Miracle-dz/UniFAR-FTUs huggingface dataset:
5G Use Cases and New Business Models for New Capital City Indonesia

Continuously, every company always finds a way to maintain its sustainability by creating new values or innovative costs strategy. The introduction of new technology such as 5G promises new capability from both technology and business aspects. Due to technology limitations, most telecom and other high technology industries are saturated with current cost structures and business strategies. Thus, there is a need to discuss new revenue streamlines from new use cases and new business models as the impact of 5G implementation. The case study of New Capital City (NCC) of Indonesia is selected due to the nature purpose of this city as a pilot project for whole 5G experience. Qualitative data was gathered from focus discussion groups (FGD) and semi-structured in-depth interviews with essential resources from multiple organizations. As the results, this study proposes 5G use cases tailored for the NCC and potential new collaboration schemes and business models such as Co-Primary Spectrum Sharing (CoPSS), Licensed Shared Access (LSA), and modified vertical micro-operator. Regulatory and business firms could nourish the ecosystem to grow the new micro and local players for both use cases and business models
Electronic Quadrature Hybrid Duplexer for Wireless Simultaneous Transmit Receive Operation

This article presents a new four-port electronic device – the directional quadrature hybrid isolator (DQHI). The topology of this duplexer is similar to the four-arm quadrature hybrid (QH) except for a 90° non-reciprocal phase shifter (NRPS) replacing one of the λ/4 Z0 arms. We derive the DQHI S-parameters and show that it performs a 0°/90° power division for an incident signal at port 2 into ports 3 and 4 exactly like a QH, functions as an ideal isolator between ports 1 and 2, enables high-isolation between port 1 to ports 3 and 4 and facilitates injection of self-interference cancellation (SIC) signal from port 4 into 3. Analytic evaluation of a dual-port N-path circuit as an NRPS is performed and the theoretical impact on the DQHI performance is examined. An 8-path, 65-nm NMOS design is utilized to simulate the performance with a 1 GHz clock resulting in 1.7 dB TX-to-antenna loss, antenna-RX loss of 3 dB, SIC-to-RX loss of 6.6 dB, TX-RX and TX-SIC isolations of 26 and 12.5 dB respectively.
An Artificial Intelligence Framework for the Detection of Emotion Transitions in Telehealth Services

Recent advancements in Artificial intelligence (AI) have led to its widespread adoption in healthcare applications and services. The global pandemic has further hastened the integration of AI into telehealth services, such as service quality improvement and new models of care. This paper focuses on the service quality improvement of telehealth, specifically, cancer information and telephone support services, in terms of its human system interaction. We present an AI framework for the detection of patient emotions and emotion transitions based on call recordings of telehealth services. The call recordings are typically a conversation between the caller (patient or carer) and the healthcare practitioner (nurse or counsellor). All primary emotions are expressed across diverse topics during these calls. It is anticipated that the caller emotion state improves during the call due to the information and support received. The proposed AI framework is designed to detect emotions expressed at all stages of the call and based on these expressions, formulate the emotion transition during the call. We have evaluated the proposed AI framework on a large real-world dataset of 60,000 call recordings of cancer information and telephone support services provided by Cancer Council Victoria, Australia. The results confirm the effectiveness of this AI framework in detection of emotions and emotion transitions during the provision of telehealth services.
Impact of Thermal Hotspots Formation in High Performance Computing

In recent years computational demands have constantly increased and it is at the highest ever demand today, as almost all applications and task computations are moving into cloud-based servers. With the node size going down and the number of parallel cores on an increasing level, power density in the substrate is increasing. High power densities lead to an exponential rise in the die temperatures and high temperatures in CMOS devices degrade the performance, reliability and life of circuits. During the workload execution, local high-temperature zones (aka. Hotspots) are created on the die (in a 3D space around the material) which needs effective mitigation. Known mitigation techniques like Dynamic Thermal Management (DTM), Dynamic Voltage and Frequency Scaling (DVFS), used for managing the performance and temperature, relies on physical sensors placed onboard and are not hotspot based. Moreover, techniques are not workload aware and apply general throttling across processor cores which has a negative impact on system performance and finishing time of all jobs. Hotspot formation depends also on the physical design space of the IC, material used, and node technology used for manufacturing. This paper proposes a method to be made available to the hardware designers and software architects, at a Pre-RTL level, to model power and temperature (hotspot) based on workload and demonstrates that by applying a novel hybrid throttling policy. A hybrid scheduling policy is workload-based throttling of tasks based on the model feedback and is measured to have instantaneous power save from 20W to 2W and temperature drop from 85oC to 45oC
A Vision Transformer Approach for Efficient Near-Field SAR Super-Resolution under Array Perturbation

