Complexity Of Process Research Articles

Prevalent harmonic interaction in the bat inferior colliculus.

Animal vocalizations and human speech are typically characterized by a complex spectrotemporal structure, composed of multiple harmonics, and patterned as temporally organized sequences. However, auditory research often employed simple artificial acoustic stimuli or their combinations. Here we addressed the question of whether the neuronal responses to natural echolocation call sequences can be predicted by manipulated sequences of incomplete constituents at the midbrain inferior colliculus (IC). We characterized the extracellular single-unit activity of IC neurons in the great roundleaf bat, Hipposideros armiger (both sexes), using natural call sequences, various manipulated sequences of incomplete vocalizations, and pure tones. We report that approximately two-thirds of IC neurons exhibited a harmonic interaction. Neurons with high harmonic interactions exhibited greater selectivity to natural call sequences, and the degree of harmonic interaction was robust to the natural amplitude variations between call harmonics. For 81% of the IC neurons, the responses to the natural echolocation call sequence could not be predicted by altered sequences of missing call components. Surprisingly, nearly 70% of the neurons that showed a harmonic interaction were characterized by a single excitatory response peak as revealed by pure tones. Our results suggest that prevalent harmonic processing has already emerged in the auditory midbrain inferior colliculus in the echolocating bat.Significance Statement Auditory cortex has long been suggested to be the main region for processing complex sound such as natural vocalizations. However, this cortex-centered view of complex stimuli processing was largely based on simple stimuli or their combinations, ignoring many crucial aspects of natural communication behaviors. Here, we took a neuroethological perspective to study the harmonic processing in the inferior colliculus, in the great roundleaf bat. We show that prevalent harmonic processing has already emerged in the auditory midbrain in the echolocating bat. Critically, harmonic interactions revealed by natural call sequences cannot be predicted by responses to either pure tones or altered sequences of missing call components. Our data suggest that auditory midbrain is involved in harmonic processing in the echolocating bat.

Event log extraction methodology for Ethereum applications

The adoption of smart contracts in decentralized blockchain-based applications enables reliable and certified audits. These audits allow the extraction of valuable information from blockchains, which can be used to reconstruct the execution of the application and facilitate advanced analyses. One of the most commonly used techniques in this context is process mining, which leverages event logs to trace and accurately represent the process execution of applications. However, extracting execution data from blockchains poses significant challenges, and the current methodologies developed have some limitations. Most approaches are tailored to specific use cases, requiring that analysis techniques are defined during the smart contract’s development. Other techniques are applied a posteriori, relying on blockchain events that often lack a standardized format. This absence of standardization requires complex processing steps to correlate logs with the executed actions and such approaches are not universally applicable to all smart contracts on the blockchain, further limiting their scope. Lastly, none of the existing techniques can extract information from event logs embedded in internal transactions of smart contracts.To address these limitations, we propose EveLog an application-agnostic methodology that can be applied to any EVM-compatible application without predefined constraints. Its primary goal is to extract information from smart contracts, capturing both public and internal transactions, and organizing the results into a structured XES event log. The EveLog methodology consists of five key steps: (i) extraction of data from smart contract transactions, (ii) decoding raw data, (iii) selection of sorting criteria, (iv) construction of traces, and (v) generation of the XES event log. EveLog has been implemented in a client–server application and tested on existing solutions, specifically the CryptoKitties application, a blockchain-based game on the Ethereum blockchain. The study was conducted using 12,996 blocks, including over 8000 real transactions from the Ethereum mainnet.

Acoustic pipette and biofunctional elastomeric microparticle system for rapid picomolar-level biomolecule detection in whole blood.

Most biosensing techniques require complex processing steps that generate prolonged workflows and introduce potential points of error. Here, we report an acoustic pipette to purify and label biomarkers in 70 minutes. A key aspect of this technology is the use of functional negative acoustic contrast particles (fNACPs), which display biorecognition motifs for the specific capture of biomarkers from whole blood. Because of their large size and compressibility, the fNACPs robustly trap along the pressure antinodes of a standing wave and separate from blood components in under 60 seconds with >99% efficiency. fNACPs are subsequently fluorescently labeled in the pipette and are analyzed by both a custom, portable fluorimeter and flow cytometer. We demonstrate the detection of anti-ovalbumin antibodies from blood at picomolar levels (35 to 60 pM) with integrated controls showing minimal nonspecific adsorption. Overall, this system offers a simple and versatile approach for the rapid, sensitive, and specific capture of biomolecules.

