Relative Output Research Articles

DETERMINATION OF DOSE REFERENCE LEVELS AT BENDAN PEKALONGAN REGIONAL HOSPITAL FOR INTERNAL DOSE AUDIT AS AN EFFORT TO OPTIMIZE RADIATION PROTECTION FOR PATIENTS

Unnecessary exposure is less of a concern because it is considered not to cause injury to patients. Radiation protection optimization is carried out for patients to reduce unnecessary exposure. Following BAPETEN Regulation No. 4 of 2020 Article 46, permit holders are required to implement radiation protection optimization [1]. This study aimed to obtain the local dose reference level (TPD) value through an internal dose audit to optimize patient radiation protection. This optimization is carried out in several steps, including conducting an internal dose audit, determining the local TPD, and evaluating the examination. This study is expected to motivate documentation of radiation parameters to obtain sufficient data for analysis. The method used in this study is a literature study through related references and X-ray radiation output data to calculate the dose in the form of incident air kerma (INAK) and entrance surface air kerma (ESAK). The results showed that the local INAK TPD value was 52% below the national TPD, and the local ESAK TPD value was 57% below the national TPD. The expected local TPD value is a TPD value that is lower than the national TPD value. This indicates that the imaging protocol at RSUD Bendan has been well-optimized. However, there are still several types of examinations where the local TPD value exceeds the national TPD, indicating the need to review further the imaging techniques and protocols used. The influence of body weight and radiological image diagnosis was analyzed as a basis for further optimization. Thus, the determination of this local TPD can be an essential reference in efforts to improve patient safety and optimize radiation protection in health facilities. Keywords: radiation protection optimization of patient, Dose Reference Level (DRL), patient dose.

The emerging role of disease-associated microglia in Parkinson’s disease

Disease-associated microglia (DAM) are a subset of microglia that appear at various stages of central nervous system neurodegenerative diseases. DAM were identified using single-cell RNA sequencing within Alzheimer’s Disease (AD) where they were characterized by their unique localization near amyloid-β plaques and their phagocytic and lipid-metabolizing features. Unfortunately, activation and etiology of DAM are only understood within the context of AD where Triggering Receptor Expressed On Myeloid Cells 2 (TREM2), a receptor for amyloid-β, appears to be the key regulator in microglial transition to a DAM state. Despite this reliance on TREM2 in AD, DAM appear across other neurodegenerative diseases in which TREM2 may not be a critical player. This begs the question of if DAM are truly the same across all neurodegenerative diseases or if there exists a heterogeneity to DAM across neurodegenerative pathologies. Investigation into this critical gap in the field regarding DAM etiology and activation, as well as DAM function, could be delineated utilizing models of Parkinson’s disease (PD) to complement studies in models of AD. Though highly underexplored regarding DAM, PD with its pattern of protein aggregation-associated pathology like AD could serve as the spatiotemporal comparison against AD findings to ascertain the nature of DAM. The experimental vehicle that could guide the future of such investigation is the multi-omics model. With a compound approach focusing on exploring triggers for DAM at the chromatin or mRNA level and related protein output, it becomes possible to strongly characterize and firmly answer the question of what is a DAM.

Cooperative Spacing Control of Vehicle Platoon via Relative Output Feedback Considering Analog Fading Networks

ABSTRACTThe influence of analog fading channels on platoon control performance is studied via relative output feedback. The main goal is to determine the existence of a distributed output feedback controller capable of achieving mean square stability (MSS) for the underlying vehicle platoon systems over analog fading channels. First, the conditions, closely associated with the statistical characteristics of fading channels, for achieving platoon MSS are derived for three cases: undirected information flow (UIF) topology with identical fading channels, balanced directed information flow (BDIF) topology with identical fading channels, and general information flow topology (IFT) with nonidentical fading channels, respectively. These conditions explicitly reveal how the statistical characteristics of fading channels and IFT jointly affect vehicular stability. Second, in order to mitigate the impact of fading channels on platoon MSS, some feasible dynamic observer‐based controllers by using the relative output feedback, are proposed for each one of the former two situations by solving the corresponding modified Riccati inequalities (MRIs); and in the last situation, by employing edge Laplacian to model the vehicle platoon dynamics, a sufficient condition based on the solution of an LMI, and a feasible state‐based controller based on the solution of an MRI, are provided. Finally, simulations are carried out to compare three distinct fading channels, namely the Nakagami channel, Rician channel, and Rayleigh channel. The outcomes substantiate the superior and effective performance of the implemented control methods in guaranteeing the platoon MSS.

