Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhanced Parkinson’s Disease Diagnosis Through Convolutional Neural Network Models Applied to SPECT DaTSCAN Images

Convolutional Neural Networks (CNNs) are highly regarded in Deep Learning (DL) and have shown promising results in medical image analysis, making them a leading model for Computer-Aided Diagnosis (CAD) systems. Their efficacy extends to the diagnosis of neurological disorders, including Parkinson’s Disease (PD), which is typically identified through Single Photon Emission Computed Tomography (SPECT) scans. However, relying solely on visual inspection of SPECT images during medical examinations can introduce inaccuracies due to subjective factors. We propose a CAD system for automatic PD diagnosis using pre-trained CNN models, Transfer Learning (TL) technique, and the Bilinear Pooling method to address this issue. The study employs several CNN architectures, specifically Efficient-Net B0, and Mobile-Net V2 models, and a custom CNN architecture. These pre-trained architectures were originally trained on ImageNet and adapted to the current task using the TL technique. These architectures are leveraged with a bilinear pooling form, resulting in three Bilinear CNN (BCNN) models. These models are applied to pre-processed SPECT image data of PD patients and Healthy Controls (HC), categorized into three distinct datasets. The proposed method is evaluated on a total of 2720 SPECT images (1360 PD and 1360 HC subjects) obtained from the Parkinson’s Progression Marker Initiative (PPMI) dataset. The findings show that the BCNN EfficientNet-B0-MobileNet-V2 model achieved the highest accuracy score of 98.47%, surpassing other developed CNN models and outperforming existing methods. In conclusion, the proposed CAD system provides an efficient diagnostic tool to assist physicians in making accurate PD diagnoses, independent of subjective factors.
Quantification and Analysis of Residual Stresses in Braking Pedal Produced via Laser–Powder Bed Fusion Additive Manufacturing Technology

This study focuses on the experimental verification of residual stress (RS) in a 3D-printed braking pedal using the Powder Bed Fusion (PBF) method with SS316L material. The RS was measured at two representative locations using the hole drilling method (HDM) and the dividing method, which are semi-destructive and destructive methods of RS measurement, respectively. The finite element method (FEM) was used with Ansys Workbench 2020R2 and Simufact Additive 2021 software to determine the magnitude of RS. The results provide insights into how RS is incorporated into metal 3D-printed components and the available tools for predicting RS. This information is essential for experts to improve the accuracy and functionality of SLM parts when post-subtractive or additive manufacturing processes are used. Overall, this study contributes to the advancement of knowledge on the effects of RS on 3D-printed metal components, which can inform future research and development in this area.
Differences in Microbial Community Composition between Uterine Horns Ipsilateral and Contralateral to the Corpus Luteum in Beef Cows on Day 15 of the Estrous Cycle

This study evaluated differences in uterine microbiota composition between uterine horns ipsilateral and contralateral to the corpus luteum of beef cows on day 15 of the estrous cycle. Cows (n = 23) were exposed to an estrus synchronization protocol to exogenously induce synchronized ovulation. Cows were then euthanized on day 15 of the estrous cycle, and individual swabs were collected from uterine horns ipsilateral and contralateral to the corpus luteum using aseptic techniques. DNA was extracted, and the entire (V1–V9 hypervariable regions) 16s rRNA gene was sequenced. Sequences were analyzed, and amplicon sequence variants (ASVs) were determined. Across all samples, 2 bacterial domains, 24 phyla, and 265 genera were identified. Butyribirio, Cutibacterium, BD7-11, Bacteroidales BS11 gut group, Ruminococcus, Bacteroidales RF16 group, and Clostridia UCG-014 differed in relative abundances between uterine horns. Rikenellaceae RC9 gut group, Bacteroidales UCG-001, Lachnospiraceae AC2044 group, Burkholderia-Caballeronia-Paraburkholderia, Psudobutyribibrio, and an unidentified genus of the family Chitinophagaceae and dgA-11 gut group differed between cows that expressed estrus and those that did not. The composition of the microbial community differed between the ipsilateral and contralateral horns and between cows that expressed estrus and cows that failed to express estrus, indicating that the uterine microbiota might play a role in cow fertility.
Priority Assessment and Ranking of Conservation-Eligible Lands (PARCEL): A Flexible GIS Tool for Watershed-Scale Land Trusts

