Open Software Development Research Articles

Numerical Simulation of Pipeline Scour and Sedimentation around Submerged Pipelines with an Open-Source Multiphase-CFD Model

This paper presents a comprehensive study on pipeline scour and sedimentation phenomena using an open-source multiphase Computational Fluid Dynamics (CFD) model. The research focuses on understanding the complex interactions between fluid flow, sediment transport, and scour formation around submerged pipelines. The proposed analysis aims to enhance the understanding of scour development and sedimentation deposition, which is crucial for the design, operation, and maintenance of various engineering structures, including offshore pipelines and underwater infrastructure. The results show that the model exhibits the ability to compute sediment transport without depending on traditional assumptions related to bed-load and suspended-load layers. The simulation results affirm the model's proficiency in replicating the underlying mechanisms accountable for the onset of these processes, notably seepage flow and piping. Furthermore, this model can successfully depict the vortex phenomenon, which promotes the accumulation of sediment around the pipe. This phenomenon arises from the contrast in pressure between the centre of the vortex and the pressure exerted on the sediment beneath it.

In-House Knowledge Management Using a Large Language Model: Focusing on Technical Specification Documents Review

In complex construction projects, technical specifications have to be reviewed in a short period of time. Even experienced engineers find it difficult to review every detail of technical specifications. In addition, it is not easy to transfer experienced knowledge to junior engineers. With the technological innovation of large language models such as ChatGPT, a fine-tuned language model is proposed as an effective solution for the automatic review of technical specification documents. Against this backdrop, this study examines the in-house technical specification documents that are not publicly available. Then, two fine-tuned large language models, GPT-3 and LLaMA2, are trained to answer questions related to technical specification documents. The results show that the fine-tuned LLaMA2 model generally outperforms the fine-tuned GPT-3 model in terms of accuracy, reliability, and conciseness of responses. In particular, the fine-tuned LLaMA2 model suppressed hallucinogenic effects better than the fine-tuned GPT-3 model. Based on the results, this study discussed the applicability and limitations of a fine-tuned large language model for in-house knowledge management. The results of this study are expected to assist practitioners in developing a domain-specific knowledge management solution by fine-tuning an open-source large language model with private datasets.

Can large language models reason about medical questions?

Although large language models often produce impressive outputs, it remains unclear how they perform in real-world scenarios requiring strong reasoning skills and expert domain knowledge. We set out to investigate whether closed- and open-source models (GPT-3.5, Llama 2, etc.) can be applied to answer and reason about difficult real-world-based questions. We focus on three popular medical benchmarks (MedQA-US Medical Licensing Examination [USMLE], MedMCQA, and PubMedQA) and multiple prompting scenarios: chain of thought (CoT; think step by step), few shot, and retrieval augmentation. Based on an expert annotation of the generated CoTs, we found that InstructGPT can often read, reason, and recall expert knowledge. Last, by leveraging advances in prompt engineering (few-shot and ensemble methods), we demonstrated that GPT-3.5 not only yields calibrated predictive distributions but also reaches the passing score on three datasets: MedQA-USMLE (60.2%), MedMCQA (62.7%), and PubMedQA (78.2%). Open-source models are closing the gap: Llama 2 70B also passed the MedQA-USMLE with 62.5% accuracy.

Real-Time Online Banking Fraud Detection Model by Unsupervised Learning Fusion

Digital trades and payments are becoming increasingly popular, as they typically entail monetary transactions. This not only makes electronic transactions more convenient for the end customer, but it also raises the likelihood of fraud. An adequate fraud detection system with a cutting-edge model is critical to minimizing fraud costs. Identifying fraud at the ideal time entails establishing and setting up ubiquitous systems to consume and analyze massive amounts of streaming data. Recent advances in data analytics methods and introducing open-source technology for big data storage and processing opened new options for detecting fraud. This study aims to tackle this critical issue by providing a newly real-time e-transaction fraud detection schema that consolidates the advantages of both unsupervised learners, including autoencoder and extended isolation forests, with cutting-edge big data gadgets such as Spark streaming and sparkling water. It addresses the shortage of non-fraudulent instances and handles the excessive dimension of the set of features. On two real-world transactional datasets, we assess our suggested technique. Compared with other current fraud identification systems, our methodology delivers an elevated accuracy yield of 99%. Furthermore, it outperforms state-of-the-art approaches in reliably identifying fraudulent samples. Doi: 10.28991/HIJ-2024-05-01-014 Full Text: PDF