Robust approximation of the conditional mean for applications of Machine Learning

Machine Learning approaches are increasingly used in a range of applications. They are shown to produce low conventional errors but in many real applications fail to model the underlying input–output relationships. This is because the error measures used only predict the conditional mean under some restrictive assumptions, often not met by the data we extract from applications. However, new approaches to Machine Learning, for example using Evolutionary Computation, allow a range of alternative error measures to be used. This paper explores the use of the Fit to Median Error measure in machine learning regression automation, using evolutionary computation in order to improve the approximation of the ground truth. When used alongside conventional error measures it improves the robustness of the learnt input–output relationships to the conditional median. It is compared to traditional regularisers to illustrate that the use of the Fit to Median Error produces regression neural networks which model more consistent input–output relationships. The problem considered is ship power prediction using a fuel-saving air lubrication system, which is highly stochastic in nature. The networks optimised for their Fit to Median Error are shown to approximate the ground truth more consistently, without sacrificing conventional Minkowski-r error values.

Distributed leader-following bipartite consensus for one-sided Lipschitz multi-agent systems via dual-terminal event-triggered mechanism

This article analyses leader-following bipartite consensus for one-sided Lipschitz multi-agent systems by dual-terminal event-triggered output feedback control approach. A distributed observer is designed to estimate unknown system states by employing relative output information at triggering time instants, and then an event-triggered output feedback controller is proposed. Dual-terminal dynamic event-triggered mechanisms are proposed in sensor–observer channel and controller–actuator channel, which can save communication resources to a great extent, and the Zeno behavior is ruled out. A new generalized one-sided Lipschitz condition is proposed to handle the nonlinear term and achieve bipartite consensus. Some stability conditions are presented to guarantee leader-following bipartite consensus. Finally, one-link robot manipulator systems are introduced to demonstrate the availability of the designed scheme. The results demonstrate that the agents of the robot manipulators can track the reference trajectories bi-directionally, and effectively reduce communication resources by 61.22% and 68.04% at the sensor–observer and controller–actuator channels, respectively.

Using punctuated equilibrium theory to explain changes in policy outputs: The case of the United States Coast Guard

AbstractThis article explores the utility of punctuated equilibrium (PE) theory in explaining revolutionary organizational change in the public sector and its effects on policy implementation, with a focus on the United States Coast Guard. We illustrate our model using data from in‐depth interviews with current and former senior Coast Guard officers and enlisted personnel, as well as secondary sources such as historical accounts and budget data. Through content analysis, we argue that PE theory begins with a change in the public sector organization's external environment, which then affects organizational strategy, structure, power distribution, and outputs. Punctuated equilibrium theory proves to be a valuable lens for understanding policy outputs in relation to organizational change.Related ArticlesKwon, Sung‐Wook, and Sylvia Gonzalez‐Gorman. 2019. “Influence of Local Political Institutions on Policy Punctuation in Three Policy Areas.” Politics & Policy 47(2): 300–25. https://doi.org/10.1111/polp.12295.Neill, Katharine A., and John C. Morris. 2012. “A Tangled Web of Principals and Agents: Examining the Deepwater Horizon Oil Spill through a Principal–Agent Lens.”Politics & Policy 40(4): 629–56. https://doi.org/10.1111/j.1747‐1346.2012.00371.x.Peng, Peiwen, and Tangzhe Cao. 2023. “Attention, Institutional Friction, and Punctuated Equilibrium in China's Budget: Changes in Social Security and Employment Expenditure.” Politics & Policy 51(2): 256–82. https://doi.org/10.1111/polp.12523.

Translating weighted probabilistic bits to synthetic genetic circuits.

