Aspects Of Process Research Articles

Computing Projects Framework for Undergraduate Research

This paper outlines the guidelines and requirements for students undertaking a computing project as part of their academic program. The project is a critical component of most undergraduate programs and is designed to provide students with the opportunity to apply theoretical knowledge to practical scenarios, demonstrating their ability to plan, execute, and present a complex piece of work. Unfortunately, there is currently a lack of clear and helpful guidance on how to conduct elementary research at this level, differentiate a computing project from other types of projects, and report it effectively. This paper aims to assist both computing students and faculty in carrying out and reporting on final-year research projects. It documents the differences in research approaches across various computing disciplines and provides a guiding framework for faculty overseeing the undergraduate research process. The guidelines cover various aspects of the project process, including the development of a project proposal, conducting the research, and writing the final report. It emphasizes the importance of early planning, regular supervision, and adherence to academic standards, particularly in the presentation and documentation of the project work. Additionally, the document details the criteria for project assessment, highlighting the need for clarity, innovation, and rigor in both the practical and written components of the project. Instructions on the submission process and the responsibilities of students and supervisors throughout the project lifecycle are also included.

Developing and Evaluating Digital Public Health Interventions Using the Digital Public Health Framework DigiPHrame: A Framework Development Study.

Digital public health (DiPH) interventions may help us tackle substantial public health challenges and reach historically underserved populations, in addition to presenting valuable opportunities to improve and complement existing services. However, DiPH interventions are often triggered through technological advancements and opportunities rather than public health needs. To develop and evaluate interventions designed to serve public health needs, a comprehensive framework is needed that systematically covers all aspects with relevance for public health. This includes considering the complexity of the technology, the context in which the technology is supposed to operate, its implementation, and its effects on public health, including ethical, legal, and social aspects. We aimed to develop such a DiPH framework with a comprehensive list of core principles to be considered throughout the development and evaluation process of any DiPH intervention. The resulting digital public health framework (DigiPHrame) was based on a scoping review of existing digital health and public health frameworks. After extracting all assessment criteria from these frameworks, we clustered the criteria. During a series of multidisciplinary meetings with experts from the Leibniz ScienceCampus Digital Public Health, we restructured each domain to represent the complexity of DiPH. In this paper, we used a COVID-19 contact-tracing app as a use case to illustrate how DigiPHrame may be applied to assess DiPH interventions. The current version of DigiPHrame consists of 182 questions nested under 12 domains. Domain 1 describes the current status of health needs and existing interventions; domains 2 and 3, the DiPH technology under assessment and aspects related to human-computer interaction, respectively; domains 4 and 5, structural and process aspects, respectively; and domains 6-12, contextual conditions and the outcomes of the DiPH intervention from broad perspectives. In the CWA use case, a number of questions relevant during its development but also important for assessors once the CWA was available were highlighted. DigiPHrame is a comprehensive framework for the development and assessment of digital technologies designed for public health purposes. It is a living framework and will, therefore, be updated regularly and as new public health needs and technological advancements emerge.

Involving lead users in firm’s standardization strategy within action groups: evidence from smart robotics