Association of aging related genes and immune microenvironment with major depressive disorder

ObjectiveTo study the relationship between aging related genes (ARGs) and Major Depressive Disorder (MDD). MethodsThe datasets GSE98793, GSE52790 and GSE39653 for MDD were obtained from the GEO database, and ARGs were obtained from the Human Aging Genome Resources database. Differential expression genes (DEGs) screening and GO, KEGG enrichment analysis were performed to uncover the underlying mechanisms. To identify key ARGs associated with MDD (key ARG-DEGs), we employed machine learning methods such as LASSO, SVM, and Random Forest, as well as the plug-ins CytoHubba-MCC and MCODE methods. SsGSEA was used to analyze the immune infiltration of MDD and healthy controls. Furthermore, we created risk prediction nomograms model and ROC curves to assess not only the ability of key ARG-DEGs to diagnose MDD, but also predicted miRNAs and transcription factors (TFs) that might interact. Finally, a two-sample Mendelian randomization (MR) study was performed to confirm the association of identified key ARG-DEGs with depression. ResultsDEGs of ARGs between MDD and healthy controls led to the identification of eight ARG-DEGs. GO and KEGG analysis revealed that the pathways associated with these eight ARG-DEGs were primarily concentrated in Foxo pathway, JAK-STAT pathway, Pl3K-AKT pathway, and metabolic diseases. A comprehensive analysis further narrowed down the 8 ARG-DEGs to 4 key ARG-DEGs: MMP9, IL7R, S100B, and EGF. Immune infiltration analysis indicated significant differences in CD8(+) T cells, macrophages, neutrophils, Th2 cells, and TIL cells between MDD and control groups, correlating with these four key ARG-DEGs. Based on these four key ARG-DEGs, a risk prediction model for MDD was developed. The miRNA-TF-mRNA interaction network of the key ARG-DEGs highlights the complexity of the regulatory process, providing valuable insights for future related research. The MR study suggested a potential causal relationship between MMP9 and the risk of depression. ConclusionThe process of aging, immune dysregulation, and MDD are closely interconnected. MMP9, IL7R, S100B, and EGF may be used as novel diagnostic biomarkers and potential therapeutic targets for MDD, especially MMP9.

Evolution of solid residue composition during inert and oxidative biomass torrefaction

Biomass torrefaction is a thermochemical pretreatment that can be used to improve biomass properties, contributing to the energy densification of biomass or increasing its grindability, burning and gasification characteristics, porous structure, etc. While it is typically carried out under inert conditions, oxygen-lean torrefaction of biomass can reduce the process cost and complexity. This work proposes a novel extended 2-step kinetics mechanism capable of accurately predicting the evolution of the chemical composition of torrefied biomass. The model parameters are obtained from data collected from an extensive experimental campaign in which the chemical composition of torrefied biomass was measured at different temperatures, residence times, and oxygen fractions. The carbon mass fraction of the torrefied products was found to increase by up to 50 % under severe inert torrefaction conditions, i.e., high temperatures and long residence times, whereas the hydrogen fraction decreased. In the presence of oxygen during torrefaction, the carbon and hydrogen contents in the solid residue decrease compared with those produced under inert torrefaction. The extended 2-step model can also be used to determine the high heating value and energy densification of the torrefied biomass, with the latter ranging from 1 to 1.4 for the operating conditions tested.

