ethicalabs/Research-Intent-Collab huggingface dataset:
THE INFLUENCE OF SOCIAL MEDIA ON ADOLESCENT MENTAL HEALTH: A LITERATURE REVIEW

Introduction/ Background: Based on WHO data, as many as 10-20% of children and adolescents in the world experience mental health problems. It is estimated that 50% of mental disorders occur by the age of 14 and 75% by the age of 18. The most common disorders in children and adolescents are generalized anxiety disorder and depression. Social media users in Indonesia reach 150 million (56%) of the total population. In Indonesia, teenagers are the most popular social media users. The impact received from the influence of social media is the disruption of mental health, such as depression caused by cyberbullying. Objective: This study aims to analyze social media's influence on adolescent mental health—method: Method used in the narrative review with PRISMA 2020. The literature search in this literature review uses two databases: Google Scholar and ScienceDirect. The critical words of the Indonesian Language are the influence of social media and adolescent mental health. English keywords are social media influence and adolescent mental health.  Results: The results of a study from 11 journals found that mental health problems owned by adolescents who use social media include emotional mental disorders, abnormal emotional symptoms, abnormal behavior problems, abnormal hyperactivity, and abnormal peer problems. Adolescent mental health problems mainly occur in adolescent girls. Based on the results of all studies, social media influences mental health problems experienced by adolescents. Conclusion: Social media is used by almost everyone in this world today. The majority of social media use is by teenagers. Social media hurts adolescent mental health. Teenagers must use social media wisely.
The Role of HRIS (Human Resource Information System) in Improving HR Management Efficiency

This study examines the role of Human Resource Information Systems (HRIS) in enhancing human resource management efficiency using a quantitative approach. In the context of digital transformation, organizations are required to optimize HR functions through the effective use of integrated information technology. HRIS facilitates the automation of administrative processes, enables real-time data availability, and supports data-driven decision-making. Data were collected from 150 HR practitioners working in various companies in the Greater Jakarta area who had implemented HRIS for at least one year, using purposive sampling. The research instrument was a structured questionnaire measured on a five-point Likert scale, which demonstrated strong validity and reliability, with Cronbach’s Alpha values exceeding 0.87 for all variables. Data analysis was conducted using SPSS version 25 and multiple linear regression after meeting classical assumption tests, including normality, multicollinearity, heteroscedasticity, and autocorrelation. The results indicate that administrative process automation (β = 0.412, p &lt; 0.001), data-driven decision-making (β = 0.358, p &lt; 0.001), and employee productivity improvement (β = 0.287, p &lt; 0.01) have significant positive effects on HR management efficiency, both individually and simultaneously. The coefficient of determination (R² = 0.723) shows that these HRIS dimensions explain 72.3% of the variance in HR management efficiency. Overall, the findings confirm that HRIS implementation enhances HR operational efficiency by reducing processing time, minimizing manual errors, and improving the quality of strategic decision-making. Nevertheless, organizations must address challenges related to implementation costs, system integration, and resistance to change through strategic planning, continuous training, strong top management support, and adequate technological infrastructure.
Artificial intelligence-assisted magnetic resonance imaging technology in the differential diagnosis and prognosis prediction of endometrial cancer

It aimed to analyze the value of deep learning algorithm combined with magnetic resonance imaging (MRI) in the risk diagnosis and prognosis of endometrial cancer (EC). Based on the deep learning convolutional neural network (CNN) architecture residual network with 101 layers (ResNet-101), spatial attention and channel attention modules were introduced to optimize the model. A retrospective collection of MRI image data from 210 EC patients was used for model segmentation and reconstruction, with 140 cases as the test set and 70 cases as the validation set. The performance was compared with traditional ResNet-101 model, ResNet-101 model based on spatial attention mechanism (SA-ResNet-101), and ResNet-101 model based on channel attention mechanism (CA-ResNet-101), using accuracy (AC), precision (PR), recall (RE), and F1 score as evaluation metrics. Among the 70 cases in the validation set, there were 45 cases of low-risk EC and 25 cases of high-risk EC. Using ROC curve analysis, it was found that the area under the curve (AUC) for the diagnosis of high-risk EC of the proposed model in this article (0.918) was visibly larger as against traditional ResNet-101 (0.613), SA-ResNet-101 (0.760), and CA-ResNet-101 models (0.758). The AC, PR, RE, and F1 values of the proposed model for the diagnosis of EC risk were visibly higher (P  < 0.05). In the validation set, postoperative recurrence occurred in 13 cases and did not occur in 57 cases. Using ROC curve analysis, it was found that the AUC for postoperative recurrence prediction of the patients by the proposed model (0.926) was visibly larger as against traditional ResNet-101 (0.620), SA-ResNet-101 (0.729), and CA-ResNet-101 models (0.767). The AC, PR, RE, and F1 values of the proposed model for postoperative recurrence prediction were visibly higher (P  < 0.05). The proposed model in this article, assisted by MRI, presented superior performance in diagnosing high-risk EC patients, with higher sensitivity (Sen) and specificity (Spe), and also demonstrated excellent predictive AC in postoperative recurrence prediction.
Searching for a message in the angular power spectrum of the cosmic microwave background