PurposeThis research explores how firms manage the complex technologies standardization in action groups. It considers the strategic issues that technology producers face when involving lead users in architecture design. Drawing on the multi-mode standardization literature, this study addresses two dilemmas regarding value creation and appropriation by technology producers within coalitions. The first dilemma is how to create value by developing solutions in compliance with industry standards. The second one is how to appropriate value while ensuring the technology sharing with action groups. The answers to these two dilemmas contribute to filling the research gap on value creation and appropriation in multi-mode standardization.Design/methodology/approachThe research focuses on technology producers participating in action groups where lead users play a crucial role. We conducted a qualitative analysis based on the standardization experience of a Japanese company specializing in smart robotics. Data are collected through semi-structured interviews with key actors. Action groups are defined operationally as a set of stakeholders including competitors of the technology producers, component suppliers, end users, services providers, research centers and academia. The case study is suitable for highlighting specific aspects of the standardization process during its manifestation. It reveals how firms create and appropriate value, providing details about its standardization strategy.FindingsOur findings show that smart robotics standardization is drivn by collaborative models, where the two dilemmas of value creation and appropriation are evident. Firstly, the case revealed that standardization is lead users oriented. Secondly, lead users’ involvement is crucial to customize technologies. Thirdly, the firm’s position is to share a part of the value with the members. The IPR policy is a matter of interest within action groups, since the collaboration is based on open innovation models to share patents and licenses related knowledge.Research limitations/implicationsThis research has some limitations attributable to the limited generalizability of the results due to the qualitative analysis. In addition, this study considers the perspective of technology producers, but should also take into account the perspective of both collective actions itself and the lead users. Findings have some implications in the strategy negotiation. Participating in action groups is not enough to ensure a competitive advantage. Involving lead users is of strategic importance to acquire a competitive advantage. Lead users contribute to the producers’ technology design, helping firms to differentiate solutions from the industry standard and create value from customized technologies.Practical implicationsThis study helps practitioners understand the competitive side of collective actions, clarifying the value capture and appropriability in standardization. The research provides insights to policymakers and standard development organizations committees when they are called to harmonize standards considering the fallouts on the sector’s competitiveness. Findings suggest appropriate property rights policies to manage the issues related to the value appropriability and technology sharing, recognizing action groups members for their contribution in value creation.Originality/valueThis study shows how firms deal within action groups with the two dilemmas of variety versus technology conformity and property rights versus technology sharing. It fills the research gap in collective actions, emphasizing the perspective of the individual firm in the group rather than the coalition strategy itself. This topic highlights the crucial role of lead users within action groups in managing the two dilemmas, offering a new perspective for understanding critical issues of multi-mode standardization. Reflecting on mechanisms and tools to manage the two dilemmas allows firms to protect their competitive advantage in coalitions.

Machine-learning classification with additivity and diverse multifractal pathways in multiplicativity

Evidence of multifractal structures has spread to a wider set of physiological time series supporting the intricate interplay of biological and psychological functioning. These dynamics manifest as random multiplicative cascades, embodying nonlinear relationships characterized by recurring division, branching, and aggregation processes implicating noise across successive generations. This investigation focuses on how well the diversity of multifractal properties can be specific to the type of cascade relationship between generation (i.e., multiplicative, additive, or a mixture) as well as to the type of noise (i.e., including additive white Gaussian noise, fractional Gaussian noise, and various amalgamations) among 15 distinct types of binomial cascade processes. Cross-correlation analysis of multifractal spectral features confirms that these features capture nuanced aspects of cascading processes with minimal redundancy. Principal component analysis using 13 distinct multifractal spectral features shows that different cascade processes can manifest multifractal evidence of nonlinearity for distinct reasons. This transparency of multifractal spectral features to underlying cascade dynamics becomes less amenable to machine-learning strategies. Fully connected neural networks struggled to classify the 15 distinct types of cascade processes based on the respective multifractal spectral features (45.5% accuracy) yet demonstrated improved accuracy when addressing single categories of cross-generation relationships, that is, additive (91.6%), multiplicative (75.4%), or additomultiplicative (70.6%). While traditional principal component analysis reveals distinct loadings attributed to individual noise processes, multiplicative relationships between generations effectively make the constituent noise processes less discernible to neural networks. Neural networks may lack sufficient hierarchical depth required to effectively distinguish among nonadditive cascading processes, recommending either elaborating multifractal geometry or using alternate architectures for machine-learning classification of cascades with multiplicative relationships. Published by the American Physical Society 2024

Evaluating the implementation of the mechanical engineering student skills competency

