Miracle-dz/UniFAR-FTUs huggingface dataset:
High-Speed IM/DD System Based on OTDM Technique for Next-Generation Datacenter Network

We report on the novel optical-time-division-multiplexing (OTDM) technique which can double the per-wavelength data rate of the intensity-modulation/direct-detection (IM/DD) system. In this technique, we utilize a sinusoidally modulated light source instead of a mode-locked laser as an input pulse source and detect the OTDM signal by using a single photodetector without the complicated optical demultiplexer. By using this technique, we demonstrate the transmission of a 300-Gb/s PAM8 signal using LiNbO3 Mach-Zehnder modulators having a 3-dB bandwidth of only 17.2 GHz. We also fabricate the proposed OTDM transmitter in a silicon-photonics (SiP) chip and use it in the same transmission experiment.
Identification of Factors Influencing the Commission of Burglaries

As in many countries, burglary is a very serious crime in Turkey. Scientific methods and techniques are needed to solve complex burglary cases. It can be argued that factors leading to the commission of a crime are important for formulating preventive strategies in the community. In this study, the contributing factors are categorized into three main groups of predictors: opportunity factors (agent), offender factors (host), and environmental factors. Criminal method (technique) and time of burglary are conceived as the opportunity factors. Four personal characteristics of offender factors are age, gender, marital status, and education level. Distance between the home addresses of burglars and target houses and distance between target houses and police stations were examined as two environmental factors. In conclusion, the best way to reduce burglary rate is to focus on offender factors. Dealing with opportunity factors and environmental factors would also contribute to a decreased burglary rate.
TỔNG QUAN HỆ THỐNG CÁC NGHIÊN CỨU ĐÁNH GIÁ KINH TẾ DƯỢC CỦA THUỐC TIÊM TRUYỀN ĐÓNG LIỀU SẴN SO VỚI THUỐC TIÊM TRUYỀN TRUYỀN THỐNG

Đặt vấn đề: Thuốc tiêm đóng liều sẵn là một trong những giải pháp có khả năng làm giảm bớt các sai sót thuốc (SST) liên quan tới quá trình chuẩn bị và thực hiện thuốc so với thuốc tiêm truyền thống, trong khi vẫn đảm bảo hiệu quả và an toàn của thuốc. Mục tiêu: Thực hiện một nghiên cứu tổng quan hệ thống về các đánh giá kinh tế y tế của thuốc tiêm truyền đóng liều sẵn so với thuốc tiêm truyền truyền thống để cung cấp các bằng chứng cho các cơ quan quản lý và các nhà hoạch định trong việc lựa chọn thuốc. Phương pháp nghiên cứu: Tổng quan hệ thống thực hiện theo hướng dẫn thực hiện tổng quan hệ thống của PRISMA (1). Kết quả nghiên cứu: Bốn nghiên cứu đạt tiêu chuẩn lựa chọn đã cho thấy các thuốc tiêm truyền đóng liều sẵn có thể đem lại lợi ích trên nhiều khía cạnh. Thuốc tiêm truyền đóng liều sẵn giúp giảm tỷ lệ SST, từ đó giảm chi phí điều trị liên quan tới SST, đồng thời có thể kéo dài thời gian sống và nâng cao chất lượng cuộc sống của bệnh nhân. Ngoài ra, thuốc tiêm đóng liều sẵn có thể làm tăng tỷ lệ bệnh nhân được nhận điều trị. Kết luận: Việc sử dụng thuốc tiêm truyền đóng liều sẵn đem lại lợi ích về cả lâm sàng và kinh tế.
Biometric Cryptosystem with Deep Learning: A New Frontier in Security

