Systems Approach Research Articles

Realising systemic justice-oriented reform in education in postcolonial contexts

Drawing on and responding to the articles in this special collection, this provocation makes the case that realising justice in education requires a focus on the processes and politics of justice-oriented reform in postcolonial, low- and middle-income counties (LMICs). In implementing reform, it is argued that it is crucial to take account of similarities and differences in context between LMICs. At the heart of reform must be a holistic, coherent and systemic approach at the level of the education system of the institution. Key priorities include reforming the curriculum, investing in educators as agents of change and developing endogenous system leadership that can drive justice-oriented reform. Here, however, it is necessary to engage with the politics of justice-oriented reform, including challenging global, neoliberal agendas, democratising the governance of education and engaging with popular struggles for social, epistemic, transitional and environmental justice.

Sensing Human Emotion using Emerging Machine Learning Techniques

Human emotion recognition using machine learning is a new field that has the potential to improve user experience, lower crime, and target advertising. The ability of today's emotion detection systems to identify human emotions is essential. Applications ranging from security cameras to emotion detection are readily accessible. Machine learning-based emotion detection recognises and deciphers human emotions from text and visual data. In this study, we use convolutional neural networks and natural language processing approaches to create and assess models for emotion detection. Instead of speaking clearly, these human face expressions visually communicate a lot of information. Recognising facial expressions is important for human-machine interaction. Applications for automatic facial expression recognition systems are numerous and include, but are not limited to, comprehending human conduct, identifying mental health issues, and creating artificial human emotions. It is still difficult for computers to recognise facial expressions with a high recognition rate. Geometry and appearance-based methods are two widely used approaches for automatic FER systems in the literature. Pre-processing, face detection, feature extraction, and expression classification are the four steps that typically make up facial expression recognition. The goal of this research is to recognise the seven main human emotions anger, disgust, fear, happiness, sadness, surprise, and neutrality using a variety of deep learning techniques (convolutional neural networks).

A qualitative study to understand the barriers and facilitators in smoking cessation practices among oncology health care practitioners in one health system.

Despite the benefits of quitting smoking for those who have cancer, including improved health outcomes and reduced therapeutic toxicities, it is unclear how many people are supported in quit attempts during this time. Variations in the availability and provision of smoking cessation (SC) services are reported, with little understanding of the challenges and solutions. This co-designed study aimed to understand the perspectives of health care professionals (HCPs) working in oncology settings to engage in SC practices and identify recommendations for developing an SC pathway. This was a qualitative study. Eighteen HCPs participated in semi-structured interviews from July 2021 to May 2022. We used thematic analysis approaches to code data and present four emergent themes and SC strategies at micro, meso and organizational levels. Four themes emerged specifically: 1) timing and knowledge, 2) building a relationship, 3) frequent asking with infrequent action, and 4) removing the barriers and tailoring the system. While HCPs discuss SC, there are variations in documentation and when conversations occur. Primarily, HCPs value the time to build therapeutic relationships with patients and thus may limit SC discussions in preference to treatment in clinical interactions. The role of structural barriers, including prescriptive authority for nurses, hinders active SC processes, as it is the lack of continuity and embedding of services supported by a clinical champion for SC. The study suggests re-evaluating the status quo in SC service, highlighting service gaps and suggesting opportunities at organizational levels to reduce structural barriers. Variations in smoking cessation services exist in designated cancer centres. The data from this study can be used to inform a real time health systems approach for SC services in oncology settings. Developing tailored smoking cessation services and interventions that are patient-centred and informed by their experiences are required. The data in this study suggests developing specialist education and training to upskill HCPs for equitable engagement if we are to meet EU and Moonshot goals for cancer reduction.

What's old is new: leveraging existing antimicrobial susceptibility test methods for rapid results in patients with bloodstream infections.

