Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementation of Business Resilience in MSMEs Malang City in Facing the Covid-19 Pandemic

The Covid-19 pandemic has had an impact on changes in all ﬁelds, including in the economic ﬁeld with its multidimensional impact. The biggest impact is felt by MSME players, especially small-scale ones. In minimizing these impacts, MSME actors need special methods that can be used as the right solution to deal with unexpected situations such as the Covid-19 pandemic. This study was presented to see the implementation of the concept of business resilience in MSMEs in facing the Covid-19 pandemic disaster using the latest literature study methods. The research method used is a literature study, consisting of collecting various sources from previous research, then comparing the results found with relevant theories before analyzing them. The results of this study concluded that theapplicationofbusinessresiliencetoMSMEactorsinMalangCitycanmaintaintheirbusiness,basedontheresultsoftheanalysisitwasfoundthatMSMEsin MalangCitycanmaintaintheirbusinessbyhavingentrepreneurialcharacteristics, theuseoftechnologyandsupportfromthegovernmentthatimplementspoliciesto helpMSMEactors.Thus,theuseoftheconceptofbusinessresilienceisimportant tobeappliedbyMSMEactorswhenexperiencingpandemicproblemsthatarise unexpectedly. Basedontheﬁndingsfromtheresearch,therecommendationsfor thenextauthoraretoraisethethemeofbusinessresilienceanditsapplication totheconditionsofthenewnormalera.Ormaybeyoucanapplytheconcept of business resilience in a larger company as an object of research, to see how this concept is applied, then the ﬁndings can be compared with this study, so that ﬁndings are obtained regarding the effectiveness of implementing more complex business resilience.
Effect of Sand and Dust Storms on the Coverage of Cellular Networks in the Millimeter Wave Band

Sand and dust storms (SDSs) threaten reliable communications over wireless links at millimeter-wave and terahertz frequency bands, which are expected to be used in future cellular networks. In such weather conditions, besides the radio frequency-dependent path-loss, additional signal attenuation is expected. SDS attenuation is related to visibility, receiver distance to the storm origin point, type of soil, climate conditions, frequency, temperature, and humidity. Leveraging on experimental measurements carried out in previous works, a novel mathematical framework is introduced to include SDS-dependent attenuation in the performance evaluation of stochastic geometric modeled wireless cellular networks in the millimeter-wave band in terms of coverage probability. Numerical results, which are validated by Monte Carlo simulations, are presented to show the effects of different parameters on system performance.
Data science based efficient and automated spectroscopy for submillimeter single-dish telescopes

In this paper, we present a new data scientific approach for efficient and automated spectroscopy with millimeter and submillimeter single-dish telescopes. The proposed approach avoids direct subtraction between two noisy spectra (i.e. on-source and off-source spectra) that is common in the current data reduction: It then offers us to improve the observation sensitivity by a factor of $\gtrsim\sqrt{2}$ and reduce artificial baseline ripples in parallel with developing observational instruments. We demonstrate such upgrades in the real observed spectra taken by existing large millimeter single-dish telescopes. We finally discuss the application of the proposed approach for the future large submillimeter single-dish telescopes that will yield petabytes of data resulting from sensitive, wide field $(\gtrsim 1\mathrm{deg}^{2})$, and wide band ($\gt100$ GHz) imaging spectroscopy.
Disturbance Rejection Enhancement of Vector Controlled PMSM Using Second-order Nonlinear ADRC

The speed control system for the Permanent Magnet Synchronous Motor (PMSM) is designed by adopting active disturbance rejection control (ADRC) techniques. This design utilizes the output of the speed regulator, specifically, the input of the current regulator, to approximately substitute for the actual q-axis stator current. This paper has been used second-order linear (L) and nonlinear (NL) ADRC controller. Simulation results indicate that under this scheme, the closed-loop system can have a stronger anti-disturbance ability.
Design of Interleaved DC-DC SEPIC Converter Based Switching Regulator Using Neural Network Based Machine Learning Algorithm

DC-DC Converter based Switching Regulators are widely utilized in industrial and domestic applications, because of high conversion efficiency with reduced power loss. This research work uses an Interleaved DC-DC SEPIC Converter (ISC) to construct a switching regulator because of following reasons: It is a two-stage SEPIC converter with phase-shifted switches operating with decreased reduced ripples, switching stress, and it also employs small size inductors and capacitors for the same output power because of high operating frequency. Here Neural Network (NN) based Cascade Controller is employed to construct ISC based switching regulator. A NN is a machine learning technique used to train data without the need of human intervention. Three different training techniques are utilized to train the NN: Levenberg-Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG) Algorithm. The time domain parameters for achieving the regulated output voltage have been lowered with the NN machine learning algorithm when compared to the Conventional Proportional Integral (PI) controller.
A physically based description of floodplain inundation dynamics in a global river routing model