This study aims to evaluate the implementation of the Expertise Competency Test (UKK) for students of Vocational High Schools (SMK) in the Machining Engineering Expertise Program implemented by the First Party Professional Certification Agency (LSP P1) in Banten Province. The evaluation was conducted using the CIPP model (Context, Input, Process, Product, and Outcome). This study is useful for assessing the performance of LSP P1 in implementing the UKK and evaluating the impact of UKK graduates on their acceptance in the industrial world. The research was conducted in three vocational schools in Banten Province with the research subjects including the head of LSP P1, the head of the machining engineering expertise programme, and competency assessors. The research instruments used were questionnaires, observations, documents and interviews, which had been validated by expert judgement. The data analysis technique used was descriptive quantitative. The results showed that the implementation of UKK by LSP P1 in Banten vocational schools received the following evaluations: 1) Context aspect scored 3.36 (very good category), 2) Input aspect scored 3.47 (very good category), 3) Process aspect scored 3.36 (very good category), 4) Product aspect scored 3.26 (very good category), and 5) Outcome aspect scored 2.94 (good category). In conclusion, the implementation of the UKK by LSP P1 in the Machining Engineering Expertise Programme at SMK Banten Province has been running very well in most aspects, although there is room for improvement in the Outcome aspect related to the impact of graduates in the industrial world. The contribution of this research is to provide an overview of the effectiveness of expertise competency certification in SMKs as well as input to improve the quality of graduates who are better prepared to compete in the world of work.

Blockchain Traceability Process for Hairy Crab Based on Cuckoo Filter

With the rapid development of global information technology, smart agriculture has gradually become an important force to promote agricultural modernisation. Taking Yangcheng Lake hairy crabs as the research object, this paper constructs a full-chain traceability process for hairy crabs integrating blockchain and Internet of Things technology in response to the demand for the enhancement of its brand value and market competitiveness. The model covers all aspects of breeding, processing, and transport to sales; realises end-to-end transparent regulation; effectively guarantees the authenticity and non-tamperability of information; and enhances consumers’ trust in the brand. At the technical level, this paper designs the ECMI (Enhanced Cuckoo Merkle Index) model, optimises the data storage and retrieval mechanism, and experimentally verifies the advantages of cuckoo filters over traditional Bloom filters in reducing false positives and improving query efficiency. This paper not only provides an efficient and safe traceability solution for the hairy crab industry in Yangcheng Lake but also provides technical support and practical cases for the further application of blockchain technology in smart agriculture, which is of great value for the realisation of the national rural revitalisation strategy.

A Study on Key Performing Indicator and their Impact on Sales at MYBAE BAGS

Key Performance Indicators (KPIs) play an important role in the sales process in a competitive business environment. This study highlights the importance of KPIs in providing a structured way to measure, evaluate and optimize sales performance. KPIs, which include metrics such as sales revenue, conversion rates, customer acquisition costs, sales growth, and average transaction value, provide valuable insight into aspects of the sales process. Based on organizational goals and industry standards, these metrics help companies make data-driven decisions, reinforce accountability, and drive continuous improvement. This study explores the bag industry, divided into fashion, travel, sports, business and specialty bags. Advances in technology have led to the use of lighter and stronger materials and the inclusion of features such as RFID blocking sensors and integrated circuits. This industry is global and has a significant contribution to the economies of the world and India. Research methods include the collection of primary and secondary data using data analysis techniques to determine market size and forecast. A PESTEL analysis of an industry examines the political, economic, social, technological, environmental and legal issues affecting the industry. The report also provides an overview of MYBAE BAGS, highlighting its business segments, revenue growth, and market strategies. This study highlights the importance of KPIs in driving strategic change and achieving sustainable growth, positioning KPIs as important tools for organizations seeking to improve sales performance and good management

Strategic priorities of regional economic development in the context of regulating non-monetary factors of inflation

A vast array of research works concentrates on such aspects of inflation processes as their essence, development factors, anti-inflationary regulation, etc., but precious few of them attempt to identify the factors of inflation in the strategic priorities of regional development. At the same time, economics and state policy at both national and regional levels recognise the significant effect of inflation on economic development. The paper deals with non-monetary factors of inflation in regions and develops a path for formulating regional policy on their regulation. The theories of regional economic development and inflation constitute the methodological framework of the study. The research methods include systematization, functional and comparative analysis, deduction. We identify the provisions of the socio-economic development strategies of the Ural macroregion’s member territories relating to the regulation of non-monetary factors in the development of inflationary processes. The identified aspects of inflation reflect the impact of its non-monetary factors on regional economic development and require anti-inflationary policy to be coupled with the areas of activity of regional authorities. We present a conceptual-logical model for framing regional policy on regulating non-monetary factors of inflation in an RF subject specifying the strategic priorities of economic development that influence regional factors of inflation. The model demonstrates that it is the state programmes of Russia’s constituent entities that are the main tool for regulating non-monetary factors of inflation in the regional government system. It coordinates price regulation measures in the regions with the goals and objectives of policy aimed at developing their economies. The findings contribute to the understanding of regional inflation factors and can be used by regional authorities when executing socio-economic development strategies.

