Miracle-dz/UniFAR-FTUs huggingface dataset:
A Systematic Literature Review of Organizational Performance through Talent Management Strategies

This research aims to deeply and thoroughly analyze various talent management strategies that have been proposed and implemented in the related literature. The research method used is Systematic Literature Review (SLR) by following the PRISMA stages to collect, review, and synthesize key findings related to optimizing organizational performance through talent management strategies over the past five years (2018-2023). This research will focus on identifying strategies that have proven successful in creating a work environment that supports the development and optimal utilization of employee potential. By involving concepts such as recruitment, employee development, retention, and overall talent management, this research aims to provide a holistic view of how organizations can integrate talent management strategies to achieve optimal performance goals. The results of this study are expected to provide valuable insights for business practitioners, HR managers, and researchers in the development and implementation of effective talent management strategies. In addition, this research can serve as a foundation for the development of conceptual models that can be adapted by organizations in the face of changing market dynamics and competition.
Continuous Online Learning-Based CSI Feedback in Massive MIMO Systems

For massive multiple-input multiple-output (MIMO) systems, downlink channel state information (CSI) compression and feedback are crucial for enhancing system performance. Deep learning (DL)-based methods have been designed and proven to perform well in this task. However, the distribution of CSI in real-world communication systems may differ from the one observed during model training, which can undermine the effectiveness of DL-based methods due to their limited generalization ability. Several methods have been proposed to facilitate online training and enable network adaptation to unknown scenarios. Nevertheless, the knowledge learned from previous scenarios is often forgotten, leading to performance degradation when encountering a previous scenario again. In this letter, we propose a novel continuous learning-based CSI feedback approach, which can effectively address the challenge of catastrophic forgetting and ensure consistent high performances across all historical scenarios, thereby enhancing the generalization capability of the model.
Comprehensive Assessment and Early Prediction of Gross Motor Performance in Toddlers With Graph Convolutional Networks–Based Deep Learning: Development and Validation Study

Background Accurate and timely assessment of children’s developmental status is crucial for early diagnosis and intervention. More accurate and automated developmental assessments are essential due to the lack of trained health care providers and imprecise parental reporting. In various areas of development, gross motor development in toddlers is known to be predictive of subsequent childhood developments. Objective The purpose of this study was to develop a model to assess gross motor behavior and integrate the results to determine the overall gross motor status of toddlers. This study also aimed to identify behaviors that are important in the assessment of overall gross motor skills and detect critical moments and important body parts for the assessment of each behavior. Methods We used behavioral videos of toddlers aged 18-35 months. To assess gross motor development, we selected 4 behaviors (climb up the stairs, go down the stairs, throw the ball, and stand on 1 foot) that have been validated with the Korean Developmental Screening Test for Infants and Children. In the child behavior videos, we estimated each child’s position as a bounding box and extracted human keypoints within the box. In the first stage, the videos with the extracted human keypoints of each behavior were evaluated separately using a graph convolutional networks (GCN)–based algorithm. The probability values obtained for each label in the first-stage model were used as input for the second-stage model, the extreme gradient boosting (XGBoost) algorithm, to predict the overall gross motor status. For interpretability, we used gradient-weighted class activation mapping (Grad-CAM) to identify important moments and relevant body parts during the movements. The Shapley additive explanations method was used for the assessment of variable importance, to determine the movements that contributed the most to the overall developmental assessment. Results Behavioral videos of 4 gross motor skills were collected from 147 children, resulting in a total of 2395 videos. The stage-1 GCN model to evaluate each behavior had an area under the receiver operating characteristic curve (AUROC) of 0.79 to 0.90. Keypoint-mapping Grad-CAM visualization identified important moments in each behavior and differences in important body parts. The stage-2 XGBoost model to assess the overall gross motor status had an AUROC of 0.90. Among the 4 behaviors, “go down the stairs” contributed the most to the overall developmental assessment. Conclusions Using movement videos of toddlers aged 18-35 months, we developed objective and automated models to evaluate each behavior and assess each child’s overall gross motor performance. We identified the important behaviors for assessing gross motor performance and developed methods to recognize important moments and body parts while evaluating gross motor performance.
Intensity Distribution of Collegiate Cross-Country Competitions

