jimnoneill/paper-to-field-training huggingface dataset:
Usable and Accessible Learning Management System: UTPL Experience

Field: Social Sciences (Social Sciences)

The purpose of this paper is describe the actions carried out by the Universidad Técnica Particular de Loja (UTPL), to provide to academic community a usable and accessible Learning Management System (LMS). For this, standards by the W3C (ISO, WCAG) are considered, as well as the vision of representative authors in this field, who highlight the educative materials into a courses, as important elements that require special attention, because these materials must be available for all students indistinct of their capacities, promoting even different styles of learning. This work contributes to one objectives of the 2030 agenda, which is "To guarantee an inclusive, equitable and quality education and to promote lifelong learning opportunities for all".
Investigation of quantum dot structures for terahertz emission

Field: Physics and Astronomy (Physical Sciences)

The terahertz (THz) frequency has many interesting and useful applications and quantum dot (QD) semiconductor devices are good candidates for generation of terahertz emission via difference frequency generation (DFG) or intraband trasitions lasing. Both techniques require that the energy difference ∆E of the energy states be less than 41.3 meV to achieve terahertz emission. This project will be on the study of QD structures, in particular the well-studied InAs QDs, and its optical properties. Characterization techniques are performed and the results obtained will be used for analysis in how QD growth conditions like monolayer coverage (ML) and growth rate (GR) affect the energy states and ∆E of the QDs.
High- versus low-dose conventional phototherapy for neonatal jaundice

Field: Medicine (Health Sciences)

This is a protocol for a Cochrane Review (intervention). The objectives are as follows: 1. To assess the effects of high‐dose conventional phototherapy versus low‐dose conventional phototherapy on bilirubin level and associated clinical outcomes that constitute the major conditions in the spectrum of bilirubin‐induced neurological dysfunction (BIND), such as acute bilirubin encephalopathy and kernicterus, as well as cerebral palsy and neurodevelopmental disabilities in infants with hyperbilirubinaemia. For the purpose of this review, we will include only studies on treatment and will exclude studies on prophylactic use of phototherapy. Prophylactic phototherapy is covered in another Cochrane Review (Okwundu 2012). 2. To assess high‐dose phototherapy defined as the use of high levels of measured spectral irradiance greater than 30 μW/cm²/nm over the same bandwidth (AAP 2004), as well as low‐dose phototherapy defined as measured levels of spectral irradiance below 30 μW/cm²/nm. 3. To restrict this review to studies that specifically state measured irradiance.
Combining Experts’ Judgments: Comparison of Algorithmic Methods using Synthetic Data

Field: Decision Sciences (Social Sciences)

Expert judgment (or expert elicitation) is a formal process for eliciting judgments from subject-matter experts about the value of a decision-relevant quantity. Judgments in the form of subjective probability distributions are obtained from several experts, raising the question how best to combine information from multiple experts. A number of algorithmic approaches have been proposed, of which the most commonly employed is the equal-weight combination (the average of the experts’ distributions). We evaluate the properties of five combination methods (equal-weight, best-expert, performance, frequentist, and copula) using simulated expert-judgment data for which we know the process generating the experts’ distributions. We examine cases in which two well-calibrated experts are of equal or unequal quality and their judgments are independent, positively or negatively dependent. In this setting, the copula, frequentist, and best-expert approaches perform better and the equal-weight combination method performs worse than the alternative approaches.
Ability of College Students to Simulate ADHD on Objective Measures of Attention

Field: Psychology (Social Sciences)

Objective: The authors examined the ability of college students to simulate ADHD symptoms on objective and self-report measures and the relationship between knowledge of ADHD and ability to simulate ADHD. Method: Undergraduate students were assigned to a control or a simulated ADHD malingering condition and compared with a clinical AD/HD group. The authors used several clinical attentional measures and symptom validity tests to differentiate experimental groups via a series of multivariate procedures. Results: Simulators successfully feigned ADHD symptoms on a retrospective self-report measure. Moreover, knowledge of ADHD was unrelated to objective attentional measure performance. Overall, participants who simulated ADHD on some objective measures (i.e., specific Wechsler Adult Intelligence Scale—III [WAIS-III] subtests) showed similar performance to the clinical ADHD comparison sample. Conclusion: The implications of these findings highlight the importance of relying on multiple vectors of information, be it objective, observational, self-report, or reports by others, when diagnosing ADHD and assessing factors related to potential secondary gain. (J. of Att. Dis. 2010; 13(4) 325-338)
Electroacupuncture in the treatment of IBS in rats: investigation of the mechanisms of CRH+ neurons in the paraventricular nucleus