In this paper, we develop a novel super-resolution algorithm for near-field synthetic-aperture radar (SAR) under array perturbation. As fifth-generation (5G) millimeter-wave (mmWave) devices become increasingly affordable and available, high-resolution SAR imaging is feasible for end-user applications and non-laboratory environments. Emerging applications, such as freehand imaging, unmanned aerial vehicle (UAV) imaging, and automotive SAR, face several unique challenges for high-resolution imaging. First, recovering a SAR image requires knowledge of the array positions throughout the scan. While recent work has introduced camera-based positioning systems capable of adequately estimating the position, recovering the algorithm efficiently is a requirement to enable edge and Internet of Things (IoT) technologies. Efficient algorithms for near-field SAR have been developed; however, they suffer from image defocusing under position estimation error and can only produce medium-fidelity images. In this paper, we introduce a mobile-friend vision transformer (ViT) architecture to perform SAR image super-resolution and artifact mitigation under array position errors. The proposed algorithm, Mobile-SRViT, is the first to employ a ViT approach for SAR image enhancement and is validated through simulation and empirical studies.
A YOLO-based Object Simplification Approach for Visual Prostheses

Visual prostheses have been introduced to partially restore vision to the blind via visual pathway stimulation. Despite their success, some challenges have been reported by the implanted patients. One of those challenges is the difficulty of object recognition due to the low resolution of the images perceived through these devices. In this paper, a deep learning-based approach combined with image pre-processing is proposed to allow visual prostheses' users to recognize objects in a given scene. The approach simplifies the objects in the scene by displaying the objects in clip art form to enhance object recognition. These clip art images are generated by, first, identifying the objects in the scene using the You Only Look Once (YOLO) deep neural network. The clip art corresponding to each identified object is then retrieved via Google Images. Three experiments were conducted to measure the success of the proposed approach using simulated prosthetic vision. Our results reveal a remarkable decrease in the recognition time, increase in the recognition accuracy and confidence level when using the clip art representation as opposed to using the actual images of the objects. These results demonstrate the utility of object simplification in enhancing the perception of images in prosthetic vision.
Comparative Evaluation of the Convolutional Neural Network based Transfer Learning Models for Classification of Plant Disease

The wide scale prevalence of diseases in agricultural crops affects both the production quality and quantity of agricultural products at local to regional scale. More often than not, the diseases remain unidentified causing huge distress to the farmers while threatening national food security. In order to circumvent this problem, early diagnosis of diseases using a fast and reliable method is beneficial. Plant disease identification from images captured by digital cameras is an area of active research. Use of various machine learning algorithms for plant disease classification and the evolution of deep convolutional neural network (CNN) based architectures have further enhanced the plant disease classification accuracy. In this context, an automated computer vision-based plant disease detection and classification scheme from plant and leaf’s photographs will be highly desirable. Although, there exist a few techniques currently used in an adhoc fashion for plant disease detection and/or classification, a systematic study to evaluate their usage and efficacy on actual plant data has largely remained unexplored.The aim of this paper is to evaluate various CNN based state-of-the-art transfer learning architectures like GoogLeNet, AlexNet, VGG16 and ResNet50V2 models for plant disease detection and classification. The models were tested on popular publicly available three plant disease benchmark database such as PlantVillage Dataset, New Plant Disease Dataset and Plant Pathology Dataset. Various validation metrics such as Precision, Recall, F1 score and overall accuracy were used to evaluate the final results of the experiments, which revealed that VGG16 rendered highest accuracy of 96.6%, 98.5% and 89% on the three dataset respectively, outperforming all other state-of-the-art models.
The Antecedents of Customer Intention to Use Mobile Health: An Application of Extended Technology Acceptance Model

The recent development of information technology has changed how individuals access services via online public transportation, e-marketplace, e-commerce, as well as ehealthcare. As the COVID-19 pandemic forces the customers to minimize contact with the service providers, they begin to use e-healthcare for their daily needs, medical consultations, online medicine purchases, even COVID-19 test appointments can be arranged using this application. The purpose of this study is to examine the main drivers of customer intention to use mobile health. The study applies the Extended Technology Acceptance Model. The study also adds external variables that are affecting customer’s intention to use were added to the model. The data was collected through online surveys, consisting of two hundred respondents of mobile health users. The data was analyzed using structural equation models, the result shows that customer’s intention to use e-healthcare application is heavily influenced by perceived usefulness and perceived ease of use, as well as subjective norm, health consciousness, and communication effectiveness. Furthermore, this study also offers tangible recommendations of improvement for current mobile healthcare companies.
Radar Target Simulation using Calibration Lines of Active Phased Array Antennas

