Two-phase Strategy Research Articles

Single candidate optimizer: a novel optimization algorithm

Single-solution-based optimization algorithms have gained little to no attention by the research community, unlike population-based approaches. This paper proposes a novel optimization algorithm, called Single Candidate Optimizer (SCO), that relies only on a single candidate solution throughout the whole optimization process. The proposed algorithm implements a unique set of equations to effectively update the position of the candidate solution. To balance exploration and exploitation, SCO is integrated with the two-phase strategy where the candidate solution updates its position differently in each phase. The effectiveness of the proposed approach is validated by testing it on thirty three classical benchmarking functions and four real-world engineering problems. SCO is compared with three well-known optimization algorithms, i.e., Particle Swarm Optimization, Grey Wolf Optimizer, and Gravitational Search Algorithm and with four recent high-performance algorithms: Equilibrium Optimizer, Archimedes Optimization Algorithm, Mayfly Algorithm, and Salp Swarm Algorithm. According to Friedman and Wilcoxon rank-sum tests, SCO can significantly outperform all other algorithms for the majority of the investigated problems. The results achieved by SCO motivates the design and development of new single-solution-based optimization algorithms to further improve the performance. The source code of SCO is publicly available at: https://uk.mathworks.com/matlabcentral/fileexchange/116100-single-candidate-optimizer.

Using Algorithmic Transformations and Sensitivity Analysis to Unleash Approximations in CNNs at the Edge.

Previous studies have demonstrated that, up to a certain degree, Convolutional Neural Networks (CNNs) can tolerate arithmetic approximations. Nonetheless, perturbations must be applied judiciously, to constrain their impact on accuracy. This is a challenging task, since the implementation of inexact operators is often decided at design time, when the application and its robustness profile are unknown, posing the risk of over-constraining or over-provisioning the hardware. Bridging this gap, we propose a two-phase strategy. Our framework first optimizes the target CNN model, reducing the bitwidth of weights and activations and enhancing error resiliency, so that inexact operations can be performed as frequently as possible. Then, it selectively assigns CNN layers to exact or inexact hardware based on a sensitivity metric. Our results show that, within a 5% accuracy degradation, our methodology, including a highly inexact multiplier design, can reduce the cost of MAC operations in CNN inference up to 83.6% compared to state-of-the-art optimized exact implementations.

Beyond QALYs and DALYs: factoring in the well-being effects of dietary supplementation with omega-3 fatty acids in dry eye syndrome

PurposeThis study aims to explore the use and relevance of WALYs (well-being-adjusted life years) in light of the utilitarian premises of neoclassical economics that continue to dominate health outcomes evaluation. QALYs (quality-adjusted life years) and DALYs (disability-adjusted life years) measure longevity and quality of life in terms of purely health-related aspects and outcomes of medical interventions. However, evaluative questions of subjective well-being may be equally important in comparing outcomes and cost-effectiveness of these interventions.Design/methodology/approachA two-phase online search strategy for refereed research on dry eye treatment with omega-3 fatty acids (omega-3s) was adopted. Phase I aimed to identify and contrast clinical parameters of efficacy in omega-3 dietary supplementation. Phase II aimed to find a preference-based, multi-attribute utility instrument specific and sensitive enough to dry eye and its consequences on patients’ subjective well-being. We then illustrate how WALYs can be conceptualized and calculated based on the search results.FindingsEmpiric therapies like omega-3s can be assessed in terms of reducing or relieving symptomatic discomfort and pain, and enabling the patient to enjoy life and derive satisfaction from daily activities. We find in VisQoL (Vision and Quality of Life Index) a viable alternative to conventional multi-attribute utility instruments, including those typically used in QALY and DALY calculations. Clinical efficacy indices of dry eye can be linked to VisQoL’s quality of life dimensions. Differently weighted outcomes can be aggregated. And WALYs per patient per year can be computed by scaling aggregated outcomes to match the WALY rating scale. The implications of subjective well-being for both patient and society can thus be approached from a broader and richer perspective.Originality/valueTo the best of our knowledge, this is the first study of its kind in pharmaceutical outcomes valuation and marketing. It offers a framework for analyzing life satisfaction and well-being among dry eye patients under treatment. It is also the first to use and adapt a multi-attribute utility measure to treatment outcomes of omega-3s in ocular diseases, from which this study suggests WALYs may be computed. However, it does not suggest that WALYs should supplant QALYs and DALYs in evaluating health outcomes. Medical economics is enriched if alternative methods of outcomes evaluations can help fill in the gaps in existing paradigms and do so by accounting for other effects of condition-specific interventions. Costs and benefits of interventions to the individual and society can then be valued not just more effectively, but also more equitably.

