Multilevel cultural evolution: From new theory to practical applications.

Evolutionary science has led to many practical applications of genetic evolution but few practical uses of cultural evolution. This is because the entire study of evolution was gene centric for most of the 20th century, relegating the study and application of human cultural change to other disciplines. The formal study of human cultural evolution began in the 1970s and has matured to the point of deriving practical applications. We provide an overview of these developments and examples for the topic areas of complex systems science and engineering, economics and business, mental health and well-being, and global change efforts.

Scalable, robust, high-throughput expression & purification of nanobodies enabled by 2-stage dynamic control.

Nanobodies are single-domain antibody fragments that have garnered considerable use as diagnostic and therapeutic agents as well as research tools. However, obtaining pure VHHs, like many proteins, can be laborious and inconsistent. High level cytoplasmic expression in E. coli can be challenging due to improper folding and insoluble aggregation caused by reduction of the conserved disulfide bond. We report a systems engineering approach leveraging engineered strains of E. coli, in combination with a two-stage process and simplified downstream purification, enabling improved, robust, soluble cytoplasmic nanobody expression, as well as rapid cell autolysis and purification. This approach relies on the dynamic control over the reduction potential of the cytoplasm, incorporates lysis enzymes for purification, and can also integrate dynamic expression of protein folding catalysts. Collectively, the engineered system results in more robust growth and protein expression, enabling efficient scalable nanobody production, and purification from high throughput microtiter plates, to routine shake flask cultures and larger instrumented bioreactors. We expect this system will expedite VHH development.

Use of artificial intelligence in critical care: opportunities and obstacles.

BACKGROUND: Perhaps nowhere else in the healthcare system than in the intensive care unit environment are the challenges to create useful models with direct time-critical clinical applications more relevant and the obstacles to achieving those goals more massive. Machine learning-based artificial intelligence (AI) techniques to define states and predict future events are commonplace activities of modern life. However, their penetration into acute care medicine has been slow, stuttering and uneven. Major obstacles to widespread effective application of AI approaches to the real-time care of the critically ill patient exist and need to be addressed. MAIN BODY: Clinical decision support systems (CDSSs) in acute and critical care environments support clinicians, not replace them at the bedside. As will be discussed in this review, the reasons are many and include the immaturity of AI-based systems to have situational awareness, the fundamental bias in many large databases that do not reflect the target population of patient being treated making fairness an important issue to address and technical barriers to the timely access to valid data and its display in a fashion useful for clinical workflow. The inherent "black-box" nature of many predictive algorithms and CDSS makes trustworthiness and acceptance by the medical community difficult. Logistically, collating and curating in real-time multidimensional data streams of various sources needed to inform the algorithms and ultimately display relevant clinical decisions support format that adapt to individual patient responses and signatures represent the efferent limb of these systems and is often ignored during initial validation efforts. Similarly, legal and commercial barriers to the access to many existing clinical databases limit studies to address fairness and generalizability of predictive models and management tools. CONCLUSIONS: AI-based CDSS are evolving and are here to stay. It is our obligation to be good shepherds of their use and further development.

Decoding the Debate: A Comparative Study of Brain-Computer Interface and Neurofeedback.

Brain-Computer Interface (BCI) and Neurofeedback (NF) both rely on the technology to capture brain activity. However, the literature lacks a clear distinction between the two, with some scholars categorizing NF as a special case of BCI while others view BCI as a natural extension of NF, or classify them as fundamentally different entities. This ambiguity hinders the flow of information and expertise among scholars and can cause confusion. To address this issue, we conducted a study comparing BCI and NF from two perspectives: the background and context within which BCI and NF developed, and their system design. We utilized Functional Flow Block Diagram (FFBD) as a system modelling approach to visualize inputs, functions, and outputs to compare BCI and NF at a conceptual level. Our analysis revealed that while NF is a subset of the biofeedback method that requires data from the brain to be extracted and processed, the device performing these tasks is a BCI system by definition. Therefore, we conclude that NF should be considered a specific application of BCI technology. By clarifying the relationship between BCI and NF, we hope to facilitate better communication and collaboration among scholars in these fields.

Strategies for automating analytical and bioanalytical laboratories.

