ethicalabs/Research-Intent-Collab huggingface dataset:
Phylogenetic Correlation Among Carpfishes and Catfishes Using a Nuclear Protein Coding Genetic Data (Gh) Retrieved from Genebank Using a Freeware Computational Evolutionary Biology Package MEGA, 4

Computational Evolutionary Biology tools can be effectively utilized to study the relatedness of genetic data from different sources. The present investigation is aimed to study the variability and relatedness among a Nuclear Protein coding Gene among candidates of two different fish groups, the Carpfishes and the Catfishes by the Phylogenetic tree topologies resulting from four different computational models as Maximum parsimony (MP), Unweighted pair group method with arithmetic mean (UPGMA), Minimum evolution and Neighbor Joining Method. The respective nucleotide sequences were retrieved from the GENBANK database, aligned by Clustal W algorithm and subjected to computational models for constructing phylogenetic trees. Tests of Neutrality for the observations are also reported. The study was conducted by a freeware Computational Evolutionary Biology package MEGA, 4.
An investigation of mass composition of ultra-high energy cosmic rays with energies above 1019 eV via the study of extensive air showers

The electron and muon components of extensive air shower (EAS) with energies    above 1019 eV are analyzed via various giant EAS arrays. A varying property    of showers is observed for two energy ranges; higher and lower than (3 ? 4) x    1019 eV. The age parameter, zenith angle, shower size dependence on muon size    and shower size dependence on primary energy show an increment of mass    composition (MC) above (3?4)x 1019eV. Comparison of the observed EAS results    with the simulations of Capdevielle et al. (2000) and Shinozaki et al. (2005)    gives at most 20% photon fraction for primary energies above 1019 eV. The    arrival directions of showers above 4x1019 eV indicate an increasing    concentration towards the super galactic plane.
Best practice guidelines for citizen science in mental health research: systematic review and evidence synthesis

Partnering with people most affected by mental health problems can transform mental health outcomes. Citizen science as a research approach enables partnering with the public at a substantial scale, but there is scarce guidance on its use in mental health research. To develop best practise guidelines for conducting and reporting research, we conducted a systematic review of studies reporting mental health citizen science research. Documents were identified from electronic databases (n = 10), grey literature, conference proceedings, hand searching of specific journals and citation tracking. Document content was organised in NVIVO using the ten European Citizen Science Association (ECSA) citizen science principles. Best practise guidelines were developed by (a) identifying approaches specific to mental health research or where citizen science and mental health practises differ, (b) identifying relevant published reporting guidelines and methodologies already used in mental health research, and (c) identifying specific elements to include in reporting studies. A total of 14,063 documents were screened. Nine studies were included, from Australia, Belgium, Canada, Denmark, Netherlands, Spain, the UK, and the United States. Citizen scientists with lived experience of mental health problems were involved in data collection, analysis, project design, leadership, and dissemination of results. Most studies reported against some ECSA principles but reporting against these principles was often unclear and unstated. Best practise guidelines were developed, which identified mental health-specific issues relevant to citizen science, and reporting recommendations. These included citizen science as a mechanism for empowering people affected by mental health problems, attending to safeguarding issues such as health-related advice being shared between contributors, the use of existing health research reporting guidelines, evaluating the benefits for contributors and impact on researchers, explicit reporting of participation at each research stage, naming the citizen science platform and data repository, and clear reporting of consent processes, data ownership, and data sharing arrangements. We conclude that citizen science is feasible in mental health and can be complementary to other participatory approaches. It can contribute to active involvement, engagement, and knowledge production with the public. The proposed guidelines will support the quality of citizen science reporting.
AI-Driven Fault Injection Testing: Enhancing System Resilience  with Automated Chaos Engineering

