Miracle-dz/UniFAR-FTUs huggingface dataset:
Designing a Performance Management System for the Digital Transformation of the Indonesian Banking Industry

This study aims to design an Indonesian digital banking transformation performance management system (PMS-DT IBI) to assist banks in managing digital transformation performance (DX). This study will answer two research questions, namely how to identify dimensions that can be used as a framework for the Indonesian digital banking transformation and how to measure the digital performance of all dimensions within the framework. This research has an important role in formulating DX performance in a comprehensive, systematic, and measurable manner. We conducted a literature review on the dimensions of digital transformation and the contribution of digital transformation to improving company performance. In PMS-DT IBI, we have formulated digital performance indicators (DPI), which are mapped into 23 dimensions spread across four perspectives: strategic management, ecosystem management, information technology, and organizational strategy. From a strategic management perspective, DPI is formulated on the dimensions of DX strategy, business model, finance, P3M (portfolio, program, and project), and operational management. In ecosystem management, DPI is mapped in terms of ecosystem design, customer, partnership, and value dimensions. The information technology perspective consists of DPI for the dimensions of enterprise architecture, IT GRC, IT capabilities, data governance, system development, new technology, and security. And the organizational strategy perspective consists of DPI for the dimensions of organizational design, competence, change management, culture, leadership, and innovation. This research contributes to the importance of defining performance indicators in all subsystems in general system theory and PMS-DT IBI will assist banks in managing DX performance as a managerial application.
Modeling Zombie Outbreaks: A Problem-Based Approach to Improving Mathematics One Brain at a Time

Abstract A great deal of educational literature has focused on problem-based learning (PBL) in mathematics at the primary and secondary level, but arguably there is an even greater need for PBL in college math courses. We present a project centered around the Humans versus Zombies moderated tag game played on the Utah State University campus. We discuss the project in the context of an undergraduate differential equations course and discuss how the project was launched. We highlight examples of students mathematical models along with their verbal and written responses, as well as discussing assessment and student learning. Results are discussed in the context of higher and lower cognition levels as well as mathematical appreciation.
An Approach to Parameter Identification using Linear Graphs and Improved Particle Swarm Optimization

Abstract In this paper, the approach of linear graphs is combined with a modified particle swarm optimization procedure for efficient and effective extraction of parameter values in a model of an engineering system. The approach is applied to the hydraulic positioning system of an industrial fish cutting machine. Both computer simulation and experimentation are used to validate the developed approach. The results show that the combined approach of model parameter identification, as developed in this paper, is able to accurately determine the parameter values in complex engineering models.
Some remarks on the Kakeya problem

Besicovitch's construction(1) of a set of measure zerot containing an infinite straight line in every direction was subsequently adapted (2, 3, 4) to provide the following answer to Kakeya's problem (5): a unit segment can be continuously turned round, so as to return to its original position with the ends reversed, inside an arbitrarily small area. The last word on Kakeya's problem itself seems to be F. Cunningham Jr.'s remarkable result(6)‡ that this can be done inside a simply connected subset of arbitrarily small measure of a unit circle.
Various Methods of Realization for Fractional-Order Elements

Fractional-order circuits are finding applications in control systems, signal processing, and other allied fields. As a result, realizing fractional-order elements or fractance devices is an essential research topic. A unique fractance device is realized in this study using continued fraction expansion (CFE) and partial fraction expansion (PFE) formulas. Continued fraction expansion is used to calculate the rational approximation. For simulation, the fourth order rational approximation for fractional order, =-1/2, -1/3, -1/4 is used. All simulations are carried out with the help of the MATLAB and TINA software packages. The theoretical and simulation results are in agreement.
Can Gross Primary Productivity Products be effectively evaluated in regions with few observation data?

