Miracle-dz/UniFAR-FTUs huggingface dataset:
Emplying Large Language Models for Surgical Education: An In-depth Analysis of ChatGPT-4

Background: The growing interest in artificial intelligence (AI) has spurred an increase in the availability of Large Language Models (LLMs) in surgical education. These LLMs hold the potential to augment medical curricula for future healthcare professionals, facilitating engagement in remote learning experiences, and assisting in personalised student feedback. Objectives: To evaluate the ability of LLMs to assist junior doctors in providing advice for common ward-based surgical scenarios with increasing complexity. Methods: Utilising an instrumental case study approach, this study explored the potential of LLMs by comparing the responses of the ChatGPT-4, BingAI and BARD. LLMs were prompted by 3 common ward-based surgical scenarios and tasked with assisting junior doctors in clinical decision-making. The outputs were assessed by a panel of two senior surgeons with extensive experience in AI and education, qualitatively utilising a Likert scale on their accuracy, safety, and effectiveness to determine their viability as a synergistic tool in surgical education. A quantitative assessment of their reliability and readability was conducted using the DISCERN score and a set of reading scores, including the Flesch Reading Ease Score, Flesch-Kincaid Grade Level, and Coleman-Liau Index. Results: BARD proved superior in readability, with Flesch Reading Ease Score 50.13 (± 5.00), Flesch-Kincaid Grade Level 9.33 (± 0.76), and Coleman-Liau Index 11.67 (± 0.58). ChatGPT-4 outperformed BARD and BingAI, with the highest DISCERN score of 71.7 (± 2.52). Using a Likert scale-based framework, the surgical expert panel further affirmed that the advice provided by the ChatGPT-4 was suitable and safe for first-year interns and residents. A T-test showed statistical significance in reliability among all three AIs (P < 0.05) and readability only between the ChatGPT-4 and BARD. This study underscores the potential for LLM integration in surgical education, particularly ChatGPT, in the provision of reliable and accurate information. Conclusions: This study highlighted the potential of LLM, specifically ChatGPT-4, as a valuable educational resource for junior doctors. The findings are limited by the potential of non-generalizability of the use of junior doctors' simulated scenarios. Future work should aim to optimise learning experiences and better support surgical trainees. Particular attention should be paid to addressing the longitudinal impact of LLMs, refining AI models, validating AI content, and exploring technological amalgamations for improved outcomes.
Characterisation of Cognitive Load Using Machine Learning Classifiers of Electroencephalogram Data

A high cognitive load can overload a person, potentially resulting in catastrophic accidents. It is therefore important to ensure the level of cognitive load associated with safety-critical tasks (such as driving a vehicle) remains manageable for drivers, enabling them to respond appropriately to changes in the driving environment. Although electroencephalography (EEG) has attracted significant interest in cognitive load research, few studies have used EEG to investigate cognitive load in the context of driving. This paper presents a feasibility study on the simulation of various levels of cognitive load through designing and implementing four driving tasks. We employ machine learning-based classification techniques using EEG recordings to differentiate driving conditions. An EEG dataset containing these four driving tasks from a group of 20 participants was collected to investigate whether EEG can be used as an indicator of changes in cognitive load. The collected dataset was used to train four Deep Neural Networks and four Support Vector Machine classification models. The results showed that the best model achieved a classification accuracy of 90.37%, utilising statistical features from multiple frequency bands in 24 EEG channels. Furthermore, the Gamma and Beta bands achieved higher classification accuracy than the Alpha and Theta bands during the analysis. The outcomes of this study have the potential to enhance the Human–Machine Interface of vehicles, contributing to improved safety.
Group-Level Interpretation of Electroencephalography Signals Using Compact Convolutional Neural Networks

