Miracle-dz/UniFAR-FTUs huggingface dataset:
A systematic review of quantitative studies concerning psychological aspects of early specialisation

Despite the intense and long-standing interest surrounding early sport specialisation, scholars still debate its nature and implications. Previous researchers have also identi ﬁ ed the need for further research relating to the psychological aspects of early specialisation such as lower quality motivation, dropout and burnout. To help guide future research it is important to build upon the quantitative literature concerning such psychological aspects of early specialisation. The speci ﬁ c aims of this paper are to provide an overview of research results of quantitative studies that set out to explore relationships between early specialisation and psychological aspects, and to critically examine the designs of such studies. As such, study design characteristics including participant demographics, the psychological aspects represented, and the research questions and results are explored. Data searches were conducted in PubMed, SportDiscus, and PsychINFO using search terms such as “ early sport speciali* ” . Twenty-one relevant papers met the inclusion criteria. The results highlight that the published papers in this area are broad in some respects (variety of sports, performance levels, and gender), but narrow in others (North American dominance, few psychological aspects explored, and few papers per psychological aspect). Many of the studies are based on cross-sectional and retrospective self-reports. Overall, this paper serves as a foundation on which to design future research studies in this area.
Adaptive Cost Function-Based Shared Driving Control for Cooperative Lane-Keeping Systems With User-Test Experiments

This article presents a new shared control method with a dynamic driver-automation conflict management for cooperative lane keeping systems (LKS) of automated vehicles. Based on a human-centered control approach, the proposed shared control design takes into account the driver's activity and availability, as well as the surrounding risk to dynamically adapt the driving assistance level via an adaptive cost function. To this end, we propose a method to characterize in real-time the driver's activity, which allows for an appropriate assistance level according to the driving conditions. Linear parameter-varying (LPV) control technique is leveraged to deal with the time-varying nature of the vehicle speed and the level of assistance. The uncertainties of the lateral tires forces are taken into account in the control design via a norm-bounded representation. Using Lyapunov stability theory, the control design conditions are derived in terms of linear matrix inequality (LMI) constraints, which can be effectively solved by semidefinite programming techniques. User-test experiments are performed with the SHERPA dynamic car simulator to demonstrate the effectiveness of the proposed shared control method from both objective and subjective viewpoints.
Suppressing high-order surface errors of an active carbon fiber reinforced composite reflector using two types of actuators

Abstract. Active feedback control is a feasible approach for maintaining the desired shape of a reflector. An architecture for an active reflector is presented in which both piezoelectric ceramic transducer (PZT) and macrofiber composite (MFC) actuators are introduced to reduce both the global and local (high-order) surface errors. An electromechanical finite element model with two types of actuators is developed using the Hamilton principle. An optimal shape controller is then developed to minimize the reflector surface error. Lower-order orthogonal Zernike polynomials are derived in a unit hexagon. The polynomials are considered to be the basic error modes of a reflector and are used to optimize the length of the PZT actuators. The arrangement of MFC actuators can be optimized in a single triangular component of the reflector because the deflections they induced are very local. An experiment is conducted to verify the performance of MFC actuators in a triangular component. The improvement in the control efficiency of the active reflector is demonstrated by two numerical examples.
Multi-Objective Optimization of Two-Stage Centrifugal Pump using NSGA-II
 Algorithm

Improving the efficiency and suction capability of a multistage centrifugal pump poses a major challenge for the designer of this type of equipment. This paper deals with the optimization of a two stage centrifugal pump using Non-dominated Sorting Genetic Algorithm II (NSGA-II), coupled with three-dimensional Reynoldsaveraged Navier-Stokes (3D-RANS) flow solver. The first stage comprises a suction impeller with a diffuser while the second stage is formed by a second impeller connected to a volute. Both impellers are of different dimensions and are inter-connected by a return channel. This arrangement increases the number of varying parameters and thus can add further constraints on the overall optimization process; as a result, a high computational complexity of NSGA-II and a higher computational fluid dynamics (CFD) simulation cost is incurred. In order to save running time, optimization with CFD simulations are performed on each stage separately shall enable to obtain better parameterization flexibility; therefore, permitting to adopt only three objective functions in as well as limiting other geometrical constraints. The objectives of this study are to maximize the head and hydraulic efficiency at a time where the net positive suction head inception (NPSHi) is kept to minimum. The overall efficiency as well as the head of the optimized pump were increased by 9.8% and 15.7%, respectively, at best efficiency point (BEP) (rotational speed N=2600 rpm); the NPSHi of suction impeller was reduced by 13.6%. At N=1450 rpm (BEP), an improvement of 14.9% in the head and 6.52% for the overall efficiency is observed. An important improvement in performance at different operating flow rates was obtained; this was in addition to other enhancements in the volumetric and hydraulic efficiencies. Unsteady CFD simulations were also performed to predict fluctuations in the pressure field, leakage flows and interactions between impellers and collectors. The obtained results were in agreement with experimental data. The head fluctuation of the optimized pump was also reduced by 22.5% in amplitude; this was favored by the presence of a tapered blade towards the trailing edge and the extended radial gap by 4.86% between the second impeller and cutwater, which was caused by the reduction of the impeller diameter.
THE BITTER END : ON PROSPECT THEORY IN THE DYNAMIC CONTEXT