Analysis of fast calcium dynamics of honey bee olfactory coding

Odour processing exhibits multiple parallels between vertebrate and invertebrate olfactory systems. Insects, in particular, have emerged as relevant models for olfactory studies because of the tractability of their olfactory circuits. Here, we used fast calcium imaging to track the activity of projection neurons in the honey bee antennal lobe (AL) during olfactory stimulation at high temporal resolution. We observed a heterogeneity of response profiles and an abundance of inhibitory activities, resulting in various response latencies and stimulus-specific post-odour neural signatures. Recorded calcium signals were fed to a mushroom body (MB) model constructed implementing the fundamental features of connectivity between olfactory projection neurons, Kenyon cells (KC), and MB output neurons (MBON). The model accounts for the increase of odorant discrimination in the MB compared to the AL and reveals the recruitment of two distinct KC populations that represent odorants and their aftersmell as two separate but temporally coherent neural objects. Finally, we showed that the learning-induced modulation of KC-to-MBON synapses can explain both the variations in associative learning scores across different conditioning protocols used in bees and the bees' response latency. Thus, it provides a simple explanation of how the time contingency between the stimulus and the reward can be encoded without the need for time tracking. This study broadens our understanding of olfactory coding and learning in honey bees. It demonstrates that a model based on simple MB connectivity rules and fed with real physiological data can explain fundamental aspects of odour processing and associative learning.

Development and architecture of Customer Relationship Management on Predictive Analytics in Smart Utilities Based

The study's scope is restricted with regard to the topics that are covered, the locations thatare covered, and the analyses that are covered. Given that the researcher's focus was on customerrelationship management, which is just one aspect of the marketing process, the topical scope of thestudy was therefore constrained. Again, concentrating on a few of the numerous units that make uporganisations in an area couldn't disclose any more details about the research. The research is focusedon the Smart Utilities based on Predictive Analytics, and because the consumers of the business aredispersed in the various areas, the geographical scope of the study is restricted because of this. Anotherconstraint of the study is its geographical or regional scope. There is also an analytical restriction to theresearch, which is that the proposed customer engagement methods are only supplied when theemphasis is focused on the predefined goals. It was also observed that customer relationshipmanagement is the most neglected activity in many organizations, which should be given prime attentionto meet the contemporary challenges in Smart Utilities based on Predictive Analytics. Therefore the needis to make in-depth study of how the relationship aspect will affect the growth profitability for the firms to what extent this requires an in-depth study of the subject entitled with intention to make acomprehensive study of customer relationship management in Smart Utilities based on PredictiveAnalytics.

Gradient Scale Monitoring for Federated Learning Systems

As the computational and communicational capabilities of edge and IoT devices grow, so do the opportunities for novel Machine Learning solutions. This leads to an increase in popularity of Federated Learning (FL), especially in cross-device settings. However, while there is a multitude of ongoing research works analyzing various aspects of the FL process, most of them do not focus on issues of operationalization and monitoring. For instance, there is a noticeable lack of research in the topic of effective problem diagnosis in FL systems. This work begins with a case study, in which we have intended to compare the performance of four selected approaches to the topology of FL systems. For this purpose, we have constructed and executed simulations of their training process in a controlled environment. We have analyzed the obtained results and encountered concerning periodic drops in the accuracy for some of the scenarios. We have performed a successful reexamination of the experiments, which led us to diagnose the problem as caused by exploding gradients. In view of those findings, we have formulated a potential new method for the continuous monitoring of the FL training process. The method would hinge on regular local computation of a handpicked metric - the gradient scale coefficient (GSC). We then extend our prior research to include a preliminary analysis of the effectiveness of GSC and average gradients per layer as potentially suitable for FL diagnostics metrics. In order to perform a more thorough examination of their usefulness in different FL scenarios, we simulate the occurrence of the exploding gradient problem, vanishing gradient problem and stable gradient serving as a baseline. We then evaluate the resulting visualizations based on their clarity and computational requirements. We introduce a gradient monitoring suite for the FL training process based on our results.

