Miracle-dz/UniFAR-FTUs huggingface dataset:
Simplified Structure of Integer Lifting Wavelet Filter Banks for Lossless Image Compression

In this paper, a simplified structure of integer lifting wavelet filter banks for lossless image compression is proposed by shifting and merging the scaling factors of the row and the column wavelet transforms. It is implemented by reducing the numbers of scaling factors and considering the scaling lifting. The numbers of scaling factors of the 2-D wavelet transform can be reduced by shifting and merging operation, and then the computing speed can be improved. Furthermore, the scaling lifting of simplified structure can be used to reduce the computing errors and get more accurate results. Experiments show that the simplified integer lifting structure results in lesser computational steps than the standard integer lifting structure and therefore improves the speed of the image compression. Besides, using the new lossless image compression system based on simplified integer lifting wavelet, the lower bit-rates are obtained.
Models for COVID-19 Data Prediction Based on Improved LSTM-ARIMA Algorithms

The global repercussions of the COVID-19 pandemic on economies and public health worldwide have been profound. This study aims to examine the developmental trends of the COVID-19 pandemic, establish predictive models, and provide insights for effective control measures against potential future disease outbreaks. Considering the coexistence of both linear and nonlinear factors in COVID-19 data, conventional single-machine learning and traditional forecasting models encounter challenges in accurately predicting pandemic trends. To enhance the precision of COVID-19 pandemic predictions by integrating linear and nonlinear factors, this study proposes three combined forecasting models: CNN-LSTM-ARIMA, TCN-LSTM-ARIMA, and SSA-LSTM-ARIMA. These models leverage the strengths of deep learning in capturing nonlinear factors and the capabilities of the traditional ARIMA model in handling linear factors. Initially, LSTM and ARIMA models are used to model and predict the COVID-19 pandemic in Quebec, Canada. Subsequently, CNN models, TCN models, and the Sparrow Search Algorithm are employed to integrate predictions from the LSTM and ARIMA models. Comparative analyses of the three combined models, it was found that the CNN-LSTM-ARIMA model exhibits the highest predictive accuracy, with an MSE of 7048.26, RMSE of 83.95, MAE of 61.18, MAPE of 0.16, and $R^{2}$ of 0.95. To validate the applicability and stability of the CNN-LSTM-ARIMA model in predicting COVID-19 pandemics, Italian COVID-19 pandemic data was employed. The three combined forecasting models are established and evaluated using model evaluation metrics. The results affirm that the CNN-LSTM-ARIMA model remains the optimal choice, underscoring its high stability and suitability for COVID-19 pandemic forecasting endeavors.
Aggregation and Oligomerization Characterization of ß-Lactoglobulin Protein Using a Solid-State Nanopore Sensor

Protein aggregation is linked to many chronic and devastating neurodegenerative human diseases and is strongly associated with aging. This work demonstrates that protein aggregation and oligomerization can be evaluated by a solid-state nanopore method at the single molecule level. A silicon nitride nanopore sensor was used to characterize both the amyloidogenic and native-state oligomerization of a model protein ß-lactoglobulin variant A (βLGa). The findings from the nanopore measurements are validated against atomic force microscopy (AFM) and dynamic light scattering (DLS) data, comparing βLGa aggregation from the same samples at various stages. By calibrating with linear and circular dsDNA, this study estimates the amyloid fibrils’ length and diameter, the quantity of the βLGa aggregates, and their distribution. The nanopore results align with the DLS and AFM data and offer additional insight at the level of individual protein molecular assemblies. As a further demonstration of the nanopore technique, βLGa self-association and aggregation at pH 4.6 as a function of temperature were measured at high (2 M KCl) and low (0.1 M KCl) ionic strength. This research highlights the advantages and limitations of using solid-state nanopore methods for analyzing protein aggregation.
A Novel Fast ICA-FBCCA Algorithm and Convolutional Neural Network for Single-Flicker SSVEP-Based BCIs

