Miracle-dz/UniFAR-FTUs huggingface dataset:
An Extreme Gradient Boosting-based Prediction for Depression

Depression, a pervasive mental health ailment impacting millions globally, necessitates precise and intelligible predictive methodologies. Physicians encounter formidable hurdles when screening large populations. Despite a plethora of proposed Artificial Intelligence (AI) techniques for depression prediction, creating transparent models remains a formidable challenge. In this investigation, we confront this challenge by constructing prediction models grounded in diverse machine-learning algorithms and employing the Hybrid Feature Selection (HFS) approach, fusing statistical hypothesis tests and LASSO. This process facilitates the identification of pivotal features enhancing the predictive model's efficacy. Consequently, the chosen features serve as inputs for our predictive model, enabling us to harness their predictive potential and delve into their connection with the depression target variable. The ensemble of models encompassed Extreme Gradient Boo sting (XGBoost), Light Gradient Boosting, Gradient Boosting, CatBoost, Ada Boost, Linear Discriminant Analysis, Ridge, Random Forest, Extra Trees, Logistic Regression, Decision Tree, K-Neighbors, Naive Bayes Gaussian, and Naive Bayes Bernoulli. These models underwent rigorous evaluation utilizing the Korea National Health and Nutrition Examination Survey (KNHANES) datasets. Our findings reveal that the XGBoost model outperformed its counterparts, achieving the highest accuracy (0.871), AUC (0.927), and F1 Score (0.673). Furthermore, our model sheds light on the intricate relationship between input variables and the predicted outcome, quantifying the influence of each input variable on the result.
Enhanced Thermodynamic Modeling of Converter Transformer Influenced by DC Bias

DC bias in high-voltage DC transformers, arising from converter operations and DC transmission, poses significant challenges to their performance. The detrimental effects of DC bias primarily manifest in increased temperature, jeopardizing the safe operation of the transformers. This article presents a novel approach by extending the utilization of the Thermal Equivalent Circuit (TEC) to accurately predict temperatures at different elements of a converter transformer under DC bias conditions. Specifically designed for a 240 MVA converter transformer, the TEC incorporates capacitances and dynamic resistances as model parameters. Additionally, an electro-thermal finite element model is implemented to comprehensively analyze the transformer's behavior under varying levels of DC bias. To estimate the TEC parameters, a hybrid GWO-CS (Grey Wolf Optimization – Cuckoo Search) algorithm is employed based on measured values. Furthermore, the paper highlights the impact of DC bias on the converter transformer's life expectancy, considering the aging acceleration factor.
Compact Circularly Polarized Metasurface Antennas for RFID Readers

Developing a circularly polarized (CP) antenna with a compact size while achieving good radiation characteristics for handheld radio frequency identification (RFID) readers is a very challenging task. This paper presents two CP antennas with compact size, high gain, and high front-to-back ratio characteristics. These proposed designs consist of a 2$\times$ 2 unit-cell metasurface and are excited by a Y-shaped patch. The first antenna has compact overall dimensions of 0.45$\lambda$$\times$ 0.45$\lambda$$\times$ 0.02$\lambda$, where $\lambda$ is the guided wavelength at the center frequency. The operating bandwidth is about 2.0%, the broadside gain is about 6.5 dBi, and the front-to-back ratio is about 22.8 dB. The second design has a more compact size of 0.36$\lambda$$\times$ 0.36$\lambda$$\times$ 0.02$\lambda$. This antenna has an operating bandwidth of 0.8%, a broadside gain of 5.9 dBi, and a front-to-back ratio of 11.8 dB.
Adenoviral Gene Therapy Vectors in Clinical Use—Basic Aspects with a Special Reference to Replication-Competent Adenovirus Formation and Its Impact on Clinical Safety