The use of geospatial information systems (GIS) by land trusts to locate and prioritize lands critical for conservation has become a crucial step in making the process of land acquisition more strategic, proactive, and fruitful. This project introduces an automated GIS tool scripted in Python for the ESRI ArcGIS environment which allows organizations to readily evaluate a select number of aspects of the conservation value of all property parcels in their target area with a minimum of GIS software literacy. The tool computes a metric of the extent to which each parcel contributes to connectivity of natural green space, draws corridors between natural patches that pass through the most optimal land uses, models areas underserved by open space, and estimates regions of relatively high sediment erosion and deposition. The analysis, with additional criteria, is demonstrated on the Ellerbe Creek watershed in the Durham, North Carolina area, highlighting the significant conservation potential in this highly impaired urban area. 3 Acknowledgments I would like to thank my advisor, Dr. Mukesh Kumar, for his invaluable assistance with and generous devotion of time to the development of this project. I would also like to thank Chris Dreps and the staff and volunteers of the Ellerbe Creek Watershed Association, for providing me with a productive, professionally valuable, and highly enjoyable internship experience which led to the development of the idea for this project. In conjunction with this, I am grateful to Mr. Fred Stanback of the Stanback Internship Program for funding that opportunity. I also owe thanks to the numerous officials at the City of Durham who generously helped me track down and make sense of some of the more elusive GIS data. Finally, I am extremely grateful to my family and my fiancée Megan Thompson for graciously enduring several months' worth of unsolicited mini-­‐lectures about sediment transport capacity.
Developmental Study on “Smart Silver Care”: A Mobile Application to Alleviate Loneliness in Older Adults within the Community

Background: Loneliness poses a significant threat to the quality of life of older adults. Therefore, it is essential to implement non-face-to-face services to solve the loneliness of older adults in the community. Objectives: This study used the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) model to develop mobile applications as a loneliness intervention for older adults living in the community. Methods: A mobile application was developed using the ADDIE model to alleviate loneliness in older adults living in the community. The development process included a systematic review, a face-to-face preference survey, and an experts’ evaluation. From 11 to 15 June 2021, the following six databases were used to search for related articles: Ovid-Medline, Ovid-EMBASE, Cochrane Library, KISS, Korea Med, RISS. A preference analysis was conducted on 100 adults aged 65 or older living in the community from 15 July to 31 August 2021. Results: A mobile application for community-dwelling older adults was developed. Its contents included emotional support, cognition, physical activity, health data, nutrition, and motivation. They were organized through a systematic review and preference survey in the analysis stage. They were also designed as main menus and sub-content at the design stage. They also designed the structure, functionality, and interface layout. The application was developed by integrating the designed content and determining the operating system, language, access method, privacy, and server history. Then, experts evaluated the validity of the application. Conclusions: The prototype mobile application incorporates emotional support, cognition, physical activity, health data, nutrition, and motivation. It is expected to help older adults achieve their goals by promoting participation. By incorporating expert validity into the content development process of early prototypes, we have improved the usability and acceptability of our products. Future pilot trials are needed to evaluate the effectiveness of this mobile application among older adults.
The Influence of Transcranial Alternating Current Stimulation on Fatigue Resistance