Brain-computer interface (BCI) systems have been developed to assist individuals with neuromuscular disorders to communicate with their surroundings using their brain signals. One attractive branch of BCI is steady-state visual evoked potential (SSVEP), which has acceptable speed and accuracy and is non-invasive. However, SSVEP-based EEG signals suffer from eye-fatigue problems, resulting in artifacts that affect the accuracy of the system. Thus, researchers are still working to improve SSVEP-based BCI systems. This paper proposes robust machine-learning algorithm for single-flicker SSVEP detection. A novel approach based on fast independent component analysis and filter-bank canonical correlation analysis (fast ICA-FBCCA) is developed to extract features from the single-flicker SSVEP signal. The clean features learned by fast ICA-FBCCA are then applied to a discrete wavelet transform (DWT) technique and fed to a convolutional neural network (CNN) with only one convolutional layer and a smaller number of parameters. The effectiveness of the proposed technique is evaluated using two datasets. The results were evaluated using two datasets. The findings clearly demonstrate that the proposed method outperforms traditional methods, with average target recognition accuracy and standard deviation values of 97 ± 3.1% among 6 subjects for dataset 1 and 82.12 ± 10.7% among 12 subjects for dataset 2. Overall, these findings suggest that the proposed method is a promising approach for improving the accuracy and reliability of the single-flicker SSVEP-based BCI systems.
Diversified Adaptive Stock Selection Using Continual Graph Learning and Ensemble Approach

Stock selection is essential for portfolio diversification to reduce risks and maximize profits. However, stock selection is challenging owing to the non-stationary nature of stock markets. In fact, stock markets experience abrupt or gradual concept drift because of their inherent volatility. Concept drift is a phenomenon in which the statistical characteristics of data change over time. Recent stock selection methods have adopted graph neural networks to capture the relational dependencies between stocks. These methods perform non-continual learning that uses a fixed set of stocks without knowledge retention. Non-continual graph learning-based methods can adapt to abrupt concept drift, while continual graph learning-based methods can adapt to gradual concept drift because they involve knowledge retention. To adapt to both abrupt and gradual concept drifts, we propose a stock selection framework called DASS, which combines non-continual and continual models for diversified adaptation. For the both models, we employ graph learning to extract both temporal and relational dependencies. Our graph learning method relies on three main components: 1) low-level temporal modeling, which extracts temporal dependencies of individual stocks; 2) relational modeling, which extracts relational dependencies between stocks; and 3) high-level temporal modeling, which extracts temporal dependencies from the learned relational dependencies. Furthermore, DASS leverages both simple graphs and hypergraphs because they complement each other. The performance of DASS is compared with that of state-of-the-art stock selection methods. Experimental results for stocks included in the Standard & Poor’s 500 index reveal that DASS achieves a compounded annual growth rate of 83.2%, outperforming the second-best method by 23.0%P.
Partial Harmonic Current Compensation for Multifunctional Photovoltaic Inverters

Recent works have addressed the ancillary services provided by multifunctional photovoltaic inverters. This concept is based on the addition of extra functions to the conventional photovoltaic systems, such as harmonic current compensation of nonlinear loads and reactive power support. However, it is important to ensure that photovoltaic inverters work below the rated current peak. Therefore, this paper proposes two current dynamic saturation techniques to limit the current peak of multifunctional photovoltaic inverters during harmonic current compensation of nonlinear loads. One technique is based on a closed loop and other on an open loop. With this approach, the photovoltaic inverters are able to perform partial harmonic current compensation, i.e., it will only be allowed to compensate harmonic currents within their maximum current capacity. Mathematical models are developed to determine the parameters of the techniques. Comparisons in simulation and experimental environments show that both techniques provide improvements in the grid power quality at the same time as the inverter rated current capacity is respected.
Intracellular Trafficking of Size-Tuned Nanoparticles for Drug Delivery