Специфика изучения игрового процесса в видеоиграх и его связи с агрессией в зарубежных исследованиях

<p style="text-align: justify;"><a><span lang="EN-US">The article addresses the problem of designing a research model applicable to study of enthusiasm for video games</span> <span lang="EN-US">and aggression. As a possible way to overcome the existing contradictions in the research results, it is important to study this interplay, taking into account the features of play activity. Video game is not an accurate model of reality. It is based on a different, significantly simplified system of rules, and has a conventional nature. Thus, it seems important to go on collecting information concerning the psychological aspects of the game process, especially the ones, connected with the reflection of the play experience and the features of behavior in the game. The purpose of this review is to identify the specificity of designing studies aimed at investigating the problem of the implementation of the game process in video games and aggression in relation to the specificity of play activity. In few publications that pay attention to the psychological components of the gameplay its individual aspects (perception of frustrating game events, realism of the game space and involvement in the game context) are often discussed without taking into account the specificity of the game. In perspective we intend to particularize the specificity of processing the play experience, namely, the way it reflects in the chosen game</span> <span lang="EN-US">tactics.</span></a></p>

Shared Religious Education through Christian–Islamic Team Teaching

The article, which is written by an interreligious team, provides comprehensive insights into the conception, implementation and accompanying research of a project on Christian–Islamic religious education in team teaching. The aim of the project is to expand the denominational religious education lessons anchored in Austrian schools through religious-cooperative units taught jointly by a Christian and an Islamic teacher. The analysis of the teaching units is carried out in the format of design-based research and thus encompasses numerous aspects of interreligious educational processes, which are examined in this article. Firstly, the design of the project is described and the legal framework associated with it is explained in the context of the Austrian school system. Subsequently, learning requirements on the part of Christian and Muslim pupils are presented, with a particular focus on their preconceptions and attitudes towards religion in general as well as other religions. Special attention is paid to the specific framework conditions of Islamic religious education teachers, which differ in many aspects from those of Catholic religious education teachers. Furthermore, interreligious competences they consider necessary are described. Specific insights into the teaching units and the complementary research provide information about the opportunities and challenges of interreligious education in team teaching by two teachers. After this focus on the teachers, an outline of the students’ perspectives on the teaching units completes the presentation of the research results. In the concluding summary, the local theories developed from the overall project are presented and discussed.

HMM for discovering decision-making dynamics using reinforcement learning experiments.

Major depressive disorder (MDD), a leading cause of years of life lived with disability, presents challenges in diagnosis and treatment due to its complex and heterogeneous nature. Emerging evidence indicates that reward processing abnormalities may serve as a behavioral marker for MDD. To measure reward processing, patients perform computer-based behavioral tasks that involve making choices or responding to stimulants that are associated with different outcomes, such as gains or losses in the laboratory. Reinforcement learning (RL) models are fitted to extract parameters that measure various aspects of reward processing (e.g. reward sensitivity) to characterize how patients make decisions in behavioral tasks. Recent findings suggest the inadequacy of characterizing reward learning solely based on a single RL model; instead, there may be a switching of decision-making processes between multiple strategies. An important scientific question is how the dynamics of strategies in decision-making affect the reward learning ability of individuals with MDD. Motivated by the probabilistic reward task within the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study, we propose a novel RL-HMM (hidden Markov model) framework for analyzing reward-based decision-making. Our model accommodates decision-making strategy switching between two distinct approaches under an HMM: subjects making decisions based on the RL model or opting for random choices. We account for continuous RL state space and allow time-varying transition probabilities in the HMM. We introduce a computationally efficient Expectation-maximization (EM) algorithm for parameter estimation and use a nonparametric bootstrap for inference. Extensive simulation studies validate the finite-sample performance of our method. We apply our approach to the EMBARC study to show that MDD patients are less engaged in RL compared to the healthy controls, and engagement is associated with brain activities in the negative affect circuitry during an emotional conflict task.

