Miracle-dz/UniFAR-FTUs huggingface dataset:
Small Cell Oesophageal Carcinoma: A Retrospective Case Series From a UK Tertiary Centre and a Review of the Literature

Background Small cell oesophageal carcinoma (SCEC), a rare neuroendocrine malignancy, presents various challenges in diagnosis and treatment. The condition is characterised by rapid dissemination, a marked responsiveness to chemotherapy, and a guarded prognosis. While the European Neuroendocrine Tumour Society has recommended platinum-based chemotherapy, ongoing debates on optimal strategies and the lack of clear guidelines underscore the need for further comprehensive research efforts. Methods This study retrospectively analysed 12 cases of localised pure SCEC treated at a UK tertiary care centre between 2006 and 2020. We systematically analysed and categorised the cases based on stage, performance status, and patient age. This comprehensive approach enabled a nuanced examination of overall survival (OS), thereby providing valuable insights into the differences between outcomes. Results The study revealed a median OS of 12.01 months for treated non-metastatic cases, highlighting the challenges of SCEC management. Conversely, treated metastatic cases exhibited a mean survival of 9.15 months, which contrasts starkly with the 2.55 months demonstrated by those receiving best supportive care (BSC). These figures underscore the urgency for refined strategies in handling advanced localised disease and the need to continue research endeavours to devise methods to enhance the precision and optimise outcomes beyond the presented data. Conclusion Based on our findings, the combination of chemoradiotherapy and surgery to manage SCEC holds promise; however, further research is needed to optimise the management approach. The lack of clear guidelines underscores the imperative for personalised treatment approaches.
Effects of iba and media on the growth performance of three cultivars of grapes (Vitis Vinifera L.)

Annual branches of three grape cultivars, namely ‘Xia Hei’ grape, ‘Eastern Star’ grape and ‘Giant Rose’ grape were first treated with Indole-3-butyric acid (IBA) solution, then inserted in different media and then n their growth performances were studied. The growth performance of the three grape cultivars were: ‘Xia Hei’ > ‘Eastern Star’ > ‘Giant Rose’. Media (M4) was the most conducive to improve the survival per cent of gape rooted cuttings. This study provides a reference for choosing a suitable method to grow rooted cuttings taken from grape annual branches, which would furthermore improve the survival rate of young vines. Bangladesh J. Bot. 52(3): 763-774, 2023 (September)
An Innovative and Time Efficient Approach to Secure Text Data Specifically on Cloud Environment

Data security is highly vital and crucial for every organization, regardless of size of organization—whether it it is a small scale organization, medium scale organization, or large scale organization. This applies to the both inside and outside the organization. The essential prerequisite for anyone's confidence and trust in any organization is data security. In day to day life human generates various data in different formats like text data, graphical data, voice data, video data etc. using various medium like youtube, twitter, facebook, instragram, emails etc. These data may be a structural data, may be a semi structural data or may be an unstructured data. In today’s life all the data which are generated is generally huge in size which we termed as big data. To handle this big data is very difficult task. Cloud computing is one of the technique which can handle this very well. But the main fact is that there are various challenges to both of these technologies. One of the biggest challenges is to provide security to this kind of data. There are various research has been done and still going on to address this issue effectively. In this paper we have also tried to address this issue after doing exhaustive literature work then we provide our own AVJ security algorithm which can provide security to text data. The writers' names—A for Mr. Ankur Shah, V for Dr. Vishal Dahiya, and J for Dr. Jay A Dave—led to the creation of the moniker AVJ***Throughout this paper we have used term base paper algorithm meaning that we are using reference paper [5] algorithm of Mohammad Anwar Hossain and et al.
Lnc-PTCHD4-AS inhibits gastric cancer through MSH2-MSH6 dimerization and ATM-p53-p21 activation