The Power of Story Slams: A Mixed-Method Analysis of Narrative Medicine Connecting, Encouraging and Comforting Healthcare Trainees and Professionals

Background: Narratives convey information and emotion, evoke understanding, empathy, and connection. Healthcare professionals can use narratives to reduce burnout, benefiting them, patients, and colleagues. Story slams are narrative sharing events used for group reflection, providing an opportunity for participants and listeners to improve their well-being by processing complex emotions and identities. Story slams have traditionally been popular with creative writers and nonprofit organizations. Methods: Following a local event hosted by The Moth, the Temple University Lewis Katz School of Medicine (LKSOM) Narrative Medicine program hosted nine story slams over five years with 93 presenters including students and health systems staff. Through a mixed-method analysis, the authors examined the stories for thematic patterns and surveyed presenters to investigate how the experience had impacted markers such as job satisfaction, stress levels, and connection to patients. Eleven presenters were interviewed to provide additional information about the impact they experienced. Results: Patient-centered care, resilience, and the value of learning comprised over half of all themes presented. Following the event, many presenters experienced improved connections with patients, and everyone reported feeling neutral or increased satisfaction with their profession. The interviews conveyed participation, created a lasting impact, fostered a sense of community, and increased appreciation for diversity. Conclusions: Overall, story slams nourish humanity and promote diversity, enthusiasm, and encouragement. This study and others suggest that story slams are inexpensive and effective events that can help reduce burnout and provide academic medical centers with a way to restore and cultivate community among students, physicians, and staff.

Research on safety risk factors of metro shield tunnel construction in China based on social network analysis

PurposeDuring metro construction using the shield method, the construction process's complexity, the construction environment's variability, and other factors can easily lead to tunnel construction accidents. This paper aims to explore the interconnections between risk factors and related accident types, as well as the risk chain formed between risk factors, and to analyze the key risk factors and vulnerabilities in shield tunnel construction through empirical data.Design/methodology/approachBased on the social network analysis theory, the connection of various risk factors in subway shield tunnel construction is explored, and the mechanism of multiple risk factors is studied. Through literature analysis, articles on safety risk factors in metro shield tunnel construction are organized and studied, and the identified safety risk factors can comprehensively reflect the significant risks that need to be concerned in metro shield tunnel construction.FindingsThe results show that a small world characterizes the SNA network of safety risk factors for metro shield tunnel construction: The frequency of association between the five risk factors “unsafe behavior,” “site management,” “safety supervision and inspection,” “safety education system” and “safety protection” is higher than that of other factors. Only a few risks, such as “site management,” “safety supervision and inspection,” and “rapid response capability,” directly lead to accidents. In addition, risk factors such as the “safety education system” and “safety protection” will indirectly cause unsafe behaviors of construction personnel.Research limitations/implicationsDuring construction, the probability of occurrence of risk factors may vary with the construction phase and area and is not considered in this paper. In addition, although this paper identifies, determines and analyzes the risk factors affecting the safety of metro shield tunnel construction, including the importance of each risk factor and the connection between them, more detailed information before and after the accident could not be obtained based on the accident investigation report alone. Therefore, future research can collect the same accident case from more sources to obtain more information.Practical implicationsThe theory of accident causation has been improved at the theoretical level. The identified safety risk factors can comprehensively reflect the significant risks that need to be paid attention to in metro shield tunnel construction. From a practical point of view, the results of the study provide a basis for the rational control of the risk factors in the construction of subway shield tunnels, which can help guide practitioners to do a good job of risk prevention before the construction of metro shield tunnels and reduce the probability of related accidents.Originality/valueThis study expands the application of social network analysis in the field of subway tunnel construction risk, quantitatively analyzes the key risk factors and vulnerabilities in shield method tunnel construction and proposes policy recommendations for future metro tunnel construction safety management.

The role of discretion and street‐level deliberative practices in the COVID‐19 crisis response: Lessons from the Philippines