Current global river routing models do not represent floodplain inundation dynamics realistically because the storage and movement of surface waters are regulated by small‐scale topography rather than the commonly used spatial resolution of global models. In this study, we propose a new global river routing model, CaMa‐Flood, which explicitly parameterizes the subgrid‐scale topography of a floodplain, thus describing floodplain inundation dynamics. The relationship between water storage, water level, and flooded area in the model is decided on the basis of the subgrid‐scale topographic parameters based on 1 km resolution digital elevation model. Horizontal water transport is calculated with a diffusive wave equation, which realizes the backwater effect in flat river basins. A set of global‐scale river flow simulations demonstrated an improved predictability of daily‐scale river discharge in many major world rivers by incorporating the floodplain inundation dynamics. Detailed analysis of the simulated results for the Amazon River suggested that introduction of the diffusive wave equation is essential for simulating water surface elevation realistically. The simulated spatiotemporal variation of the flooded area in the Amazon basin showed a good correlation with satellite observations, especially when the backwater effect was considered. The improved predictability for daily river discharge, water surface elevation, and inundated areas by the proposed model will promote climate system studies and water resource assessments.
Nappes in the internal zone of the northern Labrador Trough: Evidence for major early, NW-vergent basement transport

The westernmost hinterland zone of the northern Labrador Trough is characterized by four NW-trending, Archean basement bodies overlain by an upward-coarsening, amphibolite-facies volcanosedimentary cover sequence correlative with the Kaniapiskau Supergroup of the Labrador Trough. Two of the gneissic basement bodies occur in antiformal cores, but two occur in the cores of synforms. The synformal gneisses are thought to represent the down folded lower limb of a single, basement-cored nappe floored by a large thrust that roots down into one of the basement domes. The history of this area during the Hudsonian Orogeny involved initial westerly imbrication and transport of the cover succession along a thin decollement zone at the basement-cover interface. This was followed by NW-directed, basement-involved thrusting and associated folding, and large-amplitude, up-right to SW-vergent, SE-plunging folding. The NW-vergent, basement-involved event appears to be restricted to this portion of the hinterland zone.
Understanding Distance-Dependent Variations for Analog Circuits in a FinFET Technology

Analog circuits are sensitive to device variations. Random device variations are well modeled and quantified in the literature, but analog-relevant distance-dependent device variation measurements have not been reported for newer technology nodes. To reduce the impact of distance-dependent variations, layout patterns such as common-centroid are often used. However, these patterns use larger area and have higher parasitics than clustered (NonCC) patterns in FinFET technologies where unit parasitics are higher and design rules are more complex. This work measures variations on multiple dies in a 12nm FinFET technology, each with about 10,000 devices, and models the distance-dependent component. We then apply these findings to show that NonCC patterns can be used in lower-resolution DACs to meet mismatch specifications while reducing layout area.
Multimodal rough set transformer for sentiment analysis and emotion recognition

Sentiment analysis and emotion recognition are crucial tasks that utilize multimodal information. Transformer models have shown exceptional performance in multimodal fusion. However, traditional dot product transformers do not tolerate uncertainty inside sentiment analysis and emotion recognition data. In this study, we introduce rough set self-attention and rough set cross-attention mechanisms for multimodal sentiment analysis and emotion recognition. A common concept is established based on granulation relations to extract important features through approximation. We then investigate a multimodal fusion transformer network based on rough set theory, which facilitates the interaction of multimodal information and feature guidance through rough set cross-attention. Our empirical findings demonstrate that this is the first integration of rough set theory and transformer mechanisms for multimodal sentiment analysis and emotion recognition. Compared to traditional transformer fusion methods, our model can handle uncertain information and provides stronger global relationship guidance, allowing for better extraction of semantic information from multimodal data. We evaluate our model on sentiment analysis and emotion recognition experiments using the CMU-MOSEI and MELD datasets. The results show that our method outperforms state-of-the-art networks.
New Non-Volatile Memory Technologies and Neuromorphic Computing

Picture and audio recognition are two examples of machine learning applications that need a lot of data flow to train or categories quickly. There is a conventional von Neumann architecture still used in computer systems, which separates the computation and memory units and connects them through a data bus. Contrary to the needs of machine learning, Von-Neumann bottlenecks restrict bandwidth and increase power consumption. Using new non-volatile memory technologies, neuromorphic computing may be able to address these issues. The Von Neumann bottleneck is avoided when computing is done in a manner similar to how the human brain concurrently processes a large quantity of information. Neuromorphic computing still has a lot of unanswered problems and concerns. Three key aspects of neuromorphic computation are addressed in this article. With the most recent neuromorphic system ideas in mind, we'll look forward to seeing what's possible in the near future. System, architecture, circuit, and device simulators for this new computing paradigm are all being investigated. Neuromorphic designs' hardware security flaws may also be traced back to recent progress in the CMOS field.
Pest Detection Using Random Forest