The use of rapid disk diffusion or modified automated antimicrobial susceptibility testing (AST) system approaches demonstrates excellent performance for gram-negative organisms directly from blood cultures. In a recent study, S. Khan, A. Das, A. Mishra, A. Vidyarthi, et al. (Microbiol Spectr 12:e03081-23, 2024, https://doi.org/10.1128/spectrum.03081-23) compared the performance of three direct-from-blood AST methods against standard of care disk diffusion and automated AST. The results demonstrated high categorical agreements and low error rates across three protocols. The study suggests that locally validated direct-from-blood AST protocols offer reliable and fast results, particularly for resource-limited settings. However, local context and workflows should be considered prior to implementing rapid AST protocols, and more research is needed on the performance of rapid AST protocols for gram-positive organisms.

The legacy of Luhmann’s sociology of law

This paper aims to map the possibilities of adopting social systems theory in order to couple three usually isolated domains: social theory, jurisprudence and empirical research. It argues that these different uses would be the distinctive legacy of Luhmann’s work for legal research. The text suggests that investigations interested in adopting a systemic approach for discussions in each of these three domains (or in all of them) should focus on the entanglement among interactional, decisional and functional systems. However, this presupposes some enhancements in Luhmann’s own description of law as a social system. The core hypothesis presented here is that a functional system’s out-differentiation (its specialisation in face of other communications happening in the societal environment) depends on the inner-differentiation of this system – that is, on operations backed by the very construction of specific functional-systemic institutions and semantics (including decisional programs and self-descriptions).

Theories of immune recognition: Is anybody right?

The clonal selection theory (CST) is the centrepiece of the current paradigm used to explain immune recognition and memory. Throughout the past decades, the original CST had been expanded and modified to explain new experimental evidences since its original publication by Burnet. This gave origin to new paradigms that govern experimental immunology nowadays, such as the associative recognition of antigen model and the stranger/danger signal model. However, these new theories also do not fully explain experimental findings such as natural autoimmune immunoglobulins, idiotypic networks, low and high dose tolerance, and dual-receptor T and B cells. To make sense of these empirical data, some authors have been trying to change the paradigm of immune cognition using a systemic approach, analogies with brain processing and concepts from second-order cybernetics. In the present paper, we review the CST and some of the theories/hypotheses derived from it, focusing on immune recognition. We point out their main weaknesses and highlight arguments made by their opponents and believers. We conclude that, until now, none of the proposed theories can fully explain the totality of immune phenomena and that a theory of everything is needed in immunology.

Fault Diagnosis Method for Tractor Transmission System Based on Improved Convolutional Neural Network–Bidirectional Long Short-Term Memory

In response to the problems of limited algorithms and low diagnostic accuracy for fault diagnosis in large tractor transmission systems, as well as the high noise levels in tractor working environments, a defect detection approach for tractor transmission systems is proposed using an enhanced convolutional neural network (CNN) and a bidirectional long short-term memory neural network (BILSTM). This approach uses a one-dimensional convolutional neural network (1DCNN) to create three feature extractors of varying scales, directly extracting feature information from different levels of the raw vibration signals. Simultaneously, in order to enhance the model’s predicted accuracy and learn the data features more effectively, it presents the multi-head attention mechanism (MHA). To overcome the issue of high noise levels in tractor working environments and enhance the model’s robustness, an adaptive soft threshold is introduced. Finally, to recognize and classify faults, the fused feature data are fed into a classifier made up of bidirectional long short-term memory (BILSTM) and fully linked layers. The analytical findings demonstrate that the fault recognition accuracy of the method described in this article is over 98%, and it also has better performance in noisy environments.

Control of Grid-Connected and Standalone Microhydraulic Turbine Using a Six-Phase Induction Generator

Microhydraulic turbines offer a promising solution for decentralized energy production, suitable for both grid-connected and standalone applications, due to their compactness and high efficiency. This paper introduces a control approach for such systems employing microhydraulic turbines as distributed generators (DGs), utilizing six-phase induction generators for electricity production. This study emphasizes control strategies for both grid-connected and standalone modes utilizing proportional-integral (PI) controllers. An integrated energy storage system based on Li-Ion battery technology is also implemented to store the excess energy and compensate for production deficits to meet demand. The results obtained using MATLAB/Simulink demonstrate efficient and reliable power management among production sources, the grid and the local load, highlighting the unique contribution of employing a six-phase induction generator with the energy storage system.