AbstractThis study investigates the role of frontline workers, such as the Barangay Health Emergency Response Teams (BHERTs) in the Philippines, as street‐level bureaucrats (SLBs) in implementing COVID‐19 community quarantine policies. Drawing from SLB theory and deliberative policy analysis (DPA), I explore how SLBs adapted to rapidly changing conditions by exercising discretion in interpreting and adapting the implementation of nationally formulated pandemic management policies to meet local realities. I argue that SLBs functioned as deliberative practitioners, mediating between national policies and local realities while engaging in deliberative practices with their clients. Using the experience of BHERTs in Palompon, Leyte in the Philippines as an illustrative case, I identified the policy mandates (policy‐as‐written) of the BHERTs; the challenges and constraints they faced in implementing community quarantine policies; and the discretion they exercised in implementing community quarantine policies (policy‐as‐practiced). The findings reveal that, to meet their policy mandates and address the challenges they faced, they engaged in deliberative practices and collaborated with citizens in implementing context‐specific and creative strategies. Insights from this study highlight the adaptive and deliberative potential of SLBs in crisis situations and the value of a DPA in understanding SLB theory and research. By examining SLBs’ experiences during the pandemic, this study contributes to the SLB literature and offers practical lessons to policy practitioners in facilitating deliberation and developing context‐specific solutions in times of crisis.Points for practitioners The COVID‐19 pandemic and the subsequent government response in the Philippines have increased the challenges faced by street‐level bureaucrats (SLBs), including resource scarcity, organisational and political pressure, public resistance, and the risk of exposure to the virus. SLBs have shown their capacity to apply discretion and collaborate with their clients to resolve their own and the client's challenges and limitations through negotiation, mediation, conflict management, and active listening. This cooperative approach empowers them to resolve common issues and together formulate effective strategies that are beneficial to all parties involved. Relationships outside of SLBs’ duties as state agents (i.e. socio‐cultural relationships) can be a viable pathway for SLBs to implement alternative policy strategies to meet their policy mandates and address the challenges they face as they implement their mandates. Competing value systems, knowledge claims, and emotional narratives, compounded by a governance crisis, define the complex and informal nature of the policy process in the context of a crisis like the pandemic. Although SLBs demonstrate their capacity to creatively solve the challenges they encounter through deliberative practices, this should not serve as a justification for the government's lack of action in prioritising, safeguarding, and preserving their welfare.

Electromagnetic and Radon Earthquake Precursors

Earthquake forecasting is arguably one of the most challenging tasks in Earth sciences owing to the high complexity of the earthquake process. Over the past 40 years, there has been a plethora of work on finding credible, consistent and accurate earthquake precursors. This paper is a cumulative survey on earthquake precursor research, arranged into two broad categories: electromagnetic precursors and radon precursors. In the first category, methods related to measuring electromagnetic radiation in a wide frequency range, i.e., from a few Hz to several MHz, are presented. Precursors based on optical and radar imaging acquired by spaceborne sensors are also considered, in the broad sense, as electromagnetic. In the second category, concentration measurements of radon gas found in soil and air, or even in ground water after being dissolved, form the basis of radon activity precursors. Well-established mathematical techniques for analysing data derived from electromagnetic radiation and radon concentration measurements are also described with an emphasis on fractal methods. Finally, physical models of earthquake generation and propagation aiming at interpreting the foundation of the aforementioned seismic precursors, are investigated.

Integration of Neuro-Fuzzy Systems in Medical Diagnostics and Data Security - A Review

Adaptive Neuro-Fuzzy Systems (ANFS) have become increasingly prevalent in a variety of fields due to their ability to process complex and uncertain data with high accuracy. This research article reviews three major contributions of ANFS: their application in deep neuro-fuzzy systems (DNFS) for healthcare and industrial systems, neuro-fuzzy logic controllers for paralysis estimation, and ANFIS-based solutions for secure cloud storage in medical IoT (MIoT). The findings emphasize the importance of ANFS in improving decision-making, diagnosis, and data security. This paper concludes with a discussion on challenges, future research directions, and the need for optimization in real-time applications.

Uncertain data density peak clustering algorithm based on JS divergence

Aiming at the defects of traditional density-based uncertainty clustering algorithms, such as parameter sensitivity and poor clustering results for complex manifold uncertain data sets, a new uncertainty data density peak clustering algorithm based on JS divergence (UDPC-JS) is proposed. The algorithm first removes noise points by using the uncertain natural neighbor density factor defined by the uncertain natural neighbor. Secondly, the local density of uncertain data objects is calculated by combining the uncertain natural neighbor and JS divergence. The initial clustering center of the uncertain data set is found by combining the idea of representative points, and the distance between the initial clustering centers is defined based on JS divergence and graph. Then, the decision graph is constructed on the initial clustering center using the local density calculated based on the uncertain natural neighbor and JS divergence and the newly defined distance between the initial clustering centers based on JS divergence and graph, and the final clustering center is selected according to the decision graph. Finally, the unassigned uncertain data objects are assigned to the cluster where their initial clustering center is located. Experimental results show that the algorithm has better clustering effect and accuracy than the comparison algorithm, and has a greater advantage in processing complex manifold uncertain data sets.