The primary purpose of the current investigation was to perform an intensity distribution analysis of a collegiate cross-country (CC) competition, with a secondary purpose to compare race times (RT) with modeled performance times (MPT). Participants completed an incremental treadmill test to determine gas exchange threshold (GET), while the three-minute all-out test was conducted on a 400 m outdoor track to determine critical velocity (CV) and D prime (D′). GET and CV were used as physiological markers for the intensity zones based on heart rate (HR) and running velocity (RV), while CV and D′ were used to determine modeled performance times. Participants wore a Global Positioning System (GPS) watch and heart rate (HR) monitor during competition races. Statistically, less time was spent in HR Zone 1 (12.1% ± 13.7%) compared to Zones 2 (37.6% ± 30.2%) and 3 (50.3% ± 33.7%), while a statically greater amount of time was spent in RV Zone 2 (75.0% ± 20.7%) compared to Zones 1 (8.4% ± 14.0%) and 3 (16.7% ± 19.1%). RTs (1499.5 ± 248.5 seconds (s)) were statistically slower compared to MPTs (1359.6 ± 192.7 s). The observed differences in time spent in each zone are speculated to be related to the influence of environmental conditions on internal metrics and difference in the kinetics of HR and running velocity. Differences in RTs and MPTs are likely due to the MPT equation modeling all-out performance and not considering race strategies.
CONTEMPORARY SALT-MARSH FORAMINIFERAL DISTRIBUTION FROM THE ADRIATIC COAST OF CROATIA AND ITS POTENTIAL FOR SEA-LEVEL STUDIES

Salt-marsh foraminifera serve as proxy sea-level indicators due to a quantifiable relationship with elevation in the contemporary environment. In this paper, we document the distribution of salt-marsh foraminifera from two microtidal sites, Jadrtovac and Blace, along the Adriatic coast of Croatia and assess their suitability as proxies for elevation in transfer-function-based reconstructions of sea level, which has so far evaded the Mediterranean region. The assemblages are dominated by typical salt-marsh agglutinated taxa, Jadammina macrescens and Trochammina inflata, and the calcareous taxa Ammonia spp. and Quinqueloculina spp. Quantitative analyses revealed that the assemblages are divided into three faunal zones, which are elevation dependent, and where an assemblage dominated by J. macrescens and T. inflata extends to higher elevations in the intertidal frame. The training set was used to develop a tidal- level transfer function using linear regression due to the short environmental gradients observed. The model predicts sea level with a precision of ± 0.08 m. This study highlights the strong potential of salt-marsh foraminifera in reconstructing RSL trends for the Mediterranean region, where studies of past sea-level have previously been restricted to other indicators.
Real-time acoustic holography with physics-reinforced contrastive learning for acoustic field reconstruction

Acoustic holography (AH) provides a promising technique for arbitrary acoustic field reconstruction, supporting many applications like robotic micro-nano manipulation, neuromodulation, volumetric imaging, and virtual reality. In AH, three-dimensional (3D) acoustic fields quantified with complex-valued acoustic pressures are reconstructed by virtue of two-dimensional (2D) acoustic holograms. Phase-only hologram (POH) is recently regarded as an energy-efficient way for AH, which is typically implemented by a dynamically programmable phased array of transducers (PATs). As a result, spatiotemporal precise acoustic field reconstruction is enabled by precise, dynamic, and individual actuation of PAT. Thus, 2D POH is required per arbitrary acoustic fields, which can be viewed as a physical inverse problem. However, solving the aforementioned physical inverse problem in numerical manners poses challenges due to its non-linear, high-dimensional, and complex coupling natures. The existing iterative algorithms like the iterative angular spectrum approach (IASA) and iterative backpropagation (IB) still suffer from speed-accuracy trade-offs. Hence, this paper explores a novel physics-iterative-reinforced deep learning method, in which frequency-argument contrastive learning is proposed facilitated by the inherent physical nature of AH, and the energy conservation law is under consideration. The experimental results demonstrate the effectiveness of the proposed method for acoustic field reconstruction, highlighting its significant potential in the domain of acoustics, and pushing forward the combination of physics into deep learning.
MicroRNAs as Potential Graft Rejection or Tolerance Biomarkers and Their Dilemma in Clinical Routines Behaving like Devilish, Angelic, or Frightening Elements