ABSTRACT The dynamics of Gross Primary Productivity (GPP) is key to understand the global carbon cycle. Multiple GPP products are currently available based on remote sensing, Light Use Efficiency model (LUE) or diagnostic biophysical model. However, little knowledge is available on the spatial patterns of the uncertainty of different GPP products and their potential drivers over the Central Asia (CA), a fragile environment for accurate GPP estimation. This study investigates the sensitivity of the 8-day, monthly and yearly GPP uncertainties based on the three-cornered hat (TCH) method and Shapley additive explanation (SHAP) model in terms of vegetation, energy, water, climate and terrain factors in the dryland ecosystem during the 2003–2015 period. Ten GPP products were examined, including one product (FLUXCOM) from machine learning (ML), six products (EC-LUE, FluxSat, LUEopt, MODIS, MuSyQ and VPM) based on the (LUE), two products (GOSIF and NIRv) from satellite-based direct proxies (Proxies) and one product (PML) from the diagnostic biophysical model. The results indicate that the spatial distribution of the ten GPP products in CA showed similar patterns at different time scales, but with values varied at different products and time scales. According to the eddy covariance (EC) observations and the TCH-based uncertainties, the FLUXCOM product showed smaller relative uncertainties than other products. The attribution analysis denotes that the sources of uncertainty of the GPP varied for each product, and thus the improvement strategies for different products should be implemented separately The FLUXCOM should adapt the vegetation- related module to the dryland environment of CA. The LUE model should optimize the LUE parameters for the dryland ecosystem and incorporate the water related variables in the model. The Proxies model should incorporate the water and energy variables (such as soil moisture and radiation) as input data to improve their performance in CA. The diagnostic model should consider the elevation variable as input data, which may improve the performance of the PML in CA. Our results do not only provide an important basis for the selection of GPP products in the study of the carbon cycle in CA, but also offer a new insight into the GPP model development and improvement for the dryland ecosystem.
Decision-Making Driven by Driver Intelligence and Environment Reasoning for High-Level Autonomous Vehicles: A Survey

Autonomous vehicle (AV) is expected to reshape the future transportation system, and its decision-making is one of the most critical modules. Many current decision-making modules are designed scenario-by-scenario and thus are not capable of meeting high-level AV requirements for lack of scalability to cope with the diversity of drivers’ demands and the infinity of traffic elements. This paper surveys the decision-making design inspired by driver intelligence and environment reasoning with better scalability for future high-level AVs. It involves three aspects: human factors in driving, environment reasoning, and detailed decision methods. The current state of the art of these three sections is surveyed in this paper. The characteristics of distinguished drivers, their decision mechanism, and the factors influencing decision-making are reviewed first to learn from excellent drivers. Environment reasoning is introduced following as a three-layer structure consisting of restriction, interaction, and attention. The optimization-based decision-making algorithms are then reviewed from the aspect of optimization targets, frameworks, applied scenarios, and limitations. Inspired by the existing research on driver intelligence and environment reasoning, a promising decision-making framework is also introduced for high-level AV design.
Dairy-Protein-Based Aggregates as Additives Enriched with Tart Cherry Polyphenols and Flavor Compounds

Nowadays, the development of innovative food products with positive health effects is on the rise. Consequently, the aim of this study was a formulation of aggregates based on tart cherry juice and dairy protein matrix to investigate whether different amounts (2% and 6%) of protein matrix have an impact on the adsorption of polyphenols as well as on the adsorption of flavor compounds. Formulated aggregates were investigated through high-performance liquid chromatography, spectrophotometric methods, gas chromatography and Fourier transform infrared spectrometry. The obtained results revealed that with an increase in the amount of protein matrix used for the formulation of aggregates, a decrease in the adsorption of polyphenols occurred, and, consequently, the antioxidant activity of the formulated aggregates was lower. The amount of protein matrix additionally affected the adsorption of flavor compounds; thus the formulated aggregates differed in their flavor profiles in comparison with tart cherry juice. Adsorption of both phenolic and flavor compounds caused changes in the protein structure, as proven by recording IR spectra. Formulated dairy-protein-based aggregates could be used as additives which are enriched with tart cherry polyphenols and flavor compounds.
Analysis of electromagnetic/thermal coupling of Debye media using HIE-FDTD method

In this paper, the multi-physical working mechanism of the Debye dispersive media is studied in detail, and the coupling analysis of Maxwell's and heat conduction equations is carried out. The hybrid-implicit-explicit finite difference time domain (HIE-FDTD) method is applied to accelerate calculation of the electromagnetic(EM). The Debye model, introduced to HIE-FDTD through the auxiliary difference equation (ADE) technique by utilizing polarization current, describes the dispersion characteristics of a water-based absorber. In addition, a new expression of transient power loss density is proposed with the polarization current, which overcomes the negative power loss in the traditional method and makes the computation more accurate. Finally, the analysis proves that the ADE-HIE-FDTD method has good performance in the EM-thermal coupling calculation, and the absorption rate of the absorber shows the interaction effect between temperature distribution and dielectric material.
Relating Wind and Stress under Tropical Cyclones with Scatterometer

