ICOR Analysis of Gorontalo Province

Investment plays an important role as one of the regional economic drivers. Likewise, a various development success is determined by the quality of planning and accurate data. In general, the development planning is highly specified by an ability to provide financing sources scenario where one of which is investment as it is impactful in increasing economic growth rate and community welfare level intended.This research aims at determining ICOR value and investment needs estimate in Gorontalo Province and at determining sectors with a higher or lower capital productivity in Gorontalo Province. The research employs ICOR analysis to measure certain amount as a comparison between growth of capital (investment) with production. Through these indicators, the economic development planners can determine investments needed to increase the economy in compliance with the predetermined target. The research findings reveal that: 1) ICOR value from 2018 to 2020 is 0.29 on average and it is impacted by annual capital change (∆K) and output change (∆Y), 2) food industry sector in Gorontalo Province indicates a sufficiently low value of ICOR, and it is good due to the economic runs efficiently. Meanwhile, the chemistry and pharmacy industry sector shows massive capital productivity where a higher value of ICOR indicates capital-intensive technology use. Also, an ICOR-based sectoral investment projection signifies that the researchers set top three of future leading sectors in 2025 for investment projection in Gorontalo. They are food and plantation crops; trade and repair; and construction, 3) a stay-at-home policy urged by the government during the covid-19 pandemic has led to a significant change in community’s consumption style for basic needs such as water, electricity, gas, food, and medication.

SBP Is Superior to MAP to Reflect Tissue Perfusion and Hemodynamic Abnormality Perioperatively

Many articles have reported that intraoperative low mean artery pressure (MAP) or low systolic blood pressure (SBP) impacts on organs’ function and patients’ outcomes perioperatively. On the contrary, what type of blood pressure should be obtained still needs to be clarified. In our paper, we compared the influencing factors of MAP and SBP, and mathematical formula, arterial pulse contour calculation, and cardiovascular physiological knowledge were adopted to discuss how blood pressure can effectively reflect tissue perfusion and hemodynamic abnormality perioperatively. We concluded that MAP can reflect cardiac output change sensitively and SBP can reflect stroke volume change sensitively. Moreover, SBP can reflect the early hemodynamic changes, organs’ perfusion, and heart systolic function. Compared with MAP, perioperative monitoring of SBP and timely detection and treatment of abnormal SBP are very important for the early detection of hemodynamic abnormalities.

Sectoral Effects of Social Distancing

The outbreak of the COVID-19 virus has led many states to take the drastic measures of social distancing. Using US executive order, occupation, and survey data, we measure the fall in labor supply due to these measures. Starting from a model of production networks, we analyze the sectoral effects of these labor shocks for the United States. We find that nonlinearities in the production network account for around half of the drop in GDP associated to the implementation of social distancing measures. The model also generates realistic dispersion in sectoral output change.

Review of Static Analyzer Service Models

The static program analysis is gradually adopting advanced use cases, and integration with programming tools becomes more necessary than ever. However, each integration requires a different kind of functionality implemented within an analyzer. For example, continuous integration tools typically analyze projects from scratch, while doing the same for code querying is not efficient performance-wise. The code behind such use cases makes «service models», and it tends to differ significantly between them. In this paper, we analyze the models which might be used by the static analyzer to provide its services based on aspects of security, performance, long-term storage. All models are assigned to one of the groups: logical presence (where the actual computation is performed), resource acquisition, input/output, change accounting and historic data tracking. The usage recommendations, advantages and disadvantages are listed for each reviewed model. Input/output models are tested for actual network throughput. We also describe the model which might aggregate all these use cases. The model is partially evaluated within the work-in-progress static analyzer Equid, and the observations are presented.

Closed Loop Tuning of Cascade Controllers Based on Setpoint Experiment

This study presents online PI/PID controller tuning rules for cascade control configuration. The necessary process data are determined with the help of a simple setpoint experiment in a single closed-loop mode with the proportional controller only. The obtained process data is recorded in terms of the overshoot, controller gain, peak time, and relative output change. The data is then utilized to establish a correlation with PI/PID settings through simulations. Further, the proposed PI/PID controller tuning rule for a single loop has been extended to the cascade control configuration. The inner loop controller is tuned first, and then the primary loop is tuned by considering a well-tuned inner loop as a part of the primary plant. Finally, simulation examples demonstrate that the proposed method delivers significant disturbance rejection and better setpoint response when compared to the recently reported methods from the literature. The proposed method is also able to deliver stable closed-loop performances when subjected to large parametric uncertainties and measurement noise.

Does Unemployment Responsiveness to Output Change Depend on Age, Gender, Education, and the Phase of the Business Cycle?

The impact of economic growth on unemployment is commonly agreed and extensively studied. However, how age and gender shape this relationship is not as well explored, while there is an absence of research on whether education plays a role. We apply Okun’s law, aiming to estimate age-, gender- and educational attainment level-specific unemployment rate sensitivity to cyclical output fluctuations. Since the empirical literature provides evidence in favour of the non-linear impact of output change on the unemployment rate, supporting higher effects of recessions than that of expansions, we aim to enrich this analysis by estimating how the impact of positive/negative output change on the specific unemployment rate varies with the level of the total unemployment. The analysis is based on 28 European Union (EU) countries and covers the period of 1995–2019. The equations are estimated by least-squares dummy variables (LSDV), using Prais–Winsten standard errors. For the robustness check, we alternatively used Newey–West standard errors to address serial-correlations and heteroscedasticity, and the Arellano–Bond estimator for some specifications that assume dynamics in the panel. The results support previous findings of male- and youth-specific Okun’s coefficients and reveal that they significantly stand out just over the periods of negative output change. Additionally, we find that educational attainment level is an important factor explaining the heterogeneity of unemployment reaction to output change.

