The Amber Project: A Survey of Methods and Inks for the Reproduction of the Color of Translucent Objects

Unlike regular pigments based on selective light absorption, the so-called “effect pigments″ are based on the phenomena of structural color, or selective reflectance. Structural color has appealing aesthetic qualities, such as angle-dependent hue, and is able to produce lightfast colors. When used as a pigment, however, the gamut of the print is more limited, the color is difficult to measure, and therefore color management and preprint process become challenging. The aim of this paper is to compare the behavior of effect pigments in the processes of lithographic and screen printing with standard pigments used in so-called process inks, and to analyze their optical properties when used on their own or in combination with absorption pigments. An image of amber beads was printed as screen prints and lithographs. Three sets of inks were used: Set one: Standard process inks in the colors cyan, magenta, yellow and black (CMYK); set two: RGB inks formulated with Merck Spectraval™ pearlescent pigments which allow additive red, green, blue printing on a black substrate; and set three: golden inks formulated with pigments from the Merck Iriodin™ and Pyrisma™ effect pigment range. The image was printed on white and black paper. The optical appearance was assessed visually, and spectra and color coordinates were measured.

A 10-MHz FREE-FREE BEAM MICROMECHANICAL RESONATOR FABRICATION PROCESS USING SURFACE-MICROMACHINED TECHNOLOGY

The fabrication process for the designed MEMS resonator using surface-micromachined technology is presented in this paper. A 10-MHz Free-Free beam MEMS resonator is designed to vibrate in the second-mode shape, which is significant improvement compare to the fundamental mode. The design showed a Q value as high as 75,000, which is significant improvement compared to 8,400 VHF F-F beam MEMS resonator by K. Wang; and very low motional resistance (18kΩ). The surface-micromachined technology is used as the standard process for the design. The process is briefly described from the layout design to the experimental fabricated device.

Processing of Waste from Enrichment with the Production of Cement Clinker and the Extraction of Zinc

This paper presents studies on the processing of enrichment tailings as a component of a raw mixture in order to obtain cement clinker, with simultaneous distillation of zinc. Thermodynamic studies were carried out in the temperature range of 600–1600 °C using the software application “HSC Chemistry 6” developed by the metallurgical company Outokumpu (Finland). As a result of the conducted studies, we found that zinc contributes to the intensification of mineral formation of cement clinker. In particular, it was found that the formation of belite is possible in the temperature range from 990.7 to 1500 °C with Gibbs energy values of −0.01 and −323.8 kJ (which is better than the standard process by −11.4 kJ), respectively; the formation of alite is possible in the temperature range from 982.9 to 1500 °C with Gibbs energy values of −0.05 and −402.1 kJ (better than the standard process by −11.4 kJ), respectively; the formation of tricalcium aluminate is thermodynamically possible in the temperature range from 600 °C at ΔGTo = −893.8 kJ to 1500 °C at ΔGTo = −1899.3 kJ (better than the standard process by −1570.1 kJ), respectively; and the formation of four calcium aluminoferrite is possible in the temperature range from 600 °C at ΔGTo = −898.9 kJ to 1500 °C at ΔGTo = −1959.3 kJ (better than the standard process by −1570.2 kJ), respectively, with simultaneous distillation of zinc into a gaseous state for its further capture.

OPTIMASI PARTICLE SWARM OPTIMIZATION PADA NAÏVE BAYES UNTUK PREDIKSI KONDISI KELAHIRAN BAYI

Tehnik data mining dapat digunakan dalam berbagai bidang salah satunya dalam aspek data mining, buat memperkirakan sebuah penyakit dari data rekam medis pasien. Teknik riset yang dipakai pada riset ini mengikuti berbagai tahapan model “Cross-Industry Standard Process Data Mining” (CRISP-DM). Melalui metode klasifikasi dalam data mining, atribut data seperti usia, tekanan darah, berat badan, posisi janin, dan tinggi fundus uteri bisa dipakai buat memperkirakan kemungkinan penyakit pasien. Maka dari itu, peneliti menggunakan metode klasifikasi Naive Bayesian dan optimasi “Particle Swarm Optimization” (PSO) untuk prediksi kelahiran bayi guna mengecek prediksi status kelahiran bayi. Dari hasil prediksi itu bisa dipakai buat menetapkan rata-rata hasil kelahiran bayi setiap bulannya. Data yang kami pakai adalah jumlah ibu hamil 165. Selama pengujian digunakan perhitungan akurasi, akurasi, recall, dan AUC chart, dan model prediksi dievaluasi menggunakan 10 “fold cross-validation”. Dengan nilai akurasi 91,82% dan precission 100% serta recall 81,50% dan nilai AUC 0.90 termasuk kategori excellent classification pada model yang diujikan

A Reference Standard Process Model for Agriculture to Facilitate Efficient Implementation and Adoption of Precision Agriculture

Agriculture is a sector that today demands even greater efficiency; thus, it relies extensively on the use of precision agriculture technologies: IoT systems, mobile applications, and other digitalization technologies. Experience from a large-scale EU-funded project with a consortium made up of several software companies shows that software companies have a different and unequal knowledge/understanding of agricultural processes and the use of precision agriculture in agricultural processes. This finding coupled with what is known about the standard process model for IT governance (COBIT) triggered the idea of a reference standard process model for agriculture (RSPMA), which we present in this paper. We applied the Delphi technique to assess the RSPMA and evaluate its potential implementation in the area of agriculture. A panel of 20 members from Slovenia, Romania, Croatia, and Serbia was established for the study. The majority of RSPMA elements were identified as appropriate for the use in agriculture by the panel. The study results show that RSPMA is suitable for use in this field.

