Miracle-dz/UniFAR-FTUs huggingface dataset:
Operation Optimization Model for Photovoltaic User Group with Shared Storage and Demand Response

In recent years, with the increase of the penetration of distributed photovoltaics in the power system, the problems of power fluctuations have increased. The corresponding application of the demand side response has played a positive role in the stability and economy of the power system. First of all, a cost model related to the system net load and energy storage is proposed, which is based on the policy support and the recent technology of distributed photovoltaic in China. Secondly, an optimal operation model for photovoltaic user group with shared energy storage considering automatic demand response is proposed based on game theory and it is proved to have a unique Nash equilibrium strategy. The process of solving the Nash equilibrium strategy is equivalent to a two-level optimization model, and the solving method is proposed based on a differential evolution algorithm. Finally, a practical example is used to verify the effectiveness of the model in reducing user costs and optimizing load characteristics.
Pilot Study: The Effectiveness of Hyperbaric Oxygen Therapy in the Treatment of Periodontitis in Patients with Type 2 Diabetes

Periodontitis is a chronic inflammatory disease with multifactorial aetiology. The relationship between periodontal disease and systemic diseases such as diabetes, obesity, metabolic syndrome, atherosclerotic, cardiovascular disease, and cognitive disorders has been the subject of many studies. The purpose of this study was to evaluate the effectiveness of hyperbaric oxygen therapy on periodontal health in patients suffering from periodontitis and type 2 diabetes. The study was conducted with 14 patients. A total of 369 periodontal pockets in the study group and 431 in the control group were examined. For further analysis, the pockets were classified as moderately deep (4–5 mm) and deep (≥6 mm). All patients received standard non-surgical treatment: scaling and root planing (SRP). Additionally, a series of 30 hyperbaric chamber sessions was carried out in the study group. The following parameters were compared between groups: PD (probing depth), CAL (clinical attachment level), and BOP (bleeding on probing). The results of the study showed significantly better results in terms of PD reduction and CAL gain in the study group in comparison to the control group. Both groups showed a reduction in BOP (bleeding on probing) after treatment. The use of hyperbaric oxygen therapy seems to have considerable benefits in patients with type 2 diabetes.
Electrical Properties of Degenerate Boron Doped Graphene

P-type doping is essential for tuning the electron transport characteristics of graphene for variety of applications in electronics. We demonstrate stable p-type boron doped bi-layer graphene on quartz substrate obtained by using spin-on dopant (SOD) process. Transport properties such as sheet carrier density, sheet resistivity and Hall mobility are found to be independent of temperature (10K-300K) indicating degenerate doping of bilayer graphene.
Analytical Parasitic Resistance and Capacitance Models for Nanosheet Field-Effect Transistors

We developed analytical compact models of parasitic resistance and capacitance (RC) for nanosheet field-effect transistors (NSFETs) with metal contact structures. The proposed model accurately captures the number of sheets, sheet geometry, gate, source, drain, and contact sizes. The parasitic resistance model generated in each region was modeled using the transmission line method (TLM) and the spread resistance model. The parasitic capacitance model was modeled using the conformal mapping method based on the distribution of the electric field. The developed models were validated by changing the structural parameters. The proposed models were implemented in Berkeley short-channel IGFET model (BSIM)-common multi-gate (CMG) to enable circuit performance prediction. With our proposed model, we can accurately predict the circuit performance of NSFETs, capturing the recent scaling trends and process variations in the proposed NSFET.
On the BER Performance of Constant Envelope OFDM in Frequency Selective MIMO Channels With ML Detection

In this contribution, we investigate the issue of constant envelope orthogonal frequency-division multiplexing (CE-OFDM) transmission in multipath fading channels, by quantifying its bit-error rate (BER) performance with the aid of maximum likelihood (ML) detection in multiple-input multiple-output (MIMO) configurations. Through theoretical derivation, we demonstrate the advantage of CE-OFDM over its traditional counterparts as original OFDM and discrete Fourier transform-spread-OFDM (DFT-s-OFDM) in terms of BER performance, which is ultimately supported by the simulation results.
An Automated Scheme for Dielectrophoretic Cell Stretching Manipulation*