We show that already a small amount of probability weighting has strong implications for the application of prospect theory in the dynamic context. A naive agent will never stop a stochastic process that represents his wealth. This holds for a very large class of processes, and independently of the reference point and the curvatures of the value and weighting functions. This dynamic result is a consequence of a static result that we call skewness preference in the small: At any wealth level there exists an arbitrarily small gamble (which is sufficiently rightskewed) that a prospect theory agent wants to take. By choosing a proper stopping strategy the agent can always implement such a gamble and thus never stops. We illustrate the implications for dynamic decision problems such as irreversible investment, casino gambling, and the disposition effect.
THE BITTER END : ON PROSPECT THEORY IN A DYNAMIC CONTEXT

Many problems in finance and economics involve stopping decisions. Individuals decide when to sell an asset, when to invest in some project, or when to stop gambling in a casino. We provide a general result on the stopping behavior of a prospect theory agent who is naïve, i.e., unaware of his time-inconsistency. If a market offers a sufficiently rich set of investment strategies and, in particular, if it is complete, then the agent never stops. This extreme result applies to a very general specification of prospect theory and to a very general class of discrete and continuous, finite or infinite time stochastic processes. The dynamic result is a consequence of a static result that we call skewness preference in the small: At any wealth level there exists a right-skewed, arbitrarily small gamble that a prospect theory agent wants to take, even if it has negative expectation. In particular, this formally proves that prospect theory agents are not risk-averse, not over losses and not over gains.
Finite-Time Stabilization of Stochastic Nonlinear Systems and Its Applications in Ship Maneuvering Systems

This article extends the finite-time adaptive tracking control to stochastic nonlinear systems with multiple uncertainties, including output constraints, unknown parameters, unmodeled dynamics, and external disturbances. Most relevant results in literature have two main restrictions: 1) uncertainties and unknown items in the systems; 2) the matter of “explosion of complexity. ” By integrating an improved technique of adding fuzzy logic systems to estimate unknown parameters with a bounded command filter method, a systematic tracking control scheme is developed that eliminates both of the above restrictions. To alleviate the serious uncertainties caused by the constraints, a quartic asymmetric time-varying barrier Lyapunov function is utilized when the control coefficient is known. Subsequently, an event-triggered controller is constructed that enables boundedness and finite-time convergence of all closed-loop signals. Eventually, to validate the effectiveness of the proposed adaptation strategy, this new method is applied to ship maneuvering systems that encounters multiple uncertainties arising from disturbances, such as wind, waves, and ocean currents.
The Biological Action and Structural Characterization of Eryngitin 3 and 4, Ribotoxin-like Proteins from Pleurotus eryngii Fruiting Bodies

Ribotoxin-like proteins (RL-Ps) are specific ribonucleases found in mushrooms that are able to cleave a single phosphodiester bond located in the sarcin–ricin loop (SRL) of the large rRNA. The cleaved SRL interacts differently with some ribosomal proteins (P-stalk). This action blocks protein synthesis because the damaged ribosomes are unable to interact with elongation factors. Here, the amino acid sequences of eryngitin 3 and 4, RL-Ps isolated from Pleurotus eryngii fruiting bodies, were determined to (i) obtain structural information on this specific ribonuclease family from edible mushrooms and (ii) explore the structural determinants which justify their different biological and antipathogenic activities. Indeed, eryngitin 3 exhibited higher toxicity with respect to eryngitin 4 against tumoral cell lines and model fungi. Structurally, eryngitin 3 and 4 consist of 132 amino acids, most of them identical and exhibiting a single free cysteinyl residue. The amino acidic differences between the two toxins are (i) an additional phenylalanyl residue at the N-terminus of eryngitin 3, not retrieved in eryngitin 4, and (ii) an additional arginyl residue at the C-terminus of eryngitin 4, not retrieved in eryngitin 3. The 3D models of eryngitins show slight differences at the N- and C-terminal regions. In particular, the positive electrostatic surface at the C-terminal of eryngitin 4 is due to the additional arginyl residue not retrieved in eryngitin 3. This additional positive charge could interfere with the binding to the SRL (substrate) or with some ribosomal proteins (P-stalk structure) during substrate recognition.
Incidence and Management of Epithelial-Related Complications After SMILE

