Miracle-dz/UniFAR-FTUs huggingface dataset:
Direct-Coding DNA With Multilevel Parallelism

The cost and time to sequence entire genomes have been on a steady and rapid decline since the early 2000s, leading to an explosion of genomic data. In contrast, the growth rates for digital storage device capacity, CPU clock speed, and networking bandwidth have been much more moderate. This gap means that the need for storing, transmitting, and processing sequenced genomic data is outpacing the capacities of the underlying technologies. Compounding the problem is the fact that traditional data compression techniques used for natural language or images are not optimal for genomic data. To address this challenge, many data-compression techniques have been developed, offering a range of tradeoffs between compression ratio, computation time, memory requirements, and complexity. This paper focuses on a specific technique on one extreme of this tradeoff, namely two-bit coding, wherein every base in a genomic sequence is compressed from its original 8-bit ASCII representation to a unique two-bit binary representation. Even for this simple direct-coding scheme, current implementations leave room for significant performance improvements. Here, we show that this encoding can exploit multiple levels of parallelism in modern computer architectures to maximize encoding and decoding efficiency. Our open-source implementation achieves encoding and decoding rates of billions of bases per second, which are much higher than previously reported results. In fact, our measured throughput is typically limited only by the speed of the underlying storage media.
Left ventricular mass index versus results of diastolic stress echocardiography in patients with heart failure with preserved ejection fraction

Background: Left ventricular mass and left ventricular mass index (LVMI) is an index reflecting the thickness of the cardiac muscle, and the E/e’ ratio is a specific indicator for identifying increased left ventricular filling pressure. Limited data exist regarding the prognostic value of incorporating left ventricular mass index in heart failure with preserved ejection fraction (HFpEF). Aim: This study aimed to assess diagnostic value of LVMI at rest versus diastolic stress echo results in patients with HFpEF. Patients and methods: This study is a cross sectional descriptive study. In our study, we investigated 80 patients with unexplained exertional dyspnea with normal resting LV filling pressure at rest. Some of these patients may show elevated LV filling pressure with exercise. The current guidelines recommend to do diastolic stress echocardiography for those patients. All patients underwent complete resting echocardiography including assessment of diastolic dysfunction using the recommended 2016 guidelines and left ventricular mass index. Then we did diastolic stress echocardiography for all participants and the result of diastolic stress echocardiography was compared with the resting LVMI results.
Exploratory Investigation and Alzheimer's Disease Classification Utilizing Ensemble Models in Machine Learning

The Alzheimer's disease (AD) is a highly prevalent neurodegenerative disorder that affects an estimated 20 million people worldwide. It is characterized by changes in behavior, deteriorated cognitive abilities, and memory loss. For prompt intervention and efficient treatment of Alzheimer's disease (AD), it is imperative to identify and diagnose the disease as early as possible. In recent years, machine learning methods have become increasingly popular for detecting Alzheimer's disease (AD), with clinical data being used as the main source of information. This paper provides a comprehensive analysis of the detection of Alzheimer's disease using machine learning techniques. In this research, several key components are included, including data preprocessing, visualization of feature relevance, and evaluation of performance for five ensemble classifiers: Random Forest, AdaBoost, Gradient Boosting, Extra Trees, and Bagging. Principal Component Analysis (PCA) is used to reduce the number of attributes of a model and better understand the importance of particular features within the dataset. In addition, the research rigorously evaluates the efficacy of the classifier by utilizing a distinct test dataset. The models' prediction powers are assessed by calculating and reporting various metrics, including accuracy, precision, recall, and Fl-score.
Bayesian K-means clustering based quality of experience aware multimedia video streaming

