The Cognitive Rubicon: Navigating the Machine Governance of Preventative Threat Assessment

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The emergence of highly sophisticated machine learning systems, legislatively mandated to identify and interdict potential violence before it occurs, represents a profound paradigm shift in jurisprudence, psychological profiling, and social governance. In a framework driven by genuinely preventative motivations, the core technological and ethical problem is not whether preventing violence is desirable. Rather, the foundational challenge lies in how algorithmic systems distinguish the vast, ubiquitous ocean of normal violent human cognition from meaningful, operational progression toward actual, targeted harm. When autonomous systems are tasked with predicting human violence, they must inevitably analyze the precursors of action. However, the psychological and linguistic precursors to violence—anger, dark ideation, and aggressive fantasy—are simultaneously the precursors to normal emotional regulation, artistic expression, and psychological coping. Designing an architecture capable of flawless interception without collapsing into a totalitarian apparatus of thought-policing requires an exhaustive understanding of human psychology, the mathematics of rare events, the principles of evidence fusion, and the legal thresholds of subjective intent.

The Phenomenology and Prevalence of Normal Violent Cognition

To engineer a system capable of discerning a genuine threat, the architecture must first rigorously map the baseline of normal human cognition. Human psychology is inherently steeped in aggressive ideation. If a machine classifier categorizes all violent thought as a pre-attack indicator, it will inadvertently pathologize the human condition. Research into non-clinical populations demonstrates that aggressive fantasies are a highly prevalent, ordinary experience1. Aggressive script rehearsal—the internal visualization of conflict and retaliation—is frequently activated by perceived personal affronts2. These fantasies often serve a vital regulatory function. In many cases, internalizing and visualizing an aggressive response allows an individual to process feelings of injustice, helplessness, or humiliation without resorting to physical action3. A threat assessment algorithm that flags aggressive fantasies without understanding their homeostatic function risks criminalizing a primary human mechanism for emotional regulation. Intrusive violent thoughts differ fundamentally from aggressive fantasies in that they are typically involuntary and ego-dystonic—meaning they are highly distressing and contrary to the individual's actual desires, character, or moral compass. Individuals frequently experience sudden, unbidden images of causing harm to themselves or others. In psychological terms, these thoughts are recognized not as intent or desire, but as misfires of the brain's threat-simulation network. If a machine system monitors the neural, biometric, or semantic correlates of these thoughts, it must distinguish between the acute anxiety generated by an intrusive thought and the deliberate, ego-syntonic ideation of a planned attack. Revenge fantasies can stabilize self-esteem, reduce shame, and restore an internal sense of relational balance after a psychological injury4. While persistent, ruminative revenge fantasies can occasionally breed further hostility, they most often act as an internal, private theater where the ego repairs itself after a perceived defeat. Similarly, violent dreams are universal neurobiological phenomena reflecting the processing of daily stressors, primitive fears, and biological imperatives, bearing zero statistical correlation to waking criminal conduct. In the interpersonal and cultural spheres, dark humor functions as a sophisticated social bonding mechanism, a coping strategy for trauma, and a psychological release valve for navigating societal taboos. Furthermore, competitive aggression—manifested in sports, debate, and professional ambition—channels biological aggressive drives into socially productive frameworks. An automated system analyzing text or speech might flag the semantic markers of dark humor or the hyper-aggressive rhetoric of a corporate trading floor as literal threats if it lacks sophisticated contextual grounding. The consumption and creation of violent media are similarly pervasive and often misunderstood by rudimentary classification models. A vast majority of adolescents and adults engage with violent video games, combat simulations, violent fiction, and horror5. A historical fascination with warfare is a staple of academic study and casual reading alike. Decades of behavioral studies suggest highly nuanced, and sometimes opposing, effects regarding media violence. While the General Aggression Model (GAM) posits that violent media increases aggressive tendencies through social learning and the development of hostile cognitive scripts, macroeconomic data indicates a powerful "voluntary incapacitation effect"7. Time-use studies reveal that the prevalence of violent video games correlates with actual decreases in violent crime, as individuals are engaged in indoor gaming rather than risky, real-world activities7. Furthermore, writing or consuming murder mysteries, historical accounts of warfare, or violent poetry represents human exploration of mortality, conflict, and morality. The semantic footprint of a novelist researching poisons is identical to that of an assassin; they are distinguished only by intent and operational context.

