Blockchain Application Design and Algorithms for Traceability in Pharmaceutical Supply Chain

Blockchain technology has garnered attention from stakeholders in many domains, including healthcare, governance and supply chain management. In the context of healthcare, traceability of pharmaceutical drugs in a transparent yet secure manner can be made faster and efficient with blockchain. This paper presents a blockchain based solution for traceability known as PharmaChain. The traceability is achieved with application design and algorithms which are proposed in the work. The proposed application can be developed using hyperledger fabric deployed on dockers. The chain codes are written in javascript. The pharmaceutical blockchain proposed in this work consists of manufacturer, wholesaler, retailer and consumer. The right for registering a drug into the blockchain is granted to the manufacturers only and the ownership transfer of the drug is stored. This paper highlights the traceability of ownership transfer of the drug and validates its origin.

Pengenalan Karakter Tulisan Tangan Menggunakan Ekstraksi Fitur Bentuk Berbasis Chain Code

<p class="Abstrak">Pengenalan karakter tulisan tangan pada citra merupakan suatu permasalahan yang sulit untuk dipecahkan, dikarenakan terdapat perbedaan gaya penulisan pada setiap orang. Tahapan proses dalam pengenalan tulisan tangan diantaranya adalah <em>preprocessing</em>, ekstraksi fitur, dan klasifikasi. <em>Preprocessing</em> dilakukan untuk merubah citra tulisan tangan menjadi citra biner yang hanya mempunyai ketebalan 1 pixel melalui proses binerisasi dan <em>thining</em>. Kemudian pada tahap ekstraksi fitur, dipilih fitur bentuk karena fitur bentuk memiliki peran yang lebih penting dibanding 2 fitur visual lainnya (warna dan tekstur) pada pengenalan karakter tulisan tangan. Metode ekstraksi fitur bentuk yang dipilih dalam penelitian ini adalah metode berbasis <em>chain code</em> karena metode tersebut sering digunakan dalam beberapa penelitian pengenalan tulisan tangan. Pada penelitian ini, dilakukan studi kinerja dari ekstraksi fitur berbasis <em>chain code</em> pada pengenalan karakter tulisan tangan untuk mengetahui metode terbaiknya. Tiga metode ekstraksi fitur berbasis <em>chain code</em> yang digunakan dalam penelitian ini adalah <em>freeman chain code</em>, <em>differential chain code</em> dan <em>vertex chain code</em>. Setiap citra karakter diekstrak menggunakan 3 metode tersebut dengan tiga cara yaitu ekstraksi secara global, lokal 3x3, 5x5, dan 7x7. Setelah esktraksi fitur, dilakukan proses klasifikasi menggunakan support vector machine (SVM). Hasil eksperimen menunjukkan akurasi terbaik adalah pada model citra 7x7 dengan nilai akurasi <em>freeman chain code</em> sebesar 99.75%, <em>differential chain code</em> sebesar 99.75%, dan <em>vertex chain code</em> sebesar 98.6%.</p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p class="Abstract"><em>The recognition of handwriting characters images is a difficult problems to be solved, because everyone has a different writing style. The step of handwriting recognition process are preprocessing, feature extraction, and classification. Preprocessing is done to convert handwritten images into binary images that only have 1 pixel thickness by using binarization and thinning. Then, in the feature extraction we select shape feature because it is more important than two other visual features (color and texture) in handwriting character recognition. Shape feature extraction method chosen in this research is chain code method because this method is often used in several studies for handwriting recognition. In this study, a performance study of feature extraction based on chain codes was carried out on handwriting character recognition to know the best chain code method. The three shape feature extraction based on chain code used in this study are freeman, differential and vertex chain codes. Each character image is extracted using these 3 methods in three ways: extraction globally, local 3x3, 5x5, and 7x7. After the extraction feature, the classification process is carried out using the support vector machine (SVM). The experimental results show that the best accuracy is in the 7x7 image model with the value of freeman chain code accuracy of 99.75%, the differential chain code of 99.75%, and the vertex chain code of 98.6%.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p>

The Global Diffusion of Supply Chain Codes of Conduct: Market, Nonmarket, and Time-Dependent Effects

Why and how have supply chain codes of conduct diffused among lead firms around the globe? Prior research has drawn on both institutional and stakeholder theories to explain the adoption of codes, but no study has modeled adoption as a temporally dynamic process of diffusion. We propose that the drivers of adoption shift over time, from exclusively nonmarket to eventually market-based mechanisms as well. In an analysis of an original data set of more than 1,800 firms between the years 2006 and 2015, we find that strong nonmarket labor institutions in a firm’s home country are critical to initiating and sustaining the diffusion process. Market mechanisms, such as investor scrutiny and brand risk, emerge as important later. Contrary to prior research, we did not find a significant effect from nongovernmental organization (NGO) pressure. We conclude that markets for corporate social responsibility can and do arise, but only after they are effectuated by nonmarket institutions.

CLASSIFICATION OF STRAWBERRY FRUIT SHAPE BY MACHINE LEARNING

Shape is one of the most important traits of agricultural products due to its relationships with the quality, quantity, and value of the products. For strawberries, the nine types of fruit shape were defined and classified by humans based on the sampler patterns of the nine types. In this study, we tested the classification of strawberry shapes by machine learning in order to increase the accuracy of the classification, and we introduce the concept of computerization into this field. Four types of descriptors were extracted from the digital images of strawberries: (1) the Measured Values (MVs) including the length of the contour line, the area, the fruit length and width, and the fruit width/length ratio; (2) the Ellipse Similarity Index (ESI); (3) Elliptic Fourier Descriptors (EFDs), and (4) Chain Code Subtraction (CCS). We used these descriptors for the classification test along with the random forest approach, and eight of the nine shape types were classified with combinations of MVs&amp;thinsp;+&amp;thinsp;CCS&amp;thinsp;+&amp;thinsp;EFDs. CCS is a descriptor that adds human knowledge to the chain codes, and it showed higher robustness in classification than the other descriptors. Our results suggest machine learning's high ability to classify fruit shapes accurately. We will attempt to increase the classification accuracy and apply the machine learning methods to other plant species.