The Biometric cryptosystem uses a variety of methods to protect templates. In this work, a deep learning-based approach to improve the robustness of the fuzzy vault scheme in biometric cryptosystems. Our approach uses a CNN to extract distinctive features from biometric data and generate the polynomial equation that unlocks the vault. We evaluate our approach on a dataset of fingerprint images and demonstrate that it achieves higher accuracy of 89.9% than traditional methods. The relation between original and decrypted image is computed based on various parameters such as Cr. , MSE, MAE etc. and overall fair performance is achieved on four fingerprint databases.
Real Time Humanoid Footstep Planning With Foot Angle Difference Consideration for Cost-To-Go Heuristic

This paper proposes a real-time footstep planning framework based on the A* footstep planning algorithm. The proposed footstep planner utilizes an energy consumption-based cost function to generate energy efficient navigation plan. To increase the computational efficiency, the cost-to-go heuristic function, which approximates the remaining cost to reach the goal, is modeled more precisely by considering the angle difference between the robot and the goal position. Furthermore, an efficient dual-level feasibility check is done to avoid occlusion with the environment while stepping over small objects if possible. Finally, the proposed footstep planning algorithm is integrated with the mapping algorithm and the walking controller to solve the humanoid navigation problem and validated in simulation and real-world experiments.
Enhancement of Coil Voltage of a Galfenol-based Energy Harvester by Dimension Modification

With the increasing demand for clean energy and a good reliable form of energy, magnetostrictive energy harvesters have continuously developed, which derive energy from ambient vibrations. A perpetual source of electrical energy can be extracted from structural vibrations. This sensor is made up of Gallium (Ga) and Iron (Fe) alloy and allows it to generate electrical energy from structural vibration. This paper deals with a comparative study of different sizes of stainless steel, Galfenol, and their respective output voltages. The goal of this paper is to find an efficient configuration for a sensor. In order to evaluate the performance of the proposed design, we conduct various simulations for various dimensions of a prototype using COMSOL Multiphysics.
UNESCO Atlas on the World's Languages in Danger

Country or area All Language name Number of speakers from vulnerable definitely endangered severely endangered critically endangered UNESCO Interactive Atlas of the World’s Languages in Danger The online edition of the Atlas is complementary to the print edition It does not reproduce the regional and thematic chapters of the print version, but it offers additional information on the listed endangered languages. Via this interface, you can browse through them, using combinations of search criteria and/or zooming in the map below (see Browsing functionnalities ). For more detailed information, please consult the Languages mapping , Contribute your comments and FAQ pages.
Power Quality Control and Comparison of a Micro Grid System Using Fuzzy MPPT Technique

This paper presents a power quality control in a microgrid system using different Maximum Power Point Tracking (MPPT) techniques. The main aim of the study is to evaluate the performance of the microgrid system in terms of power quality parameters, such as harmonic distortion and voltage stability, when different MPPT techniques are employed. The microgrid system incorporates a hybrid power generation system consisting of solar panels, wind turbines, and energy storage systems, which are integrated and controlled using different MPPT techniques, including the Perturb & Observation (P&O) method and the Fuzzy MPPT technique. A single-phase sinusoidal pulse width modulation (SPWM) inverter, based on the PQ control strategy, is used to supply power to the grid and maintain a constant DC link voltage. Additionally, a fuel cell is employed to substantially reduce power fluctuations. A comparison of the power quality performance of the micro grid system using these MPPT techniques is carried out through simulation studies using MATLAB/Simulink platform. The results are analyzed and discussed in detail, and recommendations are provided for improving the power quality control of the microgrid system.
Edframe: Open-Source Library for End-to-End Energy Disaggregation in Python

Edframe is a novel open-source Python library for end-to-end energy disaggregation. It provides both researchers and engineers with a plurality of validated and robust methods for full-scale research and development. The main focus of this toolkit is to lower the entry point into the domain as well as to accelerate the prototyping of energy disaggregation algorithms by offering powerful method for generating fully synthetic datasets.
A New Method for Malware Classification Using Hyperspheres