In the agriculture sector, plants illnesses that cause crop destruction are to blame for financial losses. Whereas pesticides have been employed to alter agricultural yield, their excessive usage has a detrimental impact on the ecosystem. Determining the need for pesticides depends significantly on the ability to identify illnesses and distinguish them from nutritious deficiencies. The traditional approaches to identifying plant diseases need lengthy, laborious chemical procedures to be completed in a lab. Using machine learning and image processing techniques, this research describes an autonomous method for identifying & categorizing plants diseases. For algorithms training, the features separation technique is used on pictures. To determine which Machines Learning method is most suitable for identifying diseases, the performance of several techniques is assessed using training data. Unseen photographs are contained in the test folder, which is used to verify the system's effectiveness at identifying plant illnesses. Large numbers of photos may be used to train the system, which produces correct results more quickly. Agricultural illnesses are a danger to global agricultural security; however, it is still challenging to rapidly diagnose them since the required infrastructure is lacking in many regions of the world. Since the introduction of exact techniques, impressive accomplishments have been made within the area of categorizing images of leaves. This article makes use of Random Forest to discriminate between healthy and diseased leaves using the obtained data sets. Our plan calls for the creation of the dataset, features extraction, classifiers training, and categorization as execution activities. Ill and healthy leaf datasets were developed, and Random Forest was used to merge and train the datasets to categorize photographs of sick and healthy leaves. In conclusion, by employing machine learning to learn from those enormous data sets that are freely accessible, we may clearly detect the sickness existing in plants on a large scale.
LazyUAV: A Minimal Displacement Coverage Strategy for Multi-UAV mmWave Networks

Unmanned Aerial Vehicles (UAVs) are a potential platform for deploying millimeter wave (mmWave) base stations. Their high spatial flexibility makes them an attractive choice for the deployment in disaster management scenarios, and for offloading in crowded areas. Apart from the stringent propagation requirements of mmWaves, one challenge that has to be addressed is the limited power onboard a UAV, which is used to hover and move the device, and of course, to transmit data. In this paper, we deal with the deployment of UAVs with an aim to minimising their displacement in subsequent time. We take into consideration user mobility, and propose LazyUAV, a Set-Cover based geometric approach to minimise UAV displacement, while maintaining maximal coverage. Via extensive simulations, we demonstrate the effectiveness of our approach.
Plasma Frequency Determination from In-situ Wave Receivers

Plasma frequency is a fundamental parameter in the space plasma environment and is most accurately derived from in-situ wave receivers. Although semi-automated methodologies have been developed for use with post-processed data to extract the upper hybrid resonances from dynamic spectrograms, these such tools require significant attention from trained operators as a man-in-the-loop. Pattern recognition/image classification is an ideal technology to resolve such problem. Initial results from the signal identification algorithm developed at AFRL demonstrate its utility in solving an outstanding problem dating to the beginning of space age. Once proven successful, the fully automated application can be modified to provide space measurements as input for any assimilative model significantly improving predictive capabilities. Similar algorithms can be tailored to support future mission with similar wave receivers.
New Insights for DNA Measurement by Memristive Biosensors

This paper proposes a novel approach to DNA measure based on memristive silicon nanowire (SiNW). The SiNWs are fabricated by a lithographic technique, representing hysteric electrical response. After bio-functionalization with probe Single-Stranded DNA (ssDNA), the device is able to measure target nucleic acid molecules on dried samples as a memristive biosensor. A comparative analysis of three amineterminated ssDNA immobilization strategies is investigated to design and optimize the approach for potential label-free measurements. The silicon nanowire surface is firstly silanized by (3-Glycidyloxypropyl)trimethoxysilane (GPTMS), where the epoxy group works as a linker. The condensation occurs between expoy groups and amino groups as the case of study via: (a) Direct immobilization of probe ssDNA on nanowire surface, named as ’’Direct method’’; (b) Alkaline ambience with KOH assisted probe ssDNA immobilization by activating amino group in an alkaline ambience, named as ’’KOH method’’; (c) NHS/EDC assisted probe ssDNA immobilization by activating epoxy group in a neutral ambience, named as ’’NHS/EDC method’’. After immobilization, fluorescence-labeled target nucleic acid molecules are hybridized on the device. The two-terminal electrical responses for each step are recorded. The variation of the voltage gap (Vg) reveals the mechanism behind the chemical reactions. It is demonstrated that the NHS/EDC assisted strategy and KOH strategy (PH around 9) present a similar significant voltage gap variation for DNA hybridization performance, while direct strategy shows no voltage gap change after immobilization. With such an approach, label-free measurements are proposed to detect target single-stranded DNA (ssDNA)/RNA with custom probe nucleic acid molecules.
Action Recognition with PIR Sensor Array and Bidirectional Long Short-term Memory Neural Network

