Final Dataset Research Articles

Clinical Research in Renal Transplantation: A Bibliometric Perspective on a Half-century of Innovation and Progress.

Groundbreaking biomedical research has transformed renal transplantation (RT) into a widespread clinical procedure that represents the mainstay of treatment for end-stage kidney failure today. Here, we aimed to provide a comprehensive bibliometric perspective on the last half-century of innovation in clinical RT. The Web of Science Core Collection was used for a comprehensive screening yielding 123 303 research items during a 50-y period (January 1973-October 2022). The final data set of the 200 most-cited articles was selected on the basis of a citation-based strategy aiming to minimize bias. Studies on clinical and immunological outcomes (n = 63 and 48), registry-based epi research (n = 38), and randomized controlled trials (n = 35) dominated the data set. Lead US authors have signed 110 of 200 articles. The overall level of evidence was high, with 84% of level1 and -2 reports. Highest numbers of these articles were published in New England Journal of Medicine , Transplantation , and American Journal of Transplantation. Increasing trend was observed in the number of female authors in the postmillennial era (26% versus 7%). This study highlights important trends in RT research of the past half-century. This bibliometric perspective identifies the most intensively researched areas and shift of research interests over time; however, it also describes important imbalances in distribution of academic prolificacy based on topic, geographical aspects, and gender.

Extracting the Global 21-cm signal from Cosmic Dawn and Epoch of Reionization in the presence of Foreground and Ionosphere

ABSTRACT Detection of redshifted H i 21-cm emission is a potential probe for investigating the Universe’s first billion years. However, given the significantly brighter foreground, detecting 21-cm is observationally difficult. The Earth’s ionosphere considerably distorts the signal at low frequencies by introducing directional-dependent effects. Here, for the first time, we report the use of Artificial Neural Networks (ANNs) to extract the global 21-cm signal characteristics from the composite all-sky averaged signal, including foreground and ionospheric effects such as refraction, absorption, and thermal emission from the ionosphere’s F and D-layers. We assume a ‘perfect’ instrument and neglect instrumental calibration and beam effects. To model the ionospheric effect, we considered the static and time-varying ionospheric conditions for the mid-latitude region, where LOFAR is situated. In this work, we trained the ANN model for various situations using a synthetic set of the global 21-cm signals created by altering its parameter space based on the ‘$\rm \tanh$’ parametrized model and the Accelerated Reionization Era Simulations (ARES) algorithm. The obtained result shows that the ANN model can extract the global signal parameters with an accuracy of ${\ge}96\ \hbox{per cent}$ in the final study when we include foreground and ionospheric effects. On the other hand, a similar ANN model can extract the signal parameters from the final prediction data set with an accuracy ranging from 97 to 98 per cent when considering more realistic sets of the global 21-cm signals based on physical models.

MARVEL analysis of high-resolution rovibrational spectra of C O .

A set of empirical rovibrational energy levels, obtained through the MARVEL (measured active rotational-vibrational energy levels) procedure, is presented for the C O isotopologue of carbon dioxide. This procedure begins with the collection and analysis of experimental rovibrational transitions from the literature, allowing for a comprehensive review of the literature on the high-resolution spectroscopy of C O , which is also presented. A total of 60sources out of more than 750 checked provided 14,101uniquely measured and assigned rovibrational transitions in the wavenumber range of 579-13,735 cm . This is followed by a weighted least-squares refinement yielding the energy levels of the states involved in the measured transitions. Altogether 6318empirical rovibrational energies have been determined for C O . Finally, estimates have been given for the uncertainties of the empirical energies, based on the experimental uncertainties of the transitions. The detailed analysis of the lines and the spectroscopic network built from them, as well as the uncertainty estimates, all serve to pinpoint possible errors in the experimental data, such as typos, misassignment of quantum numbers, and misidentifications. Errors found in the literature data were corrected before including them in the final MARVEL dataset and analysis.

