Network Programming Research Articles

HAC: Hierarchical Agglomerative Clustering With Linear Programming for Wireless Sensor Networks

HAC: Hierarchical Agglomerative Clustering With Linear Programming for Wireless Sensor Networks

Programmable Filterless Optical Networks: Architecture, Design, and Resource Allocation

Programmable Filterless Optical Networks: Architecture, Design, and Resource Allocation

Evaluating the Effectiveness of the ACT Model in Enhancing Students’ Coping Skills in Higher Education Counseling

Abstract The outbreak of a new coronavirus pneumonia epidemic in early 2020 has had a great impact on the mental health status of college students, especially elevating the prevalence of PTSD among students. Based on the cognitive theory of ACT, this study develops a mobile networked intervention program to enhance students’ psychological state and students’ coping ability under the intervention of the ACT model and realizes the function of ACT intervention in the form of networked self-help. Through Latent Gold analysis software, the potential profiles of multiple characteristics of the subjects were categorized in order to scientifically examine the effect of the ACT model on enhancing students’ coping abilities. The results indicated that the ACT intervention group was able to significantly alleviate students’ depression levels (d = 0.71), and enhance students’ positive mental health (d = 0.42), compared to the waiting group. The results verified the effectiveness of the ACT network intervention application, which can provide a reference for the networked reform of psychological counseling in colleges and universities.

The Global Network Advancement Group A Next Generation System for the LHC Program and Data Intensive Sciences

This paper presents the rapid progress, vision and outlook across multiple state of the art development lines within the Global Network Advancement Group (GNA-G) and its Data Intensive Sciences and SENSE/AutoGOLE working groups, which are designed to meet the present and future needs and address the challenges of the Large Hadron Collider and other science programs with global reach. Since it was founded in the Fall of 2019 and the working groups were formed in 2020, in partnership with ESnet, Internet2, CENIC, GEANT, ANA, RNP, StarLight, NRP, N-DISE, AmLight, and many other leading research and education networks and network R&D projects, as well as Caltech, UCSD/SDSC, Fermilab, CERN, LBL, and many other leading universities and laboratories, the GNA-G working groups have deployed two virtual circuit and programmable testbeds spanning six continents which supports continuous developments aimed at the next generation of programmable networks interworking with the science programs’ computing and data management systems. The talk covers examples of recent progress in developing and deploying new methods and approaches in multidomain virtual circuits, flow steering, path selection, load balancing and congestion avoidance, segment routing and machine learning based traffic prediction and optimization.

Elephant Flow Detection With Random Forest Models Under Programmable Network Dataplane Constraints

Elephant Flow Detection With Random Forest Models Under Programmable Network Dataplane Constraints

Use of artificial neural networks for solving the problem of residual resource estimation of hoisting cranes

The work is devoted to the problems of practical application of artificial neural networks (ANN) for estimation of residual resource of hoisting machines on the example of estimation of residual resource of bridge-type cranes. The work describes in detail the programming process of ANN which allows to determine the residual resource of metal structures of a hoisting crane depending on degradation of hardness of the metal structures material. The purpose of the work was to create new software for solving the problem of estimating the residual resource of the hoisting crane which appointed service life has ended. The basis for training of ANN was the existing knowledge of hoisting cranes operation, namely the statistical database of typical crane damages, expert assessment of the technical condition of hoisting cranes, and recommendations of the existing standards to identify defect indicators of hoisting cranes. The software is written in Python programming language. The work lists different ways of programming a neural network for an estimation of residual resource of hoisting cranes. The first way is more complex and painstaking and consists of writing the programme code of the neural network manually, the second way involves using a free interactive library Skicit-learn. A new software has been created to estimate the residual resource of hoisting cranes depending on degradation of hardness of metal structures material. The programme can be used by specialists and experts as an intellectual decision-making support system for determining the residual resource of hoisting cranes. The detailed algorithm of neural network programming is presented for possible use of this information in other areas of technical diagnostics of various mechanical engineering objects.