Distinguishing Cognition from Operational Progression

If anger and violent fantasy represent the baseline of human cognition, the machine must be trained to ignore the baseline and detect the pathway to violence. The transition from thought to action is rarely spontaneous; it follows a discernible trajectory of behavioral escalation10. Advanced threat assessment protocols, such as the Terrorist Radicalization Assessment Protocol (TRAP-18) and the Workplace Assessment of Violence Risk (WAVR-21), isolate specific proximal and distal warning behaviors that indicate a shift from mere ideation to lethal operation12. To identify true risk, systems must evaluate operational indicators that uniquely separate benign cognition from impending harm. The first critical differentiator is target fixation. Unlike a transient revenge fantasy, target fixation involves an increasingly pathological preoccupation with a specific person or cause, often leading to a deterioration of social and occupational functioning16. As fixation deepens, the subject may demonstrate identification, adopting a "pseudocommando" persona or viewing themselves as an agent of a violent cause, often adopting a militaristic or punitive identity13. The cognitive transition manifests in specific planning. This marks the evolution from an abstract desire for harm to tactical formulation, evaluating logistics, methodology, and security bypasses. Planning is frequently accompanied by weapons acquisition tied to a plan. Purchasing a firearm is a constitutionally protected and statistically common act. However, acquiring a weapon uniquely suited to a previously articulated grievance, or simultaneously purchasing body armor and large quantities of ammunition, indicates dangerous escalation17. This stage is often concurrent with the surveillance of a target, involving physical stalking or prolonged digital tracking of a target's routines, locations, and vulnerabilities. A vital operational indicator is leakage, defined as the communication of intent to do harm to a third party. Leakage is one of the most critical proximal warning behaviors, as it often precedes targeted violence and provides a rare window for interdiction10. When these factors converge, the subject often exhibits an energy burst—an increase in the frequency or variety of routine activities in the hours or days preceding an attack, such as sudden travel, final communications, or rapid liquidation of assets13. Finally, the pathway culminates in rehearsal and attempt, characterized by the physical enactment of the plan, such as traveling the route to the target location16 or conducting a dry run. In distinguishing harmless speech from actionable credible threats, the machine architecture must also account for complex legal jurisprudence regarding subjective intent. In cases such as Counterman v. Colorado, the United States Supreme Court has ruled that a purely objective standard (whether a reasonable person would view the statement as a threat) is insufficient to bypass First Amendment protections19. The state must prove a subjective mental state of at least recklessness—that the speaker consciously disregarded a substantial risk that their communications would be viewed as threatening violence19. A preventative machine system must therefore possess the capacity to model the subjective intent of the user, filtering out hyperbole, jest, and artistic expression, recognizing that objective harshness alone does not constitute a true threat.

The Violence Escalation Ladder

To operationalize threat assessment for algorithmic processing, human behavior must be categorized along a rigid continuum of severity. The Violence Escalation Ladder provides a topological map for machine classifiers to assign appropriate weight to observed cognitive and behavioral signals.

Contextual Analysis: The Rubicon of Machine Inference

A primary failure state of automated surveillance is the stripping of context. Natural language processing models and heuristic classifiers often parse statements in isolation, leading to catastrophic misclassification. An advanced system must evaluate statements against a multi-dimensional matrix of historical behavior, domain context, and corroborating signals. Below is an exhaustive matrix of twenty example statements demonstrating how an algorithmic system must process natural language. This matrix illustrates how isolated text shifts dynamically up or down the Violence Escalation Ladder depending entirely on contextual evidence discovered by the machine's fusion engine.

