Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Interest Packet Forwarding Solution for NDN enabled Internet of Underwater Things

Recent growth in network architecture like the Internet of Underwater Thing (IoUT) and future Internet paradigm like Named Data Network (NDN) has caught the scientific community's attention. NDN is considered a replacement for traditional IP-based networks. By using NDN, the node address is replaced with a content name to improve data availability. On the other hand, IoUT is a promising method for actively exploring and evaluating the marine environment. Based on the available marine environment, IoUT communication faces obstacles. When NDN is applied on IoUT the broadcast storm problem arises. This problem is caused by the same kind of packets, which increases the overall number of requests and retransmissions. In IoUT, the timely and rapid transfer of crucial packets is an issue. In this paper, we proposed a Selective Neighbor-based Broadcast Storm Scheme (SNBS) in NDN-enabled IoUT (N-IoUT), which uses the counter value to solve the broadcast storm problem. To regulate the broadcast storm and ensure efficient packet transfers, our approach selects the furthest autonomous underwater vehicle (AUV) based on a counter value. The proposed method is tested using simulation, which reveals that SNBS performs better as compared to the native NDN concerning the number of Interest packets and Interest satisfaction delay.
Congestion Control and Active Queue Management During Flow Startup

Transmission Control Protocol (TCP) has served as the workhorse to transmit Internet traffic for several decades already. Its built-in congestion control mechanism has proved reliable to ensure the stability of the Internet, and congestion control algorithms borrowed from TCP are being applied largely also by other transport protocols. TCP congestion control has two main phases for increasing sending rate. Slow Start is responsible for starting up a flow by seeking the sending rate the flow should use. Congestion Avoidance then takes over to manage the sending rate for flows that last long enough. In addition, the flow is booted up by sending the Initial Window of packets prior to Slow Start. There is a large difference in the magnitude of sending rate increase during Slow Start and Congestion Avoidance. Slow Start increases the sending rate exponentially, whereas with Congestion Avoidance the increase is linear. If congestion is detected, a standard TCP sender reduces the sending rate heavily. It is well known that most of the Internet flows are short. It implies that flow startup is a rather frequent phenomenon. Â Also, many traffic types exhibit an ON-OFF pattern with senders remaining idle for varying periods of time. As the flow startup under Slow Start causes exponential sending rate increase, the link load is often subject to exponential load transients that escalate in a few round trips into overload, if not controlled properly. It is true especially near the network edge where traffic aggregation is limited to a few users.
Unexplained Massive Galactorrhea following Bilateral Subpectoral Breast Augmentation: Report of a Case

Galactorrhea, although a fairly common condition in a gravid or recently postpartum female, is rarely seen as a complication of breast augmentation or reduction. Although sporadic cases have been documented in the literature after cosmetic and reconstructive breast surgery, documented evidence of the cause of the abnormal lactation, save the identification of a pituitary prolactinoma, remains speculative at best. This case report involves a 32 y.o. Hispanic female who underwent elective bilateral subpectoral saline breast augmentation and developed massive galactorrhea in the immediate postoperative period.
Non-linear Effects of Investment in Road Infrastructure on the Structural Competitiveness of the Economy: The Case of Burkina Faso

This paper assesses the non-linear effects of road infrastructure investment on the structural competitiveness of Burkina Faso's economy. After retaining the period from 1980 to 2015, the quadratic and spline estimation revealed a non-linearity between the structural economy competitiveness and investment in road infrastructure. Indeed, the quadratic estimate identified non lineary U-shaped inverted with an optimal threshold of 10.11%. With regard to the spline estimation, it also highlighted this nonlinearity and gave an optimal interval of [5%;15%]. The economic policy implication that emerges from these results is that in order to benefit from optimal structural competitiveness, the investment’s share in road infrastructure in the total investment budget must be between5% and 15%.
Physical impairments in the operation of low-cost LIDARs

