Miracle-dz/UniFAR-FTUs huggingface dataset:
Management Information System for Micro Climate logging data using REST API with Prototype Approach in a Coffee Plantation in UB Forest

Measured at 554 hectares, Brawijaya University's forest is located in Karangploso, Malang. The university intends to utilize the forest for education purposes and as part of its community service plan. The yield of the forest is Green Beans, which is one of the finest coffee bean variants from Indonesia. Meanwhile, as El-Nino and La-Nina phenomena greatly affect the global climate nowadays, the changes also bring impacts to the yield of coffee beans. Responding to this situation, UB Forest procures a tool to measure climate indicators, such as air temperature, soil temperature, and soil moisture. However, this tool does not have an integrated monitoring system (a standalone system). Consequently, the forest conservators have to inspect the forest in person to conduct climate monitoring, requiring them to go through mountainous terrains. By taking advantage of the rapidly developing technology, this research aimed to create a website-based microclimate monitoring system that can remotely control the sensors in coffee fields. Utilizing RESTful API, the system sends data through sensors and presents them in a website built with Laravel and ReactJS. The system was subjected to testing with 100% unit test results and a value of 80 for usability testing.
The Frequency Response and Steady-State analysis on Wireless Power Transfer using Square Wave Input

Finding optimal frequency for the voltage source is important in Wireless Power Transfer (WPT) since it can make the system deliver high power to the receiver. Therefore, it is essential to visualize and analyze the frequency peak locations to ensure the system can use the correct frequency. This paper proposes a model to obtain a set of peaks that lies in some frequency range in a WPT system using square wave input. Thus, using this model, alternatives of peak frequency other than the global peak can be observed. Moreover, this study also proposes the steady-state function of the WPT system. The proposed model and steady-state analysis are compared with the experimental results and Simulation Program with Integrated Circuit Emphasis (SPICE). The comparison verified that the proposed model illustrates the steady-state response and shows the set of peak operating frequency.
A Study of English-Indonesian Neural Machine Translation with Attention (Seq2Seq, ConvSeq2Seq, RNN, and MHA): A Comparative Study of NMT on English-Indonesian

In recent years, Neural Machine Translation (NMT) with attention mechanisms has emerged in research and industry. This study discusses the essentials of NMT (Seq2Seq, Convolutional Seq2Seq (ConvSeq2Seq), Recurrent Neural Networks (RNN), and Multi-Head Attention (MHA)) while implemented in formal passages in English-Indonesian and Indonesian-English. The experimental results for ConvSeq2Seq achieve up to 38.99 BLEU sentence scores, 43.23 BLEU corpus scores, and 39.48 GLEU corpus scores over the Seq2Seq English-Indonesian. For Indonesian-English, the results for ConvSeq2Seq achieved as follows: up to 42.59 BLEU sentence scores, 42.91 BLEU corpus scores, 41.05 GLEU corpus scores, and 1356.65 WER scores over RNN and MHA. Thus, while ConvSeq2Seq tends to be the supremacy, this literature also describes the combination of architectures and specific fine-tuning strategies as a discussion.
Agile Software Development: Software Through People

Software development paradigms are shifting. The development group's "team" ability, and the effects of the individual developer, become more important as organizations recognize that the traditional approach of increasing process pressure and overworking team members is not getting the job done. The pioneers of Agile methodologies question the preconceived processes within which development teams work. Rather than adding to the burden of the individual developer, Agile asks "how can we change the process so that the team is more productive, while also improving quality? " The answer is in learning to play the "game. " Written for developers and project managers, Agile Software Development compares software development to a game. Team members play the game knowing that the ultimate goal is to win---always remembering what they have learned along the way, and always keeping in mind that they will never play the same way twice. Players must keep an open mind to different methodologies, and focus on the goal of developing quality software in a short cycle time.
Deepview: Deep-Learning-Based Users Field of View Selection in 360° Videos for Industrial Environments

The industrial demands of immersive videos for virtual reality/augmented reality applications are crescendo, where the video stream provides a choice to the user viewing object of interest with the illusion of “being there. ” However, in industry 4.0, streaming of such huge-sized video over the network consumes a tremendous amount of bandwidth, where the users are only interested in specific regions of the immersive videos. Furthermore, for delivering full excitement videos and minimizing the bandwidth consumption, the automatic selection of the user’s Region of Interest in a 360° video is very challenging because of subjectivity and difference in contentment. To tackle these challenges, we employ two efficient convolutional neural networks for salient object detection and memorability computation in a unified framework to find the most prominent portion of a 360° video. The proposed system is four-fold: 1) preprocessing; 2) intelligent visual interest predictor; 3) final viewport selection; and 4) virtual camera steerer. First, an input 360° video frame is split into three Field of Views (FoVs), each with a viewing angle of 120°. Next, each FoV is passed to the object detection and memorability prediction model for visual interestingness computation. Furthermore, the FoV is supplied as a viewport, containing the most salient and memorable objects. Finally, a virtual camera steerer is designed using enriched salient features from YOLO and LSTM that are forwarded to the dense optical flow to follow the salient object inside the immersive video. Performance evaluation of the proposed system over our own collected data from various Websites as well as on public data sets indicates the effectiveness for diverse categories of 360° videos and helps in the minimization of the bandwidth usage, making it suitable for industry 4.0 applications.
Regional Competitiveness as an Aspect Promoting Sustainability of Latvia

