ethicalabs/Research-Intent-Collab huggingface dataset:
Highly accurate protein structure prediction with AlphaFold

Abstract                                        Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort                     1–4                     , the structures of around 100,000 unique proteins have been determined                     5                     , but this represents a small fraction of the billions of known protein sequences                     6,7                     . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’                     8                     —has been an important open research problem for more than 50 years                     9                     . Despite recent progress                     10–14                     , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)                     15                     , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm.
Analisis Pengendalian Air Tak Berekening Berbasis Teknologi Melalui Pendekatan Integrasi Lean Six Sigma dan Total Quality Management di Perumda Air Minum Tirta Raharja

Air Tak Berekening (ATR) masih menjadi isu strategis nasional karena berdampak langsung terhadap kualitas pelayanan, operasional, hingga pendapatan di PDAM. Perumdam Tirta Raharja menghadapi permasalahan yang sama dengan angka Air Tak Berekening (ATR) sebesar 27,16% pada kinerja tahun 2023 dan masih perlu terus diturunkan agar menjadi 25% sesuai target RPJMN 2025-2029. Berbagai teknologi operasi yang terintegrasi seperti SCADA, IoT dan GIS dalam pemantauan SPAM-Zona-DMA nampaknya masih belum memberikan efektivitas secara signifikan terhadap penurunan ATR. Penelitian ini bertujuan untuk menganalisis strategi pengendalian Air Tak Berekening (ATR) melalui integrasi teknologi dengan pendekatan Lean Six Sigma dan Total Quality Management (TQM). Metode yang digunakan dalam penelitian ini adalah analisis deskriptif evaluatif dengan pendekatan gap analysis, yang difokuskan pada identifikasi kesenjangan antara target ideal dan kondisi aktual di lapangan. Metodologi ini juga diperkuat dengan kerangka kerja DMAIC (Define-Measure-Analyze-Improve-Control) sebagai dasar perbaikan proses, serta prinsip-prinsip Total Quality Management (TQM) yang bertujuan memperkuat budaya mutu organisasi sebagai penggerak utama dalam optimalisasi penggunaan teknologi. Hasil studi menunjukkan bahwa upaya pembentukan District Metered Area (DMA) belum dapat dijadikan solusi mutlak dalam pengendalian ATR karena masih terdapat beberapa zona distribusi yang belum dilakukan pemantauan untuk mengidentifikasi zona prioritas ATR. Selain itu, sistem manajemen alarm berbasis teknologi yang telah dikembangkan belum sepenuhnya juga dioptimalkan sebagai alat mitigasi dan respons dini terhadap potensi kehilangan air. Padahal, sistem ini dirancang untuk memperkuat budaya kerja responsif terhadap kondisi operasional di lapangan. Oleh karena itu, integrasi antara pendekatan DMAIC dan penguatan budaya mutu menjadi elemen penting dalam mendukung pencapaian target kinerja utama (KPI) perusahaan. Penelitian ini diharapkan menjadi referensi bagi organisasi dalam merancang dan menerapkan pengendalian ATR yang lebih efektif di lingkungan Perumdam Tirta Raharja.
The Factors and Strategies of Conflict Resolution and Peace Building in Nigeria

This paper examines the factors and strategies of conflict resolution and peace building in Nigeria, a country that has experienced various forms of violent conflicts since its independence in 1960. The paper analyzes the causes and consequences of these conflicts, as well as the efforts made by different actors to prevent, manage and resolve them. The paper also explores the challenges and opportunities for effective peace building in Nigeria, with a focus on the role of education, civil society, women and youth. The paper argues that conflict resolution and peace building in Nigeria require a holistic and inclusive approach that addresses the structural and root causes of conflicts, promotes dialogue and reconciliation, enhances human security and development, and builds a culture of peace and tolerance among the diverse groups in the country
Designing Algorithms for Wearable Sensors: Insights from the Framingham Heart Study Dataset for Enhanced Cardiovascular Health Monitoring

Wearable sensors hold promise for advancing cardiovascular health monitoring by enabling continuous, real-time risk assessment. This study leveraged the Framingham Heart Study dataset to develop and evaluate machine learning algorithms for predicting mortality risk based on key cardiovascular parameters. Five ML models were implemented: XGBoost, Random Forest, Logistic Regression, Ensemble Learning, and Ensemble Stacking. Among these, XGBoost and Ensemble Stacking demonstrated the highest predictive performance, with an area under the curve (AUC) value of 0.83. And feature importance analysis identified coronary artery disease, glucose levels, and diastolic blood pressure (DIABP) as significant risk factors for mortality. Given the advancements in wearable sensor technology for measuring and estimating glucose levels and blood pressure, these findings underscore the potential of wearable devices for effective cardiovascular risk prediction. This study highlights the feasibility of integrating machine learning algorithms with wearable sensors to enhance cardiovascular health monitoring and facilitate early intervention.
Midwifery-led continuity-of-care education affects health literacy, empowerment and childbirth fear: a quasi-experimental study

