ethicalabs/Research-Intent-Collab huggingface dataset:
Statistical mechanics of random geometric graphs: Geometry-induced first-order phase transition

Random geometric graphs (RGG) can be formalized as hidden-variables models where the hidden variables are the coordinates of the nodes. Here we develop a general approach to extract the typical configurations of a generic hidden-variables model and apply the resulting equations to RGG. For any RGG, defined through a rigid or a soft geometric rule, the method reduces to a non trivial satisfaction problem: Given $N$ nodes, a domain $\mathcal{D}$, and a desired average connectivity $\langle k\rangle$, find - if any - the distribution of nodes having support in $\mathcal{D}$ and average connectivity $\langle k\rangle$. We find out that, in the thermodynamic limit, nodes are either uniformly distributed or highly condensed in a small region, the two regimes being separated by a first order phase transition characterized by a $\mathop{O}(N)$ jump of $\langle k\rangle$. Other intermediate values of $\langle k\rangle$ correspond to very rare graph realizations. The phase transition is observed as a function of a parameter $a\in[0,1]$ that tunes the underlying geometry. In particular, $a=1$ indicates a rigid geometry where only close nodes are connected, while $a=0$ indicates a rigid anti-geometry where only distant nodes are connected. Consistently, when $a=1/2$ there is no geometry and no phase transition. After discussing the numerical analysis, we provide a combinatorial argument to fully explain the mechanism inducing this phase transition and recognize it as an easy-hard-easy transition. Our result shows that, in general, ad hoc optimized networks can hardly be designed, unless to rely to specific heterogeneous constructions, not necessarily scale free.
Universal Error Correction for Distributed Quantum Computing

7pages, comments are welcome
Access to Primary Care Physicians Differs by Health Insurance Coverage in Mississippi

The objective of the study was to measure how access to primary health care in Mississippi varies by type of health insurance.We called primary care physician (general practitioner, family practice, internal medicine, obstetrics/gynecology, and pediatric) offices in Mississippi three times, citing different types of health insurance coverage in each call, and asked for a new patient appointment with a physician.Of all of the offices contacted, 7% of offices were not currently accepting new patients who had private insurance, 15% of offices were not currently accepting new Medicare patients, 38% were not currently accepting new Medicaid patients, and 9% to 21% of office calls were unresolved in one telephone call to the office.Access to health insurance does not ensure access to primary health care; access varies by type of health insurance coverage.
NEIGHBORHOOD-BASED APPROACH OF COLLABORATIVE FILTERING TECHNIQUES FOR BOOK RECOMMENDATION SYSTEM

Recommendation System or Recommender System help the user to predict the "rating" or "preference" a user would give to an item. Recommender systems in general helps the users to find content, products, or services (such as digital products, books, music, movie, TV programs, and web sites) by combining and analyzing suggestions from other users, which mean rating from various people, and users. These recommendation systems use analytic technology to calculate the results that a user is willing to purchase, and the users will receive recommendations to a product of their interest. The aim of the System is to provide a recommendation based on users likes or reviews or ratings. Recommendation system comprises of content based and collaborative based filtering techniques. In this paper, collaborative based filtering has been used to get the expected outcome. The expected outcome has been achieved through collaborative filtering with the help of correlation techniques which in turn comprises of Pearson correlation, cosine similarity, Kendall’ s Tau correlation, Jaccard similarity, Spearman Rank Correlation, Mean-squared distance, etc. This paper tells about which similarity metrics such us Pearson correlation (PC), constrained Pearson correlation (CPC), spearman rank correlation (SRC) which is good in the context of book recommendation system and then applied with neighborhood algorithm.
3D chromatin architecture and epigenetic regulation in cancer stem cells

AbstractDedifferentiation of cell identity to a progenitor-like or stem cell-like state with increased cellular plasticity is frequently observed in cancer formation. During this process, a subpopulation of cells in tumours acquires a stem cell-like state partially resembling to naturally occurring pluripotent stem cells that are temporarily present during early embryogenesis. Such characteristics allow these cancer stem cells (CSCs) to give rise to the whole tumour with its entire cellular heterogeneity and thereby support metastases formation while being resistant to current cancer therapeutics. Cancer development and progression are demarcated by transcriptional dysregulation. In this article, we explore the epigenetic mechanisms shaping gene expression during tumorigenesis and cancer stem cell formation, with an emphasis on 3D chromatin architecture. Comparing the pluripotent stem cell state and epigenetic reprogramming to dedifferentiation in cellular transformation provides intriguing insight to chromatin dynamics. We suggest that the 3D chromatin architecture could be used as a target for re-sensitizing cancer stem cells to therapeutics.
Music Genre Classification Using Music Information Retrieval and Self Organizing Maps