Previous research has shown that some forms of non-invasive brain stimulation can increase fatigue resistance. The purpose of this study is to determine the influence of transcranial alternating current stimulation (tACS) on the time to task failure (TTF) of a precision grip task. The study utilized a randomized, double-blind, SHAM-controlled, within-subjects design. Twenty-six young adults completed two experimental sessions (tACS and SHAM) with a 7-day washout period between sessions. Each session involved a fatiguing isometric contraction of the right hand with a precision grip with either a tACS or SHAM stimulation applied to the primary motor cortex (M1) simultaneously. For the fatiguing contraction, the participants matched an isometric target force of 20% of the maximum voluntary contraction (MVC) force until task failure. Pre- and post-MVCs were performed to quantify the force decline due to fatigue. Accordingly, the dependent variables were the TTF and MVC force decline as well as the average EMG activity, force error, and standard deviation (SD) of force during the fatiguing contractions. The results indicate that there were no significant differences in any of the dependent variables between the tACS and SHAM conditions (p value range: 0.256–0.820). These findings suggest that tACS does not increase the TTF during fatiguing contractions in young adults.
Wideband‐Tunable on‐Chip Microwave Photonic Filter with Ultrahigh‐Q U‐Bend‐Mach–Zehnder‐Interferometer‐Coupled Microring Resonators

Microwave photonic filters are a key fundamental subsystem in microwave photonics. A wideband‐tunable on‐chip microwave photonic filter (MPF), which monolithically integrates a dual‐drive Mach‐Zehnder modulator (DDMZM), is proposed with ultrahigh‐Q Mach–Zehnder‐interferometer (MZI)‐coupled microring resonators (MRRs) in cascade and a high‐speed photodetector (PD). It is the first time an MPF has been developed by introducing U‐bend‐MZI‐coupled MRRs whose resonance wavelength and 3 dB bandwidth are tunable. In particular, the ultrahigh‐Q U‐bend‐MZI‐coupled MRRs are realized with ultralow loss broadened silicon waveguides based on a modified Euler curve. For the fabricated U‐bend‐MZI‐coupled MRRs, which have an effective radius of 29 µm, the Q‐factor is as high as 1.3 × 106, while the free spectral range (FSR) is as large as 113 GHz. Furthermore, the introduction of two cascaded U‐bend‐MZI‐coupled MRRs enables the MPF to have flat‐top bandstop (or bandpass) responses with a high extinction ratio (ER) of 20–35 dB as well as a tunable 3 dB bandwidth of 0.15–3 GHz. Meanwhile, the central frequency can be tuned thermally from 2 to 40 GHz. The work provides a feasible route to realize a compact, high spectral resolution, and wideband‐tunable MPF on silicon, enabling a range of RF applications from wireless communication to flexible high‐frequency microwave photonic signal processing.
A new offline dust cycle model that includes dynamic vegetation

Current offline dust cycle models are unable to predict variability in the extent of arid and semi-arid regions caused by the transient response of vegetation cover to the climate. As a consequence, it is not possible to test whether inter-annual variability in the dust loading is caused by vegetation changes or other processes. A new dust cycle model is presented which uses the Lund-Potsdam-Jena dynamic global vegetation model (Sitch et al., 2003) to calculate time varying dust sources. Surface emissions are calculated by simulating the processes of saltation and sandblasting (Tegen et al., 2002). Dust particles are transported as independent tracers within the TOMCAT chemical transport (Chipperfield, 2006). Dust is removed from the atmosphere by gravitational settling and sub-cloud scavenging. To improve the performance of the model, threshold values for vegetation cover, soil moisture, snow depth and threshold friction velocity, used to determine surface emissions are tuned. The effectiveness of three sub-cloud scavenging schemes are also tested. The tuning experiments are evaluated against multiple measurement datasets.
Integrating Model Predictive Control With Federated Reinforcement Learning for Decentralized Energy Management of Fuel Cell Vehicles