New Lightweight Cryptosystem for IoT in Smart City Environments

Internet of Things (IoT) devices, user interfaces (UI), software, as well as communication networks are all deployed within Smart Cities topology. The security approach designed for Internet of Things IoT should be able to prevent and detect both internal and external attacks. The problem in IoT network that not every linked node or device has an adequate amount of processing power. This means that data encryption and other related activities will be impossible and means that the security of any kind must be lightweight. A trustworthy security solution that stops illegal access to private data on the network is necessary for maintaining the privacy of information on the Internet of Things. Cryptographic processes need to be quicker and more compact without sacrificing security. The aim of this study is to reduce the execution time and power consumption of encryption processes without compromise the complexity of the encryption algorithm. This research presents a new lightweight cryptographic technique to protect various multimedia and real-time traffics across IoT network, by using two S-box in SubByte of encryption process, without affecting its performance. In this study, different audio samples will be used to test the new algorithm efficiency. Comparing the suggested method to the most advanced standard algorithm, it can reduce the cryptography process's execution time as well as energy consumption while maintaining the required security level. The outcomes demonstrate good performance in terms of power usage and delay. The new technique consumed a roughly 0.2 µJ for encryption process while the typical AES algorithm consumed 0.29 µJ, this mean the new algorithm achieved (33% power savings), while maintaining a good complexity level (security) within the process of encryption according to the results in tables I, II, and the comparison in table III. The novelty of this work can be showed by using dual XOR S-box technique which increased the complexity of SubByte process making it more secure without overload the processing performance, in addition to the reduction in encryption rounds which contribute to enhance the performance without compromise the security. Making it more suited for the Internet of Things (IoT) used in smart city environments.

Cargo adaptor identity controls the mechanism and kinetics of dynein activation.

Cytoplasmic dynein-1 (dynein), the primary retrograde motor in most eukaryotes, supports the movement of hundreds of distinct cargos, each with specific trafficking requirements. To achieve this functional diversity, dynein must bind to the multi-subunit complex dynactin and one of a family of cargo adaptors to be converted into an active, processive motor complex. Very little is known about the dynamic processes that promote the formation of this complex. To delineate the kinetic steps that lead to dynein activation, we developed a single-molecule fluorescence assay to visualize the real-time formation of dynein-dynactin-adaptor complexes in vitro. We found that dynactin and adaptors bind dynein independently rather than cooperatively. We also found that different dynein adaptors promote dynein-dynactin-adaptor assembly with dramatically different kinetics, which results in complex formation occurring via different assembly pathways. Despite differences in association rates or mechanism of assembly, all adaptors tested can generate a population of tripartite complexes that are very stable. Our work provides a model for how modulating the kinetics of dynein-dynactin-adaptor binding can be harnessed to promote differential dynein activation and reveals a new facet of the functional diversity of the dynein motor.

Investigating cross-docking criteria for raw materials with hybrid MCDM

The cross-docking process involves unloading raw materials from incoming vehicles and immediately loading them onto outgoing vehicles. This can greatly improve supply chain operations and expedite transportation to the factory. By minimizing warehouse storage expenses, space requirements, and inventory management, the process can be streamlined effectively. In industries where production profiles need to be responsive to market demands, the location of the cross-docking facility and transportation planning are crucial. This article aims to investigate the criteria for selecting a cross-docking location, using a case study of Arta Profile Company in Ardabil. The research population includes all industrial engineers and production planners in the company, with 10 industry experts selected for this study. The analytic network process (ANP) and grey complex proportional assessment (COPRAS-G) methods were used to compare and prioritize cross-docking locations. Additionally, the fuzzy DEMATEL method was used to determine the strength of connections between different locations. The COPRAS-G approach helps decision-makers make informed decisions by evaluating different options and their importance. The industry experts identified 28 criteria for this study. The results from the network analysis method showed that the most important criterion is the vehicle routing problem (VRP), with a value of 0.46. According to the ANP technique, strategic decision-making is the best option. The fuzzy DEMATEL method revealed that distribution centers are the most important factor, followed by reduced delivery/distribution operations, and finally, the cost of late or early delivery.