Purpose To investigate the incidence and management of only epithelial-related complications following small incision lenticule extraction (SMILE). Patients and Methods A retrospective, single-site study analyzed patients who underwent SMILE at Hoopes Vision Clinic in Draper, Utah, from June 2017 to February 2023. Demographic data and preoperative parameters were reviewed. Postoperatively, patients were assessed for visual acuity and complications at different time points. Statistical analyses were conducted between the control and complication groups. Results Four hundred and thirty-two eyes of 220 patients received SMILE. Postoperative epithelial-related complications were indicated in 68 (15.7%) eyes, including anterior basement membrane (ABM) changes (five [1.2%]) eyes), epithelial ingrowth (nine [2.1%] eyes), erosion (two [0.5%] eyes), rough epithelium (18 [4.2%] eyes), epithelial defect (12 [2.8%] eyes), diffuse lamellar keratitis (DLK) secondary to epitheliopathy (two [0.5%] eyes), microstriae secondary to epitheliopathy (four [0.9%] eyes), interface debris (21 [4.9%] eyes), and incisional fibrosis (one [0.2%] eye). There was a statistically significant difference in age, with older patients more likely to develop epitheliopathy postoperatively (P = 0.001). Additionally, patients with epithelial-related complications were more likely to receive photorefractive keratectomy (PRK) enhancement after SMILE than the control (P = 0.001). However, there was no statistical difference in uncorrected distance visual acuity (UDVA) better than 20/20 and corrected distance visual acuity (CDVA) between the complications group and the control at the last postoperative visit (P = 0.974 and 0.310, respectively). There was no statistically significant difference in the safety and efficacy indices between the complications and control group (P = 0.281 and 0.617, respectively). Conclusion In our study, epithelial-related complications were more prevalent in older patients and predisposed patients to require PRK enhancements after recovery from SMILE. Despite the incidence of epithelial-related complications, visual prognoses were favorable and achieved through various management strategies.
Low-Data Drug Design with Few-Shot Generative Domain Adaptation

Developing new drugs for emerging diseases, such as COVID-19, is crucial for promoting public health. In recent years, the application of artificial intelligence (AI) has significantly advanced drug discovery pipelines. Generative models, such as generative adversarial networks (GANs), exhibit the potential for discovering novel drug molecules by relying on a vast number of training samples. However, for new diseases, only a few samples are typically available, posing a significant challenge to learning a generative model that produces both high-quality and diverse molecules under limited supervision. To address this low-data drug generation issue, we propose a novel molecule generative domain adaptation paradigm (Mol-GenDA), which transfers a pre-trained GAN on a large-scale drug molecule dataset to a new disease domain using only a few references. Specifically, we introduce a molecule adaptor into the GAN generator during the fine tuning, allowing the generator to reuse prior knowledge learned in pre-training to the greatest extent and maintain the quality and diversity of the generated molecules. Comprehensive downstream experiments demonstrate that Mol-GenDA can produce high-quality and diverse drug candidates. In summary, the proposed approach offers a promising solution to expedite drug discovery for new diseases, which could lead to the timely development of effective drugs to combat emerging outbreaks.
Application of Semiconductor Metal Oxide in Chemiresistive Methane Gas Sensor: Recent Developments and Future Perspectives

The application of semiconductor metal oxides in chemiresistive methane gas sensors has seen significant progress in recent years, driven by their promising sensitivity, miniaturization potential, and cost-effectiveness. This paper presents a comprehensive review of recent developments and future perspectives in this field. The main findings highlight the advancements in material science, sensor fabrication techniques, and integration methods that have led to enhanced methane-sensing capabilities. Notably, the incorporation of noble metal dopants, nanostructuring, and hybrid materials has significantly improved sensitivity and selectivity. Furthermore, innovative sensor fabrication techniques, such as thin-film deposition and screen printing, have enabled cost-effective and scalable production. The challenges and limitations facing metal oxide-based methane sensors were identified, including issues with sensitivity, selectivity, operating temperature, long-term stability, and response times. To address these challenges, advanced material science techniques were explored, leading to novel metal oxide materials with unique properties. Design improvements, such as integrated heating elements for precise temperature control, were investigated to enhance sensor stability. Additionally, data processing algorithms and machine learning methods were employed to improve selectivity and mitigate baseline drift. The recent developments in semiconductor metal oxide-based chemiresistive methane gas sensors show promising potential for practical applications. The improvements in sensitivity, selectivity, and stability achieved through material innovations and design modifications pave the way for real-world deployment. The integration of machine learning and data processing techniques further enhances the reliability and accuracy of methane detection. However, challenges remain, and future research should focus on overcoming the limitations to fully unlock the capabilities of these sensors. Green manufacturing practices should also be explored to align with increasing environmental consciousness. Overall, the advances in this field open up new opportunities for efficient methane monitoring, leak prevention, and environmental protection.
AI application in journalism: ChatGPT and the uses and risks of an emergent technology

