appliances such as light bulb, fan, television etc. at their own convenience on their mobile phone. This system was utilizes Bluetooth technology through the connection of Bluetooth enabled. Android phone with HC-05 Bluetooth module. Arduino Nano Microcontroller was serves as the Controlling unit for the whole system. The Mobile phone was also serves as the Input Unit to the system through the Bluetooth Mobile App. It has some specific features, which enable it to communicate with the system. The Microcontroller will send impulse to the specific appliance in which command will sent to through the Relay to switch it ON or OFF. The system was tested and the result was present in this work. The result shown that the Bluetooth has some ranges in which it could cover. The Bluetooth could cover up to the distance of about 20metres when there is no obstruction. However, when there is obstruction it can cover up to 10metres. This works thus revealed the usefulness of home automation to the development of Technology in the country.

MITIGATING SPATIAL DISPROPORTIONS IN AGRICULTURE THROUGH REVEALING COMPETITIVE ADVANTAGES

Spatial development of agriculture has always been among the core agendas of food security. Amid the increased volatility of food markets globally, low diversification of agricultural production depresses competitiveness and flexibility of farmers. To narrow spatial development gaps, the study presents the five-stage approach to revealing territory-specific competitive advantages in producing nine categories of agricultural products. The data is collected across all administrative territories of Russia categorized according to the cadastral value of farmland. The revealed mismatches between the parameters of food self-sufficiency, productivity of crops, and profitability of farmers show that agricultural policy should aim at stimulating production of competitive products with due account to the spatial features of agriculture to ensure the highest return per unit of inputs along with adequate accessibility of staples for consumers. Determining proportions in which agricultural facilities should be allocated across territories would allow governments to tailor the resource provision programs, including subsidies to territories, agricultural sectors, and individual producers.

Komputerisasi dalam Pengelolaan Sekolah di SMP Swasta Pahlawan Nasional

In this digital era, the application of technology is important for the education sector. Without the help of technology, school administrative activities such as student data collection and learning achievement reports must be done manually by educators, thereby reducing the efficiency of learning activities. Therefore it is necessary to have a computer that can assist educators in managing schools and student data collection. A computer is an automatic electronic device that can calculate or process data carefully according to instructions and provide processing results, usually consisting of an input unit, an output unit, a storage unit, and a control unit. In a school computer is useful for managing and managing school management.

Efficient Industrial Wastewater and Leachate Evaporation Utilizing Heat Localization in Porous Media

In this paper, an efficient industrial wastewater and leachate evaporation method is proposed and tested experimentally. The goal of this study is to investigate whether the addition of a carbon foam (CF) porous layer can lead to energy savings by evaporating more water mass per unit of energy input. The standard boiling evaporator layout was redesigned by placing the heating element in the upper region of the tank and CF underneath the heat source. The CF purposed to localize the energy in an area by the water's surface and minimize conduction heat losses to the rest of the water. A 90.2% reduction in energy lost to regions outside of the CF isolated control volume, specifically during the evaporator preheating process was observed with the addition of 100 Pores Per Inch (PPI) CF. In addition, a reduction in evaporative energy intensity was observed yielding results of 3.344 , 3.441 , and 3.644 for the 100 PPI, 45 PPI, and 10 PPI tests, respectively. This new evaporation design provides a more energy- and cost-efficient method for reducing the volume of various industrial wastewater and leachate concentrations onsite.

Цифровизация китайских иероглифов: классификация способов ввода и предпочтения пользователей

The aim of the study is to classify the ways of inputting Chinese characters on digital devices. The paper considers the digitalization aspect of Chinese character writing and describes the structural, phonetic and special ways of inputting Chinese characters on phones and computers. The scientific novelty of the study lies in identifying the one-step input units for each input method and establishing the percentage of users choosing different ways of inputting characters on digital devices. As a result, the study found that there are several ways of inputting Chinese characters on digital devices: 1) phonetic methods (input using the pinyin transcription, input using the full pinyin transcription, the double pinyin method, zhuin or bopomofo); 2) structural methods (keyed input, сangjie input, five traits ubi input, four corners input); 3) special methods (handwritten input, voice input), each with its own advantages and disadvantages. The research provides an example of inputting the two-syllable word 汉语 “the Chinese language” on digital devices using different input methods.