Rapid and direct method to extract SARS-CoV-2 RNA from municipal wastewater using the Chemagic™ 360 12-rod head platform v1

Wastewater based epidemiology (WBE) has emerged as a strategy to identify, locate, predict, and manage outbreaks of COVID-19, as an early warning signal to public health authorities of an expected surge in cases that may overwhelm local and global health care resources.. The WBE process is based on assaying municipal wastewater for molecular markers of the SARS-CoV-2 virus. The standard process for sampling municipal wastewater is time-consuming and requires the handling of large quantities of wastewater, which negatively affects throughput and timely reporting, and can increase safety risks. We report on a rapid and direct mostly automated method to assay multiple sub-samples of a bulk wastewater sample using a 75 minute run on the Chemagic™ 360 12 rod head platform. Including a preceding setup and incubation step, twelve 10 ml samples can be processed to purified RNA in 2.5 hrs. Up to 10 ml of wastewater from 12 different collection sites can be processed in 2.5 hrs.

PENGGUNAAN DATA MINING DALAM EKSTENSIFIKASI PENELITIAN ULANG

ABSTRACT Due to increasing volume of international trade, effect on increasing customs document, Customs play a role so that trade flows run without obstacles, this causes inspection of imported goods to be less than optimal, but on the other hand Customs are required to collect state revenues optimally. This study tries to solve this problem from the post-clearance control side with re-examination by construct an analytical data model to predict the suitable classification. This study uses data on the Notification of Imported Goods during 2020 at the Regional Office of DJBC XXX which using a sample of goods that has similarities but has the potential to be misclassified. This study uses the Cross-industry Standard Process for Data Mining (CRISP-DM) model and the Rapid Miner Studio 9.9.2 application. Based on the model formed, the prediction results obtained according to the appropriate classification according to data mining. It also found the factors that most impact to goods classification, the most impact is the Importer status, whereas the least impact is the goods lane. ABSTRAK: Seiring dengan volume perdagangan internasional yang semakin tinggi, jumlah dokumen kepabeanan yang harus diperiksa juga mengalami peningkatan. Hal ini menghambat peran Bea dan Cukai sebagai fasilitator perdagangan yang menyebabkan pemeriksaan barang impor kurang optimal. Di sisi lain, Bea dan Cukai dituntut untuk menghimpun penerimaan negara secara optimum. Penelitian ini mencoba untuk menyelesaikan permasalahan tersebut pada tahap post clearance dengan penelitian ulang, yaitu dengan membangun model data analitik untuk memprediksi klasifikasi barang yang diberitahukan oleh importir sudah sesuai atau belum. Penelitian ini menggunakan data Pemberitahuan Impor Barang selama tahun 2020 pada Kanwil DJBC XXX yang sampel data barangnya memiliki kemiripan tetapi berpotensi salah klasifikasi. Penelitian ini menggunakan model Cross-industry Standard Process for Data Mining (CRISP-DM) dan aplikasi Rapid Miner Studio 9.9.2. Berdasarkan permodelan yang dibentuk, didapatkan hasil prediksi klasifikasi yang sesuai menurut data mining. Didapat pula faktor yang paling memengaruhi kebenaran pemberitahuan klasifikasi barang impor, yaitu status importir, sedangkan yang paling tidak berpengaruh adalah jalur pengeluaran barang impor. Kata Kunci: Penelitian Ulang, Data Analitik, Penerimaan Negara, Klasifikasi Barang

Artificial intelligence for last-mile logistics - Procedures and architecture

Artificial Intelligence (AI) is becoming increasingly important in many industries due to its diverse areas of application and potential. In logistics in particular, increasing customer demands and the growth in shipment volumes are leading to difficulties in forecasting delivery times, especially for the last mile. This paper explores the potential of using AI to improve delivery forecasting. For this purpose, a structured theoretical solution approach and a method for improving delivery forecasting using AI are presented. In doing so, the important phases of the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework, a standard process for data mining, are adopted and discussed in detail to illustrate the complexity and importance of each task such as data preparation or evaluation. Subsequently, by embedding the described solution into an overall system architecture for information systems, ideas for the integration of the solution into the complexity of real information systems for logistics are given.

Experimental Investigation of Thermal Conductivity from Insulation Based on Rice Hulks: Guarded and Calibrated Hot Box Method

In developing countries, large quantities of agricultural residues associated with harvests are generated, given that agriculture is one of the most important economic activities. The valorization of these residues for the construction sector could contribute to the improvement of energy efficiency in buildings. Through passive techniques, the construction of insulating thermal-acoustics panels, blocks, and aggregate for reinforced concrete can improve the energy efficiency. In this study, an experiment was performed to measure thermal conductivity of the Agricultural-Thermal Insulation Panel (ATIP) based on rice hulks. These Agricultural-Thermal Insulation Panels were elaborated follow a standard process to compaction of the rice hulks to be employed as insulation material with a panel dimension of 200x200x34.5(mm3). A “Hot Box” configuration was used to obtain the thermal conductivity of the panels, using different temperature gradients between hot and cold chambers.

Modeling Scientometric Indicators Using A SDMX Ontology

Scientometrics is the field of study and evaluation of scientific measures such as impact of research papers and academic journals. It is an important field because nowadays different rankings use key indicators for university rankings and universities themselves use them as Key Performance Indicators (KPI). The purpose of this work is to propose a semantic modeling of scientometric indicators using the ontology Satistical Data and Metadata Exchange (SDMX). We develop a case study at Tecnologico de Monterrey following the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology. We evaluate the benefits of storing and querying in a Graph Database (Neo4j) the linked data produced by our approach.