Analysis of Medical Device Reports That Contributed to a Class I Recall

The Food and Drug Administration (FDA) conducts postmarket review of medical devices as a part of its mission to protect patient safety and ensure product safety and effectiveness. Medical device reports (MDRs) are one way the FDA receives information on adverse events involving medical devices. Medical device reports can be submitted by manufacturers, importers, user facilities, and the public. User facilities are required to report device-related adverse events that resulted in a serious injury or death. In addition, user facilities are encouraged to submit voluntary medical reports where a device problem has the potential for patient harm. The FDA receives MDRs from user facilities through the MedWatch and Medical Product Safety Network programs. When reporters include detailed background information in their reports, they can directly contribute to meaningful action. Health technology management personnel, including clinical engineers, have unique insights into medical device problems and can provide valuable background information in MDRs. The highlighted examples hereinafter illustrate 2 cases where more detailed information included in MDRs was essential and contributed to a class I recall of a medical device.

Определение модуля деформации бинарного композита с помощью искусственной нейронной сети

Using of existing methods of determining the characteristics of soils which are part of current regulatory documents and which are based on the hypothesis of normal character of distribution require considerable time and material costs. According to the results of conducted laboratory researches the hypothesis wasn’t confirmed. In the paper it proposes to use trained artificial neural network for determination of the deformation modulus of binary composite «sand — granules of expanded polystyrene». Thus, it has been proven efficiency proposing method using trained artificial neural network in compare classical regression equation for determination of the deformation modulus of the binary composite. With a confidence probability of P = 95 % the absolute value of the relative error is equal to 11,8 % the proposing learning artificial neural network in 11 times less than the absolute value of the relative error of classical regression equation. Also with a confidence probability of P = 95 % the coefficient of determination is equal to 0,5641 and in 6,6 times less than it of regression equation. Further research will be directed to the selection of the values of the parameters of the artificial neural network program for increase the accuracy of determining the deformation modulus of the binary composite.

Accelerating operation optimization of complex chemical processes: A novel framework integrating artificial neural network and mixed-integer linear programming

We present an automated framework that integrates rectified linear unit activated artificial neural network (ReLU-ANN) and mixed-integer linear programming (MILP) to enable efficient operational-level optimization of complex chemical processes. Initially, data is generated through rigorous simulations to pre-train surrogate models based on ReLU-ANN (classification and regression), and subsequently, MILP is employed for optimization by linearly formulating these models. This novel framework efficiently handles complex convergence constraints through a classification neural network which will be used for high-throughput screening data for regression, while simultaneously implementing an 'optimizing while learning' strategy. By iteratively updating the neural network based on optimization feedback, our approach streamlines the optimization process and ensure the feasibility of optimum solution. To demonstrate the versatility and robustness of our proposed framework, we examine three representative chemical processes: extractive distillation, organic Rankine cycle, and methanol synthesis. Our results reveal the framework’s potential in enhancing optimization effect while concurrently reducing computational time, surpassing the capabilities of typical optimization algorithms. As for the three processes, optimization effectiveness improved by 10.11%, 28.69%, and 5.45%, respectively, while execution time were reduced by 71.71%, 54.49%, and 59.38%. This notable enhancement in optimization efficiency stems from a substantial reduction in costly while ineffective objective function evaluations. By seamless integration of ReLU-ANN and MILP, our proposed framework holds promise for improving the optimization of complex chemical processes, yielding superior results within significantly reduced timeframes compared to traditional approaches.