Adenoviral vectors are commonly used in clinical gene therapy. Apart from oncolytic adenoviruses, vector replication is highly undesired as it may pose a safety risk for the treated patient. Thus, careful monitoring for the formation of replication-competent adenoviruses (RCA) during vector manufacturing is required. To render adenoviruses replication deficient, their genomic E1 region is deleted. However, it has been known for a long time that during their propagation, some viruses will regain their replication capability by recombination in production cells, most commonly HEK293. Recently developed RCA assays have revealed that many clinical batches contain more RCA than previously assumed and allowed by regulatory authorities. The clinical significance of the higher RCA content has yet to be thoroughly evaluated. In this review, we summarize the biology of adenovirus vectors, their manufacturing methods, and the origins of RCA formed during HEK293-based vector production. Lastly, we share our experience using minimally RCA-positive serotype 5 adenoviral vectors based on observations from our clinical cardiovascular gene therapy studies.
PLaNe: Reverse Engineering of Planar Layouts to Gate-Level Netlists

With the continuous advancement of semiconductor manufacturing technology and the adoption of novel techniques, reverse engineering (RE) faces new challenges. Traditional optical images based analysis methods are no longer effective due to the diminished or non-existent separation between cells and the increasing usage of upper interconnect metal layers. This study presents a graph-based approach that successfully extracts a standard cell library and gate-level netlist from a layout description. The proposed flow involves converting the layout into an undirected transistor graph. Subsequently, a directed multigraph is generated by applying predefined rules of the hybrid flow. Library reconstruction is performed through clustering and graph isomorphism, resulting in our design's flat gate-level netlist description. To evaluate the effectiveness of our approach, we conducted tests on open-source designs using two open-source libraries. The results demonstrate a cluster performance exceeding 0.9 and a standard cell reconstruction yield ranging from 0.8 to 1. Additionally, our method exhibits commendable runtime performance, and we anticipate our technology-agnostic approach to be future-proof.
Entrepreneurship Training, Job Creation and Youth Empowerment

empowerment. The study relies solely on secondary sources from reputable books and journals. It has been determined that entrepreneurship plays a critical role in empowering young individuals, as enables them to enhance their skills and competence, thereby increasing their employment prospects and income levels. Moreover, it recognizes that entrepreneurship training encompasses interdisciplinary programs designed to cultivate an entrepreneurial mindset, foster innovation, and promote growth; these programs are instrumental in helping young people navigate social and economic challenges. The study proposes providing a comprehensive training approach for youth, particularly those residing in underdeveloped nations, while emphasizing the importance of integrating entrepreneurship policies into national plans and initiatives. Furthermore, the article suggests that development and educational institutions should implement structured entrepreneurship training programs to bolster employability by equipping young people with market-relevant competencies.
Feature Matching Machine for Cold-Start Recommendation

In recommendation systems, the cold-start issue is a long-standing problem where no historical interaction records are given for certain users or items. Under this circumstance, recommendations for new users or new items become challenging. To address this problem, most existing approaches seek to discover a latent common space for users and items. However, these methods require a strong assumption that a shared space exists where the distributions of users and items are identical, which may limit the recommendation performance. In this article, we propose a novel model called Feature Matching Machine (FMM) to learn latent informative user and item representations. Different from previous methods, for warm users (or items), FMM learns two kinds of latent features, i.e., one is constructed by a hypergraph auto-encoder based on historical interactions between users and items, and the other is built by a multi-layer perceptron based on users (or items). Subsequently, FMM matches these two latent feature representations so as to discover the relationships across users (or items) and cold-start items (or users). We conduct extensive experiments on several real-world datasets and compare the proposed method with well-known baseline methods. Promising results demonstrate the effectiveness and efficiency of the proposed model.
Cyberbullying on Instagram: How adolescents perceive risk in personal selfies?

