Dynamic Matching Research Articles

Compensation guarantees in crowdsourced delivery: Impact on platform and driver welfare

Crowdsourced delivery and other sharing economy platforms attract freelance workers by offering them flexibility in scheduling their own work hours. Those platforms, however, have been criticized for the lack of protection they offer workers. Since workers are treated as independent contractors, they do not receive minimum wage and other protection measures under labor law. In this paper, we examine the integration of driver compensation guarantees in a platform’s dynamic matching decisions. We study the problem of designing dynamic matching policies that guarantee a particular level of compensation for active workers over a time period, while maintaining work hour flexibility. We model three types of policies, that are either wage-based or utilization-based. We propose an MDP model to capture the dynamic and stochastic nature of the problem, then design a value function approximation algorithm to efficiently solve the large-scale MDP model. Extensive computational testing is conducted to assess the performance of the proposed solution methodology and the compensation guarantees, using synthetic and real-world datasets. Our findings suggest that the utilization policy results in the highest earning for drivers, though at the expense of longer empty miles from drivers’ origins to the pickup locations of matched orders. On the other hand, the effective wage policy leads to shorter average distance to pickup, but slightly lower earning to drivers. Both policies result in only a slight decrease in platform profit as compared to the base case, and exhibit lower dispersion in the distribution of driver earning while active. In contrast, the nominal wage policy shows a comparable trend to the base-case policy in terms of average driver earnings, suggesting minimal benefits for drivers.

Analysis of trans‐domain motion process of bullet‐shaped trans‐domain amphibious vehicle

AbstractA fully functional prototype of a bullet‐shaped trans‐domain amphibious vehicle with the ability to perform reciprocating cross‐medium movements has been developed. Once the bullet‐shaped underwater vehicle completes its designated underwater tasks, upon reaching a suitable location for surface observation, it will transform into a bullet‐shaped coaxial counter‐propeller aircraft to carry out cross‐domain out‐of‐water and performing aerial flight missions. The successful transition of the vehicle into amphibious mode is attributed to the design of an innovative energy attitude multiplexing mechanism. Simultaneously, a rational and effective hybrid‐switching strategy has been proposed to enhance the reliability of the vehicle's amphibious mode transition. In view of the complex and changeable cross‐domain environment, we have adopted a modular design concept, breaking down the entire cross‐domain movement process into three key phases: air operation flight, transient cross‐domain, and underwater active sailing. Detailed theoretical analysis, algorithm design, and experimental research have been conducted for each phase. This paper proposes and expounds the key design principles, evaluates the performance indicators of key components, and weighs the matching of body dynamics and propulsion performance to ensure that the vehicle achieves the expected performance. Finally, the success of the test at different phases further proves that the vehicle has the ability to achieve complete cross‐domain operation.

Deterministic Dynamic Matching in Worst-Case Update Time

We present deterministic algorithms for maintaining a (3/2 + epsilon ) and (2 + epsilon )-approximate maximum matching in a fully dynamic graph with worst-case update times {hat{O}}(sqrt{n}) and {tilde{O}}(1) respectively. The fastest known deterministic worst-case update time algorithms for achieving approximation ratio (2 - delta ) (for any delta > 0) and (2 + epsilon ) were both shown by Roghani et al. (Beating the folklore algorithm for dynamic matching, 2021) with update times O(n^{3/4}) and O_epsilon (sqrt{n}) respectively. We close the gap between worst-case and amortized algorithms for the two approximation ratios as the best deterministic amortized update times for the problem are O_epsilon (sqrt{n}) and {tilde{O}}(1) which were shown in Bernstein and Stein (in: Proceedings of the twenty-seventh annual ACM-SIAM symposium on discrete algorithms, 2016) and Bhattacharya and Kiss (in: 48th international colloquium on automata, languages, and programming, ICALP 2021, 12–16 July, Glasgow, 2021) respectively. The algorithm achieving (3/2 + epsilon ) approximation builds on the EDCS concept introduced by the influential paper of Bernstein and Stein (in: International colloquium on automata, languages, and programming, Springer, Berlin, 2015). Say that H is a (alpha , delta )-approximate matching sparsifier if at all times H satisfies that mu (H) cdot alpha + delta cdot n ge mu (G) (define (alpha , delta )-approximation similarly for matchings). We show how to maintain a locally damaged version of the EDCS which is a (3/2 + epsilon , delta )-approximate matching sparsifier. We further show how to reduce the maintenance of an alpha -approximate maximum matching to the maintenance of an (alpha , delta )-approximate maximum matching building based on an observation of Assadi et al. (in: Proceedings of the twenty-seventh annual (ACM-SIAM) symposium on discrete algorithms, (SODA) 2016, Arlington, VA, USA, January 10–12, 2016). Our reduction requires an update time blow-up of {hat{O}}(1) or {tilde{O}}(1) and is deterministic or randomized against an adaptive adversary respectively. To achieve (2 + epsilon )-approximation we improve on the update time guarantee of an algorithm of Bhattacharya and Kiss (in: 48th International colloquium on automata, languages, and programming, ICALP 2021, 12–16 July, Glasgow, 2021). In order to achieve both results we explicitly state a method implicitly used in Nanongkai and Saranurak (in: Proceedings of the twenty-seventh annual ACM symposium on theory of computing, 2017) and Bernstein et al. (Fully-dynamic graph sparsifiers against an adaptive adversary, 2020) which allows to transform dynamic algorithms capable of processing the input in batches to a dynamic algorithms with worst-case update time.