Despite the excellent performance of deep learning models for decoding electroencephalography (EEG) signals, the lack of explainability hinders the implementation of deep learning techniques in neuroscience. Although recently developed solutions ensure physiologically plausible interpretations, the robustness against subject variability and artifacts requires further improvement. This study presents a method for obtaining the group-level interpretation of EEG signals using a compact convolutional neural network (CNN). The convolutional filters of the CNN were clustered, and the clusters with high task-relevant scores were selectively interpreted. The proposed group-level analysis method was validated using a motor imagery dataset, and the results were visually and quantitatively compared with those obtained from the individual-level analysis. The cortical sources interpreted using the proposed group-level analysis exhibited a significantly smaller root mean square error of the source location from the task-relevant cortical area than those interpreted via the individual-level analysis. Furthermore, the cortical sources in the group-level analysis were concentrated in the sensorimotor area and denoted the event-related desynchronization in the alpha band, which is associated with motor imagery tasks. Conversely, the individual-level analysis resulted in unclear spatial and spectral properties of cortical sources. The findings of this study verify the feasibility of group-level analysis based on compact CNNs, which can robustly handle subject variability and artifacts.
Enhanced Limit-of-Detection of Current Sensor Based on Tunneling Magnetoresistive Effect With Multichips Differential Design

Accurately measuring weak electric currents in the order of microamperes in a noninvasive manner is crucial yet challenging for industrial electronics. In this work, we first fabricated low-noise magnetic field sensor chips based on the tunneling magnetoresistive (TMR) effect. A composite free layer and a three-step annealing process were used to improve the performance, resulting in a sensitivity of 30 V/V/T (3 mV/V/Oe), nonlinearity below 0.5%, and reduction of the noise density from <inline-formula> <tex-math notation="LaTeX">$1.4~\mu \text{V}$ </tex-math></inline-formula>/V/<inline-formula> <tex-math notation="LaTeX">$\surd $ </tex-math></inline-formula>Hz to <inline-formula> <tex-math notation="LaTeX">$0.7~\mu \text{V}$ </tex-math></inline-formula>/V/<inline-formula> <tex-math notation="LaTeX">$\surd $ </tex-math></inline-formula>Hz at 1 Hz, in comparison with the traditional two-step annealing process. We then developed a weak-current sensing module with a differential design using two TMR sensor chips arranged antiparallelly. Equipped with a magnetic flux concentration structure and a low-noise signal processing circuit, we demonstrated transient measurement of weak current well below 1 mA with a wide bandwidth of dc-to-16 kHz, a dynamic range of ±150 mA (51 dB), and a rapid response time of <inline-formula> <tex-math notation="LaTeX">$\sim 30~\mu \text{s}$ </tex-math></inline-formula>. The limit-of-detection (LoD) of the sensing module for dc and ac current reached 800 and <inline-formula> <tex-math notation="LaTeX">$300~\mu \text{A}$ </tex-math></inline-formula> (rms), respectively, which is about 30% lower than the single-chip design. This weak-current sensor based on differential TMR has the potential to be useful for industrial applications in smart grids and green energy.
Adaptive Robust Stability Control of All-Electrical Tank Gun Compensated by Radial Basis Neural Network

Stability control of the tank gun has emerged as a pivotal issue for moving tank gun control systems (TGCS). As a complex electromechanical integrated system, TGCS of moving tank inevitably possesses significant parametric uncertainties and uncertain nonlinearities. To effectively enhance the stabilization control performance of TGCS, in this study, we introduce an adaptive robust control (ARC) strategy based on radial basis function neural network (RBFNN) compensation. The adaptive technique is employed to address the parametric uncertainties, while the RBFNN is constructed to approximate the uncertain nonlinearities and realize feedforward compensation. Subsequently, to suppress the residual uncertainties, a nonlinear robust feedback control rate is devised to strengthen the robustness of the developed controller. Lyapunov analysis shows that the proposed controller achieves uniform ultimate bounded stability. Extensive simulation and electromechanical experimental results confirm the effectiveness of the proposed controller, which shows outstanding performance in handling strong parametric uncertainties and uncertain nonlinearities.
Weights of Slope Stability Evaluation Indexes Based on Factor Analysis Method

