Miracle-dz/UniFAR-FTUs huggingface dataset:
Laser-Induced Graphene on Optical Fibre: Towards Simple and Cost-Effective Electrochemical/Optical Lab-on-Fibre Bioplatforms

A 3D graphene foam made of interconnected multilayer graphene flakes was produced on optical fibres (OF) by laser-induced transformation of a polyimide (PI) film coated on the OF cladding. This material, known as laser-induced graphene (LIG), was explored in the electrochemical detection and quantification of dopamine (DA) at physiologically relevant concentrations in the presence of the most relevant interfering molecules in biological fluids, ascorbic acid (AA) and uric acid (UA). The measured limit of detection is 100 nM, the linear range is 0.1 to 5.0 μM and a maximum sensitivity of 5.0 µA µM−1 cm−2 was obtained for LIG decorated with Pt nanoparticles (NPs). Moreover, immunity to AA and UA interference and to fouling was attained by decorating the LIG electrode with Pt NPs and coating it with Nafion. These figures of merit underline the potential of these sensors for the quantification of physiologically relevant concentrations of DA in biological fluids, paving the way for the development of hybrid electrochemical/optical sensing actuating platforms in a lab-on-fibre configuration, with relevant applications in biomedical engineering. The advantages of this hybrid arrangement include the possibility of in situ counterproofing, extended measuring ranges, photoelectrochemical detection and the probing of inaccessible places. This elegant approach can also provide a simple and cost-effective way to fabricate biomedical devices with extended functionality, such as medical optical probes with added electrochemical capabilities and optogenetics combined with local electrochemical detection, among others.
Effect of UV Irradiation of Pre-Gel Solutions on the Formation of Collagen Gel Tubes

Hollow collagen gels are promising materials for drug/cell delivery systems to promote tissue regeneration because they may be able to function as carriers for these types of loads. Controlling the cavity size and swelling suppression is essential to expand the applications and improve the usability of such gel-like systems. We investigated the effects of UV-treated collagen solutions as a pre-gel aqueous mixture on the formation and properties of the hollow collagen gels in terms of their preparation range limits, morphology, and swelling ratio. The UV treatment thickened the pre-gel solutions, which allowed hollowing at lower collagen concentrations. This treatment also prevents the over-swelling of the hollow collagen rods in PBS buffer solutions. The UV-treated collagen solutions provided a large lumen space in the prepared collagen hollow fiber rods with a limited swelling ratio, allowing vascular endothelial cells and ectodermal cells to be cultured separately in the outer and inner lumen.
Buying NFTs to Support Artists? The Impact of Moral Values on the Acceptance of NFTs

Non-fungible tokens (NFTs) are unique digital assets that have recently gained significant popularity, particularly in the digital art sector. The success of NFTs and other blockchain-based innovations depends on their ac-acceptance and use by consumers. This study aims to understand the impact of moral values on the acceptance of NFTs. Based on a quantitative survey with over 800 complete responses, the analysis shows that moral aspects of NFTs are indeed important for potential users. However, there is an attitude-behavior gap, as the positive impact of moral values on the intention to use NFTs is not reflected in the actual current usage of NFTs by the respondents. This study contributes to knowledge by providing new empirical data on the acceptance of NFTs and highlighting the role of moral values on the acceptance decision.
From Relative to Absolute Heights in SAR-based Single-Image Height Prediction

Single-image height estimation has become a scientifically well-studied field in recent years with a large number of publications. However, the aspect that out of the box only patch-wise relative heights are predicted, is very rarely addressed. For many practical applications, though, it is often indispensable to also establish an absolute height reference. In this paper, such a procedure is described for the case of SAR images natively provided in slant range geometry. A coarse resolution, globally available terrain model is used for the transition to absolute heights. An experiment is conducted to test the procedure and to address the problems encountered. It is shown that the presented method reconstructs the geodetically defined urban topography very well even in an area completely unknown to the model.
Design and Implementation of Arithmetic and Logic Unit (ALU)