Synthetic genetic circuits in plants could be the next technological horizon in plant breeding, showcasing potential for precise patterned control over expression. Nevertheless, uncertainty in metabolic environments prevents robust scaling of traditional genetic circuits for agricultural use, and studies show that a deterministic system is at odds with biological randomness. We analyze the necessary requirements for assuring Boolean logic gate sequences can function in unpredictable intracellular conditions, followed by interpreted pathways by which a mathematical representation of probabilistic circuits can be translated to biological implementation. This pathway is utilized through translation of a probabilistic circuit model presented by Pervaiz that works through a series of bits; each composed of a weighted matrix that reads inputs from the environment and a random number generator that takes the matrix as bias and outputs a positive or negative signal. The weighted matrix can be biologically represented as the regulatory elements that affect transcription near promotors, allowing for an electrical bit to biological bit translation that can be refined through tuning using invertible logic prediction of the input to output relationship of a genetic response. Failsafe mechanisms should be introduced, possibly through the use of self-eliminating CRISPR-Cas9, dosage compensation, or cybernetic modeling (where CRISPR is clustered regularly interspaced short palindromic repeats and Cas9 is clustered regularly interspaced short palindromic repeat-associated protein 9). These safety measures are needed for all biological circuits, and their implementation is needed alongside work with this specific model. With applied responses to external factors, these circuits could allow fine-tuning of organism adaptation to stress while providing a framework for faster complex expression design in the field.

Interpretable Mixture of Experts for Decomposition Network on Server Performance Metrics Forecasting

The accurate forecasting of server performance metrics, such as CPU utilization, memory usage, and network bandwidth, is critical for optimizing resource allocation and ensuring system reliability in large-scale computing environments. In this paper, we introduce the Mixture of Experts for Decomposition Kolmogorov–Arnold Network (MOE-KAN), a novel approach designed to improve both the accuracy and interpretability of server performance prediction. The MOE-KAN framework employs a decomposition strategy that breaks down complex, nonlinear server performance patterns into simpler, more interpretable components, facilitating a clearer understanding of how predictions are made. By leveraging a Mixture of Experts (MOE) model, trend and residual components are learned by specialized experts, whose outputs are transparently combined to form the final prediction. The Kolmogorov–Arnold Network further enhances the model’s ability to capture intricate input–output relationships while maintaining transparency in its decision-making process. Experimental results on real-world server performance datasets demonstrate that MOE-KAN not only outperforms traditional models in terms of accuracy but also provides a more trustworthy and interpretable forecasting framework. This makes it particularly suitable for real-time server management and capacity planning, offering both reliability and interpretability in predictive models.

Optimization of a Groundwater Pollution Monitoring Well Network Using a Backpropagation Neural Network-Based Model

Selecting representative groundwater monitoring wells in polluted areas is crucial to comprehensively assess groundwater pollution, thereby ensuring effective groundwater remediation. However, numerous factors can affect the effectiveness of groundwater monitoring well network optimizations. A local sensitivity analysis method was used in this study to analyze the hydrogeological parameters of a simulation groundwater solute transport model. The results showed a strong effect of longitudinal dispersion and transverse dispersion on the output results of the simulation model, and a good fit between the backpropagation neural network (BPNN)-based alternative model’s results and those obtained using the solute transport simulation model, accurately reflecting the input and output relationship of the simulation model. The optimized groundwater monitoring layout scheme consisted of four groundwater monitoring wells, namely no. 7, no. 16, no. 23, and no. 24. These wells resulted in a groundwater fluoride pollution rate of 98.44%, which was substantially higher than that obtained using the random layout scheme. In addition, statistical analysis of the fluoride groundwater pollution results obtained using the Monte Carlo random simulation highlighted continuous and high groundwater fluoride levels in the second and third pollution sources and their downstream groundwater. Therefore, more attention should be devoted to these sources to ensure the effective remediation of groundwater pollution in the study area.