In recent years, the use of light detection and ranging (LIDAR) systems has grown exponentially due to their convenience for sensing applications. For a variety of applications, LIDARs performance degrades when they operate in environmental conditions such as rain, fog and snow. Complex processing and higher power are required to overcome these issues. Consequently, LIDARs are often inaccessible to the average consumer as this increases their cost. In this project, physical impairments due to varied materials and environmental phenomena are investigated in two low-cost LIDAR; the Benewake TFmini LIDAR Module (TFmini) and the Garmin LIDAR-Lite v3HP (LLV3HP). Using the results, the feasibility of these LIDARs in the application of advanced driver assistance systems (ADAS) is assessed. Results from these tests reveal that the TFmini and LLV3HP produce incorrect range measurements when different materials and environmental conditions are introduced. This project, therefore, demonstrates that low-cost LIDARs such as the TFmini and LLV3HP are not feasible for ADAS applications such as blind-spot monitoring and lane departure warning.
Designing Disaster Recovery Plan of Data System for University

According to the QS World University Rankings, Andalas University is a 3-star university and is in ranked 12th of the best universities in Indonesia based on the Indonesian Ministry of Research and Higher Education criteria. The university is located in three different areas which are Padang, Payakumbuh, and Dhamasraya. Currently, the university‘s Information and Communication Technology Development Unit which is responsible in managing the information and communication technology has no formal documentation of response procedures in an event of an emergency. Being used by some 26,702 students and staffs, the system is potentially corrupted by a few possible disasters like viruses, hackers, and server downs. Therefore it is crucial for the organization to plan a documented security action to protect essential archives and application software in anticipating the catastrophes. This study developed a Disaster Recovery Plan (DRP), as a document to act on accidents which may occur in occur in information system failures. It is a plan for rapid recovery from the disaster or the emergency response so that the organization does not perceive the impact given by the information system failure.
An 8-FBGs Pressure Pad for COP Measurement of Plantar

The center of pressure (COP) is one of the important indicators to estimate the stability of human body. In this paper, an 8-FBGs plantar pressure pad was designed and fabricated to measure plantar COP. According to sensitivity of FBG pressure perception, surface fixation of FBG is better than three-layer sandwich and embedded structure. A total of 8 FBG sensors are instrumented in the pad at the location of the metatarsal region and heel in light of simulation and analysis of distribution of strain on the silicon pad. , and FBGs' average sensitivity is 0.0193 nm/N. Five subjects participated in a test of the pad as a standalone platform. , by which static plantar COP was monitored under different posture condition. In comparison with a Qualisys motion capture system. , they have high relevant results in motion trajectory of lower limb. , so that the proposed pad is able to acquire the trajectory of plantar COP and evaluate the stability of human balance.
Dynamic Behavior and Off-Design Performance Analysis of Solar Driven ORC Using Scroll Expanders

Solar driven ORC system is a possible solution for small-scale power generation. A scroll expander is considered due to its better suitability among other positive displacement expanders for small-scale power outputs. This work conducted a test of an ORC system with an expansion valve by varying the working fluid mass flow rate in two scenarios. A dynamic system-level model of ORC was developed and validated with experimental data. The validated model was used to predict the ORC performance considering off-design conditions of expander and solar insolation. The experimental data showed that pressures and temperatures exhibited the same trend as that of the working fluid mass flow rate, of which the evaporation pressure was the most sensitive to this variation. The simulation results are in good agreement with the experimental results. Results from the dynamic model showed that the ORC power output was underestimated by up to 54.7%, when off-design performance of expander was not considered. Considering the expander off-design performance and solar insolation, a highest thermal efficiency of 7.6% and an expander isentropic efficiency of 80.6% were achieved.
Energy management strategy of microgrid energy storage unit based on adaptive droop control