According to the World Health Organisation, antenatal care should ensure continuity, respect and evidence-based education. Fear of childbirth (FOC), affecting 20-40% of women globally, contributes to prolonged labour, higher caesarean rates and negative birth experiences. In Türkiye, where midwifery-led support remains limited, this study examined how pregnant women's health literacy influences empowerment and FOC within antenatal care and evaluated the effectiveness of the midwifery-led continuity-of-care (MLCC) model.A quasi-experimental study with a comparative group design was conducted online between April 2024 and April 2025 using digital maternal communities and antenatal platforms in Türkiye. The intervention consisted of four structured online sessions based on the MLCC model, focusing on pregnancy adaptation, labour preparation, birth preferences and postpartum care. A total of 81 pregnant women participated, with 40 in the intervention group receiving MLCC-based antenatal education and 41 in the control group receiving standard care. Participants self-selected into groups, and data were collected online using validated scales: MHELIP, PRES and W-DEQ (Version A). All analyses were performed using SPSS 25.0 software. Independent samples t-tests and Pearson's correlations were used for analysis, and multiple regression was performed to identify predictors of empowerment and FOC. All statistical values were verified and reported as mean ± SD with exact p values.Pregnant women in the intervention group had significantly higher health literacy and empowerment scores and significantly lower FOC scores than those in the control group (p <.05). Health literacy was positively correlated with empowerment (r = .748) and negatively correlated with FOC (r = -.710).The intervention reduced fear and increased empowerment, likely through improved knowledge, self-efficacy and perceived control during pregnancy. The MLCC model enhanced health literacy and empowerment while reducing FOC. Structured education models integrated into antenatal care can support women's autonomy and psychological preparedness for birth.
Open questions on nonequilibrium thermodynamics of chemical reaction networks

Chemical reaction networks (CRNs) are prototypical complex systems because reactions are nonlinear and connected in intricate ways, and they are also essential to understand living systems. Here, I discuss how recent developments in nonequilibrium thermodynamics provide new insight on how CRNs process energy and perform sophisticated tasks, and describe open challenges in the field.
When Extension and Rhetorical Engagement Meet: Framing Public Audiences for Agricultural Science Communication

This article reports from a qualitative case study exploring how a team of agricultural scientists framed their nonscientific audiences for science communication. Our results indicate communication audiences and strategies were shaped by state extension systems. As a result, we argue that technical communicators can contribute to agricultural science communication teams by modeling rhetorically engaged communication and building capacity for audiences overlooked by extension models most focused on economic impact.
Supply Chain Optimization using Data Analytics: Analysis of Inventory Management, Transportation Logistics, & Procurement Processes

The supply chain is an organizational network, that includes various factors from producing to delivering a product, it is difficult to achieve customer satisfaction and operational efficacy in the current business environment. advanced machine learning techniques have shown significant results in predicting analysis among various domains, exploring these advancements in supply chain management to advance key aspects. Different algorithms are evaluated to recognize the most effective techniques for accurately forecasting on-time deliveries, the algorithms used in this research are SVM (Support Vector Machines), KNN (K-Nearest Neighbors), Logistic Regression, Extra Trees, XGBoost, and Random Forests. From results we have demonstrated that Random Forest followed by XGBoost AdaBoost achieved the highest accuracy, these models can improve prediction accuracy and decision-making for supply chain management. Additionally, an ensemble approach is used for more robust and less deviations in predictions achieved 99.13% accuracy where ensemble model uses average of all models considered the final result. Integrating advanced machine learning techniques into the current supply chain will enhance the existing model’s customer satisfaction, operational efficacy, and cost reduction.
On the solution of the XOR problem using the decision tree-based neural network

The decision tree-based neural network is introduced to combine neural networks with decision trees. Its key idea is using decision trees to form the structure of the neural network: to construct a decision tree and then convert the tree into a neural network. The decision tree and neural network for classification are similar and have equivalent property. Thereby a decision tree can be used to provide a systematic design method of neural network. We propose a new mapping between decision tree and neural network that can accurately specify the number of units, layers, connection and initial setting of parameters of neural network. Furthermore, we quote two theorems to show that the mapping is reasonable. In this paper, we use the decision tree-based neural network to solve the XOR problem.
Survey Descent: A Multipoint Generalization of Gradient Descent for Nonsmooth Optimization