The optimization-based energy management strategy (EMS) enables expertise to improve the performance of fuel cell vehicles (FCVs). Ongoing efforts are mostly focused on optimizing a centralized EMS using a variety of high-computing technologies without offering appropriate scalability and modularity for the onboard powertrain components. In real-time applications, the time-accomplishment capability of EMSs is crucial; hence, decentralized EMSs with low-cost components and limited processing capability are necessary. Local units handle the computation load on a modular platform. In addition, the decentralized system’s plug-and-play functionality minimizes the total cost. This paper presents a decentralized model predictive control (D-MPC) based on the consensus-based alternating direction method of multipliers (C-ADMM) that explicitly considers the coordination of the dynamic reactions of powertrain components and future driving profiles. In addition, a decentralized learning method is proposed to seek the optimal policy for the moving horizon dimensions in the D-MPC using the federated reinforcement learning (FRL) algorithm in order to improve processing time. Due to the deployment of a fully modular system in the proposed learning technique, agents are restricted from sharing their trajectories. Using a highly dynamic module-to-module communication layer in a fully decentralized arrangement, the powertrain components utilize the multi-step method to attain the global optimum. The performance of the proposed framework is evaluated with regards to its precision, convergence speed, and scalability. The results of numerical simulation and implementation demonstrated that the proposed method is superior to the centralized and fixed-horizon MPC approaches.
Exploring the past, present, and future of postoperative radiotherapy for N2 stage non-small cell lung cancer

Despite conventionally applied postoperative radiotherapy (PORT) in pathological N2 (pN2) stage non-small cell lung cancer (NSCLC) considering high locoregional recurrence, its survival benefit has been a continuous topic of debate. Although several randomized clinical trials have been conducted, many of them have been withdrawn or analyzed without statistical significance due to slow accrual, making it difficult to determine the efficacy of PORT. Recently, the results of large-scale randomized clinical trials have been published, which showed some improvement in disease-free survival with PORT, but finally had no impact on overall survival. Based on these results, it was expected that the debate over PORT in pN2 patients with NSCLC would come to an end. However, since pN2 patients have different clinicopathologic features, it has become more important to carefully select the patient population who will benefit from PORT. In addition, given the development of systemic treatments such as molecular-targeted therapy and immunotherapy, it is crucial to evaluate whether there is any benefit to PORT in the midst of these recent changes. Therefore, determining the optimal treatment approach for NSCLC pN2 patients remains a complex issue that requires further research and evaluation.
A Comprehensive Survey on Usage of Learning Analytics for Enhancing Learner's Performance in Learning Portals

Learning analytics (LA) is considered a promising field of study as it's helping to improve learning and the context in which it occurs. A learner's performance can be defined as how well students are learning in terms of knowledge and skills development and can be analyzed based on students’ outcomes and engagement in the course. We have consolidated the work carried out from 2011 to 2022 to improve learners’ performance using LA, describe criteria that define learners’ performance, discuss parameters that impact learners’ performance, and how predictive models can be created to forecast learners’ performance using these parameters. Results showed that the data collected from log files of the Learning Management System (LMS) had been used to get insights into the learner's performance in online platforms and LA could bring incredible benefits in the field of the education sector, such as improvement of learners’ involvement with learning activities as well as learning outcomes, identification of students at risk, providing real-time feedback, and personalization of learning. Hence, we can say usage of LA significantly helps learners’ performance improvement in learning portals. But we can get better results if we augment data from log files of LMS with the learner's personal data from his birth to the current moment, which is a bit challenging with respect to data collection i.e., huge and from multiple sources.
Efficiency Loss of Asymptotically Efficient Tests in an Instrumental Variables Regression

In an instrumental variable model, the score statistic can be stochastically bounded for any alternative in parts of the parameter space. These regions involve a constraint on the first-stage regression coefficients and the reduced-form covariance matrix. As a consequence, the Lagrange Multiplier (LM) test can have power close to size, despite being efficient under standard asymptotics. This loss of information limits the power of conditional tests which use only the Anderson-Rubin (AR) and the score statistic. In particular, the conditional quasi-likelihood ratio (CQLR) test also suffers severe losses because its power can be bounded for any alternative. A necessary condition for drastic power loss to occur is that the Hermitian of the reduced-form covariance matrix has eigenvalues of opposite signs. These cases are denoted impossibility designs or impossibility DGPs (ID). This restriction cannot be satisfied with homoskedastic errors, but it can happen with heteroskedastic, autocorrelated, and/or clustered (HAC) errors. We show these situations can happen in practice, by applying our theory to the problem of inference on the intertemporal elasticity of substitution (IES) with weak instruments. Out of eleven countries studied by Yogo (2004) and Andrews (2016), the data in nine of them are consistent with impossibility designs at the 95% confidence level. For these countries, the noncentrality parameter of the score statistic can be very close to zero. Therefore, the power loss is sufficiently extensive to dissuade practitioners from blindly using LM-based tests with HAC errors.
Novel α-1,3-Glucosyltransferase Variants and Their Broad Clinical Polycystic Liver Disease Spectrum