Radar target simulators are essential for calibration of the radar as well as testing of various performance parameters relevant to radar subsystems. Conventional target simulators include external systems which are positioned at the far field of radar antenna. A target simulation technique is presented in this work which can simulate multiple targets with variations in range, doppler, echo strength, azimuth etc and act as an effective means for testing radar performance using the existing antenna calibration lines of the modern phased array radar. It thus forgoes the need of additional hardware for target simulation. Target waveform modulation is maintained same as that of the radar transmit waveform. The proposed target simulator is reconfigurable to simulate multiple targets with varying range, azimuth and doppler effects. Targets can be simulated for different trajectories. This feature can be extended to test the signal and data processor and other subsystems of the radar in the laboratory, extensively.
A Treatise on Econometric Forecasting

We investigate the effects of model misspecification and stochastic dynamics in the problem of forecasting. In economics and many fields of engineering, many researchers are guilty of the dangerous practice of treating their mathematical models as the true data generating mechanisms responsible for the observed phenomena and downplaying or omitting all together the important step of model verification. In recent years, econometricians have acknowledged the need to account for model misspecification in the problems of estimation and forecasting. In particular, a large body of work has emerged to address properties of estimators under model misspecification, along with a plethora of misspecification testing methodologies. In this work, we investigate the combined effects of model misspecification and various types of stochastic dynamics on forecasts based on linear regression models. The data generating process (DGP) is assumed unknown to the forecaster except for the nature of process dependencies, i.e., independent identically distributed, covariance stationary, or nonstationary. Estimation is carried out by means of ordinary least squares, and forecasts are evaluated with the mean squared forecast error (MSFE) or mean square error of prediction. We investigate the sample size dependence of the MSFE. For this purpose, we develop an algorithm to approximate the MSFE by an expression depending only on the sample size n and moments of the processes. The approximation is constructed by Taylor series expansions of the squared forecast error which do not require knowledge of the functional form of the DGP. The approximation can be used to determine the existence of optimal observation windows which result in the minimum MSFE. We assess the accuracy of the approximating algorithm with Monte Carlo experiments.
Sensitivity of aerosol optical depth trends using long-term measurements of different sun photometers

Abstract. This work aims to assess differences in the aerosol optical depth (AOD) trend estimations when using high-quality AOD measurements from two different instruments with different technical characteristics and operational (e.g. measurement frequency), calibration and processing protocols. The different types of sun photometers are the CIMEL that is part of AERONET (AErosol RObotic NETwork) and a precision filter radiometer (PFR) that is part of the Global Atmosphere Watch Precision Filter Radiometer network. The analysis operated for two wavelengths (500 and 501 and 870 and 862 nm for CIMEL–PFR) in Davos, Switzerland, for the period 2007–2019. For the synchronous AOD measurements, more than 95 % of the CIMEL–PFR AOD differences are within the WMO-accepted limits, showing very good measurement agreement and homogeneity in calibration and post-correction procedures. AOD trends per decade in AOD for Davos for the 13-year period of analysis were approximately −0.017 and −0.007 per decade for 501 and 862 nm (PFR), while the CIMEL–PFR trend differences have been found 0.0005 and 0.0003, respectively. The linear trend difference for 870 and 862 nm is larger than the linear fit standard error. When calculating monthly AODs using all PFR data (higher instrument frequency) and comparing them with the PFR measurements that are synchronous with CIMEL, the trend differences are smaller than the standard error. Linear trend differences of the CIMEL and PFR time series presented here are not within the calculated trend uncertainties (based on measurement uncertainty) for 870 and 862 nm. On the contrary, PFR trends, when comparing high- and low-measurement-frequency datasets are within such an uncertainty estimation for both wavelengths. Finally, for time-varying trends all trend differences are well within the calculated trend uncertainties.
PPS_2000364 879..900

Knowledge creation is a social enterprise, especially in political science. Sharing new findings widely and quickly is essential for progress. Scholars can now use Twitter to rapidly disseminate ideas, and many do. What are the implications of this new tool? Who uses it, how do they use it, and what are the implications for exacerbating or ameliorating existing inequalities in terms of research dissemination and attention? We construct a novel dataset of all 1,236 political science professors at PhD-granting institutions in the United States who have a Twitter account to answer these questions. We find that female scholars and those on the tenure track are more likely to use Twitter, especially for the dissemination of research. However, we consistently find that research by men shared on Twitter is more likely to be passed along further by men than research by women.
From soil to sea: sources and transport of organic carbon traced by tetraether lipids in the monsoonal Godavari River, India