ALU is the fundamental building block of many processors like Central Processing Unit(CPU), Floating point Unit (FPU's), Graphical Processing Unit(GPU's). ALUs are used in digital computers, and they are a component of logic design whose goal is to create the right algorithms to make the most use of the hardware which was available to us. This research study describes the VLSI design of an ALU and the functionality of this design has been simulated and tested using Xilinx ISE design suite 14.7. which is used to validate the design at gate level and chip level implementation. The proposed ALU is designed using a total of 9 operations which includes Addition, Subtraction, Multiplication, Shifting, Comparison, AND, OR, NOT, and XOR.
Text Normalization by Bi-LSTM Model with Enhanced Features to Improve Tribal English Knowledge

Education is important to all children but some children have some issues with the education for example tribal children, refugees and differently disabled children. Tribal children face some barriers in their education like language problems, they are in remote areas and economic issues also. One of the strongest reasons for their study issue is language. Language is a bridge for communication but here it is a problem for these tribal students. Because learning and teaching language is different from their mother language. For this reason, they are unable to learn anything from school. Due to this reason most, tribal students discontinue their studies, to overcome this issue they studied with their own language. Artificial Intelligence will help to eliminate this barrier from them. AI based speech recognition systems with text normalization assist them to continue their studies with the mother language of tribal children. To create a text normalizer that helps to educate tribal children with proper transcription of what they are studying. Speech recognition system by using CNN model and Bi-LSTM to create a text normalizer proposed in this paper. This convergence helps to remove grammatical errors, mistakes, time, date and frequency error from the transcription. This method achieves 92.17% accuracy range during test time and it shows better feature performances efficiently.
Subspace-Based Pilot Decontamination in User-Centric Scalable Cell-Free Wireless Networks

We consider a cell-free wireless system operated in Time Division Duplex (TDD) mode with user-centric clusters of remote radio units (RUs). Since the uplink pilot dimensions per channel coherence slot is limited, co-pilot users might incur mutual pilot contamination. In the current literature, it is assumed that the long-term statistical knowledge of all user channels is available. This enables Minimum Mean-Square Error channel estimation or simplified dominant subspace projection, which achieves significant pilot decontamination under certain assumptions on the channel covariance matrices. However, estimating the channel covariance matrix or even just its dominant subspace at all RUs forming a user cluster is not an easy task. In fact, if not properly designed, a piloting scheme for such long-term statistics estimation will also be subject to the contamination problem. In this paper, we propose a new channel subspace estimation scheme explicitly designed for cell-free wireless networks. Our scheme is based on 1) a sounding reference signal (SRS) using latin squares wideband frequency hopping, and 2) a subspace estimation method based on robust Principal Component Analysis (R-PCA). The SRS hopping scheme ensures that for any user and any RU participating in its cluster, only a few pilot measurements will contain strong co-pilot interference. These few heavily contaminated measurements are (implicitly) eliminated by R-PCA, which is designed to regularize the estimation and discount the “outlier” measurements. Our simulation results show that the proposed scheme achieves almost perfect subspace knowledge, which in turns yields system performance very close to that with ideal channel state information, thus essentially solving the problem of pilot contamination in cell-free user-centric TDD wireless networks.
A Survey on Stock Market Prediction Techniques

The stock market is a meeting point for buyers and sellers to buy and sell stocks. Stock price depends on local and global events. It has a dynamic, non-stationary nature. Thus, it is exceedingly challenging to make accurate stock price predictions. If we can predict the stock prices accurately, it will be helpful for investors. Many researches are going on in this area. Machine learning and deep learning methodologies are used for stock prediction. In this paper, a survey of different stock prediction techniques is done. Additionally, an approach that can give more accurate predictions than the techniques examined is suggested.
Composite Multiple-Mode Orthogonal Frequency Division Multiplexing With Index Modulation