Cataloging variation in 16S rRNA gene sequences of female urobiome bacteria

Continued efforts to isolate and sequence bacteria of the urinary tract has increased representation of these species in publicly available databases. This in turn has improved taxonomic classifications of the urinary microbiome (urobiome). Short-read sequencing targeting a variable region(s) of the 16S rRNA gene sequence has been fundamental in characterizing the urobiomes of males and females with and without lower urinary tract symptoms, as well as cancers of the urinary tract. Here, we have compiled a data set of full-length or near-full-length 16S rRNA gene sequences for the urobiome. To generate this data set, we first plated 203 isolates from the bladder on differential media and sequenced their full-length 16S rRNA gene sequence. We combined this data set with publicly available genomes from primarily the female urinary tract. The final data set includes 399 sequences representative of 160 different species from 73 genera. We assessed the ability of publicly available databases to correctly predict these sequences based on the V1-V3, V4, and V4-V6 variable regions. As expected, species designations based upon these variable regions is often not possible or incorrect. We also detected incorrect genus-level classifications. This data set can be used to supplement existing databases, by increasing urobiome species variation, and thus improve future studies characterizing urobiomes.

Description of the Baseline Audiologic Characteristics of the Participants Enrolled in the Aging and Cognitive Health Evaluation in Elders Study.

The Aging and Cognitive Health Evaluation in Elders (ACHIEVE) study is a randomized clinical trial designed to determine the effects of a best-practice hearing intervention versus a successful aging health education control intervention on cognitive decline among community-dwelling older adults with untreated mild-to-moderate hearing loss. We describe the baseline audiologic characteristics of the ACHIEVE participants. Participants aged 70-84 years (N = 977; Mage = 76.8) were enrolled at four U.S. sites through two recruitment routes: (a) an ongoing longitudinal study and (b) de novo through the community. Participants underwent diagnostic evaluation including otoscopy, tympanometry, pure-tone and speech audiometry, speech-in-noise testing, and provided self-reported hearing abilities. Baseline characteristics are reported as frequencies (percentages) for categorical variables or medians (interquartiles, Q1-Q3) for continuous variables. Between-groups comparisons were conducted using chi-square tests for categorical variables or Kruskal-Wallis test for continuous variables. Spearman correlations assessed relationships between measured hearing function and self-reported hearing handicap. The median four-frequency pure-tone average of the better ear was 39 dB HL, and the median speech-in-noise performance was a 6-dB SNR loss, indicating mild speech-in-noise difficulty. No clinically meaningful differences were found across sites. Significant differences in subjective measures were found for recruitment route. Expected correlations between hearing measurements and self-reported handicap were found. The extensive baseline audiologic characteristics reported here will inform future analyses examining associations between hearing loss and cognitive decline. The final ACHIEVE data set will be publicly available for use among the scientific community. https://doi.org/10.23641/asha.24756948.

Optimization of Land Area Mapping and Volume Calculations using Drone Lidar Livox Mid-40 Data with the Downsampling Method

One reason for the growing acceptance of 3D point cloud-based research and applications is the quick advancement of 3D scanning technologies. However, there are still a number of serious issues that have an impact on point cloud utilization performance. Among these difficulties are controlling the quantity of points, irregular point density, and a deficiency of location proximity data. In this study, we use Livox Mid-40 Drone Lidar Data and a downsampling technique to compute land area and volume. However, it can be highly challenging and time-consuming to extract usable information from enormous amounts of gathered data. Motivated by these results, this study recommends using downsampling approaches to minimize the size of the final data set while preserving data integrity, which will facilitate and expedite. The Livox Mid-40 Lidar Drone data was optimal at 00:00:30 with a flying height of 75,719 meters and a measurement diameter of 50.3 meters. By using downsampling techniques, the number of points can be reduced by up to 40 percent from the previous number of data points. Meanwhile, the data size can be 10 percent smaller than the original data. To calculate the area of land of the same size, there is a difference of 0.53 square meters. Meanwhile, for the calculation of cubic volume, there is a difference of 1.63 cubic meters.