From HIV to Mpox: Socio-discursive Analysis of Communicable Diseases, Stigma, and Pathologization in Sexual Minorities in Brazil.

Discrimination and stigma are significant barriers to healthcare for the LGBTQIAPN+ community, necessitating a deeper analysis of their sociocultural causes. There is a notable gap in the literature regarding the understanding of socio-discursive representations and their impact on the stigmatization and pathologization of sexual minorities in the context of communicable diseases. This study aims to discuss the understanding of the sociodiscursive aspects of the health-disease process, particularly in stigmatized infectious diseases affecting the LGBTQIAPN+ community. The focus is on examining how news articles, or the set of analyzed texts (corpus), shape these perceptions. We conducted documentary research with a qualitative and discursive approach using news articles retrieved from Google NewsⓇ about diseases affecting the LGBTQIAPN+ population from 2011 to 2022. The analysis was based on critical discourse analysis, processed using MAXQDA and IRAMUTEQ software. The identified representations predominantly align with biomedical ideology, manifesting in a discourse that normalizes and medicalizes (normative-curative discourse), and notable for its pathologizing and stigmatizing nature. Six classes were found: Ethical professional dilemmas facing stigma, infection and contamination of the LGBT+ population, prejudice and discrimination in the form of information, stigma related to sexual behavior/orientation, Vulnerability and stigma related to infectious diseases, and strategies for minimizing health risk/stigma for the LGBT+ public. The most relevant analytical categories were related to infectious diseases and sexual identity. These themes were identified, indicating that media representations reinforce stigma and maintain unequal health practices (verticalization) for the LGBT+ community. Understanding these patterns within a broader historical context is crucial for promoting health education and strategies that challenge internalized prejudice. The need to reformulate cultural norms and develop health information and education policies is urgent. These policies should be led by professionals with a comprehensive and humanized vision, addressing the diverse needs of the LGBT+ population.

The research progress and Hotspot analysis of polymer cement mortar based on bibliometrics

Ordinary cement mortar is commonly used in building engineering due to its high strength, affordability, and easy access to raw materials. However, it suffers from high shrinkage and poor impermeability, which result in reduced building service life and significant carbon dioxide emissions during production. Polymer additives have been found to enhance the mechanical properties of cement mortar, leading to increased interest in polymer cement mortar by researchers. This study collected and analyzed 420 papers published between 1995 and 2023 in the field of polymer cement mortar. The analysis included publication trends, author cooperation networks, national cooperation networks, published journals, co-citation of references, and keywords. The findings reveal a rapid publication growth from 2018 to 2023, with China making the most significant contribution in this field. Among the scholars, Ru Wang has published the highest number of articles in the field of polymer cement mortar, while Ohama’s papers have been cited the most. The journal with the most articles is Construction and Building Materials. Research in polymer cement mortar focuses on mechanical properties, performance, hydration process, microstructure, and other related aspects. The reinforcement effect of polymer-modified cement mortar on reinforced concrete and applying superabsorbent polymer-modified cement mortar and polymer fiber in cement mortar have emerged as recent research frontiers. This study can help scholars quickly identify high-quality references and research frontiers in the field of polymer cement mortar while also providing research directions and ideas.

A review of advanced helical fibers: formation mechanism, preparation, properties, and applications.

As a unique structural form, helical structures have a wide range of application prospects. In the field of biology, helical structures are essential for the function of biological macromolecules such as proteins, so the study of helical structures can help to deeply understand life phenomena and develop new biotechnology. In materials science, helical structures can give rise to special physical and chemical properties, such as in the case of spiral nanotubes, helical fibers, etc., which are expected to be used in energy, environment, medical and other fields. The helical structure also has unique charm and application value in the fields of aesthetics and architecture. In addition, helical fibers have attracted a lot of attention because of their tendrils' vascular geometry and indispensable structural properties. In this paper, the development of helical fibers is briefly reviewed from the aspects of mechanism, synthesis process and application. Due to their good chemical and physical properties, helical fibers have a good application prospect in many fields. Potential problems and future opportunities for helical fibers are also presented for future studies.