Recent times have seen a wide proliferation of use of Genre as basis of music classification and documentation. This paper proposes a novel approach of music genre classification using unsupervised Neural Network method—Kohonen Self Organizing Maps (SOM). Various music features like timbrel features (attack slope), spectral distribution of energy (brightness) and tonality (major or minor) were extracted from the audio files using MATLAB toolbox. An inventory of a number of musical pieces was developed for building clusters of different genre. A database containing the extracted musical features was then clustered using SOM. Each cluster encapsulated a different genre of music. The centroid of each cluster was taken as the representative point of that genre in the considered n dimensional musical feature space. Then a number of songs were then mapped onto the said space and classified into different genre depending on their Euclidean distance from the center of each cluster.
Defects in 3D Printing and Strategies to Enhance Quality of FFF Additive Manufacturing. A Review

Additive manufacturing technologies (or 3D printing) have emerged as potent tools in the creation of a diverse array of objects, promising a paradigm shift in production methodologies across industries. However, the benefits of these technologies can be diminished by the use of suboptimal parameters or inferior materials, leading to defects that significantly degrade the quality and functionality of the resulting products. An incomplete understanding of defect formation keeps under the formulation of effective preventative strategies. In light of this, our review provides a comprehensive exploration of defects that arise during the Fused Filament Fabrication (FFF)—one of the most prevalent 3D printing methods. The defects are systematically classified according to several key characteristics, including size, type, mode of occurrence, and location. Each common defect is extensively discussed, detailing its external manifestation, root causes, the impact on the properties of printed parts, and potential preventative measures. Our findings unveil the complex interplay of material properties, printing parameters, and cooling dynamics in the defect formation process. This classification holds significant practical relevance, providing a solid foundation for the development of strategies for defect minimization and quality improvement in 3D printed products. It offers valuable insights for a broad audience, including researchers exploring additive manufacturing technologies, 3D printing engineers, 3D printer operators, and quality assurance (QA) engineers involved in production quality control. Furthermore, our review delineates the path for future research in this domain. There is a crucial need for the development of advanced machine learning and artificial intelligence models that can predict defect formation based on given printing parameters and material properties. Future investigations should also focus on the discovery of novel materials and refining of printing parameters to achieve superior quality of FFF 3D printed products. This review serves as a cornerstone for these future advancements, promoting a deeper understanding of defect formation and prevention in additive manufacturing.
Local Differential Privacy for Federated Learning

17 pages
Development of a Natural Language Processing Machine to Enhance Identification of Opioid Use Disorder in Electronic Health Records

Background/Objective: Real-world data, including electronic health records (EHRs), has shown tremendous utility in research relating to opioid use disorder (OUD). Traditional analysis of EHR data relies on explicit diagnostic codes and results in incomplete capture of cases and therefore underrepresentation of OUD rates. Machine learning can rectify this by surveying free clinical notes in addition to structured codes. This study aimed to address disparities between true OUD rates and cases identified using traditional ICD codes by developing a natural language processing (NLP) machine for identifying affected patients from EHRs.  Methods: Patients (≥12 years old) who had received an opioid prescription from IU Health or Eskenazi Health between 1/1/2009 and 12/31/2015 were identified by the Regenstrief Institute. Exclusion criteria included any cancer, sickle cell anemia, or palliative care diagnoses. Cases of OUD were identified through ICD codes and NLP. The NLP machine was developed using a dictionary of key OUD terms and a training corpus of 300 patient notes. A testing corpus of 148 patient notes was constructed and validated by manual review. The NLP machine and ICD 9/10 codes were independently tested against this corpus.  Results: Although ICD codes identified OUD cases with high specificity (98.08%), this method demonstrated moderate sensitivity (53.13%), accuracy (68.92%), and F1 score (68.92%). Testing using the NLP method demonstrated increased sensitivity (93.75%), increased accuracy (89.19%), and increased F1 score (91.84%); specificity mildly decreased (80.77%).  Conclusion: Our revised NLP machine was more effective at capturing OUD cases in EHRs than traditional identification using ICD codes. This illustrates NLP’s enhanced capability of identifying OUD cases compared to structured data.  Potential Impacts: These findings establish a role for NLP in OUD research involving large datasets. Ultimately, this is intended to improve identification of risk factors for OUD, which is of significant clinical importance during a public health crisis.
Beyond the Precautionary Principle: Protecting Public Health and the Environment in the Face of Uncertainty