Analytical measurement methods are used in different areas of production and quality control, diagnostics, environmental monitoring, or in research applications. If direct inline or online measurement methods are not possible, the samples taken have to be processed offline in the manual laboratory. Automated processes are increasingly being used to enhance throughput and improve the quality of results. In contrast to bioscreening, the degree of automation in (bio)analytical laboratories is still low. This is due in particular to the complexity of the processes, the required process conditions, and the complex matrices of the samples. The requirements of the process to be automated itself and numerous other parameters influence the selection of a suitable automation concept. Different automation strategies can be used to automate (bio)analytical processes. Classically, liquid handler-based systems are used. For more complex processes, systems with central robots are used to transport samples and labware. With the development of new collaborative robots, there will also be the possibility of distributed automation systems in the future, which will enable even more flexible automation and use of all subsystems. The complexity of the systems increases with the complexity of the processes to be automated.

"We should be resourcing their liberation:" a qualitative formative study to guide introduction of a systems engineering intervention at a King County, WA juvenile detention center clinic.

BACKGROUND: There are ongoing efforts to eliminate juvenile detention in King County, WA. An essential element of this work is effectively addressing the health needs of youth who are currently detained to improve their wellbeing and reduce further contact with the criminal legal system. This formative study sought to inform adaptation and piloting of an evidence-based systems engineering strategy - the Systems Analysis and Improvement Approach (SAIA) - in a King County juvenile detention center clinic to improve quality and continuity of healthcare services. Our aims were to describe the priority health needs of young people who are involved in Washington's criminal legal system and the current system of healthcare for young people who are detained. METHODS: We conducted nine individual interviews with providers serving youth. We also obtained de-identified quantitative summary reports of quality improvement discussions held between clinic staff and 13 young people who were detained at the time of data collection. Interview transcripts were analyzed using deductive and inductive coding and quantitative data were used to triangulate emergent themes. RESULTS: Providers identified three priority healthcare cascades for detention-based health services-mental health, substance use, and primary healthcare-and reported that care for these concerns is often introduced for the first time in detention. Interviewees classified incarceration itself as a health hazard, highlighting the paradox of resourcing healthcare quality improvement interventions in an inherently harmful setting. Fractured communication and collaboration across detention- and community-based entities drives systems-level inefficiencies, obstructs access to health and social services for marginalized youth, and fragments the continuum of care for young people establishing care plans while detained in King County. 31% of youth self-reported receiving episodic healthcare prior to detention, 15% reported never having medical care prior to entering detention, and 46% had concerns about finding healthcare services upon release to the community. CONCLUSIONS: Systems engineering interventions such as the SAIA may be appropriate and feasible approaches to build systems thinking across and between services, remedy systemic challenges, and ensure necessary information sharing for care continuity. However, more information is needed directly from youth to draw conclusions about effective pathways for healthcare quality improvement.

Enterprise risk management for automation in correctional facilities with pandemic and other stressors.

Real-time tracking of tool and equipment inventories is a critical function of many organizations and sectors. For prisons and correctional facilities, tracking and monitoring of assets such as cookware, hardware, keys, janitorial equipment, vocational/technical specialty tools, etc., is essential for safety, security, trust, efficiency, education, etc. The performance of automated systems for this purpose can be diminished by a variety of emergent and future sociotechnical factors alone and in combination. This article introduces a methodology for contractor evaluation and selection in acquisition of innovative asset management systems, with an emphasis on evolving system requirements under uncertainty. The methodology features a scenario-based preferences analysis of emergent and future conditions that are disruptive to the performance of the asset-control system. The conditions are across technologies, operating environments, regulations, workforce behaviors, offender behaviors, prices and markets, organizations, cyber threats, etc. The methodology addresses the influence and interaction of the conditions to disrupt system priorities. Examples include: (i) infectious disease disrupting priorities among requirements and (ii)  radio-frequency identification (RFID) and wireless-technology innovations disrupting priorities among stakeholders. The combinations of conditions that most and least matter for the system acquisition are characterized. The methodology constitutes a risk register for monitoring sources of risk to project performance, schedule, and cost throughout the system lifecycle. The results will be of interest to both practitioners and scholars engaged in systems acquisition as the pandemic interacts with other factors to affect risk, uncertainty, and resilience of organizational missions and operations.

Sensitivity analysis for a participatory approach to enhance the climate resilience of Venice, Italy.

