Miracle-dz/UniFAR-FTUs huggingface dataset:
Memory Complaints and Activities of Daily Living in Geriatric Depression: Is There A Difference between Early-and Late-Onset Depression?

Aim: To investigate the cognitive complaints and activities of daily living in patients with geriatric depression. Inter-group comparisons were performed by dividing geriatric depression patients into early-and late-onset subgroups. Materials and Methods: Ninety-four patients with geriatric depression and 39 individuals as a control group were included. Thirty-three of the geriatric depression patients had late-onset depression, and 61 had early-onset depression. All participants completed sociodemographic data, the Mini Mental Test, the Clock-Drawing Test, the Katz Activities of Daily Living Scale, and the Subjective Memory Complaints Questionnaire. Geriatric Depression Scale-15 was used to measure the severity of patients’ depression. Results: Late-onset depression patients exhibited significantly poorer performance on the Clock Drawing Test than the control group. Significantly greater subjective memory complaints were determined in patients with early-and late-onset depression than in the control group. A negative relationship was found between the severity of depression and activities of daily living in patients with geriatric depression. Conclusion: Geriatric depression may differ in terms of cognitive functions according to the age of onset.
Globally enhanced calci�cation across the coccolithophore Gephyrocapsa complex during the Mid-Brunhes interval

Evolutionary or adaptative changes in Noelaerhabdaceae coccolithophores occurred in parallel with major changes in carbonate export and burial during scenarios of low orbital eccentricity, with a ~ 400 kyr recurrence, during the Pleistocene. Coeval with these conditions of enhanced proliferation, here we report a globally enhanced calci�cation intensity of specimens across multiple species or morphotypes within the Gephyrocapsa complex during the Mid-Brunhes (MB) interval, 400 ka. Seawater alkalinity is proposed as the environmental trigger for the increased production of both the inorganic and organic carbon, possibly implemented by a coupled increase in nutrient delivery. The strong biological pump triggered by the enhanced proliferation of highly calci�ed Gephyrocapsa, together with respiration dissolution, would have contributed to the associated deep sea dissolution event at the ~ 400 kyr scale, limiting the removal of alkalinity by burial, and maintaining constant levels at this scale. This new perspective highlights, �rst, the role of orbital forcing in phytoplankton evolution or adaptation through changes in the seawater carbon chemistry. Second, the capacity of the Noelaerhabdaceae acmes to modify the typical behavior of carbonate compensation in the ocean. Our �ndings suggests that changes in coccolith calci�cation intensity may indicate changes in past ocean carbonate chemistry and the operation of the global carbon cycle under contrasting background conditions during the Cenozoic.
Information for Australian impact and adaptation planning in response to sea-level rise

Sea levels along Australia's coastline are influenced by natural climate variability and anthropogenic climate change. Projections of sea-level rise (SLR) for 2090 for the Australian coast-line are similar to the global mean sea-level projections. The global and regional projections are almost independent of the Representative Concentration Pathways (RCPs) for greenhouse gas emissions chosen for the first decades of the 21st century, but they begin to diverge significantly from about 2050. For the business-as-usual scenario (RCP8.5), the rates increase steadily through the 21st century, reaching almost 12 mm yr-1 by 2100 at all locations. For the inter-mediate scenarios of RCP 6.0 and RCP 4.5, the rates stabilise in about 2090 and 2060 at about 7-8 and 6 mm yr-1, respectively. For the strong mitigation scenario (RCP 2.6, requiring significant and urgent mitigation of greenhouse gas emissions), the rate of rise stabilises much earlier than the other scenarios and then reduces slightly to about 4 mm yr-1. On the north and west coasts of Australia, the observed sea-level trends from the start of the projections in 1996 to 2010 are larger than the model projections as a result of the combined effect of SLR and internal modes of climate variability. The impacts of SLR will be felt most profoundly during extreme sea-level events. The meteorological and climate processes that contribute to extreme sea levels around the coast of Australia are highly regionally heterogeneous. Allowances were calculated that provide estimates of the height that present assets or their protective measures would need to be raised to ensure that the likelihood of exceedance of those levels in the future does not change from the present climate. These allowances vary according to SLR projections, their uncertainties and the variability of extreme sea levels. For 2030, when the uncertainty surrounding future SLR scenarios is small, the allowances are approximately the median projected SLR. However by 2090, the larger uncertainties associated with the projections lead to allowances that typically lie to-wards the upper end of the range of projected SLR.
Changes in multiple cytokine concentrations in the aqueous humour of neovascular age-related macular degeneration after 2 months of ranibizumab therapy