Decarbonising the power sector of the Dominican Republic: An approach from electric mobility transition

The electrical power and land transportation systems of the Dominican Republic are facing significant challenges due to growing demand in both sectors. These two systems are responsible for around 62% of greenhouse gas (GHG) emissions. With the deployment of electric mobility, the power and transport sectors can synergise and contribute significantly to long-term sustainable planning. This article analyses the impact of the penetration of electric mobility programmed by the Dominican Republic's National Institute of Transit and Land Transport (INTRANT), the country's goal of reducing GHG emissions by 27% by 2030 and the decarbonisation process. Using the open-source modelling system OSeMOSYS, four scenarios are built to explore strategies that contribute to sustainable development in the long term. Meeting climate change commitments necessitates that all coal-fired plants in the country will be closed or transformed by 2040. Despite the positive role of electric mobility in reducing GHG, decarbonising the electricity sector while facing the energy crisis requires investments totalling around 16 billion USD. Failing to make these investments will incur expenses of approximately 6 billion USD on the user's side in the period from 2024 to 2030 to cover energy needs; this figure represents more than 270% of the amount that should be invested in the electrical power system to supply these users' needs.

High-resolution, open-source modeling of inland flooding impacts on the North Carolina bulk electric power grid

Although damages to local distribution systems from wind and fallen trees are typically responsible for the largest fraction of electricity outages during hurricanes, outages caused by flooding of electrical substations pose a unique risk. Electrical substations are a key component of electric power systems, and in some areas, the loss of a single substation can cause widespread power outages. Before repairing damaged substations, utilities must first allow floodwaters to recede, potentially leaving some customers without power for weeks following storms. As economic losses from flooding continue to increase in the U.S., there has been increasing attention paid to the potential impacts of flooding on power systems. Yet, this attention has mostly been limited to geospatial risk assessments that identify what assets are in the path of flooding. Here, we present the first major attempt to understand how flooding from hurricanes and other extreme precipitation events affects the dynamic behavior of power networks, including losses of demand and generation, and altered power flows through transmission lines. We use North Carolina, hit by major hurricanes in three of the past seven years, as a test case. Using open-source data of grid infrastructure, we develop a high-resolution direct current optimal power flow model that simulates electricity production and generators and power flows through a network consisting of 662 nodes and 790 lines. We then simulate grid operations during the historical (2018) storm Hurricane Florence. Time series of flooding depth at a discrete set of ‘high water’ mark points from the storm are used to spatially interpolate flooding depth across the footprint area of the storms on an hourly basis. Outages of substations and solar farms due to flooding are translated to location-specific losses of demand and solar power production throughout the network. We perform sensitivity analysis to explore grid impacts as a function of the height of sensitive equipment at substations. Results shed light on the potential for localized impacts from flooding to have wider impacts throughout the grid (including in areas not affected by flooding), with performance tracked in terms of transmission line flows/congestion, generation outputs, and customer outages.

Machine Learning Accelerates De Novo Design of Antimicrobial Peptides.

Efficient and precise design of antimicrobial peptides (AMPs) is of great importance in the field of AMP development. Computing provides opportunities for peptide de novo design. In the present investigation, a new machine learning-based AMP prediction model, AP_Sin, was trained using 1160 AMP sequences and 1160 non-AMP sequences. The results showed that AP_Sin correctly classified 94.61% of AMPs on a comprehensive dataset, outperforming the mainstream and open-source models (Antimicrobial Peptide Scanner vr.2, iAMPpred and AMPlify) and being effective in identifying AMPs. In addition, a peptide sequence generator, AP_Gen, was devised based on the concept of recombining dominant amino acids and dipeptide compositions. After inputting the parameters of the 71 tridecapeptides from antimicrobial peptides database (APD3) into AP_Gen, a tridecapeptide bank consisting of de novo designed 17,496 tridecapeptide sequences were randomly generated, from which 2675 candidate AMP sequences were identified by AP_Sin. Chemical synthesis was performed on 180 randomly selected candidate AMP sequences, of which 18 showed high antimicrobial activities against a wide range of the tested pathogenic microorganisms, and 16 of which had a minimal inhibitory concentration of less than 10μg/mL against at least one of the tested pathogenic microorganisms. The method established in this research accelerates the discovery of valuable candidate AMPs and provides a novel approach for de novo design of antimicrobial peptides.

Supportive Interventions of Chinese Police in Domestic Violence: Do Officer Knowledge and Training Matter?

