Miracle-dz/UniFAR-FTUs huggingface dataset:
Dual-polarization Dual-mode Silicon 3dB Beam Splitter Based on Shallow Etched Multimode Interference Coupler

In this paper, we propose a dual polarization dual mode 3dB beam splitter. By utilizing the shallow etched multimode interference (MMI) coupler, the proposed device can handle TE<inf>0</inf>, TE<inf>1</inf>, TM<inf>0</inf> and TM<inf>1</inf> modes simultaneously.
Classification and Identification of Certain Types of Car Accidents Based on Sound Information

In this paper, the purpose was the implementation of a solution to recognize the type of car crash using the audio signals generated by the simulation of the most common categories of accidents from the ENCAP (European New Car Assessment Programme) database. This can determine a better reaction from the emergency services that respond to a real world event that happens on public roads. In order to achieve this, a Python application was created that computes an MFCC features vector for each of the 400 sounds in the created database. For the classification the machine learning algorithms used were k-Nearest Neighbor and Support Vector Machine. The kNN algorithm obtained an accuracy score of 62.18%, while the SVM algorithm obtained an accuracy score of 67.22%.
Exposure of Prooxidant Potential of CeO2 and GdYVO4/Eu3+ Nanoparticles in Model Systems Containing Low-Molecular Antioxidants

Nanotechnology-based pro-oxidant strategy is perspective for cancer therapy. For instance, pro-oxidant properties of cerium oxide nanoparticles (nanpceria) observed under certain conditions, as well as its antitumor effect, have drawn a lot of attention in recent years. Thus, the aim of the study is to compare the pro-oxidant potential of CeO2 and GdYVO4/Eu3+ nanoparticles (NPs) as the promoters of ROS-mediated antitumor activity. Extra-small 2 nm CeO2 NPs or 2 nm GdYVO4/Eu3+ NPs were used in this research. The oxidation of ascorbic acid (AA), NADPH, and GSH molecules was monitored by spectroscopic methods at pH 6.7, 7.4, and 7.8. The solutions of NPs (with final concentrations of $\mathbf{10}-\mathbf{40}\ \boldsymbol{\mu} \mathbf{g}/\mathbf{mL}$) were added to organic (20 mM Tris or Hepes) buffers contained $\mathbf{200}\ \boldsymbol{\mu} \mathbf{mol}$ AA or $\mathbf{200}\ \boldsymbol{\mu} \mathbf{mol}$ NADPH or $\mathbf{50} \boldsymbol{\mu} \mathbf{mol}$ GSH. The mixture was kept for 60 min or 3 h, or 24 h at 37 °C, respectively. The AA oxidative damage was quantified by measuring its absorbance at the characteristic wavelengths $\boldsymbol{\lambda}\ \mathbf{max}=\mathbf{265}\ \mathbf{nm}$ (AA), 340 nm (NADPH), and 412 nm (GSH, after reaction with Elman reagent) using Specord 200 spectrophotometer (Analytik Jena, Germany). The literature reports on the potential antitumor activity of rare earth elements (REE) and REE-based nanoparticles, and in particular, of CeO2 NPs and their combinations with antitumor agents. Data on the antitumor activity of GdYVO4/Eu3+ nanoparticles were also obtained. The pro-oxidant potential of nanoceria and, in particular, the ability to exhibit oxidizing activity under certain conditions with oxidation of organic substrates is discussed. We have carried out a comparative analysis of the oxidation of the most important low-molecular components of the antioxidant defense system in cells - AA, NADPH, and GSH in the presence of CeO2 and GdYVO4/Eu3+ NPs. Oxidation of all studied compounds in the presence of CeO2 NPs was shown. A drop in the level of AA compared with the control was noticed at all studied concentrations of nanoparticles and pH values. At the same time, CeO2 NPs accelerated the oxidation of NADPH and glutathione only at neutral pH values. Unlike CeO2, GdYVO4/Eu3+ NPs decrease GSH level only under weakly alkaline conditions at NPs concentration of $\mathbf{10}\ \boldsymbol{\mu}\mathbf{g}/\mathbf{mL}$ and more. It was noted that the oxidation of GSH at pH 7.8 is accompanied by irreversible agglomeration of nanoparticles. Thus, under certain conditions, extra-small CeO2 NPs demonstrate a significant pro-oxidant activity. This may create prerequisites for further aggravation of the redox imbalance against the background of constant oxidative stress of tumor cells and, as a result, more pronounced oxidative damage.
Bidirectional Loss for Robust Change Detection in Satellite Images