Media streaming is an essential approach for delivering multimedia information from the source distributor to the end-user through the Internet. Along with the development of more number of users and the spread of mobile devices, the availability and diversity of multimedia applications has also increased. Multimedia users primarily prioritize quality of experience (QoE), as they seek to access multimedia content with high availability and enjoy smooth video streaming in the shortest possible time. The impact of video delivery plays a significant role in QoE, which is efficiently made by delivering the content through a specialized content delivery network architecture. In this research, a Bayesian K-means clustering algorithm is proposed for the identification of QoE in multimedia video streaming. In this multimedia video streaming, the Amazon Prime video dataset is utilized for determining the performance of the proposed model. The proposed method is developed from the ‘Patching Up’ the video quality problem (PatchVQ) model, the from patches to pictures (PaQ-2-PiQ) model is utilized for the spatial feature extraction, and 3D ResNet-18 is utilized for temporal feature extraction. The proposed Bayesian K-means achieved a QoE reward function of 5,237.42 and 5841.36 as well as a fairness reward function of 5,841.36 and 8,732.08 at the speed of 1,500 kB/s and 2,000 kB/s respectively.
Understanding watershed hydrogeochemistry: 1. Development of RT‐Flux‐PIHM

Model development in hydrology and geochemistry has been advancing separately with limited integration. We developed a watershed hydrogeochemical code RT‐Flux‐PIHM to understand complex interactions between hydrological processes (PIHM), land‐surface processes (FLUX—Noah Land Surface Model), and multicomponent subsurface reactive transport (RT). The RT module simulates geochemical processes including aqueous complexation, surface complexation, mineral dissolution and precipitation, and cation exchange. The RT module is verified against the widely used reactive transport code CrunchFlow. The code uses semidiscrete finite volume method and irregular gridding and offers data harvesting capabilities from national databases. The application of RT‐Flux‐PIHM is demonstrated in the Susquehanna Shale Hills Critical Zone Observatory (SSHCZO). We aim to understand key processes that govern hydrogeochemical dynamics of the nonreactive chloride and reactive magnesium. Simulation results indicate that watershed characteristics, in particular topography, dictate the spatial distributions of water content and soil dissolution rates. Ion exchange provides buffering capacities and leads to a hysteresis loop of concentration and discharge relationship of magnesium, which differs from the open hysteresis of chloride. RT‐Flux‐PIHM offers physics‐based modeling capabilities to integrate the vast amount of water and chemistry data that have now become available, to differentiate the relative importance of competing processes, and to test hypotheses at the interface of hydrology and geochemistry.
Estimating the AGV load and a battery lifetime based on the current measurement and random forest application

The paper describes the energy consumption from the battery based on the current measurements for various cases, i.e., speed (PWL adjustment) and loads. The main purpose of the research is to have additional and reliable information about power consumption and battery life estimation for autonomous guided vehicles (AGV). The authors propose a two-step algorithm. In the first step, a linear classifier was proposed. Then, the KNN classifier was tested; however, it did not give satisfactory results, so it was finally decided to use the random forest to estimate the load and PWL. The time domain current measurement is evaluated, and the beforementioned algorithms process the selected statistical measures. It has been proven that a two-step algorithm allows for achieving high accuracy. Based on the current observation, the paper is a good starting point for further investigation of the AGV because it is usually implemented in the AGV – so it does not require additional hardware. Moreover, it can lead to better energy management and increase battery lifetime.
On mission Twitter Profiles: A Study of Selective Toxic Behavior

The argument for persistent social media influence campaigns, often funded by malicious entities, is gaining traction. These entities utilize instrumented profiles to disseminate divisive content and disinformation, shaping public perception. Despite ample evidence of these instrumented profiles, few identification methods exist to locate them in the wild. To evade detection and appear genuine, small clusters of instrumented profiles engage in unrelated discussions, diverting attention from their true goals [34]. This strategic thematic diversity conceals their selective polarity towards certain topics and fosters public trust [49]. This study aims to characterize profiles potentially used for influence operations, termed “on-mission profiles,” relying solely on thematic content diversity within unlabeled data. Distinguishing this work is its focus on content volume and toxicity towards specific themes. Longitudinal data from 138K Twitter (rebranded as X) profiles and 293M tweets enables profiling based on theme diversity. High thematic diversity groups predominantly produce toxic content concerning specific themes, like politics, health, and news—classifying them as “on-mission” profiles. Using the identified on-mission” profiles, we design a classifier for unseen, unlabeled data. Employing a linear SVM model, we train and test it on an 80/20% split of the most diverse profiles. The classifier achieves a flawless 100% accuracy, facilitating the discovery of previously unknown “on-mission” profiles in the wild.
Annual to Inter-Decadal Variability of Surface Air Temperature along the Coast of Tanzania

