ethicalabs/Research-Intent-Collab huggingface dataset:
Integrative Prediction of Gene Function and Platinum-Free Survival from Genomic and Epigenetic Features in Ovarian Cancer

The identification of genetic and epigenetic alterations from primary tumor cells has become a common method to discover genes critical to the development, progression, and therapeutic resistance of cancer. We seek to identify those genetic and epigenetic aberrations that have the most impact on gene function within the tumor. First, we perform a bioinformatics analysis of copy number variation (CNV) and DNA methylation covering the genetic landscape of ovarian cancer tumor cells. We were specifically interested in copy number variation as our base genomic property in the prediction of tumor suppressors and oncogenes in the altered ovarian tumor. We identify changes in DNA methylation and expression specifically for all amplified and deleted genes. We statistically define tumor suppressor and oncogenic gene function from integrative analysis of three modalities: copy number variation, DNA methylation, and gene expression. Our method (1) calculates the extent of genomic and epigenetic alterations of defined tumor suppressor and oncogenic features for the functional prediction of significant ovarian cancer gene candidates and (2) identifies the functional activity or inactivity of known tumor suppressors and oncogenes in ovarian cancer. We applied our protocol on 42 primary serous ovarian cancer samples using MOMA-ROMA representational array assays. Additionally, we provide the basis for incorporating epigenetic profiles of ovarian tumors for the purposes of platinum-free survival prediction in the context of TCGA data.
Natural Language Processing (NLP) in Analyzing Electronic Health Records for Better Decision Making

Natural Language Processing (NLP) is transforming healthcare decision-making by extracting valuable insights from Electronic Health Records (EHR). This paper explores the integration of NLP with EHR systems, focusing on its potential to enhance clinical workflows, patient outcomes, and the accuracy of healthcare decision-making. Using advanced NLP techniques, such as BERT and spaCy, the study analyzes both structured and unstructured EHR data to uncover patterns in diagnosis, treatment recommendations, and patient outcomes. The study compares NLP-based analysis with traditional data analysis methods, demonstrating its effectiveness in improving clinical decision support systems. Despite the promising potential, challenges such as data quality, model interpretability, and seamless integration with existing healthcare systems are highlighted. The paper concludes by emphasizing the need for continued advancements in NLP models and real-time data processing, suggesting future research directions to optimize the implementation of NLP in healthcare settings.
Constitutional Courts as Guardians of EU Fundamental Rights? Centralised Judicial Review of Legislation and the Charter of Fundamental Rights of the EU

This paper discusses Judgment of 11 September 2014, Case C-112/13, A v B and others of the Court of Justice of the EU, and its implications on centralised judicial review of legislation in the Member States.
INTERFAITH DIALOGUE AND ITS ROLE IN PEACE BUILDING PARTIES, AND CONFLICT RESOLUTION IN RAYA AZEBO WEREDA, SOUTHERN TIGRAY, ETHIOPIA

This thesis explores the role of interfaith dialogue in peace-building and conflict resolution between Orthodox Christians and Muslims in Raya Azebo Woreda, Ethiopia. The primary objective is to examine how interfaith dialogue contributes to reconciling disputing parties and fostering peaceful coexistence in a religiously diverse context. Specific objectives include analyzing the impact of community-based religious values on relationships among believers, assessing the mechanisms of community faith-based institutions in peace-building, and investigating their effectiveness in promoting collaboration among different faiths. Utilizing a qualitative research methodology, the study employs personal observations, in-depth interviews, and focus group discussions to gather insights from key participants, including local religious leaders and community peace builders. The findings reveal that interfaith dialogue significantly aids conflict prevention and reconciliation at the community level. It highlights the vital role of faith-based actors, such as “አአአአ”s, “አአአአአ”s, and “Debartes,” in facilitating dialogue and fostering mutual respect among diverse religious groups. The significance of this research lies in its potential to inform policymakers and community leaders about the constructive role of interfaith dialogue in promoting long-term peace and social cohesion. Furthermore, it emphasizes the importance of integrating peace education into curricula in conflict-affected regions. The study concludes that the ongoing practices of interfaith dialogue in Raya Azebo Woreda demonstrate a tradition of religious tolerance and communal harmony, underscoring the potential of faith-based initiatives in building a peaceful society.
HRIS Model Design (Human Resources Information System) at PT. Poultry Diamond Partners