Assessing groundwater level modelling using a 1-D convolutional neural network (CNN): linking model performances to geospatial and time series features

Abstract. Groundwater level (GWL) forecasting with machine learning has been widely studied due to its generally accurate results and low input data requirements. Furthermore, machine learning models for this purpose can be set up and trained quickly compared to the effort required for process-based numerical models. Despite demonstrating high performance at specific locations, applying the same model architecture to multiple sites across a regional area can lead to varying accuracies. The reasons behind this discrepancy in model performance have been scarcely examined in previous studies. Here, we explore the relationship between model performance and the geospatial and time series features of the sites. Using precipitation (P) and temperature (T) as predictors, we model monthly groundwater levels at approximately 500 observation wells in Lower Saxony, Germany, applying a 1-D convolutional neural network (CNN) with a fixed architecture and hyperparameters tuned for each time series individually. The GWL observations range from 21 to 71 years, resulting in variable test and training dataset time ranges. The performances are evaluated against selected geospatial characteristics (e.g. land cover, distance to waterworks, and leaf area index) and time series features (e.g. autocorrelation, flat spots, and number of peaks) using Pearson correlation coefficients. Results indicate that model performance is negatively influenced at sites near waterworks and densely vegetated areas. Longer subsequences of GWL measurements above or below the mean negatively impact the model accuracy. Besides, GWL time series containing more irregular patterns and with a higher number of peaks might lead to higher model performances, possibly due to a closer link with precipitation dynamics. As deep learning models are known to be black-box models missing the understanding of physical processes, our work provides new insights into how geospatial and time series features link to the input–output relationship of a GWL forecasting model.

12547 Spatiotemporal Analysis Of Steroidogenesis Pathway Genes Expression In Mouse Fetal Leydig Cells

Abstract Disclosure: K. Jiang: Other; Self; NIH ORIP P51OD011106&S10OD028626. A. Kothandapani: None. Z. Fu: None. T.W. Kearse: None. J. Jorgensen: Grant Recipient; Self; NIH R01HD090660. Male mouse embryos experience a masculinization programming window (MPW, embryonic day, E13-16), during which a critical threshold of androgens is produced to ensure male-pattern development. Insufficient fetal androgens during the MPW leads to variations of masculinization, such as cryptorchidism and hypospadias. In rodents, fetal Leydig cells (FLC) are the primary source of androgens; however, our knowledge about their maturation and steroid synthesis regulation is limited. Hence, the objective for this research is to reveal spatiotemporal patterns of steroidogenic pathway genes in mouse fetal testes using high-resolution technologies. To understand the temporal aspect of steroidogenesis, we quantified transcript numbers of steroidogenic pathway genes via copy number qPCR from E11 to postnatal day 6 (P6). Results showed a gradual increase in transcripts as FLCs differentiate until E14, which then surge during the MPW until E16, followed by a rapid drop in expression to pre-FLC differentiation levels by the time of birth (P0). The profound changes persisted after data were normalized to FLC numbers, suggesting active stimulation and downregulation phases of transcription regulation. Next, to assess androgen output in relation to gene transcript levels, we collected testes at 24 hour intervals from E12 to P3 to measure progesterone, androstenedione, and testosterone via LC/MS/MS; results are pending. Guided by the temporal expression profile, we examined the spatial patterns of Star (steroidogenic acute regulatory protein) at the onset (E13), peak (E16), and end (P0) of steroidogenesis using single-molecule fluorescent in situ hybridization (smFISH). We were surprised to observe three different stages for Star transcription based on the subcellular localization of it: Stage I-only at gene loci in the nucleus, newly differentiated; Stage II-in both the nucleus and cytoplasm, peak activity; and Stage III-in cytoplasm only, repressed transcription. The proportion of Stage I FLC decreased over time (15% at E13, 2% at E16, and 0.4% at P0). Most (55%) FLCs at E16 were at Stage II, when testes exhibit maximum steroidogenic gene transcript numbers. Stage III FLCs dominated in P0 testes with 68% of the total FLC number, reflecting diminishing steroidogenesis. Of note, we also discovered that the global testis pattern for Star transcripts exhibited a unique pattern. At the onset of differentiation (E13), FLCs accumulated in the middle of the dorsal-ventral axis and at either pole of the anterior-posterior axis. By E16 and at P0, FLCs were evenly distributed throughout the testis. In conclusion, we observed that FLC steroidogenic pathway gene transcription is not synchronized but individual FLCs contribute to global testis androgen output as they differentiate in a coordinated pattern towards maximal output to coordinate masculinization. Presentation: 6/1/2024