Conserved long non-coding RNAs (lncRNAs) have not thoroughly been studied in many cancers, including gastric cancer (GC). We have identified a novel lncRNA PTCHD4-AS which was highly conserved between humans and mice and naturally downregulated in GC cell lines and tissues. Notably, PTCHD4-AS was found to be transcriptionally induced by DNA damage agents and its upregulation led to cell cycle arrest at the G2/M phase, in parallel, it facilitated the cell apoptosis induced by cisplatin (CDDP) in GC. Mechanistically, PTCHD4-AS directly bound to the DNA mismatch repair protein MSH2-MSH6 dimer, and facilitated the binding of dimer to ATM, thereby promoting the expression of phosphorylated ATM, p53 and p21. Here we conclude that the upregulation of PTCHD4-AS inhibits proliferation and increases CDDP sensitivity of GC cells via binding with MSH2-MSH6 dimer, activating the ATM-p53-p21 pathway.
A Dynamic Spatio-temporal Synchronous Spectral Filtering Method for Distributed Anode Current Signals Classification

In the modern industrial aluminum electrolysis production process, distributed anode current signals (DACS) classification is an effective means to mine knowledge from data. Considering that DACS is a spatio-temporal sequence signal with complex dynamic spatio-temporal dependencies that are difficult to be captured by traditional methods. To solve this problem, a dynamic spatio-temporal synchronous spectral filtering method for distributed anode current signals classification is proposed. Firstly, the edit distance on real sequences (EDR) distance function with strong robustness is used to calculate the similarity between one-dimensional anode current time sequence corresponding to different carbon anode positions and the similarity between one-dimensional anode current spatial sequence corresponding to different time points, and dynamic spatial graph and dynamic temporal graph are constructed, respectively. Secondly, dynamic spatial spectral filtering and dynamic temporal spectral filtering are performed synchronously on DACS based on dynamic spatial graph and dynamic temporal graph respectively to capture complex dynamic spatio-temporal dependencies. Then, based on the mechanism knowledge of industrial aluminum electrolysis production process, the attenuation intervention is performed on spatial spectral filtering to make the model adapt to different cell conditions, and the corresponding matrix is spliced with the results of dynamic temporal spectral filtering to obtain the feature input matrix for DACS classification. Finally, a pre-defined static graph is designed based on the electrolyte flow field distribution. Meanwhile, to prevent over-smooth issues, a single-layer graph convolutional networks (GCN) is used to perform DACS classification tasks. And, the classification results on the actual DACS datasets compared with existing methods demonstrate the effectiveness of proposed method.
ChatGPT backend: A comprehensive analysis

Artificial intelligence (AI) has transformed the field of natural language processing, enabling substantial advances in understanding, interpreting, and producing human language. The ability of AI to find new solutions to difficult linguistic expressions has led to the birth of sophisticated language models such as ChatGPT. This model uses cutting-edge deep learning algorithms to produce high-quality, human-like writing in response to natural language inputs. ChatGPT has an amazing capacity to recognize context, evaluate sentiment, and provide coherent and appropriate replies. It has a wide range of applications, from virtual assistants and customer support bots to language translation and content development. Therefore, understanding its backend has become essential. In this paper, we summarize the key principles underlying the operation of ChatGPT's back-end. This study is required reading for ChatGPT researchers because it covers critical aspects of the ChatGPT backend. It includes essential information for researchers looking to improve ChatGPT's performance or create new language models based on its architecture.
Design and Implementation of Data Concentrator Unit supported with Multiple Synchronized Cameras for Object-Detection

The real-time performance of Autonomous Vehicles (AVs) is crucial to swiftly analyze dynamic road conditions, make split-second decisions, and ensure passenger safety and efficient navigation in complex and ever-changing environments. Synchronized cameras play a pivotal role in AVs due to their ability to provide a multi-perspective and comprehensive view of the vehicle's surroundings. DCU (Data Concentrator Unit) is a device to collect, process, and transmit data from several sensors in AVs. In this paper, we propose Data Concentrator Unit (DCU) that is supported by Multiple Synchronized Cameras for Object-Detection. It performs object-detection analysis on multiple high-definition video streams captured by multiple synchronized cameras.
Anomaly Based Intrusion Detection System: A Deep Learning Approach