Increases in the magnitudes and frequencies of climate-related extreme events are redistributing risk across coastal systems, including their environmental, economic, and social components. Consequently, stakeholders (SHs) are faced with long-term challenges and complex information when managing assets, services, and uses of the coast. In this context, SH engagement is a key step for risk management and in the preparation of resilience plans to respond and adapt to climate change. This paper develops a participatory method to identify and prioritize a set of risk measures, combining multi-criteria analysis with sensitivity analysis. The process involved local and regional authorities of the Veneto region testing the method, including national, regional, and local government, catchment officers, research organizations, natural parks managers and Non-Governmental Organizations (NGOs). SHs identified and ranked a range of adaptation measures to increase climate resilience, with a focus on coastal risk in the Venice lagoon. Results demonstrate that the sensitivity analysis provides useful information on how different sectors of expertise can influence the ranking of the identified risk management measures, highlighting the value of investigating the preferences or priorities of different SH groups within the definition of adaptation plans.

Exploring patient flow management through a lens of cognitive systems engineering.

Hospitals work to provide quality, safety, and availability to patients with a wide variety of care needs, which makes efficient prioritisation and resource utilisation essential. Anticipation of each patients' trajectory, while monitoring available resources across the hospital, are major challenges for patient flow management. This study focuses on how hospital patient flow management is realised in situ with the help of concepts from cognitive systems engineering. Five semi-structured interviews with high level managers and shadowing observations of seven full work-shifts with management teams were conducted, to explore how patient flow is coordinated and communicated across the hospital. The data has been analysed using qualitative content analysis. The results describe patient flow management using an adapted Extended Control Model (ECOM) and reveal how authority and information might be better placed closer to clinical work for increased efficiency of patient flow.Practitioner summary: This study describes how a large tertiary paediatric hospital's patient flow management functions. The results offer a new understanding of how patient flow management is communicated and coordinated across organisational levels of the hospital and how authority and information might be better placed closer to clinical work for increased efficiency.

An improved algorithm for flux variability analysis.

Flux balance analysis (FBA) is an optimization based approach to find the optimal steady state of a metabolic network, commonly of microorganisms such as yeast strains and Escherichia coli. However, the resulting solution from an FBA is typically not unique, as the optimization problem is, more often than not, degenerate. Flux variability analysis (FVA) is a method to determine the range of possible reaction fluxes that still satisfy, within some optimality factor, the original FBA problem. The resulting range of reaction fluxes can be utilized to determine metabolic reactions of high importance, amongst other analyses. In the literature, this has been done by solving [Formula: see text] linear programs (LPs), with n being the number of reactions in the metabolic network. However, FVA can be solved with less than [Formula: see text] LPs by utilizing the basic feasible solution property of bounded LPs to reduce the number of LPs that are needed to be solved. In this work, a new algorithm is proposed to solve FVA that requires less than [Formula: see text] LPs. The proposed algorithm is benchmarked on a problem set of 112 metabolic network models ranging from single cell organisms (iMM904, ect) to a human metabolic system (Recon3D). Showing a reduction in the number of LPs required to solve the FVA problem and thus the time to solve an FVA problem.

The hot-to-cold spot quotient for SAR-based treatment planning in deep microwave hyperthermia.

BACKGROUND: A necessary precondition for a successful microwave hyperthermia (HT) treatment delivered by phased arrays is the ability of the HT applicator to selectively raise the temperature of the entire tumor volume. SAR-based treatment plan (HTP) optimization methods exploit the correlation between specific absorption rate (SAR) and temperature increase in order to determine the set of steering parameters for optimal focusing, while allowing for lower model complexity. Several cost functions have been suggested in the past for this optimization problem. However, their correlation with high and homogeneous tumor temperatures remains sub-optimal in many cases. Previously, we proposed the hot-to-cold spot quotient (HCQ) as a novel cost function for SAR-based HTP optimization and showed its potential to address these issues. MATERIALS AND METHODS: In this work, we validate the HCQ on a standard ESHO patient repository within single and multi-frequency contexts. We verify its correlation with clinical SAR and temperature indexes, and compare it to HTPs obtained using a commonly accepted cost-function for SAR-based HTP (hot-spot to target quotient, HTQ). RESULTS AND DISCUSSION: The results show that low HCQ values produce better SAR (TC50, TC75) and temperature metrics (T50, T90) than HTQ in most patient models and frequency settings. For the deep-seated tumors, the correlation between the clinical indicators and 1/HCQ is more favorable than the correlation exhibited by 1/HTQ. CONCLUSION: The validation confirms the ability of HCQ to promote target coverage and hot-spot suppression in SAR-based HTP optimization, resulting in higher SAR and temperature indexes for deep-seated tumors.

