ethicalabs/Research-Intent-Collab huggingface dataset:
INTEGRATING 3D PRINTING PARAMETERS INTO TECHNICAL DRAWINGS: A FRAMEWORK FOR SEAMLESS ADDITIVE MANUFACTURING

3D printing, or additive manufacturing, has become a transformative technology that enables rapid prototyping, customized production, and the creation of complex geometries unattainable through traditional methods. It is widely used in aerospace, healthcare, automotive, and consumer industries, offering cost-effective and efficient production solutions. However, a critical gap remains: 3D printing parameters are rarely integrated into technical drawings, leading to a disconnect between digital design and physical manufacturing. Incorporating 3D printing data into technical drawings is essential to streamline the transition from digital models to physical parts. While conventional drawings include fabrication details such as tolerances and material specifications, they lack the parameters necessary for additive manufacturing. This research proposes a framework to embed key 3D printing parameters—such as layer height, printing speed, infill density, support structures, extrusion temperature, and cooling settings—directly within technical drawings. To achieve this, a standardized annotation system should be developed, similar to conventions used in machining and casting. Such a system would define how 3D printing information is encoded within CAD-generated drawings, ensuring consistency across software and production environments. Like geometric dimensioning and tolerancing (GD&amp;T) in CNC machining, this framework would allow engineers to convey precise additive manufacturing instructions through an extended metadata layer or specialized symbols, reducing the need for separate process sheets. Integrating these annotations into common CAD formats like STEP and IGES would enhance interoperability and communication between design and production teams. By embedding 3D printing parameters directly into technical drawings, manufacturers can minimize errors, improve repeatability, and accelerate workflows. This approach strengthens the link between design intent and fabrication, promoting a more seamless and standardized implementation of additive manufacturing. Keywords: 3D printing, Additive manufacturing, Technical drawing, 3D modelling, Dimensioning rules
Aligning Medical Conversational AI through Online Reinforcement Learning with Information-Theoretic Rewards

We present Information Gain Fine-Tuning (IGFT), a novel approach for training medical conversational AI to conduct effective patient interviews and generate comprehensive History of Present Illness (HPI) without requiring pre-collected human conversations. IGFT combines online Group Relative Policy Optimization (GRPO) with information-theoretic rewards, enabling models to learn from self-generated conversations with simulated patients. Unlike existing approaches that rely on expensive expert-annotated conversations or static datasets, our online RL framework allows models to discover effective questioning strategies through exploration. Our key innovation is an information gain reward function that tracks which clinical entities such as symptoms, temporal patterns, and medical history, are revealed during conversation. Each question's reward is computed based on its expected information gain combined with GPT-4o-mini quality assessments across dimensions including clinical relevance, patient engagement, and specificity. This hybrid approach ensures models learn to ask targeted, clinically appropriate questions that efficiently gather diagnostic information. We fine-tune two models using LoRA: Llama-3.1-8B-Instruct and DeepSeek-R1-Distill-Qwen-7B (a reasoning-optimized model). Training exclusively on Avey data containing concise HPIs, we evaluate generalization to MIMIC data with longer, more elaborate HPIs. DeepSeek-R1-Distill-Qwen-7B (IGFT) achieves F1 scores of 0.408 on Avey (10.9% improvement over base) and 0.289 on MIMIC (12.9% improvement), while Llama-3.1-8B-Instruct (IGFT) reaches 0.384 and 0.336 respectively. Both models outperform OpenAI's model on MIMIC and surpass medical domain-specific baselines like HuatuoGPT and UltraMedical, which were optimized for single-turn medical QA rather than multi-turn conversations.
DEVELOPING MACHINE LEARNING METHODOLOGY FOR PRECISION HEALTH