Biomechanical factors are closely related to the biological properties of cells. Therefore, the physiological function of cells can be characterized by studying the biomechanical properties of cells, which becomes an important signal for the occurrence and development of some diseases. The main method to obtain Young’s modulus is to stretch to deform cells by applying force. At present, there are various methods to deform cells, but the complexity of the operation and human factors limit the amount of data. In this work, a scheme for the automatic acquisition of large amounts of cellular deformation data is presented, which integrates the functions of cell capture, dielectrophoretic stretching, and release. The whole process does not require human intervention, and the mechanical properties of human umbilical vein endothelial cells (HUVECs) were measured to reveal the performance of the automated manipulation scheme. The presented method provides a more convenient and effective method for measuring the mechanical properties of cells, which improves the significance of biostatistics.
Using exoplanet systems with highly elliptical orbits to search for star-planet interactions

Abstract We are investigating if the orbital geometry of exoplanets affects the activity of their host star by studying a sample of planetary systems known to contain massive planets on short period, highly elliptical orbits. While recent studies in the optical, UV, and X-Ray have shown enhanced chromospheric activity for stars hosting exoplanets with orbital semi-major axes less than 0.1 AU (Krejcova 2012, Shkolnik 2013, Kashyap 2008, Poppenhaeger 2010), it is not yet clear whether this activity is driven by magnetic or tidal interactions. We are probing the dependence of star-planet interactions (SPI) on the orbital geometry of the planetary systems by analyzing chromospheric lines (such as Ca II H & K) for variability phased with the exoplanet's orbit. We have obtained high resolution spectra of several systems with the McDonald 2.1-m Sandiford echelle spectrograph, ARCES on the APO 3.5-m, and for HIRES on Keck I from the Keck Observatory Archive. We describe our methodology and review how our results will use orbital geometry to deduce how planets may affect the activity of their host stars.
New chorus wave properties near the equator from Van Allen Probes wave observations

The chorus wave properties are evaluated using Van Allen Probes data in the Earth's equatorial magnetosphere. Two distinct modes of lower band chorus are identified: a quasi‐parallel mode and a quasi‐electrostatic mode, whose wave normal direction is close to the resonance cone. Statistical results indicate that the quasi‐electrostatic (quasi‐parallel) mode preferentially occurs during relatively quiet (disturbed) geomagnetic activity at lower (higher) L shells. Although the magnetic intensity of the quasi‐electrostatic mode is considerably weaker than the quasi‐parallel mode, their electric intensities are comparable. A newly identified feature of the quasi‐electrostatic mode is that its frequency peaks at higher values compared to the quasi‐parallel mode that exhibits a broad frequency spectrum. Moreover, upper band chorus wave normal directions vary between 0° and the resonance cone and become more parallel as geomagnetic activity increases. Our new findings suggest that chorus‐driven energetic electron dynamics needs a careful examination by considering the properties of these two distinct modes.
Extraction of Key Foreground Information from Visual Feedback Images for Contact Micromanipulation in Liquid Environment

Contact micromanipulation for cells is an important branch in the field of micromanipulation. Limited by the size of the sensor, it is difficult to integrate the sensor in the macroscopic scene into the micromanipulator. So in most operations, images are often the only reliable source of information. The first prerequisite for realizing automated micromanipulation is to be able to extract key information from images. The occlusion phenomenon will inevitably occur in the contact micromanipulation. Although it is possible to design a specific algorithm to identify the target edge information in the occlusion state according to the characteristics of the operating environment and the end-effector. But there is still no universal image processing method to solve this problem. In this paper, we propose an image processing function based on a composite deep learning network structure to solve this problem. Our algorithm is divided into two steps:In the first step, we input the original image into the target detection network to get the position information of the target and end-effector, and cut the region of interest from the original image according to this information. In the second step, we preprocess these candidate sub-images containing key foreground information, and then input them into the image segmentation network to obtain the contour information of the end-effector and target. We designed a cell aspiration experiment based on the digital holographic microscope imaging system to validate our algorithm. In future work, we will continue to improve the algorithm to have better robustness and generalization.
Bibliometric Review of Corporate Governance of Islamic Financial Institutions Through AI-Based Tools