Polymeric nanoparticles (NPs) are widely used as drug delivery systems in nanomedicine. Despite their widespread application, a comprehensive understanding of their intracellular trafficking remains elusive. In the present study, we focused on exploring the impact of a 20 nm difference in size on NP performance, including drug delivery capabilities and intracellular trafficking. For that, poly(ethylene glycol) methyl ether-block-poly(lactide-co-glycolide) (PLGA-PEG) NPs with sizes of 50 and 70 nm were precisely tailored. To assess their prowess in encapsulating and releasing therapeutic agents, we have employed doxorubicin (Dox), a well-established anticancer drug widely utilized in clinical settings, as a model drug. Then, the beneficial effect of the developed nanoformulations was evaluated in breast cancer cells. Finally, we performed a semiquantitative analysis of both NPs’ uptake and intracellular localization by immunostaining lysosomes, early endosomes, and recycling endosomes. The results show that the smaller NPs (50 nm) were able to reduce the metabolic activity of cancer cells more efficiently than NPs of 70 nm, in a time and concentration-dependent manner. These findings are corroborated by intracellular trafficking studies that reveal an earlier and higher uptake of NPs, with 50 nm compared to the 70 nm ones, by the breast cancer cells. Consequently, this study demonstrates that NP size, even in small increments, has an important impact on their therapeutic effect.
Flammutoxin, a Degradation Product of Transepithelial Electrical Resistance-Decreasing Protein, Induces Reactive Oxygen Species and Apoptosis in HepG2 Cells

Proteins from Flammulina filiformis were prepared by sodium chloride extraction and fractionated by ammonium sulfate precipitation with increasing saturation degrees to obtain the protein fractions Ffsp-30, Ffsp-50, Ffsp-70, Ffsp-90, and Ffp-90. Among these protein fractions, Ffsp-50 possessed the most significant cytotoxic effect against three human gastrointestinal cancer cell lines, viz. HT-29, SGC-7901, and HepG2. SDS-PAGE and MALDI-TOF/TOF MS/MS analyses revealed that flammutoxin (FTX) was present as a dominating protein in Ffsp-50, which was further evidenced by HPLC-MS/MS determination. Furthermore, native FTX was purified from Ffsp-50 with a molecular weight of 26.78 kDa, exhibiting notable cytotoxicity against gastrointestinal cancer cell lines. Both Ffsp-50 and FTX exposure could enhance intercellular reactive oxygen species (ROS) generation and induce significant apoptosis in HepG2 cells. FTX was identified to be relatively conserved in basidiomycetes according to phylogenetic analysis, and its expression was highly upregulated in the primordium as well as the pileus of the fruiting body from the elongation and maturation stages, as compared with that in mycelium. Taken together, FTX could remarkably inhibit cell growth and induce ROS and apoptosis in HepG2 cells, potentially participating in the growth and development of the fruiting body. These findings from our investigation provided insight into the antigastrointestinal cancer activity of FTX, which could serve as a biological source of health-promoting and biomedical applications.
A Constant Switching Frequency Multiple-Vector-Based Model Predictive Current Control of Five-Phase PMSM With Nonsinusoidal Back EMF

Most of the existing finite-control-set model predictive current control (FCS-MPCC) schemes for multi-phase motor suffer from heavy computational burden, inevitable low-order harmonic currents, and variable switching frequencies. Some virtual voltage vector (V3)-based FCS-MPCC schemes can effectively suppress harmonic current by zeroing the harmonic subspace voltage on average during one sampling period. However, they fail when the motor has a nonsinusoidal back electromotive force. In order to solve the aforementioned issues, this paper proposes a constant switching frequency multiple-vector-based FCS-MPCC scheme. Unlike the traditional FCS-MPCC schemes, the proposed scheme selects optimal V3s and their duty ratios in two orthogonal subspaces. Thus, it can simultaneously track the references in both orthogonal subspaces. In this approach, the optimal V3s and their duty ratios are directly obtained from the principle of deadbeat current control without time-consuming enumeration-based state predictions and cost function calculations. In addition, the obtained optimal V3s and their duty ratios are adopted to rearrange the pulse sequence to obtain constant switching frequency and can be simply synthesized by carrier-based pulsewidth modulation. Furthermore, a discrete time disturbance observer is designed to improve the robustness of the proposed FCS-MPCC against parameter mismatch. Finally, comparative experiments with traditional MPCC schemes for five-phase surface-mounted permanent magnet synchronous machine are carried out to verify the effectiveness of the proposed scheme.
Efficient commodity price forecasting using long short-term memory model