Field: Medicine (Health Sciences)

The purpose of this research was to determine the central regulatory mechanism of electroacupuncture (EA) in rats with irritable bowel syndrome (IBS). Our results showed that combined with the serum changes in corticotropin-releasing hormone (CRH), corticosterone (CORT), and adrenocorticotropic hormone (ACTH), the improvement of IBS by EA was related to them. Furthermore, EA could regulate intestinal functional activity through the central CRH + nervous system.
Role of Agronomic Interventions on Quality and Yield of Oil in Transplanted Canola (Brassica napus L.)

Field: Agricultural and Biological Sciences (Life Sciences)

Canola is an important oilseed crop throughout the world and is grown in more than fifty-three countries. Planting geometry plays an important role in achieving higher production, because of an optimum number of crop plants per unit area and efficient utilization of available nutrients. Planting geometry has an effect on yield through its influence on light interception, rooting pattern and moisture extraction. Besides the research for best plant geometry, it is also essential to screen suitable varieties for procuring a higher yield of seed as well as oil. A field experiment entitled “Role of agronomic interventions on quality and yield of oil in transplanted canola (Brassica napus L.)” was carried out at Student’s Research Farm, P.G. Department of Agriculture, Khalsa College, Amritsar during winter (rabi) season of 2021-2022 on sandy loam soil. The experiment was laid out in split plot design (SPD) with three replications and twelve treatment combinations comprising of three different varieties in main-plots viz., V1 (Kenola-1001), V2 (Hyola Adv-45) and V3 (GSC-7) with four planting geometries viz., S1 (45 cm x 10 cm), S2 (45 cm x 20 cm), S3 (45 cm x 30 cm) and S4 (45 cm x 40 cm) allotted in sub-plots. Among the varieties, GSC-7 recorded significantly higher oil yield and oil content (8.63 q ha-1 and 42.31%) as compared to Hyola Adv-45 (6.85 q ha-1 and 40.17%) and Kenola-1001 (5.70 q ha-1 and 38.33%). Among various planting geometries, S1 (45 cm x 10 cm) recorded significantly higher oil yield and oil content (8.44 q ha-1 and 42.92%). Significantly higher content of stearic acid (2.69%), palmitic acid (5.04%), linoleic acid (16.39%), linolenic acid (7.57%) and erucic acid (1.73%) were recorded in GSC-7 variety. Sub-plot treatment S1 recorded arithmetically improved stearic acid (2.51%), palmitic acid (4.60%), linoleic acid (15.72%), linolenic acid (7.21%) and erucic acid (1.62%) content compared to other treatments. Statistically improved protein content (24.72%) and NPandK (3.97%, 0.73% and 0.95%) was recorded in GSC-7.
Lightweight Road Environment Segmentation using Vector Quantization

Field: Computer Science (Physical Sciences)

Road environment segmentation plays a significant role in autonomous driving. Numerous works based on Fully Convolutional Networks (FCNs) and Transformer architectures have been proposed to leverage local and global contextual learning for efficient and accurate semantic segmentation. In both architectures, the encoder often relies heavily on extracting continuous representations from the image, which limits the ability to represent meaningful discrete information. To address this limitation, we propose segmentation of the autonomous driving environment using vector quantization. Vector quantization offers three primary advantages for road environment segmentation. (1) Each continuous feature from the encoder is mapped to a discrete vector from the codebook, helping the model discover distinct features more easily than with complex continuous features. (2) Since a discrete feature acts as compressed versions of the encoder's continuous features, they also compress noise or outliers, enhancing the image segmentation task. (3) Vector quantization encourages the latent space to form coarse clusters of continuous features, forcing the model to group similar features, making the learned representations more structured for the decoding process. In this work, we combined vector quantization with the lightweight image segmentation model MobileUNETR and used it as a baseline model for comparison to demonstrate its efficiency. Through experiments, we achieved 77.0 % mIoU on Cityscapes, outperforming the baseline by 2.9 % without increasing the model's initial size or complexity.
Validation data confusion matrix.