Maximum energy entropy: A novel signal preprocessing approach for data-driven monthly streamflow forecasting

In recent years, the application of Data-Driven Models (DDMs) in ecological studies has garnered significant attention due to their capacity to accurately simulate complex hydrological processes. These models have proven invaluable in comprehending and predicting natural phenomena. However, to achieve improved outcomes, certain additive components such as signal analysis models (SAM) and input variable selections (IVS) are necessary. SAMs unveil hidden characteristics within time series data, while IVS prevents the utilization of inappropriate input data. In the realm of ecological research, understanding these patterns is pivotal for grasping the ecological implications of streamflow dynamics and guiding effective management decisions. Addressing the need for more precise streamflow forecasting, this study proposes a novel SAM called “Maximum Energy Entropy (MEE)” to forecast monthly streamflow in the Ajichai basin, located in northwestern Iran. A comparative analysis was conducted, pitting MEE against well-known methods such as Discreet Wavelet (DW) and Discreet Wavelet-Entropy (DWE), ultimately demonstrating the superiority of MEE. The results showcased the superior performance of our proposed method, with an NSE value of 0.72, compared to DW (NSE value of 0.68) and DWE (NSE value of 0.68). Furthermore, MEE exhibited greater reliability, boasting a lower Standard Deviation value of 0.13 compared to DW (0.26) and DWE (0.19). The utilization of MEE equips researchers and decision-makers with more accurate predictions, facilitating well-informed ecological management and water resource planning. To further evaluate MEE's accuracy using various DDMs, we integrated MEE with Artificial Neural Network (ANN) and Genetic Programming (GP). Additionally, GP served as an IVS method for selecting appropriate input variables. Ultimately, the combination of MEE and GP within the ANN forecasting model (MEE-GP-ANN) yielded the most favorable results.

A quantitative model to assess the human consequences of a natural gas pipeline rupture in urban distribution networks

Natural gas is known as a hazardous fuel that could cause human casualties, especially in urban areas. For this reason, assessing the consequences of natural gas pipeline ruptures is settled as an important element in determining the potential effects of such ruptures on human health and developing risk management programs for natural gas networks. Several efforts have been made in the literature to establish potential consequence areas; however, there are many limitations on the methodologies proposed so far; or alternatively, consequence analyses have been performed in specific cases using a defined location, but not along a natural gas distribution network. The model proposed in this research consists of two main components: (i) a spatial assessment of gas dispersion through the ALOHA software to obtain the potential impact areas, and (ii), the count of the number of individual people located within the potential impact areas through a geographic information system (GIS) model, which proves to be a valuable tool for assessing the consequences of natural gas distribution networks. The proposed model has been applied on a case study to validate the ability to quantify the number of people exposed per linear meter of pipeline, and provide the opportunity to relatively assess the level of consequences of the pipeline network.

SENNET CONSORTIUM: OPPORTUNITIES AND CHALLENGES

Abstract The Cellular Senescence Network (SenNet) Program was established in 2021 by the NIH Director’s Office Common Fund with the goal of comprehensively identifying and characterizing senescent cells across human lifespan in numerous organs. Mapping senescent cells in mice was added in 2022 to leverage genetic and pharmacologic approaches to drive, report, and ablate senescence in vivo. Senescent cells are implicated in driving most if not all age-related chronic diseases as well as accelerated aging caused by chronic diseases and their treatments (e.g., cancer chemotherapy). Furthermore, therapeutics specifically targeting senescent cells (senotherapeutics) have been developed and are currently being tested in clinical trials for multiple disease and geriatric endpoints. Thus, identifying and characterizing senescent cells throughout human aging and disease states has the potential to significantly impact human health. The SenNet Consortium is a highly collaborative network of scientists across the globe jointly benchmarking tools, technologies, and data analytic approaches, and aligning their activities with other NIH Common Fund initiatives, such as HuBMAP. While mapping senescent cells holds great biomedical promise, there are challenges such as the absence of specific biomarker to identify senescent cells, the appearance of senescence under physiological and pathological conditions, and little knowledge of what drives senescence in vivo. Analogous to the Human Genome Project, to achieve SenNet goals, innovative tools and technologies for single cell and spatial tissue analysis must be developed. Products of SenNet, in addition these new approaches, will include common nomenclature to define senescent cells, novel biomarkers of senescent cells, and publicly accessible databases and 4D atlases of senescent cells in 18 human and murine organs that enables 4D visualization.