4 pages, 2 figures
Topological phenomena at topological defects

There are two prominent applications of the mathematical concept of topology to the physics of materials: band topology, which classifies different topological insulators and semimetals, and topological defects that represent immutable deviations of a solid lattice from its ideal crystalline form. While these two classes of topological phenomena have generally been treated as separate topics, recent experimental advancements have begun to probe their intricate and surprising interactions, in real materials as well as synthetic metamaterials. Topological lattice defects in topological materials offer a platform to explore a diverse range of novel phenomena, such as topological pumping via topological defects, embedded topological phases, synthetic dimensions, and non-Hermitian skin effects. In this Perspective, we survey the developments in this rapidly moving field, and give an outlook of its impact on materials science and applications.
Next Generation Chemoproteomic Platforms for Functional Diagnostics and Small Molecule Drug Discovery

Targeting the proteome to treat diseases has been a long-standing goal in biomedical research. Yet less than 3% of the human proteome have FDA approved drugs against them. This is largely because over 35% of the proteome remains poorly understood. Moreover, we need to identify those proteins within the proteome that directly drive disease. To uncover new therapeutic targets, we must assess the activity of the proteins under relevant backgrounds—not just their abundance. Furthermore, many disease-driving proteins are still considered "undruggable," underscoring the need for innovative strategies to therapeutically target these challenging proteins. Here we report single cell Activity Dependent Proximity Ligation (scADPL) which integrates automated microfluidic single-cell capture and nanoliter-volume handling to translate activity of proteins into multiplexed, amplifiable oligonucleotide barcodes. Activity-based probes targeting serine hydrolases and cysteine proteases were used to profile a panel of six enzymes known to be associated with cancer aggressiveness. Using the single-cell ADPL (scADPL) workflow on cancer cell lines and primary breast cancer organoids, we identified an activity signature that correlates with metastatic progression in breast cancer. This platform enables measurement of activity signatures at the single-cell level, allowing us to distinguish signals from cancer cells versus normal cells—signatures that would be masked in bulk analyses. Additionally, the low sample input requirement makes it possible to profile patient-derived cells under physiologically relevant conditions, helping to identify true disease drivers. To enable efficient targeting of traditionally undruggable proteins, we developed a high-throughput chemoproteomics screening platform called In-situ Proximity Amplified Rapid Chemoproteomics (SPARC). SPARC employs activity-based probes to convert protein engagement into a barcoded DNA signal that is amplified and quantified. Drug binding to the target protein leads to signal loss or signal gain, indicating inhibition or activation of target protein. Using SPARC, we identified inhibitors and activators for a panel of five full-length kinase-GFP fusion proteins in their native, intracellular context. In addition to detecting drug–protein interactions, SPARC provides multiple layers of information that help reduce false discovery rates and accelerate drug development. It captures protein localisation and abundance, allowing us to distinguish true target engagement from changes in protein levels. SPARC is also coupled with a viability assay to assess whether observed effects are due to specific interactions or general cytotoxicity from off-target effects. Overall, these chemoproteomic platforms advance drug discovery by enabling the identification of disease-relevant targets through scADPL and the rapid discovery of modulators—activators or inhibitors—of these targets using the SPARC screening pipeline.
Stabilising priors for robust Bayesian deep learning

Bayesian neural networks (BNNs) have developed into useful tools for probabilistic modelling due to recent advances in variational inference enabling large scale BNNs. However, BNNs remain brittle and hard to train, especially: (1) when using deep architectures consisting of many hidden layers and (2) in situations with large weight variances. We use signal propagation theory to quantify these challenges and propose self-stabilising priors. This is achieved by a reformulation of the ELBO to allow the prior to influence network signal propagation. Then, we develop a stabilising prior, where the distributional parameters of the prior are adjusted before each forward pass to ensure stability of the propagating signal. This stabilised signal propagation leads to improved convergence and robustness making it possible to train deeper networks and in more noisy settings.
The Legal Basis for the Exercise of Jurisdiction by the International Criminal Court and the Preparatory Work of the Rome Statute (Ⅱ)

application/pdf
Parate Execution After the Indonesian Constitutional Court’s Judicial Review of Fiducia Law and Mortgage Law