Providing of sustainability is one of the main priorities in normative documents in various countries. Factors affecting regional competitiveness is seen as close to them determining sustainability in many researches. The aim of this research was to identify and evaluate main factors of competitiveness for statistical regions of Latvia to promote sustainable development of the country, applying the complex regional competitiveness assessment system developed by the author. The analysis of the Regional Competitiveness Index (RCI) and its sub-indexes showed that each statistical region has both: factors promoting and hindering competitiveness. Overall the most competitive is Riga statistical region, but the last place takes Latgale statistical region. It is possible to promote equal regional development and sustainability of Latvia by implementing well-developed regional development strategy and National Action Plan. To develop such strategies, it is necessary to understand the concept of sustainable competitiveness. To evaluate sustainable competitiveness of Latvia and its regions it is necessary to develop further the methodology of regional competitiveness evaluation.
Assessment of forecasting management in international pharmaceutical companies : a grounded theory study

Despite the improvements in mathematical forecasts and teh creation of new formulas in forecasting, the increase in the accuracy forecasts is not yet significant, thus, leading to an increase in the size of the gap between academia and practice. Furthermore, Applying mathematical forecasting techniques alone is not sufficient in light of the existing lack of knowledge about forecasting, which, thus, gives rise to the importance of judgements and the possible contributions they may add to the forecasting management process.
Characterization of the complete chloroplast genome of Ammopiptanthus mongolicus (Leguminosae)

Abstract Ammopiptanthus mongolicus is the only evergreen broadleaf shrub in the northwest desert of China, which can survive in long-term aridity and extremely cold environments. In this study, the complete chloroplast genome sequence of A. mongolicus was reported based on the Illumina NovaSeq Platform (Illumina, San Diego, CA). The chloroplast genome is 156,077 bp in length, containing a pair of inverted repeated (IR) regions (14,698 bp) that are separated by a large single copy (LSC) region of 88,025 bp, and a small single copy (SSC) region of 36,606 bp. Moreover, a total of 115 functional genes were annotated, including 81 mRNA, 30 tRNA genes, and 4 rRNA genes. Phylogenetic analysis based on 16 chloroplast genomics indicates that A. mongolicus is closely related to A. nanus.
Behavioral Explanations of Spatial Disparities in Productivity: The Role of Cultural and Psychological Profiling

Abstract This article argues that theories of economic development and productivity should move beyond the generally known factors and mechanisms of such development. It is theoretically proposed and empirically illustrated that differences in human behavior are significant deep-rooted causes of spatial economic and productivity disparities. The article examines the relationship between behavioral constructs and productivity using data for local areas across England. Measures of personality psychology and community culture (including both living culture and cultural heritage) are hypothesized to be related to activities impacting upon productivity performance at the local level. The analysis indicates that underlying human behavioral factors play a role in determining rates of productivity and levels of economic development in localities and regions. Culture and psychological traits, as manifested in the form of the psychocultural behavior of localities and regions, appear to shape their long-term development trajectories. Localities that have relatively atomized behavioral environments with high levels of individual commitment tend to enjoy productivity benefits. Similarly, places with high rates of cultural diversity and extravert individuals have relatively high rates of productivity. It is concluded that from a policy perspective, governments looking to level up local and regional economies should pay greater attention to understanding behavioral influences on productivity, especially related to strategies focused on behavioral nudges, institutional changes, and education systems.
People or Places that Don’t Matter? Individual and Contextual Determinants of the Geography of Discontent

abstract The rise of a geography of political discontent in the EU documented in recent studies highlights a strong spatial association between antisystem voting, regional economic decline, and poor occupational opportunities, suggesting that economic disparities within the EU are the origin of some of the most recent and shocking political events like Brexit. This article reexamines this statement by disentangling the effect on individual and political discontent of different socioeconomic disadvantage conditions at the interregional, intraregional, and individual level. Making use of a large data set on the individual and political discontent perceived by EU citizens between 2013 and 2018, our analysis confirms that a geography of discontent exists across EU regions. Nevertheless, our findings also highlight that intraregional inequalities do matter for individual discontent, and individual socioeconomic disadvantage conditions amplify further this negative effect.
Models of Human Phone Transcription in Noise Based on Intelligibility Predictors