For rapid evaluation of slope stability,44 typical slopes along Tangtun highway were investigated. The factor analysis method was adopted to analyze 7 evaluation indicators of slope stability,i. e. ,slope gradient,slope height,lithology,slope structure,structural plane cementation,main discontinuity and rock mass integrity. These indexes were sorted in light of their effects on slope stability,and evaluation indexes' grades after normalization were taken as evaluation indexes' weights.The research shows that from their importance,main factors are listed as slope shape,slope structure and structural plane characteristics in proper order,and sorting of evaluation indexes is in turn rock mass integrity,lithology,slope structure,main discontinuity,structural plane cementation,slope gradient and slope height.
Study on Change Detection Framework of Land Use and Land Cover by Remote Sensing

The quantitative and automatic level of change detection is not high,the main reason is that it is a very complicated and multi-factor composited process for land use and land cover change (LUCC) information extraction from remotely sensed images.To solve this problem,this paper decomposed the LUCC information extraction into four processes: change location finding, change region extraction, change class labeling, and change information presentation. Further, the technical procedure for LUCC information extraction was constructed, and the key techniques and methods were discussed. Methods in the paper can be effectively used to improve the quantitative,automatic and reliability level of change information extraction.
Quantifying the effects of alternative surface inlet protection strategies on water quality

Subsurface drainage systems with surface inlets are widely used to divert water in crop producing areas, but pose problems because they can allow unfiltered, sediment-laden water to travel quickly to other waterways. The project tested several modest, uncomplicated inlet protection practices with potential to keep nutrient and sediment flows in check.
Parameter estimates for fishes of the upper Paranß river floodplain and Itaipu reservoir (Brazil)

Estimates of the growth (K), natural mortality (M), consumption/biomass (Q/B) rate and trophic level (TL) for 35 species in the upper Parans river floodplain and the Itaipu reservoir (interconnected ecosystems) are presented. A compilation of these biological statistics is made for comparison purposes and some general trends are briefly discussed.
A Systematic Review to Inform the Development of a Reporting Guideline for Concept Mapping Research

Concept mapping is a phased, mixed-method approach that is increasingly used in health research to develop an understanding of complex phenomena. The six phases of concept mapping are preparation, idea generation, structuring (clustering and prioritization), data analysis, interpretation, and utilization of the map. The reporting of concept mapping research requires the development of a specific reporting guideline. We conducted a systematic review to identify candidate reporting items for inclusion in a reporting guideline. Three databases (MEDLINE, CINAHL, and PsycInfo) were searched to identify studies that used concept mapping methodology. We included 75 concept mapping studies published since 2019 from which we extracted information about the quality of reporting. A third of the studies focused on public health. We identified 71 candidate items that relate to the quality of reporting concept mapping research. The rationale for the study, the focus prompt, procedures for brainstorming, and structuring statements were consistently reported across the included studies. The process for developing the focus prompt, the rationale for the size of the stakeholder groups, and the process for determining the final concept map were generally not reported. The findings from the review will be used to inform the development of our reporting guideline for concept mapping research.
Suicidal Ideation in Victims Bullying: The Role of Non-disclosure in the Family and School Contexts

The main objective of this study was to analyze the influence of adolescents’ family and school contexts in the suicidal ideation of victims of peer bullying, considering the influence of the victim’s non-disclosure of the victimization versus the victimized adolescent’s communicating the situation. The results show that parental styles of rejection and indifference are positively related to victims’ suicidal ideation and non-disclosure of bullying. Conversely, a positive school climate shows a negative relationship with victims’ suicidal ideation and the disclosure of the bullying situation. The findings also indicate that non-disclosure mediates the relationship between the mother’s parental style and suicidal ideation, whereas the school climate moderates the relationship between negative parenting styles and non-disclosure in adolescent bullying victim
Supersonic Flow Over Straight Shallow Cavities Including Leading and Trailing Edge Modifications