Field: Psychology (Social Sciences)

<div><p>In addition to affecting people’s bodily and mental health, the Covid-19 epidemic has also altered the emotional and mental well-being of many workers. Especially in the realm of institutions and privately held enterprises, which encountered a plethora of constraints due to the peculiar circumstances of the epidemic. It was thus anticipated that the present study would use a group method of data handling (GMDH) neural network for analyzing the relationship of demographic factors, Coronavirus, resilience, and the burnout in startups. The test methodology was quantitative. The research examined 384 startup directors and representatives, which is a sizable proportion of the limitless community. The BRCS, the MBI-GS, and custom-made assessments of stress due to the Coronavirus were all used to collect data. Cronbach’s alpha confirmed the polls’ dependability, and an expert panel confirmed the surveys’ authenticity. The GMDH neural network’s inherent potential for self-organization was used to choose the most useful properties automatically. The trained network has a three-layered topology with 4, 3, and 2 neurons in each of the hidden layers. The GMDH network has significantly reduced the computational load by using just 7 parameters of marital status, stress of covid-19, job experience, professional efficiency, gender, age, and resilience for burnout categorization. After comparing the neural network’s output with the acquired data, it was determined that the constructed network accurately classified all of the information. Among the achievements of this research, high accuracy in predicting job burnout, checking the performance of neural network in determining job burnout and introducing effective characteristics in determination of this parameter can be mentioned.</p></div>
Hybrid Transformer-Based Classification for Web-Based Injection Attack Detection: A Novel Roberta-XLNet Approach

Field: Computer Science (Physical Sciences)

Web-based injection attacks such as SQL Injection (SQLi) and Cross-Site Scripting (XSS) remain significant cybersecurity threats, enabling adversaries to manipulate databases, execute unauthorized commands, and compromise sensitive data. Traditional detection mechanisms—including rule-based and anomaly-based intrusion detection systems—struggle with high false positive rates and limited adaptability to evolving attack vectors. This research introduces a novel hybrid transformer-based classification model, integrating RoBERTa and XLNet architectures to enhance web-based injection attack detection. The hybrid model capitalizes on RoBERTa’s dynamic contextual embeddings and XLNet’s permutation-based language understanding to provide a robust and generalized detection mechanism capable of handling obfuscated and zero-day payloads. The study utilizes two labeled datasets: 43,135 SQLi and 16,985 XSS payloads, preprocessed through standardized cleaning, tokenization, and padding techniques. The hybrid architecture extracts [CLS] and finaltoken embeddings from RoBERTa and XLNet respectively, concatenates them into a 1536-dimensional feature vector, and classifies through a three-layer dense neural network. Evaluation metrics include Accuracy, Precision, Recall, F1 Score, False Positive Rate, and Computational Cost.Results reveal that the hybrid model outperforms standalone BERT, RoBERTa, and XLNet implementations, achieving 97.66% accuracy, 98% precision, and 97% recall, while maintaining efficient computational performance via frozen transformer layers. The model demonstrates superior robustness against complex payloads, reduced overfitting, and scalable potential for Security Operations Centers (SOCs). This approach offers a novel and effective solution for intelligent, real-time web-based threat detection.
PARADIGMA PENGEMBANGAN KURIKULUM PAI DI LEMBAGA PENDIDIKAN

Field: Arts and Humanities (Social Sciences)