This paper presents a novel approach to enhancing system resilience through AI-driven fault injection testing, leveraging automated chaos engineering. As modern distributed systems grow in complexity, traditional resilience testing techniques—often limited by static fault models and insufficient adaptability—struggle to expose hidden vulnerabilities under dynamic real-world scenarios. To address these challenges, we propose an intelligent framework that integrates artificial intelligence, specifically reinforcement learning, with automated chaos tools to dynamically generate and execute context-aware fault scenarios. The system continuously learns from observed behaviors, identifies weak points, and adapts its strategies to maximize test coverage and impact. Experimental results demonstrate a 28% improvement in fault detection accuracy and a 35% reduction in system recovery time compared to conventional static methods. Furthermore, the approach generalizes effectively across various cloud-native and microservice-based architectures. This work contributes to the evolution of autonomous resilience testing, offering a scalable and proactive solution for building more robust, self-healing systems in highly dynamic environments.
Electrochemistry of inorganic nanocrystalline electrode materials for lithium batteries

Aditional electrode materials are based on the redox potential difference of the electrode in the course of intercalation/deintercalation reactions...
PENGARUH HUMAN RESOURCE INFORMATION SYSTEM (HRIS) DAN PELATIHAN TERHADAP PRODUKTIVITAS KARYAWAN PADA PT. BPR TANAOBA LAIS MANEKAT KOTA KUPANG NUSA TENGGARA TIMUR

Penelitian ini bertujuan untuk mendeskripsikan Human Resource Information System (HRIS), pelatihan dan Produktivitas karyawan berdasarkan persepsi karyawan dan untuk mengetahui pengaruh Human Resource Information System (HRIS) dan pelatihan secara parsial dan simultan terhadap Produktivitas karyawan pada PT. BPR Tanaoba Lais Manekat Kota Kupang. Populasi dalam penelitian ini adalah seluruh karyawan PT. BPR Tanaoba Lais Manekat Kota Kupang. Penentuan sampel dalam penelitian   adalah  probability sampling dengan teknik simple random sampling menggunakan rumus slovin diperoleh 55 responden. Teknik pengumpulan data menggunakan kuesioner, wawancara, observasi dan dokumentasi.Teknik analisis data yang di gunakan adalah analisis statistik deskriptif dan model regresi linear berganda menggunakan aplikasi SPSS versi 27.  Hasil analisis deskriptif menunjukan bahwa variabel Human Resource Information System (HRIS) dan pelatihan berada pada kategori sangat tinggi dan produktivitas karyawan berada pada kategori tinggi. Hasil uji t menunjukan bahwa Human Resource Information System (HRIS)  dan pelatihan secara parsial berpengaruh positif dan signifikan terhadap poduktivitas karyawan pada PT. BPR Tanaoba Lais Manekat Kota Kupang. Hasil uji F menunjukan bahwa  Human Resource Information System (HRIS)  dan pelatihan secara simultan berpengaruh positif dan signifikan terhadap poduktivitas karyawan pada PT. BPR Tanaoba Lais Manekat Kota Kupang. Hasil koefesien determinasi menunjukan angka sebesar 51% dan sisanya di pengaruhi oleh variabel lain yang tidak dimasukan dalam penelitian ini.  Kata Kunci : Human Resource Information System (HRIS), Pelatihan, dan Produktivitas Karyawan
Advanced Edge Computing Frameworks for Optimizing Data Processing and Latency in IoT Networks

The advent of edge computing has been revolutionary in improving data processing efficiency andreducing latency in IoT networks. By decentralizing computational tasks, edge computing enables real-timeanalytics and scalability while reducing reliance on centralized cloud infrastructures. This paper exploresadvanced frameworks, including hierarchical and decentralized architectures, that integrate AI and machinelearning to enhance predictive optimization and resource management. It also addresses challenges such asprotocol compatibility, energy efficiency, and operational complexities. The review provides insights into currentadvancements and identifies future opportunities for improving IoT network performance and supportinglatency-sensitive applications like smart cities and industrial automation.
The Right of Management Originating From Indigenous People Ulayat Land: A Curse or A Solution?