Purpose: Although corporate governance vis-à-vis Shariah governance (SG) has remained a cornerstone of research in Islamic financial institutions (IFIs), little is known about the themes covered by the plethora of studies conducted in this domain. Accordingly, this research aims to unpack the major themes covered in the literature on SG from 2007 to 2023 employing bibliometric analysis technique. Design/methodology/approach: The evaluation of 440 published articles in Scopus is done through different analytical tools i.e., Gephi, VOSviewer, and Scholarcy (artificial intelligence-based research tool) to decode publication trends, outlets, performance, authors, and themes. Findings: The findings elucidate the double-digit increase in the publication, Malaysia is the leading country, Journal of Islamic Accounting and Business Research is the most popular publication outlet, and the International Islamic University (IIUM) is the spearhead institution on SG research. Our results further reveal that SG research broadly covers four main topics; significance of SG, fundamental ingredients of effective SG, impact of SG on IFIs, and latent issues in SG. Originality/novelty: This study contributes to strengthening the governance and management system of IFIs by identifying the novel research avenues.
AI-Based Decision Support System for Traumatic Brain Injury: A Survey

Traumatic brain injury (TBI) is one of the major causes of disability and mortality worldwide. Rapid and precise clinical assessment and decision-making are essential to improve the outcome and the resulting complications. Due to the size and complexity of the data analyzed in TBI cases, computer-aided data processing, analysis, and decision support systems could play an important role. However, developing such systems is challenging due to the heterogeneity of symptoms, varying data quality caused by different spatio-temporal resolutions, and the inherent noise associated with image and signal acquisition. The purpose of this article is to review current advances in developing artificial intelligence-based decision support systems for the diagnosis, severity assessment, and long-term prognosis of TBI complications.
A Review on Clinical Named Entity Recognition

Named Entity Recognition (NER) is a commonly followed standard approach in natural language processing for recognizing category of the textual term such as noun, pronoun or any other pre-defined class. Consequently, the natural language processing domain can more effectively tackle complex tasks, such as answering questions from text and machine transformation. The deep learning models which are already proposed are quite complicated and needs high execution time. In this research previous research work will be analyzed. The bio medical entity recognition models are generally derived for the entity recognition. The entity recognition models are used to generate new entities based on the input data. From the previous research work analysis, the deep learning and pattern recognition algorithms are commonly used for the entity recognition.
Current Levels of Environmental Exposure to Cadmium in Industrialized Countries as a Risk Factor for Kidney Damage in the General Population: A Comprehensive Review of Available Data

The growing number of reports indicating unfavorable outcomes for human health upon environmental exposure to cadmium (Cd) have focused attention on the threat to the general population posed by this heavy metal. The kidney is a target organ during chronic Cd intoxication. The aim of this article was to critically review the available literature on the impact of the current levels of environmental exposure to this xenobiotic in industrialized countries on the kidney, and to evaluate the associated risk of organ damage, including chronic kidney disease (CKD). Based on a comprehensive review of the available data, we recognized that the observed adverse effect levels (NOAELs) of Cd concentration in the blood and urine for clinically relevant kidney damage (glomerular dysfunction) are 0.18 μg/L and 0.27 μg/g creatinine, respectively, whereas the lowest observed adverse effect levels (LOAELs) are >0.18 μg/L and >0.27 μg/g creatinine, respectively, which are within the lower range of concentrations noted in inhabitants of industrialized countries. In conclusion, the current levels of environmental exposure to Cd may increase the risk of clinically relevant kidney damage, resulting in, or at least contributing to, the development of CKD.
Medication Adherence and Its Associated Factors Among Outpatients with Heart Failure

