Miracle-dz/UniFAR-FTUs huggingface dataset:
An Entropy Framework for Evaluating Reflectance Observations for Climate Studies

This study evaluated the comparative entropy represented in shortwave (300–1,750 nm) passive remote sensing measurements from conceptual instruments with varying systematic measurement uncertainties and spectral resolutions. The focus was on spectral and broadband reflectance averaged over large spatial scales typically relevant for climate change studies. Information theory was applied to quantify how the normalized Shannon entropy changed for different conceptual instruments and over different spatial scales. Reflectance measurements from the Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) were used to represent the observed reflectance, and simulated reflectance from climate Observing System Simulations Experiments (OSSEs) were used to represent decadal‐ and centennial‐length data sets free from instrument artifacts. Similar to previous studies, the simulated OSSE spectra were shown to be a sufficient proxy for the observed reflectance. As hypothesized, the normalized entropy decreased with increasing measurement uncertainty and increasing spectral bandwidth. Additionally, the entropy decreased for increasingly large spatial scales, particularly for measurement uncertainties larger than 2%. When applied to OSSE reflectance simulated from a forced CMIP3 climate model simulation, the change in entropy with measurement uncertainty and spectral resolution provided insight into measurement attributes needed to monitor a changing climate system and highlighted the importance of sufficiently high accuracy and spectral resolution for detecting and attributing climate trends. These preliminary studies illustrate the value of this information theory‐based framework in instrument design by calculating the entropy, used to represent information in measurements from different conceptual instruments.
The Impact of Online Gambling Among Indonesian Teens and Technology

This research is motivated by the development of technological advances. With advances in technology there is an increase in the problem of crime using sophisticated modus operandi. This form of crime is Cyber ? Crime. One of the cases of cybercrime that is rife at this time is gambling via the internet. Article 27 paragraph (2) of Law Number 11 of 2008 concerning Information and Electronic Transactions regulates the prohibition of electronic gambling with a maximum imprisonment of 6 (six) years and/or a maximum fine of Rp. 1,000,000,000.00 (one billion rupiah). Online gambling is a game that is carried out using money as a bet with the terms of the game and the amount of the bet determined by online gamblers and using electronic media with internet access as an intermediary. Online gambling is a phenomenon that cannot be denied and is found in society, especially among teenagers. They consciously know that gambling is included in deviant behavior which is considered bad by some people and is prohibited by state law, but this does not cover their interest in participating in it. This study aims to describe the behavior of teenagers who are addicted to online gambling. This research is a type of qualitative research with data analysis techniques using qualitative analysis. The results explained that the behavior of adolescents who were addicted to online gambling showed the following characteristics: spending more time playing online gambling, decreasing physical activity and neglecting health, avoiding important life activities, decreasing socialization processes, finding it difficult to break the desire to play online gambling, ignoring work and personal obligations and mental illness.
Functionalisation of Inorganic Material Surfaces with Staphylococcus Protein A: A Molecular Dynamics Study

Staphylococcus protein A (SpA) is found in the cell wall of Staphylococcus aureus bacteria. Its ability to bind to the constant Fc regions of antibodies means it is useful for antibody extraction, and further integration with inorganic materials can lead to the development of diagnostics and therapeutics. We have investigated the adsorption of SpA on inorganic surface models such as experimentally relevant negatively charged silica, as well as positively charged and neutral surfaces, by use of fully atomistic molecular dynamics simulations. We have found that SpA, which is itself negatively charged at pH7, is able to adsorb on all our surface models. However, adsorption on charged surfaces is more specific in terms of protein orientation compared to a neutral Au (111) surface, while the protein structure is generally well maintained in all cases. The results indicate that SpA adsorption is optimal on the siloxide-rich silica surface, which is negative at pH7 since this keeps the Fc binding regions free to interact with other species in solution. Due to the dominant role of electrostatics, the results are transferable to other inorganic materials and pave the way for new diagnostic and therapeutic designs where SpA might be used to conjugate antibodies to nanoparticles.
The Application of Speech Recognition Techniques to Radar Target Doppler Recognition: A Case Study

This paper reports some preliminary results of an examination into the feasibility of recognising the Doppler signatures of targets using speech recognition processing techniques. The rationale is that human operators typically listen to the Doppler audio output from the surveillance radar to detect and possibly identify targets. A feature of speech recognition is that pre-processing is used that takes account of the voice mechanisms that produce speech and the characteristics of the human ear. Three different recognition techniques, with identical pre-processing, were implemented. After validating the recognition algorithms with speech the recognisers were retrained with Doppler signals from a number of sources. It was found that the best of the speech recognisers, HMM-GMM, was also the best of the Doppler recognisers with 88% recognition. The work has been compared with that of others using a similar technique and a good agreement has been found. Some recent discoveries in neuroimaging are quoted that suggest that the human brain and that of several other mammals performs visual recognition in a manner common in speech recognition.
LiDAR-Inertial-GNSS Fusion Positioning System in Urban Environment: Local Accurate Registration and Global Drift-Free

