Miracle-dz/UniFAR-FTUs huggingface dataset:
Qualitative Analysis of Modified Advanced-Platelet-Rich Fibrin Buffy Coat among Diabetic Patients and Tobacco Smokers with Chronic Periodontitis: A Cell Block Cytology Study

Aim: The aim of this study was to evaluate and compare histologically the pattern of distribution of platelet and leukocyte concentration, quality of fibrin network, and the aggregation of platelets in the three zones of modified advanced platelet-rich fibrin (A-PRF) buffy coat among uncontrolled type 2 diabetic patients, tobacco smokers, and healthy individuals with chronic periodontitis. Materials and Methods: In this cross-sectional cytology study, 180 generalized chronic periodontitis patients (46–55 years) were enrolled – Group 1 (control group): 60 systemically healthy participants, Group 2 (test group): 60 heavy tobacco smokers, and Group 3 (test group): 60 uncontrolled type 2 diabetic patients. Fifteen milliliters of blood was drawn from all study participants. Modified A- PRF membrane was prepared and then processed histologically. Results: The distribution pattern of platelet and leukocyte concentration in modified (A-PRF) gradually declines from the serum to the red blood cell (RBC) end of a clot in all groups. We have assessed that the serum and middle end of modified (A-PRF) membrane had an increasingly moderate distribution of platelets and leukocytes in both type 2 diabetics and tobacco smokers. RBC end had more of sparse distribution in all the three groups. Healthy individuals exhibited 95% of reversible pattern, whereas tobacco smokers had 78.33% and uncontrolled type 2 diabetic patients had 93.33% of irreversible aggregation pattern of platelets. Loose fibrin network pattern was seen in all the three groups. These observations conclude that tobacco smokers had a high percentage of loose fibrin network with sparse distribution of cells. Males showed more loose fibrin network pattern of modified (A-PRF) membrane than compared to females. Conclusion: In the present study, it can be concluded that the application of modified (A-PRF) may provide enhanced periodontal healing in uncontrolled type 2 diabetic patients and tobacco smokers; furthermore, females may have better regenerative capacity compared to males.
Uso de la inteligencia artificial en la investigación para el reposicionamiento de fármacos

El desarrollo de algoritmos para su utilización en la inteligencia artificial y la disponibilidad masiva de datos biomédicos han impulsado y acelerado la investigación, con el resultado de descubrimientos y de tratamientos novedosos utilizando fármacos patentados y aprobados para el tratamiento de diferentes enfermedades. Este proceso se conoce como reposicionamiento de fármacos (REFA), y puede ser abordado por una rama de la inteligencia artificial (IA) conocida como aprendizaje automático (AA). El aprendizaje automático se basa en un conjunto de algoritmos que, combinados con técnicas computacionales bien establecidas en el campo del descubrimiento de fármacos, han sido capaces de dar lugar, con una alta eficacia, a nuevas propiedades y relaciones farmacológicas anteriormente desconocidas. Así, se han identificado nuevos blancos y tratamientos potenciales contra diversos padecimientos como el cáncer, y las enfermedades neurodegenerativas e infecciosas, entre otras. El objetivo de este trabajo de revisión es contribuir a la literatura en español sobre el uso de la inteligencia artificial y el aprendizaje de máquina en la investigación para el reposicionamiento de fármacos.
A Systematic Literature Review and Bibliometric Analysis on Mobile Payments

Mobile payments (m-payments) have become a way of life as it enables transactions without the need for physical cash. Systematic literature review (SLR) and bibliometric analysis are rare contributions in the field of m-payment research. The basis of this work is to explore and analyze the existing literature on m-payments. This article attempts to provide a holistic view on m-payment research. The current research article analyzes both peer reviewed and open access papers. The papers are selected from ScienceDirect, EBSCO and ProQuest databases for the time period 2007–2021. A total of 250 papers were chosen as a part of this study to conduct SLR and bibliometric analysis, out of which 62 relevant papers, published in 48 peer reviewed and open access journals, were taken for the final analysis. VOSviewer is used to carry out the bibliometric analysis. The bibliometric coupling helped in identifying three clusters: factors influencing m-payment adoption, consumer’s attitude towards m-payments and the effect of technological and behavioural factors on the adoption of m-payments. This study would help future researchers in gaining significant insights on the current status of the existing literature and assist in conducting a qualitative literature review.
Developing ecological endpoints for valuation of semi-arid riparian ecosystem services