AbstractOcean surface stress, the turbulent transport of momentum, is largely derived from wind through a drag coefficient. In tropical cyclones (TCs), scatterometers have difficulty measuring strong wind and there is large uncertainty in the drag coefficient. This study postulates that the microwave backscatter from ocean surface roughness, which is in equilibrium with local stress, does not distinguish between weather systems. The reduced sensitivity of scatterometer wind retrieval algorithms under the strong wind is an air–sea interaction problem that is caused by a change in the behavior of the drag coefficient rather than a sensor problem. Under this assumption, a stress retrieval algorithm developed over a moderate wind range is applied to retrieve stress under the strong winds of TCs. Over a moderate wind range, the abundant wind measurements and the more established drag coefficient value allow for sufficient stress data to be computed from wind to develop a stress retrieval algorithm for the scat.
EnGauge: Engagement Gauge of Meeting Participants Estimated by Facial Expression and Deep Neural Network

Measuring the level of engagement among participants in a meeting is crucial for evaluating collective understanding. While previous studies have utilized multiple sensors, such as wearable devices, to gauge engagement levels in offline environments, the shift to remote meetings due to the COVID-19 pandemic presents new challenges. In this study, we propose a method for measuring student engagement during online meetings using only the built-in web cameras on their devices. We collect high, middle, and low engagement level recording data from 24 students. We decided to collect data using the role-acting approach instead of conventional self-reporting or post-experiment annotation. With the feature extraction based approach, we achieved a classification rate of 46.7%. With a deep learning based approach, we achieved a classification rate of 89.5% using MobileNetV2 for leave-one-participant-out cross-validation, which demonstrated higher accuracy than previous studies. From the model, we implement an application EnGauge and conduct a pilot study. The results demonstrate a new approach to data collection, an optimal engagement level recognition model, and application scenarios.
Horizontal reduction of pressure to sea level : comparison against the NMC's Shuell method

Abstract It is suggested that there are two major problems with the “standard” methods of reducing pressure to sea level based on the surface temperature or the lowest-layer(s) temperature of a numerical model. The first is that using air temperatures above elevated terrain for reducing pressure to sea level is in conflict with the presumed objective of the reduction. The authors take this to be the derivation of a pressure field appropriate to sea level that to the extent possible maintains the shape of the constant-elevation isobars and reflects the changes in the horizontal of the magnitudes of horizontal pressure gradients, as these exist at the ground surface. The other problem is that evidence is emerging showing that with the increasing realism in the representation of mountains in numerical models the performance of the standard reduction methods is about to deteriorate to the point of becoming unacceptable. Fortunately, as proposed earlier by the first author, an alternative exists that is both s.
Efficient Anonymous Batch Authentication Scheme With Conditional Privacy in the Internet of Vehicles (IoV) Applications

The Internet of Vehicles (IoV) has emerged as a robust solution to improve real-time road traffic conditions, safety, and driving comfort. However, the road’s dynamic environment and network heterogeneity make communication insecure. It poses many security threats like man-in-the-middle attacks, impersonation attacks, replay attacks, etc. Therefore, the message exchanges and real identities of nodes over the IoV network need to be preserved. The existing authentication schemes are inefficient and incur computational and communication overhead. They are unsuitable for resource-constrained IoV networks due to their highly dynamic nature, open communication, and chaotic road environment. Therefore, this paper proposes an elliptic curve cryptography-based secure and efficient anonymous batch authentication scheme with conditional privacy (EABAS-CP) for the IoV environment. The EABAS-CP scheme achieves conditional privacy by tracing the malicious vehicle efficiently. The RSU’s distributed parameters generate a pseudo-identity for vehicles and unique signatures that do not require secure channels and trusted authorities (TAs) for secure communication. Furthermore, the EABAS-CP scheme supports batch signature verification in which multiple signatures can be verified simultaneously and efficiently. The Random Oracle Model (RoM) provides unforgeability against adaptive message attacks by using the Elliptic Curve Discrete Logarithm Problem (ECDLP). The AVISPA simulation tool is used to evaluate the proposed protocol for its reliability and protection from the attacks like man-in-the-middle and replay attacks. The proposed EABAS-CP scheme is also proven secure against replay, impersonation, stolen verifier, ephemeral secret leakage, and linkability attacks. The network performance of the EABAS-CP scheme has been evaluated in terms of end-to-end delay, throughput, average message delay, average message loss ratio, communication cost, and computation cost through the NS-2 simulator and compared with existing schemes to demonstrate its efficiency.
Academic Success through Engagement and Trust Fostered by Professor Leadership Style