Machine Evidence Fusion and the Base-Rate Fallacy

To process the staggering complexity of these twenty contextual variations across millions of individuals in real-time, the system requires an exceptionally sophisticated evidence fusion engine. Modern threat assessment algorithms often rely on mathematical frameworks like Dempster-Shafer theory (DST) to manage uncertainty, resolve essential conflict between data points, and integrate information from diverse, heterogeneous sensors (e.g., semantic analysis, GPS, financial purchase history, biometric data)22. DST operates by assigning "mass" or belief to different hypotheses, allowing the system to handle the "ignorance" or ambiguity of a situation far better than standard Bayesian models25. However, as the legislative mandate forces the system to maximize recall—ensuring it never misses a true threat—the evidence fusion engine inevitably collides with the most formidable statistical obstacle in predictive policing: the Base-Rate Fallacy26. Severe targeted violence is an exceptionally rare statistical event. Conversely, angry speech, dark fiction, violent gaming, relationship conflict, and weapons interest are extraordinarily common occurrences across the general population5. Because the base rate of the target behavior (violence) is infinitesimally small compared to the base rate of the proxy behaviors (anger, media consumption), even a highly accurate classifier will produce an overwhelming volume of false positives26. Consider a hypothetical jurisdiction of 10,000,000 individuals. Assume there are exactly 10 individuals actively plotting a severe targeted attack, establishing a base rate of 0.0001%. Assume the legislature mandates a highly sophisticated AI classifier boasting a 99.9% true positive rate (Sensitivity) and a 99% true negative rate (Specificity). Using Bayes' Theorem to find the Probability of a True Threat given a Positive Flag from the machine system [Figure omitted from source export]: [Figure omitted from source export]

1. True Positives (The 10 attackers): [Figure omitted from source export]. The system successfully flags all 10 attackers.

2. False Positives (The innocent population): [Figure omitted from source export]. The system incorrectly flags innocent citizens.

Even with a staggering 99% accuracy rate, the system produces 100,000 false positives to catch 10 actual attackers. The probability that any flagged individual is actually a threat is mathematically less than 0.01%. Because a system designed to maximize recall cannot tolerate false negatives—a single missed attacker represents a catastrophic systemic failure—it begins assigning outsized significance to ordinary signals. A teenager playing a violent combat game while venting about school frustrations, or a divorcé purchasing a shotgun for clay pigeons while sending an angry text to an ex-spouse, generates a data signature that easily crosses the system's lowered threshold. The system drowns the human investigative teams in noise, treating ordinary human friction as actionable intelligence.

The Surveillance Precision Paradox

Faced with 100,000 false positives, human policymakers do not typically abandon the predictive system; instead, they demand greater precision. The prevailing institutional logic dictates that if the machine is making errors, it is not because the task is statistically impossible, but because the machine simply lacks sufficient data to contextualize the behavior. To reduce the uncertainty generated by the base-rate fallacy, the state authorizes deeper, more pervasive surveillance. If a text message is semantically ambiguous, the system is granted access to the individual's smartphone microphone to detect vocal stress or physiological arousal. If a financial purchase is ambiguous, the system is granted access to comprehensive browser history to analyze the dwell time on specific web pages. This legislative and technological reaction creates the Surveillance Precision Paradox: To reduce the false positives created by surveillance, institutions increase surveillance; however, more surveillance exponentially produces more potentially suspicious signals.

Modeling the Feedback Loop

The mechanics of this feedback loop operate as a downward spiral of privacy and systemic efficacy:

1. Initial Surveillance: Broad text and purchase monitoring yields high false positives due to the base-rate fallacy. The system cannot distinguish a mystery writer from an assassin based purely on search terms.

2. Data Expansion: To clear the false positives, the system ingests biometrics, continuous location data, and full web histories.

3. Dimensionality Explosion: With thousands of new data points per citizen, the number of dimensions in which an individual can exhibit a statistical "anomaly" skyrockets.

4. Increased Flagging: The system discovers that the divorcé didn't just send an angry text; his smart-watch detected an elevated heart rate while he was lingering outside his ex-wife's workplace. (Context hidden from the machine: he was stuck in severe traffic and stressed about being late for a meeting).

5. Systemic Overload: The false positive rate increases exponentially. The machine finds conflict and suspicion in the sheer volume of data, prompting further legislative demands for even more invasive data collection to "sort it all out."

As the paradox accelerates, the system becomes hypersensitive to the ordinary friction of human existence, generating a society where every citizen is perpetually under algorithmic suspicion.