Policing domestic violence (DV) poses significant challenges in China due to cultural, legal, and organizational complexities. Policing DV in China favors mediation over assertive interventions, complicating law enforcement's role. While previous research has focused on coercive interventions by Chinese police, there is limited information on non-coercive, supportive approaches. This study investigates the relationship between police officers' knowledge and training regarding the Anti-DV law and their willingness to provide supportive services to DV victims in China. It also considers various individual and organizational factors. The data used in this study are derived from the Policing DV in China project, with a sample of 1,353 respondents who had experience dealing with DV cases within the past 3 years. The study focuses on three dependent variables representing supportive approaches to DV cases: Referral, Counseling, and Protection orders. Independent variables include officers' knowledge of the Anti-DV law and agency training. Control variables include the use of body-worn cameras (BWC) and attitudes toward Violence Tolerance, Male Dominance, and Gender Equality. Additionally, demographic variables, working environment, length of service, and police rank are considered. The analytical approach involves a three-step strategy, incorporating descriptive, bivariate analyses, and regression analyses. The results are interpreted using odds ratios and average marginal effects, and statistical software such as SPSS by IBM and R by Open-Source Model is utilized for data analysis. Key findings indicate that more than half of the officers referred intimate partner violence survivors to shelters and assisted victims in filing protection orders. Counseling practices varied across provinces and between male and female officers. Agency training and the use of BWC were positively associated with non-coercive and supportive approaches, while knowledge of the DV Act, male dominance score, and gender equality score did not predict the use of such approaches. Demographic characteristics, including police rank, length of service, and province of employment, influenced the utilization of non-coercive and supportive approaches. This study examines the challenges faced by Chinese police officers when responding to DV cases and their willingness to provide supportive interventions. The study highlights the complexities surrounding the initiation of protection orders due to officers' legal knowledge and discretion. The study emphasizes the importance of police support in addressing DV in China and the role of agency training in promoting non-coercive responses. It highlights regional variations in police support and underscores the need for addressing disparities in service provision across different provinces.

SNVstory: inferring genetic ancestry from genome sequencing data

BackgroundGenetic ancestry, inferred from genomic data, is a quantifiable biological parameter. While much of the human genome is identical across populations, it is estimated that as much as 0.4% of the genome can differ due to ancestry. This variation is primarily characterized by single nucleotide variants (SNVs), which are often unique to specific genetic populations. Knowledge of a patient's genetic ancestry can inform clinical decisions, from genetic testing and health screenings to medication dosages, based on ancestral disease predispositions. Nevertheless, the current reliance on self-reported ancestry can introduce subjectivity and exacerbate health disparities. While genomic sequencing data enables objective determination of a patient's genetic ancestry, existing approaches are limited to ancestry inference at the continental level.ResultsTo address this challenge, and create an objective, measurable metric of genetic ancestry we present SNVstory, a method built upon three independent machine learning models for accurately inferring the sub-continental ancestry of individuals. We also introduce a novel method for simulating individual samples from aggregate allele frequencies from known populations. SNVstory includes a feature-importance scheme, unique among open-source ancestral tools, which allows the user to track the ancestral signal broadcast by a given gene or locus. We successfully evaluated SNVstory using a clinical exome sequencing dataset, comparing self-reported ethnicity and race to our inferred genetic ancestry, and demonstrate the capability of the algorithm to estimate ancestry from 36 different populations with high accuracy.ConclusionsSNVstory represents a significant advance in methods to assign genetic ancestry, opening the door to ancestry-informed care. SNVstory, an open-source model, is packaged as a Docker container for enhanced reliability and interoperability. It can be accessed from https://github.com/nch-igm/snvstory.

A Federated Database for Obesity Research: An IMI-SOPHIA Study.

Obesity is considered by many as a lifestyle choice rather than a chronic progressive disease. The Innovative Medicines Initiative (IMI) SOPHIA (Stratification of Obesity Phenotypes to Optimize Future Obesity Therapy) project is part of a momentum shift aiming to provide better tools for the stratification of people with obesity according to disease risk and treatment response. One of the challenges to achieving these goals is that many clinical cohorts are siloed, limiting the potential of combined data for biomarker discovery. In SOPHIA, we have addressed this challenge by setting up a federated database building on open-source DataSHIELD technology. The database currently federates 16 cohorts that are accessible via a central gateway. The database is multi-modal, including research studies, clinical trials, and routine health data, and is accessed using the R statistical programming environment where statistical and machine learning analyses can be performed at a distance without any disclosure of patient-level data. We demonstrate the use of the database by providing a proof-of-concept analysis, performing a federated linear model of BMI and systolic blood pressure, pooling all data from 16 studies virtually without any analyst seeing individual patient-level data. This analysis provided similar point estimates compared to a meta-analysis of the 16 individual studies. Our approach provides a benchmark for reproducible, safe federated analyses across multiple study types provided by multiple stakeholders.