Background Poor adherence to heart failure (HF) medications represents a major barrier to achieve the desired health outcomes in those patients. Objective To assess medication adherence and to explore the factors associated with medication non-adherence among patient with HF in Jordan. Methods The current cross-sectional study was conducted at the outpatient cardiology clinics at two main hospitals in Jordan from August 2021 through April 2022. Variables including socio-demographics, biomedical variables, in addition to disease and medication characteristics were collected using medical records and custom-designed questionnaire. Medication adherence was assessed using the 4-item Morisky Medication Adherence Scale. Multinomial logistic regression analysis was performed to identify the factors that are significantly and independently associated with medication non-adherence. Results Of the 427 participating patients, 92.5% had low to moderate medication adherence. Results of the regression analysis revealed that that patients who had higher education level (OR=3.36; 95% CI 1.08–10.43; P=0.04) and were not suffering from medication-related side effects (OR=4.7; 95% CI 1.91–11.5; P=0.001) had significantly higher odds of being in the moderate adherence group. Patients who were taking statins (OR=16.59; 95% CI 1.79–153.98; P=0.01) or ACEIs/ ARBs (OR=3.95; 95% CI 1.01–15.41; P=0.04) had significantly higher odds of being in the high adherence group. Furthermore, Patients who were not taking anticoagulants had higher odds of being in the moderate (OR=2.77; 95% CI 1.2–6.46; P=0.02) and high (OR=4.11; 95% CI 1.27–13.36; P=0.02) adherence groups when compared to patients who were taking anticoagulants. Conclusion The poor medication adherence in the present study sheds the light on the importance of implementing intervention programs which focus on improving patients’ perception about the prescribed medications particularly for patients who have low educational levels, receive an anticoagulant, and do not receive a statin or an ACEI/ ARB.
Using Physiological Signals and Machine Learning Algorithms to Measure Attentiveness During Robot-Assisted Social Skills Intervention: A Case Study of Two Children with Autism Spectrum Disorder

Individuals with autism spectrum disorder (ASD) often face barriers in accessing opportunities across a range of educational, employment, and social contexts. One of these barriers is the development of effective communication skills sufficient for navigating the social demands of everyday environments. Fortunately, researchers have established evidence-based practices (EBP) for teaching critical communication skills to individuals with ASD [1]. One EBP that has received a great deal of attention over the last few decades is technology-aided instruction and intervention (TAII) [1], [2]. TAII is an instructional practice in which technology is an essential component and is used to facilitate behavior change. Further, it encompasses a wide range of applications including computer-assisted instruction, virtual and augmented reality, augmentative and alternative communication, and robot-assisted intervention [2].
Effects of bamlanivimab alone or in combination with etesevimab on subsequent hospitalization and mortality in outpatients with COVID-19: a systematic review and meta-analysis