The curriculum is a teaching and learning tool that needs to be developed in accordance with the existing developments in society. There are 3 kinds of paradigms in curriculum development, among others: 1. Diotomous paradigm, 2. Paradigm nikanisme, 3. Paradigm Organisem. Therefore, curriculum development is the process that determines how the curriculum will work. So in the preparation of curriculum development should consider the steps as follows: Formulation of Objectives; Define Content; Selecting Activities; And Formulate Evaluation. So in the preparation of curriculum development should consider the steps: 1) Formulation of Objectives, 2) Determining Content, 3) Selecting Activities, 4) Formulating Evaluation
Fine Root Biomass Mediates Soil Fauna Community in Response to Nitrogen Addition in Poplar Plantations (Populus deltoids) on the East Coast of China

Field: Agricultural and Biological Sciences (Life Sciences)

Soil fauna is critical for maintaining ecosystem functioning, and its community could be significantly impacted by nitrogen (N) deposition. However, our knowledge of how soil-faunal community composition responds to N addition is still limited. In this study, we simulated N deposition (0, 50, 100, 150, and 300 kg N ha−1 year−1) to explore the effects of N addition on the total and the phytophagous soil fauna along the soil profile (0–10, 10–25, and 25–40 cm) in poplar plantations (Populus deltoids) on the east coast of China. Ammonium nitrate (NH4NO3) was dissolved in water and sprayed evenly under the canopy with a backpack sprayer to simulate N deposition. Our results showed that N addition either significantly increased or decreased the density (D) of both the total and the phytophagous soil fauna (Dtotal and Dp) at low or high N addition rates, respectively, indicating the existence of threshold effects over the range of N addition. However, N addition had no significant impacts on the number of groups (G) and diversity (H) of either the total or the phytophagous soil fauna (Gtotal, Gp and Htotal, Hp). With increasing soil depth, Dtotal, Dp, Gtotal, and Gp largely decreased, showing that the soil fauna have a propensity to aggregate at the soil surface. Htotal and Hp did not significantly vary along the soil profile. Importantly, the threshold effects of N addition on Dtotal and Dp increased from 50 and 100 to 150 kg N ha−1 year−1 along the soil profile. Fine root biomass was the dominant factor mediating variations in Dtotal and Dp. Our results suggested that N addition may drive changes in soil-faunal community composition by altering belowground food resources in poplar plantations.
The surface tension and cloud condensation nuclei (CCN) activation of sea spray aerosol particles

Field: Environmental Science (Physical Sciences)

In marine environments, sea spray aerosol (SSA) particles have been found to contain surface-active substances (surfactants) originating from the sea surface microlayer. These surfactants can lower the surface tension of the SSA particles, facilitating their activation to cloud droplets. This effect is not considered in classical K & ouml;hler theory, which assumes droplets to be homogeneous with a surface tension of pure water. In this study the cloud condensation nuclei (CCN) activity of SSA particles calculated with classical K & ouml;hler theory is compared to a more complex calculation that considers bulk-surface partitioning, surface tension lowering, and liquid-liquid phase separation. The model approach presented here combines the multi-component Eberhart model for surface tension with the monolayer model and an activity model (Aerosol Inorganic-Organic Mixtures Functional groups Activity Coefficients, AIOMFAC). This combination allows for calculating K & ouml;hler curves of surfactant-containing particles with a large number of compounds for the first time. In a sensitivity study we show that organic compounds can be categorized into weak, intermediate, and strong surfactants for CCN activation based on their separation factor in water S1i and their pure component surface tension sigma i. For a quaternary model system of SSA particles, it is shown that high content of hydrophobic organic material (i.e. strong surfactants) in Aitken mode particles does not necessarily prevent good CCN activation but rather facilitates effective activation via surface tension lowering. Since common climate models use parameterizations that are based on classical K & ouml;hler theory, these results suggest that the CCN activity of small SSA particles might be underestimated in climate models.
Le "fleuve cotier": Un phenomene hydrologique important en Manche orientale. Exemple du Pas-de-Calais

Field: Earth and Planetary Sciences (Physical Sciences)