In this article, we scrutinize the ability of the Precautionary Principle to serve as a unifying principle for public health. Although most commonly invoked in environmental health regulatory debates, implicit and explicit invocations of the Principle have spread to other contexts. Here we seek to understand the potential uses of the Precautionary Principle for those concerned with population health by considering its invocation in five cases: vaccination, quarantine for SARS, needle exchange to prevent the spread of HIV/AIDS, e-cigarettes as an alternative to tobacco cigarettes, and climate change mitigation. We ask whether the Precautionary Principle offers a philosophical approach precise and sufficiently stringent to guide health policy in a range of circumstances where evidence may be less than definitive and the course of action contested. We find there are far more ambiguities in the Principle’s application than might appear at first and conclude that it is best used in concert with other frameworks for guiding action in the face of uncertainty.
Prompt Compression in Production Task Orchestration: A Pre-Registered Randomized Trial

The economics of prompt compression depend not only on reducing input tokens but on how compression changes output length, which is typically priced several times higher. We evaluate this in a pre-registered six-arm randomized controlled trial of prompt compression on production multi-agent task-orchestration, analyzing 358 successful Claude Sonnet 4.5 runs (59-61 per arm) drawn from a randomized corpus of 1,199 real orchestration instructions. We compare an uncompressed control with three uniform retention rates (r=0.8, 0.5, 0.2) and two structure-aware strategies (entropy-adaptive and recency-weighted), measuring total inference cost (input+output) and embedding-based response similarity. Moderate compression (r=0.5) reduced mean total cost by 27.9%, while aggressive compression (r=0.2) increased mean cost by 1.8% despite substantial input reduction, consistent with small mean output expansion (1.03x vs. control) and heavy-tailed uncertainty. Recency-weighted compression achieved 23.5% savings and, together with moderate compression, occupied the empirical cost-similarity Pareto frontier, whereas aggressive compression was dominated on both cost and similarity. These results show that "compress more" is not a reliable production heuristic and that output tokens must be treated as a first-class outcome when designing compression policies.
The 5 A’s of firearm safety counseling: Validating a clinical counseling methodology for firearms in a simulation-based randomized controlled trial

The 5 A's of Firearm Safety Counseling is a novel framework by which clinicians can approach firearm injury prevention counseling. To evaluate this methodology as a tool for clinicians, a single-center, simulation-based randomized controlled trial was performed with clinical trainees in psychiatry, medicine, and pediatrics in an urban quaternary care center. Participants received didactic education on firearm injury epidemiology and evidence-based policies and training on a specific counseling framework, the 5 A's of Firearm Safety Counseling which they then implemented in a simulation setting with standardized patients. Of the 29 participants who were randomized, 28 completed the trial. Most participants were psychiatry trainees (residents or subspecialty fellows). While over 60% of participants were uncomfortable or extremely uncomfortable counseling on firearm injury prior to the interventions, only 4% reported being uncomfortable after receiving education and participating in simulated encounters. There was no significant difference between the quality and content of the counseling provided before and after the didactic-only session. There was a significant difference between the quality and content of the counseling provided before and after the specific training on the 5 A's for Firearm Safety Counseling strategy. The 5 A's for Firearm Safety Counseling is a promising educational tool to improve quality, content, and comfort delivering patient-centered counseling on firearm injury prevention in a simulation-based setting. These findings suggest that further validation in a clinical setting is warranted given there is an urgent need for feasible and effective firearm injury prevention strategies among clinicians.
Optimal Design and Experimental Modification of Multi-row Valve Ports Generating Drilling Fluid Continuous Wave

Technology of downhole data transmission based on drilling fluid continuous pressure waves is extensively applied to the drilling operation of oil/gas wells due to high transmission rate. For generation of the continuous pressure waves, this paper designs a signal generator with multi-row valve ports, and an optimization model is established to preliminarily solve the valve ports distribution. In addition, a laboratory experiment is carried out to determine the relationship between flow and pressure of the drilling fluid flowing through the multi-row valve ports, and the flow coefficient and valve port exponent herein are adopted to modify the previous optimization result of the valve ports distribution. Ultimately, the pressure waves produced by the preliminary and modified optimization results are compared with sine wave. Results show that the multi-row valve port exponent and flow coefficient are experimentally measured to be 0.3 and 0.75, respectively, and based on them, the modified optimization result of the valve ports distribution is capable of generating a pressure wave that has a higher similarity with sine wave. This is because that the correlation coefficient between them is as high as 0.9978, which is very close to 1. Research results indicate the optimization model for calculating the valve ports distribution is correct. Furthermore, the experimental data are reliable and can accurately reveal the flow and pressure characteristics of the multi-row valve ports.
Neural Network Blind Equalization Algorithm Applied in Medical CT Image Restoration