The patterns of atmospheric surface temperature along the coast of Tanzania were investigated for variability and decadal trends over the past 50 years (1960-2009). Various statistical tools were employed in the research, including non-parametric models as well as trend, spectral and wavelet analyses. The effects of large scale climatic systems were investigated through partial correlations. Results show the predominance of annual oscillations, but the El Nino–Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and Pacific Decadal Oscillation (PDO) phenomena are also evident in the record. Generally around equinoxes, the surface temperatures are directly influenced by ENSO, and indirectly by the IOD and the PDO. Conversely around solstices, the temperatures are directly influenced by the IOD and PDO, and indirectly by the ENSO. A key finding of this study is the revelation of significant warming trends of surface temperature along the coast. The increases per decade are consistent with global trends, being generally greater in minimum temperature (0.14-0.72 o C) than in maximum temperature (0.07-0.13 o C). Within the last half century, the warmest year in minimum temperature differed slightly between stations but all fell within the last decade (2000-2009). The past decade, which coincided with a warm PDO inter-decadal signal, was also the warmest.
FUNGSI MANAJEMEN DALAM MENINGKATKAN PELAYANAN NASABAH DI PT. BPR RAGA SURYA NUANSA PONOROGO

Penerapan fungsi manajemen dalam kegiatan operasional di BPR mempunyai fungsi yang sangat penting dalam pencapaian target dan tujuan yang sudah direncanakan. Manajemen merupakan kegiatan mengatur dan mengelola SDM agar tujuan dari BPR dapat tercapai. Penelitian ini bertujuan untuk meneliti pelaksanaan fungsi manajemen di BPR Raga Surya Nuansa Ponorogo dalam meningkatkan pelayanan nasabah. Metode penelitian menggunakan pendekatan kualitatif deskriptif dengan teknik pengumpulan data melalui wawancara, observasi, dan dokumentasi selain itu juga menggunakan data skunder yang diperoleh dari buku, artikel, dan web. Hasil penelitian menunjukkan bahwa di BPR Raga Surya Nuansa Ponorogo, peningkatan pelayanan nasabah dicapai melalui penerapan berbagai fungsi manajemen yaitu perencanaan, pengorganisasian, pengarahan, dan pengendalian.
Study on a Portable Electrode Used to Detect the Fatigue of Tower Crane Drivers in Real Construction Environment

In view of the serious accidents caused by the fatigue operation of tower crane drivers in construction, this study puts forward a novel type of portable semi-dry electrode to detect the mental fatigue state of tower crane drivers in real time and reduce the occurrence of unsafe operation behavior. The electrode not only possesses the advantages of low contact impedance and convenient use as traditional dry and wet electrodes but also has a more stable performance compared with the semi-dry electrodes designed by previous scholars. It is suitable for experimental occasions such as fatigue detection of tower crane drivers and collecting electroencephalogram (EEG) signals for a long time. In addition, combined with the advantages of the Gramian angular difference field-convolutional neural network (GADF-CNN) algorithm that can extract multiple features of EEG signals for comprehensive fatigue determination, this study chose the GADF-CNN algorithm to analyze the driving fatigue features of tower crane drivers. The results show that the combination of the novel semi-dry electrode and GADF-CNN algorithm can detect the driving fatigue features of tower crane drivers in real time and conveniently, thus improving the safety of building construction.
New SOA Design With Large Gain, Small Noise Figure, and High Saturation Output Power Level