Creating measurable ecological accounting units has become a point of emphasis in valuing ecosystem services. Understanding which ecological endpoints, which emanate from biophysical production functions, are important to individuals could help to create measurable ecological accounting units. Using two semi-arid riparian ecosystems we create a suite of ecological endpoints and using benefits transfer techniques compare their ability to be transferred to similar riparian ecosystems. If clearly defined, ecological accounting units can be developed for ecosystem services. This could lead to ecosystem services being properly incorporated into benefit cost analyses that maximize economic product of both market and non-market goods and services.
Industrial Internet of Things: Requirements, Architecture, Challenges, and Future Research Directions

Industry 4.0 relates to the digital revolution of manufacturing and other sectors, such as retail, distribution, oil and gas, and infrastructure. Meanwhile, the Industrial Internet of Things (IIoT) is a technological advancement that leads to Industry 4.0 implementation by boosting the manufacturing sector’s productivity and economic impact. IIoT provides the ability to provide global connectivity between components in different locations. The manufacturing sector has had various difficulties implementing IIoT, primarily due to IIoT characteristics. This paper offers an in-depth review of Industry 4.0 and IIoT, where the primary motivation behind this is to introduce the most recent advancements related to Industry 4.0 and IIoT, as well as to address the existing limitations. Firstly, this paper presents a novel taxonomy of IIoT challenges that includes aspects of each challenge, such as the terminology and approaches utilized to solve these challenges. Besides IIoT challenges, this survey provides an in-depth demonstration of the many concepts related to IIoT, such as architecture and use cases. Secondly, this paper provides a comprehensive review of the state-of-the-art of Industry 4.0 in terms of concepts, requirements, and supporting technology. In addition, the correlation between enabling technology and technical requirements is discussed in detail. Finally, this paper highlights deep learning, edge computing, and big data as key techniques for the future directions of IIoT. Furthermore, the presented techniques are thoroughly examined to present an alternative method for future adoption. In addition to the showcased techniques, a new architecture for the future of IIoT based on these three primary techniques is also proposed.
A Novel Simple Convoluted Geometry to Design Frequency Selective Surfaces for Applications at ISM and UNII Bands

In this paper, we use a novel convoluted geometry to design a dual-band Frequency Selective Surface (FSS) with angular stability and independent polarization operation. The geometry allows the design of a dual-band FSS to block ISM and UNII radio bands. We performed a parametric study with the commercial software ANSYS HFSS. A prototype of the designed FSS was built and its transmission characteristics were measured and compared with simulated results. We observed good agreement between numerical and experimental results. The FSS realized a dual-band response at the required bands and it had angular stability and polarization independence.
The Influence of Soft and Hard Quality Management Practices on Organizational Performance at The Vocational High Schools

The aim of this re search was to examine the influence of soft quality management and hard quality management on organizational performance at the Vocational High School s. The research proposed several hypotheses relating to the influence between soft quality management fact ors, hard quality management factors, and organizational performance at the Vocational High School s. The t ype of research was expost - facto by the survey method. This research was carried out at the Vocational High S chools located in Yogyakarta Special Reg ion, covering Yogyakarta, Sleman, Bantul, Kulon progo, and Gunungkidul. T his research used a sample of 139 the Vocational High School s. Subjects were the managers staffs at the Vocational High S chools includ ing the principal, deputy principal, and head of department at the V ocational High Schools. Data were collected by using questionnaires. The test for validity and reliability of r esearch instruments was used the total item correlation and Alpha Croncbach coefficient. While for testing the validity and re liability of the indicators was done through confirmatory factor analysis. This research use d structural equation modeling (SEM) analysis. The findings showed that soft quality management factors ha d a positive , direct , and significant effect on organizati onal performance at the V ocational High S chools ; hard quality management factors ha d a positive , direct , and significant effect on organizational performance at the V ocational High S chools
Cross-Collaborative Fusion-Encoder Network for Robust RGB-Thermal Salient Object Detection