Precision-Targeted Injection Laryngoplasty and Longitudinal Biomaterial Effects Evaluation Using High-resolution Ultrasonography in a Rat Model.

Current laryngeal injection models rely on the transoral route and are suboptimal due to limited view, narrowed working space, and the need to sacrifice animals for investigation of the injectables. In the present study, a novel surgical model for laryngeal intervention therapy utilizing an ultra-high frequency ultrasound imaging system was proposed. Based on this system, we developed a systemic evaluation approach, from guidance of the injection process, documentation of the injection site of the material, to in vivo longitudinal follow-up on the augmentation and medialization effect by analyzing the ultrasonography data. In vivo animal study. Academic institution. Injection laryngoplasty with hyaluronic acid under ultrasonography guidance was performed on Sprague-Dawley rats one week after induced unilateral vocal paralysis. Ultrasonography was performed at preinjection, immediately postinjection, on Day 2, Day 7 and then weekly for 4 weeks to obtain measurements, including the glottic area, angle between bilateral folds, and vocal fold width ratio. Laryngoscopic and histologic studies were also performed. Unilateral injections to the paralyzed fold were successfully performed as demonstrated by ultrasonographic, laryngoscopic, and histologic studies. The width ratio was significantly increased after injection for 4 weeks, while the glottic airway area was unchanged. Here, a novel surgical model for laryngeal injection utilizing ultrasonography in rats was established. In addition to providing visual guidance for precise localization of the injection, robust documentation of the treatment effect was also demonstrated. This methodology could be beneficial for screening therapeutic agents for treatment of glottic insufficiency.

Healthcare professionals’ perceptions of challenges in vaccine communication and training needs: a qualitative study

BackgroundHealthcare professionals (HCPs) can play an important role in encouraging patients and their caregivers to be vaccinated. The objective of this qualitative study was to investigate HCPs’ perspectives on challenges in vaccine communication and unmet training needs in this domain.MethodsSemi-structured interviews were conducted with 41 HCPs (mainly nurses and physicians) with vaccination roles (23 in England; 18 in France), gathering information on: (1) HCPs’ approach to vaccine conversations with patients; (2) Challenges of communicating about vaccines; (3) Vaccine-related training and learning resources available to HCPs, and; (4) HCPs’ training needs around vaccine communication.ResultsHCPs described a range of communication experiences that indicated insufficient time, information, and skills to confidently navigate difficult conversations with vaccine-hesitant patients. Communication skills were especially important to avoid conflict that could potentially damage the patient-provider relationship. Some HCPs interviewed had received communication training, but for most, this training was not specific to vaccination. Although general communication skills were transferable to vaccine conversations, most HCPs welcomed specific training and informational resources to support countering patients’ misconceptions or misinformation about vaccines.ConclusionsHCPs would benefit from training tailored to address vaccine communication with patients, and this should be part of a systemic approach that also provides time and space to have effective vaccine conversations.

Modular Digital Twin – an approach for generating and exploiting product sustainability information towards service-oriented business models

ABSTRACT Companies receive numerous customer inquiries and regulatory requirements while attempting to offer new services to enhance sustainability, leading in total to an increase in the number of product-related requirements. Digital Twins serve as enablers for meeting these requirements, whereas new challenges arise for companies through the introduction of Digital Twins. Digital Twins represent an order-specific configuration and must be structured differently according to requirements and thus as modularly as possible. This paper aims to provide a systemic approach with a concept for a Modular Digital Twin for generating and exploiting Product Sustainability Information to leverage sustainability potentials towards Service-oriented Business Models. The approach is concretised by means of a selected use case regarding energy-efficient production control of an automated model factory. We can show with this use case that the systemic approach with the concept of a Modular Digital Twin is applicable for energy consumption of machinery motors' simulation and for the calculation of an optimised motor speed.