In recent years, computer networks have seen a considerable proliferation in terms of performance and total traffic volume. At the same time, cyber attacks have been on the rise ever since, which led to the emergence of Intrusion Detection Systems (IDSs) to deal with them. Conversely, artificial intelligence has been a popular technique that can be applied to a variety of purposes including detection of cyber attacks. However, most related work that leveraged artificial intelligence classifiers to address this problem used outdated datasets. In this paper, we implemented an anomaly-based intrusion detection system using deep learning algorithms with the goal of achieving higher performance while using a newer dataset. That is why we used the NSL-KDD dataset, which constitutes an improvement over the widely used KDD Cup 99 dataset, as it addresses some of its imperfections such as duplicated records and obsolete attack types. Then, we developed three deep learning classifiers that are Recurrent Neural Networks (RNNs), Multi-Layer Perceptron (MLP), and a hybrid Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) model. Also, we compared the effectiveness of our proposed model with Machine Learning classifiers such as Support Machine Vector (SVM), K-Nearest Neighbor (KNN), and Gradient Boosting (GB). Finally, we validate our findings with a performance evaluation of our model, which showed encouraging results.
Application of a CNN-based Acoustic Classification Model to Detecting a Target Signal with Interference

In recent years, there have been several collapse accidents in Korea that have increased anxiety about disaster accidents. Several preliminary predictive studies have been conducted to prevent collapse accidents. Still, there are few studies on detecting survivors who need rescue after an accident. In this paper, we propose a convolutional neural networks-based abnormal sound classification model for detecting collapse survivors. The proposed model classifies nine types of sound, and its accuracy is 96.34 %. As an additional experiment, accuracy is measured for various SIR values to investigate the performance of this model when input signals are mixed with interfering signals.
Field Observations of Regional Controls of Soil Hydraulic Properties on Soil Moisture Spatial Variability in Different Climate Zones

Knowledge of soil moisture spatial variability (SMSP) is important for many practical reasons. However, a significant gap still exists in our understanding of different controls on SMSP, especially the roles of soil hydraulic parameters due to their limited availability. Although modeling approaches have been used to assess the impacts of those parameters on SMSP, they have led to inconsistent findings. In this study, soil moisture data from Utah (5 yr) and the US Southeast (2 yr) were obtained from the Soil Climate Analysis Network (SCAN), along with estimated van Genuchten parameters. The method of mean relative difference (MRD) of soil moisture was used as a diagnostic tool for assessing different climate and soil controls on SMSP. The results show that instead of being controlled by climate variables (e.g., precipitation and potential evapotranspiration) as traditionally believed at regional scales (∼105 km2), MRD is mainly dependent on soil hydraulic properties. In Utah with a drier climate, the residual soil moisture content (θr) is the dominant control on MRD, followed by the saturated soil moisture content (θs). With wetter climates in the US Southeast, the impacts of θr and θs on MRD become comparable, mostly due to the high correlation between θr and θs in this region, and there exists a nonlinear negative relationship between MRD and the parameter n for coarser soils, indicated by larger n values tending to have lower MRDs. The findings of this study have important implications for verifying remotely sensed moisture data and initializing and parameterizing regional land surface and climate models.
Correlation Between Body Mass Index with Menstrual Cycle on Female Adolescent

Menstruation is the primary sexual sign that marks the period of puberty for women. Menstrual cycle disorders often occur in adolescents and are caused by several factors including psychological, hormonal, genetic disorders, organics abnormalities, and nutritional status. The nutritional situation is measured using a body mass index. The purpose of this study is to determine the correlation of body mass index and the menstrual cycle on the female adolescent. This research is an observational analytic study and uses a cross-sectional approach. The population of the study is all female students in Sunan Ampel State Islamic University. Accidental sampling technique is used in this study to obtain 36 respondent. Data collection from respondents was carried out by administering questionnaires and measurement of body weight and height. Data analysis uses the chi-square test. The result of the study is that most of body mass index (BMI) of respondents is standard (77,8%) and had regular menstrual cycles (75%). Chi-square test shows that there is a correlation between body mass index (BMI) and the length of the menstrual cycle (p=0,030).
Hardware Accelerator for VSLAM using Fisheye Image in Embedded System