Predicting commodity prices, particularly food prices, is a significant concern for various stakeholders, especially in regions that are highly sensitive to commodity price volatility. Historically, many machine learning models like autoregressive integrated moving average (ARIMA) and support vector machine (SVM) have been suggested to overcome the forecasting task. These models struggle to capture the multifaceted and dynamic factors influencing these prices. Recently, deep learning approaches have demonstrated considerable promise in handling complex forecasting tasks. This paper presents a novel long short-term memory (LSTM) network-based model for commodity price forecasting. The model uses five essential commodities namely bread, meat, milk, oil, and petrol. The proposed model focuses on advanced feature engineering which involves moving averages, price volatility, and past prices. The results reveal that our model outperforms traditional methods as it achieves 0.14, 3.04%, and 98.2% for root mean square error (RMSE), mean absolute percentage error (MAPE), and R-squared (R2), respectively. In addition to the simplicity of the model, which consists of an LSTM single-cell architecture that reduced the training time to a few minutes instead of hours. This paper contributes to the economic literature on price prediction using advanced deep learning techniques as well as provides practical implications for managing commodity price instability globally.
Effect of 3-Dimensional Robotic Therapy Combined with Electromyography-Triggered Neuromuscular Electrical Stimulation on Upper Limb Function and Cerebral Cortex Activation in Stroke Patients: A Randomized Controlled Trial

(1) Background: This study investigated the effect of 3-dimensional robotic therapy (RT) combined with electromyography-triggered neuromuscular electrical stimulation (RT–ENMES) on stroke patients’ upper-limb function and cerebral cortex activation. (2) Methods: Sixty-one stroke patients were assigned randomly to one of three groups. The stroke patients were in the subacute stage between 2 and 6 months after onset. The three groups received 20 min of RT and 20 min of electromyography-triggered neuromuscular electrical stimulation (ENMES) in the RT–ENMES group (n = 21), 40 min of RT in the RT group (n = 20), and 40 min of ENMES in the ENMES group (n = 20). The treatments were for 40 min, 5 days per week, and for 8 weeks. Upper-extremity function was evaluated using the Fugl–Meyer assessment for upper extremity (FMA-UE), Wolf motor function test, and action research arm test (ARAT); cerebral cortex activation and motor-evoked potential (MEP) amplitude were evaluated before and after the study. (3) Results: The analysis showed significant changes in all evaluation items for all three groups in the before-and-after comparisons. Significant changes were observed in the FMA-UE, ARAT, and MEP; in the posttest, the RT–ENMES group showed more significant changes in the FMA-UE, ARAT, and MEP than the other two groups. (4) Conclusions: The study analysis suggests that RT–ENMES effectively improves upper-limb function and cerebral cortex activation in patients with stroke.
Pestalotiopols E–J, Six New Polyketide Derivatives from a Marine Derived Fungus Pestalotiopsis sp. SWMU-WZ04-1

Chemical epigenetic cultivation of the sponge-derived fungus Pestalotiopsis sp. SWMU-WZ04-1 contributed to the identification of twelve polyketide derivatives, including six new pestalotiopols E–J (1–6) and six known analogues (7–12). Their gross structures were deduced from 1D/2D NMR and HRESIMS spectroscopic data, and their absolute configurations were further established by circular dichroism (CD) Cotton effects and the modified Mosher’s method. In the bioassay, the cytotoxic and antibacterial activities of all compounds were evaluated. Chlorinated benzophenone derivatives 7 and 8 exhibited inhibitory effects on Staphylococcus aureus and Bacillus subtilis, with MIC values varying from 3.0 to 50 μg/mL. In addition, these two compounds were cytotoxic to four types of human cancer cells, with IC50 values of 16.2~83.6 μM. The result showed that compound 7 had the probability of being developed into a lead drug with antibacterial ability.
The Relationship between Language and Technology: How Screen Time Affects Language Development in Early Life—A Systematic Review