Investigation and Demonstration for immunoassay technology based on impedance Amplifies of magnetic and dielectric microbeads

BackgroundBead-based immunoassays typically require researchers to perform the analysis in a laboratory setting, involving complex processing procedures, most of the time, constant potential or current instruments are used to measure the bio-impedance change. However, almost all biological materials are decorated on the electrode surface to obtain the maximum sensitivity, resulting in difficulty to clean, separate, and other applications of fixed bead-based immunoassay. ResultWe present an innovative unfixed bead-based immunoassay technology that utilizes electrical impedance characteristics, offering the advantages of being rapid, portable, miniaturized, and cost-effective. This technique which is known as Impedance-enhanced electrochemical immunoassay (IE-ECIA) employs antibody-coated Magnetic beads (MBs) and Dielectric beads (DBs) linked to antigens for quantitative with miniaturization electronic devices, which has been successfully applied in an immune impedance sensor for detecting the Dengue virus (DENV), which is an affordable test with a low-cost microfluidic impedance biosensor combined with immune magnetic and dielectric micro/nanobeads for antigen detection. HighlightThe proposed immunoassay technology introduces a novel microfluidic platform incorporating magnetic and dielectric beads alongside with an electrochemical impedance biosensor, therefore facilitating continuous-fluid quantitative analysis. This innovative and unconventional approach leverages free-motion multiplex beads housing magnetic and dielectric bead-bound antigens within a simple linear microchannel of microfluidic chip architecture, making the high-through measurements more efficient, enhanced Electrochemical immunoassay (ECIA) signals of Bio-on-magnetic-beads (BOMB). This advancement holds significant potential for enhancing diagnostic capabilities for emerging infectious diseases within clinical environments, thereby bolstering rapid detection capabilities and aiding in the advancement of novel pharmaceuticals, benefiting all the stakeholders.

Structure-Stability Relation of Single-Atom Catalysts under Operating Conditions of CO2 Reduction.

Single-atom catalysts (SACs) have exhibited exceptional atomic efficiency and catalytic performance in various reactions but suffer poor stability. Understanding the structure-stability relation is the prerequisite for stability optimization but has been rarely explored due to complexity of the degradation process and reaction environments. Herein, we successfully established the structure-stability relation of N-doped carbon-supports SACs (MN4 SACs) under working conditions of CO2 reduction, by using advanced constant-potential density functional theory calculations. Systematic mechanism investigation that considered different factors identifies the key role of initial hydrogen adsorption on the coordination N atom in catalytic stability, where the feasibility of the adsorption eventually determines the leaching of the metal atom. On this basis, a simple descriptor consisting of electron number and electronegativity is constructed, realizing accurate and rapid prediction of the stability of SACs. Furthermore, strategies via modifying the local geometric structure to improve the stability without changing the active centers are proposed accordingly, which are supported by related experiments. These findings fill the current void in understanding SAC stability under practical working conditions, potentially advancing the widespread application of SACs in sustainable energy conversion systems.

Online Self-Learning-Based Raw Material Proportioning for Rotary Hearth Furnace and Intelligent Batching System Development

With the increasing awareness of environmental protection, the rotary hearth furnace system has emerged as a key technology that facilitates a win-win situation for both environmental protection and enterprise economic benefits. This is attributed to its high flexibility in raw material utilization, capability of directly supplying blast furnaces, low energy consumption, and high zinc removal rate. However, the complexity of the raw material proportioning process coupled with the rotary hearth furnace system’s reliance on human labor results in a time-consuming and inefficient process. This paper innovatively introduces an intelligent formula method for proportioning raw materials based on online clustering algorithms and develops an intelligent batching system for rotary hearth furnaces. Firstly, the ingredients of raw materials undergo data preprocessing, which involves using the local outlier factor (LOF) method to detect any abnormal values, using Kalman filtering to smooth the data, and performing one-hot encoding to represent the different kinds of raw materials. Afterwards, the affinity propagation (AP) clustering method is used to evaluate past data on the ingredients of raw materials and their ratios. This analysis aims to extract information based on human experience with ratios and create a library of machine learning formulas. The incremental AP clustering algorithm is utilized to learn new ratio data and continuously update the machine learning formula library. To ensure that the formula meets the actual production performance requirements of the rotary hearth furnace, the machine learning formula is fine-tuned based on expert experience. The integration of machine learning and expert experience demonstrates good flexibility and satisfactory performance in the practical application of intelligent formulas for rotary hearth furnaces. An intelligent batching system is developed and executed at a steel plant in China. It shows an excellent user interface and significantly enhances batching efficiency and product quality.