Development of a Self-Reported Communication Competence Questionnaire for Patients with Schizophrenia

Purpose: To develop a self-reported communication competency scale for patients with schizophrenia. Methods: Twenty-nine preliminary items were selected from 42 candidate items derived from literature review and qualitative interviews using content validity analysis of expert opinions to evaluate 637 patients’ schizophrenia communication competence. Data from 383 individuals were analyzed. Confirmatory factor analysis was conducted using data of 280 individuals. The final dataset included data from 254 participants. Data were subjected to exploratory and confirmatory factor analyses using SPSS and AMOS 24.0.Results: After evaluating the model fit index for the last 24 items, the following results were obtained: x2=633.95 (p<.001), x2/df (degree of freedom)=2.26, SRMR=0.04, RMSEA=0.06, TLI=0.93, CFI=0.94, NFI=0.96. Therefore, this tool’s suitability was evaluated as acceptable. The final scale comprised 24 items and 3 subscales that could measure basic, interpersonal, and communicative expression of patients with schizophrenia. Conclusion: A self-reported communication competency scale was developed for patients with schizophrenia. It is expected to help measure the effectiveness of intervention programs to improve communication among patients with schizophrenia.

SALES PROMOTION, LIVE STREAMING, SUBJECTIVE NORM TERHADAP IMPULSIVE BUYING DENGAN PAYLATER SEBAGAI VARIABEL MODERASI

E-commerce has profoundly altered the shopping behaviors of consumers in Indonesia. Additionally, fintech companies have introduced "paylater" services, revolutionizing the payment process for online purchases. Paylater operates on a Buy Now Pay Later (BNPL) system, allowing consumers to make purchases and defer payment. This innovation enhances the ease and security of online shopping, enticing users with enticing sales promotions. A survey targeting e-commerce users in Indonesia was conducted and subsequently analyzed using structural equation modeling (SEM - PLS). Employing a quantitative descriptive approach with a cross-sectional design, data was collected offline over 14 days in DKI Jakarta, Indonesia, yielding a final dataset from 122 respondents out of 210 obtained. Research outcomes revealed that sales promotion, subjective norms, and e-commerce live streaming features significantly influence impulsive buying behavior among consumers. The use of paylater services moderates this relationship, indicating that frequent paylater users tend to exhibit more impulsivity in their purchasing patterns. Despite the convenience offered by paylater services, there are associated risks such as debt accumulation and financial mismanagement. The findings of this research can serve as an educational tool for potential paylater users, encouraging responsible use and financial management. This research substantially contributes to the growth of fintech companies and micro, small, and medium-sized enterprises (MSMEs), ultimately fostering economic development and providing a reference for future researchers exploring similar topics.

HIV drug resistance among patients experiencing antiretroviral therapy failure in Russia, 2019–2021

Increasing HIV drug resistance is an important public health concern. The current study aimed to assess HIV drug resistance among people who live with HIV (PLWH) experiencing virological failure.Blood samples and epidemiological characteristics were collected in four Siberian regions from PLWH experiencing ART failure. Partial pol gene sequences were obtained for the study individuals. Drug resistance mutations (DRMs) were predicted using the Stanford HIVdb Program. The association of HIV DRM with epidemiological characteristics was estimated using logistic regression analysis. Further analysis was performed for children (0–14 y old) and adults (≥15 y old) separately.In total, 815 (89.4%) patients were included in the final dataset. Overall, 501 (61.5%) patients had DRM detected. NRTI DRM was more common in children, while NRTI+NNRTI DRM was more frequent in adults (P < 0.001). Krasnoyarsk region, male sex and high viral load were positively associated with the presence of DRM in adults, while higher CD4 cell count and PI/INSTI-based ART had a negative association. No association between epidemiological characteristics and DRM was identified in children. The remaining 38.5% of patients with virological failure had no DRM detected; those patients were likely to have insufficient ART adherence. Most (55.5%) patients had HIV CRF63_02A6, followed by sub-subtype A6 (39.2%).This study revealed poor ART adherence as a main factor driving ART failure among PLWH in the Siberian region. DRM was detected in over 60% of PLWH experiencing ART failure. The current results highlight an urgent need for the introduction of special programs focusing on ART adherence improvement.