In this paper, we propose a composite multiple-mode orthogonal frequency division multiplexing with index modulation (C-MM-OFDM-IM) scheme to increase the spectral efficiency (SE) of OFDM-IM systems by extending the indexing to the energy and constellation domains. In C-MM-OFDM-IM, the information bits are mapped to not only the subcarrier activation patterns (SAPs) and modulation symbols, but also the energy allocation patterns (EAPs) and constellation activation patterns (CAPs). To cope with the practical situations, we propose a variant IM scheme named C-MM-OFDM-IM-II to build a new mapping rule between information bits and the increased CAPs, capable of further increasing the SE of C-MM-OFDM-IM. Upper-bounded bit error rate (BER) and lower-bounded achievable rate are both derived in closed-form to evaluate the performance of C-MM-OFDM-IM(-II). Moreover, we further propose two enhanced schemes, named generalized C-MM-OFDM-IM(-II) and C-MM-OFDM with in-phase/quadrature IM(-II), where the former jointly considers all SAPs, EAPs, CAPs and modulated symbols, while the latter expands the index implementation to the in-phase and quadrature constellation domains. Simulation results show that C-MM-OFDM-IM(-II) outperforms the conventional OFDM-IM related schemes, especially in the high signal-to-noise ratio (SNR) region, and verify the accuracy of the theoretical analysis for the upper-bounded BER and achievable rate.
Modular Multilevel Converters for Fast Charging Stations of Electric Vehicles: An Overview

This paper reviews the different configurations of Modular Multilevel Converters (MMCs) used in Fast Charging Stations (FCSs) and Extreme Fast Charging Stations (XFCs) of Electric Vehicles (EVs), It enables a solid reference for charging stations operating at the megawatt's level and employing novel topologies to interface with the grid. First, the paper delivers a general overview on FCSs and XFCs along with their requirements and challenges. Then, the paper describes in detail the different configurations of MMC structure and highlights the importance of using MMC in terms of reducing the overall size of the charging station, and in reducing the impact of the massive integration of FCSs and XFCs on the grid. A comparative study is presented to focus on various configurations of MMC-based FCSs and XFCs and compares them in a table in terms of structure components, target objectives within the structure, and outcomes benefits of each type. Common benefit associated with several proposed MMC configurations is interfacing directly with the medium voltage grid, thus the bulky, costly, and heavy line frequency transformer is removed. Additional benefits include limiting the grid-side filter due to the high-quality waveforms of MMC, in addition to the easy integration of renewable energy sources and energy storage systems which helps in managing the impact of the charging station on the grid in terms of power requirement.
Artificial Intelligence (AI) based Detection of Traffic Violations by Two-Wheeler Vehicles

Increasing commuters, bad traffic signal management, and rider mentality are some of the factors contributing to traffic violations in India. Monitoring large traffic volumes physically and tracking violations at the same time is clearly insufficient with only physical traffic police. The result has been that many violators have gone undetected. By violating the traffic laws, the violators cause more serious mishaps on the road, thereby putting both themselves and others in danger. To avoid manual intervention in detecting and catching violators, Artificial Intelligence (AI)-based techniques should be incorporated. This research study demonstrates a novel technique to discover multiple offences on Indian roads electronically, which include helmet detection, using a smartphone while driving, tri cruising, wheeling, and parking illegally, and ultimately model the issuing of tickets by monitoring the infractions and affiliated car number together in record. Through all the automated AI-based traffic offense and booking system, the software will be extremely beneficial in determining diverse safety-related guidelines, facilitating in the imposing of harsher traffic restrictions, and fostering the development of such a green technology atmosphere.
Anti-grouped Pseudo-BCI Algebras and Anti-grouped Filters