Purpose To determine changes in multiple cytokine concentrations in the anterior chamber during the induction phase of ranibizumab treatment in patients with neovascular age-related macular degeneration (AMD). Methods This prospective study included 48 treatment-naïve neovascular AMD eyes of 48 patients who received three consecutive monthly injections of ranibizumab at the
Electric Power Systems Research

Lightning data has become an integral part of weather observation and has been shown to be an effective tool for alerting and nowcasting. The Earth Networks Total Lightning Network (ENTLN) is a global lightning detection network established in 2009. ENTLN consists of roughly 1800 broadband electric field sensors globally that detect intra-cloud (IC) and cloud-to-ground (CG) lightning, with over 140 sensors in South America. In this study, we analyze the geographical variation of lightning characteristics such as density, peak current, and polarity, with the goal of better understanding the meteorological processes that produce these thunderstorms. Results indicate significant variation in average peak current per flash, with a maximum occurring throughout Northern Argentina, and a localized strong minimum North of São Paulo, Brazil. This minimum is coincident with a region dominated by negative IC flashes. In Central and Northern Argentina, 40%–50% of CG flashes were observed to be positive. Conventional thunderstorms typically have around 10% positive CG flashes. The large-scale patterns observed in this study support the findings of previous case studies regarding inverted polarity storms, mesoscale convective systems, and transient luminous event production, most of which had more limited scopes.
INTERNET USAGE AND ACADEMIC PERFORMANCES OF UNIVERSITY STUDENTS IN CROSS RIVER STATE-NIGERIA AND THE NEED FOR GUIDANCE AND COUNSELLING

This study was aimed on the investigation of internet usability and academic performance of university students in Cross River State, Nigeria two (2) research hypotheses were framed that guided the study. Both empirical and conceptual literatures were reviewed according to the sub-variables raised for the study. Ex-post facto research design was adopted in the study with simple and stratified sampling technique to collect a sample size of five hundred and thirty two (532) respondents from two public universities; university of Calabar, Calabar and Cross River University of Technology Calabar. Two research instrument titled internet use questionnaire (IUQ) was developed by the researchers while Achievement Test in English (ATE) was adopted from the University of Calabarcentre for General Studies were used for data collection. Data collected were subjected to analysis using simple percentages and pearson product moment correlations. The result of the study revealed that; there is significant relationship between facebook usage and academic performance of students (2), there is significant relationship between whatsapp usage and academic performance of students. It was recommended that government, university authorities, and the network service provider should work together to improve on broadband internet infrastructures to help students performance in their academic activities considering the modern method of learning in education. Also on effective counselling and human capacity development centre should be created in every university to help in educating undergraduate students on how to moderate their internet usage to avoid it negative impacts.
Neural Adaptive Control for a Class of Uncertain Switched Nonlinear Systems with Input and Output Constraints

—A neural network adaptive control scheme for a class of uncertain switched nonlinear systems with input and output constraints is presented in this paper. A smooth function is adopted to approximate the input dead zone and saturation function, which can be used to solve the non differentiable phenomenon in system equations. Radial basis function (RBF) based neural networks are introduced to estimate the nonlinear functions of the system and the barrier Lyapunov function is selected to solve the problem of system output constraint, based on which a neural adaptive controller is designed by backstepping technique. Under the condition that the switching system meets a certain average dwell time, the designed controller can ensure that all signals in the closed-loop system are bounded and the tracking error of the system can converge to a compact set. The effectiveness of the designed controller is veriﬁed by the simulation example.
THE IMPACT OF INTEREST RATE SPREAD ON BANK PROFITABILITY IN GHANA

