Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediction of Flight Arrival Delay Time Using U.S. Bureau of Transportation Statistics

According to the data from the Bureau of Trans-portation Statistics (BTS), the number of passengers and flights has been increasing year by year. However, flight delay has become a pervasive problem in the United States in recent years due to various factors, including human factors such as security regulations, as well as natural factors such as bad weather. Flight delay not only affects the profits of airlines but also affects the satisfaction of passengers. Therefore, a model that can predict the arrival time of airplanes needs to be developed. Machine learning methods have been widely applied to prediction problems. In this paper, a variety of machine learning and computational intelligence methods, including linear regression, decision tree (DT), random forest (RF), gradient boosting (GB), gaussian regression models and genetic programming were trained on the U.S. Department of Transportation's (DOT) BTS dataset. The results show that genetic programming performs best and can be used to predict the arrival time of the U.S. flights in advance, which is beneficial for airlines and passengers to make timely decisions.
Multilevel Polar-Coded PAM-8 with MSB Shaping over Turbulent FSO Communication Link

Thanks to the flexibility and adaptability of polar codes, a Free-space Optical (FSO) communication scheme combining it with probabilistic shaping (PS) and pulse amplitude modulation (PAM) is proposed to achieve capacity improvement in atmospheric turbulence (AT) channels. The proposed scheme generates a non-uniform probability distribution PAM-8 transmission symbol by a multilevel polar-coded modulation (MLPC) method equipped with the most significant bit (MSB) level shaping precoder. Under the selected turbulence conditions, it provides up to 1.09dB theoretical shaping gain of capacity at the specified information rate (AIR) of 1.859 bit/channel use and 0.82dB performance gain in block error rate.
Seasonal climate summary southern hemisphere (spring 2015): El Niño nears its peak

Southern hemisphere circulation patterns and associated anomalies for the austral spring 2015 are reviewed, with an emphasis on Pacific climate indicators and Australian rainfall and temperature patterns. A strong El Niño persisted in the tropical Pacific Ocean with sea-surface temperature anomalies in excess of +2 °C in central and eastern parts, strongly negative outgoing longwave radiation near the Date Line, and the Southern Oscillation Index showing large negative departures. The positive Indian Ocean Dipole that had established in winter dissipated in late November, but was particularly influential on Australia's climate during the months of September and October.
The Nordic Monitoring System 2011-2014. Status and development of diet, physical activity, smoking, alcohol and overweight

This report describes the results of the first and second collection of data in the joint Nordic Monitoring System of diet, physical activity and overweight. It is well recognized that health in the European Region needs to be improved and Nordic public health researchers have since 2008 developed and validated a Nordic method to assess diet and physical activity among adults and children. Data has collected on health behaviour and overweight in 2011 and 2014 among 18,000 adults and 5,000 children. During the three year survey period, significant changes have occurred in the Nordic countries, especially among adults. The Nordic Monitoring System shows that lifestyle and weight status differ between the Nordic countries despite highly comparable societal structures. Favorable and unfavorable health behaviour is present in all countries and there are areas to be improved in all countries.
Applying the Simple Partial Discard Method to Crystals-Kyber

In certain cryptographic applications random numbers are required (e.g., when generating cryptographic keys and generating digital signatures). To obtain these random numbers, the typical approach involves obtaining random bits first and then converting them into random numbers. Several methods to convert a sequence of random bits into a sequence of random numbers are known and some of them are standardized. Recently, ISO/IEC JTC 1/SC 27/WG 2 decided to add two more methods, the simple partial discard method and the complex partial discard method, to the existing four standard methods. Meanwhile, CRYSTALS-Kyber is the only public-key encryption and key-establishment algorithm selected for the post-quantum cryptography standardization project by NIST (National Institute of Standards and Technology). It uses an algorithm called Parse that takes a byte stream as input and outputs a polynomial of degree <inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> with coefficients in <inline-formula> <tex-math notation="LaTeX">$\mathbb {Z}_{q}$ </tex-math></inline-formula> (for some positive integer <inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> and prime <inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula>) using the simple discard method to generate key pairs. In this paper, we apply the simple partial discard method to Parse. We show that using the simple partial discard method instead of the simple discard method can reduce the number of bits required by up to 12%. Furthermore, we show that, in some cases, using the simple partial discard method instead of the simple discard method can experimentally generate a polynomial up to 8% faster.
Vision Based Micro-UAV Navigation Through Narrow Passages