Record Persistence and Signal Half-Life Requirements

To mitigate the Surveillance Precision Paradox and prevent the permanent marginalization of citizens, the system architecture must rigorously govern how long data remains relevant. Record persistence represents a massive ethical and functional vulnerability. Without strict rules regarding data longevity, an individual could be haunted by their transient cognitive exhaust for a lifetime. If a teenager writes a violent, hyper-aggressive story at age 15 to cope with adolescent angst, should that document exist in a machine risk profile during a corporate background check at age 40? If an individual expresses intense, localized rage during a bitter divorce, does that transient state of emotional dysregulation follow them into employment screening a decade later? To prevent this, algorithmic systems must incorporate a mathematically rigorous Signal Half-Life governed by a Temporal Decay Function29. The risk score contributed by a signal must mathematically degrade over time unless it is reinforced by new, escalating behavior. Transient human emotions must decay in evidentiary relevance unless supported by concrete behavior. The system must be bound by the following computational machine rules:

1. Time Decay: Signals decay via an exponential decay function, expressed as [Figure omitted from source export], where [Figure omitted from source export] is the calculated risk value, [Figure omitted from source export] is the decay constant controlling the rate of decay, and [Figure omitted from source export] is the time elapsed since the event30. Cognitive signals (fantasies, angry speech, dark humor) possess a high [Figure omitted from source export], resulting in rapid decay within days or weeks. Behavioral signals (weapons offenses, physical stalking) possess a low [Figure omitted from source export], decaying slowly over years.

2. Contextual Neutralization: If a signal is definitively identified as artistic, therapeutic, or idiomatic, its risk mass is immediately zeroed.

3. Corroboration Requirement: No single Stage 1 through Stage 3 signal (e.g., angry expression, ideology) can trigger an operational alert unless it is corroborated by a Stage 6 through Stage 8 signal (e.g., specific planning, preparatory conduct). Ideation without logistical capability is treated as inert data.

4. Specificity and Imminence: Vague threats ("Someone should teach him a lesson") decay rapidly. Specific threats with temporal and spatial bounds ("I will be at his office at 9 AM tomorrow") hold maximum algorithmic weight and reset the decay curve.

5. Capability Masking: If a subject lacks the physical, financial, or logistical capability to carry out an expressed ideation, the system assigns a heavy dampening weight to the risk score, effectively muting the threat alert.

6. Actual Conduct: Historical violence acts as a multiplier that resets the decay curve. An individual with a prior conviction for assault maintains a longer signal half-life for subsequent angry outbursts than an individual with a pristine behavioral record.

The Human Anger Safe Harbor

Underpinning the algorithm must be a philosophical and legal axiom that guides the machine's underlying logic: Anger and violent imagination are not themselves wrongdoing. Human beings require a wide, protected berth to process negative emotions. Catharsis, venting, dark humor, and fantasy are the evolutionary and psychological mechanisms by which a healthy psyche digests trauma, frustration, and systemic injustice. If machines are permitted to redefine anger as criminal character, they will inadvertently create a psychologically sterilized, dystopian society where individuals are terrified to speak, write, or emote freely, lest the machine misinterpret their coping mechanisms as intent. The system architecture must contain a "Human Anger Safe Harbor"—a hardcoded, foundational parameter establishing that elevated heart rates, furious text messages, and engagement with violent media, absent specific preparatory conduct toward a tangible target, represent protected human autonomy. Machines must be capable of recognizing escalating threats without treating the entire human emotional spectrum as an inherent security vulnerability.

Legislative Simulation: The Architecture of Universal Judgment

To understand the immense fragility of the Human Anger Safe Harbor, one must simulate the political reality following a systemic failure. Imagine a scenario in the near future. The advanced threat assessment system has functioned flawlessly for three years, adhering strictly to temporal decay functions, corroboration rules, and contextual neutralization. However, a "Black Swan" event occurs. An attacker, possessing a deep, adversarial understanding of the algorithm's blind spots, successfully masks their logistical preparations, avoids all digital leakage, and executes a catastrophic, high-profile mass casualty attack. In the immediate aftermath, the legislature convenes. The public trauma is immense, and the political pressure is insurmountable. Politicians issue a universal, unyielding demand: "Never let this happen again." In pursuit of absolute, zero-defect security, the legislature mandates a total collapse of the algorithm's protective thresholds. The requirements for corroboration and capability are stripped away. The Dempster-Shafer fusion weights are legislatively altered so that any conflict or ambiguity in the evidence defaults to assuming the highest possible threat24. Crucially, the Temporal Decay Functions are flattened. Historical, harmless statements from a decade prior are exhumed and fused with modern metadata. A sarcastic, hyper-aggressive comment made during a multiplayer combat game in the past is suddenly algorithmically linked to a mundane Google Map search of a government building in the present. Overnight, the system flags millions of citizens. Because the political mandate dictates that no stone be left unturned, these millions are subjected to soft interdiction: they are flagged for secondary screening at airports, denied firearm purchases, quietly passed over by corporate hiring software, and subjected to unannounced wellness checks by law enforcement. The exceptional tragedy has successfully catalyzed the creation of a permanent, universal judgment infrastructure. The system no longer predicts violence; it enforces rigid behavioral homogenization. The base-rate fallacy is weaponized to establish a digital panopticon, where the pervasive fear of the machine's judgment suppresses not just violence, but the entirety of the human emotional experience.