Precision health has been an increasingly popular solution to improve health care quality and guide the decision making process. This includes precision medicine (at the individual level) and precision public health (at the population level such as communities and institutions). By learning from the available medical data with advanced analytical tools, precision health recommends the treatments that are individualized to each patient or entity to maximize clinical outcomes for each individual. We extend and develop three machine learning methods to improve the estimation of optimal individualized treatment regimes in precision health: the jackknife estimator of value function of precision medicine models compared with zero-order models, doubly robust outcome-weighted estimators with deep neural network structures for complex and large data, and risk-adjusted adverse event monitoring for survival data. First, motivated by a knee osteoarthristis trial, we estimate value functions and select the optimal treatment with the jackknife method whose consistency is established under weak assumptions. Next, we implement deep learning architecture in augmented outcome-weighted learning to increase model flexibility and computation efficiency, especially for high-dimensional data such as medical imaging. Lastly, we develop a risk-adjusted survival model to monitor adverse events and estimate its variance for hierarchical, right-censored data with recurrent events. All three methodologies aim to solve practical, health-related challenges and provide data-driven decision support and operations.
A system for simulation and monitoring of robot-assisted and navigation-assisted surgical interventions (Part 1)

Location monitoring systems for surgical applications are getting more and more common in clinical daily life. Navigation treatment systems are also a form of a location monitoring system. These systems always consist of an optical, US-, electromagnetic or mechanical navigation system. If two or more navigation systems are combined or one navigations system is combined with a robot system the kinematic model of the entire system consists of closed kinematic loops. In this paper, an approach is presented to gain more information from the closed kinematic chains instead of breaking the kinematic loops.
Comparative performance of semi-empirical based remote sensing and crop simulation model for cotton yield prediction

Early prediction of crop yield serves as a strategic plan for policymakers in making suitable import–export policies. In this research, remote sensing driven semi-empirical light use efficiency (LUE) model and a process-based crop simulation model (CSM) approach were attempted to predict cotton yield in the five districts of Maharashtra state, India. Time-series satellite data from different earth observation (EO) missions were retrieved from 2011 to 2017. Satellite-based measurements of fraction of absorbed photosynthetically active radiation (FAPAR) and photosynthetically active radiation (PAR) data were directly incorporated into the LUE model. The LUE model approach used observed minimum (0.07) and maximum (0.12) lint harvest index (HI) values to predict the cotton yield. On the other hand, the CSM model was parameterized with observed field management, soil, weather, and cultivar data for crop yield prediction. The inter-evaluation of models has revealed that the LUE model with HI value 0.07 (HI0.07) performed better when compared to the LUE model with HI value 0.12 (HI0.12) and CSM model. LUE model HI0.07 had shown better accuracy in terms of root mean square error (RMSE) of 154 kg ha−1 and correlation coefficient value of (R2 = 0.46) when compared with LUE model HI0.12 (RMSE 310 kg ha−1 and R2 = 0.41) and CSM model (RMSE 235 kg ha−1 and R2 = 0.38). The predicted yield from the models was found satisfactory for the normal year with acceptable accuracy. However, overestimation of yield was seen for the years affected by drought. This study has shown the potential of geospatial data that can be coupled with crop models to predict the crop yield with limited field observations.
Laser and Photonics Reviews photonic single perceptron based on time-wavelength multiplexing with a microcomb for optical neural networks

Optical artificial neural networks (ONNs) — analog computing hardware tailored for machine learning [1, 2] — have significant potential for ultra-high computing speed and energy efficiency [3]. We propose a new approach to architectures for ONNs based on integrated Kerr micro-comb sources [4] that is programmable, highly scalable and capable of reaching ultra-high speeds. We experimentally demonstrate the building block of the ONN — a single neuron perceptron — by mapping synapses onto 49 wavelengths of a micro-comb to achieve a high single-unit throughput of 11.9 Giga-FLOPS at 8 bits per FLOP, corresponding to 95.2 Gbps. We test the perceptron on simple standard benchmark datasets — handwritten-digit recognition and cancer-cell detection — achieving over 90% and 85% accuracy, respectively. This performance is a direct result of the record small wavelength spacing (49GHz) for a coherent integrated microcomb source, which results in an unprecedented number of wavelengths for neuromorphic optics. Finally, we propose an approach to scaling the perceptron to a deep learning network using the same single micro-comb device and standard off-the-shelf telecommunications technology, for high-throughput operation involving full matrix multiplication for applications such as real-time massive data processing for unmanned vehicle and aircraft tracking.
Existence and Uniqueness of Solution to a Functional Integro-differential Fractional Equation

