Miracle-dz/UniFAR-FTUs huggingface dataset:
Theoretical Accuracy of the Raytracing Method for Intraocular Calculation of Lens Power in Myopic Eyes after Small Incision Extraction of the Lenticule

Abstract Aim To evaluate the accuracy of the raytracing method for the calculation of intraocular lens (IOL) power in myopic eyes after small incision extraction of the lenticule (SMILE). Methods Retrospective study. All patients undergoing surgery for myopic SMILE between May 1, 2020, and December 31, 2020, with Scheimpflug tomography optical biometry were eligible for inclusion. Manifest refraction was performed before and 6 months after refractive surgery. One eye from each patient was included in the final analysis. A theoretical model was invited to predict the accuracy of multiple methods of lens power calculation by comparing the IOL-induced refractive error at the corneal plane (IOL-Dif) and the SMILE-induced change of spherical equivalent (SMILE-Dif) before and after SMILE surgery. The prediction error (PE) was calculated as the difference between SMILE-Dif–IOL-Dif. IOL power calculations were performed using raytracing (Olsen Raytracing, Pentacam AXL, software version 1.22r05, Wetzlar, Germany) and other formulae with historical data (Barrett True-K, Double-K SRK/T, Masket, Modified Masket) and without historical data (Barrett True-K no history, Haigis-L, Hill Potvin Shammas PM, Shammas-PL) for the same IOL power and model. In addition, subgroup analysis was performed in different anterior chamber depths, axial lengths, back-to-front corneal radius ratio, keratometry, lens thickness, and preoperative spherical equivalents. Results A total of 70 eyes of 70 patients were analyzed. The raytracing method had the smallest mean absolute PE (0.26 ± 0.24 D) and median absolute PE (0.16 D), and also had the largest percentage of eyes within a PE of ± 0.25 D (64.3%), ± 0.50 D (81.4%), ± 0.75 D (95.7%), and ± 1.00 D (100.0%). The raytracing method was significantly better than Double-K SRK/T, Haigis, Haigis-L, and Shammas-PL formulae in postoperative refraction prediction (all p < 0.001), but not better than the following formulae: Barrett True-K (p = 0.314), Barrett True-K no history (p = 0.163), Masket (p = 1.0), Modified Masket (p = 0.806), and Hill Potvin Shammas PM (p = 0.286). Subgroup analysis showed that refractive outcomes exhibited no statistically significant differences in the raytracing method (all p < 0.05). Conclusion Raytracing was the most accurate method in predicting target refraction and had a good consistency in calculating IOL power for myopic eyes after SMILE.
Leboh 2: An Android Application for Solid Waste Detection

—Garbage or waste will be a serious problem if they are not managed properly. Sorting garbage is necessary before putting them in the bins. This paper develops an Android application called Lebooh 2 for garbage detection using EfﬁcientDet-Lite, a Convolutional Neural Network’s architecture for object detection. Lebooh 2 is designed for Indonesian speakers. This paper applies two models: Model 1 and Model 2. Model 1 is trained to recognize 11 types of garbage, i.e., biological, battery, cardboard, metal, glass, plastic, paper, clothes, shoes, electronics, and trash. Model 2 is developed to detect whether the garbage is recyclable or not-recyclable. The experiment using EfﬁcientDet-Lite 3 obtains the mean average precision are around 75% - 77%. User testing involves participants as testers and they collect 400 screenshots of Leboh 2 for solid waste detection. The user testing produces an accuracy of around 78% and 82% for Model 1 and Model 2, respectively. A simple survey of 172 respondents reveals that 22.7% of them have not yet learned how to sort the garbage.
Assessment and management of respiratory function in patients with Duchenne muscular dystrophy: current and emerging options