The livestock business is one of the businesses that contribute quite significantly in developing quality human resources for a country. The development of quality human resources must certainly be supported by the adequacy of quality animal food so over the past few years the government has focused on encouraging the growth and development of livestock businesses to strengthen the country's food security.  The method used in this research is action research. Methods in action research are based on the assumption that theory and practice can be integrated with learning from the results of planned interventions after carrying out a diagnosis process against the context of the existing problem.  Based on the results obtained from the diagnostic stage, PT. Mitra Berlian Unggas currently does not have a standard and effective MSDM management system. With the limited number of members of the HR Division and the existence of a plan to develop and grow the livestock network, the company realized the need for a system that could accommodate "improvement" efforts and facilitate the work of the HR Division in managing employees. HRIS design at PT. Mitra Berlian Unggas is carried out by considering the company's need for the ability of the system to facilitate the management of human resources in the company today, without excluding the characteristics of the company that has a distribution of livestock networks in several regions, so that employees need to work remotely.
Force fields and molecular dynamics simulations

The objective of this review is to serve as an introductory guide for the non-expert to the exciting field of Molecular Dynamics (MD). MD simulations generate a phase space trajectory by integrating the classical equations of motion for a system of N  particles. Here I review the basic concepts needed to understand the technique, what are the key elements to perform a simulation and which is the information that can be extracted from it. I will start defining what is a force field, which are the terms composing a classical force field, how the parameters of the potential are optimized, and which are the more popular force fields currently employed and the lines of research to improve them. Then the Molecular Dynamics technique will be introduced, including a general overview of the main algorithms employed to integrate the equations of motion, compute the long-range forces, work on different thermodynamic ensembles, or reduce the computational time. Finally the main properties that can be computed from a MD trajectory are briefly introduced.
A Review on Neural Network Models of Schizophrenia and Autism Spectrum Disorder

This survey presents the most relevant neural network models of autism spectrum disorder and schizophrenia, from the first connectionist models to recent deep network architectures. We analyzed and compared the most representative symptoms with its neural model counterpart, detailing the alteration introduced in the network that generates each of the symptoms, and identifying their strengths and weaknesses. We additionally cross-compared Bayesian and free-energy approaches, as they are widely applied to modeling psychiatric disorders and share basic mechanisms with neural networks. Models of schizophrenia mainly focused on hallucinations and delusional thoughts using neural dysconnections or inhibitory imbalance as the predominating alteration. Models of autism rather focused on perceptual difficulties, mainly excessive attention to environment details, implemented as excessive inhibitory connections or increased sensory precision. We found an excessive tight view of the psychopathologies around one specific and simplified effect, usually constrained to the technical idiosyncrasy of the used network architecture. Recent theories and evidence on sensorimotor integration and body perception combined with modern neural network architectures could offer a broader and novel spectrum to approach these psychopathologies. This review emphasizes the power of artificial neural networks for modeling some symptoms of neurological disorders but also calls for further developing these techniques in the field of computational psychiatry.
AI-driven Natural Language Processing and Stochastic Modeling for Transforming Unstructured Electronic Health Records into Structured Clinical Data

Electronic health records (EHRs) contain large amounts of valuable clinical information, but a substantial portion of this information exists in unstructured form, including physician notes, discharge summaries, and narrative clinical reports. Because these data are recorded as free text, they are difficult to aggregate, standardize, and analyze using conventional statistical or database methods. As a result, a significant amount of clinically relevant information remains underutilized in healthcare analytics and decision support systems. This study proposes a hybrid framework that combines artificial intelligence–based natural language processing (NLP) with stochastic modeling to transform unstructured EHR narratives into structured clinical datasets. The approach first applies AI-driven NLP techniques to identify and extract clinically meaningful entities such as diagnoses, symptoms, medications, laboratory values, and procedures from free-text clinical notes. The extracted information is then organized into relational tables suitable for large-scale analytics. To account for patient heterogeneity and uncertainty in clinical observations, stochastic analytical methods are applied to reconstruct latent health trajectories and estimate time-dependent risk patterns. The proposed framework enables the integration of narrative clinical information into structured data environments, facilitating more comprehensive analysis of patient health dynamics. By combining AI-based text extraction with stochastic modeling, the method improves the ability to analyze complex clinical datasets and supports more realistic representation of disease progression and patient variability. This approach has the potential to enhance population health analytics, clinical research, and predictive modeling using electronic health record data.
Optimal genetic circuit design for mammalian synthetic biology applications

Synthetic biology combines biology and engineering to redesign living organisms by conferring them new properties. Synthetic biologists harness the power of nature to solve problems in medicine, manufacturing, and agriculture and use engineering principles to control and perturb cell pathways to gather novel insights into biological functions. Specifically, the programming of new functions into mammalian cells has tremendous application in research and medicine. The rapid advances in the ability to synthesize DNA and RNA have increased our understanding of gene function and regulation and have enabled the expansion of genetic parts available for programming cell biology. Genetic parts can be multiplexed to build complex genetic circuits to (i) confer new functions to the host cells, (ii) regulate endogenous pathways, or (iii) sense inputs of interest to actuate a cellular response. This thesis focuses on the optimization of genetic circuit design for mammalian synthetic biology applications. It covers the need for additional regulatory tools for tight spatiotemporal control of gene expression and cell response and the need for a better understanding of how regulation processes work in cells. Finally, it explores strategies to select biologically relevant inputs for sensor-actuator devices that control cell fate.
First-principles design and subsequent synthesis of a material to search for the permanent electric dipole moment of the electron