We prove, using a fixed point theorem in a Banach algebra, an existence result for a fractional functional differential equation in the Riemann-Liouville sense. Dependence of solutions with respect to initial data and an uniqueness result are also derived.
OPTIMIZATION OF THE EXISTING TECHNOLOGY OF CARGO TRANSPORTATION FROM OIL REFINING ENTERPRISES AND THE STRUCTURE OF INTERACTION OF PARTICIPANTS OF THE LOGISTICS SYSTEM

This article analyzes the existing technology for transporting goods from oil refineries, identifies problematic moments and risks of this technology, and proposes a set of measures to solve the identified problems in 2 areas: optimization of the existing technology for transporting goods from oil refineries, which consists in organizing container transportation of products road transport and improving the structure of interaction between participants in the system for the delivery of petroleum products to end consumers. The effectiveness of the proposed solutions was evaluated.
Health-related quality of life, palliative care needs and 12-month survival among patients with end stage renal disease in Uganda: protocol for a mixed methods longitudinal study

Abstract Background The prevalence of chronic kidney disease is on the rise globally and in sub-Saharan Africa. Due to its “silent” nature, many patients often present with advanced disease. At this point options for care are often limited to renal replacement therapies such as hemodialysis and kidney transplantation. In resource limited settings, these options are associated with catastrophic expenditures and increased household poverty levels. Early palliative care interventions, if shown to ensure comparable quality of life (QoL), can significantly mitigate this by focusing care on comfort, symptom control and QoL rather than primarily on prolonging survival.  Methods A mixed methods longitudinal study, recruiting patients with End Stage Renal Disease (ESRD) on hemodialysis or conservative management and following them up over 12 months. The study aims are to: 1) measure and compare the health-related quality of life (HRQoL) scores of patients with ESRD receiving hemodialysis with those receiving conservative management, 2) measure and compare the palliative care needs and outcomes of patients in the two groups, 3) explore the impact of treatment modality and demographic, socio-economic and financial factors on QoL and palliative care needs and outcomes, 4) review patient survival over 12 months and 5) explore the patients’ lived experiences. The Kidney Disease Quality Of Life Short Form version 1.3 (KDQOL-SF) will be used to measure HRQoL; the African Palliative Care Association Palliative care Outcome Score (APCA POS) and the Palliative care Outcome Score for renal symptoms (POS-S Renal) will be used to assess palliative care needs and outcomes; and semi-structured in-depth interviews to explore the patients’ experiences of living with ESRD. Data collection will be carried out at 0, 3, 6, 9 and 12 months.  Discussion To the best of our knowledge, no similar study has been conducted in sub-Saharan Africa. This will be an important step towards raising awareness of patients’ need and preferences and the strengths and limitations of available health care services for ESRD in resource limited settings.
Transformers are Graph Neural Networks

We establish connections between the Transformer architecture, originally introduced for natural language processing, and Graph Neural Networks (GNNs) for representation learning on graphs. We show how Transformers can be viewed as message passing GNNs operating on fully connected graphs of tokens, where the self-attention mechanism capture the relative importance of all tokens w.r.t. each-other, and positional encodings provide hints about sequential ordering or structure. Thus, Transformers are expressive set processing networks that learn relationships among input elements without being constrained by apriori graphs. Despite this mathematical connection to GNNs, Transformers are implemented via dense matrix operations that are significantly more efficient on modern hardware than sparse message passing. This leads to the perspective that Transformers are GNNs currently winning the hardware lottery.
Balancing public health and group privacy: Ethics, rights, and obligations for wastewater surveillance systems