Scalable musculoskeletal model for dynamic simulations of lower body movement

A musculoskeletal (MSK) model is an important tool for analysing human motions, calculating joint torques during movement, enhancing sports activity, and developing exoskeletons and prostheses. To enable biomechanical investigation of human motion, this work presents an open-source lower body MSK model. The MSK model of the lower body consists of 7 body segments (pelvis, left/right thigh, left/right leg, and left/right foot). The model has 20 degrees of freedom (DoFs) and 28 muscle torque generators (MTGs), which are developed from experimental data. The model can be modified for different anthropometric measurements and subject body characteristics, including sex, age, body mass, height, physical activity, and skin temperature. The model is validated by simulating the torque within the range of motion (ROM) of isolated movements; all simulation findings exhibit a good level of agreement with the literature.

A comprehensive patient-specific prediction model for temporomandibular joint osteoarthritis progression

Temporomandibular joint osteoarthritis (TMJ OA) is a prevalent degenerative disease characterized by chronic pain and impaired jaw function. The complexity of TMJ OA has hindered the development of prognostic tools, posing a significant challenge in timely, patient-specific management. Addressing this gap, our research employs a comprehensive, multidimensional approach to advance TMJ OA prognostication. We conducted a prospective study with 106 subjects, 74 of whom were followed up after 2 to 3 y of conservative treatment. Central to our methodology is the development of an innovative, open-source predictive modeling framework, the Ensemble via Hierarchical Predictions through Nested cross-validation tool (EHPN). This framework synergistically integrates 18 feature selection, statistical, and machine learning methods to yield an accuracy of 0.87, with an area under the ROC curve of 0.72 and an F1 score of 0.82. Our study, beyond technical advancements, emphasizes the global impact of TMJ OA, recognizing its unique demographic occurrence. We highlight key factors influencing TMJ OA progression. Using SHAP analysis, we identified personalized prognostic predictors: lower values of headache, lower back pain, restless sleep, condyle high gray level-GL-run emphasis, articular fossa GL nonuniformity, and long-run low GL emphasis; and higher values of superior joint space, mouth opening, saliva Vascular-endothelium-growth-factor, Matrix-metalloproteinase-7, serum Epithelial-neutrophil-activating-peptide, and age indicate recovery likelihood. Our multidimensional and multimodal EHPN tool enhances clinicians' decision-making, offering a transformative translational infrastructure. The EHPN model stands as a significant contribution to precision medicine, offering a paradigm shift in the management of temporomandibular disorders and potentially influencing broader applications in personalized healthcare.

Development of a Facenet Enhanced Secured Smart Office System

A secured smart office system is the one that is capable of recognizing and granting access to authorized persons only and manage the office appliances autonomously. The goals are access control, security and automation. Over the years, several studies have been carried out to meet these needs using RFID cards, access codes and biometrics resulting in weak security with long computational period. Switching of electrical appliances and smoke detection in case of fire outbreak were used but real time electrical appliances management that could prevent fire outbreak is yet to be achieved. This research focused its attention on the design and implementation of a smart office system that meet these needs. The system was developed using a raspberry pi 4 board. Ultrasonic sensor, camera, servo motor, relay, current and voltage sensors were interfaced with the raspberry pi for image capturing, opening the door, switching and power monitoring respectively. The system captures the image of an approaching person and process it for recognition using FaceNet; an open source model for face recognition. Information was transmitted via SIM800L GSM module as SMS to the administrator. The system shuts down the office electrical network once the supply voltage exceeds 220v ac or less than 161v ac, thus preventing any chance of fire outbreak due to irregular power supply. The accuracy of image recognition model was 93.13%. This research has shown a simple way of implementing an autonomous smart office system that is capable of providing adequate security, efficiency and convenience in offices.