The rapid increase in scale and complexity of malware attacks has made traditional signature-based defense approaches less effective due to the inability to detect new forms of malware. Therefore, there is a need for more advanced malware classification methods, which can identify both known and unknown malware efficiently enough, without using signatures. In this paper, we propose a new machine-learning technique for open-world malware classification, using hyperspheres for the succinct representation of different malware families. For each malware sample that needs to be classified, we calculate the probability for it to belong to each hypersphere, then assign the sample to the family having the hypersphere with the highest probability of containing the sample point. Results from experiments have demonstrated the effectiveness of our proposed method on malware datasets for personal computers.
Adaptive Control with Disturbance Modelling for BG Regulation in TIDM Patient

In order to control blood glucose levels in TIDM patients, this paper explains the creation of a Teaching Learning Based Optimization-PID (TLBO-PID) controller that delivers appropriate insulin doses through an artificial pancreas (AP). Using the Teaching Learning Based Optimization (TLBO), that adjusts the controller gains to improve the BG control of the proposed patient model. This classic controller with TLBO is intended to increase the performance and toughness of patient's problems with glycemic management which are resulting from nonlinearities in the patient model. The nonlinearity of patient models can be effectively handled by using an AP-based TLBO, which also helps to keep blood sugar levels in the glycemic range (70–120 mg/dL). The accuracy, robustness, stability, noise reduction, and enhanced capacity to handle uncertainties are examined while using the proposed patient model with TLBO-PID. A comparison of data from different control strategies indicates the reasons for the suggested approach's superior control performance.
Key Technologies of Mobile Application of Marketing Business Based on Universal Network

With the continuous development of mobile terminals and wireless communication technology, the equipment of the existing office automation information system of marketing enterprises may not be able to meet the needs of system development - this is an urgent problem to be solved. At the same time, with the continuous development of smart terminals, mobile smart terminals are developing in the direction of multiculturalism. In order to attract more customers, many terminal manufacturers choose different operating systems and adapt special functions for their operating systems. The main purpose of this paper is to research the key technologies of mobile application (MA) in marketing business based on general network. This paper adopts the test model combined with the actual software situation of the mobile trading platform, and carries out various test steps in the mobile trading platform. According to the detailed analysis of the platform test results, the final analysis shows that the test success rate of the mobile trading platform exceeds the set upper limit, there are no major problems, and it meets the initial and test performance requirements of the program.
Data-driven Energy Evaluation and Optimization Method for Industrial Robots

With the rapid development and wide application of industrial robots (IRs), it inevitably brings huge energy consumption (EC), which has become an important part of manufacturing EC. Therefore, the EC optimization of IRs has become the key to the green transformation and upgrading of the manufacturing industry, and it is of great significance for the realization of "carbon neutrality" and "carbon peaking". Therefore, this paper focuses on the energy evaluation and optimization of IR, and realizes the power and EC evaluation and motion parameter optimization of its trajectories based on data-driven method. First, the convolutional neural network (CNN) and Transformer models are combined to build the energy model of IR, and then accurate modeling of its power and EC is realized based on the deep learning algorithms. Based on the above energy model, the exhaustive method and genetic algorithm (GA) are used to find the optimal motion parameters and obtain the optimal trajectory with the least EC. Finally, the experimental results show that the proposed method can achieve more than 98% and 99% of the power and EC modeling of IR, respectively. The optimization of the trajectory motion parameters of IR is realized through exhaustive method and GA, and the maximum optimization potential on the test dataset can reach 52.77%, which verifies the effectiveness and accuracy of the proposed method.
Interpreting Energy Utilisation with Shapley Additive Explanations by Defining a Synthetic Data Generator for Plausible Charging Sessions of Electric Vehicles