The English Channel has a "megatidal" regime: in the Dover Strait, the tidal range varies between 3 and 9 m in neap-tide and spring-tide respectively. The tide currents are alternating and essentially parallel to the coast. Along the French coast, the fluvial supplies, distributed from the Bay of Seine to Cape Gris-Nez, generate a coastal water mass which drifts nearshore, separated from the open sea by a frontal area: this coastal flow is characterized by its freshness, turbidity, phytoplankton richness, and pollution content. Its width is only 3 to 5 miles, but because of the high concentrations of dissolved and suspended components, it is possible that the coastal flow plays an important role in the fluxes between the Eastern Channel and the North Sea.
The Migration Industry and the H‐2 Visa in the United States: Employers, Labour Intermediaries, and the State

Field: Social Sciences (Social Sciences)

Abstract Research on the migration industry has demonstrated the wide variety of roles played by private actors in international migration. However, so far little of this work has attempted to quantitatively measure the size and composition of these industries within particular migration schemes. Using the case of the H‐2 temporary working visa in the US, this article looks to better understand the prevalence, impact, and dynamics of the private labour intermediaries that offer services to US employers looking to hire workers from abroad. Using data from applications to hire foreign workers made to the US Department of Labor, this article finds that private intermediaries are extensively involved. Their broad inclusion raises questions of public and private authority in the visa programme, as the use of private intermediaries becomes necessary for employers to access and navigate the state institutions that oversee the programme.
An IT Value Management Capability Model for Portuguese Universities: A Delphi Study

Field: Business, Management and Accounting (Social Sciences)

One of the most common dilemmas faced today by organizations and their leaders is how to guarantee value from high level IT investments, i.e. how organizations ensure expected benefits from growth in IT investments. Knowledgeable about this reality, organizations seek solutions to solve this problem, either through the adoption of frameworks developed and proposed by the professional community (COBIT5; VAL IT 2.0; IT-CMF), or alternatively, by designing and implementing their own models. The aforementioned, for organizations in general, is not different in the context of Higher Education Institutions (HEIs). This paper adopts a Resource-Based View theory (RBV) to identify a set of competences and resources, which contribute to develop and conceptualize an IT Value Management Capability Model. The identified items were submitted to a panel of experts through a Delphi study in order to validate and propose a baseline to assist academic and practitioners understand essential requirements to implement an IT Value Management Capability Model (ITVMCM) in Portuguese public universities.
Post-translational modification of the 26S proteasome subunits.

Field: Biochemistry, Genetics and Molecular Biology (Life Sciences)

Most of proteins are modified post-translationally to function in a normal fashion. The incompleteness of post-translational modification can cause various diseases. The yeast 26S proteasome is a large protease complex that catalyzes the degradation of ubiquitin-tagged proteins. This complex is composed of two subcomplexes, a catalytic core particle (20S proteasome) and a pair of 19S regulatory particles. There are 28 subunits in the 20S proteasome and at least 17 subunits in the 19S regulatory particle. These subunits are post-translationally modified with functional groups such as phospho, N-acetyl, N-myristoyl groups. The phosphorylated subunits of the 26S proteasome have been identified by two-dimensional electrophoresis and mass spectrometry, and three phosphorylation sites of alpha-7 subunit of the 20S proteasome by mass spectrometry. The N-acetylated subunits also have been identified by two-dimensional electrophoresis. These phosphorylation and N-acetylation of the 26S proteasome were found to affect the proteolytic activity of the 20S proteasome and ATPase activity of the 19S regulatory particle.
Machine learning for lip reading

Field: Computer Science (Physical Sciences)

Lip-reading is one of the most challenging task in visual recognition system. It decodes the text from the movement of lips from the speaker. In the previous approach, the lip-reading problem is divided by two stages: feature extraction and prediction. The Hidden Markov Model is implemented to solve the sequence problem. However, the traditional approaches require a lot of effort on feature extraction. Also, the models are trained to perform single word classification instead of sentence-level. This project aims to build an end-to-end sentence level system of lip-reading, by using the neural network and deep learning method. The convolutional neural network(CNN), recurrent neural network (RNN) and connectionist temporal classification (CTC) method will be implemented on the neural network. The GRID dataset is used in this project. Several speech videos from the GRID dataset will be used as training data.