Creating a spatially continuous air temperature dataset for Taiwan using thermal remote-sensing data and machine learning algorithms

Weather stations can provide accurate and high temporal resolution air temperature (Ta) measurements, but their limited spatial coverage due to sparse distribution poses an issue and challenge. However, satellite data can offer land surface temperature (LST) observations with high spatial coverage, which have a strong relationship with Ta, making them ideal for enhancing Ta estimation. This study uses satellite-derived and auxiliary data to create a monthly mean Ta dataset with a 1 km resolution over Taiwan from 2003 to 2020. We employed three machine learning (ML) algorithms and seven different datasets comprising 12 explanatory variables with LST obtained from the MODIS to find the optimal combination of algorithm and dataset for Ta estimation in Taiwan. We applied recursive feature elimination (RFE) to reduce the model complexity and overfitting issues. For model assessment, we used five-fold cross-validation to evaluate the ML models, and indicators such as the coefficient of determination (R2), mean absolute error (MAE), and root mean square of error (RMSE) were employed. The results show that the XGB regressor performed the best among the three models with the highest accuracy. The RFE using the XGB model suggested eight selected variables, including nighttime LST, daytime LST, elevation, longitude, latitude, distance to the sea, month, and year. Based on the variance importance analysis, nighttime LST was the most crucial variable, followed by daytime LST and month. We found that the final monthly Ta dataset using the XGB model had an excellent five-fold cross-validated performance (R2 = 0.986, MAE = 0.477 °C, and RMSE = 0.639 °C). Furthermore, the XGB model not only performed well throughout all four seasons but also had high and consistent accuracy across months, years, and subsets, indicating its potential for accurately estimating Ta in Taiwan's complex topographic features with varying climate conditions. The resulting monthly Ta dataset created by our model can be an essential input for environmental studies.

An AI-based Image Recognition System for Early Detection of Forest and Field Fires

Forest fires and field fires (agricultural areas, grasslands, etc.) have severe global implications, causing significant environmental and economic harm. Traditional fire detection methods often rely on human personnel, which can pose safety risks and reduce their efficiency in large-scale monitoring. There is an urgent need for real-time fire detection technology to address these challenges and minimize losses. In this research, we propose the utilization of artificial intelligence techniques, specifically Deep Learning with Convolutional Neural Networks (CNN), to tackle this issue. Our proposed system analyzes real-time images captured by IP cameras and stored on a cloud server. Its primary objective is to detect signs of fires and promptly notify users through a mobile application, ensuring timely awareness. We meticulously assembled a dataset to train our model by merging three existing datasets comprising both fire and non-fire images. Also, we incorporated images that could potentially be misinterpreted as fire, such as red trees, individuals wearing red clothing, and red flags. Furthermore, we supplemented the dataset with images of unaffected areas obtained from online sources. The final dataset consisted of 1,588 fire images and 909 non-fire images. During evaluations, our model achieved an accuracy of 93.07%. This enables effective detection, thus rapid intervention and damage reduction. It is a proactive and preventive solution to combat these devastating fires.

Multi-institutional investigation into the robustness of intra-cranial multi-target stereotactic radiosurgery plans to delivery errors.