Prioritization of Resilience Initiatives for Climate-Related Disasters in the Metropolitan City of Venice.

Increases in the magnitude and frequency of climate and other disruptive factors are placing environmental, economic, and social stresses on coastal systems. This is further exacerbated by land use transformations, urbanization, over-tourism, sociopolitical tensions, technological innovations, among others. A scenario-informed multicriteria decision analysis (MCDA) was applied in the Metropolitan City of Venice integrating qualitative (i.e., local stakeholder preferences) and quantitative information (i.e., climate-change projections) with the aim of enhancing system resilience to multiple climate-related threats. As part of this analysis, different groups of local stakeholders (e.g., local authorities, civil protection agencies, SMEs, NGOs) were asked to identify critical functions that needs to be sustained. Various policy initiatives were considered to support these critical functions. The MCDA was used to rank the initiatives across several scenarios describing main climate threats (e.g., storm surges, floods, heatwaves, drought). We found that many climate change scenarios were considered to be disruptive to stakeholders and influence alternative ranking. The management alternatives acting on physical domain generally enhance resilience across just a few scenarios while cognitive and informative initiatives provided resilience enhancement across most scenarios considered. With uncertainty of multiple stressors along with projected climate variability, a portfolio of cognitive and physical initiatives is recommended to enhance resilience.

Sensor-Driven Human-Robot Synergy: A Systems Engineering Approach.

Knowledge-based synergistic automation is a potential intermediate option between the opposite extremes of manual and fully automated robotic labor in agriculture. Disruptive information and communication technologies (ICT) and sophisticated solutions for human-robot interaction (HRI) endow a skilled farmer with enhanced capabilities to perform agricultural tasks more efficiently and productively. This research aspires to apply systems engineering principles to assess the design of a conceptual human-robot synergistic platform enabled by a sensor-driven ICT sub-system. In particular, this paper firstly presents an overview of a use case, including a human-robot synergistic platform comprising a drone, a mobile platform, and wearable equipment. The technology framework constitutes a paradigm of human-centric worker-robot logistics synergy for high-value crops, which is applicable in operational environments of outdoor in-field harvesting and handling operations. Except for the physical sub-system, the ICT sub-system of the robotic framework consists of an extended sensor network for enabling data acquisition to extract the context (e.g., worker's status, environment awareness) and plan and schedule the robotic agents of the framework. Secondly, this research explicitly presents the underpinning Design Structure Matrix (DSM) that systematically captures the interrelations between the sensors in the platform and data/information signals for enabling synergistic operations. The employed Systems Engineering approach provides a comprehensible analysis of the baseline structure existing in the examined human-robot synergy platform. In particular, the applied DSM allows for understanding and synthesizing a sensor sub-system's architecture and enriching its efficacy by informing targeted interventions and reconfiguring the developed robotic solution modules depending on the required farming tasks at an orchard. Human-centric solutions for the agrarian sector demand careful study of the features that the particular agri-field possesses; thus, the insight DSM provides to system designers can turn out to be useful in the investigation of other similar data-driven applications.

Engineering Approaches for Addressing Opioid Use Disorder in the Community.

Many communities in the United States are struggling to deal with the negative consequences of illicit opioid use. Effectively addressing this epidemic requires the coordination and support of community stakeholders in a change process with common goals and objectives, continuous engagement with individuals with opioid use disorder (OUD) through their treatment and recovery journeys, application of systems engineering principles to drive process change and sustain it, and use of a formal evaluation process to support a learning community that continuously adapts. This review presents strategies to improve OUD treatment and recovery with a focus on engineering approaches grounded in systems thinking.

Using Health Systems Engineering Approaches to Prepare for Tailoring of Implementation Interventions.