: Duchenne muscular dystrophy (DMD) is an X-linked myopathy resulting in progressive weakness and wasting of all the striated muscles including the respiratory muscles. The consequences are loss of ambulation before teen ages, cardiac involvement and breathing difficulties, the main cause of death. A cure for DMD is not currently available. In the last decades the survival of patients with DMD has improved because the natural history of the disease can be changed thanks to a more comprehensive therapeutic approach. This comprises interventions targeted to the manifestations and complications of the disease, particularly in the respiratory care. These include: 1) pharmacological intervention, namely corticosteroids and idebenone that significantly reduce the decline of spirometric parameters; 2) rehabilitative intervention, namely lung volume recruitment techniques that help prevent atelectasis and slows the rate of decline of pulmonary function; 3) scoliosis treatment, namely steroid therapy that is used to reduce muscle inflammation/degeneration and prolong ambulation in order to delay the onset of scoliosis, being an additional contribution to the restrictive lung pattern; 4) cough assisted devices that improve airway clearance thus reducing the risk of pulmonary infections; and 5) non-invasive mechanical ventilation that is essential to treat nocturnal hypoventilation, sleep disordered breathing, and ultimately respiratory failure. Without any intervention death occurs within the first 2 decades, however, thanks to this multidisciplinary therapeutic approach life expectancy of a newborn with DMD nowadays can be significantly prolonged up to his fourth decade. This review is aimed at providing state-of-the-art methods and techniques for the assessment and management of respiratory function in DMD patients.
POPULATION STRUCTURE AND ECOLOGY OF A HIGH ANDEAN FOREST: POLYLEPIS RUGULOSA (ROSACEAE) FROM PERU

Thirteen plots of 500m 2 were established in the forest of quenoa at Muylaque, district of San Cristóbal (Moquegua), southern of Peru. The population structure showed a predominance of saplings (239 individuals), followed by adults (217 individuals), and seedlings (164 individuals). The average of individuals per plot was higher for the seedlings (18.4 ± 3.6), followed by adults (16.7 ± 4.3) and saplings (12.6 ± 4.5). It was estimated 334 adult individuals per hectare. P. rugulosa yielded floral buds during the wet season (December to February), while in the dry season (July to September) individuals in a vegetative stage predominate. The fruiting stage predominated at the end of the wet season (February to April). The plants affected by anthropogenic activities were accounted up to 13% of the plants evaluated. The associated flora to the P. rugulosa forest is composed of 72 species of herbaceous and shrubby plants distributed in 28 families.
RNN-LSTM Model based Forecasting of Cryptocurrency Prices using Standard Scaler Transform

Bitcoin is the worlds’ most valuable cryptocurrency and is traded on over 40 exchanges worldwide accepting over 30 different currencies. It has a current market capitalization of 9 billion USD according to https://www.blockchain. info/ and sees over 250,000 transactions taking place per day. As a currency, Bitcoin offers a novel opportunity for price prediction due to its relatively young age and resulting volatility, which is far greater than that of fiat currencies. It is also unique in relation to traditional fiat currencies in terms of its open nature; no complete data exists regarding cash transactions or money in circulation for fiat currencies. Prediction of mature financial markets such as the stock market has been researched at length. Bitcoin presents an interesting parallel to this as it is a time series prediction problem in a market still in its transient stage. Traditional time series prediction methods such as Holt-Winters exponential smoothing models rely on linear assumptions and require data that can be broken down into trend, seasonal and noise to be effective. This type of methodology is more suitable for a task such as forecasting sales where seasonal effects are present. Therefore, the analysis of financial data for predicting the future bitcoin price has always been an important field of research with a direct and indirect effect on world economy. Due to the lack of seasonality in the Bitcoin market and its high volatility, these methods are not highly effective for this task. Given the complexity of the task, machine learning makes for an interesting technological solution based on its performance in similar areas. Hence, a time series analysis is utilized in this paper in order to find out the pattern of bitcoin price movement and forecasting the closing price of the next few days as well as analyzing the performance of the time series models
2015) Coupling and electrical control of structural, orbital and magnetic orders in