Electric vehicles (EVs) are an effective solution for reducing reliance on non-renewable energy sources. However, the lack of charging infrastructure and concerns over their range are some of the biggest hurdles to adopting EVs. Charging infrastructure for EVs is, however, on the rise. Proper planning of charging stations vis-à-vis road networks and related points of interest such as transportation hubs, schools, shopping centres, etc., alongside such roads become vital to laying out a plan for such infrastructure, particularly for developing countries like India where EV adoption is relatively in a nascent stage. Synthetic datasets can help overcome these hurdles and promote EV adoption. This article presents a synthetic dataset mechanism for EV charging infrastructure planning, taking the Indian city of Berhampur, Odisha with its existiing EV charging infrastructure as a reference. The dataset includes information on the number of charging sessions for EVs, allocation to chargers in EVCS, reach time, charging start and end time, waiting time, total time spent at EVCS, total charged amount, energy used, and cost for charging. This information can help city planners and utilities identify the optimal locations for charging stations and plan for future charging infrastructure augmentation. The dataset can also be used to predict energy usage for the near future and identify the key factors affecting the planning with the help of Explainable AI (XAI) techniques. This information can help forecast the demand for charging services and optimize energy usage in the city. The article contributes to the EV charging behaviour and infrastructure planning and aims to promote broader EV adoption for future sustainable transportation.
Chemogenomics: Looking at biology through the lens of chemistry

Chemogenomic approaches are gaining importance currently due to increased interest in the polypharmacology of drugs (i.e. their activity against multiple target proteins) and the ligand‐side relationships between different targets. This is not least due to the availability of increasingly larger amounts of ligand bioactivity data in the public domain; PubChem or the recent announcement to make the former Inpharmatica StARLITe database available through the EBI/EMBL being only the two of the many examples of this development. In this work we firstly summarize recent advances in the chemogenomics area. In the second part of the article we present current primary research origination from our group. This addresses various topics from the chemogenomics field such as substructure mining and chemogenomics analyses of GPCR ligands; novel feature selection methods such as two‐entropies analysis which are now translated from the bioinformatics into the chemogenomics field; as well as proteochemometric studies on subfamilies of G‐protein‐coupled receptors which not only take ligand and sequence information into account, but as a third variable are also able to consider quantitative activity values assigned to a ligand‐receptor pair. Copyright © 2009 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 2: 149–160, 2009
Rankformer: Leveraging Rank Correlation for Transformer-based Time Series Forecasting

Long-term forecasting problem for time series has been actively studied during the last several years, and preceding Transformer-based models have exploited various self-attention mechanisms to discover the long-range dependencies. However, the hidden dependencies required by the forecasting task are not always appropriately extracted, especially the nonlinear serial dependencies in some datasets. In this paper, we propose a novel Transformer-based model, namely Rankformer, leveraging the rank correlation function and decomposition architecture for long-term time series forecasting tasks. Rankformer outperforms four state-of-the-art Transformer-based models and two RNN-based models for different forecasting horizons on different datasets on which extensive experiments were conducted.
Wearable Electro-Phonocardiography Device for Cardiovascular Disease Monitoring

In this paper, we present a new wearable multichannel phonocardiography (PCG) and electrocardiography (ECG) device for cardiovascular disease (CVD) pre-screening and monitoring developed recently by researchers at Curtin University in collaboration with Ticking Heart, a health-tech start-up. An iterative Wiener filter based noise cancelation algorithm is proposed to improve the integrity of heart sound signals. We show that compared with an existing approach, the proposed algorithm has a better performance in suppressing the noise at 200-300 Hz. A convolutional neural network based classifier is implemented which exploits both the ECG and PCG signals to improve the pre-screening accuracy of CVD.
Measurement of jet charge in dijet events from sqrt(s)=8 TeV pp collisions with the ATLAS detector