&lt;p&gt;According to the Law No. 42 of 1999 on Fiducia Security (“Fiducia Law”) as well as the Law No. 4 of 1996 on Mortgage, if there is a breach of fiduciary guarantee and mortgage rights, the secured creditors can undertake a parate execution, as the expedient, simple and cost-efficient method by means of a public auction. However, the Indonesian Constitutional Court’s (MKRI) Decision Number 18/PUU-XVII/2019 has interpreted Parate Execution of Fiduciary Guarantee must firstly obtain the debtor’s consent that a breach has indeed occurred and the voluntarily surrenders of the guarantee object to the creditor. On the other hand, in the Decision No. 21/PUU-XVIII/2020, MKRI did not define the same process for Parate Execution of Mortgage Rights. From the substance point of view, the two MK verdicts provide a different interpretation of the principle of “&lt;em&gt;pacta sunt servanda” &lt;/em&gt;and fiducia security. This has caused the execution of Fiduciary Guarantee becomes not easy, expedient and cost efficient any longer. This normative research attempts to analyse the legal and economic impact of the two verdicts and their implementation from a law and justice perspective. The results show the need of consistency in the implementation of Parate Execution for both. This means that an agreement regarding the existence of a breach is not required. In addition, if the debtor does not voluntarily surrender the guarantee object, then the creditor by law reserves the rights to seize the object. Arguably, it is necessary to amend the Fiducia Law in accordance with the MKRI’s Decisions, in line with the general principles of security in parallel with the principles of justice, legal certainty and utility.&lt;/p&gt;
Translating culture through machines: a comparative analysis of AI and student simultaneous interpretations from Arabic to Spanish

The growing integration of artificial intelligence (AI) into language services has had a significant impact on the field of interpretation, particularly through the use of large language models and increasingly sophisticated algorithms. However, as AI-mediated solutions become more prevalent in professional settings, it is crucial to critically evaluate their performance in comparison to human interpreting, particularly in contexts involving complex sociocultural content. This study presents a comparative analysis of AI-based voice translation (AI-VT) and human interpreting (HI), as performed by two students enrolled on the Master's degree in Conference Interpreting at the University of Granada. The focus is on simultaneous interpretation from Arabic into Spanish at a live academic conference entitled 'Ahat al-Masrah al-Jazairi li-Masrah al-Haifa al-Sari' ('The Sigh of Algerian Theatre for the Theatre of the Fast Bus'), delivered by Rabah Ait Abba, a doctoral researcher at the University of Algiers 2. The analysis centers on how each interpreting system handles sociocultural references, such as dialectal Arabic, intertextuality and culturally embedded metaphors, within a multimodal presentation format incorporating audiovisual materials. The event took place on 11 April 2025 at the Faculty of Translation and Interpreting, and was recorded and transcribed for detailed comparison. By evaluating the outputs of both AI-VT and HI in terms of accuracy, cultural transfer and audience comprehension, this paper highlights the significant limitations of current AI systems when it comes to processing linguistically and culturally nuanced material. Preliminary findings suggest that AI-VT significantly underperforms in interpreting tasks involving dialectal Arabic and multimodal content. This emphasizes the ongoing requirement for skilled human interpreters in such contexts.
Systems Biology of Metabolic and Signaling Networks

International audience
A federated collaborative short-term industrial load forecasting method integrating meta-learning and differentiated privacy allocation

Short-term load forecasting for industrial users is often limited by scarce local data and heterogeneous load patterns, while cross-factory collaboration raises serious privacy concerns due to production-sensitive information. This paper proposes a privacy-preserving federated forecasting framework for industrial short-term load prediction that integrates meta-learning and differentiated differential privacy allocation. The framework is built on federated learning, where factories perform local training and upload encrypted model updates for secure aggregation, ensuring data availability without data visibility. To address negative transfer and poor personalization caused by load heterogeneity, a meta-learning strategy is introduced to enable rapid adaptation to new factories or operating conditions using only a small amount of local data. In addition, a differentiated privacy-weight mechanism is designed to dynamically allocate privacy budgets based on data contribution and sensitivity, achieving a better trade-off between privacy protection and forecasting accuracy. Experiments on real-world industrial load datasets show that the proposed method reduces MAPE by 1.99 percentage points compared with independent training, and by 2.63 percentage points in new operating-condition scenarios, demonstrating its effectiveness for secure and collaborative industrial load forecasting.
The simulated features of heliospheric cosmic-ray modulation with a time-dependent drift model. III - General energy dependence

The time-dependent cosmic-ray transport equation is solved numerically in an axially symmetric heliosphere. Gradient and curvature drifts are incorporated, together with an emulated wavy neutral sheet. This model is used to simulate heliospheric cosmic-ray modulation for the period 1985-1989 during which drifts are considered to be important. The general energy dependence of the modulation of Galactic protons is studied as predicted by the model for the energy range 1 MeV to 10 GeV. The corresponding instantaneous radial and latitudinal gradients are calculated, and it is found that, whereas the latitudinal gradients follow the trends in the waviness of the neutral sheet to a large extent for all energies, the radial gradients below about 200 MeV deviate from this general pattern. In particular, these gradients increase when the waviness decreases for the simulated period 1985-1987.3, after which they again follow the neutral sheet by increasing rapidly.
Can social resilience inform SA/SIA for adaptive planning for climate change in vulnerable regions?