SKYQUANT 3D: Quantifying Vascular Anatomy With an Open-Source Workflow for Comprehensive Analysis of Volumetric Optoacoustic Angiography Data.

Efficient visualization of the vascular system is of key importance in biomedical research into tumor angiogenesis, cerebrovascular alterations, and other angiopathies. Optoacoustic (OA) angiography offers a promising solution combining molecular optical contrast with high resolution and deep penetration of ultrasound. However, its hybrid nature implies complex data collection and processing workflows, with significant variability in methodologies across developers and users. To streamline interoperability, we introduce SKYQUANT 3D, a Python-based set of instructions for the Thermo Fisher Scientific Amira/Avizo 3D Visualization & Analysis Software. Our workflow simplifies the batch processing of volumetric optoacoustic angiography images, extracting meaningful quantitative information while also providing statistical analysis and graphical representation of the results. Quantification performance of SKYQUANT 3D is demonstrated using functional preclinical and clinical in vivo 3D OA angiographic tests involving ambient temperature variations and repositioning of the imaged limb.

Trusted microelectronics: reverse engineering chip die using U-Net convolutional network

In the field of integrated circuits (IC) reverse engineering, accurate IC die image segmentation is critical for ensuring trust and detecting counterfeits. This study introduces a deep learning-based methodology utilizing a U-Net Convolutional Neural Network (CNN) tailored for IC image segmentation. Our model excels in processing complex IC images with noise, achieving superior segmentation accuracy. The model was evaluated on a dataset of 512 × 512 pixel IC die images, achieving a mean Intersection over Union (IoU) of 81.2%, which is a significant improvement over traditional image processing techniques that achieve an IoU around 65.3%. During training, the model’s Dice Loss showed a sharp decrease, as depicted in the provided graph, highlighting the model’s ability to effectively learn and refine segmentation boundaries. Simultaneously, the training accuracy, as illustrated in the accompanying accuracy graph, improved steadily, reaching approximately 60% but still rising. This convergence of Dice Loss and the upward trend in accuracy demonstrate the model’s robust performance across varying noise levels and its effectiveness in producing precise segmentation outputs. This work underscores the effectiveness of CNNs, particularly the U-Net architecture, in enhancing the accuracy and reliability of IC die image analysis, paving the way for improved IC manufacturing quality assurance.

3D Printing of Hierarchical Structures Made of Inorganic Silicon-Rich Glass Featuring Self-Forming Nanogratings.

Hierarchical structures are abundant in nature, such as in the superhydrophobic surfaces of lotus leaves and the structural coloration of butterfly wings. They consist of ordered features across multiple size scales, and their advantageous properties have attracted enormous interest in wide-ranging fields including energy storage, nanofluidics, and nanophotonics. Femtosecond lasers, which are capable of inducing various material modifications, have shown promise for manufacturing tailored hierarchical structures. However, existing methods, such as multiphoton lithography and three-dimensional (3D) printing using nanoparticle-filled inks, typically involve polymers and suffer from high process complexity. Here, we demonstrate the 3D printing of hierarchical structures in inorganic silicon-rich glass featuring self-forming nanogratings. This approach takes advantage of our finding that femtosecond laser pulses can induce simultaneous multiphoton cross-linking and self-formation of nanogratings in hydrogen silsesquioxane. The 3D printing process combines the 3D patterning capability of multiphoton lithography and the efficient generation of periodic structures by the self-formation of nanogratings. We 3D-printed micro-supercapacitors with large surface areas and a high areal capacitance of 1 mF/cm2 at an ultrahigh scan rate of 50 V/s, thereby demonstrating the utility of our 3D printing approach for device applications in emerging fields such as energy storage.