Back-propagating errors through artificial neural networks for variable selection

Variable selection is one of the most important contents of machine learning research and a large number of variable selection methods have been proposed at present. From deep learning perspective, we propose a new variable selection method based on multi-layer artificial neural networks in this paper. The method propagates errors gained form a trained network model through the network from output layer to input layer and the values gained by input layer give information about the relative contribution. The variables with big contribution value are selected, which we call neural inverse propagation (NIP). Specifically, after training a neural network, the gained errors between the actual network outputs and the desired outputs go through the network from top to down and arrive at the input layer. The absolute values gained by input units provide information about the relative importance (contribution) of input units, which is that the larger the value is, the more important the corresponding input variable is. We remove k variables corresponding to the first k minimum absolute value gained by input units to the predictive variables, to perform variable selection. The efficiency of the proposed method is illustrated through its application to classification and regression tasks on a number of synthetic datasets and a real-world image classification dataset.

Crop Water Productivity from Cloud-Based Landsat Helps Assess California’s Water Savings

Demand for food and water are increasing while the extent of arable land and accessible fresh water are decreasing. This poses global challenges as economies continue to develop and the population grows. With agriculture as the leading consumer of water, better understanding how water is used to produce food may help support the increase of Crop Water Productivity (CWP; kg/m3), the ratio of crop output per unit of water input (or crop per drop). Previous large-scale CWP studies have been useful for broad water use modeling at coarser resolutions. However, obtaining more precise CWP, especially for specific crop types in a particular area and growing season as outlined here are important for informing farm-scale water management decision making. Therefore, this study focused on California’s Central Valley utilizing high-spatial resolution satellite imagery of 30 m (0.09 hectares per pixel) to generate more precise CWP for commonly grown and water-intensive irrigated crops. First, two products were modeled and mapped. 1. Landsat based Actual Evapotranspiration (ETa; mm/d) to determine Crop Water Use (CWU; m3/m2), and 2. Crop Productivity (CP; kg/m2) to estimate crop yield per growing season. Then, CWP was calculated by dividing CP by CWU and mapped. The amount of water that can be saved by increasing CWP of each crop was further calculated. For example, in the 434 million m2 study area, a 10% increase in CWP across the 9 crops analyzed had a potential water savings of 31.5 million m3 of water. An increase in CWP is widely considered the best approach for saving maximum quantities of water. This paper proposed, developed, and implemented a workflow of combined methods utilizing cloud computing based remote sensing data. The environmental implications of this work in assessing water savings for food and water security in the 21st century are expected to be significant.

Hybrid, Asymmetric and Reconfigurable Input Unit Designs for Energy-Efficient On-Chip Networks

The complexity and scale of Networks-on-Chip (NoCs) are growing as more processing elements and memory devices are implemented on chips. However, under strict power budgets, it is also critical to lower the power consumption of NoCs for the sake of energy efficiency. In this paper, we therefore present three novel input unit designs for on-chip routers attempting to shrink their power consumption while still conserving the network performance. The key idea behind our designs is to organize buffers in the input units with characteristics of the network traffic in mind; as in our observations, only a small portion of the network traffic are long packets (composed of multiple flits), which means, it is fair to implement hybrid, asymmetric and reconfigurable buffers so that they are mainly targeting at short packets (only having a single flit), hence the smaller power consumption and area overhead. Evaluations show that our hybrid, asymmetric and reconfigurable input unit designs can achieve an average reduction of energy consumption per flit by 45%, 52.3% and 56.2% under 93.6% (for hybrid designs) and 66.3% (for asymmetric and reconfigurable designs) of the original router area, respectively. Meanwhile, we only observe minor degradation in network latency (ranging from 18.4% to 1.5%, on average) with our proposals.