Allograft rejection is a widespread complication in allograft recipients with chronic kidney disease. Undertreatment of subclinical and clinical rejection and later post-transplant problems are caused by an imperfect understanding of the mechanisms at play and a lack of adequate diagnostic tools. Many different biomarkers have been analyzed and proposed to detect and monitor these crucial events in transplant outcomes. In this sense, microRNAs may help diagnose rejection or tolerance and indicate appropriate treatment, especially in patients with chronic allograft rejection. As key epigenetic regulators of physiological homeostasis, microRNAs have therapeutic potential and may indicate allograft tolerance or rejection. However, more evidence and clinical validation are indispensable before microRNAs are ready for clinical prime time.
Implementation of Schnorr Signcryption Algorithm on DSP

The lack of data security in public mobile telecommunication system increases the need for a strong data protection and security mechanisms. Cryptography is considered as one of the key elements that provides security for mobile systems. Signcryption algorithm, which is based on public key cryptography, provides both confidentiality and authenticity in one step. Moreover, hardware implementations of cryptographic algorithms provide scalable solutions to enhance the level of security of the existing hardware. In this paper, we present a case study on using a DSP board to construct a secure communication channel. We use the constructed DSP board as a hardware cryptosystem to increase the security of transmitted data using any mobile communication system. We evaluate the performance by computing the consumed time by encryption/decryption process, while implementing the Schnorr Signcryption scheme on a DSP. Furtheremore, we propose an enhanced model of applying multiple DSP using pipelines and parallelization technique to decrease the consumed time in the whole process.
Association of Barrett's esophagus with obstructive sleep apnea syndrome: a bidirectional analysis of Mendelian randomization

Background Observational studies have reported associations between Barrett's esophagus (BE) and obstructive sleep apnea syndrome (OSAS), but the causal relationship remained unclear due to potential confounding biases. Our study aimed to elucidate this causal relationship by deploying a two-sample Mendelian randomization (MR) methodology. Methods Instrumental variables (IVs) for Barrett's esophagus were obtained from a public database that comprised 13,358 cases and 43,071 controls. To investigate OSAS, we utilized summary statistics from a comprehensive genome-wide association study (GWAS) encompassing 38,998 cases of OSAS and 336,659 controls. Our MR analyses adopted multiple techniques, including inverse variance weighted (IVW), weighted median, weighted mode, MR-Egger, and simple mode. Results The IVW analysis established a causal relationship between Barrett's esophagus and OSAS, with an odds ratio (OR) of 1.19 and a 95% confidence interval (CI) of 1.11–1.28 (p = 8.88E-07). Furthermore, OSAS was identified as a contributing factor to the onset of Barrett's esophagus, with an OR of 1.44 and a 95% CI of 1.33–1.57 (p = 7.74E-19). Notably, the MR–Egger intercept test found no evidence of directional pleiotropy (p > 0.05). Conclusion This study identifies a potential association between BE and an increased occurrence of OSAS, as well as the reverse relationship. These insights could influence future screening protocols and prevention strategies for both conditions.
A PPN-Based Improved QAM-FBMC System With Jointly Optimized Mismatched Prototype Filters