12547 Spatiotemporal Analysis Of Steroidogenesis Pathway Genes Expression In Mouse Fetal Leydig Cells

Abstract Disclosure: K. Jiang: Other; Self; NIH ORIP P51OD011106&S10OD028626. A. Kothandapani: None. Z. Fu: None. T.W. Kearse: None. J. Jorgensen: Grant Recipient; Self; NIH R01HD090660. Male mouse embryos experience a masculinization programming window (MPW, embryonic day, E13-16), during which a critical threshold of androgens is produced to ensure male-pattern development. Insufficient fetal androgens during the MPW leads to variations of masculinization, such as cryptorchidism and hypospadias. In rodents, fetal Leydig cells (FLC) are the primary source of androgens; however, our knowledge about their maturation and steroid synthesis regulation is limited. Hence, the objective for this research is to reveal spatiotemporal patterns of steroidogenic pathway genes in mouse fetal testes using high-resolution technologies. To understand the temporal aspect of steroidogenesis, we quantified transcript numbers of steroidogenic pathway genes via copy number qPCR from E11 to postnatal day 6 (P6). Results showed a gradual increase in transcripts as FLCs differentiate until E14, which then surge during the MPW until E16, followed by a rapid drop in expression to pre-FLC differentiation levels by the time of birth (P0). The profound changes persisted after data were normalized to FLC numbers, suggesting active stimulation and downregulation phases of transcription regulation. Next, to assess androgen output in relation to gene transcript levels, we collected testes at 24 hour intervals from E12 to P3 to measure progesterone, androstenedione, and testosterone via LC/MS/MS; results are pending. Guided by the temporal expression profile, we examined the spatial patterns of Star (steroidogenic acute regulatory protein) at the onset (E13), peak (E16), and end (P0) of steroidogenesis using single-molecule fluorescent in situ hybridization (smFISH). We were surprised to observe three different stages for Star transcription based on the subcellular localization of it: Stage I-only at gene loci in the nucleus, newly differentiated; Stage II-in both the nucleus and cytoplasm, peak activity; and Stage III-in cytoplasm only, repressed transcription. The proportion of Stage I FLC decreased over time (15% at E13, 2% at E16, and 0.4% at P0). Most (55%) FLCs at E16 were at Stage II, when testes exhibit maximum steroidogenic gene transcript numbers. Stage III FLCs dominated in P0 testes with 68% of the total FLC number, reflecting diminishing steroidogenesis. Of note, we also discovered that the global testis pattern for Star transcripts exhibited a unique pattern. At the onset of differentiation (E13), FLCs accumulated in the middle of the dorsal-ventral axis and at either pole of the anterior-posterior axis. By E16 and at P0, FLCs were evenly distributed throughout the testis. In conclusion, we observed that FLC steroidogenic pathway gene transcription is not synchronized but individual FLCs contribute to global testis androgen output as they differentiate in a coordinated pattern towards maximal output to coordinate masculinization. Presentation: 6/1/2024