Perovskite oxides are already widely used in industry and have huge potential for novel device applications thanks to the rich physical behaviour displayed in these materials. The key to the functional electronic properties exhibited by perovskites is often the so-called Jahn-Teller distortion. For applications, an electrical control of the Jahn-Teller distortions, which is so far out of reach, would therefore be highly desirable. Based on universal symmetry arguments, we determine new lattice mode couplings that can provide exactly this paradigm, and exemplify the effect from first-principles calculations. The proposed mechanism is completely general, however for illustrative purposes, we demonstrate the concept on vanadium based perovskites where we reveal an unprecedented orbital ordering and Jahn-Teller induced ferroelectricity. Thanks to the intimate coupling between Jahn-Teller distortions and electronic degrees of freedom, the electric field control of Jahn-Teller distortions is of general relevance and may find broad interest in various functional devices.
Seeing Test at Samorkraeng Mountain

Seeing test based on DIMM method was employed to investigate the atmospheric turbulence at Samorkraeng Mountain, Wangtong district, Phitsanulok province. The latitude and the longitude of the selected site for an observatory are 16􀁄50′. 59 N and 100􀁄24′. 08 E, respectively; the altitude is about 180 meters above the sea level; and the distance is about 15 kilometers north east from the downtown of Phitsanulok. The statistics of the seeing in FWHM, temperature, and humidity which measured and calculated for 20 nights during December 8, 2004 to January 4, 2006 are as follows: maximum FWHM, minimum FWHM, and average FWHM are 3.6, 0.8, and 1.7 arc seconds, respectively; maximum temperature, minimum temperature, and average temperature are 29.2, 17.4, and 22.8 °C, respectively; and maximum humidity, minimum humidity, average humidity are 80, 55, and 68.8 percent, respectively. The average seeing value at Samorkraeng Mountain is not much different from the average values at Siding Springs in Australia, Kitt Peak in the United States of America, Devasthal in India, and South Pole in Antarctica which are the famous observatories in the world. Keywords: Site testing-seeing, Astronomical seeing, Atmospheric effects, Seeing disk, DIMM
A primate-specic (GCC) repeat in SMAD9 undergoes natural selection in humans and harbors unambiguous genotypes in late-onset neurocognitive disorder.

Across numerous primate species and tissues, SMAD9 (SMAD Family Member 9) reaches the highest level of expression in the human brain. This gene contains a (GCC) short tandem repeat (STR) at the interval between + 1 and + 60 of the transcription start site, which is in the 1st percent of high-ranking (GCC)-repeats in respect of length. Here we sequenced this (GCC)-repeat in 396 Iranian individuals, consisting of late-onset neurocognitive disorder (NCD) (N = 181) and controls (N = 215). We detected two predominantly abundant alleles of 7 and 9 repeats, forming 96.2% of the allele pool. The ratio of the (GCC)7 and (GCC)9 alleles was in the reverse order in the NCD group versus controls (p = 0.005), resulting from excess of (GCC)7 in the NCD group (p = 0.003) and the 9-repeat in the controls (p = 0.01). Five genotypes, predominantly consisting of (GCC)7 and lacking (GCC)9 were detected in the NCD group only (p = 0.008). Those patients received probable diagnoses of Alzheimer’s disease and/or cerebrovascular dementia. Five genotypes consisting of (GCC)9 and lacking (GCC)7 were detected in the control group only (p = 0.002). The group-specic genotypes formed approximately 4% of the genotype pool in human samples studied. In conclusion, we propose natural selection and a novel locus for late-onset NCD at the SMAD9 (GCC)-repeat in humans. Although the percentage of individuals harboring the speci�c genotypes in each group was modest, those genotypes represent an underappreciated feature, which may enhance the perspective of disorders that are considered to be complex, and yet may be linked to unambiguous genotypes at certain STR loci.
The study of urban form in Turkey