As the threat of COVID-19 recedes, wastewater surveillance - unlike other pandemic-era public health surveillance methods - seems here to stay. Concerns have been raised, however, about the potential risks that wastewater surveillance might pose towards group privacy. Existing scholarship has focused upon using ethics- or human rights-based frameworks as a means of balancing the public health objectives of wastewater surveillance and the potential risks it might pose to group privacy. However, such frameworks greatly lack enforceability. In order to further the strong foundation laid by such frameworks - while addressing their lack of enforceability - this paper proposes the idea of the 'obligation' as an alternative way to regulate wastewater surveillance systems. The legal codification of said obligations provides a method of ensuring that wastewater surveillance systems can be deployed effectively and equitably. Our paper proposes that legal obligations for wastewater surveillance can be created and enforced through transparent and purposeful legislation (which would include limits on power and grant institutions substantial oversight) as well as paying heed to non-legislative legal means of enforcement, such as through courts or contracts. Introducing legal obligations for wastewater surveillance could therefore be highly useful to researchers, policymakers, corporate technologists, and government agencies working in this field.
Multilevel Data Processing Using Parallel Algorithms for Analyzing Big Data in High-Performance Computing

The growing gap between users and the Big Data analytics requires innovative tools that address the challenges faced by big data volume, variety, and velocity. Therefore, it becomes computationally inefficient to analyze such massive volume of data. Moreover, advancements in the field of Big Data application and data science poses additional challenges, where High-Performance Computing solution has become a key issue and has attracted attention in recent years. However, these systems are either memoryless or computational inefficient. Therefore, keeping in view the aforementioned needs, there is a requirement for a system that can efficiently analyze a stream of Big Data within their requirements. Hence, this paper presents a system architecture that enhances the working of traditional MapReduce by incorporating parallel processing algorithm. Moreover, complete four-tier architecture is also proposed that efficiently aggregate the data, eliminate unnecessary data, and analyze the data by the proposed parallel processing algorithm. The proposed system architecture both read and writes operations that enhance the efficiency of the Input/Output operation. To check the efficiency of the proposed algorithms exploited in the proposed system architecture, we have implemented our proposed system using Hadoop and MapReduce. MapReduce is supported by a parallel algorithm that efficiently processes a huge volume of data sets. The system is implemented using MapReduce tool at the top of the Hadoop parallel nodes to generate and process graphs with near real-time. Moreover, the system is evaluated in terms of efficiency by considering the system throughput and processing time. The results show that the proposed system is more scalable and efficient.
Use of Project Management Knowledge Areas in Civil Infrastructure Projects: Implications for Sustainability Assessment and Risk Analysis

The success of civil infrastructure projects hinges on effective project management. Building on the PMBOK® Guide framework, this study investigates how project management knowledge areas are used in practice and how their use relates to the integration of sustainability and risk-management principles. 272 construction professionals in Türkiye were surveyed and their responses were analyzed using reliability testing, normality checks, and a combination of non-parametric tests (Mann–Whitney U, Kruskal–Wallis) and ANOVA. There were found significant differences in perceived use of knowledge areas by education level, project role, project profile, and prior project-management training; in applied practice, company profile explains variation, whereas project type does not. The results indicate that wider, more systematic adoption—particularly in integration, schedule/time, quality, and risk—supports transparent, traceable processes aligned with sustainability objectives. These behavioral determinants were interpreted as enablers of life-cycle sustainability assessment and risk-informed decision making across civil-infrastructure contexts. There were discussed managerial and policy implications for asset owners and contractors, identifying leverage points for training and capability building, and outlining how standardized use of PMBOK knowledge areas can accelerate sustainability assessment and risk analysis in practice.
Improving Adversarial Transferability via Model Alignment