Aiming at the problems of large bus voltage fluctuations and non-proportional output of energy storage unit(ESU) charging and discharging power resulting in unbalanced state of charge(SOC) in microgrid, this paper proposes an adaptive droop control(ADC) strategy based on SOC, which uses the SOC value of the ESU as the droop coefficient, and considering the fluctuation range of bus voltage, uses different control strategies for the charging and discharging of ESUs, so that the charging and discharging power of the ESU can be proportionally distributed according to its SOC value, stabilizing the fluctuating voltage, and achieving SOC balance. Energy management strategy uses a secondary control method to simultaneously monitor the bus voltage and the SOC value of the ESU. it coordinates the flexible switching between various working modes in different environments of the microgrid, effectively stabilize the bus voltage, and improve the quality of microgrid. A simulation model of the control strategy was built on the Matlab/Simulink platform, and the effectiveness of the control strategy is verified on the optical storage DC microgrid experimental platform based on TMS320F28335.
Evidence of an extreme weather‐induced phenological mismatch and a local extirpation of the endangered Karner blue butterfly

In 2011, an experiment was undertaken to examine spring synchrony between the endangered Karner blue butterfly (Lycaeides melissa samuelis) (Kbb) and its obligate host plant, wild blue lupine (Lupinus perennis) at Indiana Dunes National Lakeshore (INDU), where the southernmost population of Kbb occurred at the time of this study. From 2012 to 2014, field‐placed Kbb eggs were observed for larvae hatching in conjunction with observations of lupine emergence in oak savanna habitat. In 2012, 61% of Kbb hatched when <5% of lupine had emerged due to an extreme early spring event as compared to subsequent years where temporal overlap was >15% between Kbb and lupine. Laboratory experiments testing the sensitivity of Kbb hatching to warm temperatures during the winter of 2011–2012 confirmed that Kbb eggs were susceptible to temperature‐induced hatching. In the summer of 2012, second generation Kbb larvae feeding on sun‐exposed lupine had higher mortality due to the heat and drought conditions that resulted in earlier plant senescence. Following 2012, Kbb were no longer observed at INDU. This observation illustrates the pressing need for adaptive management strategies that account for extreme weather events brought on by climate change.
Classification of Aglaonema Using Machine Learning

Recently, Aglaonema has become an ornamental plant with high economic value. Various Aglaonema variants are available in the market with various prices offered, e.g., Lipstick, Suksom, Red cochin, Widuri, and Pride of Sumatra (pos). People often need help distinguishing the variants to prevent being deceived when buying the plants. A classifier to distinguish several local Aglaonema variants with high similarities is proposed to solve this problem. A collection of images from each Aglaonema variant are used to train the classifier. Both traditional classification algorithms, such as the $k$-nearest neighbours and support vector machines, and modern algorithms, specifically the Inception-v3, were employed. Classifier evaluation is performed by applying 5-folds cross-validation. Experimental results show that the best level of accuracy is obtained from the Inception-v3 algorithm by achieving an accuracy rate of 83.02%. This result indicates that the classification of Aglaonema is relatively hard, but feasible to continue for improvements.
Malicious PDF detection Based on Machine Learning with Enhanced Feature Set

PDF is one of the most popular document file formats due to its flexibility, platform independence and ability to embed different types of content. Over the years, PDF has become a popular attack vector for spreading malware and compromising computer systems. Existing signature-based defense systems have extremely high recall rates, but quickly become obsolete and ineffective against zero-day attacks, which makes them easy to circumvent by malicious PDF files. Recently, Machine Learning (ML) has emerged as a viable tool to improve discovery of previously unseen attacks. Hence, in this paper we present enhanced ML-based models for the detection of malicious PDF documents. We develop an approach for ML-based detection with static features derived from PDF documents leveraging existing tools and propose new, previously unused features to enhance the performance of the ML-based classifiers. Our investigative study is conducted on the recently published Evasive-PDFMal2022 dataset, which was used to evaluate seven ML classifiers based on our proposed method. The EvasivePDFMal2022 dataset consists of 4,468 benign samples and 5,557 malicious PDF samples. The results of the experiments show that our proposed approach with the enhanced features enabled improved accuracies in five out of seven of the classifiers that were evaluated. The results demonstrate the potential of the new features to increase the robustness of feature-based PDF malware detection.
Selecting Students for Medical Education : Exploring novel approaches