This research paper presents a novel low computational approach for navigating a micro UAV (Unmanned Aerial Vehicle) through narrow passages using only its onboard camera feed and a PID control system. The proposed method uses edge detection and homography techniques to extract the key features of the passage from the camera feed, and then employs a tuned PID controller to guide the UAV through and out of the passage while avoiding collisions with the walls. To evaluate the effectiveness of the proposed approach, a series of experiments were conducted using a micro-UAV navigating in and out of a custom-built test environment (constrained rectangular box). The results demonstrate that the system is able to successfully guide the UAV through the passages while avoiding collisions with the walls.
Advancing Precision Medicine: VAE Enhanced Predictions of Pancreatic Cancer Patient Survival in Local Hospital

In this research, we address the urgent need for accurate prediction of in-hospital survival periods for patients diagnosed with pancreatic cancer (PC), a disease notorious for its late-stage diagnosis and dismal survival rates. Utilizing machine learning (ML) technologies, we focus on the application of Variational Autoencoders (VAE) for data augmentation and ensemble learning techniques for enhancing predictive accuracy. Our dataset comprises biochemical blood test (BBT) results from stage II/III PC patients, which is limited in size, making VAE’s capability for data augmentation particularly valuable. The study employs several ML models, including Elastic Net (EN), Decision Trees (DT), and Radial Basis Function Support Vector Machine (RBF-SVM), and evaluates their performance using metrics such as Mean Absolute Error (MAE) and Mean Squared Error (MSE). Our findings reveal that EN, DT, and RBF-SVM are the most effective models within a VAE-augmented framework, showing substantial improvements in predictive accuracy. An ensemble learning approach further optimized the results, reducing the MAE to approximately 10 days. These advancements hold significant implications for the field of precision medicine, enabling more targeted therapeutic interventions and optimizing healthcare resource allocation. The study can also serve as a foundational step towards more personalized and effective healthcare solutions for PC patients.
Clustered Cascaded Optical-Electrical Feedforward Equalization for C-Band Single-Lane 100G ER PAM4 IM/DD Systems

A low-complexity clustered cascaded optical-electrical feedforward equalization (OE-FFE) scheme is proposed and verified for C-band dispersion-limited high-speed IM/DD PAM4 systems. 7% FEC threshold is achieved for 122Gb/s 40-km PAM4 system with only 35 real-valued multiplications.
Long-Term Erosion Trends on Cropland in the Pacific Northwest

Winter erosion from non-irrigated cropland in northwestern Oregon, southeastern Washington, andnorthern Idaho appears to have decreased during the past 20 years. Assuming this effect is real and not justapparent, is there a single cause? Is this the effect of climate change, changes as the result of the 1985 farmbill, or a combination of these and other factors? We cant answer all these questions, but a unique 43-yeardata set obtained from monitoring winter erosion on a large number of sample fields in Whitman Co, WA fromWater Year (WY) 1940 through WY 1982, provides an opportunity to examine historic trends in erosion andcorresponding climate conditions. During this period, the winter wheat/summer fallow rotation was used onmuch of the area, including the higher precipitation zone more suitable for annual cropping. There were severalconsecutive years in the 43-year data set when erosion was low. Weather records for these years indicatereduced freeze/thaw activity with rain or snowmelt during the period of thaw. We examined diurnal freeze/thawcycles, length and severity of frozen periods, snow-melt accumulation during cold periods, and rain during earlystages of the thawing process. Our analysis of 1983 through 2005 climate data indicate reduced erosionhazard from freeze/thaw effects. U.S. Department of Agriculture progress records for 1979 and 1994 indicateincreased application of conservation practices in 1994 as compared to 1979, with a large estimated reductionin erosion. Measurement of sediment at the mouth of the Palouse River indicated a large reduction from the1962 through 1971 period to the 1993 to 1996 period. Sediment in runoff and flooding from snowmelt on frostimpacted soil occurred in WY 1996 and WY 1997, but erosion rates were not catastrophic. A close inspection of weather records from near the McCredie Experiment Station in Central Missouri, withmean winter temperatures and precipitation values comparable to those at Pullman, WA, and source of a largedata set frequently used in calibrating or validating erosion models, provides confirmation of the unique climatecharacteristics of the non-irrigated cropland region of the Pacific Northwest.
Efficiency of InN/InGaN/GaN Intermediate-Band Solar Cell under the Effects of Hydrostatic Pressure, In-Compositions, Built-in-Electric Field, Confinement, and Thickness

This paper presents a thorough numerical investigation focused on optimizing the efficiency of quantum-well intermediate-band solar cells (QW-IBSCs) based on III-nitride materials. The optimization strategy encompasses manipulating confinement potential energy, controlling hydrostatic pressure, adjusting compositions, and varying thickness. The built-in electric fields in (In, Ga)N alloys and heavy-hole levels are considered to enhance the results’ accuracy. The finite element method (FEM) and Python 3.8 are employed to numerically solve the Schrödinger equation within the effective mass theory framework. This study reveals that meticulous design can achieve a theoretical photovoltaic efficiency of quantum-well intermediate-band solar cells (QW-IBSCs) that surpasses the Shockley–Queisser limit. Moreover, reducing the thickness of the layers enhances the light-absorbing capacity and, therefore, contributes to efficiency improvement. Additionally, the shape of the confinement potential significantly influences the device’s performance. This work is critical for society, as it represents a significant advancement in sustainable energy solutions, holding the promise of enhancing both the efficiency and accessibility of solar power generation. Consequently, this research stands at the forefront of innovation, offering a tangible and impactful contribution toward a greener and more sustainable energy future.
Use of Machine Learning on Satellite and Drone Images for Agriculture Application