Aiming at the insufficient accuracy and accumulated error of the point cloud registration of LiDAR-inertial odometry (LIO) in an urban environment, we propose a LiDAR-inertial-GNSS fusion positioning algorithm based on voxelized accurate registration. Firstly, a voxelized point cloud downsampling method based on curvature segmentation is proposed. Rough classification is carried out by the curvature threshold, and the voxelized point cloud downsampling is performed using HashMap instead of a random sample consensus algorithm. Secondly, a point cloud registration model based on the nearest neighbors of the point and neighborhood point sets is constructed. Furthermore, an iterative termination threshold is set to reduce the probability of the local optimal solution. The registration time of a single frame point cloud is increased by an order of magnitude. Finally, we propose a LIO-GNSS fusion positioning model based on graph optimization that uses GNSS observations weighted by confidence to globally correct local drift. The experimental results show that the average root mean square error of the absolute trajectory error of our algorithm is 1.58m on average in a large-scale outdoor environment, which is approximately 83.5% higher than that of similar algorithms. It is fully proved that our algorithm can realize a more continuous and accurate position and attitude estimation and map reconstruction in urban environments.
Mathematical Modeling Finds Disparate Interferon Production Rates Drive Strain-Specific Immunodynamics during Deadly Influenza Infection

The timing and magnitude of the immune response (i.e., the immunodynamics) associated with the early innate immune response to viral infection display distinct trends across influenza A virus subtypes in vivo. Evidence shows that the timing of the type-I interferon response and the overall magnitude of immune cell infiltration are both correlated with more severe outcomes. However, the mechanisms driving the distinct immunodynamics between infections of different virus strains (strain-specific immunodynamics) remain unclear. Here, computational modeling and strain-specific immunologic data are used to identify the immune interactions that differ in mice infected with low-pathogenic H1N1 or high-pathogenic H5N1 influenza viruses. Computational exploration of free parameters between strains suggests that the production rate of interferon is the major driver of strain-specific immune responses observed in vivo, and points towards the relationship between the viral load and lung epithelial interferon production as the main source of variance between infection outcomes. A greater understanding of the contributors to strain-specific immunodynamics can be utilized in future efforts aimed at treatment development to improve clinical outcomes of high-pathogenic viral strains.
The Influence of Precipitate Morphology on the Growth of Austenite Grain in Nb-Ti-Al Microalloyed Steels

The present study investigates the morphological evolution of carbonitrides and the effect of these precipitates on grain boundary pinning during pseudo-carburizing a Nb-Ti-Al microalloyed steel. The result indicated that three kinds of complex precipitates with different morphologies containing Nb, Ti, and Al respectively were observed in samples austenitized at different temperatures and times. The NbC and TiN precipitates played an important role in pinning grain boundaries and suppressing the growth of austenite grains, relying on the high thermal stability of TiN precipitates and small size of NbC precipitates. The precipitate characteristics affected the size of austenite grain. Based on the Zener pinning model, the effect of precipitate characteristic on austenite grain size was quantitatively analyzed. It is found that the existence of NbC and TiN precipitated at high temperature makes austenite grain growth difficult when austenite grain boundaries were pinned by fine and diffused precipitates.
Brillouin Frequency Shift Extraction Based on AdaBoost Algorithm

The Brillouin Optical Time-Domain Analyzer assisted by the AdaBoost Algorithm for Brillouin frequency shift (BFS) extraction is proposed and experimentally demonstrated. The Brillouin gain spectrum classification under different BFS is realized by iteratively updating the weak classifier in the form of a decision tree, forming several base classifiers and combining them into a strong classifier. Based on the pseudo-Voigt curve training set with noise, the performance of the AdaBoost Algorithm is studied, and the influence of different signal-to-noise ratio (SNR), frequency range, and frequency step is also studied. Results show that the performance of BFS extraction decreases with the decrease in SNR, the reduction in frequency range, and the increase in frequency step.
Saikosaponin A and Saikosaponin C Reduce TNF-α-Induced TSLP Expression through Inhibition of MAPK-Mediated EGR1 Expression in HaCaT Keratinocytes