Protein-truncating variants in α-1,3-glucosyltransferase (ALG8) are a risk factor for a mild cystic kidney disease phenotype. The association between these variants and liver cysts is limited. We aim to identify pathogenic ALG8 variants in our cohort of autosomal dominant polycystic liver disease (ADPLD) individuals. In order to fine-map the phenotypical spectrum of pathogenic ALG8 variant carriers, we performed targeted ALG8 screening in 478 ADPLD singletons, and exome sequencing in 48 singletons and 4 patients from two large ADPLD families. Eight novel and one previously reported pathogenic variant in ALG8 were discovered in sixteen patients. The ALG8 clinical phenotype ranges from mild to severe polycystic liver disease, and from innumerable small to multiple large hepatic cysts. The presence of <5 renal cysts that do not affect renal function is common in this population. Three-dimensional homology modeling demonstrated that six variants cause a truncated ALG8 protein with abnormal functioning, and one variant is predicted to destabilize ALG8. For the seventh variant, immunostaining of the liver tissue showed a complete loss of ALG8 in the cystic cells. ALG8-associated ADPLD has a broad clinical spectrum, including the possibility of developing a small number of renal cysts. This broadens the ADPLD genotype–phenotype spectrum and narrows the gap between liver-specific ADPLD and kidney-specific ADPKD.
Channel Parameter and Read Reference Voltages Estimation in 3-D NAND Flash Memory Using Unsupervised Learning Algorithms

In 3-D NAND flash memory, the channel is always offset due to the complicated interference from the program/erase (PE), including the data retention and layer interference, so that the channel estimation is desired. However, due to the physical structure of 3-D flash memory, there exists significant variation in channel parameters and inconsistent channel offsets among different wordlines. As a result, the process of estimating channel parameters for each individual wordline typically requires a considerable amount of computational resources, resulting in the high latency of system, which becomes a new challenge. To tackle this problem, two unsupervised learning algorithms are proposed to estimate the channel parameters, based on analyzing the error distribution in 3-D flash memory. To address the longer read latency introduced by the unsupervised learning algorithm for the channel estimation, we further propose a low-latency detection algorithm, which first detects whether the current channel needs to be updated. In the event that an update is required, the algorithm only periodically estimates the channel parameters during system idle times, resulting in a more efficient and streamlined process. Compared to the existing methods, the proposed algorithms can efficiently estimate channel parameters with lower-computational complexity. Moreover, combining with the search algorithm, a correction scheme is proposed to minimize the error between the estimated read reference voltage (RRV) and the optimal RRV in the actual device. Theoretical analysis and simulation results demonstrate that the proposed method can improve the lifetime of flash memory and reduces the number of read retries.
Financial cycles: how long and how certain?

Understanding and measuring financial cycles is important to academics and policy-makers, particularly those in charge of macroprudential policy. While business cycle periodic components have been largely estimated with different methods, empirical evidence related to financial cycles’ periodicities is scarce. Worse, ad hoc filters, are commonly used by policy-makers to calculate the credit gap and make important policy decisions based on the amplitude of this gap. To address the issue of depicting financial cycles, we estimate the credit gap and credit periodic components in 28 countries using Bayesian Structural Time Series Methods (STM) and Singular Spectrum Analysis (SSA). We find financial cycles of 13 to 20 years in most countries, but some present periodicities close to those of the business cycle. We also find discrepancies between our estimated cycles and the ad hoc filter calibration usually adopted by policy-makers to measure the credit gap, which conceal uncertainty and can be misleading.