For strongly convex objectives that are smooth, the classical theory of gradient descent ensures linear convergence relative to the number of gradient evaluations. An analogous nonsmooth theory is challenging. Even when the objective is smooth at every iterate, the corresponding local models are unstable and the number of cutting planes invoked by traditional remedies is difficult to bound, leading to convergences guarantees that are sublinear relative to the cumulative number of gradient evaluations. We instead propose a multipoint generalization of the gradient descent iteration for local optimization. While designed with general objectives in mind, we are motivated by a ``max-of-smooth'' model that captures the subdifferential dimension at optimality. We prove linear convergence when the objective is itself max-of-smooth, and experiments suggest a more general phenomenon.
Simulation of ICA-PI Controller of DC Motor in Surgical Robots for Biomedical Application

The medical device industry is considered a hub of innovation with constantly improving designs and processes, the possibilities seem endless. Over the past 20 years, dc motors have played its role in the development of medical instruments like surgical robots, plate readers, liquid and specimen handling systems, chromatography, and In Vitro diagnostic machines. Regardless of what ever the application may be, all devices in the medical industry should have high Reliability and flexibility. This paper presents a new controller model using Imperialist Competitive Algorithm (ICA) for efficient search and optimization of Proportional Integral controller parameter in order to achieve a better close loop control of the dc motor in bio-medical application. This control method was simulated using MATLAB/SIMULINK to control dc motor. From the simulation, it was found that the proposed method gives better performance with improved response in any user defined function as the settling time, overshoot and speed error are reduced.
The Impact of Social Media on Adolescent Identity Formation and Mental  Health: Opportunities, Risks, and Policy Implications

Psychological resilience is a critical factor in mental health and well-being, particularly in the face of increasing global stressors such as the COVID-19 pandemic. This study conducts a comprehensive narrative review to examine the key determinants of resilience, focusing on social support, stress management, cultural influences, and spirituality. A systematic literature search was conducted using Scopus, PubMed, and Google Scholar to identify empirical studies on resilience and its contributing factors. The findings underscore the significant role of social networks in enhancing resilience, demonstrating that individuals with strong support systems experience lower levels of anxiety and depression. Effective stress management techniques, including mindfulness and structured coping strategies, further contribute to resilience by promoting emotional regulation. Additionally, cultural and spiritual factors influence resilience development, with collectivist societies benefiting from community-driven support structures, while individualist cultures rely more on personal coping mechanisms. These results highlight the need for targeted interventions to strengthen resilience across diverse populations. Policymakers must prioritize mental health infrastructure, integrate resilience training into education and workplace settings, and promote digital mental health interventions. Future research should explore longitudinal effects of resilience-building strategies and assess the potential of digital platforms in enhancing psychological resilience. By adopting a multi-dimensional approach, societies can better equip individuals to withstand adversity and improve overall mental well-being.
3D printing for Medical Product Development: The Advantages of Additive Manufacturing to Reduce Cost of Design and Development in the Medical Industry: Paxman Case Study

Paxman Research team were invited to give a presentation at Medtec, Stuttgart Germany about the use of 3D printing for Medical Product Development. The team also exhibited the patented scalp cooling cap with Primasil, the manufacturer partner of the scalp cooling project. The MedTec visitors were given the opportunity to visit a selection of educational presentations as well as workshops and pre- planned meetings in a select meeting area. They were offered a number of innovation tours and labs, focusing on computerisation and personalisation of medical care. The KTP associate presented and chaired in the area of ‘Additive Manufacturing’ with the presentation title ‘Reducing the cost of design and development using additive manufacturing’. The presentation was 45 minutes long, including questions and answers. This report covers the exhibition and presentation.
On Gravitational Waves: Did We Simply Detect the Gravitational Effect of the Sun on the Photons Moving in the Cavity of Interferometers LIGO and VIRGO?

On September 14, 2015 09:50:45 UTC, the two laser interferometers of the LIGO program simultaneously observed a first gravitational wave signal called GW150914. With the commissioning of the VIRGO interferometer in 2017, two other detections, GW170814 and GW170817, were observed and their positions given accurately by LIGO and VIRGO. In this article, I argue that the photons circulating in the cavities of the three interferometers of LIGO and VIRGO were sensitive to the field of attraction of the planets of our Solar System and more particularly to that of the Sun, and would not be due to a coalescence of black hole or neutron stars. The shape of the signals obtained by my interaction model (called GEAR) between the photons in the interferometer cavity and the gravitational field of the Sun is very similar to that of a compact binary coalescence, identical to those obtained by general relativity. Solving the equations of GEAR also gives the exact positions and pseudo-date of the coalescences of all the LIGO and VIRGO detections detected so far, and probably those that will come at the end of 2018 and beyond.
Putting the HR into the HRIS: A study of the implementation of Human Resource Information Systems