The nature of social networking sites and the overconfidence and lack of awareness that characterize the adolescent stage favor the assumption of risk behaviors on Instagram (e.g., self-objectified photos, that is, photos showing sexualized body parts such as skin cleavage or the abdomen). Two experimental studies examine the social perceptions of adolescents regarding the risk of suffering from cyberbullying behaviors by posting selfies on Instagram. Study 1 (N = 373 adolescents) revealed that participants perceived more risk when an individual uploaded an objectified selfie (vs. a non-objectified). Participants perceived that the target will be more likely to suffer from cyberbullying behaviors when they: (a) uploaded on Instagram an objectified selfie (vs. a non-objectified) and (b) were female (vs. male). Study 2 (N = 210 adolescents) showed that when a girl (vs. a boy) uploaded an objectified selfie, participants perceived more risk in her behavior and perceived that she would be more likely to suffer from cyberbullying behaviors. Specifically, female adolescents (vs. males) perceived that a girl (vs. a boy) would be a target of cyberbullying behaviors to a greater extent. Finally, participants perceived that uploading an objectified photo was more risky when the target was a girl (vs. a boy), which led to suffering from cyberbullying behaviors to a greater extent. These results can help to further understand the risk to which adolescents are exposed on Instagram, being higher among adolescent girls. Therefore, this work highlights the importance of considering gender differences in the design of cyberbullying prevention programs.
An overlapping generations version of Krugman’s world’s smallest macroeconomic model and fiscal deficit

<p class="MsoNormal" style="margin-top: 12pt; text-align: justify;"><span lang="EN-US" style="font-family: arial, helvetica, sans-serif;">This paper attempts to introduce an overlapping generations structure into Paul Krugman's "The world's smallest macroeconomic model" (Krugman (1999)) to examine the implications of fiscal policy, particularly fiscal deficits, in a framework suitable for policy analysis. In that paper, Krugman argued that under the price rigidity assumption, a shortage in the money supply leads to underemployment and recession, so increasing the money supply would eliminate underemployment and restore full employment. But how can the money supply be increased? I show that in order to restore full employment out of a recession, a fiscal deficit is needed to increase the money supply. I also show that in a growing economy, fiscal deficits are necessary to maintain full employment at constant prices or inflation. Fiscal deficits are not only effective in pulling the economy out of recession, they are even necessary for growth to continue without recession or inflation. The fiscal deficit in this paper represents the difference between government spending and government revenues. If this difference is positive, we say that the government is in deficit. Krugman's original model is a one-period static model. I intend to extend this model to a dynamic overlapping generations model. </span></p>
Sequential Texts Driven Cohesive Motions Synthesis with Natural Transitions

The intelligent synthesis/generation of daily-life motion sequences is fundamental and urgently needed for many VR/metaverse-related applications. However, existing approaches commonly focus on monotonic motion generation (e.g., walking, jumping, etc.) based on single instruction-like text, which is still not intelligent enough and can’t meet practical demands. To this end, we propose a cohesive human motion sequence synthesis framework based on free-form sequential texts while ensuring semantic connection and natural transitions between adjacent motions. At the technical level, we explore the local-to-global semantic features of previous and current texts to extract relevant information. This information is used to guide the framework in understanding the semantics of the current moment. Moreover, we propose learnable tokens to adaptively learn the influence range of the previous motions towards natural transitions. These tokens can be trained to encode the relevant information into well-designed transition loss. To demonstrate the efficacy of our method, we conduct extensive experiments and comprehensive evaluations on the public dataset as well as a new dataset produced by us. All the experiments confirm that our method outperforms the state-of-the-art methods in terms of semantic matching, realism, and transition fluency. Our project is public available. https://druthrie.github. io/sequential-texts-to-motion/
Paleotemperature Analysis of the Plio-Pleistocene Section at Le Castella, Calabria, Southern Italy