Evidence exists that speech recognition by humans is simple patt rn recognition at some level. It would be worth knowing the level at which human speech perc eption operates in this manner, and the structure of the pattern recognition system employed. The approach taken here is to collect human classifications for phones (i.e., phone transcription ), model the auditory signals these speech sounds will evoke (to the extent possib le), then use tools from statistics, pattern recognition, and information theory to constrain a model of human phone transcription. It can be inferred from statistics that human speech recogni ti n must use a much simpler representation of speech than sampling at full bandwidth. T he representation used (1) results from the statistics of speech signals in the auditory periph ry, (2) is probably optimal in a sense related to the capabilities of the human pattern recog nizer, and (3) has a strong effect on the resulting error rates and patterns exhibited by humans i n speech recognition. A wealth of research exists about human behavior in speech recognition tasks. One such investigation, resulting in theArticulation Indexmethod of predicting speech intelligibility, is especiall y relevant because it models human error rates in the conditio s of noise, amplification, and filtering. These conditions are readily analyzed using stat istics and information theory. The Articulation Index studies revealed that human error rates for phone transcription are relatively insensitive to changes in amplification, while they are rela tively sensitive to changes in the speech-to-noise ratio. This dependence on the speech-to-noise ratio has been inter preted to imply some things about the human pattern recognizer. This dissertation exam ines this interpretation more thoroughly using new perceptual data which could not have be en available at the time the Articulation Index was developed. Some of the data are phone classifications in two noise spectra and at various noise levels, which permits us to dete rmin the specificity of the
Multimechanism-Deformation Parameters of Domal Salts Using Transient Creep Analysis

Use of Gulf Coast salt domes for construction of very large storage caverns by solution mining has grown significantly in the last several decades. In fact, among the largest developers of storage caverns along the Gulf Coast is the Strategic Petroleum Reserve (SPR) which has purchased or constructed 62 crude oil storage caverns in four storage sites (domes). Although SPR and commercial caverns have been operated economically for many years, the caverns still exhibit some relatively poorly understood behaviors, especially involving creep closure volume loss and hanging string damage from salt falls. Since it is possible to postulate that some of these behaviors stem from geomechanical or reformational aspects of the salt, a method of correlating the cavern response to mechanical creep behavior as determined in the laboratory could be of considerable value. Recently, detailed study of the creep response of domal salts has cast some insight into the influence of different salt origins on cavern behavior. The study used a simple graphical analysis of limited non-steady state data to establish an approach or bound to steady state, as an estimate of the steady state behavior of a given salt. This permitted analysis of sparse creep databases for domal salts.more » It appears that a shortcoming of this steady state analysis method is that it obscures some critical differences of the salt material behavior. In an attempt to overcome the steady state analysis shortcomings, a method was developed based on integration of the Multimechanism-Deformation (M-D) creep constitutive model to obtain fits to the transient response. This integration process permits definition of all the material sensitive parameters of the model, while those parameters that are constants or material insensitive parameters are fixed independently. The transient analysis method has proven more sensitive to differences in the creep characteristics and has provided a way of defining different behaviors within a given dome. Characteristics defined by the transient analysis are related quantitatively to the volume loss creep rate of the SPR caverns. This increase in understanding of the domal material creep response already has pointed to the possibility y of delineating the existence of material spines within a specific dome. Further definition of the domal geology and structure seems possible only through expansion of the creep databases for domal salts. « less
Knowledge acquisition in knowledge transfer partnerships: An activity theory based study of business process improvement using process mapping

Knowledge and its acquisition are seen to be an increasingly important aspect of business management. Universities, through initiatives such as Knowledge Transfer Partnerships (KTP), are influential in the effective co-production and transfer of business management knowledge, however the mechanisms of knowledge acquisition within KTPs have been under researched. This thesis explores the acquisition of knowledge in three KTPs where process mapping was used in order to deliver business process improvements. It utilises Activity Theory as its research framework, adopting a Participatory Action Research approach with multi-site triangulation and employing cyclic data capture and analysis. Data is captured using semi-structured interviews and instantaneously sampled field notes. The research identifies the organisational community in which the KTP is conducted to be the most significant source of tensions or disturbances that impinge upon the work that is being undertaken. It makes a contribution to knowledge by classifying a source of tensions or disturbances that are not currently identified by Activity Theory and proffers an extension to the framework to include the ‘Tacit Skills’ of the individual undertaking an activity. It also identifies issues of governance, such as the stewardship of public funds that pervade KTPs and threaten to undermine the stated aims of the KTP initiative. Furthermore it highlights the complexities of KTPs and emphasises the need to support the psychological, emotional and training needs of those that are involved in their execution.
Portfolio Selection using DEA-COPRAS at Risk – Return Interface Based on NSE (India)