With the prevalence of thermal cameras, RGB-T multi-modal data have become more available for salient object detection (SOD) in complex scenes. Most RGB-T SOD works first individually extract RGB and thermal features from two separate encoders and directly integrate them, which pay less attention to the issue of defective modalities. However, such an indiscriminate feature extraction strategy may produce contaminated features and thus lead to poor SOD performance. To address this issue, we propose a novel CCFENet for a perspective to perform robust and accurate multi-modal expression encoding. First, we propose an essential cross-collaboration enhancement strategy (CCE), which concentrates on facilitating the interactions across the encoders and encouraging different modalities to complement each other during encoding. Such a cross-collaborative-encoder paradigm induces our network to collaboratively suppress the negative feature responses of defective modality data and effectively exploit modality-informative features. Moreover, as the network goes deeper, we embed several CCEs into the encoder, further enabling more representative and robust feature generation. Second, benefiting from the proposed robust encoding paradigm, a simple yet effective cross-scale cross-modal decoder (CCD) is designed to aggregate multi-level complementary multi-modal features, and thus encourages efficient and accurate RGB-T SOD. Extensive experiments reveal that our CCFENet outperforms the state-of-the-art models on three RGB-T datasets with a fast inference speed of 62 FPS. In addition, the advantages of our approach in complex scenarios (e.g., bad weather, motion blur, etc.) and RGB-D SOD further verify its robustness and generality. The source code will be publicly available via our project page: https://git.openi. org.cn/OpenVision/CCFENet.
Reliability and Validation of the Japanese Version of the Patient Empowerment Scale

Empowerment scales for inpatients have been developed worldwide, but their validity and reliability have not been adequately tested and applied to the health promotion and care among older adults during hospitalization. In this study, the Patient Empowerment Scale developed by Faulkner was translated into Japanese, and Japanese patients were surveyed to test its clinical applicability. To test its applicability, 151 patients in rehabilitation wards were surveyed in four municipalities. After considering ceiling/floor effects and validating the structure, the Patient Empowerment Scale—Japanese comprised 37 items and six factors: subject–staff interaction, environmental adjustment through collaboration, necessary information gathering and problem awareness, proactive behavioral practices, self-disclosure, and self-management of activities. Criteria-related validity assessment confirmed the scale’s correlation with the Health Locus of Control Scale, General Self-Efficacy Scale, 13-item Sense of Coherence Scale, Rosenberg Self-Esteem Scale, and Philadelphia Geriatric Center Morale Scale. Regarding internal consistency, the Cronbach’s alpha was 0.93 for all 37 items. The Cronbach’s alphas for the six factors were 0.93, 0.91, 0.92, 0.92, 0.91, and 0.75, respectively. In our test/re-test of reliability, Spearman’s rank correlation coefficient between the first and second total scores was ρ = 0.96, p < 0.01. These results confirm the scale’s validity and reliability, and its applicability to older hospitalized patients.
Gender and Leadership in Sport: Girl Team Captains

Employing personal narrative, observations, and overview of literature, the notions of gender and leadership are examined through captaining. Battling the gender norms in female team sports and leadership has been a non-topic for decades in physical education and recreational sports. This study intends to interrogate and highlight the obstacles and possibilities of leadership in female sports and to rectify the often antiquated and masculinist points of view on female sports.
COMPUTATIONAL STUDIES OF POTENTIAL EBOLA VP40 INHIBITORS USING BIOACTIVE COMPOUNDS FROM MEDICINAL PLANTS OF MALAYSIA