BACKGROUND: Implementation of evidence-based practices often requires tailoring implementation strategies to local contextual factors, including available resources, expertise, and cultural norms. Using an exemplar case, we describe how health systems engineering methods can be used to understand system-level variation that must be accounted for prior to broad implementation. METHODS: Within the context of a single-center quality improvement activity, a multi-disciplinary stakeholder team used health systems engineering methods to describe how pre-endoscopy antithrombotic management was executed, and implemented a redesigned process to improve clinical care. The research team then conducted multiple stakeholder focus groups at four different health-care systems to describe and compare current processes for pre-endoscopy antithrombotic medication management. Detailed work flow maps for each health-care system were developed, analyzed, and integrated to develop an overarching current work flow map, identify key process steps, and describe areas of process variation. RESULTS: Five key process steps were identified across the four health systems: (1) place an endoscopy order, (2) screen for antithrombotic use, (3) coordinate medication management, (4) instruct the patient, and (5) confirm appropriate medication management before procedure. Across health systems, we found a high degree of variation in each step (e.g., who performed, use of technology, systematic vs. ad hoc process). This variation was influenced by two key system-level contextual factors: (1) degree of health system integration and (2) role and training level of available staff. These key steps, areas of variation, and contextual factors were integrated into an assessment tool designed to facilitate tailoring of a future implementation and dissemination strategy. CONCLUSIONS: Tools from health systems engineering can be used to identify key work flow process steps, variations in how those steps are executed, and influential contextual factors. This process and the associated assessment tool may facilitate broader implementation tailoring.

A patient-centered framework for health systems engineering in gastroenterology: improving inpatient colonoscopy bowel preparation.

BACKGROUND: Inpatient colonoscopy bowel preparation (ICBP) is frequently inadequate and can lead to adverse events, delayed or repeated procedures, and negative patient outcomes. Guidelines to overcome the complex factors in this setting are not well established. Our aims were to use health systems engineering principles to comprehensively evaluate the ICBP process, create an ICBP protocol, increase adequate ICBP, and decrease length of stay. Our goal was to provide adaptable tools for other institutions and procedural specialties. METHODS: Patients admitted to our tertiary care academic hospital that underwent inpatient colonoscopy between July 3, 2017 to June 8, 2018 were included. Our multi-disciplinary team created a protocol employing health systems engineering techniques (i.e., process mapping, cause-effect diagrams, and plan-do-study-act cycles). We collected demographic and colonoscopy data. Our outcome measures were adequate preparation and length of stay. We compared pre-intervention (120 ICBP) vs. post-intervention (129 ICBP) outcomes using generalized linear regression models. Our new ICBP protocol included: split-dose 6-L polyethylene glycol-electrolyte solution, a gastroenterology electronic note template, and an education plan for patients, nurses, and physicians. RESULTS: The percent of adequate ICBPs significantly increased with the intervention from 61% pre-intervention to 74% post-intervention (adjusted odds ratio of 1.87, p value = 0.023). The median length of stay decreased by approximately 25%, from 4 days pre-intervention to 3 days post-intervention (p value = 0.11). CONCLUSIONS: By addressing issues at patient, provider, and system levels with health systems engineering principles, we addressed patient safety and quality of care provided by improving rates of adequate ICBP.

Visualization of thermal washout due to spatiotemporally heterogenous perfusion in the application of a model-based control algorithm for MR-HIFU mediated hyperthermia.

PURPOSE: This article will report results from the in-vivo application of a previously published model-predictive control algorithm for MR-HIFU hyperthermia. The purpose of the investigation was to test the controller's in-vivo performance and behavior in the presence of heterogeneous perfusion. MATERIALS AND METHODS: Hyperthermia at 42°C was induced and maintained for up to 30 min in a circular section of a thermometry slice in the biceps femoris of German landrace pigs (n=5) using a commercial MR-HIFU system and a recently developed MPC algorithm. The heating power allocation was correlated with heat sink maps and contrast-enhanced MRI images. The temporal change in perfusion was estimated based on the power required to maintain hyperthermia. RESULTS: The controller performed well throughout the treatments with an absolute average tracking error of 0.27 ± 0.15 °C and an average difference of 1.25 ± 0.22 °C between T10 and T90. The MPC algorithm allocates additional heating power to sub-volumes with elevated heat sink effects, which are colocalized with blood vessels visible on contrast-enhanced MRI. The perfusion appeared to have increased by at least a factor of ∼1.86 on average. CONCLUSIONS: The MPC controller generates temperature distributions with a narrow spectrum of voxel temperatures inside the target ROI despite the presence of spatiotemporally heterogeneous perfusion due to the rapid thermometry feedback available with MR-HIFU and the flexible allocation of heating power. The visualization of spatiotemporally heterogeneous perfusion presents new research opportunities for the investigation of stimulated perfusion in hypoxic tumor regions.