The study examines the effect of interest rate spread on the profitability of commercial banks in Ghana. The study measured interest rate spread using net interest income (IntSp) and net interest margin (NIM) and bank profitability using Return on Assets (ROA) and Return on Equity (ROE). The study is based on a sample of 24 banks over a ten - year period using a panel data. The results of the study show that there is a positive and statistically significant association between interest rate spread and bank profitability in Ghana. The findings could be interpreted within the context of the loanable funds theory to suggest that the demand for loans exceed the supply of same allowing banks to charge higher interest on lending relative to deposits to increase profitability. The results of the study have significant implications on research on interest rate spread and more especially on government policy to reduce interest rate spread in Ghana. With profit as a motivation, banks will only reduce interest rate spread if its reduce their profitability but the current evidence shows that banks charge higher interest margin to maximize profitability.
Proto-Lexicon Size and Phonotactic Knowledge are Linked in Non-Māori Speaking New Zealand Adults

Orthographic Cues : The results of Oh et al. (2020) showed a small effect by the presence of a macron in the stimuli. Non-Māori speakers showed a tendency to rate stimuli containing macrons as more Māori-like. Macrons are a very salient feature of Māori orthography for NMSs, and consequently the presence of a macron impacted the results of the experiments. Our experiment does not include any stimuli with macrons.
Sensor-independent LAI/FPAR CDR: reconstructing a global sensor-independent climate data record of MODIS and VIIRS LAI/FPAR from 2000 to 2022

Abstract. Leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR) are critical biophysical parameters for the characterization of terrestrial ecosystems. Long-term global LAI/FPAR products, such as the moderate resolution imaging spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS), provide the fundamental dataset for accessing vegetation dynamics and studying climate change. However, existing global LAI/FPAR products suffer from several limitations, including spatial–temporal inconsistencies and accuracy issues. Considering these limitations, this study develops a sensor-independent (SI) LAI/FPAR climate data record (CDR) based on Terra-MODIS/Aqua-MODIS/VIIRS LAI/FPAR standard products. The SI LAI/FPAR CDR covers the period from 2000 to 2022, at spatial resolutions of 500 m/5 km/0.05∘, 8 d/bimonthly temporal frequencies and available in sinusoidal and WGS1984 projections. The methodology includes (i) comprehensive analyses of sensor-specific quality assessment variables to select high-quality retrievals, (ii) application of the spatial–temporal tensor (ST-tensor) completion model to extrapolate LAI and FPAR beyond areas with high-quality retrievals, (iii) generation of SI LAI/FPAR CDR in various projections and various spatial and temporal resolutions, and (iv) evaluation of the CDR by direct comparisons with ground data and indirectly through reproducing results of LAI/FPAR trends documented in the literature. This paper provides a comprehensive analysis of each step involved in the generation of the SI LAI/FPAR CDR, as well as evaluation of the ST-tensor completion model. Comparisons of SI LAI (FPAR) CDR with ground truth data suggest an RMSE of 0.84 LAI (0.15 FPAR) units with R2 of 0.72 (0.79), which outperform the standard Terra/Aqua/VIIRS LAI (FPAR) products. The SI LAI/FPAR CDR is characterized by a low time series stability (TSS) value, suggesting a more stable and less noisy dataset than sensor-dependent counterparts. Furthermore, the mean absolute error (MAE) of the CDR is also lower, suggesting that SI LAI/FPAR CDR is comparable in accuracy to high-quality retrievals. LAI/FPAR trend analyses based on the SI LAI/FPAR CDR agree with previous studies, which indirectly provides enhanced capabilities to utilize this CDR for studying vegetation dynamics and climate change. Overall, the integration of multiple satellite data sources and the use of advanced gap filling modeling techniques improve the accuracy of the SI LAI/FPAR CDR, ensuring the reliability of long-term vegetation studies, global carbon cycle modeling, and land policy development for informed decision-making and sustainable environmental management. The SI LAI/FPAR CDR is open access and available under a Creative Commons Attribution 4.0 License at https://doi.org/10.5281/zenodo.8076540 (Pu et al., 2023a).
“Snowplow” injection front effects