Assessing the risks and opportunities posed by AI-enhanced influence operations on social media

AbstractLarge language models (LLMs) like GPT-4 have the potential to dramatically change the landscape of influence operations. They can generate persuasive, tailored content at scale, making campaigns using falsified content, such as disinformation and fake accounts, easier to produce. Advances in self-hosted open-source models have meant that adversaries can evade content moderation and security checks built into large commercial models such as those commercialised by Anthropic, Google, and OpenAI. New multi-lingual models make it easier than ever for foreign adversaries to pose as local actors. This article examines the heightened threats posed by synthetic media, as well as the potential that these tools hold for creating effective countermeasures. It begins with assessing the challenges posed by a toxic combination of automated bots, human-controlled troll accounts, and more targeted social engineering operations. However, the second part of the article assesses the potential for these same tools to improve detection. Promising countermeasures include running internal generative models to bolster training data for internal classifiers, detecting statistical anomalies, identifying output from common prompts, and building specialised classifiers optimised for specific monitoring needs.

Automated multiclass segmentation, quantification, and visualization of the diseased aorta on hybrid PET/CT-SEQUOIA.

Cardiovascular disease is the most common cause of death worldwide, including infection and inflammation related conditions. Multiple studies have demonstrated potential advantages of hybrid positron emission tomography combined with computed tomography (PET/CT) as an adjunct to current clinical inflammatory and infectious biochemical markers. To quantitatively analyze vascular diseases at PET/CT, robust segmentation of the aorta is necessary. However, manual segmentation is extremely time-consuming and labor-intensive. To investigate the feasibility and accuracy of an automated tool to segment and quantify multiple parts of the diseased aorta on unenhanced low-dose computed tomography (LDCT) as an anatomical reference for PET-assessed vascular disease. A software pipeline was developed including automated segmentation using a 3D U-Net, calcium scoring, PET uptake quantification, background measurement, radiomics feature extraction, and 2D surface visualization of vessel wall calcium and tracer uptake distribution. To train the 3D U-Net, 352 non-contrast LDCTs from (2-[18F]FDG and Na[18F]F) PET/CTs performed in patients with various vascular pathologies with manual segmentation of the ascending aorta, aortic arch, descending aorta, and abdominal aorta were used. The last 22 consecutive scans were used as a hold-out internal test set. The remaining dataset was randomly split into training (n=264; 80%) and validation (n=66; 20%) sets. Further evaluation was performed on an external test set of 49 PET/CTs. The dice similarity coefficient (DSC) and Hausdorff distance (HD) were used to assess segmentation performance. Automatically obtained calcium scores and uptake values were compared with manual scoring obtained using clinical softwares (syngo.via and Affinity Viewer) in six patient images. intraclass correlation coefficients (ICC) were calculated to validate calcium and uptake values. Fully automated segmentation of the aorta using a 3D U-Net was feasible in LDCT obtained from PET/CT scans. The external test set yielded a DSC of 0.867±0.030 and HD of 1.0 [0.6-1.4] mm, similar to an open-source model with a DSC of 0.864±0.023 and HD of 1.4 [1.0-1.8] mm. Quantification of calcium and uptake values were in excellent agreement with clinical software (ICC: 1.00 [1.00-1.00] and 0.99 [0.93-1.00] for calcium and uptake values, respectively). We present an automated pipeline to segment the ascending aorta, aortic arch, descending aorta, and abdominal aorta on LDCT from PET/CT and to accurately provide uptake values, calcium scores, background measurement, radiomics features, and a 2D visualization. We call this algorithm SEQUOIA (SEgmentation, QUantification, and visualizatiOn of the dIseased Aorta) and is available at https://github.com/UMCG-CVI/SEQUOIA. This model could augment the utility of aortic evaluation at PET/CT studies tremendously, irrespective of the tracer, and potentially provide fast and reliable quantification of cardiovascular diseases in clinical practice, both for primary diagnosis and disease monitoring.

Large-Scale Agrivoltaics Visualisations for Assessing Landscape Impacts and Social Acceptance

Visual landscape impacts are of great importance when it comes to social acceptance measures of renewable energy. Although agrivoltaics seems to have higher acceptance values than other renewable energy infrastructures due to the dual land use approach, it is expected that they have a bigger visual impact on the landscape scenery than ground-mounted installations due to the increased land requirements and clearance heights. This article presents the development of a game-based visualisation approach using open-source software and open data (open government data) for visualising energy landscapes by creating large-scale interactive and immersive 3D visualisations. The results show that the use of open geodata and available open-source gaming technologies can be used to create comprehensive digital VR landscapes for assessing the visual impacts of agrivoltaics. Furthermore, the data-driven approach can provide additional indicators for evaluating planning scenarios and investigating the social acceptability due to renewable energy expansion.