The Plio-Pleistocene boundary has been located at the place where certain northern species of marine invertebrates first appear in the continuous Plio-Pleistocene sections of Italy. The section at Le Castella, near Crotone, southern Italy, consists of clays with diatomaceous layers and a few thin sand layers. The Plio-Pleistocene boundary there is clearly marked by the appearance of Anomalina baltica and other northern species of Foraminifera. Calabnan (lower Pleistocene) sediments 210m thick overlie the boundary, and upper Pliocene sediments 1405 m thick underlie it. The fossil fauna of the Plio-Pleistocene section consists essentially of abundant pelagic and benthonic Foraminifera, and diatoms. The benthonic microfauna indicates a depositional depth of about 500 m. There is no evidence of turbidity currents and submarine slumping. A section 167.80 m thick, including the paleontologically defined Plio-Pleistocene boundary, was logged and sampled at close stratigraphic intervals. Oxygen isotopic analyses of different species of pelagic and benthonic Foraminifera and of shell fragments of benthonic mollusks at successively higher stratigraphic positions in the section have revealed numerous temperature oscillations. Surface temperatures, probably representing summer averages, as indicated by Globigerinoides rubra and G. sacculifera, ranged from 21°C. to more than 30°C. in the late Pliocene, from 16°C. to more than 30°C. in the late Pliocene-early Pleistocene, and from 12° C. to 28° C. in the late Pleistocene. Temperatures at some depth and/or at a season other than the summer, as indicated by Globigerina bulloides and G. inflata, ranged from 20°C. to 28°C. in the late Pliocene, from 11°C. to 22°C. in the late Pliocene-early Pleistocene, and from 9°C. to 18°C. in the late Pleistocene. Temperatures given by benthonic Foraminifera ranged from 14°C. to 20°C. in the late Pliocene and from 11°C. to 22°C. in the late Pliocene-early Pleistocene (no data available for the late Pleistocene). Temperatures above 30°C. may be due, in part, to isotopic effects of the sea water. The above figures clearly indicate a major shift of the temperature ranges toward lower values from the late Pliocene to the late Pleistocene. No major temperature change seems to have occurred across the paleontologically defined Plio-Pleistocene boundary. Since the secular temperature minima of the late Pliocene are somewhat lower than present average summer temperature, an areal extension of the ice somewhat greater than that at present is suggested. Such extension probably increased at the times corresponding to the even lower temperature minima of the early Pleistocene. Major continental glaciations, however, may not have started until later, because none of the temperature minima in the section at Le Castella appear to have reached values as low as those obtaining during the glacial ages in the eastern Mediterranean. The temperature changes in the section at Le Castella are paralleled by important changes in the microfauna.
CBCT-to-CT Synthesis for Cervical Cancer Adaptive Radiotherapy via U-Net-Based Model Hierarchically Trained with Hybrid Dataset

Simple Summary Adaptive radiotherapy ensures precise radiation dose deposition to the target volume while minimizing radiation-induced toxicities. However, due to poor image quality and inaccurate HU values, it is currently challenging to realize adaptive radiotherapy with cone beam computed tomography (CBCT) images. Previous studies on CBCT image enhancement have rarely focused on cervical cancer and are often limited to training datasets from a single linear accelerator. The aim of this study is to develop a deep learning framework based on a hybrid dataset to enhance the quality of CBCT images and achieve accurate HU values, for application to cervical cancer adaptive radiotherapy. The synthetic pseudo-CT images generated by our model have accurate HU values and well-preserved edges of soft tissues. It can be further explored in multi-center datasets to promote its clinical applications. Abstract Purpose: To develop a deep learning framework based on a hybrid dataset to enhance the quality of CBCT images and obtain accurate HU values. Materials and Methods: A total of 228 cervical cancer patients treated in different LINACs were enrolled. We developed an encoder–decoder architecture with residual learning and skip connections. The model was hierarchically trained and validated on 5279 paired CBCT/planning CT images and tested on 1302 paired images. The mean absolute error (MAE), peak signal to noise ratio (PSNR), and structural similarity index (SSIM) were utilized to access the quality of the synthetic CT images generated by our model. Results: The MAE between synthetic CT images generated by our model and planning CT was 10.93 HU, compared to 50.02 HU for the CBCT images. The PSNR increased from 27.79 dB to 33.91 dB, and the SSIM increased from 0.76 to 0.90. Compared with synthetic CT images generated by the convolution neural networks with residual blocks, our model had superior performance both in qualitative and quantitative aspects. Conclusions: Our model could synthesize CT images with enhanced image quality and accurate HU values. The synthetic CT images preserved the edges of tissues well, which is important for downstream tasks in adaptive radiotherapy.
Autonomous and intelligent optical tweezers for improving the reliability and throughput of single particle analysis