The promise and peril of comparing fluorescence lifetime in biology revealed by simulations.

Signaling dynamics are crucial in biological systems, and biosensor-based real-time imaging has revolutionized their analysis. Fluorescence lifetime imaging microscopy (FLIM) excels over the widely used fluorescence intensity imaging by allowing the measurement of absolute signal levels, independent of sensor concentration. This capability enables the comparison of signaling dynamics across different animals, body regions, and timeframes. However, FLIM's advantage can be compromised by factors like autofluorescence in biological experiments. To address this, we introduce FLiSimBA, a flexible computational framework for realistic F luorescence Li fetime Sim ulation for B iological A pplications. Through simulations, we analyze the signal-to-noise ratios of fluorescence lifetime data, determining measurement uncertainty and providing necessary error bars for lifetime measurements. Furthermore, we challenge the belief that fluorescence lifetime is unaffected by sensor expression, and establish quantitative limits to this insensitivity in biological applications. Additionally, we propose innovations, notably multiplexed dynamic imaging that combines fluorescence intensity and lifetime measurements. This innovation can transform the number of signals that can be simultaneously monitoring, thereby enabling a systems approach in studying signaling dynamics. Thus, by incorporating diverse factors into our simulation framework, we uncover surprises, identify limitations, and propose advancements for fluorescence lifetime imaging in biology. This quantitative framework supports rigorous experimental design, facilitates accurate data interpretation, and paves the way for technological advancements in fluorescence lifetime imaging.

Characterization Methods to Determine Interpenetrating Polymer Network (IPN) in Hydrogels.

Significant developments have been achieved with the invention of hydrogels. They are effective in many fields such as wastewater treatment, food, agriculture, pharmaceutical applications, and drug delivery. Although hydrogels have been used successfully in these areas, there is a need to make them better for future applications. Interpenetrating polymer networks (IPNs) can be created to make hydrogels more adjustable and suitable for a specific purpose. IPN formation is an innovative approach for polymeric systems. It brings two or more polymer networks together with entanglements. The properties of IPNs are controlled by its chemistry, crosslinking density, and morphology. Therefore, it is necessary to understand characterization methods in order to detect the formation of IPN structure and to develop the properties of hydrogels. In recent studies, IPN structure in hydrogels has been determined via chemical, physical, and mechanical methods such as Fourier transform infrared spectroscopy (FTIR), Raman spectroscopy, scanning electron microscopy (SEM), field emission scanning electron microscopy (FESEM), differential scanning calorimetry (DSC), dynamic mechanical analysis (DMA), X-ray diffraction (XRD), and rheology methods. In this paper, these characterization methods will be explained, recent studies will be scrutinized, and the effectiveness of these methods to confirm IPN formation will be evaluated.

Stratified health care for low back pain using the STarT Back approach: Holy Grail or doomed to fail?

There have been at least 7 separate randomised controlled trials published between 2011 and 2023 that have examined primary care for nonspecific low back pain informed by the STarT Back approach to stratified care based on risk prediction, compared with care not informed by this approach. The results, across 4 countries, have been contrasting-some demonstrating effectiveness and/or efficiency of this approach, others finding no benefits over comparison interventions. This review considers possible explanations for the differences, particularly whether this is related to poor predictive performance of the STarT Back risk-prediction tool or to variable degrees of success in implementing the whole STarT Back approach (subgrouping and matching treatments to predicted risk of poor outcomes) in different healthcare systems. The review concludes that although there is room for improving and expanding the predictive value of the STarT Back tool, its performance in allocating individuals to their appropriate risk categories cannot alone explain the variation in results of the trials to date. Rather, the learning thus far suggests that challenges in implementing stratified care in clinical practice and in changing professional practice largely explain the contrasting trial results. The review makes recommendations for future research, including greater focus on studying facilitators of implementation of stratified care and developing better treatments for patients with nonspecific low back pain at high risk of poor outcomes.