An experimental investigation was conducted to examine the effects of adding serrated edges to shallow straight cavities at a freestream Mach number of 2. Measurements consisted of mean and time resolved schlieren visualisation and unsteady pressure spectra. The length to depth ratio of the cavity was 8. The tests conducted at different Reynolds numbers with the straight cavity showed that increasing the Reynolds number increases the root mean square pressure values inside the cavity. Addition of serrations to the cavity leading edge or the trailing edge did not have any significant effect on the separating shear layer nor in stabilising the oscillations of the shear layer. There was also no noticeable effect on the overall sound pressure levels (OASPL) inside the cavity.
Adult Ossabaw Pigs Prefer Fermented Sorghum Tea over Isocaloric Sweetened Water

Simple Summary Fermented sorghum tea has previously been proposed to improve satiety and health. A pig model for adult weight management and fermented sorghum tea will be used for future studies; however, the literature has indicated that pigs may reject sour or bitter flavors. Previous literature studies focused on piglets and lactating sows fed ad libitum, whereas our model is focused on male and female adult pigs with restricted calorie intake. Therefore, the objective of the current work was to determine the appetitive response to fermented sorghum tea using a novel, three-way preference maze. For this study, limit-fed adult pigs not only have high affinity for fermented sorghum tea, but they also prefer the tea over the equally sweetened control solution. This is the first preference research in many years where adult pigs do not reject sour or bitter flavors if they are formulated in fermented tea. Our novel preference maze is now commercially available; therefore, our preference test methods can be replicated across laboratories, thus impacting the scientific community that uses pigs in research. Furthermore, future work using the adult pig as a model for understanding the health benefits of fermented sorghum tea will not be impeded by the avoidance behaviors of adult pigs. Abstract Ossabaw pigs (n = 11; 5—gilts, 6—barrows; age 15.6 ± 0.62 SD months) were exposed to a three-choice preference maze to evaluate preference for fermented sorghum teas (FSTs). After conditioning, pigs were exposed, in four sessions, to choices of white FST, sumac FST, and roasted sumac-FST. Then, pigs were exposed, in three sessions, to choices of deionized H2O (−control; avoidance), isocaloric control (+control; deionized H2O and sucrose), and blended FST (3Tea) (equal portions: white, sumac, and roasted sumac). When tea type was evaluated, no clear preference behaviors for tea type were observed (p > 0.10). When the 3Tea and controls were evaluated, pigs consumed minimal control (p < 0.01;18.0 ± 2.21% SEM), and they consumed great but similar volumes of +control and 3Tea (96.6 and 99.0 ± 2.21% SEM, respectively). Likewise, head-in-bowl duration was the least for −control, but 3Tea was the greatest (p < 0.01; 5.6 and 31.9 ± 1.87% SEM, respectively). Head-in-bowl duration for +control was less than 3Tea (p < 0.01; 27.6 vs. 31.9 ± 1.87% SEM). Exploration duration was the greatest in the area with the −control (p < 0.01; 7.1 ± 1.45% SEM), but 3Tea and +control exploration were not different from each other (1.4 and 3.0 ± 1.45% SEM, respectively). Regardless of tea type, adult pigs show preference for FST, even over +control. Adult pigs likely prefer the complexity of flavors, rather than the sweetness alone.
How does subgrid‐scale parametrization influence nonlinear spectral energy fluxes in global NWP models?