Visual Simultaneous Localization and Mapping (VSLAM) is a key technology for Autonomous Vehicles. Among them, the Indirect method of VSLAM is suitable for use in a vehicle environment where the surrounding situation changes quickly. However, it has the disadvantage of requiring additional resources to extract Feature Points. Separately, using Fisheye images for VSLAM can be utilized more effectively through a wide angle of view. However, the nature of Fisheye imagery introduces distortion, which requires additional resources to compensate for. These resource consumption is more critical in Embedded Systems such as vehicles. In addition, the execution time of VSLAM applied to Embedded Systems is too long to be used in the vehicle environment. In this paper, we propose a hardware accelerator for distortion correction of Fisheye images and feature point extraction of images. We use a Look-Up Table (LUT) for acceleration of distortion correction and simultaneously generate multiple scale images for Feature Point extraction. The Feature Points extractor is implemented by modified the SIFT algorithm, which has the best generalization performance. We applied it to CubemapSLAM, an Indirect VSLAM using Fisheye image. Experimental results show that the execution time of CubemapSLAM's tracking thread can be significantly reduced on embedded systems to about 5300 ms without hardware accelerator and 98.597 ms with hardware accelerator.
Carbonate cements and their formation mechanism in Palaeogene sandstones of Lishui sag,East China Sea basin

Carbonate cements are the most abundant authigenic minerals in the Palaeogene sandstones of Lishui sag,East China Sea basin.They mainly consist of ankerite and calcite and have distinctive stratigraphic distribution and geometry.The ankerite cement just occurs beneath the unconformity of Ojian movement and nearby coal beds and the calcite occurs in a relatively deep bed far from the unconformity.The stable-isotope data for ankerite are δ13C(VPDB)=-2.1‰~-2.5‰,δ18O(VPDB) =-3.7‰~-4.1‰(precipitation temperature is 37℃ to 39℃) and those for calcite are δ13C(VPDB) =-2.4‰~-17.3‰(most of the samples are less than-10‰),δ18O(VPDB) =-6.8‰~-15.3‰(precipitation temperature for most of samples is more than 90℃). Based on the results,the authors have the following suggestions:(1) the precipitation of ankerite is from meteoric fluids and some kind of acids from the coal bed during early diagenesis,input into sediments during times of relative sea-level fall;(2) the source of ankerite cement is the dissolution of coeval carbonate matierals;(3) the precipitation of calcite cement is from buried fluids during later diagenesis;(4) the source of calcite cement is transformation of clay minerals and dissolution of feldspar and other aluminosilicates;and(5) the earlier cementation by ankerite has enhanced the mechanical strength of rocks and resistance ability to compaction.This is of positive significance for the preservation of porosity in under deeply buried conditions.
PAVEMENT SLABS RESTING ON ELASTIC FOUNDATION

THE DEVELOPMENT OF A RATIONAL METHOD OF ANALYSIS OF ANY SYSTEM MUST INCLUDE THE SELECTION AND ANALYSIS OF A MODEL FOR REALISTIC INPUT PARAMETERS. BASED ON THIS IDEA, A SIMPLE METHOD HAS BEEN DEVELOPED FOR SOLUTION OF OFTEN ENCOUNTERED PROBLEMS OF ENGINEERING PRACTICE INVOLVING SLABS RESTING ON SUBGRADE. THE SLAB IS REPRESENTED BY A PHYSICAL MODEL, WHICH IS HELPFUL IN VISUALIZING THE PROBLEM AND FORMING A SOLUTION. REGARDING SUBGRADE, MOST OF THE AVAILABLE ANALYSES ASSUME IT TO BE A WINKLER MODEL, A PHYSICAL MODEL OF A HEAVY LIQUID, OR A BED OF SPRINGS. IN THIS PAPER THE SOIL IS TREATED AS AN ELASTIC SOLID. WITH THESE TWO MODELS FOR SLAB AND SUBGRADE, A COMPUTER METHOD BASED ON MATRIX ANALYSIS HAS BEEN DEVELOPED. FROM THE SOLUTION OF REACTIVE SUBGRADE PRESSURES, THE DEFLECTIONS ARE THEN SUBSEQUENTLY USED TO COMPUTE STRESSES AND BENDING MOMENTS. TWO EXEMPLARY PROBLEMS, ONE WITH A CORNER LOAD AND ONE WITH A CENTER LOAD, HAVE BEEN INCLUDED. COMPARISON OF THE LATTER WITH THE WINKLER MODEL IS ILLUSTRATED.
Comparison of Evaluation Metrics for Short Story Generation