Indonesia has extensive forest areas and a rich diversity of cultures and customs. This is related to Indigenous Legal Communities, which, according to the constitution, must be recognized and respected. This research aims to examine whether granting the Right of Management over Ulayat Land represents a solution for sustainable land management or whether it could be detrimental to the land rights of Indigenous Communities. This study employs a Normative Juridical approach, utilizing both a Legislative and Conceptual approach. The legal materials used in this research include both Primary and Secondary Legal Materials, which are analysed using a perspective analysis technique. The research findings indicate that granting the Right of Management over Ulayat Land held by Indigenous Legal Communities can positively impact sustainable land management, provided that Regional Regulations and Regional Decrees accommodating the recognition and protection of Indigenous Legal Communities are in place. Conversely, it could be detrimental to the existence of Indigenous Communities if Regional Regulations and/or Regional Decrees concerning the recognition and protection of Indigenous Legal Communities are not properly regulated.
Integrating Product Information Management (PIM) with Internet-Mediated Transactions (IMTs)

This research takes advantage of the features of the Extensible Markup Language (XML), constructing an Internet-mediated, knowledge-sharing environment where product information management (PIM) is fully implemented. Therein XML files serve as the basic exchange and storage format of knowledge texts in the transaction-matching process. Under this configuration, all sorts of product information stored by the user in the system platform which contains various concepts and related features involving such different fields as the design, production, and marketing of a product, are integrated into the programmed process of an intermediary transaction. With XML technology adopted as the foundation of the programmed intermediary transaction, the transaction knowledge will not only be stored structurally but be transmitted conveniently among the heterogeneous systems of the users, and the file data can be used uniformly. Besides, the specific text file markers and text definitions in the XML make it possible for the users to search the information more accurately, efficiently, and quickly.
A Novel Dataset for Flood Detection Robust to Seasonal Changes in Satellite Imagery

This study introduces a novel dataset for segmenting flooded areas in satellite images. After reviewing 77 existing benchmarks utilizing satellite imagery, we identified a shortage of suitable datasets for this specific task. To fill this gap, we collected satellite imagery of the 2019 Midwestern USA floods from Planet Explorer by Planet Labs (Image \c{opyright} 2024 Planet Labs PBC). The dataset consists of 10 satellite images per location, each containing both flooded and non-flooded areas. We selected ten locations from each of the five states: Iowa, Kansas, Montana, Nebraska, and South Dakota. The dataset ensures uniform resolution and resizing during data processing. For evaluating semantic segmentation performance, we tested state-of-the-art models in computer vision and remote sensing on our dataset. Additionally, we conducted an ablation study varying window sizes to capture temporal characteristics. Overall, the models demonstrated modest results, suggesting a requirement for future multimodal and temporal learning strategies. The dataset will be publicly available on <https://github.com/youngsunjang/SDSU_MidWest_Flood_2019>.
Mental Health Crisis Detection via Shopping Behavior for Ecological Digital Phenotyping (Preprint)

BACKGROUND                   Background: Mental health crises occur in five out of every 20 adults throughout the year, but medical professionals do not detect 60% of these cases until the condition reaches its most severe point. Current psychiatric diagnosis relies on categorical frameworks and episodic clinical assessments, missing critical transitions in mental state and creating barriers to early intervention.                                                     OBJECTIVE                   : This research presents a privacy-preserving framework for continuous mental health crisis detection using digital shopping behavior. We introduce the Decision Paralysis–Impulsivity Spectrum (DPIS) as a transdiagnostic behavioral model grounded in dual-process neuroscience and demonstrate its application for detecting episodes of depression and anxiety through e-commerce behavioral signatures.                                                     METHODS                   We generated synthetic cohorts (N=12,847 participants) with embedded causal mechanisms producing 847,392 shopping sessions over 12 months. We extracted 67 behavioral features across five domains: temporal patterns, cart dynamics, purchase characteristics, search behavior, and interaction patterns. Machine learning models (XGBoost, Temporal Convolutional Networks, Random Forest) were trained with federated learning and differential privacy (ε=1.0, δ=10⁻⁵). Causal relationships were established through Granger causality analysis, instrumental variables regression, and propensity score matching.                                                     RESULTS                   The ensemble model achieved 85.3% sensitivity and 86.1% accuracy in detecting mental health crises 8-14 days before onset, with an AUC of 0.913 and 18.7% false positive rate. Granger causality analysis demonstrated that DPIS temporally precedes PHQ-9 changes (F=47.3, p&lt;0.001). Instrumental variables regression controlling for confounders yielded a causal coefficient of β=4.2 (SE=0.7, p&lt;0.001). The system maintained strong performance across demographic subgroups with no significant bias (Kruskal-Wallis H=2.31, p=0.68). Adaptive Personalized Differential Privacy improved utility by 23% compared to fixed-ε approaches while maintaining privacy guarantees.                                                     CONCLUSIONS                   This work establishes Commercial Behavioral Psychiatry as a new interdisciplinary domain and demonstrates the feasibility of continuous, privacy-preserving mental health monitoring through ubiquitous e-commerce platforms. The DPIS framework provides a neurocognitively grounded, transdiagnostic alternative to categorical psychiatric diagnosis, enabling early intervention before crisis onset. With strong causal validation and robust privacy protections, this approach offers a scalable, stigma-free pathway to addressing the global mental health diagnostic gap.
PRIMUS: Pretraining IMU Encoders with Multimodal Self-Supervision