Portfolio formation holds paramount importance in the process of the investment decision making since, a single door investment (SDI) option is much riskier than a multiple door investment (MDI) option. Among available financial instruments, the stock market (SM) has allured investors because of its liquidity and growth opportunities. However, the effectiveness of the investment decision is largely reflected in the selection of the constituent elements of the portfolio by an investor while trading off risk and return. In this paper, after an initial level selection of for formulating a possible portfolio by using Perceptual Map (PM), we have applied DEA to calculate the efficiency of the stocks at the risk-return interface based on the market performance. In order to ascertain that the stock selection is logical and worthwhile, we further probe the fundamental performances over a time period of five consecutive financial years using the method of Multi-Criteria Decision Analysis (MCDA) framework based on the Complex Proportional Assessment (COPRAS) method, where, the criteria weights are calculated by using the entropy method. A consistency is visible in the yearly fundamental performances and a significant pattern with regard to the portfolio selection.
Eosinophilic perimyocarditis associated with eosinophilic granulomatosis with polyangiitis: a case report

Abstract Background Eosinophilic myocarditis (EM) is a rare and potentially life-threatening form of myocarditis, frequently (but not always) associated with eosinophilia, and presents with acute chest pain, or signs and symptoms of acute or chronic heart failure. Eosinophilic myocarditis has various aetiologies, including eosinophilic granulomatosis with polyangiitis (EGPA). Case summary A 52-year-old female with a long-standing history of asthma, acral paraesthesia, subcutaneous nodules, and recurrent chest pain treated with anti-inflammatory drugs was admitted to our hospital with chest pain, repolarization disturbances, eosinophilia, and increased troponin levels. After an initial evaluation by coronary angiography, echocardiography and cardiac magnetic resonance, a definitive diagnosis of EM was made with the help of an endomyocardial biopsy. The aetiological diagnosis of EM as a manifestation of tissue involvement in EGPA was concluded after ruling out other possible causes of eosinophilia and with the help of other diagnostic criteria for EGPA (asthma, eosinophilia, and neuropathy). Therefore, we started with a high dosage of glucocorticoids, and attained relief of symptoms and normalization of eosinophilic count after a few days. Discussion In cases of myocarditis (particularly if associated with eosinophilia), EM is a manifestation of EGPA and should be considered for a prompt differential diagnosis. Endomyocardial biopsy represents the gold standard for the diagnosis of EM. The mainstay of therapy for EM is immunosuppressive drugs to help prevent its evolution to a fulminant form and chronic progression towards restrictive cardiomyopathy.
Regression-Based Artificial Neural Network Methodology In Response Surface Methodology

Response surface methodology (RSM) is an important tool for process parameter optimization, robust design and other quality improvement efforts. When the relationship between influential input variables and output response is very complex, it' s hard to find the real response surface using RSM. In recent years artificial neural network(ANN) has been used in RSM. But the classical ANN does not work well under the constraints of real applications. An algorithm of regression-based ANN(R-ANN) is proposed in this paper, which is a supplement to the classical ANN methodology. It makes network closer to the response surface, so that training time is reduced and robustness is strengthened. The procedure of improving ANN by regressions is described and the comparisons among R-ANN, RSM and classical ANN are computed graphically in three examples. Our research shows that the R-ANN methodology is a good supplement to the RSM and classical ANN methodology,which can yield lower standard error of prediction under conditions that the scope of experiment is rigidly restricted.
RESISTANCE AND SEAKEEPING CHARACTERISTICS OF FAST TRANSOM STERN HULLS WITH SYSTEMATICALLY VARIED FORM

Reliable methods to predict resistance and seakeeping characteristics form the basic tools for the preliminary hydrodynamic design of high-speed vessels. To supplement the older systematic hull forms suited for operation in the shallow Finnish archipelago were designed. Characteristic of the new hull forms is a shallow draft and a slightly higher block coefficient than in the older series to accommodate a waterjet propulsion system. Extensive systematic resistance tests were carried out with all model, including one typical hard chine planing hull form. New resistance prediction equations were developed by using regression analysis, which was based on parameters and resistance data of some older systematic series, the new series and some suitable models from the records. Seakeeping tests were run with the narrowest and the widest round bottom model in head waves. Motion transfer functions and phases predicted by two versions of the strip theory were compared with the results of the seakeeping model experiments. The accuracy of the strip theory were compared with the results of the seakeeping model experiments. The accuracy of the strip theory and the resistance prediction methods are discussed. The results of the investigation are presented in such a form that they can be used in the design of high-speed mono-hull vessels.