A 13-Level SC DAC Achieving High Linearity With a Simple DEM for Wideband CT DSMs

Wideband continuous-time (CT) Delta-Sigma modulators (DSMs) generally demand multi-level digital-to-analog converters (DACs), which require a linearization technique to overcome the problem of component mismatches. Dynamic element matching (DEM) is a popular DAC linearization approach but can be challenging to implement in a wideband DSM, particularly when there are many DAC elements. In this manuscript, we introduce a 13-level switched-capacitor (SC) DAC achieving high linearity with a simple DEM that rotates three unit elements only and does not incur excess loop delay. In a traditional CT DSM, a DAC of the SC type increases the amplifier settling requirements and damages the alias rejection. Earlier works have shown that these problems can be solved if the CT DSM adopts a passive RC frontend. A 10-MHz bandwidth CT DSM is designed in the 65-nm CMOS technology to validate the proposed DAC. Simulations show that the DAC enables a spurious-free dynamic range greater than 87.7 dB and a signal-to-noise pulse distortion ratio greater than 80.5 dB under a 1% capacitor mismatch, a 2% reference voltage error, and process variations.

Dynamical matching in a three-dimensional Caldera potential-energy surface.

In a previous paper, we used a recent extension of the periodic-orbit dividing surfaces method to distinguish the reactive and nonreactive parts in a three-dimensional (3D) Caldera potential-energy surface. Furthermore, we detected the phenomenon of dynamical matching in a 3D Caldera potential-energy surface. This happened for a specific value of the radius r of the periodic orbit dividing surfaces (r=0.25). In this paper, we demonstrated that the chemical ratios of the number of reactive and nonreactive trajectories to the total number of trajectories converges for a range of the radius r of the periodic-orbit dividing surfaces. This is important not only for validating the previous paper and to confirm that the method can detect the phenomenon of dynamical matching independently of the chosen radius of the construction of the dividing surface but also for investigating the application of the method to other Hamiltonian models.

Dynamic Matching and Spatial Optimization of Land Use and Resource-Environment Constraints in Typical Regions of the Yellow River Basin in China

Accurately identifying the matching relationships between territorial space evolution and the resources and environment carrying capacity will directly guide the sustainable use of territorial space. Based on the evaluation of the territorial space dynamics of the lower Yellow River, this paper evaluates the suitability of territorial space development by focusing on ecological protection, agricultural development, and urban construction. Specifically, the resources and environment carrying capacity is estimated by identifying and mediating potential conflicts in the development of territorial space. The matching relationship between the evolution of territorial space and the resources and environment carrying capacity is identified using the matching degree model. The results demonstrated that: (1) Between 2000 and 2020, the agricultural space of the lower Yellow River was relatively stable, while the ecological space was generally shrinking, and the urban space continued to increase; (2) The characteristics of suitability for the agricultural development and urban construction of the lower Yellow River are characterized by landform and land-sea differentiation. The carrying scale of resources and the environment is based on agricultural space and is increasing yearly, followed by ecological space, which is gradually decreasing, and urban space, which first increased and then decreased; (3) Between 2000 and 2020, the matching index of the ecological and agricultural space evolution and the resource and environmental carrying capacity in the lower Yellow River exhibited a downward trend, while the regional difference increased. Furthermore, the matching index of urban space and the resources and environment carrying capacity indicated an upward trend, while the regional difference decreased.