The proposed study's primary goal is to evaluate land use change and land cover (LULC) utilizing machine learning techniques and algorithms. This was done using Sentinel-2 satellite images, which is viewable and available through the Copernicus Earth observation system. These Sentinel-2 photos cover four spectral bands and fall into four categories (urban, water, tall vegetation, short vegetation, and barren ground), and they are identified as Google Earth image data. Different land use classifications represent how much simpler the proposed method makes the process of image classification. The findings and discussion show that Random Forest, ANN, SVM, and KNN were the most effective algorithms for attaining our main goal. For each dataset, cross validation and random sampling were used to determine the best models for achieving the best results with less over fitting and finally different band combinations were used for classification of SAR image.
Artificial Hummingbird Optimization Algorithm With Hierarchical Deep Learning for Traffic Management in Intelligent Transportation Systems

Intelligent Transportation Systems (ITS) make use of advanced technologies to optimize interurban and urban traffic, reduce congestion and enhance overall traffic flow. Deep learning (DL) approaches can be widely used for traffic flow monitoring in the ITS. This manuscript introduces the Artificial Hummingbird Optimization Algorithm with Hierarchical Deep Learning for Traffic Management (AHOA-HDLTM) technique in the ITS environment. The purpose of the AHOA-HDLTM technique is to predict traffic flow levels in smart cities, enabling effective traffic management. Primarily, the AHOA-HDLTM model involves data preprocessing and an Improved Salp Swarm Algorithm (ISSA) for feature selection. For the prediction of traffic flow, the Hierarchical Extreme Learning Machine (HELM) model can be used. The HELM extracts complex features and patterns, with an additional Artificial Hummingbird Optimization Algorithm (AHOA)-based hyperparameter selection process to enhance predictive outcomes. The simulation result analysis under different traffic data demonstrates the better performance of the AHOA-HDLTM technique over existing models. The hierarchical structure of the HELM model along with AHOA-based hyperparameter tuning helps to accomplish enhanced prediction performance. The AHOA-HDLTM technique presents a robust solution for traffic management in ITS, showcasing enhanced performance in forecasting traffic patterns and congestion. The AHOA-HDLTM technique can be used in various smart cities and urban regions. Its abilities in real-time traffic flow prediction can be helpful in the design of efficient, sustainable, and resilient transportation networks.
Negative Predictors of Outcomes of Flexor Tendon Repairs

The current trend in hand surgery has streamlined the treatment of acute hand trauma to the modern-day surgery unit. As the volume of hand trauma caseloads continues to increase, it is becoming increasingly difficult to schedule patients for theater on the day of injury. It, therefore, becomes paramount to adequately triage patients in accordance with best clinical evidence and predictors of poor clinical outcomes. Animal models suggest that the earlier flexor tendons are repaired, the better the patient functional outcome. The largest study to date examining the timing of injury to functional post-operative outcome also recognizes that the faster these injuries are repaired, the better the patient outcome. Age-related changes to tendon biomechanics and structure are well-documented. However, no conclusive evidence exists specific to the degenerative changes and mechanical properties of flexor tendons in humans. The animal model strongly suggests that increasing age is associated with local architectural and biological changes that directly affect the tendon repair functional outcome. Although retrospective analyses to date suggest that smoking is a negative outcome predictor for functional tendon outcome, no prospective large-scale studies exist. A large, single-center prospective study specifically examining the positive and negative outcome predictors of flexor tendon repairs and functional post-operative outcome is warranted. The negative predictive model of patient care may enable us to further council patients preoperatively and stratify patients according to clinical need.
Fusion of Body and Environment with Movable Carabiners for Wire-Driven Robots Toward Expansion of Physical Capabilities

Many wire-driven robots have been developed due to the flexibility in wire placement. Generally, conventional robots have fixed performance at the time of manufacture, making it difficult to deal with unforeseen environments and tasks. We focus on the flexibility in wire placement to solve this problem. In this study, the robot's body is fused with the environment to expand physical capabilities. To achieve this, we designed a movable carabiner that allows the wire ends to be easily attached to and detached from the environment. We added the movable carabiners to an existing wire-driven robot and implemented the proposed method in a real-world setup. With this method, we were able to accomplish expansion of the robot's physical capabilities, such as reinforcing its own arm and carrying a heavy object. By these results, the proposed method has demonstrated its effectiveness as a viable solution to the problem.
Classification of Coronary Stenosis using a Support Vector Machine with Automatic Parameter Tuning