Learning to Search in Local Branching

Finding high-quality solutions to mixed-integer linear programming problems (MILPs) is of great importance for many practical applications. In this respect, the refinement heuristic local branching (LB) has been proposed to produce improving solutions and has been highly influential for the development of local search methods in MILP. The algorithm iteratively explores a sequence of solution neighborhoods defined by the so-called local branching constraint, namely, a linear inequality limiting the distance from a reference solution. For a LB algorithm, the choice of the neighborhood size is critical to performance. Although it was initialized by a conservative value in the original LB scheme, our new observation is that the "best" size is strongly dependent on the particular MILP instance. In this work, we investigate the relation between the size of the search neighborhood and the behavior of the underlying LB algorithm, and we devise a leaning-based framework for guiding the neighborhood search of the LB heuristic. The framework consists of a two-phase strategy. For the first phase, a scaled regression model is trained to predict the size of the LB neighborhood at the first iteration through a regression task. In the second phase, we leverage reinforcement learning and devise a reinforced neighborhood search strategy to dynamically adapt the size at the subsequent iterations. We computationally show that the neighborhood size can indeed be learned, leading to improved performances and that the overall algorithm generalizes well both with respect to the instance size and, remarkably, across instances.

A two-phase evasive strategy for a pursuit-evasion problem involving two non-holonomic agents with incomplete information

In this paper, we consider the problem of pursuit-evasion between two non-holonomic agents. To make the problem more realistic, we assume that the pursuer and evader have incomplete information regarding each other’s motion. We propose a novel, two-phase, evasive strategy against a higher speed pursuer, based on worst-case scenario planning and proximity-based maneuver. In the first phase, the evader assumes the pursuer to have zero turn radius and executes a best-response strategy, which is analytically shown to be movement along a tangent to the turn circle of the evader. In the second phase, when the pursuer gets close to the evader, the latter switches to movement along high-curvature paths to side-step the former. The effectiveness of the two phases is shown through simulations.

An adaptive deep learning framework for day-ahead forecasting of photovoltaic power generation

Accurate forecasts of photovoltaic power generation (PVPG) are essential to optimize operations between energy supply and demand. Recently, the propagation of sensors and smart meters has produced an enormous volume of data, which supports the data-driven models for PVPG forecasting. Although emerging deep learning (DL) models, such as the long short-term memory (LSTM), based on historical data, have provided effective solutions for PVPG forecasting with great successes, these models utilize offline learning. As a result, DL models cannot take advantage of the opportunity to learn from newly-arrived data, and are unable to handle concept drift caused by installing extra PV units and unforeseen PV unit failures. Consequently, to improve day-ahead PVPG forecasting accuracy, as well as eliminate the impacts of concept drift, this paper proposes an adaptive LSTM (AD-LSTM), which is a DL framework that can not only acquire general knowledge from historical data, but also dynamically learn specific knowledge from newly-arrived data. A two-phase adaptive learning strategy (TP-ALS) is integrated into AD-LSTM, and a sliding window (SDWIN) algorithm is proposed, to detect concept drift in PV systems. Multiple datasets from PV systems are utilized to assess the feasibility and effectiveness of the proposed approaches. The developed AD-LSTM model demonstrates greater forecasting capability than the conventional offline LSTM model, particularly in the presence of concept drift. The forecasting skill of AD-LSTM can be improved up to 73.11%. Additionally, the proposed AD-LSTM model also achieves superior performance in terms of day-ahead PVPG forecasting for individual days and multiple days to other reference models in the literature.