The paper examines horizontal wind variance (kinetic energy spectra) and available potential energy spectra in simulations conducted with a state‐of‐the‐art global numerical weather prediction (NWP) model: the Integrated Forecasting System (IFS) of the European Centre for Medium‐Range Weather Forecasts. The formulation of the spectral energy budget of the atmosphere by Augier and Lindborg (2013) is used to illustrate how the nonlinear spectral fluxes differ for a hierarchy of reduced models, adiabatic dynamical core, Held‐Suarez dry, and idealized moist aquaplanet simulations, compared to NWP simulations with full complexity. The results identify surface drag and momentum vertical mixing as the key processes for influencing the transfer of energy in a stratified atmosphere. Moreover, the circulation generated by topography plays a significant role in these transfers. Given that subgrid‐scale vertical mixing is parametrized, and that the treatment of orography filtering varies vastly between NWP models, the magnitude and scale of the nonlinear interactions can differ substantially from model to model, and depends on the choices made for the physical parametrizations. The need to appropriately parametrize the essential influence of subgrid‐scale processes in global NWP and climate simulations has the effect that the physical energy cascade is replaced by a parametrized energy transfer. This explains the seeming failure of the IFS to produce a shallower mesoscale energy spectrum. In contrast, neither the horizontal filtering, typically applied in NWP models to avoid a spectral blocking at the smallest scales, nor implicit numerical dissipation significantly constrain, at sufficiently high resolution, the nonlinear interactions or the dominant slope of the energy spectra at synoptic and mesoscales.
Novel Quinoline-Based Thiosemicarbazide Derivatives: Synthesis, DFT Calculations, and Investigation of Antitubercular, Antibacterial, and Antifungal Activities

The discovery of new antimicrobial agents as a means of treating drug-resistant microbial pathogens is of utmost significance to overcome their immense risk to human well-being. The current investigation involves the development, synthesis, and assessment of the antimicrobial efficacy of novel quinoline derivatives incorporating a thiosemicarbazide functionality. To design the target compounds (QST1–QST14), we applied the molecular hybridization approach to link various thiosemicarbazides to the quinoline core with a sulfonyl group. Upon the synthesis and completion of structural characterization via spectroscopic techniques (1H NMR, 13C NMR, 15N NMR, IR, and HRMS), the title molecules were extensively evaluated for their potential antitubercular, antibacterial, and antifungal activities. N-(3-Chlorophenyl)-2-(quinolin-8-ylsulfonyl)hydrazine-1-carbothioamide (QST4), the most effective compound against Mycobacterium tuberculosis H37Rv, was also tested on isoniazid-resistant clinical isolates with katG and inhA promoter mutations. Based on molecular docking studies, QST4 was also likely to demonstrate its antimycobacterial activity through inhibition of the InhA enzyme. Furthermore, three derivatives (QST3, QST4, and QST10) with preferable antimicrobial and drug-like profiles were also shown to be nontoxic against human embryonic kidney (HEK) cells. All compounds were optimized by the density functional theory method using B3LYP with the 6-31+G(d,p) basis set. Structural analysis, natural bond orbital calculations of donor–acceptor interactions, molecular electrostatic potential analysis, and frontier molecular orbital analysis were carried out. Quantum chemical descriptors and charges on the atoms were determined to compare the strengths of the intramolecular hydrogen bonds formed and their stabilities. We determined that the sulfur atom forms a stronger intramolecular hydrogen bond than the nitrogen, oxygen, and fluorine atoms in these sulfonyl thiosemicarbazide derivatives.
Feature Selection For Crop Yield Prediction Using Optimization Techniques

Agriculture sector forms the basis of Indian economy. Prediction of yield of crops is an important issue in agriculture as the domain is facing a huge challenge of producing effective crops having high yield while maintaining the sustainability of natural resources. In addition, early prediction of yield enable the farmers to take precautionary actions to improve the productivity of crops. Crop yield depends on various factors including weather parameters, soil parameters, irrigation availability, fertilizer’s needs, farm capacity, etc. In general, early prediction is being done by analyzing the above data which is archived over several years using machine learning techniques. Identifying relevant and more useful attributes from the set of all available attributes(called feature selection) which really affect the yield becomes important before performing the prediction process using machine learning algorithms. The focus of this work lies with feature selection. In this work, four different feature selection techniques, namely, Binary Cuckoo Search(BCS), Relief, Grey Wolf Optimization(GWO) and Principal Component Analysis(PCA) have been employed over the agricultural data collected from field and preprocessed using Monte Carlo method. The selected features are given for yield prediction using multiple regression. The accuracy of prediction obtained using the above four methods are presented. Results are discussed.