(165) Assessing Artificial Intelligence Quality in the Evaluation and Treatment of Erectile Dysfunction

Abstract Introduction Since November 2022, Artificial Intelligence (AI) chatbots have grown in popularity including in urologic conditions. However, their accuracy and quality has not been evaluated systematically. In this study, we sought to assess the accuracy and quality of AI chatbots in the management of Erectile Dysfunction (ED). Objective We aim to evaluate the accuracy and quality of open-source language models in fielding common clinical questions pertaining to ED compared to board certified urologists. Methods Two AI open-source language models, ChatGPT and Google Bard, were fielded 15 standard questions related to ED some of which included causes, risk factors and treatment options of ED. Two board certified urologists were given the same questions on a standard survey. A third blinded board-certified urologist served as a grader using the AUA guidelines for ED on a Likert scale to assess the accuracy, robustness, and bias of each response. Urologist and AI responses were graded and aggregated using Likert scales. Results Overall AI responses were significantly more accurate (p<0.01), robust (p<0.01), and unbiased (p<0.01). Additionally, Google Bard had the highest scores all around followed by ChatGPT. The urologists’ responses were approximately 38% lower compared to the AI responses. Conclusions This study suggests that AI responses were superior compared to urologists in the areas of accuracy, robustness, and bias pertaining to the management of ED. Albeit, the chatbots have promising role in urologic conditions including ED, its widespread clinical use and adoption warrants further evaluation in the context of clinical decision making and enhancing patient care. Disclosure No.

Rethinking use-restricted open-source licenses for regulating abuse of generative models

The rapid progress of Artificial intelligence in generative modeling is marred by widespread misuse. In response, researchers turn to use-based restrictions—contractual terms prohibiting certain uses—as a “solution” for abuse. While these restrictions can be beneficial to artificial intelligence governance in API-gated settings, their failings are especially significant in open-source models: not only do they lack any means of enforcement, but they also perpetuate the current proliferation of tokenistic efforts toward ethical artificial intelligence. This observation echoes growing literature that points to useless efforts in “AI ethics,” and underscores the need to shift from this paradigm. This article provides an overview of these drawbacks and argues that researchers should divert their efforts to studying deployable, effective, and theoretically grounded solutions like watermarking and model alignment from human feedback to effect tangible changes in the current climate of artificial intelligence.

The openOCHEM consensus model is the best-performing open-source predictive model in the First EUOS/SLAS joint compound solubility challenge

The EUOS/SLAS challenge aimed to facilitate the development of reliable algorithms to predict the aqueous solubility of small molecules using experimental data from 100 K compounds. In total, hundred teams took part in the challenge to predict low, medium and highly soluble compounds as measured by the nephelometry assay. This article describes the winning model, which was developed using the publicly available Online CHEmical database and Modeling environment (OCHEM) available on the website https://ochem.eu/article/27. We describe in detail the assumptions and steps used to select methods, descriptors and strategy which contributed to the winning solution. In particular we show that consensus based on 28 models calculated using descriptor-based and representation learning methods allowed us to obtain the best score, which was higher than those based on individual approaches or consensus models developed using each individual approach. A combination of diverse models allowed us to decrease both bias and variance of individual models and to calculate the highest score. The model based on Transformer CNN contributed the best individual score thus highlighting the power of Natural Language Processing (NLP) methods. The inclusion of information about aleatoric uncertainty would be important to better understand and use the challenge data by the contestants.

Printed smart devices for anti-counterfeiting allowing precise identification with household equipment

Counterfeiting has become a serious global problem, causing worldwide losses and disrupting the normal order of society. Physical unclonable functions are promising hardware-based cryptographic primitives, especially those generated by chemical processes showing a massive challenge-response pair space. However, current chemical-based physical unclonable function devices typically require complex fabrication processes or sophisticated characterization methods with only binary (bit) keys, limiting their practical applications and security properties. Here, we report a flexible laser printing method to synthesize unclonable electronics with high randomness, uniqueness, and repeatability. Hexadecimal resistive keys and binary optical keys can be obtained by the challenge with an ohmmeter and an optical microscope. These readout methods not only make the identification process available to general end users without professional expertise, but also guarantee device complexity and data capacity. An adopted open-source deep learning model guarantees precise identification with high reliability. The electrodes and connection wires are directly printed during laser writing, which allows electronics with different structures to be realized through free design. Meanwhile, the electronics exhibit excellent mechanical and thermal stability. The high physical unclonable function performance and the widely accessible readout methods, together with the flexibility and stability, make this synthesis strategy extremely attractive for practical applications.