The notion of Pseudo-BCKalgebras can be regarded as an algebraic framework(implicational fragment)of non-commutative fuzzy logics.This paper investigates the structures of pseudo-BCI algebras which are generalization pseudo-BCKalgebras.First,a characterization of pseudo-BCI algebra is constructed by the notion of BZ-algebra(or weak BCC-algebra). Second,the relationship between pseudo-BCIalgebras and(non-commutative)groups are investigated by introducing the notion of anti-grouped pseudo-BCIalgebra.Third,the notions of anti-grouped(well,normal)filter are introduced,and by these notions the necessary and sufficient conditions for pseudo-BCIalgebra to be anti-grouped(and filter to be p-filter)are given.Finally,the following results are proved:(1)medial pseudo-BCI algebra is equivalent to p-semisimple BCI-algebra;(2)every filter of a pseudo-BCIalgebra Xis p-filter,if and only if,Xis anti-grouped and every subgroup of adjoint group of Xis normal subgroup.
Early anthropogenic CH4 emissions and the variation of CH4 and (CH4)-C-13 over the last millennium

[ 1 ] This study presents a new hypothesis to explain the observed variation of CH 4 and d 13 C-CH 4 over the last millennium. It was originally proposed that the observed minimum of d 13 C-CH 4 prior to the start of industrialization is caused by a large shift in biomass burning emissions between 1400 and 1700 A.D. According to our new hypothesis, however, the d 13 C-CH 4 minimum is the first sign of the global rise of anthropogenic CH 4 emissions. The main idea is that emissions of isotopically depleted CH 4 , from, for example, rice cultivation, domestic ruminants, and waste treatment started increasing earlier than the isotopically enriched emissions from fossil fuel, which started with the start of industrialization. However, because the observed increase of atmospheric methane only started around 1750 A.D., these preindustrial anthropogenic emissions must have been accompanied by a net reduction of natural CH 4 sources during the Little Ice Age (LIA) compensating for the increase of anthropogenic emissions during that period. Results of transient box model simulations for the last millennium show that under the new hypothesis a close agreement can be obtained between model and measurements. Prior to 1400 A.D., low emissions from anthropogenic biomass burning require a sizable contribution of methane emissions from vegetation to explain the observed high level of d 13 C-CH 4 . During the Little Ice Age, a larger than expected reduction of natural sources is needed, which calls for further verification using a more sophisticated modeling approach and additional constraints from ice core measurements.
Elevating Perceptual Sample Quality in Probabilistic Circuits through Differentiable Sampling

Deep generative models have seen a dramatic improvement in recent years, due to the use of alternative losses based on perceptual assessment of generated samples. This improvement has not yet been applied to the model class of probabilistic circuits (PCs), presumably due to significant technical challenges concerning differentiable sampling, which is a key requirement for optimizing perceptual losses. This is unfortunate, since PCs allow a much wider range of probabilistic inference routines than main-stream generative models, such as exact and efficient marginalization and conditioning. Motivated by the success of loss reframing in deep generative models, we incorporate perceptual metrics into the PC learning objective. To this aim, we introduce a differentiable sampling procedure for PCs, where the central challenge is the non-differentiability of sampling from the categorical distribution over latent PC variables. We take advantage of the Gumbel-Softmax trick and develop a novel inference pass to smoothly interpolate child samples as a strategy to circumvent non-differentiability of sum node sampling. Our hypothesis is that perceptual losses, unlocked by our novel differentiable sampling procedure, will elevate the generative power of PCs and improve their sample quality to be on par with neural counterparts like probabilistic auto-encoders and generative adversarial networks.
Influence of the RHS on the convergence behaviour of the curl-curl equation