The use of modulated techniques for intra-cranial stereotactic radiosurgery (SRS) results in highly modulated fields with small apertures, which may be susceptible to uncertainties in the delivery device. This study aimed to quantify the impact of simulated delivery errors on treatment plan dosimetry and how this is affected by treatment planning system (TPS), plan geometry, delivery technique, and plan complexity. A beam modelling error was also included as context to the dose uncertainties due to treatment delivery errors. Delivery errors were assessed for multiple-target brain SRS plans obtained through the Trans-Tasman Radiation Oncology Group (TROG) international treatment planning challenge (2018). The challenge dataset consisted of five intra-cranial targets, each with a prescription of 20Gy. Of the final dataset of 54 plans, 51 were created using the volumetric modulated arc therapy (VMAT) technique and three used intensity modulated arc therapy (IMRT). Thirty-five plans were from the Varian Eclipse TPS, 17 from Elekta Monaco TPS, and one plan each from RayStation and Philips Pinnacle TPS. The errors introduced included: monitor unit calibration errors, multi-leaf collimator (MLC) bank offset, single MLC leaf offset, couch rotations, and collimator rotations. Dosimetric leaf gap (DLG) error was also included as a beam modelling error. Dose to targets was assessed via dose covering 98% of planning target volume (PTV) (D98%), dose covering 2% of PTV (D2%), and dose covering 99% of gross tumor volume (GTV) (D99%). Dose to organs at risk (OARs) was assessed using the volume of normal brain receiving 12Gy (V12Gy), mean dose to normal brain, and maximum dose covering 0.03cc brainstem (D0.03cc). Plan complexity was also assessed via edge metric, modulation complexity score (MCS), mean MLC gap, mean MLC speed, and plan modulation (PM). PTV D98% showed high robustness on average to most errors with the exception of a bank shift of 1.0mm and large rotational errors ≥1.0° for either the couch or collimator. However, in some cases, errors close to or within generally accepted machine tolerances resulted in clinically relevant impacts. The greatest impact upon normal brain V12Gy, mean dose to normal brain, and D0.03cc brainstem was found for DLG error in alignment with other recent studies. All delivery errors had on average a minimal impact across these parameters. Comparing plans from the Monaco TPS and the Eclipse TPS, showed a lesser increase to V12Gy, mean dose to normal brain, and D0.03cc brainstem for Monaco plans (p<0.01) when DLG error was simulated. Monaco plans also correlated to lower plan complexity. Using Spearman's correlation coefficient (r) a strong negative correlation (r≤-0.8) was found between the mean MLC gap and dose to OARs for DLG errors. Reducing MLC complexity and using larger mean MLC gaps is recommended to improve plan robustness and reduce sensitivity to delivery and modelling errors. For cases in which the calculated dose distribution or dose indices are close to the clinically acceptable limits, this is especially important.

Truth-telling to the seriously ill child - Nurses' experiences, attitudes, and beliefs.

Nurses play an integral role in the care of children hospitalised with a serious illness. Although information about diagnostics, treatments, and prognosis are generally conveyed to parents and caregivers of seriously ill children by physicians, nurses spend a significant amount of time at the child's bedside and have an acknowledged role in helping patients and families understand the information that they have been given by a doctor. Hence, the ethical role of the nurse in truth disclosure to children is worth exploring. A systematic academic database and grey literature search strategy was conducted using CINAHL, Medline Psych Info, and Google Scholar. Keywords used included truth, children, nurse, disclosure, serious illness, and communication. A total of 17 publications of varying types were included in the final data set. As this was a review of the literature, there were no direct human participants. Empirical studies included in the review had received ethics approval. Of the 17 articles included in the review, only one directly reported on the experiences of nurses asked to withhold the truth from patients. Empirical studies were limited to HIV-positive children and children diagnosed with cancer and the dying child. A paucity of literature exploring the experiences, attitudes, and beliefs of nurses with regard to truth-telling to seriously ill children is evident. Little consideration has been given to the role nurses play in communicating medical information to children in a hospital setting. The 17 articles included in the review focused on cancer, and HIV, diagnosis, and end-of-life care. Further research should be undertaken to explore the experiences and attitudes of nurses to clinical information sharing to children hospitalised with a wide range of serious illnesses and in diverse clinical scenarios.

Comparison of state-of-the-art deep learning architectures for detection of freezing of gait in Parkinson's disease.

Freezing of gait (FOG) is one of the most debilitating motor symptoms experienced by patients with Parkinson's disease (PD). FOG detection is possible using acceleration data from wearable sensors, and a convolutional neural network (CNN) is often used to determine the presence of FOG epochs. We compared the performance of a standard CNN for the detection of FOG with two more complex networks, which are well suited for time series data, the MiniRocket and the InceptionTime. We combined acceleration data of people with PD across four studies. The final data set was split into a training (80%) and hold-out test (20%) set. A fifth study was included as an unseen test set. The data were windowed (2 s) and five-fold cross-validation was applied. The CNN, MiniRocket, and InceptionTime models were evaluated using a receiver operating characteristic (ROC) curve and its area under the curve (AUC). Multiple sensor configurations were evaluated for the best model. The geometric mean was subsequently calculated to select the optimal threshold. The selected model and threshold were evaluated on the hold-out and unseen test set. A total of 70 participants (23.7 h, 9% FOG) were included in this study for training and testing, and in addition, 10 participants provided an unseen test set (2.4 h, 11% FOG). The CNN performed best (AUC = 0.86) in comparison to the InceptionTime (AUC = 0.82) and MiniRocket (AUC = 0.76) models. For the CNN, we found a similar performance for a seven-sensor configuration (lumbar, upper and lower legs and feet; AUC = 0.86), six-sensor configuration (upper and lower legs and feet; AUC = 0.87), and two-sensor configuration (lower legs; AUC = 0.86). The optimal threshold of 0.45 resulted in a sensitivity of 77% and a specificity of 58% for the hold-out set (AUC = 0.72), and a sensitivity of 85% and a specificity of 68% for the unseen test set (AUC = 0.90). We confirmed that deep learning can be used to detect FOG in a large, heterogeneous dataset. The CNN model outperformed more complex networks. This model could be employed in future personalized interventions, with the ultimate goal of using automated FOG detection to trigger real-time cues to alleviate FOG in daily life.