Abstract                Social resilience concepts are gaining momentum in environmental planning through an emerging understanding of the socio-ecological nature of biophysical systems. There is a disconnect, however, between these concepts and the sociological and psychological literature related to social resilience. Further still, both schools of thought are not well connected to the concepts of social assessment (SA) and social impact assessment (SIA) that are the more standard tools supporting planning and decision-making. This raises questions as to how emerging social resilience concepts can translate into improved SA/SIA practices to inform regional-scale adaptation. Through a review of the literature, this paper suggests that more cross-disciplinary integration is needed if social resilience concepts are to have a genuine impact in helping vulnerable regions tackle climate change.
Simulation for Optimal Design of Hand-Held Surgical Robots

Hand-held surgical robots are manipulators with dexterous effectors to be used by surgeons in minimally invasive surgery and especially in laparoscopy. Mechanical manipulators for laparoscopy have appeared on the markets in recent years with various interfaces and dexterities. 2 examples are RealHand and Laparo-Angle. The question of which interface and control mode is the best has not been answered yet. Also, the effector kinematics has not been studied much. We have made a simulator to study the robot's interface, control mode and kinematics to design a hand-held surgical robot for laparoscopic surgery. We asked test subjects to use the simulator and try to make sutures in a virtual environment. Users opinion is that a joystick as interface is easier to use, compared to a jointed interface translating hand's orientation to that of the effector. It appears that a 6 DOF effector coupled to the shaft is necessary and dexterous enough to make sutures in different angles.
11 TOPS photonic convolutional accelerator for optical neural networks

Convolutional neural networks, inspired by biological visual cortex systems, are a powerful category of artificial neural networks that can extract the hierarchical features of raw data to provide greatly reduced parametric complexity and to enhance the accuracy of prediction. They are of great interest for machine learning tasks such as computer vision, speech recognition, playing board games and medical diagnosis1-7. Optical neural networks offer the promise of dramatically accelerating computing speed using the broad optical bandwidths available. Here we demonstrate a universal optical vector convolutional accelerator operating at more than ten TOPS (trillions (1012) of operations per second, or tera-ops per second), generating convolutions of images with 250,000 pixels-sufficiently large for facial image recognition. We use the same hardware to sequentially form an optical convolutional neural network with ten output neurons, achieving successful recognition of handwritten digit images at 88 per cent accuracy. Our results are based on simultaneously interleaving temporal, wavelength and spatial dimensions enabled by an integrated microcomb source. This approach is scalable and trainable to much more complex networks for demanding applications such as autonomous vehicles and real-time video recognition.
Health Care Utilization Research using Secondary Data

In the last 20 years, administrative data (‘secondary data’ and ‘claims data’) from German statutory health insurances (SHIs) were made available for health, especially utilization research. These data cover information from all services billed by the SHI: outpatient and inpatient care, drug prescription, prescriptions of non-pharmaceutical therapies and technical aids, and data on incapacity to work.
The Physics of Gamma-Ray Burst Afterglows and the Nature of Their Host Galaxies

Gamma-ray bursts are brief flashes of 7-rays, discovered by the cold-war Vela satellites in the early 1970s. For over two decades the distance scale of these explosions was unknown. In the early 1990s, the Burst and Transient Source experiment onboard the Compton Gamma-Ray Observatory showed gamma-ray bursts to have an isotropic sky distribution, suggestive of a cosmological origin. Thanks to the discovery of X-ray and optical afterglows by BeppoSAX, their distant extra-galactic nature was definitely established in 1997. We discuss the history and current status of the study of gamma-ray burst afterglows, and future VLT observations that can significantly advance the field.
Interdisciplinary Quality Improvement Conference

The Veterans Healthcare Administration (VA) has embraced patient safety and quality improvement in the quest to improve care for veterans. The New Mexico VA Health Care System introduced a new morbidity and mortality conference, called the Interdisciplinary Quality Improvement Conference (IQIC), using patient case presentations to focus on underlying systems in the clinical care environment. The revised conference design also effectively teaches the 6 Accreditation Council for Graduate Medical Education (ACGME) core requirements for resident education. A formal process was established for case selection, presentation, systems issue identification, tracking, and follow-up. The IQIC has enabled the identification of more than 20 system issues at the study institution. Outcome data show lasting improvement in system issues that were addressed by this mechanism. The VA IQIC is an effective method to both identify and correct systems issues that affect patient care and is an effective method for teaching residents the 6 ACGME requirements for residency education.