12674 Spatiotemporal Analysis Of Steroidogenesis Pathway Genes Expression In Mouse Fetal Leydig Cells

Abstract Disclosure: K. Jiang: Other; Self; NIH ORIP P51OD011106&S10OD028626. A. Kothandapani: None. Z. Fu: None. T.W. Kearse: None. J. Jorgensen: Grant Recipient; Self; NIH R01HD090660. Male mouse embryos experience a masculinization programming window (MPW, embryonic day, E13-16), during which a critical threshold of androgens is produced to ensure male-pattern development. Insufficient fetal androgens during the MPW leads to variations of masculinization, such as cryptorchidism and hypospadias. In rodents, fetal Leydig cells (FLC) are the primary source of androgens; however, our knowledge about their maturation and steroid synthesis regulation is limited. Hence, the objective for this research is to reveal spatiotemporal patterns of steroidogenic pathway genes in mouse fetal testes using high-resolution technologies. To understand the temporal aspect of steroidogenesis, we quantified transcript numbers of steroidogenic pathway genes via copy number qPCR from E11 to postnatal day 6 (P6). Results showed a gradual increase in transcripts as FLCs differentiate until E14, which then surge during the MPW until E16, followed by a rapid drop in expression to pre-FLC differentiation levels by the time of birth (P0). The profound changes persisted after data were normalized to FLC numbers, suggesting active stimulation and downregulation phases of transcription regulation. Next, to assess androgen output in relation to gene transcript levels, we collected testes at 24 hour intervals from E12 to P3 to measure progesterone, androstenedione, and testosterone via LC/MS/MS; results are pending. Guided by the temporal expression profile, we examined the spatial patterns of Star (steroidogenic acute regulatory protein) at the onset (E13), peak (E16), and end (P0) of steroidogenesis using single-molecule fluorescent in situ hybridization (smFISH). We were surprised to observe three different stages for Star transcription based on the subcellular localization of it: Stage I-only at gene loci in the nucleus, newly differentiated; Stage II-in both the nucleus and cytoplasm, peak activity; and Stage III-in cytoplasm only, repressed transcription. The proportion of Stage I FLC decreased over time (15% at E13, 2% at E16, and 0.4% at P0). Most (55%) FLCs at E16 were at Stage II, when testes exhibit maximum steroidogenic gene transcript numbers. Stage III FLCs dominated in P0 testes with 68% of the total FLC number, reflecting diminishing steroidogenesis. Of note, we also discovered that the global testis pattern for Star transcripts exhibited a unique pattern. At the onset of differentiation (E13), FLCs accumulated in the middle of the dorsal-ventral axis and at either pole of the anterior-posterior axis. By E16 and at P0, FLCs were evenly distributed throughout the testis. In conclusion, we observed that FLC steroidogenic pathway gene transcription is not synchronized but individual FLCs contribute to global testis androgen output as they differentiate in a coordinated pattern towards maximal output to coordinate masculinization. Presentation: 6/1/2024

12674 Spatiotemporal Analysis Of Steroidogenesis Pathway Genes Expression In Mouse Fetal Leydig Cells

Abstract Disclosure: K. Jiang: Other; Self; NIH ORIP P51OD011106&S10OD028626. A. Kothandapani: None. Z. Fu: None. T.W. Kearse: None. J. Jorgensen: Grant Recipient; Self; NIH R01HD090660. Male mouse embryos experience a masculinization programming window (MPW, embryonic day, E13-16), during which a critical threshold of androgens is produced to ensure male-pattern development. Insufficient fetal androgens during the MPW leads to variations of masculinization, such as cryptorchidism and hypospadias. In rodents, fetal Leydig cells (FLC) are the primary source of androgens; however, our knowledge about their maturation and steroid synthesis regulation is limited. Hence, the objective for this research is to reveal spatiotemporal patterns of steroidogenic pathway genes in mouse fetal testes using high-resolution technologies. To understand the temporal aspect of steroidogenesis, we quantified transcript numbers of steroidogenic pathway genes via copy number qPCR from E11 to postnatal day 6 (P6). Results showed a gradual increase in transcripts as FLCs differentiate until E14, which then surge during the MPW until E16, followed by a rapid drop in expression to pre-FLC differentiation levels by the time of birth (P0). The profound changes persisted after data were normalized to FLC numbers, suggesting active stimulation and downregulation phases of transcription regulation. Next, to assess androgen output in relation to gene transcript levels, we collected testes at 24 hour intervals from E12 to P3 to measure progesterone, androstenedione, and testosterone via LC/MS/MS; results are pending. Guided by the temporal expression profile, we examined the spatial patterns of Star (steroidogenic acute regulatory protein) at the onset (E13), peak (E16), and end (P0) of steroidogenesis using single-molecule fluorescent in situ hybridization (smFISH). We were surprised to observe three different stages for Star transcription based on the subcellular localization of it: Stage I-only at gene loci in the nucleus, newly differentiated; Stage II-in both the nucleus and cytoplasm, peak activity; and Stage III-in cytoplasm only, repressed transcription. The proportion of Stage I FLC decreased over time (15% at E13, 2% at E16, and 0.4% at P0). Most (55%) FLCs at E16 were at Stage II, when testes exhibit maximum steroidogenic gene transcript numbers. Stage III FLCs dominated in P0 testes with 68% of the total FLC number, reflecting diminishing steroidogenesis. Of note, we also discovered that the global testis pattern for Star transcripts exhibited a unique pattern. At the onset of differentiation (E13), FLCs accumulated in the middle of the dorsal-ventral axis and at either pole of the anterior-posterior axis. By E16 and at P0, FLCs were evenly distributed throughout the testis. In conclusion, we observed that FLC steroidogenic pathway gene transcription is not synchronized but individual FLCs contribute to global testis androgen output as they differentiate in a coordinated pattern towards maximal output to coordinate masculinization. Presentation: 6/1/2024