Social media influencers: literature review, trends and research agenda

PurposeThis study aims to provide a holistic review of social media influencers (SMIs) research based on a unique approach of bibliometric analysis and content analysis between 2011 and 2020. The review examines the main influential aspects, themes and research streams to identify research directions for the future.Design/methodology/approachThe sample selection and data collection were done from the Scopus database. The sample dataset was refined based on the inclusion and exclusion criteria to determine the final dataset of 183 articles. The dataset was exported in the BibTeX format and then imported into the BiblioShiny app for bibliometric analysis. The content analysis was done following the theory-context-methodology framework.FindingsThe several findings of this study include (1) Co-word analysis of most used keywords; (2) Longitudinal thematic evolution; (3) The focus of the research papers as per the theory-context-methodology review protocol are persuasion knowledge model, fashion and beauty industries, Instagram and content analysis, respectively; and (4) The network analysis of the research studies is known as the co-citation analysis and depicts the intellectual structure in the domain. This analysis resulted in four clusters of the research streams from the literature and two emergent themes (Chen et al., 2010)Originality/valueIn general, the previous reviews in the area are either domain, method or theory-based. Thus, this study aims to complement and extend the existing literature by presenting the overall picture of the SMI research with the help of a unique combined approach and further highlighting the trends and future research directions based on the findings of this study.

Shedding light on the relationship between ESG ratings and systematic risk

The role of environmental, social and governance (ESG) factors is growing in importance in finance studies. However, the relationship of this role with systematic risk is largely controversial and underinvestigated. We study this relationship considering a 17-year time span from 2005 to 2021 using a sample of all active constituent companies of the NYSE, with a final dataset of 791 firms for a total of 7,177 firm-year observations. The findings indicate that the relationship between overall ESG score and systematic risk takes an inverted U-shape. These results are solid considering ESG components at both an aggregate and disaggregate level. When companies invest little in ESG, they could be viewed as having more funds to allocate in, presumably, more rewarding projects and are more flexible and less risky, at least in the short term. However, beyond a certain threshold, more attention to sustainability facets results in lower systematic risk. These findings are also supported by checking them through different robustness tests.

Year-long buoy-based observations of the air–sea transition zone off the US west coast

Abstract. Two buoys equipped with Doppler lidars owned by the US Department of Energy (DOE) were deployed off the coast of California in autumn of 2020 by Pacific Northwest National Laboratory. The buoys collected data for an entire annual cycle at two offshore locations proposed for offshore wind development by the Bureau of Ocean Energy Management. One of the buoys was deployed approximately 50 km off the coast near Morro Bay in central California in 1100 m of water. The second buoy was deployed approximately 40 km off Humboldt County in northern California in 625 m of water. The buoys provided the first-ever continuous measurements of the air–sea transition zone off the coast of California. The atmospheric and oceanographic characteristics of the area and estimates of annual energy production at both the Morro Bay and Humboldt wind energy areas show that both locations have a high wind energy yield and are prime locations for future floating offshore wind turbines. This article provides a description and comprehensive analysis of the data collected by the buoys, and a final post-processed dataset is uploaded to a data archive maintained by the DOE. Additional analysis was conducted to show the value of the data collected by the DOE buoys. All post-processed data from this study are available on the Wind Data Hub website: https://a2e.energy.gov/data# (last access: 14 September 2023). Near-surface, wave, current, and cloud datasets for Humboldt and Morro Bay are provided at https://doi.org/10.21947/1783807 (Krishnamurthy and Sheridan, 2023b) and https://doi.org/10.21947/1959715 (Krishnamurthy and Sheridan, 2023a), respectively. Lidar datasets for Humboldt and Morro Bay are provided at https://doi.org/10.21947/1783809 (Krishnamurthy and Sheridan, 2023d) and https://doi.org/10.21947/1959721 (Krishnamurthy and Sheridan, 2023c), respectively.