Sensing human motions through Inertial Measurement Units (IMUs) embedded in personal devices has enabled significant applications in health and wellness. Labeled IMU data is scarce, however, unlabeled or weakly labeled IMU data can be used to model human motions. For video or text modalities, the "pretrain and adapt" approach utilizes large volumes of unlabeled or weakly labeled data to build a strong feature extractor, followed by adaptation to specific tasks using limited labeled data. However, pretraining methods are poorly understood for IMU data, and pipelines are rarely evaluated on out-of-domain tasks. We propose PRIMUS: a method for PRetraining IMU encoderS that uses a novel pretraining objective that is empirically validated based on downstream performance on both in-domain and out-of-domain datasets. The PRIMUS objective effectively enhances downstream performance by combining self-supervision, multimodal, and nearest-neighbor supervision. With fewer than 500 labeled samples per class, PRIMUS improves test accuracy by up to 15%, compared to state-of-the-art baselines. To benefit the broader community, we have open-sourced our code at github.com/nokia-bell-labs/pretrained-imu-encoders.
A comprehensive phylogenetic analysis of termites (Isoptera) illuminates key aspects of their evolutionary biology

The first comprehensive combined molecular and morphological phylogenetic analysis of the major groups of termites is presented. This was based on the analysis of three genes (cytochrome oxidase II, 12S and 28S) and worker characters for approximately 250 species of termites. Parsimony analysis of the aligned dataset showed that the monophyly of Hodotermitidae, Kalotermitidae and Termitidae were well supported, while Termopsidae and Rhinotermitidae were both paraphyletic on the estimated cladogram. Within Termitidae, the most diverse and ecologically most important family, the monophyly of Macrotermitinae, Foraminitermitinae, Apicotermitinae, Syntermitinae and Nasutitermitinae were all broadly supported, but Termitinae was paraphyletic. The pantropical genera Termes, Amitermes and Nasutitermes were all paraphyletic on the estimated cladogram, with at least 17 genera nested within Nasutitermes, given the presently accepted generic limits. Key biological features were mapped onto the cladogram. It was not possible to reconstruct the evolution of true workers unambiguously, as it was as parsimonious to assume a basal evolution of true workers and subsequent evolution of pseudergates, as to assume a basal condition of pseudergates and subsequent evolution of true workers. However, true workers were only found in species with either separate- or intermediate-type nests, so that the mapping of nest habit and worker type onto the cladogram were perfectly correlated. Feeding group evolution, however, showed a much more complex pattern, particularly within the Termitidae, where it proved impossible to estimate unambiguously the ancestral state within the family (which is associated with the loss of worker gut flagellates). However, one biologically plausible optimization implies an initial evolution from wood-feeding to fungus-growing, proposed as the ancestral condition within the Termitidae, followed by the very early evolution of soil-feeding and subsequent re-evolution of wood-feeding in numerous lineages.
Price and the person: markets, discrimination, and personhood