Given an image pair that consists of a reference image and a query input, the objective of pixel-level change detection (CD) is to label each pixel with either 0 (no change ) or 1 (change) class to locate the modified region. In this work, we evaluate the performance of deep-learning-based methods for pixel-wise change detection in satellite images. Specifically, we compare three models, namely, SNUNet (CNN-based), BIT (a combination of CNN and transformer), and ChangeFormer (transformer-based) under various settings, including query preprocessing, cloud overlay, resolution change and reversal of input pair. We have identified a common discrepancy in existing approaches, i.e., their inability to cope with input in reverse order. To address this issue, two possible solutions have been proposed. The first method utilizes a data-augmentation scheme by adding reversely ordered data to the training set. The second approach modifies the loss function to incorporate bidirectional change detection during the optimization process. Both strategies have proven to work effectively, maintaining comparable performance regardless of the input order or change type.
Do Robots Dream of Passing a Programming Course?

Programming typically involves humans formulating instructions for a computer to execute computations. If we adhere to this definition, a machine would seemingly lack the capability to autonomously design algorithms. However, recent generative Artificial Intelligence models, such as GPT, have demonstrated an impressive ability to perform complex human tasks with remarkable precision. In this paper, we initially showcase how an AI model can successfully complete an entire college-level programming course, akin to one of the top-performing students in the class. We then put forward strategies for crafting programming exercises that enable educators to effectively integrate these innovative technologies into their teaching methods. Lastly, we illustrate how these models can transition from being perceived as a potential threat to educators to becoming a valuable opportunity when employed judiciously.
W-band 30 dB Waveguide Directional Coupler with H-plate Slot Bridge Structure

This paper presents a W-band 30dB broadband waveguide directional coupler with H-plate slot bridge structure, which shows good bandwidth characteristics. By proposing a sandwich-like three-layer structure, the reliability of the device is improved. A sample of the proposed coupler is tested. In the range of 75 - 110 GHz, the measured return loss is better than 20dB, the isolation is better than 30dB, the coupling is 30 ± 1.5 dB, and the average insertion loss is 0.17 dB. The experimental results are in good agreement with the simulation results.
Achieving Cyber-Informed Engineering Through Bayesian Belief Network and Sensitivity Analysis

The security of critical infrastructures is a challenge facing nations and states today as a result of the increased complexity and interconnectivity of these systems and their control from remote locations. Ensuring the security of critical systems requires engineering cybersecurity-related risks that attackers can exploit to cause severe consequences, such as equipment damage, environmental/water pollution, monetary loss, or even loss of life. It is important to identify and prioritize actions or attacks that can lead to high-consequence events (HCEs) capable of crippling critical functions of any organization. In this work, we proposed a new approach to cybersecurity risk assessment by proposing the Consequence-Driven Cyber-Informed Engineering (CCE) approach and the Bayesian Belief Network (BBN) with Sensitivity Analysis (SA). For proof of concept, we tested the proposed approach at the Tennessee Eastman chemical plant and were able to uncover and prioritize ripple effects caused by disturbance or noise-induced attacks on critical infrastructure.
Using the Deep Learning-Based Approaches for Program Debugging and Repair