As the Polar spacecraft apogee precessed through the magnetic equator in 2001, Polar encountered numerous substorm events in the region between geosynchronous orbit and 10 RE geocentric distance; most of them in the plasma sheet boundary layers. Of these, a small number was recorded near the neutral sheet in the evening sector. Polar/Thermal Ion Dynamics Experiment provides a unique perspective on the lowest‐energy ion plasma, showing that these events exhibited a damped wavelike character, initiated by a burst of radially outward flow transverse to the local magnetic field at ~80 km/s. They then exhibit strongly damped cycles of inward/outward flow with a period of several minutes. After one or two cycles, they culminated in a hot plasma electron and ion injection, quite similar to those observed at geosynchronous orbit. Cold plasmaspheric plasmas comprise the outward flow cycles, while the inward flow cycles contain counterstreaming field‐parallel polar wind‐like flows. The observed wavelike structure, preceding the arrival of an earthward moving substorm injection front, suggests an outward displacement driven by the inward motion at local times closer to midnight, that is, a “snowplow” effect. The damped in/out flows are consistent with interchange oscillations driven by the arrival at the observed local time by an injection originating at greater radius and local time.
Sentiment Analysis of Air Quality Perception in Major Metro Cities of India

Air pollution is a major problem in cities across the world, especially in developing countries like India. This study aimed to compare the sentiment analysis of air quality perception in two major metro cities, Delhi and Mumbai using natural language processing techniques. The hypothesis was that the sentiment expressed on social media platforms like Twitter and news articles can provide insights into the public perception of air pollution. The process involved use of machine learning models such as support vector machines and naive bayes, along with tf-idf and bag-of-words algorithms, to analyze a data set of 0.1 million tweets related to air quality collected in the time period of Jan 2022 - Dec 2022. The findings of this study have important implications for policymakers and stakeholders concerned with improving air quality in India. The strong correlation observed between the air quality index and sentiment of tweets suggests that social media can be an effective tool for monitoring air quality and understanding public perception of air pollution. The results also highlight the need for targeted interventions to address the problem of air pollution in these cities. For instance, the negative sentiment expressed in tweets related to air quality in Delhi indicates the need for urgent measures to mitigate the sources of pollution in the city. In contrast, the less negative sentiment expressed in Mumbai suggests that the city may have implemented some successful interventions that could be studied and potentially replicated in other cities. This study only focuses on two of the major metro cities in India and may not be representative of the entire country. However, by utilizing sentiment analysis of social media and news articles, policymakers and stakeholders can gain valuable insights into public perception of air pollution, which can inform policy decisions and public awareness campaigns aimed at improving air quality in major metro cities.
Predictive Selective Repeat - an Optimized Selective Repeat for Noisy Channels

Automatic Repeat reQuest (ARQ) is a technique used in two-way communication systems to make sure that the transmitted data is received properly without any errors. The underlying mechanism on which ARQ operates is the acknowledgment (ACK) sent by the receiver to the sender on the orderly arrival of data frames. Some protocols which have been employed include Stop and Wait ARQ, Go-Back-N ARQ, and Selective Repeat ARQ. There have been many attempts to optimize these protocols to decrease the number of Round-Trips and increase the throughput. Predictive Selective Repeat (PSR) ARQ is the technique introduced in this paper to send multiple copies of frames without waiting for ACK. The main goal of PSR ARQ model is to optimally predict the number of required copies for a particular frame so as to reduce the number of Round-Trips by using a Time series Forecasting Long Short-Term Memory (LSTM) model.
The Value of Wetlands for Flood Mitigation