Atopic dermatitis (AD) is one of the most common chronic inflammatory skin diseases worldwide, characterized by intense pruritus and eczematous lesions. Aberrant expression of thymic stromal lymphopoietin (TSLP) in keratinocytes is associated with the pathogenesis of AD and is considered a therapeutic target for the treatment of this disease. Saikosaponin A (SSA) and saikosaponin C (SSC), identified from Radix Bupleuri, exert anti-inflammatory effects. However, the topical effects of SSA and SSC on chronic inflammatory skin diseases are unclear. In this study, we investigated the effects of SSA and SSC on TSLP suppression in an AD-like inflammatory environment. We observed that SSA and SSC suppressed tumor necrosis factor-α-induced TSLP expression by downregulating the expression of the transcription factor early growth response 1 (EGR1) via inhibition of the extracellular signal-regulated kinase 1/2, c-Jun N-terminal kinase 1/2, and p38 mitogen-activated protein kinase pathways. We also confirmed that topical application of SSA or SSC reduced AD-like skin lesions in BALB/c mice challenged with 2,4-dinitrochlorobenzene. Our findings suggest that suppression of EGR1-regulated TSLP expression in keratinocytes might be attributable to the anti-inflammatory effects of SSA and SSC in AD-like skin lesions.
Mobile Robots for In-Process Monitoring of Aircraft Systems Assemblies

Currently, systems installed on large-scale aerospace structures are manually equipped by trained operators. To improve current methods, an automated system that ensures quality control and process adherence could be used. This work presents a mobile robot capable of autonomously inspecting aircraft systems and providing feedback to workers. The mobile robot can follow operators and localise the position of the inspection using a thermal camera and 2D lidars. While moving, a depth camera collects 3D data about the system being installed. The in-process monitoring algorithm uses this information to check if the system has been correctly installed. Finally, based on these measurements, indications are shown on a screen to provide feedback to the workers. The performance of this solution has been validated in a laboratory environment, replicating a trailing edge equipping task. During testing, the tracking and localisation systems have proven to be reliable. The in-process monitoring system was also found to provide accurate feedback to the operators. Overall, the results show that the solution is promising for industrial applications.
Electrical Conductivity and Crystallization of Polylactic Acid Nanocomposites Containing Surfactant Modified Carbon Nanotubes

In order to extend the usage of PLA-based nanocomposites, the modification of multi-walled carbon nanotubes (MWCNT) and preparation of surfactant modified carbon nanotubes (SMCNT) involved PLA nanocomposites (PLA/SMCNT) were investigated. The morphologies, electrical properties and crystallization behavior of PLA/SMCNT composites were investigated. The TEM images indicated that SMCNTs were dispersed homoge-nously in the PLA matrix without forming aggregates. The electrical conductivity of PLA/SMCNT was greatly improved and the percolation threshold of PLA/SMCNT was calculated to be 0.61% which is much lower than 1.45% of PLA/MWCNT composite. The crystallization behavior suggested that SDBS together with MWCNT acted as heterogeneous nucleating agent and accelerated the nucleation. Meanwhile, the SMCNT also devoted further restrained effect to spherulites growth due to its well dispersion and improved compatibility with matrix.
Event‐driven strain cycle monitoring of railway bridges using a wireless sensor network with sentinel nodes

Due to the increasing traffic volume on the European railway network, the remaining fatigue life of existing steel bridges is a major concern. Several investigations demonstrated that supplementing the assessment with monitoring data enables to achieve more reliable remaining fatigue life estimations. In this paper, an event‐driven monitoring system based on a wireless sensor network that consists of two functionally different components was designed and tested. Sentinel nodes, which were mounted on the track, were used for detecting approaching trains and alerting with alarm messages the monitoring nodes. These nodes, which were mounted on the bridge, started strain sensing and data recording after receiving the alarm message and went back to a power saving mode upon completion. An embedded data processing algorithm transformed the recorded raw data into a much smaller data set representing strain cycles. A test deployment on a railway bridge demonstrated that train detection and alarming was fast and reliable. The combination of event‐driven monitoring and embedded data processing allowed to extend the battery lifetime of monitoring nodes to several months.
MC-SPWM and MC-THIPWM Methods for Symmetric and Asymmetric Design of CHB-MLI: A Study

: Cascaded H-bridge multilevel inverters (CHB-MLI) are employed in a variety of medium/high power applications. These inverters are known to inject unwanted harmonics into the grid, which negatively affects the grid and connected loads. CHB-MLI topology can reduce many of these harmonics by producing multiple output voltage levels and improves the fundamental component using a suitable modulation technique. However, the CHB-MLI topology configuration requires multiple isolated input sources which must be balanced either with the modulation technique or with an additional method. This paper analyzes multi-carrier pulse width modulation (MC-PWM) techniques for CHB-MLIs. In this study, two basic configurations of CHB-MLI symmetrical and asymmetrical are reviewed, followed by their mathematical analysis. Also, this paper analyses multi-carrier based sinusoidal pulse width modulation (MC-SPWM) and multi-carrier based third-harmonic injected pulse width modulation (MC-THIPWM) techniques with phase-shifted (PS) and level-shifted (LS) carrier arrangements for the CHB-MLI. Moreover, a simulation study has been conducted using MATLAB Simulink to analyze the performance of MC-PWM techniques for the symmetrical and asymmetrical type CHB-MLIs. The 7-level and 9-level CHB-MLIs were evaluated for the stated MCPWM techniques in terms of harmonics and fundamental components. In addition, discharging current of all input sources was checked to verify the ability of all MC-PWM techniques to balance all input sources.
Dentinal Occlusion of Two Desensitizing Toothpastes Using Manual Versus Electric Toothbrushes