Ebola virus (EBOV) belongs to Filoviridae family, a deadly virus that can cause severe viral haemorrhagic fevers (VHF) with a high fatality rate between 25 to 90 percent. Amongst EBOV proteins, the EBOV matrix protein VP40 is crucial in facilitating the transcription of the viral gene in the early stage of infection. To date, there is no cure for EBOV and available chemical drugs were known to cause severe side effects. It is known that bioactive compounds from natural products can potentially combat this viral disease with fewer side effects. Therefore, this study aims to screen 15 bioactive compounds of medicinal plants from Malaysia. These compounds were docked against the RNA active site (Phe125 and Arg134) on VP40 matrix protein using AutoDock Vina. The ADMET properties and the toxicity class of the compounds were predicted computationally, and the compounds with good oral bioavailability were chosen for docking simulations. The top three docked compounds namely apigenin, epiexcelsin and kaempferol have a binding affinity of -4.6, -4.4 and -4.3 kJ/mol respectively. Our MD simulation study showed that epiexcelsin is the best candidate among the three selected compounds. Binding free energy calculation via molecular-mechanics Poisson Boltzmann surface area (MM-PBSA) method showed that epiexcelsin has the lowest binding free energy of -56.503 kJ/mol compared to apigenin (-40.344 kJ/mol) and kaemferol (-27.329 kJ/mol). Our results suggest that epiexcelsin from local herbal plants can potentially be explored as a good candidate for further development of EBOV inhibitor targeting VP40.
Context-Free Grammars for Several Triangular Arrays

In this paper, we present a unified grammatical interpretation of the numbers that satisfy a kind of four-term recurrence relation, including the Bell triangle, the coefficients of modified Hermite polynomials, and the Bessel polynomials. Additionally, as an application, a criterion for real zeros of row-generating polynomials is also presented.
Topological and Statistical Analysis for the High-Voltage Transmission Networks in the Korean Power Grid

A power grid is one of the most complex networks and is critical infrastructure for society. To understand the characteristics of a power grid, complex network analysis has been used from the early 2000s mainly for US and European power grids. However, since the power grids of different countries might have different structures, the Korean power grid needs to be examined through complex network analysis. This paper performs the analysis for the Korean power grid, especially for high-voltage transmission networks. In addition, statistical and small-world characteristics for the Korean power grid are analyzed. Generally, the Korean power grid has similar characteristics to other power grids, but some characteristics differ because the Korean power grid is concentrated in the capital area.
An enhanced Genetic Folding algorithm for prostate and breast cancer detection

Cancer’s genomic complexity is gradually increasing as we learn more about it. Genomic classification of various cancers is crucial in providing oncologists with vital information for targeted therapy. Thus, it becomes more pertinent to address issues of patient genomic classification. Prostate cancer is a cancer subtype that exhibits extreme heterogeneity. Prostate cancer contributes to 7.3% of new cancer cases worldwide, with a high prevalence in males. Breast cancer is the most common type of cancer in women and the second most significant cause of death from cancer in women. Breast cancer is caused by abnormal cell growth in the breast tissue, generally referred to as a tumour. Tumours are not synonymous with cancer; they can be benign (noncancerous), pre-malignant (pre-cancerous), or malignant (cancerous). Fine-needle aspiration (FNA) tests are used to biopsy the breast to diagnose breast cancer. Artificial Intelligence (AI) and machine learning (ML) models are used to diagnose with varying accuracy. In light of this, we used the Genetic Folding (GF) algorithm to predict prostate cancer status in a given dataset. An accuracy of 96% was obtained, thus being the current highest accuracy in prostate cancer diagnosis. The model was also used in breast cancer classification with a proposed pipeline that used exploratory data analysis (EDA), label encoding, feature standardization, feature decomposition, log transformation, detect and remove the outliers with Z-score, and the BAGGINGSVM approach attained a 95.96% accuracy. The accuracy of this model was then assessed using the rate of change of PSA, age, BMI, and filtration by race. We discovered that integrating the rate of change of PSA and age in our model raised the model’s area under the curve (AUC) by 6.8%, whereas BMI and race had no effect. As for breast cancer classification, no features were removed.
Design, steady performance and wake characterization of a scaled wind turbine with pitch, torque and yaw actuation