We describe the first-principles design and subsequent synthesis of a new material with the specific functionalities required for a solid-state-based search for the permanent electric dipole moment of the electron. We show computationally that perovskite-structure europium barium titanate should exhibit the required large and pressure-dependent ferroelectric polarization, local magnetic moments, and absence of magnetic ordering even at liquid helium temperature. Subsequent synthesis and characterization of Eu$_{0.5}$Ba$_{0.5}$TiO$_3$ ceramics confirm the predicted desirable properties.
Collaborative learning in action: empowering students as authors of open educational resources case studies on public health

Seminar-style honors courses provide an opportunity for participants to share perspectives and learn from different disciplines. Seminars on current topics challenge students to identify reliable information and work with others to understand and form opinions on transdisciplinary issues. When a textbook is not accessible to all or the subject matter is constantly evolving, students can research topics and create case studies that their peers can learn from. The Honors                     Outbreak                     course at NC State engages students in co-creating Open Educational Resources (OER) case studies describing recent public health outbreaks. Students familiarize themselves with the outbreak's source and impact through asynchronous activities. Groups are tasked with writing sections of an outbreak investigation. All students work on concept maps, graphics, and questions. Students work together to fact-check the content and format it as an engaging case study for the Learn Gala site. Students revise the text and glossaries, improving the case studies and creating critical thinking questions for learners. Future cohorts develop new case studies and use the existing ones. These resources are openly licensed, making them available for others to benefit from. This framework can be adapted for a variety of topics, highlighting how honors seminars can engage scholars in the creation of knowledge and in the peer-review process to produce high-quality open educational resources. Importantly, the process empowers students to become researchers, authors, reviewers, and critical evaluators of information. These skills, along with teamwork and collaboration, help prepare honors scholars to navigate and contribute to the deluge of information.
On the Design of Parallel Discrete Algorithms for High Performance Computing Systems

The use of high performance computing systems is steadily increasing. For instance, systems composed of PC clusters interconnected by high performance local networks is one of the major trends in parallel/distributed computing nowadays, since these clusters yield parallel systems more powerful than some super-computers, for a fraction of the price. Although a great deal of effort has been undertaken on system-level and programming environment issues for such systems, little attention has been paid to computing issues. In this paper we discuss theoretical (BSP-like) models and their adaptation to solve important classes of problems on high performance computing systems. We show as an example a coarse-grained parallel algorithm to solve the maximum weighted clique problem in interval graphs. It is theoretically optimal, since it requires only a constant number of communication rounds, and is also efficient in practice as demonstrated by reported experimental results obtained on a Myrinet-connected PC cluster. Noticeably, even super-linear speedups can occur for large data because of swapping factors. We conclude that these models are well adapted to the task of algorithm design for high performance computing systems.
Physics-informed Meta-instrument for eXperiments (PiMiX) with applications to fusion energy

Data-driven methods (DDMs), such as deep neural networks, offer a generic approach to integrated data analysis (IDA), integrated diagnostic-to-control (IDC) workflows through data fusion (DF), which includes multi-instrument data fusion (MIDF), multi-experiment data fusion (MXDF), and simulation-experiment data fusion (SXDF). These features make DDMs attractive to nuclear fusion energy and power plant applications, leveraging accelerated workflows through machine learning and artificial intelligence. Here we describe Physics-informed Meta-instrument for eXperiments (PiMiX) that integrates X-ray (including high-energy photons such as $γ$-rays from nuclear fusion), neutron and others (such as proton radiography) measurements for nuclear fusion. PiMiX solves multi-domain high-dimensional optimization problems and integrates multi-modal measurements with multiphysics modeling through neural networks. Super-resolution for neutron detection and energy resolved X-ray detection have been demonstrated. Multi-modal measurements through MIDF can extract more information than individual or uni-modal measurements alone. Further optimization schemes through DF are possible towards empirical fusion scaling laws discovery and new fusion reactor designs.
erancangan Aplikasi Human Resource Information System (HRIS) Berbasis Website