Floods cause billions of dollars in property damages each year in the United States. While programs like flood insurance and disaster relief help to lessen the burden on those harmed, they do nothing to mitigate the severity of floods. Wetlands have a natural ability to mitigate the impacts of flooding through the slowing of runoff and absorption of excess rainwater. For this purpose they are inherently valuable. This study implements a hedonic analysis to study the complicated interactions between property values, wetlands, and flood risk using detailed property transaction data in the Hudson River Valley of New York State. We augment this analysis with GIS data including FEMA flood risk maps, the location of wetlands and other land uses, and various geographic layers. We find, as expected, that higher levels of flood risk, as well as the realization of actual flooding events, negatively impact property values. We also find that the presence of wetlands mitigates the negative effect of floods to properties in flood zones, which is likely due to a reduced severity of damages. The magnitudes of these effects suggest that wetlands provide considerable benefits to property owners and the results provide a partial benefit measure for policymakers, especially to state and federal programs, such as the USDA’s wetlands and conservation reserve programs whose primary aim is to preserve and restore these natural buffers.
Design and Implementation of Visible Light Positioning Systems Based on Photodiode Arrays

Photodiode-based visible light positioning (VLP) systems can achieve centimeter-level accuracy, high positioning frequency, and low signal processing complexity. Thus they can serve as infrastructures for smart cities and smart villages, providing coordinates information for navigation, mobile communication, monitoring, and sensing systems. However, the implementation of such systems requires expensive devices and positioning accuracy is hampered by the imperfection of electronics and beacon light-emitting diode (LED) databases. In this work, a simple and efficient framework for implementing VLP systems is developed. New techniques are proposed for calibrating the non-ideal electronics, and for constructing the beacon LED databases. The framework is compatible with various VLP algorithms and can be realized with economical devices. As an illustrative example, an angle-of-arrival VLP system with four beacon LEDs is built, which has an average 3D positioning error of 1.45 cm, an average rotation estimation error of 1.90°, and a positioning frequency over 100 Hz. All these experimental results show advantages over current state-of-the-art.
Hierarchical Swin Transformers and Graph Convolutional Nets for Iris Antispoofing

Iris recognition is widely employed as a biometric authentication technique. It is valued for its high precision and user-friendliness. However, these systems are susceptible to spoofing attacks, wherein fraudulent irises or photographs of genuine irises are presented to deceive the system. To address this vulnerability, iris antispoofing techniques are utilized to differentiate between authentic and fake iris samples. In order to tackle the subtle distinctions between real and fake iris images, our research incorporates fine-grained vision classification. Fine-grained vision classification is a method employed to discern genuine iris images from spoofed ones, given the minute differences in their characteristics. We employ Swin-Transformaer backbone and attention mechanism to focus on pertinent areas within an image. By limiting self-attention computations to cross-window connections while allowing for non-overlapping local windows, this option offers increased efficiency. Furthermore, the backbone network was upgraded with the feature pyramid network (FPN), enabling effective fusion of features at different scales using graph convolution network, ultimately to produce more accurate recognition results. Our proposed approach aims to strengthen the iris antispoofing systems, thereby enhancing the overall security and reliability of iris recognition technology. Moreover, the proposed approach yields superior outcomes compared to other state-of-the-art results.
Gap Coupled Multiple Input Multiple Output MSA for 5G and WLAN Applications

This paper presents a simple four ports gap coupled MIMO (multiple input multiple output) MSA (microstrip antenna) desirable for 5G, WLAN, and other modern wireless applications. The presented antenna exhibits very good input characteristics with proper isolation between the adjacent ports. An input impedance bandwidth of 39.10% was obtained from the measurements. The measured adjacent ports isolation coefficient are better than -15dB & other ports isolation is found to be nearly -20dB. The envelop correlation coefficient (ECC) value was found to be less than 0.03 which is well within the prescribed limit. The measured diversity gain (DG) was above 9.8dB in the band of interest Simulated results were compared with measured values and good matching was found between these two results.