Abstract. Monsoonal rivers play an important role in the land-to-sea transport of soil-derived organic carbon (OC). However, spatial and temporal variation in the concentration, composition, and fate of this OC in these rivers remains poorly understood. We investigate soil-to-sea transport of soil OC by the Godavari River in India using glycerol dialkyl glycerol tetraether (GDGT) lipids in soils, river suspended particulate matter (SPM), and riverbed sediments, as well as in a marine sediment core from the Bay of Bengal. The abundance and composition of GDGTs in SPM and sediments in the Godavari River differs between the dry and wet season. In the dry season, SPM and riverbed sediments from the whole basin contain more 6-methyl branched GDGTs (brGDGTs) than the soils. In the upper basin, where mobilisation and transport of soils is limited due to deficient rainfall and damming, contributions of 6-methyl brGDGTs in SPM and riverbed sediments are relatively high year-round, suggesting that they have an aquatic source. Aquatic brGDGT production coincides with elevated values of the isoprenoid GDGT-0 / crenarchaeol ratio in SPM and riverbed sediments from the upper basin, indicating low-oxygen conditions. In the wet season, brGDGT distributions in SPM from the lower basin closely resemble those in soils, mostly from the north and east tributaries, corresponding to precipitation patterns. The brGDGT composition in SPM and sediments from the delta suggests that soil OC is only effectively transported to the Bay of Bengal in the wet season, when the river plume extends beyond the river mouth. The sediment geochemistry indicates that also the mineral particles exported by the Godavari River primarily originate from the lower basin, similar to the brGDGTs, suggesting that they are transported together. However, river depth profiles in the downstream Godavari reveal no hydrodynamic sorting effect on brGDGTs in either season, indicating that brGDGTs are not closely associated with mineral particles. The similarity of brGDGT distributions in bulk and fine-grained sediments (≤ 63 µm) further confirms the absence of selective transport mechanisms. Nevertheless, the composition of brGDGTs in a Holocene, marine sediment core near the river mouth appears substantially different from that in the modern Godavari basin, suggesting that terrestrial-derived brGDGTs are rapidly lost upon discharge into the Bay of Bengal and/or overprinted by marine in situ production. The large change in brGDGT distributions at the river–sea transition implies that this zone is key in the transfer of soil OC, as well as that of the environmental signal carried by brGDGTs from the river basin.
Noncommutative Reduction of Nonlinear Schrödinger Equation on Lie Groups

We propose a new approach that allows one to reduce nonlinear equations on Lie groups to equations with a fewer number of independent variables for finding particular solutions of the nonlinear equations. The main idea is to apply the method of noncommutative integration to the linear part of a nonlinear equation, which allows one to find bases in the space of solutions of linear partial differential equations with a set of noncommuting symmetry operators. The approach is implemented for the generalized nonlinear Schrödinger equation on a Lie group in curved space with local cubic nonlinearity. General formalism is illustrated by the example of the noncommutative reduction of the nonstationary nonlinear Schrödinger equation on the motion group E(2) of the two-dimensional plane R2. In this particular case, we come to the usual (1+1)-dimensional nonlinear Schrödinger equation with the soliton solution. Another example provides the noncommutative reduction of the stationary multidimensional nonlinear Schrödinger equation on the four-dimensional exponential solvable group.
Loanblends in the speech of Greek heritage spe k r : a corpus-based lexicological approach

Found in situations of language contact between Greek and English, Greek heritage speakers living in the US, Canada, Australia, etc. produce loanblends, which combine an English stem e.g. fence and a Greek affix e.g. -ι, as in fénsi ‘fence’. These loanblends are very frequent contact-induced formations that have become part of the Heritage Speakers’ everyday language usage. This study analyses fifty (50) such loanblends found in the Greek Heritage Language Corpus, which contains data from Greek Heritage Speakers living in Chicago, US, tests the borrowability scale constraint and the unmarked gender hypothesis for loanwords, and discusses the lexicographic protocol for the compilation of an online dictionary of loanblends of Greek Heritage Speakers.
Influence of Experimental Skull Defects on Brain

Abstract: This study was aimed to observe the effects of skull defects on the brain in rats and further to investigate its underlying pathophysiological. Three different sizes of skull were removed in rats to produce models of skull defect, and then the behavioral changes were detected using a grip strength meter and neurobehavioral severity scale scores. The authors further examined the levels of cell apoptosis and autophagy, the cerebral blood flow with immunoblotting, and immunofluorescence micro-ultrasound system, respectively. The authors found that the sensory function but not the grip was impaired on the 6th day after a 5 × 10 mm defect while the motor function was on the 2nd day. In addition, the authors found an increment in B-cell lymphoma-2/BCL2-Associated X (Bcl2/Bax) and LC3 II/I expression, a maker of apoptosis and autophagy, respectively, in the defective hemisphere especially at the edge of the defective area. Importantly, the blood flow of internal carotid artery began to decline at 2 hours, and reached minimum on the 4th day, but began to recover on the 6th day in the hemi-defect group. In conclusion, a larger skull defect could impair the cognitive function but not the motor function and its underlying pathophysiology were mainly related to a decrease in cerebral flow.