An estimated 90% of HR departments in organisations use some form of technology to manage their data and processes. While these systems have gained traction over the last 10 years there is signific...
Quantum computing with error mitigation for data-driven computational homogenization

As a crossover frontier of physics and mechanics, quantum computing is showing its great potential in computational mechanics. However, quantum hardware noise remains a critical barrier to achieving accurate simulation results due to the limitation of the current hardware. In this paper, we integrate error-mitigated quantum computing in data-driven computational homogenization, where the zero-noise extrapolation (ZNE) technique is employed to improve the reliability of quantum computing. Specifically, ZNE is utilized to mitigate the quantum hardware noise in two quantum algorithms for distance calculation, namely a Swap-based algorithm and an H-based algorithm, thereby improving the overall accuracy of data-driven computational homogenization. Multiscale simulations of a 2D composite L-shaped beam and a 3D composite cylindrical shell are conducted with the quantum computer simulator Qiskit, and the results validate the effectiveness of the proposed method. We believe this work presents a promising step towards using quantum computing in computational mechanics.
BeamVLM for Low-altitude Economy: Generative Beam Prediction via Vision-language Models

For low-altitude economy (LAE), fast and accurate beam prediction between high-mobility unmanned aerial vehicles (UAVs) and ground base stations is of paramount importance, which ensures seamless coverage and reliable communications. However, existing deep learning-based beam prediction methods lack high-level semantic understanding of dynamic environments, resulting in poor generalization. On the other hand, the emerging large language model (LLM) based approaches show promise in enhancing generalization, but they typically lack rich environmental perception, thereby failing to capture fine-grained spatial semantics essential for precise beam alignment. To tackle these limitations, we propose in this correspondence a novel end-to-end generative framework for beam prediction, called BeamVLM, which treats beam prediction as a vision question answering task capitalizing on powerful existing vision-language models (VLMs). By projecting raw visual patches directly into the language domain and judiciously designing an instructional prompt, the proposed BeamVLM enables the VLM to jointly reason over UAV trajectories and environmental context. Last, experimental results on real-world datasets demonstrate that the proposed BeamVLM outperforms state-of-the-art methods in prediction accuracy and also exhibits superior generalization for other scenarios such as vehicle-to-infrastructure (V2I) beam prediction.
Dehazing algorithm's high performance and parallel computing for GF-1 Satellite Images

Recent years, China has launched a series of remote sensing satellites, such as HJ-1, FY3, GF-1, etc. Making the extreme rapid growth of high resolution remote sensing images. In this case, preliminary data processing is becoming more and more important, dehazing is an important step of radiometric processing in prfeliminary data processing. However, the existing dehazing methods are quite slow. In this paper, we are going to discuss a faster dehazing algorithm based on large-scale median filtering, by using the multi-resolution remote sensing image data of GF-1 satellite, we can highly optimize the algorithm itself‥Parallel computing techniques are used to improve the processing speed, and the proposed algorithm is implemented in standard C++. On the computer with Intel core i7-3770 and CPU 3.4 GHz, our algorithm can decrease the dehazing time from one hour to less than five minutes for a 4-band, 10000×10000 pixel size, 16 bits unsigned integer data type image.
Differentially Private Knowledge Graph Neural Network for Recommeder System

Abstract         Knowledge Graphs (KGs) have been instrumental in mitigating the challenges of cold start and data scarcity in recommender systems, serving as a kind of auxiliary information. However, recent studies reveal that KGs, while beneficial, may also inadvertently furnish malicious attackers with more comprehensive background knowledge, escalating the risk of user privacy data breaches within these recommender systems. Current frameworks, which amalgamate KGs with privacy-preserving techniques, predominantly concentrate on enhancing privacy preservation, often at the expense of recommendation efficacy. In this research, we introduce a framework, named Differential Privacy Knowledge graph Neural network for Recommender systems (DPKNRec), designed to harmonize the trade-off between differential privacy noise and recommendation performance. Firstly, we formulate an integrated KG convolutional neural network adept at mining interrelationships between KG entities and item correlations effectively. Secondly, we incorporate a differential privacy technique to impose uniform Laplacian noise on feature embeddings and obfuscate genuine user-item interactions through pseudo-interaction item sampling, thereby augmenting user data security. Lastly, we evaluate our model using three distinct datasets-movies, music, and books. Empirical results from three benchmarks indicate that DPKNRec markedly supersedes contemporary benchmarks.