Neural networks are susceptible to adversarial perturbations that are transferable across different models. In this paper, we introduce a novel model alignment technique aimed at improving a given source model's ability in generating transferable adversarial perturbations. During the alignment process, the parameters of the source model are fine-tuned to minimize an alignment loss. This loss measures the divergence in the predictions between the source model and another, independently trained model, referred to as the witness model. To understand the effect of model alignment, we conduct a geometric analysis of the resulting changes in the loss landscape. Extensive experiments on the ImageNet dataset, using a variety of model architectures, demonstrate that perturbations generated from aligned source models exhibit significantly higher transferability than those from the original source model.
A Recommender System for Suggested Sites using Multi-Armed Bandits : Initialising Bandit Contexts by Neural Collaborative Filtering

The abundance of information available on the internet necessitates means of quickly finding what is relevant for the individual user. To this end, there has been much research concerning recommender systems and lately specifically methods using deep learning for such systems. This work proposes a Multi-Armed Bandit as a recommender for suggested sites on a browser start page. The system is compared to a pre-existing baseline and does not manage to outperform it in the setting used in controlled experiments. A Neural Collaborative Filtering system is then constructed using a stacked autoencoder and is used to produce user preference vectors that are inserted in the bandit in the hope of improving its performance. Analysis indicates that the bandit solution works better as the number of items grows. The user-informed initialisation used in this work shows a trend of improving over a randomly-initialised bandit, but results are inconclusive. This work also contributes an analysis of the problem domain including which factors impact the performance on the model training for preference vectors, and the performance of the bandit algorithms.
Masked Conditional Neural Networks for Audio Classification

We present the ConditionaL Neural Network (CLNN) and the Masked ConditionaL Neural Network (MCLNN) designed for temporal signal recognition. The CLNN takes into consideration the temporal nature of the sound signal and the MCLNN extends upon the CLNN through a binary mask to preserve the spatial locality of the features and allows an automated exploration of the features combination analogous to hand-crafting the most relevant features for the recognition task. MCLNN has achieved competitive recognition accuracies on the GTZAN and the ISMIR2004 music datasets that surpass several state-of-the-art neural network based architectures and hand-crafted methods applied on both datasets.
Supplementary Figure S2 from Blood-Based Genomic Alteration Signature for Predicting Progression-Free Survival in &lt;i&gt;De Novo&lt;/i&gt; Metastatic Hormone-Sensitive Prostate Cancer: A Real-World Study

&lt;p&gt;Figure S2. Nomogram for predicting progression-free survival (PFS) based on t.TRPC and disease volume score.&lt;/p&gt;
Spontaneous disclosure of BRCA1/2 genetic test results to employers: a French prospective study

The aim of this study was to examine the patterns of disclosure of BRCA1/2 genetic test results to employers by unaffected carriers. In a national prospective cohort study on unaffected BRCA1/2 mutation carriers, disclosure to employers was assessed prospectively, using self-administered questionnaires, up to 2 years after their test results were delivered by cancer geneticists. Kaplan-Meier curves and Cox-regression analysis were used to assess the factors associated with time to disclosure to the employer. Mean age of the 146 women BRCA1/2 carriers who were employed when their test results were delivered was 37.1 years (range: 19-57). At the end of the second year of follow-up, 47 of them (32.2%) had disclosed their results to their employers; median time to disclosure was 6 months. Reasons spontaneously expressed were first to inform the employer that medical surveillance/surgery was necessary for cancer risk management although these carriers did not actually have cancer. After multivariate adjustment on age, women with a lower educational level (HRadj=2.00, P=0.026) and those who had undergone prophylactic surgery during the 2 years of follow-up (HRadj=2.18, P=0.019) had disclosed their BRCA status to their employers earlier and more frequently. One-third of the female carriers not affected by breast/ovarian cancer disclosed their BRCA1/2 genetic test results spontaneously to their employers, mainly to inform them that they were disease-free but required medical surveillance or a surgical intervention to reduce the risk of cancer.