The paper explores how pricing has historically been involved in the making up of persons (Hacking 1986; Carrier 1994), and how the ability to ‘personalize’ price is reconfiguring the ability of markets to discriminate persons. We discuss a variety of contemporary pricing practices, and three types of personhood they produce: generic, protected, and transcontextual. While some contemporary developments in pricing draw on understandings of the person that are quite familiar, others are novel and likely to be contested. We argue that many newer pricing techniques make it harder for consumers to identify themselves as part of a recognized group. We conclude that contemporary price personalization should be understood in terms of the intensification of individualization in combination with dividualization (Strathern 1988), and as such, contributes to what Fourcade and Healy (2013) describe as ‘classification situations that shape life-chances’.
Artificial Intelligence in Oncology: Integrating Screening, Diagnosis, Prognosis and Treatment

Cancer is a global health challenge characterized by complex, adaptive biological processes and substantial mortality rates. Early detection and precise treatment personalization of cancer is elusive despite  all the advances in oncological screening, diagnosis and treatment. Artificial intelligence (AI) emerges as a transformative technology in oncology by leveraging vast and varied datasets, including clinical records, genomic profiles, imaging, and pathology to augment decision-making and improve patient outcomes. AI encompasses machine learning (ML) and deep learning (DL) techniques capable of identifying intricate patterns in high-dimensional data unavailable to human cognition alone. These techniques are applied across the cancer care continuum, enabling improved risk stratification in screening, early diagnosis through radiological and histopathological image analysis, and enhanced prognostic modelling that surpasses traditional statistical methods. AI-driven models facilitate individualized treatment planning with adaptive radiotherapy, robotic surgery assistance, and optimization of chemotherapy and immunotherapy regimens, improving therapeutic efficacy while minimizing toxicity. This review synthesizes evidence across AI-driven screening, diagnosis, prognosis, treatment optimization and drug discovery, highlighting methodologies, clinical performance and future directions for AI in oncology. Key findings demonstrate that AI achieves expert-level performance in imaging-based cancer screening (mammography, colonoscopy, dermoscopy), surpasses traditional statistical models in multi-cancer prognostic prediction, and enables individualized treatment planning through adaptive radiotherapy, robotic surgical guidance, optimization of chemotherapy and immunotherapy regimes. Emerging applications in generative drug engineering and multi-cancer liquid biopsy detection further highlight AI’s expanding translational potential.
Regulation of epigenetic modifications of chromatin by the VRK1 kinase

Chromatin organization is regulated by covalent modifications of histones such as acetylation or methylation in specific residues, which can be regulated by phosphorylations. VRK1 is a nuclear Ser-Thr kinase implicated in many nuclear processes, such as proliferation, cell cycle or DNA Damage Repair (DDR). VRK1 is present in the chromatin fraction and is able to interact and phosphorylate H2AX in Ser139 after DNA Damage induced by irradiation, and also phosphorylates histone H3 in Thr3. In this work we have studied the role of human VRK1 Ser-Thr in the control of DNA damage response to IR and the role of specific acetylation and methylation of histones. We have shown that VRK1 is necessary for a proper formation of 53BP1 and γH2AX foci formation at double strand-breaks induced by ionizing radiation in and ATM-independent manner. DSB imply a local relaxation of chromatin and therefore it is likely that it might alter the modifications of histones. Fist we tested if acetylation is required for formation of DSB foci, and whether these acetylations require VRK1. The depletion of VRK1 by siRNA i affected the acetylation of H3 in Lys14 and Lys9 and also the H4 acetylation in Lys16, important markers of transcriptional activation. In contrast, siVRK1 enhanced the H3 methylation in Lys9 (a gene repressor marker) but not in Lys4 (transcriptional activator residue), confirming the role of VRK1 in positively mediating gene transcription. Also we have determined if manipulation of VRK1 levels can alter the tumour cell sensitivity to drugs currently used in clinical trials that target histone acetylation or methylation. Pharmacological inhibition of histone acetyl transferases (HATs) or VRK1 depletion did not rescue H3K9 and H4K16 acetylations, indicating that VRK1 is a regulator of HATs. We conclude that VRK1 regulates epigenetic mechanisms and has a role in chromatin condensation.