Search results

The Agriculture Improvement Act of 2018 directed the US Secretary of Agriculture to report on the profitability and viability of beginning farmers and ranchers. Many beginning operations use local food markets as they provide more control, or a premium over commodity prices, and beginning operations cannot yet take advantage of economies of scale and subsequently have higher costs of production. Little research assesses the relationship between beginning farmer profitability and sales through local food markets. In this paper, the profitability implications of sales through local food markets for beginning farmers and ranchers are explored.

The authors utilize 2013–2016 USDA agricultural resource management survey data to assess the financial performance of US beginning farmers and ranchers who generate sales through local food markets.

The results point to four important takeaways to support beginning operations. (1) Local food channels can be viable marketing opportunities for beginning operations. (2) There are differences when using short- and long-term financial performance indicators, which may indicate that there is benefit to promoting lean management strategies to support beginning operations. (3) Beginning operations with intermediated local food sales, on average, perform better than those operations with direct-to-consumer sales. (4) Diversification across local food market channel types does not appear to be an indicator of improved financial performance.

This article is the first to focus on the relationship beginning local food sales and beginning farmer financial performance. It incorporates short-term and long-term measures of financial performance and differentiates sales by four local food market type classifications: direct-to-consumer sales at farmers markets, other direct-to-consumer sales, direct-to-retail sales and direct-to-regional distributor or institution sales.

This work presents a system based on an ensemble of Convolutional Neural Networks (CNNs) and descriptors for bioimage classification that has been validated on different datasets of color images. The proposed system represents a very simple yet effective way of boosting the performance of trained CNNs by composing multiple CNNs into an ensemble and combining scores by sum rule. Several types of ensembles are considered, with different CNN topologies along with different learning parameter sets. The proposed system not only exhibits strong discriminative power but also generalizes well over multiple datasets thanks to the combination of multiple descriptors based on different feature types, both learned and handcrafted. Separate classifiers are trained for each descriptor, and the entire set of classifiers is combined by sum rule. Results show that the proposed system obtains state-of-the-art performance across four different bioimage and medical datasets. The MATLAB code of the descriptors will be available at https://github.com/LorisNanni.

Diagnosing pain in neonates is difficult but critical. Although approximately thirty manual pain instruments have been developed for neonatal pain diagnosis, most are complex, multifactorial, and geared toward research. The goals of this work are twofold: 1) to develop a new video dataset for automatic neonatal pain detection called iCOPEvid (infant Classification Of Pain Expressions videos), and 2) to present a classification system that sets a challenging comparison performance on this dataset. The iCOPEvid dataset contains 234 videos of 49 neonates experiencing a set of noxious stimuli, a period of rest, and an acute pain stimulus. From these videos 20 s segments are extracted and grouped into two classes: pain (49) and nopain (185), with the nopain video segments handpicked to produce a highly challenging dataset. An ensemble of twelve global and local descriptors with a Bag-of-Features approach is utilized to improve the performance of some new descriptors based on Gaussian of Local Descriptors (GOLD). The basic classifier used in the ensembles is the Support Vector Machine, and decisions are combined by sum rule. These results are compared with standard methods, some deep learning approaches, and 185 human assessments. Our best machine learning methods are shown to outperform the human judges.

An accurate detection of overhead ground wire under open surroundings with varying illumination is the premise of reliable line grasping with the off-line arm when the inspection robot cross obstacle automatically. This paper aims to propose an improved approach which is called adaptive homomorphic filter and supervised learning (AHSL) for overhead ground wire detection.

First, to decrease the influence of the varying illumination caused by the open work environment of the inspection robot, the adaptive homomorphic filter is introduced to compensation the changing illumination. Second, to represent ground wire more effectively and to extract more powerful and discriminative information for building a binary classifier, the global and local features fusion method followed by supervised learning method support vector machine is proposed.

Experiment results on two self-built testing data sets A and B which contain relative older ground wires and relative newer ground wire and on the field ground wires show that the use of the adaptive homomorphic filter and global and local feature fusion method can improve the detection accuracy of the ground wire effectively. The result of the proposed method lays a solid foundation for inspection robot grasping the ground wire by visual servo.

This method AHSL has achieved 80.8 per cent detection accuracy on data set A which contains relative older ground wires and 85.3 per cent detection accuracy on data set B which contains relative newer ground wires, and the field experiment shows that the robot can detect the ground wire accurately. The performance achieved by proposed method is the state of the art under open environment with varying illumination.