This study aimed to analyze the correlation among different automatic evaluation metrics for short story generation. In the study, texts were generated from short stories using different language models: the N-gram model, the Continuous Bag-of-Word (CBOW) model, the Gated recurrent unit (GRU) model and the Generative Pre-trained Transformer 2 (GPT-2) model. All models were trained on short Aesop’s fables. The quality of the generated text was measured with various metrics: Perplexity, BLEU score, the number of grammatical errors, Self-BLEU score, ROUGE score, BERTScore, and Word Mover’s Distance (WMD). The resulting correlation analysis of the evaluation metrics revealed four groups of correlated metrics. Firstly, perplexity and grammatical errors were moderately correlated. Secondly, BLEU, ROUGE and BERTScore were highly correlated. Next, WMD was negatively correlated with BLEU, ROUGE and BERTScore. On the other hand, Self-BLEU, which measures text diversity within the model, did not correlate with the other metrics. In conclusion, to evaluate text generation, a combination of various metrics should be used to measure different aspects of the generated text.
Non-Linear Joint AVO Inversion of PP and PS Waves in a VTI Medium

Summary This paper aims to estimate elastic parameters such as Pwave and S-wave velocity, Thomsen’s anisotropy parameters (, ε density, and layer thickness of a layered medium using pre-stack seismic data and some prior information. Three different algorithms have been developed in an object oriented environment: Travel time inversion, waveform inversion, and joint traveltime and reflectivity inversion. These algorithms can use compressional or joint compressional and converted shear waves data respectively. We have implemented exact parametric travel time equations in a VTI medium for a stack of either anisotropic or isotropic homogeneous layers. Reflection and transmission coefficients for the whole ray path have been calculated. A convolution model is used to calculate synthetic seismograms. To minimize an objective function a non-linear conjugate gradient (CG) algorithm has been implemented. All of the partial derivatives have been calculated analytically which results in a fast and robust differentiation of traveltimes and reflection and transmission coefficients. A numerical example of a stack of 10 layers has been tested for different elastic parameters and thicknesses. Compressional and shear waves velocities and thicknesses have been varied 25 percent from true values while  and ε have been varied 100 percent from their true values. Joint traveltime inversion of P  P & P  Sv has successfully estimated the true values of the parameters. Waveform inversion is highly non-linear; hence only anisotropic parameters and density while varying up to 100 percent are estimated. Waveform inversion results in a full estimation of the true values of  and ε and a fairly good estimate of density.
Using Portraiture Methodology to Tell Stories of Teachers’ Lived Experiences

Sara Lawrence-Lightfoot pioneered a new qualitative research design, portraiture, in the 1980s. The aim of this integrative review was to analyze the use of portraiture in studies about general secondary education teachers to determine researchers’ motivation for choosing portraiture over other similar qualitative research designs such as ethnography and phenomenology, and synthesize the findings to provide a research agenda about the use of portraiture methodology in studies about teachers. Electronic databases Jstor, Taylor & Francis, and Springer were searched to identify studies about pre-service and in-service general secondary education teachers that used portraiture as the research design. Publications about early childhood education teachers and higher education teachers were excluded. Likewise, studies that portrayed students or schools were excluded. 10 publications were included in the review. Within the publications, the most frequently reported reason for using portraiture methodology was that it allowed to portray the richness and complexity of teachers’ lived experiences in a language that could be understood by readers beyond the scientific community. For now, portraiture is not as widely utilized as other qualitative research designs. It has potential to become more accepted in educational research, as it allows researchers to share the nuanced and complex stories of teachers’ strengths and lived experiences and, thus, drive social change.