AI integration in media newsrooms is changing professional routines, required profiles and journalistic products. The acceleration in the development of this technology since the end of 2022 makes it necessary to review these tools in order to analyse their uses and risks, as well as their possible applications in the journalistic field. OpenAI launched ChatGPT in November 2022 open to the public. This tool has been a key element in this technological revolution. This paper analyses ChatGPT applications in journalistic newsrooms through its introduction during a working day in the routine of different professionals. A mixed methodology was used to carry out this experience: a benchmarking of AI tools applied to journalism was created, a walkthrough experience was carried out with ChatGPT and, based on the results obtained in these first two phases, the experiment with journalists was designed. A total of 12 journalists of different ages and sectors with little or no previous contact with technological tools based on artificial intelligence, as the aim of the study is to observe the application in newsrooms without the need for technical training, participated in this research. The results show the usefulness of the tool to automate mechanical processes, rewrite texts, analyse data and even serve as a content idea creator. On the other hand, journalists have also perceived significant risks such as inaccuracy of AI as well as lack of ‘empathy’.
Toward Better Automated Content Moderation in Low-Resource Languages

Social media companies have learned the hard way that poor moderation of content in languages other than English can have grave consequences. Leaving harmful content up, particularly in regions where social media platforms are primary news and communication channels, has fueled conspiracy theories, violence, and even genocide around the world (Fink 2018; Iyengar 2018). American social media companies have long faced criticism for underinvesting in regions outside the US and Europe. In response, Meta, Google, and others have begun to deploy newmultilingual language models that they claim can effectively detect and take action on harmful content in dozens if not hundreds of languages (Jigsaw 2021; Meta AI 2021b).
SHOCK UNLOADING CHARACTERISTICS OF CRUSHABLE ROCKS.

Abstract : The unloading characteristics of dry volcanic tuff were observed for two initial shock states. The unloading paths in the stress-particle velocity plane differed substantially from the reflected Hugoniot curve passing through the corresponding initial shock state. Thus the unloading proc ess does not follow the usually assumed Hugoniot unloading path. The net results of this behavior are that (1) the quantity of energy dissipated in the shock process is considerably greater than that normally assumed, and (2) the propagation speeds of the unloading waves are considerably greater than those normally assumed. For shock stress intensities of approximately 100 and 200 kilobars it was determined that the amount of energy dissipated at the shock front has been underestimated by 20 to 30 percent, and that the remaining useful energy has been overestimated by approximately 50 percent. Furthermore, the unloading wave speed for these cases has been underestimated by approximately 20 to 25 percent. A correlation was made between Hugoniot data obtained during this program and other Hugoniot data for dry volcanic tuff of various initial densities. It was found that, when presented in terms of absolute density, all of the Hugoniot states above a critical stress level were located on a single Hugoniot curve. This resulting aggregate mineral Hugoniot correlates well with Hugoniots for the basic nonporous constituents of the geologic materials. (Author)
Expanding the Analytical Toolbox: Developing New Lys-C Peptide Mapping Methods with Minimized Assay-Induced Artifacts to Fully Characterize Antibodies

Peptide mapping is an important tool used to confirm that the correct sequence has been expressed for a protein and to evaluate protein post-translational modifications (PTMs) that may arise during the production, processing, or storage of protein drugs. Our new orally administered drug (Ab-1), a single-domain antibody, is highly stable and resistant to proteolysis. Analysis via the commonly used tryptic mapping method did not generate sufficient sequence coverage. Alternative methods were needed to study the Ab-1 drug substance (75 mg/mL) and drug product (3 mg/mL). To meet these analytical needs, we developed two new peptide mapping methods using lysyl endopeptidase (Lys-C) digestion. These newly developed protein digestion protocols do not require desalting/buffer-exchange steps, thereby reducing sample preparation time and improving method robustness. Additionally, the protein digestion is performed under neutral pH with methionine acting as a scavenger to minimize artifacts, such as deamidation and oxidation, which are induced during sample preparation. Further, the method for low-concentration samples performs comparably to the method for high-concentration samples. Both methods provide 100% sequence coverage for Ab-1, and, therefore, enable comprehensive characterization for its product quality attribute (PQA) assessment. Both methods can be used to study other antibody formats.