In this paper an improved method for stenosis classification which consists of three stages is proposed. In the first stage are extracted 49 different feature types such as texture, intensity and morphology. In the second stage, an automatic feature selection driven by the Boltzmann Univariate Marginal Algorithm and a Support Vector Machine-based (SVM) classifier is performed. Finally, in third step, an automatic fine tuning of several parameters for the SVM is applied. Relevant results were obtained in the second stage were a decreasing rate of 71% of the initial feature set was achieved using only 14 features to achieve the highest classification rate in terms of the accuracy metric. Additionally, the SVM classification performance was improved with the automatic parameter tuning achieving a classification efficiency of 0.91 in terms of the accuracy metric.
Reduced Phase Shifter Design “Switched line”

This paper presents the results of a study aimed at miniaturizing a microstrip phase shifter with switched transmission lines. Two compact phase shifters were obtained-one in which the classic section of the transmission line was replaced by a low-pass filter, and the second in which the power supply filters were made on SMD elements. When comparing the theoretical and experimental characteristics of phase shifters, a discrepancy was discovered between them, which is associated with the difference in the dielectric constant of the substrates in the model and the prototype, as well as due to differences in the values of lumped elements and tolerances in the manufacture of prototypes.
Ten Steps to Conduct a Systematic Review

This article introduces a concise 10-step guide tailored for researchers engaged in systematic reviews within the field of medicine and health, aligning with the imperative for evidence-based healthcare. The guide underscores the importance of integrating research evidence, clinical proficiency, and patient preferences. It emphasizes the need for precision in formulating research questions, utilizing tools such as PICO(S)(Population Intervention Comparator Outcome), PEO (Population Exposure Outcome), SPICE (setting, perspective, intervention/exposure/interest, comparison, and evaluation), and SPIDER (expectation, client group, location, impact, professionals, service and evaluation), and advocates for the validation of research ideas through preliminary investigations. The guide prioritizes transparency by recommending the documentation and registration of protocols on various platforms. It highlights the significance of a well-organized literature search, encouraging the involvement of experts to ensure a high-quality search strategy. The critical stages of screening titles and abstracts are navigated using different tools, each characterized by its specific advantages. This diverse approach aims to enhance the effectiveness of the systematic review process. In conclusion, this 10-step guide provides a practical framework for the rigorous conduct of systematic reviews in the domain of medicine and health. It addresses the unique challenges inherent in this field, emphasizing the values of transparency, precision, and ongoing efforts to improve primary research practices. The guide aims to contribute to the establishment of a robust evidence base, facilitating informed decision-making in healthcare.
A Survey on Forensics and Compliance Auditing for Critical Infrastructure Protection

The broadening dependency and reliance that modern societies have on essential services provided by Critical Infrastructures is increasing the relevance of their trustworthiness. However, Critical Infrastructures are attractive targets for cyberattacks, due to the potential for considerable impact, not just at the economic level but also in terms of physical damage and even loss of human life. Complementing traditional security mechanisms, forensics and compliance audit processes play an important role in ensuring Critical Infrastructure trustworthiness. Compliance auditing contributes to checking if security measures are in place and compliant with standards and internal policies. Forensics assist the investigation of past security incidents. Since these two areas significantly overlap, in terms of data sources, tools and techniques, they can be merged into unified Forensics and Compliance Auditing (FCA) frameworks. In this paper, we survey the latest developments, methodologies, challenges, and solutions addressing forensics and compliance auditing in the scope of Critical Infrastructure Protection. This survey focuses on relevant contributions, capable of tackling the requirements imposed by massively distributed and complex Industrial Automation and Control Systems, in terms of handling large volumes of heterogeneous data (that can be noisy, ambiguous, and redundant) for analytic purposes, with adequate performance and reliability. The achieved results produced a taxonomy in the field of FCA whose key categories denote the relevant topics in the literature. Also, the collected knowledge resulted in the establishment of a reference FCA architecture, proposed as a generic template for a converged platform. These results are intended to guide future research on forensics and compliance auditing for Critical Infrastructure Protection.
Continuous-wave Terahertz Mode-beating Signal Generation Based on High-power Multi-wavelength DFB Semiconductor Laser Array

We demonstrated a widely tunable terahertz (THz) source based on optical heterodyne method, with a generation range exceeding 0.075-2.23 THz. The terahertz light source chip is composed of parallel high-power multi-wavelength distributed feedback (DFB) laser chips, and the maximum single mode output power of the laser array is greater than 120 mW.