Active and Incremental Learning with Weak Supervision

Large amounts of labeled training data are one of the main contributors to the great success that deep models have achieved in the past. Label acquisition for tasks other than benchmarks can pose a challenge due to requirements of both funding and expertise. By selecting unlabeled examples that are promising in terms of model improvement and only asking for respective labels, active learning can increase the efficiency of the labeling process in terms of time and cost. In this work, we describe combinations of an incremental learning scheme and methods of active learning. These allow for continuous exploration of newly observed unlabeled data. We describe selection criteria based on model uncertainty as well as expected model output change (EMOC). An object detection task is evaluated in a continuous exploration context on the PASCAL VOC dataset. We also validate a weakly supervised system based on active and incremental learning in a real-world biodiversity application where images from camera traps are analyzed. Labeling only 32 images by accepting or rejecting proposals generated by our method yields an increase in accuracy from 25.4 to 42.6%.

An Active Learning Framework for Alpha Matting

Good trimap is essential for high-quality alpha matte. However, making high-quality trimap is hardwork, especially for complex images. In this paper, an active learning framework is proposed to make high quality trimap. There are two active learning methods which are employed: minimization of uncertainty sampling (MUS) and maximization of expected model output change (EMOC). MUS model finds the informative area in image which can decrease the uncertain sampling of alpha matte. EMOC model finds the important areas in image which can give the maximum expected output change of alpha matte. Two methods are combined to define the active map. Active map shows important areas which are informative in image. It can help users to make high quality trimap. The analysis and evaluation of benchmark datasets show that proposed method is effective.

The Calibration Test Of Hybrid Landfill Gas -Photovoltaic-Genset Model In Bontang, East Borneo

The study is aimed to determine the measurement of sensor tool made in Model Real Time Hybrid LFG-PV-Genset by finding the correction factor produced at TPA Bontang City, East Borneo. The research was conducted by using ampere clammeter, variable voltage regulator, thermometer, odalog7000 series. The calculation results show that the calibration test of Model Real Time Hybrid LFG-PV-Genset from MQ4 sensor data that has been designed capable of having errors of 1,91% and the LM35 temperature sensor is capable of monitoring tempertures of 30°C to 120°C and has an error value of 0,519°C. ZMPT 101B voltage sensor has a linear output change to input changes and has an average error of 1.499V. While the current sensor SCT 013 has an aerage error value of 0,0022A CO2.

KONTRIBUSI PERMINTAAN AKHIR DAN TEKNOLOGI TERHADAP PERUBAHAN OUTPUT SEKTOR PERIKANAN Pendekatan Analisis Input Output

Kajian bertujuan untuk mengetahui kontribusi komponen permintaan akhir dan teknologi terhadap perubahan output sektor perikanan Indonesia selama periode 1990-2000 dengan menggunakan pendekatan Analisis Input-Output (I-O) telah dilakukan pada tahun 2007. Data yang digunakan berupa data sekunder berupa Tabel I-O Tahun 1990, 1995 dan 2000 yang bersumber dari BPS. Hasil kajian menunjukkan bahwa permintaan akhir dan teknologi memberikan kontribusi terhadap perubahan output sektor perikanan. Perubahan teknologi pada perikanan laut sebesar -5,98% berkontribusi terhadap perubahan output sebesar 124,91%, lebih tinggi daripada dampak nasional sebesar 98,22%. Perubahan teknologi sebesar -6,45% pada perikanan darat memberikan kontribusi terhadap perubahan output sebesar 106,3%. Pada sektor industri pengeringan dan penggaraman ikan perubahan teknologi sebesar 40,86% menciptakan perubahan output sebesar 15,37%. Pada industri pengolahan dan pengawetan ikan, perubahan teknologi sebesar 17,32%. Oleh karena itu perlu adanya peningkatan kajian dan pengembangan teknologi menurut dimensi agribisnis komoditas perikanan dalam mendorong peningkatan produsksi dan pengembangan produk sektor perikanan. Tittle: Contribution of Final Demand and Technology to the Fisheries Sector Output : An Input - Output approachResearch intended to ilutrate the contribution of final demand component and technology to the changes in output of the fisheries sector in Indonesia during 1990 - 2000 with Input - Output (I-O) Analytical Approach was carried out in 2007. The I-O tables of 1990,1995 and 2000 from CBS of Indonesia was used in this study. The results showed that there were positive contribution of final demand and technology to fisheries output charge. Technology change of -5,98% in marine fisheries give an output change of 124,91%, higher than national impact of 98,22%. It is similar to inland fisheries which technology change of -6,45% contribute to output change of 106,36%. On the other hand, technology change of 40,86% in drying and salting industres contribute to output change of 15,37% much lower than procesing and preseving industries which output change of 64,26% produced by technology change of 17,32%. In conclusion, assesment and development of technology by commodity dimension in agribusiness must be increased to push the production of fisheries sector.