Recently, it is shown that a spectrally-efficient low-complexity quadrature amplitude modulation (QAM) filter-bank multicarrier (FBMC) system can be designed by relaxing the constraints on the time-frequency (TF) product at the transmitter (TX) and the time-domain localization at the receiver (RX). However, it turns out that the attainable signal-to-interference-plus-noise ratio (SINR) is not satisfactory enough to support certain high-order modulations. This is because, given the PHYDYAS TX prototype filter, only the RX filter is optimized under a sparsity constraint. In this paper, we propose an improved QAM-FBMC system to further reduce self-interference by jointly optimizing the TX and RX prototype filters. The sparsity constraint on the RX prototype filter is now removed, and a new polyphase network (PPN)-based structure is introduced to maintain the complexity at the RX to almost the same level. The joint optimization is formulated as an approximate SINR maximization and converted to a line search, under the constraint on the fall-off rate of the TX prototype filter for high spectral confinement. For each search-parameter value which is a lower bound on the post-processing signal-to-noise ratio (SNR), the prototype filters are optimized to maximize the signal-to-interference ratio (SIR). The line search stops at a saturation point of the SINR, and the pair of fixed TX and RX filters obtained at the point is used for all SNR ranges. Numerical results show that the prototype filters combined with the PPN-based structure achieve low self-interference and lead to a spectrally-efficient low-complexity QAM-FBMC system.
Evolution of trace elements in the planetary boundary layer in southern China: Effects of dust storms and aerosol‐cloud interactions

Aerosols and cloud water were analyzed at a mountaintop in the planetary boundary layer in southern China during March–May 2009, when two Asian dust storms occurred, to investigate the effects of aerosol‐cloud interactions (ACIs) on chemical evolution of atmospheric trace elements. Fe, Al, and Zn predominated in both coarse and fine aerosols, followed by high concentrations of toxic Pb, As, and Cd. Most of these aerosol trace elements, which were affected by dust storms, exhibited various increases in concentrations but consistent decreases in solubility. Zn, Fe, Al, and Pb were the most abundant trace elements in cloud water. The trace element concentrations exhibited logarithmic inverse relationships with the cloud liquid water content and were found highly pH dependent with minimum concentrations at the threshold of pH ~5.0. The calculation of Visual MINTEQ model showed that 80.7–96.3% of Fe(II), Zn(II), Pb(II), and Cu(II) existed in divalent free ions, while 71.7% of Fe(III) and 71.5% of Al(III) were complexed by oxalate and fluoride, respectively. ACIs could markedly change the speciation distributions of trace elements in cloud water by pH modification. The in‐cloud scavenging of aerosol trace elements likely reached a peak after the first 2–3 h of cloud processing, with scavenging ratios between 0.12 for Cr and 0.57 for Pb. The increases of the trace element solubility (4–33%) were determined in both in‐cloud aerosols and postcloud aerosols. These results indicated the significant importance of aerosol‐cloud interactions to the evolution of trace elements during the first several cloud condensation/evaporation cycles.
Identification of hub genes in heart failure by integrated bioinformatics analysis and machine learning

Objective To screen feature genes of heart failure patients through machine learning methods, in order to identify characteristic genes driving heart failure and investigate the progression of heart failure Methods Heart failure patient samples were downloaded from the public database GEO (Gene Expression Omnibus), including the datasets GSE116250, GSE120895, and GSE59867. GSE116250 and GSE120895 were used as the testing set, while GSE59867 was used as the validation set. LASSO regression analysis and SVM-RFE were utilized to identify feature genes. Results Analysis showed that among the differentially expressed genes between normal and heart failure patients, 9 genes were upregulated and 10 genes were downregulated. ROC curve analysis in the training set showed that TAGLN and SGPP2 had AUC values greater than 0.7. Moreover, SDSL and SMTNL2 had even higher AUC values of greater than 0.9. However, further analysis in the validation set showed that only SDSL had an AUC value greater than 0.7. Western blot experiments, RT-PCR, and ISO-induced experiments confirmed that SDSL was highly expressed in heart failure patients and promoted heart failure progression. In addition, SDSL promoted PARP1 expression and knockdown of SDSL expression led to decreased Cleaved-PARP1 expression and reduced cardiomyocyte apoptosis. Conversely, overexpression of SDSL resulted in increased PARP1 expression and myocardial cell apoptosis. These results suggest that elevated expression of SDSL in cardiomyocytes from heart failure patients may be an important factor promoting the occurrence and development of heart failure. Conclusions Using machine learning methods and experimental validation, it has been demonstrated that SDSL is a driving gene in patients with heart failure, providing a new treatment direction for clinical treatment.
Brain tumor segmentation using U-Net in conjunction with EfficientNet