Objective: To investigate dentinal occlusion of two desensitizing toothpastes using manual versus electric toothbrushes. Methods: A total of 40 sound human molars were used in this study. From each tooth, one dentin disc specimen was prepared 2 mm in thickness. Specimens were divided randomly into 2 main groups (n=20), according to the type of toothpaste used; Arginine-based toothpaste and Nanohydroxyapatite-based toothpaste. Each main group was subdivided into 2 subgroups (n=10) according to the type of toothbrush; manual toothbrush and electric toothbrush. Each specimen was brushed twice daily, 1 min each, for 4 days and subjected to acidic challenge using grapefruit juice. Specimens were examined under scanning electron microscope to determine degree of dentinal occlusion before and after acidic challenge. Degree of dentinal occlusion were determined using occlusion scoring system on a scale of 1-5. Data were tabulated and statistically analysed. Results: Hydroxyapatite toothpaste revealed significantly higher dentinal occlusion score than arginine toothpaste in all experimental groups. Electric toothbrush recorded higher dentinal occlusion score than manual toothbrush with arginine toothpaste before and after acidic challenge. However, no significant difference was recorded between both toothbrushes with hydroxyapatite toothpaste. Hydroxyapatite toothpaste was not affected by the acidic challenge in contrast to arginine toothpaste which was negatively affected. Conclusion: Hydroxyapatite toothpaste was effective in dentinal tubules occlusion and able to resist acidic challenge whether brushed using manual or electric toothbrush. Use of electric toothbrush with arginine toothpaste was beneficial in improving dentinal tubules occlusion before and after acidic challenge.
Stacking Machine Learning Model for the Assessment of R&D Product’s Readiness and Method for Its Cost Estimation

The modern technology universities have the necessary resource and material base for developing and transferring R&D products. However, the cost estimation process is not formalized. There are many methods of estimating the cost of R&D products’ commercialization processes. However, in some cases, we cannot consider any single technique to be the best one as each of them has advantages and disadvantages. In such conditions, all efforts should be made to use a combination of the estimation techniques to arrive at a better cost and quality estimate. The effectiveness of the valuation of R&D products is of particular importance in today’s economy and due to the need to analyze large data sets prepared for transfer from universities to the business environment. This paper presents the model, two methods, and general information technology for R&D products’ readiness level assessment and R&D products’ cost estimation. The article presents the complex method for determining the cost of R&D products, which will allow: increasing the efficiency of the transfer, commercialization, and market launch of R&D products, and promoting the interaction of all components of the national innovation infrastructure, innovations, etc. The need to consider many different indicators when evaluating R&D products has determined the need to use machine learning algorithms. We have designed a new machine learning-based model for the readiness assessment of R&D products, which is based on the principle of “crowd wisdom” and uses a stacking strategy to integrate machine learning methods. It is experimentally established that the new stacking model based on machine learning algorithms that use random forest as a meta-algorithm provides a minimum of a 1.03 times higher RMSE compared to other ensemble strategies.
Digital Government Stage Evaluation in Population and Civil Registry Office (Disdukcapil) of Kutai Kartanegara Regency

Recent developments show scholars argue that when electronic government systems reach certain stages, they will develop into Digital Government. However, studies on Digital Government evaluation, especially in the Indonesian context, are still few. With that in mind, this article aims to describe the maturity of the digital government system in the Population and Civil Registry Office (Disdukcapil) of the Kutai Kartanegara Regency. It is based on the assumption that Disdukcapil has successfully implemented electronic government, as proven by their multiple recognitions and can be considered at the digital government stage. Field observation and in-depth interviews with key informants are employed to collect the relevant data. All the data are then analyzed qualitatively, and then it uses digital government evolution variables developed by Janowski (2015) to determine the stage of Disdukcapil digital government. The result shows that within a four-stage maturity level, the digital government of Disdukcapil of Kutai Kartanegara Regency has reached the third stage, based on the fulfilment of each stage's variables. It is hoped that this study can contribute to Digital Government studies in Indonesia