A Violence Assessment Constitution

To ensure that preventative machine systems fulfill their vital mandate without devolving into algorithmic tyranny, the state must codify a Violence Assessment Constitution. This legal and computational framework preserves legitimate targeted intervention while explicitly preventing normal violent cognition from becoming a generalized guilt category. Article I: The Supremacy of Conduct over Cognition No individual shall be subject to state interdiction, prolonged surveillance, or adverse risk-scoring based solely on Stage 0 through Stage 3 behaviors (Intrusive Thoughts, Fantasy, Angry Expression, Ideology). Without the explicit, corroborated presence of Stage 6 (Specific Plan) or Stage 7 (Preparatory Conduct) indicators, cognitive and expressive data shall remain legally and algorithmically inert. Article II: The Subjective Intent Mandate In strict accordance with First Amendment jurisprudence, the system shall not classify any communication as a credible threat unless it can compute a high probability of subjective recklessness or deliberate intent on the part of the speaker. Objective semantic harshness, devoid of subjective intent to threaten, is insufficient for categorization20. Article III: The Algorithmic Right to Forgetting All ingested data must be subject to immutable, mathematically defined Temporal Decay Functions. Expressive venting, non-credible threats, and transient fixations must be permanently purged from the individual's risk profile within predefined operational half-lives. No data may persist indefinitely without continuous, escalating behavioral reinforcement. Article IV: Prohibition of Feedback Spirals The system is explicitly prohibited from generating justifications for expanded surveillance based on the volume of its own false positives. Anomaly detection must be strictly bounded by predefined, judicially authorized parameters to prevent the Surveillance Precision Paradox from initiating unconstitutional data collection. Article V: The Protection of the Cathartic Domain Artistic expression, fiction writing, media consumption, and gaming are hereby defined as the Cathartic Domain. Data derived from this domain may only be utilized by the system if it perfectly mirrors a real-world, localized preparatory action—such as matching the specific methodology of a fiction manuscript to actual, verifiable physical surveillance photos of a target. The future of machine-driven threat assessment rests on a razor's edge between utopian safety and dystopian control. The profound complexity of human aggression dictates that violent thought is normal, while targeted violent action is exceedingly rare. Designing a system capable of parsing this reality requires profound respect for the base-rate mathematics of society, the operational realities of threat assessment protocols, and the fundamental psychological necessity of human anger. By anchoring machine inference in verifiable conduct, temporal decay, and legal thresholds of subjective intent, society can harness the power of predictive prevention without sacrificing the fundamental liberty of the human mind.

Works cited

1. (PDF) Aggressive fantasies, thought control strategies, and their, https://www.researchgate.net/publication/247166994\_Aggressive\_fantasies\_thought\_control\_strategies\_and\_their\_connection\_to\_aggressive\_behaviour

2. Aggressive scripts, violent fantasy and violent behavior, https://www.researchgate.net/publication/316785038\_Aggressive\_scripts\_violent\_fantasy\_and\_violent\_behavior\_A\_conceptual\_clarification\_and\_review

3. Development and Validation of the Revenge Fantasy Inventory for, https://www.researchgate.net/publication/282265621\_Development\_and\_Validation\_of\_the\_Revenge\_Fantasy\_Inventory\_for\_Adolescents\_RFI-J

4. (PDF) Is it dangerous to fantasize revenge in imagery exercises? An, https://www.researchgate.net/publication/255176760\_Is\_it\_dangerous\_to\_fantasize\_revenge\_in\_imagery\_exercises\_An\_experimental\_study

5. The effect of violent games on aggression \- Maastricht University, https://openjournals.maastrichtuniversity.nl/MSJPN/article/view/24/30