Enhancing prestack seismic data by sparse radon transform and dynamic waveform matching

Abstract In the field of complex underground geological structures and irregular topography, prestack seismic data often have a low signal-to-noise ratio (SNR), in which weakly reflected signals are buried beneath strong incoherent, and scattered noise. Stacking, such as beamforming along the moveout surfaces of coherent local events, can significantly improve seismic data quality. Accurate and efficient estimation of the moveout for an irregular acquisition geometry and uneven illumination is important in a complex environment. In this paper, a new optimal stacking approach for enhancing weak prestack reflection signals is presented. The proposed method mainly includes regional division and moveout estimation. Optimal stacking should be implemented within local time and space domains. Based on beam-ray theory, we designed a reasonable regional division of the common-shot (CS), common-receiver (CR) and common-middle-point (CMP) domains. Then, we proposed using the sparse radon transform and dynamic waveform matching method to estimate the moveout surfaces of local reflection events. The sparse radon transform was applied to obtain the linear moveout to ensure the correctness of the reflection wave direction. The residual nonlinear disturbance was estimated using the dynamic waveform matching method. Tests on synthetic and field data demonstrated the effectiveness of the proposed method, which can effectively improve the SNR of prestack seismic data and attenuate incoherent noise.

Design of High Precision Temperature Sensor with Current Gain Compensation Technology for On-Chip Application

The integration of on-chip temperature sensors within various systems, industrial Internet of Things (IoT), and wireless sensor networks is greatly facilitated by their small size, cost-effectiveness, and capability to provide direct digital output. However, the diverse application scenarios pose challenges in designing these sensors. On one hand, real-time clock calibration demands high-precision temperature sensors, while on-chip heat management emphasizes compactness and low-voltage operation. Additionally, streamlining the calibration cost for mass production holds significant practical value. Addressing these challenges, this study systematically investigates on-chip complementary metal-oxide-semiconductor (CMOS) temperature sensors based on distinct signal domains processed by temperature readout circuits. Specifically, the research commences by analyzing the issues of several degeneracy points in the front-end circuit of a bipolar junction transistor (BJT) temperature sensor with current gain compensation technology. To address the intricate design challenges in advanced technologies and calibration complexities in industrial applications, dynamic component matching, current gain compensation, and chopper stabilization are harnessed. A novel dynamic current gain canceling technique for temperature readout is introduced, enhancing temperature measurement accuracy without incurring additional power consumption or area overhead. Ultimately, an all-digital CMOS temperature sensor is realized using the SMIC 55 nm CMOS process. Occupying a mere 0.29 mm2 of core area, the design operates efficiently across a wide supply voltage range of 1.2 V to 3.6 V. Covering a temperature spectrum from −40 °C to 125 °C, the sensor demonstrates a calibration error of just ±0.7 °C. This achievement is attributed to the incorporation of the proposed dynamic current gain compensation technique.

Dynamic Matching: Characterizing and Achieving Constant Regret

We study how to optimally match agents in a dynamic matching market with heterogeneous match cardinalities and values. A network topology determines the feasible matches in the market. In general, a fundamental tradeoff exists between short-term value—which calls for performing matches frequently—and long-term value—which calls, sometimes, for delaying match decisions in order to perform better matches. We find that in networks that satisfy a general position condition, the tension between short- and long-term value is limited, and a simple periodic clearing policy (nearly) maximizes the total match value simultaneously at all times. Central to our results is the general position gap ϵ; a proxy for capacity slack in the market. With the exception of trivial cases, no policy can achieve an all-time regret that is smaller, in terms of order, than [Formula: see text]. We achieve this lower bound with a policy, which periodically resolves a natural matching integer linear program, provided that the delay between resolving periods is of the order of [Formula: see text]. Examples illustrate the necessity of some delay to alleviate the tension between short- and long-term value. This paper was accepted by David Simchi-Levi, revenue management and market analytics. Funding: This work was supported by the National Science Foundation [Grant CMM-2010940] and the U.S. Department of Defense [Grant STTR A18B-T007].

Star map matching method for optical circular rotation imaging based on graph neural networks.