Integration of ATAC-Seq and RNA-Seq Analysis to Identify Key Genes in the Longissimus Dorsi Muscle Development of the Tianzhu White Yak.

During the postnatal stages, skeletal muscle development undergoes a series of meticulously regulated alterations in gene expression. However, limited studies have employed chromatin accessibility to unravel the underlying molecular mechanisms governing muscle development in yak species. Therefore, we conducted an analysis of both gene expression levels and chromatin accessibility to comprehensively characterize the dynamic genome-wide chromatin accessibility during muscle growth and development in the Tianzhu white yak, thereby elucidating the features of accessible chromatin regions throughout this process. Initially, we compared the differences in chromatin accessibility between two groups and observed that calves exhibited higher levels of chromatin accessibility compared to adult cattle, particularly within ±2 kb of the transcription start site (TSS). In order to investigate the correlation between alterations in chromatin accessible regions and variations in gene expression levels, we employed a combination of ATAC-seq and RNA-seq techniques, leading to the identification of 18 central transcriptional factors (TFs) and 110 key genes with significant effects. Through further analysis, we successfully identified several TFs, including Sp1, YY1, MyoG, MEF2A and MEF2C, as well as a number of candidate genes (ANKRD2, ANKRD1, BTG2 and LMOD3) which may be closely associated with muscle growth and development. Moreover, we constructed an interactive network program encompassing hub TFs and key genes related to muscle growth and development. This innovative approach provided valuable insights into the molecular mechanism underlying skeletal muscle development in the postnatal stages of Tianzhu white yaks while also establishing a solid theoretical foundation for future research on yak muscle development.

Predicting discharge coefficient of triangular side orifice using ANN and GEP models

ABSTRACT This study utilized machine learning models to predict the discharge coefficient for a sharp-crested triangular side orifice (TSO). The chosen models were the Artificial Neural Network (ANN) and Gene Expression Programming (GEP). Development of the models was based on 570 experimental datasets, with 70% allocated for training and the remaining 30% for testing. Five nondimensional parameters were utilized as inputs for the models, including TSO’s crest height to its height (W * =W/H), main channel width to TSO’s base length (L*=B/L), main channel width to TSO’s height (H * =B/H), upstream flow depth to the TSO’s height (Y * =y 1/H), and upstream Froude number of the main channel (F r ). While the discharge coefficient (C d ) was defined as the output. Then, the developed models were evaluated by three performance metrics, violin boxplots, and Taylor diagrams to ensure their reliability and accuracy. Furthermore, a sensitivity analysis was conducted to indicate the most effective parameter affecting the C d value. The findings revealed that both models predicted very well compared to the actual values, with the ANN model emerging as the most reliable predictor. It exhibited the highest determination coefficient (R 2 ), nearing 1, along with the lowest Mean-Square-Error (MSE) and Mean-Absolute-Error (MAE) values, both close to zero. The sensitivity analysis highlighted that the orifice crest height and Froude number significantly impacted the C d value, contributing to more than 36%. In addition, the predicted discharge coefficient stayed within the range of ± 5.0% of the experimental values. Finally, the developed models demonstrated a high level of equivalence compared to previous studies, especially the ANN model. Therefore, these models are recommended as accurate, robust, and rapid tools to predict the TSO’s discharge coefficient.