The spatio-temporal sequence of human body movements provides important information about daily action patterns. This article presents a pyroelectric infrared (PIR) sensor array for detecting human motion features and develops a bidirectional long short-term memory (LSTM) neural network for sequence recognition. A PIR sensor array with five direction-sensitive modules is proposed to detect the changing infrared field induced by the human head, upper limb, and lower limb. The low-dimensional output of the sensor array is directly fed to a multi-layer bidirectional LSTM-based neural network for sequential dependency learning. A PIR data set of seven daily actions is generated by four subjects imitating the predefined movements. Experimental results demonstrate that the presented approach achieves high accuracy in action recognition.
Empowering Cardiologists with Deep Learning YOLOv8 Model for Accurate Coronary Artery Stenosis Detection in Angiography Images

Coronary artery stenosis is a common manifestation of cardiovascular disease. For a prompt diagnosis and successful treatment of cardiovascular disease, precise stenosis identification and quantification are essential. Manual interpretation of angiography images is subjective and time-consuming, leading to potential errors and delays in diagnosis. In Present time, deep learning techniques such as You Only Look Once (YOLO) algorithms have shown promising results in image recognition tasks. In this type of study, YOLOv8 algorithm has been used for accurate detection of coronary artery stenosis in angiography images. The collected dataset of angiography images from Mendeley website. There are 236 images that have been utilised in the experiment, with 80% of the images being used for training and the remaining of 20% being used for testing. All of these techniques aid patients who have been given a diagnosis of coronary artery disease and annotate the images to show whether or not there is stenosis. The YOLOv8 algorithm was subjected to transfer learning. Which also includes fine tuning on pre trained model. The experimental results shown that after fine tuning of YOLOv8 algorithm provides better detection of coronary arteries stenosis. The Performance to this approach includes Precision, Recall and Mean Average Precision (mAP) with value of 82%, 58% and 65% respectively. The investigation of results shows that parameter tunned YOLOv8 can help to empower cardiologist for fast and reliable diagnosis of cardiovascular disease and ultimately leading to patient survival rate
Improving Power Cycle Lifetime of SiC Power Modules with Double-Bonded Wire: Experimental and Simulation Analysis

The trend of using silicon carbide power modules is driven by applications in traction, electric vehicles, and renewable energy generation. However, higher power density and temperature pose more severe challenges to the reliability of wire bonds in the package. This paper investigates the effects of geometric parameters on wire bond reliability through a comparison of single and double bonds and finds that double bonding can significantly enhance the thermal-mechanical performance of bonding wire.
A multiobjective ant colony optimization approach for scheduling environmental flow management alternatives with application to the River Murray, Australia

In regulated river systems, such as the River Murray in Australia, the efficient use of water to preserve and restore biota in the river, wetlands, and floodplains is of concern for water managers. Available management options include the timing of river flow releases and operation of wetland flow control structures. However, the optimal scheduling of these environmental flow management alternatives is a difficult task, since there are generally multiple wetlands and floodplains with a range of species, as well as a large number of management options that need to be considered. Consequently, this problem is a multiobjective optimization problem aimed at maximizing ecological benefit while minimizing water allocations within the infrastructure constraints of the system under consideration. This paper presents a multiobjective optimization framework, which is based on a multiobjective ant colony optimization approach, for developing optimal trade‐offs between water allocation and ecological benefit. The framework is applied to a reach of the River Murray in South Australia. Two studies are formulated to assess the impact of (i) upstream system flow constraints and (ii) additional regulators on this trade‐off. The results indicate that unless the system flow constraints are relaxed, there is limited additional ecological benefit as allocation increases. Furthermore the use of regulators can increase ecological benefits while using less water. The results illustrate the utility of the framework since the impact of flow control infrastructure on the trade‐offs between water allocation and ecological benefit can be investigated, thereby providing valuable insight to managers.
HAMR Areal Density Optimization Using Deep Neural Networks

Deep neural networks (DNNs) have been used to investigate geometrical scaling of heat-assisted magnetic recording (HAMR). Our investigation considers areal density capability (ADC) sensitivity with respect to distinctive design features. We study HAMR performance by numerical scaling of key parameters including thermal profile spot size and reader dimension. DNNs are trained based on a full recording system model, which includes the writer, the reader, and the channel detector, to accelerate ADC optimization through geometrical scaling. In this article, we show that by providing adequate training data through sampling the entire design space, a deep feedforward neural network can learn the behavior of HAMR and accelerate BER estimation for triple track recording.