According to the Ten Leading Causes of Death Statistics Report by the Ministry of Health and Welfare in 2021, cancer ranks as the leading cause of mortality. Among them, pleomorphic glioblastoma is a common type of brain cancer. Brain cancer often occurs in the brain with unclear boundaries from normal brain tissue, necessitating assistance from experienced doctors to distinguish brain tumors before surgical resection to avoid damaging critical neural structures. In recent years, with the advancement of deep learning (DL) technology, artificial intelligence (AI) plays a vital role in disease diagnosis, especially in the field of image segmentation. This technology can aid doctors in locating and measuring brain tumors, while significantly reducing manpower and time costs. Currently, U-Net is one of the primary image segmentation techniques. It utilizes skip connections to combine high-level and low-level feature information, leading to significant improvements in segmentation accuracy. To further enhance the model’s performance, this study explores the feasibility of using EfficientNetV2 as an encoder in combination with U-net. Experimental results indicate that employing EfficientNetV2 as an encoder together with U-net can improve the segmentation model’s Dice score (loss = 0.0866, accuracy = 0.9977, and Dice similarity coefficient (DSC) = 0.9133).
Deep Learning Segmentation of Protein EM Maps

Protein Electron Microscopy (EM) maps are critical in determining the three-dimensional structures of bio-molecules, including proteins and their interactions. The task of identifying regions that correspond to specific proteins is challenging, but crucial for gaining insight into their function and designing drugs to enhance or suppress their processes. Conventional methods of protein EM map segmentation use algorithms that assign a voxel to a region, but can result in difficulties in obtaining a segmentation that maps each region to a single protein unit. Our approach incorporates an interactive mechanism that allows for user guidance of the segmentation process. Our deep learning model is trained on a dataset of protein EM maps and uses a U - Net architecture. Results show that our approach has potential to overcome limitations of traditional state-of-the-art approaches.
Are People With Type 1 Diabetes Mellitus Appropriately Following Insulin Injection Technique Practices: A Review of Literature

People with type 1 diabetes mellitus (T1DM) need to take multiple doses of insulin injections daily throughout their lives. However, a notable portion of people with diabetes mellitus (DM) show suboptimal insulin injection technique practices. They are supposed to follow the recommended insulin injection technique guidelines. Our explorative literature search, including studies from the past 30 years, is expected to identify the deficiencies of self-injection insulin techniques and the associated complications in people with T1DM, where we have summarised the overall incidence of complications that have occurred due to nonadherence of the prescribed guidelines, along with their associated risk factors. We have attempted to include multiple systematic reviews, meta-analyses, literature reviews, case reports, and original articles from the search engines and databases like PubMed, Scopus, ScienceDirect, Cochrane Library, Google Scholar, and BioMed Central, and studies with only human participants were included in this search. The knowledge sharing from this research may be utilised for enhancing the structured education diabetes programme and implementing the population-based corrective measures, including the thrust areas in future multi-centre longitudinal research studies and recommendations, which can prevent unnecessary complications and enhance their quality of life. Correct insulin administration technique, abstaining from administration of injection at the areas with lipohypertrophy, rotation of injection sites, and ultrasound scanning can be used as a complimentary method to detect the lipohypertrophy at an early stage. Liposuction is beneficial in reducing the extensive lipohypertrophic tissues but helps achieve only cosmetically satisfactory outcome; thus, empowering people to follow insulin injection technique guidelines is one of the best strategies to reduce the high prevalence of lipohypertrophy. To conclude, education among the people with DM, especially T1DM who have to take insulin regularly, needs to be carried out consistently in the clinical settings, to prevent the severe complications caused due to inappropriate insulin injection techniques.