Urban form is studied in a variety of disciplines in Turkey, but it has recently become central to urban studies. Histories of individual towns have usually been preoccupied with political, socio-economic and cultural issues. Archaeological excavations have made an important contribution to the reconstruction and analysis of Hellenistic, Roman, Byzantine and Turkish historic cities located in Anatolia and Thrace. Urban form is often considered as a container of socio-economic processes and as a marker in the search for cultural identity in Turkey. Architectural studies have focused mainly on important buildings and housing areas. The increasingly strong morphological perspective in urban studies is part of a reaction to the loss of historical fabric associated with large-scale urban renewal schemes. Urban morphological research in Turkey is reviewed here mainly since the 1970s. A number of disciplines are covered, particularly architecture, urban planning, urban design, geography and urban history. The historical evolution of Anatolian civilizations and its impact on settlement forms is outlined. Attention is drawn in particular to the contribution of urban planning and urban history to urban morphological research.
The Role of Management in the Development of Culture and Art

--------------------------------------------------------------***------------------------------------------------------------- Annotation: This article contains analytical comments about the role and importance of management in the field of culture and art. In particular, scientific research was conducted on the activities of culture and art institutions and the activities of leaders in these processes. Also, suggestions and recommendations are given for the development of management in the further improvement of cultural management.
Athletic groin pain ( part

Background Athletic groin pain (AGP) is prevalent in sports involving repeated accelerations, decelerations, kicking and change-of-direction movements. Clinical and radiological examinations lack the ability to assess pathomechanics of AGP, but three-dimensional biomechanical movement analysis may be an important innovation. Aim The primary aim was to describe and analyse movements used by patients with AGP during a maximum effort change-of-direction task. The secondary aim was to determine if speci ﬁ c anatomical diagnoses were related to a distinct movement strategy. Methods 322 athletes with a current symptom of chronic AGP participated. Structured and standardised clinical assessments and radiological examinations were performed on all participants. Additionally, each participant performed multiple repetitions of a planned maximum effort change-of-direction task during which whole body kinematics were recorded. Kinematic and kinetic data were examined using continuous waveform analysis techniques in combination with a subgroup design that used gap statistic and hierarchical clustering.
Handwritten Character Recognition

Touchpad based devices like phones and tablets are now ubiquitous and growing even more in popularity. Due to their form factors, however, otherwise standard means of input like keyboards are less effective in these devices. Infact using scribbling to recognize handwriting is a viable alternative. In this project we investigated a method of recognizing handwritten characters to allow automatic recognition of characters. We used a gaussian mixture model for modelling the feature distributions.
Genetic-Algorithm-Based Tuning of PID Controllers for a Multipurpose Water Tank Plant

PID controllers for industrial processes have been usually tuned by methods such as Ziegler-Nichols, Cohen-Coon, frequency-based, root-locus-based and many others. Metaheuristic methods including genetic algorithms can optimize parameters to minimize cost functions, thus these techniques can be also used to tune PID controllers. The present study focuses on the design of PID controllers tuned by a simplified genetic algorithm for a multipurpose water tank plant in Peru, in order to optimize the parameters and improve its performance for single-loop and multi-loop control. For this purpose, the experimentally obtained transfer functions of the industrial multi-input multi-output plant are utilized, and a simplified genetic algorithm is proposed. It is demonstrated that in most of the cases, the PID gains obtained by the genetic algorithm compared to pid-tune tool of Matlab is superior according to the performance indexes in flow, pressure, level and temperature loops.
Fake News Detection Using Semantic Analysis and Machine Learning Techniques

In the era of online information dissemination, the challenge of fake news and disinformation has become increasingly severe, making reliable detection crucial for maintaining the integrity of information ecosystems. Using semantic analysis and several forms of machine learning, the authors of this research study provide an innovative method for identifying instances of false news. To describe the contextual semantics of textual data more precisely, we use Word Embeddings and Long Short-Term Memory (LSTM) networks. From multiple internet sources, we gathered a dataset of real and false news stories, which we then preprocessed using tokenization, stopword removal, stemming, and sequence padding. To capture the semantic links between words and phrases, we employed the Global Vectors for Word Representation (GloVe) to convert each word into a dense vector representation. This was used to feed a stacked LSTM model, a kind of RNN that can recognise long-distance relationships in text data. Our suggested approach beat conventional models such as Support Vector Machines (SVM), Naive Bayes, Convolutional Neural Networks (CNN), and LSTM without Word Embeddings during rigorous testing. Our method achieved an accuracy of 0.92, demonstrating the effectiveness of combining LSTM networks with Word Embeddings for fake news detection. Despite these promising results, challenges related to language generalization, sensitivity to word embedding quality, and inclusion of additional sources of information remain. Our research signifies a key contribution to the ongoing fight against fake news and disinformation, providing an effective approach to detect fake news with potential for further enhancement and application in real-world scenarios. Future work could focus on fine-tuning the model for different languages and domains, improving word embeddings, and integrating additional information sources to further improve fake news detection.
Analysis and study of IDSs for cybersecurity vulnerability detection and prevention: A Comprehensive Review