Distributed adaptive secure consensus control for multiagent systems under denial-of-service attacks

This paper investigates the security consensus of multiagent systems (MASs) under denial-of-service (DoS) attacks. First, we introduce a novel dynamic consensus protocol against DoS attacks by utilising relative measurement outputs. Then, based on the edge pinning technique, the novel distributed synchronous adaptive laws are designed to tune a small fraction of coupling strengths between neighbouring agents, under which all updating of coupling strengths will stop when DoS attacks are activated. To extend the application of secure dynamic edge pinning consensus scheme, we further develop the distributed asynchronous adaptive laws, under which only the coupling strengths of the attacked edges stop updating rather than all coupling strengths when DoS attacks are activated. Note that the proposed secure dynamic edge pinning consensus protocols with distributed synchronous and asynchronous adaptive laws are fully distributed and operate independently of any global information. At last, we validate the validity of the proposed distributed adaptive secure dynamic consensus schemes via an example.

Consensus Control of Multiagent Systems Subject to Biasing Faults: Relative Output Information-Based Approach

Consensus Control of Multiagent Systems Subject to Biasing Faults: Relative Output Information-Based Approach

BOATMAP: Bayesian Optimization Active Targeting for Monomorphic Arrhythmia Pace-mapping

Recent advances in machine learning and deep learning have presented new opportunities for learning to localize the origin of ventricular activation from 12-lead electrocardiograms (ECGs), an important step in guiding ablation therapies for ventricular tachycardia. Passively learning from population data is faced with challenges due to significant variations among subjects, and building a patient-specific model raises the open question of where to select pace-mapping data for training. This work introduces BOATMAP, a novel active learning approach designed to provide clinicians with interpretable guidance that progressively assists in locating the origin of ventricular activation from 12-lead ECGs. BOATMAP inverts the input–output relationship in traditional machine learning solutions to this problem and learns the similarity between a target ECG and a paced ECG as a function of the pacing site coordinates. Using Gaussian processes (GP) as a surrogate model, BOATMAP iteratively refines the estimated similarity landscape while providing suggestions to clinicians regarding the next optimal pacing site. Furthermore, it can incorporate constraints to avoid suggesting pacing in non-viable regions such as the core of the myocardial scar. Tested in a realistic simulation environment in various heart geometries and tissue properties, BOATMAP demonstrated the ability to accurately localize the origin of activation, achieving an average localization accuracy of 3.9±3.6mm with only 8.0±4.0 pacing sites. BOATMAP offers real-time interpretable guidance for accurate localization and enhancing clinical decision-making.

Peer-Reviewed Publications by Successfully Matched Orthopedic Surgery Residency Applicants in the 2022-2023 Match.