CV. Sita Deceng merupakan badan usaha yang bergerak dalam bidang pertambangan galian golongan C yang merupakanpenambangan berupa tambang pasir, kerikil, sirtu, gelondongan. Sistem pemasaran yang dijalankan saat ini masihmenggunakan cara konvensional yaitu melalui chatting/telepon, face to face serta memanfaatkan media sosial  dimanasistem tersebut belum menjangkau secara luas. Selain itu, dalam pengelolaan data perusahaan baik dalam pengolahan datatransaksi, data barang, data pelanggan, data  karyawan sampai dengan pembuatan laporan masih menggunakan cara manual  dengan menggunakan media kertas untuk mencatat data yang ada di perusahaan. Untuk menangani permasalahan tersebutdibuatlah aplikasi Human Resources Information System (HRIS) yang dapat membantu bagian personalia/HRD dan stafuntuk membuat laporan dan pendataan yang lebih rapi dan akurat. HRIS adalah prosedur sistematik untuk mengumpulan,menyimpan, mempertahankan, menarik dan memvalidasi data yang dibutuhkan oleh perusahaan untuk meningkatkankeputusan sumber daya manusia. Penggunaan HRIS di dalam perusahaan dapat mempercepat informasi untuk mengambilkeputusan dengan cepat dan tepat, metode pengembangan sistem yang digunakan adalah Waterfall yang terdiri dari analisis,perancangan, desain, implementasi dan pengujian. Diharapkan sistem ini dapat meningkatkan SDM menjadi lebih baikdalam menjalankan tugas yang diberikan direktur perusahaan.
Studying the Properties of Accretion Disks and Coronae in Black Hole X‐Ray Binaries with Monte Carlo Simulation

Accepted version, appear on ApJ 20 January 2005 issue
Marine biodiversity conservation: The cultural aspect of marine protected areas in Ghana

Abstract  Studying fishers and fishing communities' social‐cultural structures is vital in policy‐making concerning local marine ecosystem sustainability. Previous studies on marine communities seldom neglected the role of culture in marine biodiversity conservation, especially a marine protected area (MPA) establishment. This study first explores the cultural practices in coastal communities in Ghana that have the potential for the conservation of the marine ecosystem (MPA); second, it examines the willingness of coastal communities to welcome an MPA establishment. Primary data were collected using focus group discussions with 9 fishermen, key informant interviews with 20 chief fishermen, and self‐administered questionnaires to 491 fishermen across 9 semi‐urban, 2 urban, and 16 villages along the coast of Ghana. Adopting the convergent parallel mixed‐methods research design (quantitative method includes descriptive analysis using tables, charts, diagrams, and χ2 bivariate analysis; and qualitative method involves a narrative format), the study underscored that the socioeconomic characteristics of fishermen are diverse and mixed. Most of the fishermen do not know what an MPA is but are willing to accept an MPA initiative. Certain cultural practices such as the prohibition of gathering certain species, traditional close season, and fishing holidays are found to be conducive to an MPA initiative. These cultural practices should be integrated into an MPA policy. Fisheries practitioners should consider an ecosystem‐based approach (including cultural values) to fisheries management and fishermen should endeavour to learn new skills outside the industry.
Experimental Research on the Optimal Operation Mode of Integrated Oxidation Ditch with Continuous Flow and Intermittent Aeration

The influence of HRT , oxidic time and anoxidic time on the carbon and nitrogen removal of integrated oxidation ditch with continuous flow and intermittent aeration was studied by cross experiment method. The results show that the anoxidic time has the most distinct influence on removal ratio of BOD5 and TN in the system and the oxidic time has a distinct influence on BOD5 as well .When HRT , oxidic time and anoxidic time of the system is 8h , 2 h and 2 h respectively , the effluent quality and the removal ratio for BOD5 are &lt;20 mg/ l and 85. 2 % and for TN are &lt; 20 mg/ l and 55. 5 % respectively.
Diffusion of innovations in a social network: the influence of network topology

Recently, there has been a great interest in modelling the diffusion of innovations. In our work, we model the adopters as nodes and the connections among them as the links of network of agents. We try to investigate the relationship between topology and the growth of the global technological level of the network.
Generative AI for Drug Molecule Discovery

This research paper explores the role of Generative Artificial Intelligence in accelerating drug molecule discovery. Traditional drug discovery processes are often time-consuming and expensive, requiring extensive laboratory experimentation. This study reviews how modern AI techniques such as Transformer models, Diffusion models, and Reinforcement Learning can generate and optimize molecular structures efficiently.  Transformer-based architectures like ChemBERTa and MolFormer help understand chemical structures and generate valid molecular sequences. Diffusion models focus on creating accurate 3D molecular conformations that maintain chemical and spatial properties. Reinforcement Learning frameworks guide molecular generation toward desired pharmaceutical properties such as stability, efficacy, and reduced toxicity.  The paper highlights how integrating these AI approaches can significantly reduce the time and cost involved in drug discovery while enabling researchers to explore a larger chemical space. This work demonstrates the growing importance of artificial intelligence in modern pharmaceutical research and molecular design.