Maintenance is the most labor-intensive stage in the Software System Development Life Cycle (SDLC). When an error is detected, an Automatic Program Repair (APR) system can locate the error by utilizing fault prediction and then employ search-based or semantic-based repair techniques to attempt to fix it. Finally, the program repair system runs test cases to verify that the errors have been properly repaired and the program's functionality has not been compromised. In this study, we present a novel approach called Deep Learning Based GenProg (DLBGP), which combines deep learning with APR. Our proposed method involves extracting node vectors from the program's Abstract Syntax Tree (AST) and identifying semantic features using a Deep Brief Network (DBN). These semantic features are then used to predict the accuracy of the repair candidates. In experiments conducted on ten subject files in the IntroClass database, our proposed DLBGP model reduced execution time by 64% compared to GenProg while achieving the same number of successful test cases. In summary, we offer a fast, efficient, and easily deployable deep learning program repair method that could prove beneficial to the field of APR.
The Effect of Voltage and Current Harmonic Distortion on Power Indicators in Power Circuits a Dynamic Voltage Restorer

On the basis of the developed model of the dynamic voltage restorer, a subsystem for measuring power indicators was built to assess its distribution in circuit elements. Experiments were conducted to determine the effect of harmonic distortion of voltage and current on integral power indicators in the power circuits of the dynamic voltage restorer. It is noted that there is distortion power in the converter circuits and on the filter elements, which for the inductance is comparable to the full power. Significant reactive power is distributed between the filter elements and the output lines of the converter. In this case, the reactive power of the capacitor is balanced by the reactive power of the inductor and the converter. The storage capacitor in the investigated mode gives active power to the external circuits.
Wide-Range Parametric Modeling of an Inter-Digital Bandpass Filter

This paper introduces a novel decomposition technique for parametric modeling microwave components whose geometric variable values vary over a wide range. This technique includes generating parallel data, training sub-models in parallel, and modifying sub-models in parallel. A second-derivative-based algorithm is used to divide a large range of geometric variables into a series of sub-ranges, leading to the decomposition of a highly nonlinear region into numerous sub-regions, each of which is trained to obtain an artificial neural network (ANN) model with a simple structure. However, the problem of complex multidimensional discontinuities occurs in the process of connecting all the sub-models into an overall model. Therefore, a solution to this problem is proposed by introducing a sub-model modification process, so that this overall model can be used for design optimization. The decomposition technique can enhance the accuracy with short model-development time compared with the methods that train the overall wide range directly into a model. The proposed technique is demonstrated to be valid through an example of an Inter-Digital Bandpass Filter.
An Efficient Security Closure Methodology for EM-based Attacks on Power Grid Structures

Hardware Trojans (HTs) are a significant issue in hardware security, as they have the capability to compromise the integrity of critical electronic systems. Power grid structures of modern integrated circuits are an important component that is vulnerable to electromigration (EM)-based attacks, which can affect the security and reliability of the chip simply by modifying the reservoirs, which are passive wire segments that are employed to increase reliability robustness. To address this issue, a security closure methodology is essential to identify potential vulnerabilities in the power grid by analyzing EM-induced Trojans capable of stealthily degrading chip lifetime by removing reservoirs. In this paper, we propose an efficient methodology that is based on the semi-analytical solution of the EM transient analysis and can be effectively employed in parallel for all the different lines of the power grid. Experimental evaluation on the OpenROAD power grid benchmarks demonstrates that our method can locate all the susceptible lines of the power grid that are vulnerable from the reservoir removal in less than 33 seconds, while it calculates the time-to-failure (TTF) with and without the reservoirs.
Work-in-Process: Error-Compensation-Based Energy-Efficient MAC Unit for CNNs

Approximate circuits sacrifice accuracy in exchange for energy efficiency and have been widely used in hardware deployment of neural networks (NNs). Since convolution accounts for most of the power consumption in NNs, it is necessary to design an approximate multiplication and accumulation (MAC) unit which improve the energy efficiency of hardware with ignorable accuarcy loss. In this work, an error-compensation-based energy-efficient MAC unit is proposed in which approximate multipliers are designed by Boolean matrix factorization and approximate adders are generated by Cartesian genetic programming. The proposed MAC unit is conducted on CIFAR10 using ResNet-18, where PDP is reduced by 58.8% with an accuracy loss of 0.81%.
Fate Of Anthropogenic Micropollutants DuringWastewater Treatment And Influence OnReceiving Surface Water