Core problems in information systems implementation: An analytical review and implications for AI systems

Research on information systems (IS) has identified diverse problems encountered in system implementation and organizational change. We reviewed and analyzed the implementation problems explored in prior research to determine the core problems and the important causal relations between the problems. The causal mechanism behind the relations was articulated with illustrating examples. This analytical review provides a deeper understanding of the fundamental aspects of IS implementation processes, which are conducive to preparing for the implementation of artificial intelligence (AI) systems. AI systems are an information system as well. However, by embedding machine learning elements and necessary data flows, the implementation of AI systems would be much more complicated and challenging. We derived useful implications for AI systems implementation by advancing the understanding of the core problems of IS implementation and connecting them with the fundamental problems inherent in AI systems. The analyses and discussions motivate further studies on implementing IS with or without AI.

Vamos: Middleware for best-effort third-party monitoring

As the complexity and criticality of software increase every year, so does the importance of runtime monitoring. Third-party and best-effort monitoring are especially valuable, yet under-explored areas of runtime monitoring. In this context, third-party monitoring means monitoring with a limited knowledge of the monitored software (as it has been developed by a third party). Best-effort monitoring keeps pace with the monitored software at the cost of possibly imprecise verdicts when keeping up with the monitored software would not be feasible. Most existing monitoring frameworks do not support the combination of third-party and best-effort monitoring because they either require the full access to the monitored code or the ability to process all observable events, or both.We present a middleware framework, Vamos, for the runtime monitoring of software. Vamos is explicitly designed to support third-party and best-effort scenarios. The design goals of Vamos are (i) efficiency (tracing events with low overhead), (ii) flexibility (the ability to monitor a variety of different event channels, and to connect to a wide range of monitors), and (iii) ease-of-use. To achieve its goals, Vamos combines aspects of event broker and event recognition systems with aspects of stream processing systems.We implemented a prototype toolchain for Vamos and conducted a set of experiments demonstrating the usability of the scheme. The results indicate that Vamos enables writing useful yet efficient monitors, and simplifies key aspects of setting up a monitoring system from scratch.

Regenerated Fat Induced by a Decellularized Adipose Matrix Can Survive Long-term In Vivo

Decellularized adipose matrix (DAM) is considered to be the most potential biological scaffold for soft tissue repair and reconstruction, as it is able to induce the regeneration of adipose tissue in situ in adulthood. But how does this adipose tissue regeneration happen and develop in vivo? Is it the same as the original autologous one? Temporary existence or long-term survival? These are the key questions that will determine the future applications of DAM. In this study, we investigated the composition, structure and biomechanical properties of DAM before implanting it into the subcutaneous back of immunodeficient mice. The entire regeneration process in vivo was closely monitored histologically from 3 days to 1 year after implantation, including fat regeneration, vascular growth, inflammatory responses, and matrix degradation and remodeling. Transcriptome sequencing was used to analyze the difference in gene expression between regenerated fat and autologous fat at different periods. The results showed that the DAM-induced regenerated fat first appeared at 1w and remained stable over 6m, indicating remarkable similarity to autologous fat at the later stages of implantation. And about (18.3 ± 29.3) % of the regenerated adipocytes were still viable after one year. The process of adipogenesis was enhanced by the decrease in inflammatory infiltration and proceeded in parallel with angiogenesis. Statement of significance: The decellularized adipose matrix (DAM) is the only biological scaffold that can spontaneously generate adipocytes in vivo without the need to add exogenous cells. However, in the previous studies, the longest DAM-related animal experiments were about 3 months. The different stages and characteristics of DAM implantation cannot be fully captured. Comprehensive preclinical researches on the initiation, characteristics, and long-term outcomes of DAM-induced adipose tissue regeneration in adulthood is crucial.In this study, we closely observed various aspects of the entire process in vivo from 3 days to 1 year after implantation including fat regeneration, vascular growth, inflammatory reactions as well as matrix degradation and remodeling. The thorough research will contribute to the understanding of stability and dynamic remodeling of DAM regeneration models.