This paper focuses on a dynamic star image acquisition and matching method for space situational awareness, which can quickly search for widely distributed resident space objects. First, the optical circular rotation imaging method performed by a single space camera is proposed to obtain a series of star images. And then, the image matching method based on graph neural networks is proposed for generating a wide observation star image. Experiment results show that compared with baseline matching algorithms, the matching accuracy and matching precision of the proposed algorithm are improved significantly.

A 4.21-μV Offset Voltage and 42-nV/√Hz Input Noise Chopper Operational Amplifier with Dynamic Element Matching

This paper presents a high-efficiency operational amplifier (op-amp) used in the readout circuit of micro-sensor. The chopping and dynamic element matching (DEM) techniques are used in the proposed design, which greatly reduce the offset voltage and flicker noise [Formula: see text] noise) of the op-amp. By optimizing the circuit structure, the maximum chopping frequency is increased to 2[Formula: see text]MHz when the offset voltage is less than [Formula: see text][Formula: see text]V. Therefore, the maximum bandwidth of signal processing can be up to 1MHz. The proposed circuit is designed and fabricated using TSMC 0.18-[Formula: see text]m 1P5M CMOS technology. It occupies an area of 0.16[Formula: see text]mm2 and consumes [Formula: see text]A from a 1.8-V supply. When the chopping frequency is 100[Formula: see text]kHz, the input-referred offset voltage is 4.21[Formula: see text][Formula: see text]V and the input-referred noise is 42[Formula: see text]nV[Formula: see text]Hz. It achieves a noise efficiency factor of 7.82 and a power efficiency factor of 110.07.

Special Connections for Representing Multiscale Heterogeneities in Reservoir Simulation

Summary The significant quantities of oil contained in fractured karst reservoirs in Brazilian presalt fields add new challenges to the development of upscaling procedures to reduce time on numerical simulations. This work aims to represent multiscale heterogeneities in reservoir simulators based on special connections between matrix, karst, and fracture mediums, both modeled in different grid domains within a single porosity flow model. The objective of this representation is to strike a good balance between accuracy and simulation time. Therefore, this work extends the approach of special connections developed by Correia et al. (2019) to integrate both karst and fracture mediums modeled in different grid domains and block scales. The transmissibility calculation between the three domains is a combination of the conventional formulation based on two-point flux approximation schemes and the matrix-fracture fluid transfer formulation. The flow inside each domain is governed by Darcy’s equation and implicitly solved by the simulator. For proper validation and numerical verification, we applied the methodology to a simple case (two-phase and three-phase flow) and a real case (two-phase flow). For the simple case, the reference model is a refined grid model with (1) an arrangement of large conduits (karsts), which are poorly connected; (2) a well-connected and orthogonal system of fractures; and (3) a background medium (matrix). The real case is a section of a Brazilian presalt field, characterized as a naturally fractured carbonate reservoir. The reference is the geological model. The simulation model consists of a structural model with different gridblock sizes according to the scale of the heterogeneities—small-scale karst geometries, medium-scale matrix properties, and larger-scale fracture features—interconnected by special connections. The results for both cases show a significant performance improvement regarding a dynamic matching response with the reference model, within a suitable simulation time and maintaining the dynamic resolution according to the representative elementary volume of heterogeneities, without using an unstructured grid. In comparison to the reference model, for the simple case and the real case, the simulation time was reduced by 42% and 87%, respectively. The proposed method contributes to a multiscale flow simulation solution to manage heterogeneous geological scenarios using structured grids while preserving the high resolution of small-scale heterogeneities and providing a good relationship between accuracy and simulation time.

Data mining based network intrusion detection method in the environment of IoT

AbstractWith the rapid development of Internet of Things (IoT) and Internet technology, the network is becoming more and more important and strict. The IoT networks that access to a large number of devices make network intrusion more convenient. However, because the traditional network intrusion detection methods have the problems of low detection rate and high false alarm rate, this paper proposes a network intrusion detection method based on data mining. Firstly, a pre‐processing model is established to process the initial data. Then, according to the characteristics of network behavior, a new feature subset selection process is designed. Based on the above data mining and analysis process, a dynamic detection generation rule matching with data mining is set to detect abnormal network intrusion traffic. Then, the network intrusion detection based on data mining is completed. The experimental results show that, compared with other methods, the network intrusion detection method based on data mining has higher network intrusion detection rate and lower false alarm rate, which is helpful to improve the detection accuracy.