Classifying moving objects in video sequences has been extensively studied, yet it is still an ongoing problem. In this paper, we propose to solve moving objects classification problem via an extended version of two-dimensional principal component analysis (2DPCA), named as category-wise 2DPCA (CW2DPCA). A key component of the CW2DPCA is to independently construct optimal projection matrices from object-specific training datasets and produce category-wise feature spaces, wherein each feature space uniquely captures the invariant characteristics of the underlying intra-category samples. Consequently, on one hand, CW2DPCA enables early separation among the different object categories and, on the other hand, extracts effective discriminative features for representing both training datasets and test objects samples in the classification model, which is a nearest neighbor classifier. For ease of exposition, we consider human/vehicle classification, although the proposed CW2DPCA-based classification framework can be easily generalized to handle multiple objects classification. The experimental results prove the effectiveness of CW2DPCA features in discriminating between humans and vehicles in two publicly available video datasets.

In this article, the author discusses works from the French Documentation Movement in the 1940s and 1950s with regard to how it formulates bibliographic classification systems as documents. Significant writings by Suzanne Briet, Éric de Grolier and Robert Pagès are analyzed in the light of current document-theoretical concepts and discussions.

Conceptual analysis.

The French Documentation Movement provided a rich intellectual environment in the late 1940s and early 1950s, resulting in original works on documents and the ways these may be represented bibliographically. These works display a variety of approaches from object-oriented description to notational concept-synthesis, and definitions of classification systems as isomorph documents at the center of politically informed critique of modern society.

The article brings together historical and conceptual elements in the analysis which have not previously been combined in Library and Information Science literature. In the analysis, the article discusses significant contributions to classification and document theory that hitherto have eluded attention from the wider international Library and Information Science research community. Through this, the article contributes to the currently ongoing conceptual discussion on documents and documentality.

Purpose – This research study aims to answer the question of how good is the development of the extended regency, and which shows better autonomy of development—before or after expanding. The implications of this study is to answer whether expanding a regent is truly needed to improve the economic development and welfare of the remote regions and their people. This study analyzes the autonomous state of three regencies, North Aceh, Bireuen and Lhokseumawe districts, which have expanded. The analysis takes into consideration the difference in the proportion of their regional revenues, budgeting perfomance, and economic growth as indicators of regional autonomy.

Design/Method/Approach – The data used in this research are secondary data sourced from the budget realization report and the accountability report of North Aceh, Bireuen, and Lhokseumawe districts from 2006 to 2013. The data analysis methods used in this study are the analysis of financial ratios and the comparative mean of one way anova.

Finding – The results showed a significant value or a probability value more than 0.05. Thus, the hypothesis (H1) is rejected, and therefore the hypothesis (H0) is received.

Research Impication – The implication is that there is no difference in the average of regional autonomy of North Aceh Regency, Bireuen, and Lhokseumawe districts as seen from the proportion of local revenue, budgeting perfomance, and regional growth. It means that with regard to financial performance there is no difference in the level of independence in autonomy among the three regions. The proportion of local revenue, financial permormance area, and the development of North Aceh, Bireuen, and Lhokseumawe districts demonstrate no influence on the level of independence in autonomy.

Automatic DNA-binding protein (DNA-BP) classification is now an essential proteomic technology. Unfortunately, many systems reported in the literature are tested on only one or two datasets/tasks. The purpose of this study is to create the most optimal and universal system for DNA-BP classification, one that performs competitively across several DNA-BP classification tasks.

Efficient DNA-BP classifier systems require the discovery of powerful protein representations and feature extraction methods. Experiments were performed that combined and compared descriptors extracted from state-of-the-art matrix/image protein representations. These descriptors were trained on separate support vector machines (SVMs) and evaluated. Convolutional neural networks with different parameter settings were fine-tuned on two matrix representations of proteins. Decisions were fused with the SVMs using the weighted sum rule and evaluated to experimentally derive the most powerful general-purpose DNA-BP classifier system.

The best ensemble proposed here produced comparable, if not superior, classification results on a broad and fair comparison with the literature across four different datasets representing a variety of DNA-BP classification tasks, thereby demonstrating both the power and generalizability of the proposed system.

Most DNA-BP methods proposed in the literature are only validated on one (rarely two) datasets/tasks. In this work, the authors report the performance of our general-purpose DNA-BP system on four datasets representing different DNA-BP classification tasks. The excellent results of the proposed best classifier system demonstrate the power of the proposed approach. These results can now be used for baseline comparisons by other researchers in the field.