We introduce a semiconductor optical amplifier (SOA) chip with high gain (>40 dB) and high saturation power (>21 dBm) with moderate drive current (1.3A). A design model for optimizing the new dual-section SOA concept is presented. The model predictions are in very good agreement with the measurement results on fabricated chips. Using the gain and saturation output power product as the figure of merit, it shows the best-reported trade-off result so far. However, due to the slight degradation of the noise figure that ensued, an advanced design is introduced, enabling the optimization of the noise figure in addition to the gain and saturation output power.
Internet Regulated ESP32 Cam Robot

The activities around any or any other information that changes need to be looked into and monitored. This process is the basis of surveillance and can be expanded into many forms depending on the scenario and environment. Thus, to identify and prevent crime and ensure public safety, we must use the latest devices and technology to identify potential threats. Also, to ensure the monitoring and prevention of the spread of many different diseases, we need some method to look at the area of containment of the disease. A system using an Esp32 cam, wifi module, motor driver system, and FTDI USB-UART interface board has been developed to fulfill this requirement. In such scenarios, we cannot hamper human life and put another human's life in danger by exposing them to such acute and severe diseases just for monitoring. We should not waste lives, especially of those of great value to the system due to their knowledge, such as doctors and nurses, and we should try and provide them with a device that could enter the contaminated areas and allow them to see what is happening.
Investigating Intention to Use Central Bank Digital Currency in Indonesia

Central Bank Digital Currency (CBDC) is a digital currency issued by a government-controlled central bank of a particular country. Bank Indonesia as the central bank in Indonesia has been trying to develop a CBDC known as digital rupiah. Digital rupiah is expected to complement and perform same function as the fiat money in Indonesia. Research is needed to identify people’s views on the use of CBDCs. This study seeks to understand the factors that influence intention to use CBDC by adapting Technology Acceptance Model. Data were collected through questionnaires from 565 valid respondents. Partial Least Square-Structural Equation Modeling (PLS-SEM) was used to evaluate the proposed model. This study verifies that perceived usefulness, perceived ease of use, hard trust, and soft trust can influence intention to use CBDC in Indonesia, while personal innovativeness is known to influence perceived usefulness and perceived ease of use.
Bronchial Progenitor Cells in Obstructive and Neoplastic Lung Disease: A Pilot Study

The alteration of progenitor/stem cells present in the airway epithelium has been observed in patients with COPD. Smoking exposure induces remodeling patterns in bronchial progenitor cells (BPCs), encompassing squamous metaplasia, hyperplasia of basal and of mucus-secreting cells, and the depletion of ciliated and non-mucous secretory cells. Our aim was to assess the expression of p63 and vimentin as potential markers of airway remodeling and the regulation of stem cell populations in obstructive and neoplastic lung disease patients. A retrospective single-center observational study was conducted, including patients undergoing bronchoscopy with bronchial biopsies for suspected lung cancer. p63 and vimentin expression were evaluated via immunohistochemical analysis. There were 25 patients, of which 21 with COPD were included, and 17 were diagnosed with lung cancer. We observed that FEV1% was negatively correlated with p63+ basal cell number (r = −0.614, p = 0.019) and positively correlated with vimentin expression (r = 0.670; p = 0.008). p63 was significantly higher in biopsies from the trachea and main bronchi compared to more distal areas (p = 0.040), whereas vimentin was prevalent in the more distal areas (p = 0.042). Our preliminary data suggest the initial evidence of structural changes in BPCs among patients with COPD and lung cancer. Further research efforts are warranted to investigate additional morphologic and functional respiratory parameters in these patients.
Examining Trends in ICT Statistics: How Does the Philippines Fare in ICT?