markdownabstractAbstract Medical schools all over the world have to select their students as the number of applicants highly exceeds the number of places available. In this dissertation, we examined why the current selection method employed by Erasmus MC Medical School is successful in including students who perform well during medical school, and approaches from other domains are investigated to examine whether they are of use to medical school selection.
Speaker Identification and Verification using Deep Learning

Many voice assistants gained importance across globe in the recent times, for example, Cortana, Siri, Ok Google. These assistants are part of everyone's life these days. The main motive behind the proposed system is to improve recognition assistant system. The speaker prediction model is trained using features MFCC, Chroma, Tonnetz, Mel spectrogram, and Spectral contrast extracted from audio samples. The proposed system has numerous real-world applications, such as meeting transcription, unlocking smart devices using voice, and online viva voice verification. It can replace the existing biometric system for faculty attendance and traditional fingerprint recognition. A Dense Neural Network was created for each audio feature and finally concatenated using a concatenation layer which fetched the best performance output compared to LSTM. Dense Neural Network successfully predicted the speaker with an accuracy of more than 95% most of the times. In the case of LSTM, due to fewer samples, the accuracy of speaker prediction is around 79%. In the case of CNN, the accuracy of speaker prediction is around 86%; this behavior can be attributed to the noise environment. When an unknown speaker tries to speak, the Dense Neural network can manage the task by placing them in an anonymous class.
Conformational state switching and pathways of chromosome dynamics in cell cycle

Cell cycle is a process and function of a cell with different phases essential for cell growth, proliferation, and replication. Cell cycle depends on the structure and dynamics of the underlying DNA molecule, which underpins the genome function. A microscopic structural-level understanding of how genome or its functional module chromosome performs the cell cycle in terms of large-scale conformational transformation between different phases such as the interphase and the mitotic phase is still challenging. Here, we develop a non-equilibrium excitation-relaxation energy landscape-switching model to quantify the underlying chromosome conformational transitions through (de-)condensation for a complete microscopic understanding of the cell cycle. We show that the chromosome conformational transition mechanism from the interphase to the mitotic phase follows a two-stage scenario, in good agreement with the experiments. In contrast, the mitotic exit pathways show the existence of an over-expanded chromosome that recapitulates the chromosome in the experimentally identified intermediate state at the telophase. We find the conformational pathways are heterogeneous and irreversible, as a result of the non-equilibrium dynamics of the cell cycle from both structural and kinetic perspectives. We suggest that the irreversibility is mainly due to the distinct participation of the ATP-dependent structural maintenance of chromosomal protein complexes during the cell cycle. Our findings provide crucial insights into the microscopic molecular structural and dynamical physical mechanism for the cell cycle beyond the previous more macroscopic descriptions. Our non-equilibrium landscape framework is general and applicable to study diverse non-equilibrium physical and biological processes such as active matter, differentiation/development and cancer.
Whole-cell bioreporters for evaluating petroleum hydrocarbon contamination