More than 110 volatile and base-neutral compounds or groups of compounds with concentrations exceeding 0.1 μg/L were identified in a wastewater treatment plant, at different stages of treatment. Individual concentrations ranged from 0.1 μg/L to 1 mg/L. The concentrations of many micropollutants were increased after mixing the raw water with recycled waters from the sludge drying process. Six compounds listed among the 33 priority pollutants from the Water Framework Directive, were detected. Due to the high efficiency of the treatment process and to the high dilution ratio at the outlet, this plant showed little influence on the receiving water.
Representation of Mean Arctic Precipitation from NCEP-NCAR and ERA Reanalyses

Abstract An improved monthly precipitation climatology for the Arctic is developed by blending the Legates and Willmott gridded product with measurements from Russian “North Pole” drifting stations and gauge-corrected station data for Eurasia and Canada. The improved climatology is used to examine the accuracy of mean precipitation forecasts from the National Centers for Environmental Prediction (NCEP) and European Reanalysis Agency (ERA) reanalysis models, based on data for the period 1979–88. Both models capture the major spatial features of annual mean precipitation and general aspects of the seasonal cycle but with some notable errors. Both underestimate precipitation over the Atlantic side of the Arctic. NCEP overestimates annual totals over land areas and to a somewhat lesser extent over the central Arctic Ocean. Except for the North Atlantic–Scandinavia sector, the NCEP model also depicts the seasonal precipitation maximum consistently one month early in July. Overall, the ERA predictions are bette.
Hunters or herders? Evidence from the cultural assemblages at Bakoond, Western Cape, South Africa

Hunters and herders have long been distinguished in the Western Cape based on a now hotly debated model in which different cultural material dictated ethnicity and economy. Bakoond lies near Yzerfontein on the west coast of South Africa and provides observations pertinent to the debate. Stone artefacts were dominated by silcrete in the lower levels (before c 1000 BC) in both excavations with almost all other artefacts being quartz. Among the quartz the high incidence of small backed tools distinguishes the later assemblage as possibly belonging to an industry better known from Namaqualand to the north. Pottery occurs, with three sherds displaying decoration engraved into the surface post-firing; this technique has not been recorded elsewhere. With one exception, all ostrich eggshell beads were less than 6 mm maximum dimension and nine Donax scrapers, one perforated, were recovered. Although Holocene occupation began some 5500 years ago, the more interesting deposits seem to date within the last 2000 years.
Reinforcement Learning Approach to Sedation and Delirium Management in the Intensive Care Unit

Common treatments in Intensive Care Units frequently involve prolonged sedation. Maintaining adequate sedation levels is challenging and prone to errors including: incorrect dosing, omission/delay in administration and, selecting a sub-optimal combination of sedatives. In this single-center retrospective study of 1,346 patients, we use a Deep Q Network approach to develop a multi-objective sedation management agent. The agent's objective was to achieve an adequate level of patient sedation without moving the patient's Mean Arterial Pressure (MAP) outside of a therapeutic range. To achieve this objective, the agent was allowed to periodically (every 4 hours) recommend how the dose of two commonly used sedatives (propofol, midazolam) and an opioid (fentanyl) should be adjusted: increased, decreased, or stay the same. To inform it's recommendations, the agent was provided with the patient's demographym and periodic measures including: vital signs, and depth of sedation. To mitigate the potential risk of delirium and the adverse effects of over sedation, a delirium control variable was integrated into the agent's reward function. We found that Physicians with dosing policies that agreed with our agent were 29% more likely to maintain the patient's sedation in a therapeutic range, compared to those that disagreed with our agent's policy.
Investigation of Valence Band Dispersion in (Ga, Mn)(Bi, As) Epitaxial Nanolayers