Smart environments are worried about private keys and security problems. To prevent security attacks, which take benefit of a few of these intrusion detection systems (IDSs) and security vulnerabilities, were modeled. In the age of the Internet of Things (IoT), data generated by connected objects presents the fundamentals for big data analytics that could be used to recognize the anomalies and patterns in the data. Numerous cyber security systems use IDSs to detect intrusions that employ a variety of approaches and architectures. In this work, several research papers have been utilized based on diverse approaches used for detecting and preventing cyber security vulnerabilities. Moreover, this research work is reviewed by classifying them based on the detection approaches used in the preceding works. In addition, the research gaps and problems recognized in the previous works are stated; thus, the researchers can bring elucidation and improve their research in the future. The works in the literature are analyzed based on several features, such as performance evaluation measures, datasets utilized and approaches used.
The Use Of Deep Learning In Music Genre Classification

There has been an ever growing demand for a better music recommendation system which beats the traditional ones. Before recommending any song it is crucial that a prior classification of songs is achieved with the highest level of accuracy. This research paper is primarily focussed on categorizing the songs based on its genre. In order to achieve this, the sequential Neural network model with several layers of densely connected neurons, dropout regularization, and a Softmax output layer is trained on a large dataset containing the features extracted from the audio samples. The previous model has been upgraded with the aid of the CNN deep learning model.
Data Transformation, Modelling and Prediction of Customer Churn using Deep Learning

Client prediction of churn is a difficult field of study that adds to client retention strategies. In a saturated and competitive market, telecommunications companies must rely on a viable churn customers prediction model to retain consumers. Previous research has focused on predicting customer attrition in the present moment or next month, when telecom businesses have not got enough time to create and implement churn control solutions. The predictive performance of present machine learning algorithms, which are frequently used by churn communities, appears to be at a stalemate, owing in part to the algorithms' weak feature extraction capacity. As a result, this research proposes a unique technique, a mix of neural networks with self- attention augmentation, to increase the efficiency of features filtering and feature extraction, hence enhancing model prediction performance. This study is being undertaken in the context of forecasting customer churn (CCP) in the telecommunications industry (TCI), wherein customer attrition is a typical occurrence. For the CCP problem, we developed a unique solution that combines data transformation approaches with machine learning models. We ran our tests on publicly accessible TCI datasets and evaluated performance using widely used assessment metrics (e.g., AUC, accuracy, recall, and F-measure). In this work, we used extensive comparisons to verify the impact of modification strategies.
A Brief Review on Optimal Routing in Vehicular Ad-hoc Network

Vehicular Ad-hoc Networks (VANET) are rapid and reliable networks that transmit safety messages between vehicles to vehicles and vehicles to infrastructure in a multi-hop manner. However, it seems to have some limitations in complex road scenarios, less performance, and issues related to routing strategy and fault tolerance. Thus, many routing protocols are developed to enhance the efficiency of VANETs with highly efficient and reliable routing protocols. This paper gives a brief review of 35 papers on optimal solutions for VANET routing protocol and analyzes various proposed protocols and the performance of these routing protocols. On the other hand, the analysis of link breakage handling for VANET, simulators used for VANET protocols, and the measurements of QoS parameters are evaluated to find the efficiency of each routing protocol. As well as limitations of these routing protocols are also described in this work to pave the way for an efficient and reliable routing protocol for VANET.