An assessment of microvascular hemodynamics in human macula

An adequate blood supply to meet the energy demands is essential for any tissue, particularly for high energy demand tissues such as the retina. A critical question is: How is the dynamic match between neuronal demands and blood supply achieved? We present a quantitative assessment of temporal and spatial variations in perfusion in the macular capillary network in 10 healthy human subjects using a non-invasive and label-free imaging technique. The assessment is based on the calculation of the coefficient of variation (CoV) of the perfusion signal from arterioles, venules and capillaries from a sequence of optical coherence tomography angiography images centred on the fovea. Significant heterogeneity of the spatial and temporal variation was found within arterioles, venules and capillary networks. The CoV values of the capillaries and smallest vessels were significantly higher than that in the larger vessels. Our results demonstrate the presence of significant heterogeneity of spatial and temporal variation within each element of the macular microvasculature, particularly in the capillaries and finer vessels. Our findings suggest that the dynamic match between neuronal demands and blood supply is achieved by frequent alteration of local blood flow evidenced by capillary perfusion variations both spatially and temporally in the macular region.

Flexible Resource Management in High-Throughput Satellite Communication Systems: A Two-Stage Machine Learning Framework

With digitization and globalization in the era of 5G and beyond, research on high-throughput satellites (HTS) to increase communication capacity and improve flexibility is becoming essential. To achieve efficient resource utilization and dynamic traffic demand matching, the multi-dimensional resource management (MDRM) problem of the HTS communication system has been studied in this paper. Since the MDRM problem is a non-convex mixed integer problem, we decompose it into two tractable sub-problems. First, the beam-domain resource configuration problem is formed to enable on-demand coverage. Next, the user-domain resource allocation problem is modeled to enable on-demand communication. Considering the two-domain optimization problem, a two-stage framework is developed based on the combination of self-supervised learning and deep reinforcement learning. Specifically, in the first stage, a maximum co-channel interference based self-supervised learning method is proposed to perform traffic demand matching through demand awareness. In the second stage, a soft frequency reuse based proximal policy optimization approach is presented to further increase the system capacity through interference coordination. The simulation results demonstrate that our proposed two-stage algorithm outperforms the benchmark schemes in terms of spectrum efficiency and demand satisfaction.

Capability matching between suppliers and customers in solution co-creation: a process-based model

PurposeThe purpose of this study is to examine supplier–customer capabilities in solution co-creation and how they are matched from a relational process perspective.Design/methodology/approachUsing a qualitative approach, the authors identified 20 sets of supplier–customer capability matches by conducting in-depth interviews with 34 matched informants and retrieving suppliers’ archival data (project documents and success stories).FindingsThe authors identified 20 capability matching sets (21 supplier and 23 customer capabilities) and developed a process-based model of bilateral capabilities that match at the organizational level in solution co-creation. The authors reveal their match forms (complementarity and compatibility) and offer suggestions for future research.Research limitations/implicationsThis paper is qualitative; quantitative studies are required for testing and extending the initial conclusions.Practical implicationsThis study guides the supplier and customer to cultivate different capabilities at different stages of solution co-creation and alerts them to the importance of capability complementarity and compatibility.Originality/valueTo the best of the authors’ knowledge, this study is the first to introduce the bilateral perspective into dynamic capability research in the context of solution co-creation. The authors discuss the abilities the supplier and customer must possess at different stages and how they match dynamically. The analysis extends the research on solution-specific capabilities and dynamic matching, offering useful implications for solution co-creation in practice.

Full waveform inversion based on dynamic data matching of convolutional wavefields

Cycle skipping problem caused by the absent of low frequencies and inaccurate initial model makes full waveform inversion (FWI) deviate from the true model. A novel method is proposed to mitigate cycle skipping phenomenon by dynamic data matching which improves the matching of synthetic and observed events to regulate the updating of initial model in a correct direction. 1-dimentional (1-D) Gaussian convolutional kernels with different lengths are used to extract features of each time sample in each trace which represents the integrated properties of wavefield at different time ranges centered on each time sample. According to the minimum Euclidean distance of the features, the optimally matched pairs of time samples in the observed and synthetic trace can be found. A constraint evaluates the reliability of dynamic matching by attenuating the amplitude of synthetic data according to the values of traveltime differences between each pairs of optimally matched time samples is proposed to improve the accuracy of data matching. In addition, Gaussian kernels have the capability to extract features of time samples contaminated by strong noises accurately to improve the robustness of the propose method further. The selection scheme of optimal parameters is discussed and concluded to ensure the convergence of the proposed method. Numerical tests on Marmousi model verify the feasibility of the propose method. The proposed method provides a new approach to tackle the convergence problem of FWI when using the field seismic data.