Micro Knowledge as a Driver for Systemic Emergencies Management: The Case of Xylella in Italy

AbstractDuring emergency production systems, supply chain stakeholders’ interactions, media activities, government actions, community behaviors are stressed bringing, all, to a high-risk perception about safety. For agri-food products, it became in a variation of consumers’ purchasing behaviors, guided by uncertainty and influenced by trust. The extant knowledge about consumers’ willingness to buy for agri-food products in emergency condition does not provide evidences during a phytosanitary warning. The proposed study, considering the Xylella fastidiosa phytosanitary emergency as case study, aims to fulfill this gap analyzing the possible variations of consumers’ perception and willingness to buy of Apulian olive oil in the era of Xylella emergence by adopting a multi-perspective and systemic approach. For achieving this scope, the study follows a Process-Person-Context-Time perspective for conceiving a systemic psychological model based on the Bronfenbrenner ecological model. Seven hypotheses were tested through Structural Equation Modeling thanks to a survey in a representative sample of 779 Italian consumers. Results showed that Italian consumer’s willingness to buy the post-Xylella olive oil can fluctuate due to the hypothesized system vulnerability: family, friends, and colleague such as trust in farmers and the credibility of Italian food market are impactful; trust in processors, transporters, and government and media is not. The findings, from theoretical and sectorial perspectives, provide new knowledge on the topic, empirical evidences, and a multi-systemic model able to analyze consumers’ behaviors, understand and stimulate the search for new (or more appropriate) marketing and communication strategies to face Xylella serious economic shortages, and to act a requalification of territory at all.

Artificial Intelligence and the Transformation of Higher Education Institutions: A Systems Approach

Artificial intelligence (AI) advances and the rapid adoption of generative AI tools, like ChatGPT, present new opportunities and challenges for higher education. While substantial literature discusses AI in higher education, there is a lack of a systems approach that captures a holistic view of the structure and dynamics of the AI transformation of higher education institutions (HEIs). To fill this gap, this article develops a causal loop diagram (CLD) to map the causal feedback mechanisms of AI transformation in a typical HEI. We identify important variables and their relationships and map multiple reinforcing and balancing feedback loops accounting for the forces that drive the AI transformation and its impact on value creation in a typical HEI. The model shows how, motivated by AI technology advances, the HEI can invest in AI to improve student learning, research, and administration while dealing with academic integrity problems and adapting to job market changes by emphasizing AI-complementary student skills. We explore model insights, scenarios, and policy interventions and recommend that HEI leaders become systems thinkers to manage the complexity of the AI transformation and benefit from the AI feedback loops while avoiding policy traps that may lead to decline. We also discuss the notion of HEIs influencing the direction of AI and directions for future research on AI transformation and the sustainability of HEIs.

An efficient implementation of three-level boost converter with capacitor voltage balancing for an advanced MPPT approach in PV Systems

Ensuring optimal extraction of power from photovoltaic (PV) systems under diverse climatic circumstances is a significant challenge, which requires an efficient Maximum Power Point (MPP) Tracking (MPPT) algorithm and an adequate conversion stage. Therefore, this paper proposes an advanced MPPT strategy implemented by a Three-Level Boost converter (TLB), which serves as the interface between the PV generator and the DC load, offering advantages such as reduced output voltage distortion, minimized inductor current ripple, and decreased switching losses. To achieve more accurate tracking, a robust voltage balance control across the TLB converter capacitors is also suggested. The proposed system is meticulously implemented and evaluated using MATLAB/Simulink, demonstrating its robust control capabilities in MPP tracking and voltage balancing across the TLB converter capacitors under diverse climatic scenarios. Furthermore, comparative analysis under various test circumstances including static and dynamic insolation conditions and sudden load variations, shows that the suggested MPPT algorithm offers noteworthy improvements over the InC algorithm. Additionally, a thorough comparison with existing research methods proves its effectiveness over these methods. The advanced MPPT strategy achieves a steady-state error of 0 W, an average tracking efficiency between 99.13 % and 99.44%, and fast-tracking velocity. Further, a comprehensive real-time evaluation using the cutting-edge RT-LAB platform confirms the consistency of the results with the simulation outputs, emphasizing the robustness and low implementation complexity of the overall proposed control strategy.