Abstract. This paper describes the design and characterization of a scaled wind turbine model, conceived to support wake and wind farm control experiments in a boundary layer wind tunnel. The turbine has a rotor diameter of 0.6 m and was designed to match the circulation distribution of a target conceptual full-scale turbine at its design tip speed ratio. In order to enable the testing of plant-level control strategies, the model is equipped with pitch, torque and yaw actuation and is sensorized with integrated load cells, rotor azimuth and blade pitch encoders. After describing the design of the turbine, its steady-state performance and wake characteristics are assessed by conducting experiments in two different wind tunnels, in laminar and turbulent conditions, collecting wake data with different measurement techniques. A large-eddy simulator coupled to an actuator-line model is used to develop a digital replica of the turbine and of the wind tunnel. For increased accuracy, the polars of the low-Reynolds-number airfoil used in the numerical model are tuned directly from measurements obtained from the rotor in operation in the wind tunnel. Results indicate that the scaled turbine performs as expected: measurements are repeatable and consistent, and the wake appears to have a realistic behavior in line with expectations and with a similar but slightly larger scaled model turbine. Furthermore, the predictions of the numerical model are well in line with experimental observations.
Performance evaluation of frequency division duplex (FDD) massive multiple input multiple output (MIMO) under different correlation models

Massive multiple-input multiple-output (massive-MIMO) is considered as the key technology to meet the huge demands of data rates in the future wireless communications networks. However, for massive-MIMO systems to realize their maximum potential gain, sufficiently accurate downlink (DL) channel state information (CSI) with low overhead to meet the short coherence time (CT) is required. Therefore, this article aims to overcome the technical challenge of DL CSI estimation in a frequency-division-duplex (FDD) massive-MIMO with short CT considering five different physical correlation models. To this end, the statistical structure of the massive-MIMO channel, which is captured by the physical correlation is exploited to find sufficiently accurate DL CSI estimation. Specifically, to reduce the DL CSI estimation overhead, the training sequence is designed based on the eigenvectors of the transmit correlation matrix. To this end, the achievable sum rate (ASR) maximization and the mean square error (MSE) of CSI estimation with short CT are investigated using the proposed training sequence design. Furthermore, this article examines the effect of channel hardening in an FDD massive-MIMO system. The results demonstrate that in high correlation scenarios, a large loss in channel hardening is obtained. The results reveal that increasing the correlation level reduces the MSE but does not increase the ASR. However, exploiting the spatial correction structure is still very essential for the FDD massive-MIMO systems under limited CT. This finding holds for all the physical correlation models considered.
A Comprehensive Review of Micro UAV Charging Techniques

The groundbreaking Unmanned Aerial Vehicles (UAVs) technology has gained significant attention from both academia and industrial experts due to several applications, such as military missions, power lines inspection, precision agriculture, remote sensing, delivery services, traffic monitoring and many more. UAVs are expected to become a mainstream delivery element by 2040 to address the ever-increasing demand for delivery services. Similarly, UAV-assisted monitoring approaches will automate the inspection process, lowering mission costs, increasing access to remote locations and saving time and energy. Despite the fact that unmanned aerial vehicles (UAVs) are gaining popularity in both military and civilian applications, they have a number of limitations and critical problems that must be addressed in order for missions to be effective. One of the most difficult and time-consuming tasks is charging UAVs. UAVs’ mission length and travel distance are constrained by their low battery endurance. There is a need to study multi-UAV charging systems to overcome battery capacity limitations, allowing UAVs to be used for a variety of services while saving time and human resources. Wired and Wireless Power Transfer (WPT) systems have emerged as viable options to successfully solve this difficulty. In the past, several research surveys have focused on crucial aspects of wireless UAV charging. In this review, we have also examined the most emerging charging techniques for UAVs such as laser power transfer (LPT), distributed laser charging (DLC), simultaneous wireless information and power transfer (SWIPT) and simultaneous light wave information and power transfer (SLIPT). The classification and types of UAVs, as well as various battery charging methods, are all discussed in this paper. We’ve also addressed a number of difficulties and solutions for safe operation. In the final section, we have briefly discussed future research directions.