A new algorithm for iterative blind image restoration is presented in this paper. The method extends blind equalization found in the signal case to the image. A neural network blind equalization algorithm is derived and used in conjunction with Zigzag coding to restore the original image. As a result, the effect of PSF can be removed by using the proposed algorithm, which contributes to eliminate intersymbol interference (ISI). In order to obtain the estimation of the original image, what is proposed in this method is to optimize constant modulus blind equalization cost function applied to grayscale CT image by using conjugate gradient method. Analysis of convergence performance of the algorithm verifies the feasibility of this method theoretically; meanwhile, simulation results and performance evaluations of recent image quality metrics are provided to assess the effectiveness of the proposed method.
Deep neural networks integrating genomics and histopathological images for predicting stages and survival time-to-event in colon cancer

Motivation There exists an unexplained diverse variation within the predefined colon cancer stages using only features from either genomics or histopathological whole slide images as prognostic factors. Unraveling this variation will bring about improved staging and treatment outcomes. Hence, motivated by the advancement of Deep Neural Network (DNN) libraries and complementary factors within some genomics datasets, we aggregate atypia patterns in histopathological images with diverse carcinogenic expression from mRNA, miRNA and DNA methylation as an integrative input source into a deep neural network for colon cancer stages classification, and samples stratification into low or high-risk survival groups.   Results The genomics-only and integrated input features return Area Under Curve–Receiver Operating Characteristic curve (AUC-ROC) of 0.97 compared with AUC-ROC of 0.78 obtained when only image features are used for the stage’s classification. A further analysis of prediction accuracy using the confusion matrix shows that the integrated features have a weakly improved accuracy of 0.08% more than the accuracy obtained with genomics features. Also, the extracted features were used to split the patients into low or high-risk survival groups. Among the 2,700 fused features, 1,836 (68%) features showed statistically significant survival probability differences in aggregating samples into either low or high between the two risk survival groups. Availability and Implementation: https://github.com/Ogundipe-L/EDCNN
BUSINESS ETHICS AND CORPORATE SOCIAL RESPONSIBILITY

The conversation about sustainable development in modern business practices has come to revolve around corporate social responsibility (CSR) and business ethics. This study explores the relationship between corporate social responsibility (CSR) and ethics, with the goal of clarifying the importance, difficulties, and effects on corporate behaviour and the well-being of society. Starting with a definition of CSR and business ethics, the study goes on to discuss their conceptual foundations and development within the corporate environment. After that, it delves into the theoretical frameworks of corporate citizenship, normative ethics, and stakeholder theory that support CSR and ethical decision-making processes. In addition, the article looks at how business ethics and corporate social responsibility are applied in a variety of sectors and places. It examines case studies of businesses that have successfully incorporated ethical behaviour and CSR programs into their main business plans, demonstrating the benefits to the organization and its stakeholders. In addition, the article critically assesses the difficulties and conundrums that companies face when putting CSR and ethical principles into practice, including problems with greenwashing, stakeholder prioritization, and competing stakeholder interests. It also covers how corporate governance practices, industry standards, and legal frameworks support ethical business practices. In reaction to the bad press surrounding encounters with diverse settings, organizations are beginning to implement ethics programs. For organizations to continue receiving support from suppliers, customers, and other stakeholders, they must uphold public trust. A strategic plan's formulation involves considerations of ethics. But it makes sense that stakeholders are naturally focused on profitability, frequently ignoring the importance of ethics and corporate social responsibility. After all, businesses primarily focus on marketing campaigns, sales advertisements, and other ploys to outmanoeuvre rivals and hit budget targets in an effort to deliver agreed returns to their stakeholders. Finally, the article addresses prospects and trends for CSR and business ethics in the future, taking into account new subjects including corporate activism, ethical AI, and stakeholder participation in decision-making. In order to promote long-term value generation and societal well-being, it emphasizes in its conclusion how important it is for firms to embrace CSR and ethical practices as essential elements of sustainable development. In summary, this study adds to the current discussion on corporate social responsibility (CSR) and business ethics by offering perspectives and suggestions for companies trying to negotiate the complicated terrain of 21st-century responsible business practices. Keywords: Corporate Social Responsibility (CSR), Business Ethics, Sustainable Development, Ethical Decision-Making, Corporate Citizenship, Stakeholder Engagement