A Framework and Taxonomy for Characterizing the Applicability of Software Architecture Recovery Approaches: A Tertiary‐Mapping Study

SummarySoftware architecture assists developers in addressing non‐functional requirements and in maintaining, debugging, and upgrading their software systems. Consequently, consistency between the designed architecture and the implemented software system itself is important; without this consistency the non‐functional requirements targeted may not be addressed and architectural documentation may mis‐direct maintenance efforts that target the associated code‐base. But often, when software is initially implemented or subsequently evolved, the designed architecture and software architecture become inconsistent, with the implemented structure degraded due to issues like developer time‐pressures, or ambiguous communication of the designed architecture. In such cases, Software Architecture Recovery (SAR) or consistency approaches can be applied to reconstruct the architecture of the software system and possibly to compare it to/re‐align it with the designed architecture. Many SAR approaches have been proposed in the research. However, choosing an appropriate architecture recovery approach for software systems is still an open issue. Consequently, this research aims to conduct a tertiary‐mapping study based on available secondary studies of architecture recovery approaches, to uncover important characteristics, towards the selection of appropriate SAR approaches. This research has aggregated 13 secondary studies and 10 primary studies beyond 2020 from 5 databases and, in doing so, identified 111 architecture recovery approaches. Based on these approaches, a taxonomy, containing nine main SAR‐selection categories is proposed and a framework (in the form of a supporting tool to help developers select an appropriate SAR approach) has been developed. Finally, this research identifies six potential open research gaps related to the underlying research that could be helpful for guiding research in the future.

Aproximación al Buen Vivir: Una experiencia de emprendimiento por mujeres de la zona rural

The concept of Good Living plays a crucial role in relation to the community and becomes an opportunity to improve the quality of life. This article systematizes the entrepreneurship experience with peasant women from the rural area of Villamaría, Caldas, Colombia, as a contribution to human flourishing. This research followed the guidelines of the Alforja Network, which proposes a systematization framed within dialectical, systemic, and holistic approaches to popular education. The results are presented in three phases: characterization of the families, the environment, and the entrepreneurship production; interdisciplinary accompaniment and sustainability through the structuring of networks and alliances. The experience reflects the ability of women for the Good Living of themselves and their families, collective organization, and innovation. Additionally, it highlights the importance of the role of the State in strengthening local development through the production chain, preserving its rural identity in the context of equitable processes.

Opportunistic Sensor-Based Authentication Factors in and for the Internet of Things.

Communication between connected objects in the Internet of Things (IoT) often requires secure and reliable authentication mechanisms to verify identities of entities and prevent unauthorized access to sensitive data and resources. Unlike other domains, IoT offers several advantages and opportunities, such as the ability to collect real-time data through numerous sensors. These data contains valuable information about the environment and other objects that, if used, can significantly enhance authentication processes. In this paper, we propose a novel idea to building opportunistic sensor-based authentication factors by leveraging existing IoT sensors in a system of systems approach. The objective is to highlight the promising prospects of opportunistic authentication factors in enhancing IoT security. We claim that sensors can be utilized to create additional authentication factors, thereby reinforcing existing object-to-object authentication mechanisms. By integrating these opportunistic sensor-based authentication factors into multi-factor authentication schemes, IoT security can be substantially improved. We demonstrate the feasibility and effectivenness of our idea through illustrative experiments in a parking entry scenario, involving both mobile robots and cars, achieving high identification accuracy. We highlight the potential of this novel method to improve IoT security and suggest future research directions for formalizing and comparing our approach with existing techniques.