Screen time refers to the amount of time a child is exposed to a screen, that is, television, computer, smartphone, or any other digital medium. Prolonged screen time in the first years of life may affect a child’s cognitive abilities, especially language acquisition. A systematic review was conducted, following the PRISMA-P guidelines, with the aim to explore the available literature relating to the impact of screen time on children’s language development. This review identified 18 articles. The articles reviewed showed that prolonged screen time and exposure to screens in the first 2 years of life can negatively affect language development and communication skills, in terms of comprehension and vocabulary range. In addition, overexposure to screens in the early years can affect overall cognitive development, especially attention to environmental stimuli, social experiences, problem solving, and communication with others, e.g., the alternance of rhythms and roles in a conversation. In conclusion, our systematic review supports the idea that preschool screen time has negative effects on children’s cognitive and language development. Television seems to be the medium most detrimental to children’s skills, as it is used in a passive manner and is often characterised by language and content that do not suit the child’s processing mode. Future studies should increasingly focus on the digital media that children possess at an early age, such as mobile phones and tablets, and on how children relate to the online world, such as social networks.
Implementasi Metode Collaborative Filtering pada Aplikasi Rekomendasi Hotel dan Wisma di Kota Palangka Raya Berbasis Website

Abstrak. Hotel merupakan suatu lembaga yang menyediakan para tamu untuk menginap, di mana setiap orang dapat menginap, makan, minum dan menikmati fasilitas yang lainnya dengan melakukan transaksi pembayaran. Sedangkan wisma adalah bangunan untuk tempat tinggal, kantor atau kumpulan rumah, komplek perumahan, permukiman yang di peruntukkan untuk menunjang urusan atau kegiatan pada bidang tertentu. Karena semakin banyaknya pembangunan hotel dan wisma yang di bangun di kota Palangka Raya, sering kali menimbulkan permasalahan bagi para wisatawan yaitu dalam melakukan pencarian dan menentukan hotel dan wisma berdasarkan fasilitas jasa yang disediakan. Berdasarkan hal tersebut, dibuatlah suatu sistem yang dapat membantu memberikan rekomendasi hotel dan wisma kepada wisatawan. Metodologi yang digunakan dalam aplikasi Rekomendasi Hotel Dan Wisma Di Kota Palangka Raya ini adalah waterfall. Pengujian menggunakan metode blackbox. Hasil penelitian ini adalah sebuah aplikasi Rekomendasi Hotel Dan Wisma Di Kota Palangka Raya Berbasis Website memfasilitasi wisatawan atau pengunjung dalam mendapatkan informasi serta rekomendasi hotel dan wisma tanpa harus terlebih dahulu mengunjungi satu persatu website hotel dan wisma. Pengujian dilakukan menggunakan blackbox. Abstract. Hotel is an institution that provides guests to stay, where everyone can stay, eat, drink and enjoy other facilities by making payment transactions. Wisma is a building for residence, office or group of houses, housing complexes, settlements that are intended to support business or activities in certain fields. Due to the increasing number of hotel and guest house constructions being built in the city of Palangka Raya, it often creates problems for tourists, namely in searching and determining hotels and guest houses based on the service facilities provided. Based on this, a system was created that could help provide hotel and guest house recommendations to tourists. The methodology used in the Hotel and Wisma Recommendation application in the City of Palangka Raya is a waterfall. Testing using the blackbox method. The results of this study are a Website-Based Hotel and Guesthouse Recommendation Application in Palangka Raya City facilitating tourists or visitors in obtaining information and recommendations for hotels and guesthouses without having to first visit the hotel and guest house websites one by one. The tests using blackbox method.
Evaluation of terrain and canopy height products in central African tropical forests

ABSTRACT Since the introduction of space-based altimetry data into the science community, global products associated with elevation and vegetation metrics have been heavily utilized for a variety of ecological applications. Satellite remote sensing enables the collection of global (or near-global), standardized data sets, which can be used in their original form or used as inputs along with other data sets in generating new products. Recent effort has focused on using available data to generate maps of tree heights at a global scale in the service of a better understanding of above ground biomass distribution and its effects on global carbon storage. However, global data sets, while validated at a global scale, often display local and regional variations in accuracy which must be quantified before applying those data sets to smaller scale studies. This work addresses the need for a better understanding of the quality of the Shuttle Radar Topography Mission (SRTM) 90 m digital elevation model and a global 1 km canopy height model in the dense tropical forests of Gabon by using a small-footprint airborne lidar survey and large-footprint, space-based waveform lidar data from teh National Air and Space Administration’s Ice, Cloud, and land Elevation Satellite (ICESat) for validation. As expected, the study found SRTM elevations to be heavily biased by vegetation in this biome, with elevations consistently located within the canopy volume. In addition, the global canopy height model consistently underestimates maximum canopy height at both local and regional scales.