The impact of incorporating Bi into (Ga,Mn)As layers on their electronic, band structure, magnetic, and structural properties has been studied. The low-temperature molecular beam epitaxy technique was employed to grow homogeneous (Ga,Mn)(Bi,As) layers with high structural perfection. Post-growth annealing treatment resulted in improved structural and magnetic properties, as well as an increase in the hole concentration in the layers. Modulation photoreflectance spectroscopy confirmed modifications to the valence and split-off band in the (Ga,Mn)(Bi,As) layers. The experimental results are consistent with the valence band model of hole-mediated ferromagnetism in the layers. The (Ga,Mn)(Bi,As) compound combines the properties of (Ga,Mn)As and Ga(Bi,As) ternary compounds, offering the possibility of tailoring the bandgap structure to the requirements of novel device functionalities for future spintronic and photonic applications.
Discovering the development habits of software development projects in academic scenarios

Software development sessions are a source of information to understand the software evolution. In the field of software engineering education, tracking the development software projects is usually applied using a control system version or a particular strategy. However, this information is generally lost in control system version, because the checkin/checkout model do not allow users to recover the particular changes or actions between two commits. In academic scenarios, the control system versions do not offer the registration of events and student actions that can be useful for the teaching-learning process. In this article we present an exploratory case study in a software development course at the University of Talca using BlueLogger. BlueLogger is a plugin for Netbeans IDE that allows teachers to discover software development habits using a remote monitoring system. We obtain several findings about habits that students apply in software project development sessions in terms of activity time, effective programming, work timeline and technical actions/events.
Designing an Effective System Architecture for Detecting Propaganda and Spam in Social Media News Feed

In this article suggested the system architecture designed to effectively detect propaganda and spam in social media news feeds. The system is created to handle the dynamic environment of social media, with a focus on scalability, prioritization of user connections, and real-time generation of news feeds, all while maintaining low latency. In the beginning, outlined the system design of a news feed application and discussed technologies and structures in order to support user operations. To address scalability issues used a partitioning strategy and also an algorithm of offline precomputation of news feeds. The design also includes Recurrent Neural Network and Convolutional Neural Network for spam and propaganda detection trained on manually created datasets and a combination of public datasets. The approach involves asynchronously writing each post or comment into a Kafka message queue, the batching process of these messages, storing them in Amazon S3 buckets, and applying the trained neural network for detection. The overall article provides a comprehensive blueprint for an optimized and efficient system that ensures a cleaner, propaganda, and spam-free user experience in navigating social media news feeds.
The 'wylding' of Te Urewera National Park : analysis of (re)creation discourses in Godzone (Aotearoa/New Zealand)

Throughout the world, there are many struggles over resource definition and use; Te Urewera National Park within the North Island ofNew Zealand is a site of such contestation. Currently the Maori of this region are struggling to regain ownership of the park lands through the Waitangi Tribunal. In the meantime, current legislation requires park management to take into account the principles of the Treaty ofWaitangi. This thesis is an analysis of selected texts about the aforementioned park. In it I examine the underlying ideologies of recent management plans and compare them with colonial era literature about the region as well as dominant discursive trends that have emerged from Europe. This study demonstrates different means by which the management plans reflect colonial ways of thinking about nature as separate from culture. I argue that until these persistent colonial attitudes are carefully examined, colonial injustices will never be
Convolutional Neural Network based Digital Twin of Rolling Bearings for CNC Machine Tools in Cloud Computing

CNC machine tool is the mother of industry, and the realization of Intelligent CNC machine tool is an important link to promote the development of intelligent manufacturing. The fault diagnosis of key components of CNC machine tools is very important for the machining process. The traditional fault diagnosis methods have problems such as poor real-time performance and inaccurate prediction. Therefore, with the help of digital twin technology and cloud computing, this paper proposes a five-dimensional digital twin system architecture of CNC machine tool rolling bearing based on cloud computing to solve the above problems. And a deep learning technology for data processing is established, which is a hybrid network structure based on CNN-LSTM. Taking the fault diagnosis and prediction of rolling bearing as an example, it is verified that the prediction effect of the algorithm is good, and it can be used for system data processing.