Background Coronavirus disease 2019 (COVID-19) has caused an enormous loss of life worldwide. The spike protein of the severe acute respiratory syndrome coronavirus 2 is the cause of its virulence. Bamlanivimab, a recombinant monoclonal antibody, has been used alone or in combination with etesevimab to provide passive immunity and improve clinical outcomes. A systematic review and meta-analysis was conducted to investigate the therapeutic effects of bamlanivimab with or without etesevimab (BAM/ETE) treatment. Methods Our study was registered in PROSPERO (registry number CRD42021270206). We searched the following electronic databases, without language restrictions, until January 2023: PubMed, Embase, medRxiv, and the Cochrane database. A systematic review and meta-analysis was conducted based on the search results. Results Eighteen publications with a total of 28,577 patients were identified. Non-hospitalized patients given bamlanivimab with or without etesevimab had a significantly lower risk of subsequent hospitalization (18 trials, odds ratio (OR): 0.37, 95% confidence interval (CI): [0.29–0.49], I2: 69%; p < 0.01) and mortality (15 trials, OR: 0.27, 95% CI [0.17–0.43], I2: 0%; p = 0.85). Bamlanivimab monotherapy also reduced the subsequent risk of hospitalization (16 trials, OR: 0.43, 95% CI [0.34–0.54], I2: 57%; p = 0.01) and mortality (14 trials, OR: 0.28, 95% CI [0.17–0.46], I2: 0%; p = 0.9). Adverse events from these medications were uncommon and tolerable. Conclusions In this meta-analysis, we found the use of bamlanivimab with or without etesevimab contributed to a significantly-reduced risk of subsequent hospitalization and mortality in non-hospitalized COVID-19 patients. However, resistance to monoclonal antibodies was observed in COVID-19 variants, resulting in the halting of the clinical use of BAM/ETE. Clinicians’ experiences with BAM/ETE indicate the importance of genomic surveillance. BAM/ETE may be repurposed as a potential component of a cocktail regimen in treating future COVID variants.
Sleep Architecture and EEG Power Spectrum Following Cumulative Sleep Restriction: A Comparison between Typically Developing Children and Children with ADHD

No studies have looked at the effects of cumulative sleep restriction (CSR) on sleep architecture or the power spectrum of sleep EEG (electroencephalogram) in school-age children, as recorded by PSG (polysomnography). This is true for both typically developing (TD) children and children with ADHD (attention deficit/hyperactivity disorder), who are known to have more sleep difficulties. Participants were children (ages 6–12 years), including 18 TD and 18 ADHD, who were age- and sex-matched. The CSR protocol included a two-week baseline and two randomized conditions: Typical (six nights of sleep based on baseline sleep schedules) and Restricted (one-hour reduction of baseline time in bed). This resulted in an average of 28 min per night difference in sleep. Based on ANOVAs (analysis of variance), children with ADHD took longer to reach N3 (non-rapid eye movement), had more WASO (wake after sleep onset) (within the first 5.1 h of the night), and had more REM (rapid eye movement) sleep than TD children regardless of condition. During CSR, ADHD participants had less REM and a trend toward longer durations of N1 and N2 compared to the TD group. No significant differences in the power spectrum were found between groups or conditions. In conclusion, this CSR protocol impacted some physiological aspects of sleep but may not be sufficient to cause changes in the power spectrum of sleep EEG. Although preliminary, group-by-condition interactions suggest that the homeostatic processes in children with ADHD may be impaired during CSR.
Environmental CSR in Industrial Cities: A Collaborative Governance Approach

Corporate social responsibility (CSR) initiatives based on charity and philanthropy have not alleviated many sustainable environmental issues, particularly in industrial towns. The collaborative governance strategy is seen to be an option for resolving difficulties of coordination and communication between businesses, the government, and the community so that the goals of urban environmental management can be met via collaborative efforts. The purpose of this research is to identify the different forms of environmental CSR implementation by corporate entities and to create a CSR collaborative governance model in environmental management. This qualitative investigation was carried out in 2020 in Cilegon City, one of Indonesia’s industrial cities. To investigate their support, a total of 20 informants from three stakeholder groups, namely the government, corporate entities, and the community, were questioned. According to the study’s findings, CSR implementation in the environmental sector covers cleaner production, eco-office, energy and natural resource conservation, waste management, renewable energy, climate change adaptation, and environmental education. CSR implementation in the environmental potential to implement collaborative governance. Business entities have an essential role in establishing the starting condition, while the government provides facilitative leadership and the CSR forum launches institutional design. These three elements are critical to the effectiveness of collaborative governance in environmental management in industrial cities.