The momentum-weighted sum of the charges of tracks associated to a jet is sensitive to the charge of the initiating quark or gluon. This paper presents a measurement of the distribution of momentum-weighted sums, called jet charge, in dijet events using 20.3 fb$^{-1}$ of data recorded with the ATLAS detector at $\sqrt{s}=8$ TeV in $pp$ collisions at the LHC. The jet charge distribution is unfolded to remove distortions from detector effects and the resulting particle-level distribution is compared with several models. The $p_T$-dependence of the jet charge distribution average and standard deviation are compared to predictions obtained with several LO and NLO parton distribution functions. The data are also compared to different Monte Carlo simulations of QCD dijet production using various settings of the free parameters within these models. The chosen value of the strong coupling constant used to calculate gluon radiation is found to have a significant impact on the predicted jet charge. There is evidence for a $p_{T}$-dependence of the jet charge distribution for a given jet flavor. In agreement with perturbative QCD predictions, the data show that the average jet charge of quark-initiated jets decreases in magnitude as the energy of the jet increases.
A Conversational Social Robot for Detecting Emotions Through Drawing Journal Activity

This study proposes a conversational social robot with a drawing journal activity that can express children's subjective and honest thoughts and emotions through drawing. The robot leads to conversation during the drawing activity and can detect the child's emotion by analyzing the children's utterance explaining the picture they drew. The children's emotions were classified as one of four emotions which are anger, happiness, sadness and neutral through a BERT-based emotion detecting model. The suitability and readiness of the proposed conversational robot with a drawing activity were then validated by the group of experts in the field of child education.
Reinforcement Learning Based Pallet Loading Algorithm and its Application to a Real Manipulator System

Manufacturers pallet loading problem (MPLP) aims to fit the maximum number of boxes into a fixed-size pallet capacity. Solving MPLP can be time-consuming due to its complexity, leading to the use of heuristic methods which may not produce optimal results. This paper proposes a pallet loading algorithm using reinforcement learning to find the optimal solution. Simulation results indicate that the proposed method utilizes the given pallet space more efficiently than the existing heuristic methods. In addition, we introduce a real-life automatic pallet loading system and demonstrate the effectiveness of the proposed algorithm.
Deep Reinforcement Learning Trajectory Planning for Vibration Suppression via Jerk Control

Vibration suppression methods using passive control, active control, and input shaping have traditionally been used. Recently, vibration suppression studies using deep reinforcement learning (DRL) controllers have been conducted. Instead of directly approaching the low-level controller variables, this paper proposes a method to generate a trajectory that reduces jerk using the output of neural networks trained with reinforcement learning (RL) with an environment where the system is complex to analyze the model and direct force input to the oscillations generated is not possible. As a result, it is possible to have easier access to vibration control using DRL in a complex system.
A Generalized Scheme for Attaining Maximum Power Point Tracking and Voltage Regulation in PV Integrated System

In this paper a generalized scheme for attaining the two significant objectives in Photo-voltaic (PV) integrated system i.e., Maximum Power Point Tracking (MPPT) and Voltage regulation has been proposed under different environmental and operating scenarios. A single converter- based control scheme has been proposed to extract maximum power under varying degree of solar irradiance and voltage regulation under dynamic loading conditions. The controller is tuned to execute the desired objective as per the requirement of the application. The scheme has been extensively validated for real time environment in terms of extracting maximum power during irradiance change and maintaining the desired voltage under varying load.
Overcoming Silicon Limitations Short Review: How Geometrical Innovation Can Revolutionize Nanophotonics and Nanoelectronics

Traditional CMOS and FinFET shrinking techniques have begun to reach the physical limits of available materials, and a revolution in silicon photonics communication is necessary to continue to meet the ever-growing demands on microprocessors. Although on-chip optical communication presents a promising direction for circumventing the scaling bottleneck, silicon-based solutions are constrained by several factors, such as the element’s indirect energy band gap, limited absorption spectrum, native oxide, and more. However, the employment of innovative design geometries has enabled the development of a series of silicon nanophotonic and nanoelectronic devices that both overcome these limitations as well as improve on existing physical phenomena. Presented in this short review are a few working examples of these Si nano-devices, which are part of a larger family of components being developed for optical communication and advanced sensing applications.