The Vibration Response of the Helicopter Coupling with Aircraft Gun

The vibration response characteristics of the helicopter due to the launching load of the aircraft gun has been studied. Based on the finite element method, a fully coupled dynamics analysis model by considering the helicopter and aircraft gun was constructed. The effect of the aircraft gun launching load on the vibration response of the helicopter was analyzed with the different firing angles of the gun. The results show that the front-back and up-down vibration of the airframe is greatly affected by the elevating firing angles, and the left-right vibration displacement is greatly affected by the azimuth firing angle. The front-back and up-down vibration of the helicopter decreases with the increase of the azimuth firing angle, and the left-right vibration displacement increases with the azimuth firing angle. When the elevating firing angles are at the two limit firing angles of +10 degree and -60 degree, the vibration direction at the same position of the airframe is opposite. The results are useful for the control of airframe vibration and the dynamic matching between the airframe and the gun.

Force/moment tracking performance during constant-pose, force-varying, bilaterally symmetric, hand-wrist tasks.

Bilateral movement is widely used for calibration of myoelectric prosthesis controllers, and is also relevant as rehabilitation therapy for patients with motor impairment and for athletic training. Target tracking and/or force matching tasks can be used to elicit such bilateral movement. Limited descriptive accuracy data exist in able-bodied subjects for bilateral target tracking or dominant vs non-dominant dynamic force matching tasks requiring more than one degree of freedom (DoF). We examined dynamic trajectory (0.75Hz band-limited, white, uniform random) constant-posture, hand open-close, wrist pronation-supination target tracking and matching tasks. Tasks were normalized to maximum voluntary contraction (MVC), spanning a±30% MVC force range, in four 1-DoF and 2-DoF tasks: (1, 2) unilateral dominant limb tracking with/without visual feedback, and (3, 4) bilateral dominant/non-dominant limb tracking with mirror visual feedback. In 12 able-bodied subjects, unilateral tracking error with visual feedback averaged 10-15%MVC, but up to 30%MVC without visual feedback. Bilateral matching error averaged∼10%MVC and was affected little by visual feedback type, so long as feedback was provided. In 1-DoF bilateral tracking, the dominant side had statistically lower error than the non-dominant side. In 2-DoF bilateral tracking, the side providing mirror visual feedback exhibited lower error than the opposite side. In 2-DoF tasks (assumed to be more challenging than their constituent 1-DoF tracking tasks), hand grip force errors grew disproportionately larger than those of each wrist DoF. In unilateral 1-DoF tasks, both hand vs target and wrist vs target latency averaged 250-350ms. In unilateral 2-DoF tasks, wrist vs target latency also averaged 250-350ms, while hand vs target latency averaged>500ms. These results provide guidance on bilateral 2-DoF hand-wrist performance in target tracking, and dominant vs non-dominant force matching tasks.

Direct Dynamics Trajectories Demonstrate Dynamic Matching and Nonstatistical Radical Pair Intermediates during Fe-Oxo-Mediated C–H Functionalization Reactions

The generally proposed mechanism for the reaction between non-heme Fe-oxo complexes and alkane C-H bonds involves a hydrogen atom transfer (HAT) reaction step with a radical pair intermediate that then has competitive radical rebound, dissociation, or desaturation pathways. Here, we report density functional theory-based quasiclassical direct dynamics trajectories that examine post-HAT reaction dynamics. Trajectories revealed that the radical pair intermediate can be a nonstatistical type intermediate without complete internal vibrational redistribution and post-HAT selectivity is generally determined by dynamic effects. Fast rebound trajectories occur through dynamic matching between the rotational motion of the newly formed Fe-OH bond and collision with the alkane radical, and all of this occurs through a nonsynchronous dynamically concerted process that circumvents the radical pair intermediate structure. For radical pair dissociation, trajectories proceeded to the radical pair intermediate for a very brief time, followed by complete dissociation. These trajectories provide a new viewpoint and model to understand the inherent reaction pathway selectivity for non-heme Fe-oxo-mediated C-H functionalization reactions.