The Orthopaedic Surgery Match is highly competitive, with more applicants than residency spots. With the Step 1 Exam moving to a pass/fail result, residency programs and applicants have fewer objective data to determine applicants' relative competitiveness. Through this study, we sought to provide the mean number of accepted publications on PubMed a successfully matched orthopedic surgery applicant has by the time of submission of their application. Orthopedic surgery residency programs participating in the National Resident Matching Program were identified by their ranking on the Doximity Residency Navigator. Each program's intern class and their medical schools were identified. Their names were searched in PubMed and Scopus and articles with their name and affiliations were recorded. In total, 877 orthopedic surgery interns published a mean of 3.30±5.27 articles each on PubMed. They were first or second author on 1.44±2.58, and 1.96±3.89 publications were related to orthopedic surgery. There were no statistical differences between degree, sex, or residency program rank from the Doximity Residency Navigator. The mean number of publications from a successful applicant was approximately 3. There was a great range in the number of publications, and 27.3% of successful applicants did not have a single publication. Future applicants and programs can use this number to gauge relative research output. [Orthopedics. 202x;4x(x):xx-xx.].

Interpretable physics-aware alkali-silica reaction expansion prediction

The alkali-silica reaction (ASR) is a major contributor to the aging and degradation of infrastructure. Understanding ASR-induced expansion in concrete structures and accurately predicting its future progression are critical components of effective risk assessment frameworks. This paper presents a study focused on developing and interpreting an advanced machine learning model specifically designed to predict ASR expansion. The model strategically integrates two powerful algorithms to achieve this goal. A comprehensive database comprising 2000 samples of ASR expansion data with various attributes was used to train the model. The first algorithm, eXtreme Gradient Boosting (XGBoost), was employed to establish a predictive model for ASR expansion, achieving approximately 90% validation accuracy. The second algorithm, SHapley Additive exPlanations (SHAP), was applied to assess the relative importance of the factors influencing the XGBoost model’s predictions. This approach provided valuable physical and quantitative insights into the input–output relationships, which are often obscured in conventional machine learning methods. The study revealed that higher silica content, elevated alkali levels, and longer reaction times are strongly correlated with increased ASR expansion. In contrast, larger aggregate sizes and higher water-to-cement ratios were associated with reduced expansion. Additionally, the analysis identified alkali content as the most influential factor in determining the ultimate ASR expansion, while silica content emerged as the key parameter affecting the characteristic time of the ASR curve.

The Neurostimulationist will see you now: prescribing direct electrical stimulation therapies for the human brain in epilepsy and beyond.

As the pace of research in implantable neurotechnology increases, it is important to take a step back and see if the promise lives up to our intentions. While direct electrical stimulation applied intracranially has been used for the treatment of various neurological disorders, such as Parkinson's, epilepsy, clinical depression, and Obsessive-compulsive disorder, the effectiveness can be highly variable. One perspective is that the inability to consistently treat these neurological disorders in a standardized way is due to multiple, interlaced factors, including stimulation parameters, location, and differences in underlying network connectivity, leading to a trial-and-error stimulation approach in the clinic. An alternate view, based on a growing knowledge from neural data, is that variability in this input (stimulation) and output (brain response) relationship may be more predictable and amenable to standardization, personalization, and, ultimately, therapeutic implementation. In this review, we assert that the future of human brain neurostimulation, via direct electrical stimulation, rests on deploying standardized, constrained models for easier clinical implementation and informed by intracranial data sets, such that diverse, individualized therapeutic parameters can efficiently produce similar, robust, positive outcomes for many patients closer to a prescriptive model. We address the pathway needed to arrive at this future by addressing three questions, namely: (1) why aren't we already at this prescriptive future?; (2) how do we get there?; (3) how far are we from this Neurostimulationist prescriptive future? We first posit that there are limited and predictable ways, constrained by underlying networks, for direct electrical stimulation to induce changes in the brain based on past literature. We then address how identifying underlying individual structural and functional brain connectivity which shape these standard responses enable targeted and personalized neuromodulation, bolstered through large-scale efforts, including machine learning techniques, to map and reverse engineer these input-output relationships to produce a good outcome and better identify underlying mechanisms. This understanding will not only be a major advance in enabling intelligent and informed design of neuromodulatory therapeutic tools for a wide variety of neurological diseases, but a shift in how we can predictably, and therapeutically, prescribe stimulation treatments the human brain.