The vector potential formulation is widely used in electromagnetic field computation due to its robustness. Numerical experiences on the convergence behaviour of the non-gauged vector potential formulation (curl curl equation) are reported. The convergence of the system depends on the discretisation of the source variable (the right hand side of the equation). The system converges if the matrix equation is compatible, i.e. if the RHS is in the range of the curl-curl matrix. The compatibility is ensured when the current density is expressed by the curl of a source field (vector potential) and when this source field is projected on the space curl W/sup 1/, where W/sup 1/ is the space of the Whitney edge element. An explanation of the convergence behaviour is given through the analysis of the matrix structure: under the condition of the compatibility, the curl-curl equation is implicitly ganged by an iterative solver.
Measuring Vulnerability in Grief: The Psychometric Properties of the Italian Adult Attitude to Grief Scale

Although experiences of loss and the consequent grief are natural in human life, some individuals may have difficulty managing these events, to the point of developing significant impairment in their functioning in important life areas. Given this, the present research aimed to explore the psychometric properties of the Italian version of the Adult Attitude to Grief scale (AAG) to facilitate research on adult vulnerability to grief among Italian-speaking populations. A sample of 367 participants (Mage = 30.44, SD = 11.21; 78% females) participated in this research. A back-translation procedure was implemented to develop the Italian AAG. Then, participants completed the Italian AAG alongside a battery of other self-report psychometric scales in order to assess aspects of the construct validity of the AAG: the Forty-Item Defense Style Questionnaire, the Impact of Event Scale—Revised, and the Beck Depression Inventory–II. A bifactor structure was found to have the best fit to the data, supporting the possibility of using both the general factor (i.e., vulnerability) and three dimensions (i.e., overwhelmed, controlled, and resilient). Unlike the original version, the control dimension emerged as a “protective” factor in the Italian population, together with the resilient factor. Furthermore, results provided satisfactory indications of internal consistency and construct validity. In conclusion, the Italian AAG was shown to be a valid, reliable, quick, and easy-to-use scale that can be used both for research and clinical practice in the Italian context.
Literature Review of Accreditation Systems in Higher Education

This study investigates the accreditation processes in higher education across various countries, focusing on the time and bureaucratic burden associated with accrediting new courses. The aim is to identify strategies to accelerate the accreditation process for new courses in higher education institutions. A comprehensive literature review was conducted to achieve this objective, examining the accreditation processes in Portugal, Spain, the United States, France, China, Japan, Sweden, the United Kingdom, India, and Germany. The study’s key findings revealed that the accreditation process is generally efficient in most countries, with courses receiving accreditation within a reasonable timeframe. However, the process can be more complex and time-consuming for institutions seeking accreditation for the first time or offering new or innovative courses. Institutions must meet all established criteria and promptly provide all required documentation to expedite the accreditation process. The implications of these findings suggest that higher education institutions should collaborate closely with relevant accrediting agencies to ensure a streamlined accreditation process. Institutions should also consider agency requirements and course specialization when developing new courses. Furthermore, governments play a crucial role in promoting transparency and competition among higher education institutions, which can lead to enhanced quality assurance and increased customer satisfaction in the education sector.
Multi-Label Emotion Classification in Urdu

With the massive growth and widespread usage of social media platforms, the rates of its misuse and its corresponding impact on society have seen an exponential rise in numbers. The comfort of anonymity and wide reach offered by social media has led to the convenient spread of hatred and incitement to threats, that are often targeted against particular users and communities. Thus identifying hate speech, threats and intense emotions in the digital arena has gained attention recently. This is also the aim of the EmoThreat: Emotions and Threat Detection in Urdu 2022 Challenge. In this paper, we describe a few traditional machine learning models and deep neural networks submitted by our team Aces for Task A: Multi-label emotion classification in Urdu. The models tested include Classifier Chains, MLKNN, RNN and LSTM Networks implemented with a combination of feature extraction methods such as the Count Vectorizer and TF-IDF, as well as embedding models like Word2Vec and FastText. Each model has been discussed in detail in Section 4 after a brief overview of the dataset adopted, in Section 3. Out of these tested permutations, the Classifier Chains model with TF-IDF vectorization proved to give the most promising results, which has been detailed in Section 5.