Programmable fluidic networks on centrifugal microfluidic discs

BackgroundBiomedical diagnostic and lab automation solutions built on the Lab-on-a-Disc (LoaD) platform has great potential due to their independence from specialised micro-pumps and their ease of integration, through direct pipetting, with manual or automated workflows. However, a challenge for all microfluidic chips is their cost of manufacture when each microfluidic disc must be customized for a specific application. In this paper, we present centrifugal discs with programmable fluidic networks. ResultsBased on dissolvable film valves, we present two technologies. The first, based on recently introduced pulse-actuated dissolvable film valves, is a centrifugal disc which, depending on how it is loaded, is configured to perform either six sequential reagent releases through one reaction chamber or three sequential reagent releases through two reaction chambers. In the second approach, we use the previously introduced electronic Lab-on-a-Disc (eLoaD) wireless valve array, which can actuate up to 128 centrifugo-pneumatic dissolvable film valves in a pre-defined sequence. In this approach we present a disc which can deliver any one of 8 reagent washes to any one of four reaction chambers. We use identical discs to demonstrate the first four sequential washes through two reaction chambers and then two sequential washes through four reaction chambers. SignificanceThese programmable fluidic networks have the potential to allow a single disc architecture to be applied to multiple different assay types and so can offer a lower-cost and more integrated alternative to the standard combination of micro-titre plate and liquid handling robot. Indeed, it may even be possible to conduct multiple different assays concurrently. This can have the effect of reducing manufacturing costs and streamlining supply-chains and so results in a more accessible diagnostic platform.

A Study on Role of Women in Shaping Digital Marketing: Empowerment and Innovation

The landscape of digital marketing is undergoing a transformative shift, and this research investigates the pivotal role of women in driving innovation and shaping the future of this dynamic field. Drawing upon existing literature and empirical data, this study examines the impact of women's participation, leadership, and empowerment on fostering innovation within digital marketing strategies and practices. Research suggests that gender diversity contributes significantly to innovation and creativity in professional settings (Bell et al., 2018; O'Mara et al., 2020). By analyzing case studies and success stories of women leaders in digital marketing, this study aims to highlight the unique perspectives, contributions, and innovative approaches that women bring to the industry. The study explores the challenges and barriers faced by women in advancing within the digital marketing sphere, emphasizing the need for inclusive policies and initiatives that empower women to excel and lead in this evolving landscape. It also delves into the impact of mentorship, networking, and skill development programs in facilitating women's empowerment and fostering innovation within digital marketing contexts (Dobbin &Kalev, 2016; Eagly& Carli, 2007). Ultimately, this research aims to provide actionable insights for industry leaders, policymakers, and educational institutions to create an environment that not only empowers women in digital marketing but also leverages their contributions for fostering innovation and sustained growth

Comparison of endovascular therapies for chronic limb-threatening ischemia and claudication

ObjectiveAnalysis of regional data from the Vascular Quality Initiative (VQI) suggested improved survival for patients undergoing stent placement compared with balloon angioplasty and atherectomy. Using national data from the VQI linked to Medicare claims data through the Vascular Implant Surveillance and Interventional Outcomes Network program, this study aimed to compare the rates of mortality, reintervention, and amputation after endovascular interventions (atherectomy, stenting, and balloon angioplasty) for two separate cohorts: patients with chronic limb-threatening ischemia (CLTI) and patients with claudication. MethodsThis was a secondary data analysis of Society for Vascular Surgery National VQI data linked to Medicare claims, between October 2016 and December 2019. Patients aged ≥65 years with symptoms of claudication or CLTI and a diagnosis of occlusive disease were included. Urgent or emergent interventions or those with concurrent procedures (endarterectomy, bypass, or bilateral intervention) were excluded. Interventions were grouped into (1) balloon angioplasty only; (2) stent (with or without balloon angioplasty); or (3) atherectomy (alone, with or without stent, with or without balloon angioplasty). Propensity score-matched cohorts were constructed to conduct pairwise intervention comparisons of mortality, reintervention, and amputation rates. Multivariable logistic regression was used to derive propensity scores for each patient. Kaplan-Meier estimates and Cox proportional hazards ratios (HRs) (95% confidence interval [CI]) analyses were performed. ResultsA total of 9785 (2665 claudication, 7120 CLTI) eligible patients were identified. After propensity score matching for the CLTI group, 2826, 3608, and 2796 pairs of cases were used to compare balloon angioplasty vs atherectomy, balloon angioplasty vs stent, and stent vs atherectomy, respectively. No statistically significant difference in mortality was observed among all interventions. However, atherectomy was associated with a significant increase in reintervention rate compared with balloon angioplasty (HR, 1.22; 95% CI, 1.06-1.39; P = .01) and compared with stenting (HR, 1.27; 95% CI, 1.10-1.46; P < .01) within the first year after the index procedure. Of note, both atherectomy (HR, 0.82; 95% CI, 0.68-0.98; P < .05) and stenting (HR, 0.76; 95% CI, 0.64-0.90; P < .01) showed lower rates of major amputation when compared with balloon angioplasty within 1 year after the index procedure. In the claudication group, there were no significant differences observed among interventions for peripheral arterial disease for mortality, reintervention, or amputation rates. ConclusionsFurther studies are needed to identify appropriate indications for atherectomy, because there may be a subset of patients with CLTI who benefit from this therapy with respect to amputation rates. Until then, caution should be exercised when using atherectomy because it is also associated with higher reintervention rates.