In the past one and a half decades, the world has vastly changed economic transactions, data sharing, and the entire general way of life given the dynamic and innovative landscape brought about by information and communications technologies (ICT). This paper first describes the deluge of digital data known as Big Data and its potentials for generating socioeconomic statistics given issues of veracity and privacy. It also gives a brief history of the Internet in the Philippines and discusses the increased Internet access and usage in the country. Other ICT statistics that describe a host of issues regarding the ICT sector, particularly infrastructure and the policy environment, are also examined. Finally, the paper provides some suggestions on how the country can make its digital dividends more inclusive.
Quality of melatonin use in children and adolescents: findings from a UK clinical audit

Background Melatonin is commonly used to treat sleep disturbance in children and adolescents, although uncertainties about its optimal use remain. Objective To determine to what extent prescribing of melatonin complies with evidence-based clinical practice standards. Methods As part of a quality improvement programme, the Prescribing Observatory for Mental Health conducted a retrospective clinical audit in UK services for children and adolescents. Findings Data were submitted for 4151 children and adolescents up to 18 years of age, treated with melatonin: 3053 (74%) had a diagnosis of neurodevelopmental disorder. In 2655 (73%) of the 3651 patients prescribed melatonin to be taken regularly, the main reason was to reduce sleep latency (time taken to fall asleep). In 409 patients recently starting melatonin, a non-pharmacological intervention had already been tried in 279 (68%). The therapeutic response of patients early in treatment (n=899) and on long-term treatment (n=2353) had been assessed and quantified in 36% and 31%, respectively, while for review of side effects, the respective proportions were 46% and 43%. Planned treatment breaks were documented in 317 (13%) of those on long-term treatment. Conclusions Melatonin was predominantly prescribed for evidence-based clinical indications, but the clinical review and monitoring of this treatment fell short of best practice. Clinical implications With limited methodical review of melatonin use in their patients, clinicians will fail to garner reliable information on its risks and benefits for individual patients. The lack of such practice-based evidence may increase the risk of melatonin being inappropriately targeted or continued despite being ineffective or no longer indicated.
The Performance of Machine and Deep Learning Algorithms in Detecting Fake Reviews

The advent of the Internet has enabled everyone with access to it to provide their views online. This freedom of expression has also resulted in an increasing amount of unstructured text data daily, which can be leveraged to build models that can help make better business decisions. Customer reviews have become an integral part of the decision-making process as there is a tremendous increase in online products and services. Reviews provided by users online have a major problem regarding reliability and authenticity. It is an arduous task to make business decisions based on unstructured reviews whose trustworthiness is not established. Hence, this paper focuses on classifying the reviews of certain restaurants available on the Internet using different machine/deep learning techniques and summarises the findings. The results show that deep learning methods are more efficient in identifying fake reviews. More specifically, combining BERT and a 4-layered Feed Forward network gave 96% accuracy in detecting fake reviews.
Predicting the Price of used Cars using Machine Learning

A significant element of digitizing solutions that have gained widespread awareness in the digital sphere is Machine Learning (ML), a field of artificial intelligence. Every sector uses ML to its advantage, from automating laborious activities to providing insightful analysis. The modern world already employs the tools necessary to solve these issues. For instance, a smart home companion like Alexa or Google Home or a wearable fitness device like Smart Band. There are, however, a lot more applications for machine learning. The purpose of this model is to calculate the value of a used car. Our objective is to establish which factors influence the cost of a used car and how those factors influence the cost of a car. The dataset for vehicles was obtained from the Kaggle database. The Linear Regression approach is utilized to calculate the automobile costs in this case. The goal of this research is to create models utilizing the vehicle dataset and the aforementioned machine learning approaches. The purpose of this study is to show how well the Linear Regression model handles regression problems. This research has been attempted to forecast the price of a used automobile and develop a statistical model based on the data. The obtained results of MAE, MSE and R2 as score of the Linear Regression Model are 1.259 , 3.493 and 0.829 respectively.