Optical tweezers are an interesting tool to enable single cell analysis, especially when coupled with optical sensing and advanced computational methods. Nevertheless, such approaches are still hindered by system operation variability, and reduced amount of data, resulting in performance degradation when addressing new data sets. In this manuscript, we describe the deployment of an automatic and intelligent optical tweezers setup, capable of trapping, manipulating, and analyzing the physical properties of individual microscopic particles in an automatic and autonomous manner, at a rate of 4 particle per min, without user intervention. Reproducibility of particle identification with the help of machine learning algorithms is tested both for manual and automatic operation. The forward scattered signal of the trapped PMMA and PS particles was acquired over two days and used to train and test models based on the random forest classifier. With manual operation the system could initially distinguish between PMMA and PS with 90% accuracy. However, when using test datasets acquired on a different day it suffered a loss of accuracy around 24%. On the other hand, the automatic system could classify four types of particles with 79% accuracy maintaining performance (around 1% variation) even when tested with different datasets. Overall, the automated system shows an increased reproducibility and stability of the acquired signals allowing for the confirmation of the proportionality relationship expected between the particle size and its friction coefficient. These results demonstrate that this approach may support the development of future systems with increased throughput and reliability, for biosciences applications.
Standardized CPUE of bluefin tuna (Thunnus thynnus) caught by the Moroccan traps in the area of influence of the Strait of Gibraltar for the period 1986- 2006

SUMMARY Relative abundance indices of bluefin tuna (Thunnus thynnus) caught by the Moroccan traps in the area of influence of the Strait of Gibraltar for the period 1986- 2006 were estimated through a General Linear Modeling (GLM) approach under a negative binomial error distribution assumption.
Iris depigmentation and cytomegalovirus in aqueous humor as predictors of uveitic activity and recurrence in chronic and recurrent anterior uveitis

Purpose: To examine the clinical outcomes in chronic or recurrent anterior uveitis in the presence or absence of cytomegalovirus (CMV) and investigate the predictive factors for uveitic activity and recurrence. Methods: Polymerase chain reaction (PCR) was performed in a prospective cohort of immunocompetent adults with recurrent or chronic anterior uveitis to detect CMV in aqueous humor. The clinical outcomes were compared between eyes with and without CMV DNA. Logistic regression was performed to evaluate associations between iris depigmentation, CMV-PCR status, uveitic activity, and recurrence. Results: Thirty-eight eyes of 38 subjects with a mean age of 61.1 ± 11.2 years old were analyzed. Fifteen eyes were positive for CMV. More eyes with CMV developed recurrences and remained actively inflamed at 6, 12, and 24 weeks though the differences were insignificant. The presence of iris depigmentation was predictive of a greater odd of uveitic recurrences by 12 and 24 weeks (Odds ratio (OR) = 9.17 and 5.72, P = 0.007 and 0.034 respectively), whereas positive CMV-PCR predicts a greater odd of uveitic activity at postoperative 12 and 24 weeks (OR = 13.08, 34.30; P = 0.027, 0.007). Conclusion: Eye with and without detectable CMV behaved similarly in their clinical course. Our findings suggested that iris depigmentation was predictive of more frequent uveitic recurrence, regardless of the PCR status, whereas the presence of CMV in aqueous humor was associated with persistent uveitic activity. Iris changes may be present during the earlier phase of the disease and precede the detection of virus from the aqueous humor at a later stage of CMV infection.