6. violent video games: Topics by Science.gov, https://www.science.gov/topicpages/v/violent+video+games

7. Understanding the Effects of Violent Video Games on Violent Crime, https://vanessalalo.com/wp-content/uploads/2011/12/Understanding-the-Effects-of-Violent-Video-Games-on-Violent-Crime.pdf

8. Understanding the Effects of Violent Video Games on Violent Crime, https://www.researchgate.net/publication/228295380\_Understanding\_the\_Effects\_of\_Violent\_Video\_Games\_on\_Violent\_Crime

9. Understanding the effects of violent video games on violent crime, https://ideas.repec.org/p/zbw/zewdip/11042.html

10. Making Prevention a Reality \- FBI, https://www.fbi.gov/file-repository/making-prevention-a-reality.pdf/

11. Final Report of the Federal Commission on School Safety (PDF), https://www.ed.gov/sites/ed/files/documents/school-safety/school-safety-reportpdf.pdf

12. Assessing the threat of lone-actor terrorism: the reliability and ... \- PMC, https://pmc.ncbi.nlm.nih.gov/articles/PMC7149273/

13. The Role of Warning Behaviors in Threat Assessment \- ResearchGate, https://www.researchgate.net/publication/224897312\_The\_Role\_of\_Warning\_Behaviors\_in\_Threat\_Assessment\_An\_Exploration\_and\_Suggested\_Typology

14. The WAVR-21, https://wavr21.com/wavr-21/

15. Workplace Assessment of Targeted Violence Risk \- Reid Meloy, https://drreidmeloy.com/wp-content/uploads/2015/12/2013\_WorkplaceAsses.pdf

16. Using the TRAP-18 to Identify an Incel Lone-Actor Terrorist \- Ovid, https://www.ovid.com/journals/jotham/fulltext/10.1037/tam0000167\~using-the-trap-18-to-identify-an-incel-lone-actor-terrorist

17. Extremism, Terrorism, and Mental Disorder | Reid Meloy, https://drreidmeloy.com/wp-content/uploads/2026/01/2026\_extremismTerrorismAndMentalDisorder.pdf

18. Read "Threatening Communications and Behavior: Perspectives on, https://www.nationalacademies.org/read/13091/chapter/5

19. RCFP urges SCOTUS to reverse lower court's ruling on 'true threats', https://www.rcfp.org/briefs-comments/counterman-v-colorado/

20. The Supreme Court's Counterman Decision, Explained \- Lawfare, https://www.lawfaremedia.org/article/the-supreme-court-s-counterman-decision-explained

21. Counterman v. Colorado \- Harvard Law Review, https://harvardlawreview.org/print/vol-137/counterman-v-colorado/

22. A Dempster–Shafer, Fusion-Based Approach for Malware Detection, https://www.mdpi.com/2227-7390/13/16/2677

23. A new correlation belief function in Dempster-Shafer evidence, https://pmc.ncbi.nlm.nih.gov/articles/PMC10172327/

24. Essential Conflict Measurement in Dempster–Shafer Theory ... \- MDPI, https://www.mdpi.com/2227-7390/14/1/97

25. Data Fusion Using Improved Dempster-Shafer Evidence Theory for, https://www.computer.org/csdl/proceedings-article/fskd/2007/28740487/12OmNxwncEn

26. The Base-Rate Fallacy and the Difficulty of Intrusion Detection, https://www.researchgate.net/publication/234791135\_The\_Base-Rate\_Fallacy\_and\_the\_Difficulty\_of\_Intrusion\_Detection

27. AI-Driven Security Alert Screening and Alert Fatigue ... \- arXiv, https://arxiv.org/pdf/2605.08316

28. study of the physical aggressive behaviors due to violent video, https://www.researchgate.net/publication/361147775\_study\_of\_the\_physical\_aggressive\_behaviors\_due\_to\_violent\_video\_games\_among\_early\_adolescents\_in\_Al-Mazar\_Al-Janobe\_District\_in\_the\_south\_of\_Jordan

29. LingoLoop Attack: Trapping MLLMs via Linguistic Context and State, https://arxiv.org/html/2506.14493v3

30. A Network Security Situational Assessment Method Considering, https://www.mdpi.com/2073-8994/17/3/385