Optimizing rural healthcare through improved team function: a case study of the Rural Surgical Obstetrical Networks programme

ABSTRACT We explored enablers and mechanisms of optimal team function within rural hospital teams, and the impact of these factors on health service sustainability in British Columbia. The data were drawn from interviews and focus groups with healthcare providers and administrators (n = 169) who participated in the Rural Surgical Obstetrical Networks (RSON) initiative to support low-volume rural surgical and obstetrical services in British Columbia, Canada. The 5-year programme (2018–2022) provided evidence-based system interventions across eight rural sites with the objective of providing sustainable, quality health services to meet population needs. To explore the impact of RSON interventions on local team function, we performed a scoping review, to assess the current literature surrounding enablers of effective rural hospital teamwork. Through inductive thematic analysis of interview data, we identified five enablers of good team function at RSON sites, including emphasis on local leadership, shared direction, commitment to sustainability, respect and solidarity among colleagues, and meaningful communication. The RSON project led to a shift in team culture in participating sites, improved team function, and contributed to improved clinical processes and patient outcomes. The findings have implications for rural health policy and practice in British Columbia and other jurisdictions with similar health service delivery models and geographic contexts.

Dynamic Upgrade to SDN From a Global Perspective: Model and Its Heuristic Solutions

Software Defined Network (SDN) has been considered as one of the most promising next-generation network solutions due to its network programmability. However, the upgrade from legacy IP network to pure SDN network is in general a gradual process. From a global perspective, a dynamic upgrade strategy should not only aim to pursue the local goal at each step, but also strive to optimize the final global solution when the upgrading process terminates. This raises three essential questions: which switches to upgrade, when to upgrade, and how to deploy controllers. Due to the interaction between the local goals at intermediate steps and the global goal at the final step, answering these questions altogether from a global perspective is challenging. In this paper, we study the dynamic SDN upgrade problem from a global perspective and answer these three questions altogether. We formulate the problem as a dynamic optimization problem that optimizes the global and local goals at the same time. We then propose two new formulations to combine the global and local goals, and two heuristic algorithms to solve them, respectively. We evaluate the proposed model and algorithms on realistic network topologies. The results show their feasibility and superiority.

Dismantling the Golden Age: Broadcast scheduling strategies on 1950s US network radio

From the late 1940s to early 1960s, the US radio industry underwent a dramatic transformation. Instead of scripted programming and live musical performances, which characterized the era known as the Golden Age of Radio, stations began to focus on local advertising markets and relied upon recorded music for content. This study explores the programming and scheduling strategies of the major commercial networks in the United States as they dealt with this transformation. Using the theoretical framework of remediation, the analysis provides some context for a contemporary change in electronic media, the transition from linear programming to streaming and on-demand forms of delivery.