This study focused on professor leadership styles and investigated the impacts of various leadership characteristics on student academic performance as well as students’ perceptions of class engagement and trust toward a professor. Using multiple leaderships styles to estimate a model of the relationship between faculty leadership styles and student outcomes provides for a broader view compared to previous research. The empirical results indicated that student perception of class engagement was significantly influenced by a professor’s emotional leadership, Pygmalion leadership, and charismatic leadership. Further, student perception of trust toward a professor was significantly affected by the professor’s transformational leadership, Pygmalion leadership, and charismatic leadership. Lastly, student academic performance was significantly impacted by class engagement. This research provides practical applications for enhancing student academic performance with the adoption of relevant leadership styles in higher education classes.
Robust Accurate Lane Detection and Tracking for Automated Rubber-Tired Gantries in a Container Terminal

Lane detection and tracking technique is the autonomous driving basis for Rubber-Tired Gantries (RTGs), vital to the automation and intelligence updating of man-driven container terminals. However, the existing lane detection methods developed for common road scenarios cannot meet the high-precision and robust all-weather requirements of RTG autonomous driving. In this article, we propose an Adaptive Edge-based Lane Detection and Tracking method considering RTG lanes’ characteristics in this paper. First, the candidate edges of lane lines are detected and paired based on the enhanced gradient features. Next, inverse perspective mapping is employed to search the right edges, followed by an adaptive sliding-window method. Ultimately, we develop an adaptive Kalman filter to track lane lines robustly, detecting confidence weighting by relaxing the constraint of lane line width. The proposed method is tested in an actual container yard, the lane centerline’s average position error is 2.051 pixels, and the detection success rate is close to 100%.
Determination of small field output factors in 6 and 10 MV flattening filter free photon beams using various detectors

The study aimed to determine appropriate detectors for output factor measurement of small fields in 6 and 10 MV flattening filter free photon beams using five different detectors. Field sizes were varied between 0.6 × 0.6 and 4.0 × 4.0 cm2. An indirect method (daisy-chaining) was applied to normalize the output factors. For the smallest field size, the variations of output factors compared among the detectors were 13%. Exradin A16 had the lowest output factor and increasing in sequence with CC01, microDiamond, microLion and EDGE detectors, respectively, for both energies. The similarity between CC01 and microDiamond output factor values were within 1.6% and 1% for all field sizes of 6 and 10 MV FFF, respectively. EDGE and microLion presented the highest values while ExradinA16 gave lowest values. In conclusion, IBACC01, Exradin A16, microLion, microDiamond and EDGE detectors seem to be the detectors of choices for small field output factor measurement of FFF beams down to 1.6 × 1.6 cm2. However, we could not guarantee which detector is the most suitable for output factor measurement in small field less than 1.6 × 1.6 cm2 of FFF beams. Further studies are required to provide reference information for validation purposes.
The Effect of Entrepreneurial Marketing on Business Performance Moderated by Environmental Turbulence on MSMEs in Indonesia

The purpose of this study is to investigate the effect of entrepreneurial marketing which consists of innovation-focused, proactiveness, opportunity, calculated risk-taking, customer intensity, resource leveraging, and value creation on business performance moderated by environmental turbulence in SMEs. The approach used is a quantitative approach and data collection is done by distributing online questionnaires. The population of this research is MSMEs in Batam City which are registered with the Online Database System (ODS) as many as 81,575. This research uses the purposive sampling method. The sampling technique used in the study was purposive sampling based on the following criteria: The duration of active micro, small and medium enterprises (MSMEs) in Batam City has been operating for at least 3 years, the business sector is engaged in: trading, manufacturing/fabrication, and services. The samples collected and used in this study were 421 respondents. The results show that proactiveness, opportunity, value creation, and environmental turbulence have a direct effect on business performance, while innovation-focused, calculated risk-taking, customer intensity, and resource leveraging have no significant effect on business performance, while the overall environmental turbulence variable does not moderate the relationship. between entrepreneurial marketing and business performance.
Age‐Specific Incidence and Prevalence: A Statistical Perspective

In epidemiology incidence denotes the rate of occurrence of new cases (of disease), while prevalence is the frequency in the population (of diseased people). From a statistical point of view it is useful to understand incidence and prevalence in the parameter space, incidence as intensity (hazard) and prevalence as probability, and to relate observable quantities to these via a statistical model. In this paper such a framework is based on modelling each individual's dynamics in the Lexis diagram by a simple three‐state stochastic process in the age direction and recruiting individuals from a Poisson process in the time direction. The resulting distributions in the cross‐sectional population allow a rigorous discussion of the interplay between age‐specific incidence and prevalence as well as of the statistical analysis of epidemiological cross‐sectional data. For the latter, this paper focuses on methods from modern nonparametric continuous time survival analysis, including random censoring and truncation models and estimation under monotonicity constraints. The exposition is illustrated by examples, primarily from the author's epidemiological experience.