FAMILY INVOLVEMENT IN EDUCATION OF SPECIAL NEEDS STUDENTS: A SYSTEMATIC LITERATURE REVIEW

Family involvement is an essential element in the education concerning special needs students, and this systematic literature review is focused on investigating its significance and impact. It investigates the factors and elements that influence family involvement, its impact on student outcomes, and strategies for bolstering collaboration between families and educational institutions. While the importance of family involvement is widely recognized, challenges and knowledge gaps remain regarding its best practices and effects. The study utilized the PRISMA approach, conducting a systematic review by scouring academic databases and journals for pertinent articles published within the last five years. The systematic search employed academic databases like Scopus and Web Of Science. After rigorous searching, the final dataset comprised (n=24) studies. Three core themes emerged from this approach: Teacher-Parent Collaboration, Barriers and Challenges, and Effects on the Achievement of Students. The review underscores essential components of family involvement, including communication, collaboration, shared decision-making, and parent-teacher partnerships. It provides an in-depth exploration of how family engagement positively affects students' academic and socio-emotional development while also shedding light on the obstacles and hindrances that can disrupt productive family-school collaborations. In conclusion, family involvement plays a pivotal role in the education of special needs students, significantly contributing to their holistic development. This review offers valuable insights for policymakers, educators, and researchers to further fortify family involvement in special education.

ETHICAL LEADERSHIP PRACTICE IN EDUCATION: A SIGNIFICANT SYSTEMATIC REVIEW

This systematic literature review explores the significance of ethical leadership practices in the context of education. The introduction provides an overview of the importance of ethical leadership within educational settings. However, these leaders contend with complex moral dilemmas, navigating the delicate balance between adhering to laws and potentially disobeying some, which could result in a competitive disadvantage. This study employed the PRISMA methodology, conducting a systematic search across academic databases, specifically Scopus and the Web of Science, resulting in a comprehensive selection of studies, reviews, and articles pertaining to ethical leadership in educational contexts. By utilizing an advanced search strategy involving keywords such as 'ethical leadership,' 'ethical management,' and 'education,' the analysis revealed the pivotal role played by ethical leadership in educational settings and organizations, contributing to higher-quality leadership. The final dataset consisted of 35 items, with the review identifying key themes that were subsequently validated by experts and categorized into three distinct themes: (1) the impact of ethical leadership on workplace behavior, (2) ethical leadership within educational contexts, and (3) ethical leadership across diverse industries and sectors. In conclusion, ethical leadership stands as a foundational element of responsible and effective leadership within the contemporary educational system.

Car Full View Dataset: Fine-Grained Predictions of Car Orientation from Images

The orientation of objects plays an important role in accurate predictions for the tasks of classification, detection, and trajectory estimation. This is especially important in the automotive domain, where estimating an accurate car orientation can significantly impact the effectiveness of the other prediction tasks. This work presents Car Full View (CFV), a novel dataset for car orientation prediction from images obtained by video recording all possible angles of individual vehicles in diverse scenarios. We developed a tool to semi-automatically annotate all the video frames with the respective car angle based on the walking speed of the recorder and manually annotated key angles. The final dataset contains over 23,000 images of individual cars along with fine-grained angle annotations. We study the performance of three state-of-the-art deep learning architectures on this dataset in three different learning settings: classification, regression, and multi-objective. The top result of 3.39° in circular mean absolute error (CMAE) shows that the model accurately predicts car orientations for unseen vehicles and images. Furthermore, we test the trained models on images from two different datasets and show their generalization capability to realistic images. We release the dataset and the best models while publishing a web service to annotate new images.