Fiducial markers and their impact on ablation outcome for patients treated with MR-guided transurethral ablation (TULSA): a retrospective technical analysis.

OBJECTIVES: Fiducial markers improve accuracy in external beam radiation therapy (EBRT) for treatment of prostate cancer (PCa). However, many patients recur after EBRT necessitating additional treatment, such as MR-guided transurethral ultrasound ablation (TULSA). Residual markers may compromise TULSA through ultrasound field distortions and generation of local susceptibility artifacts. The objective was to investigate how markers affect the ablation outcome during clinical TULSA treatments. SUBJECTS AND METHODS: A retrospective analysis was performed on nine patients with radiorecurrent PCa and residual markers who received TULSA. The MR susceptibility artifact was quantified as a function of marker type, size and orientation, in particular for thermometry. The spatial distribution of markers inside the prostate was recorded, and the resulting impact on the thermal dose was measured. The thermal dose measurements were directly compared to the residual enhancing prostatic tissue observed on the immediate and control post-TULSA contrast enhanced (CE) image. RESULTS: Successful thermal dose accumulation to the target boundary occurred for 14/20 (70%) of markers, confirmed with CE imaging. Gold markers situated simultaneously close to the urethra (≤12 mm) and far from the target boundary (≥13 mm) reduced the ultrasound depth of heating. Nitinol markers produced large, hypointense artifacts that disrupted thermometry and compromised treatment. Artifacts from gold markers were less pronounced, but when located near the target boundary, also affected treatment. CONCLUSION: Marker composition, orientation and location inside the prostate can all potentially impact treatment outcome. Proper patient selection through detailed MRI screening is critical to ensure successful radiorecurrent PCa treatment outcomes with TULSA.

Vision for a systems architecture to integrate and transform population health.

Entities involved in population health often share a common mission while acting independently of one another and perhaps redundantly. Population health is in everybody's interest, but nobody is really in charge of promoting it. Across governments, corporations, and frontline operations, lack of coordination, lack of resources, and lack of reliable, current information have often impeded the development of situation-awareness models and thus a broad operational integration for population health. These deficiencies may also affect the technical, organizational, policy, and legal arrangements for information sharing, a desired practice of high potential value in population health. In this article, we articulate a vision for a next-generation modeling effort to create a systems architecture for broadly integrating and visualizing strategies for advancing population health. This multipurpose systems architecture would enable different views, alerts, and scenarios to better prepare for and respond to potential degradations in population health. We draw inspiration from systems engineering and visualization tools currently in other uses, including monitoring the state of the economy (market performance), security (classified intelligence), energy (power generation), transportation (global air traffic control), environment (weather monitoring), jobs (labor market dynamics), manufacturing and supply chain (tracking of components, parts, subassemblies, and products), and democratic processes (election analytics). We envision the basic ingredients for a population health systems architecture and its visualization dashboards to eventually support proactive planning and joint action among constituents. We intend our ambitious vision to encourage the work needed for progress that the population deserves.

Continuous Productivity Improvement Using IoE Data for Fault Monitoring: An Automotive Parts Production Line Case Study.

This paper presents a case study of continuous productivity improvement of an automotive parts production line using Internet of Everything (IoE) data for fault monitoring. Continuous productivity improvement denotes an iterative process of analyzing and updating the production line configuration for productivity improvement based on measured data. Analysis for continuous improvement of a production system requires a set of data (machine uptime, downtime, cycle-time) that are not typically monitored by a conventional fault monitoring system. Although productivity improvement is a critical aspect for a manufacturing site, not many production systems are equipped with a dedicated data recording system towards continuous improvement. In this paper, we study the problem of how to derive the dataset required for continuous improvement from the measurement by a conventional fault monitoring system. In particular, we provide a case study of an automotive parts production line. Based on the data measured by the existing fault monitoring system, we model the production system and derive the dataset required for continuous improvement. Our approach provides the expected amount of improvement to operation managers in a numerical manner to help them make a decision on whether they should modify the line configuration or not.