Abstract Whole-cell bioreporter (WCB) is a category of biological analytical tools using living microorganisms to sense chemicals by producing quantifiable signals. Comparing to conventional chemical analysis, WCBs have the features of rapidness, cost-effectiveness and easy operation. This paper critically reviews the concept and recent progress in constructing and applying WCBs for sensing petroleum hydrocarbons, including n-alkanes and aromatic hydrocarbons. Additionally, some WCBs can evaluate the toxicity of petroleum hydrocarbons, linking their natural occurrence to ecological risks. Further discussions address the state-of-art design of WCBs for petroleum hydrocarbons, comprehensively assessing the key challenges in improving their sensitivity and limit of detection, including host strains, reporter genes and regulatory systems. Regarding the significant gaps between laboratory trials and real-world scenarios, we also discuss some restriction factors influencing bioreporter performance in sensing petroleum hydrocarbons in environmental samples, e.g., low solubility, and suggest some solutions as biosurfactant and chemotaxis. Further development of sensing devices and multisensing WCBs can provide more feasibilities overcoming the applicable barriers. This review comprehensively summarizes the current progress of WCBs for petroleum hydrocarbons, and raises new insights into their bright future in practices of environmental monitoring and risk assessment. Graphical Abstract
IR Artificial Compound Eye

In this work, a 3D IR artificial compound eye (ACE) optical component is fabricated using thermoplastic IR polymer material. The femtosecond laser‐assisted wet etching and the 3D nano‐imprinting techniques are proposed for fabricating this thin shell multi‐aperture component. The prepared component is close‐packed with over 1000 high‐quality micro‐facet‐lenslets (ommatidia) on the semispherical thin shell dome with base radius of 2.7 mm and sag height of 2.1 mm, enabling enlarged field of view to 148°. The ommatidia with diameter of 150 µm and numerical aperture of 0.35 are demonstrated to have a practical spatial resolution up to 160 lp mm−1. IR imaging experiments (both in passive and active scenarios) confirm its outstanding optical properties and uniformity, and demonstrate the probability of thermal target imaging with such component in the near‐infrared band. The proposed fabrication strategy shows remarkable efficiency and advantage in massive replication, and such ACE component predicts its potential in emerging applications, such as IR imaging, target tracking, and so on.
The Public Blockchain Ecosystem: An Empirical Analysis

This paper provides an empirical overview of the largely unexplored public blockchain ecosystem. Our overview highlights that only a few blockchains dominate the ecosystem although no single blockchain, not even Bitcoin, dominates uniformly. We explain our empirical findings with a simple theoretical framework that establishes three key economic attributes - scale, security, and adoption - as the determinants of blockchain user utility. We examine each blockchain along those dimensions empirically. We then establish that whether a blockchain could be welfare enhancing for some user profile can be determined by a comparison across our attributes. Applying that comparison yields that only a few blockchains could be welfare enhancing. Our results thus explain why only a few blockchains dominate the broader blockchain ecosystem and further provide an empirical framework from which to evaluate progress within the blockchain ecosystem.
Korean Morphological Analyzer for Neologism and Spacing Error based on Sequence-to-Sequence

In order to analyze Internet text data from Korean internet communities, it is necessary to accurately perform morphological analysis even in a sentence with a spacing error and adequate restoration of original form for an out-of-vocabulary input. However, the existing Korean morphological analyzer often uses dictionaries and complicate preprocessing for the restoration. In this paper, we propose a Korean morphological analyzer model which is based on the sequence-to-sequence model. The model can effectively handle the spacing problem and OOV problem. In addition, the model uses syllable bigram and grapheme as additional input features. The proposed model does not use a dictionary and minimizes rule-based preprocessing. The proposed model showed better performance than other morphological analyzers without a dictionary in the experiment for Sejong corpus. Also, better performance was evident for the dataset without space and sample dataset collected from Internet.
Fractional Order PI-Based Control Applied to the Traction System of an Electric Vehicle (EV)

This paper presents a design of a cruise control based on a fractional-order proportional and integral (PI) direct torque control applied to the traction system of an electric vehicle (EV). The paper also discusses the modeling, control design and simulation, resulting in a numerical simulator composed of detailed models of the main components: transmission system, induction motor, power electronics, control system, and vehicle dynamics. The simulation was developed in MATLAB/Simulink and will allow the estimation of the energy consumption of an EV under specific configurations. Simulation results show the efficiency of the designed control. These simulations were carried out using the velocity profiles given by the New Europe Drive Cycle (NEDC).