Acceptability of Overdose Prevention Sites in the Business Community in Baltimore, Maryland

Intervetions are urgently needed to reduce the trajectory of the US opioid overdose epidemic, yet implementation is often hampered by resistance or opposition from key community stakeholders. While businesses are economically and physically impacted by the opioid epidemic, they are rarely engaged in efforts to reduce its impact. The establishment of overdose prevention sites (OPS) is being discussed throughout many US jurisdictions with limited attention to the potential positive role of businesses in that process. We surveyed business owners and employees of businesses located in neighborhoods with concentrated drug markets. The study’s primary aim was to examine their attitudes to locally-placed OPS. An iterative, two-phase sampling strategy was used to identify recruitment zones. In person (December 2019–March 2020) and telephone-based (April–July 2020) surveys were administered to distinct business owners and employees (N = 149). Sixty-five percent of participants supported OPS in their neighborhood and 47% had recently witnessed an overdose in or around their workplace. While 70% had heard of naloxone, and 38% reported having it on the premises. Correlates of supporting an OPS locally included living in the same neighborhood as work (adjusted odds ratio (aOR) 1.99, 95% confidence intervals (CI): 1.30–3.05); having a more positive attitude towards people who use drugs (aOR 1.33, 95% CI: 1.13–1.58); and having recently seen an overdose in/around the workplace (aOR 2.86, 95% CI: 1.11–7.32). Lack of support being an owner (aOR 0.35, 95% CI: 0.15–0.83). These data indicate the extent to which businesses are directly impacted by the opioid epidemic and the power of personal experience in shaping OPS support in advocacy efforts.

Mitigating China’s Ozone Pollution with More Balanced Health Benefits

China is confronting the challenge of opposite health benefits (OHBs) during ambient ozone (O3) mitigation because the same reduction scheme might yield opposite impacts on O3 levels and associated public health across different regions. Here, we used a combination of chemical transport modeling, health benefit assessments, and machine learning to capture such OHBs and optimize O3 mitigation pathways based on 121 control scenarios. We revealed that, for the China mainland, Beijing-Tianjin-Hebei and its surroundings ("2 + 26" cities), Yangtze River Delta, and Pearl River Delta, there could be at most 2897, 920, 1247, and 896 additional O3-related deaths in urban areas, respectively, accompanying 21,512, 3442, 5614, and 642 avoided O3-related deaths in rural areas, respectively, at the same control stage. Additionally, potential disbenefits during O3 mitigation were "pro-wealthy", that is, residents in developed regions are more likely to afford additional health risks. In order to avoid OHBs during O3 abatement, we proposed a two-phase control strategy, whereby the reduction ratio of NOX (nitrogen oxide) to VOCs (volatile organic compounds) was adjusted according to health benefit distribution patterns. Our study provided novel insights into China's O3 attainment and references for other countries facing the dual challenges of environmental pollution and associated inequality issues.

A two-phase design strategy based on the composite of mortar and coarse aggregate for 3D printable concrete with coarse aggregate

In this investigation, a two-phase design strategy based on the composite of mortar phase and coarse aggregate phase is proposed for designing 3D printable concrete (3DPC) with coarse aggregate. Coussot model and Krieger-Dougherty model were utilized to establish a quantitative relationship between the rheological property of 3DPC and the volume fraction of coarse aggregate, aiming to design 3DPC with coarse aggregate based on the mix proportion of printable mortar and thus facilitate the mixture design. It is found that the plastic viscosity of 3DPC with coarse aggregate is excessively high when the volume fraction of coarse aggregate reaches to a certain value, which is the main reason restricting the printability of 3DPC with coarse aggregate. The printability of 3DPC with coarse aggregate cannot be solely evaluated by yield stress, the plastic viscosity should be taken into consideration together with yield stress for evaluation as well.

Terahertz Meets Untrusted UAV-Relaying: Minimum Secrecy Energy Efficiency Maximization via Trajectory and Communication Co-Design

Unmanned aerial vehicles (UAVs) and Terahertz (THz) technology are envisioned to play paramount roles in next-generation wireless communications. In this paper, we present a novel secure UAV-assisted mobile relaying system operating at THz bands for data acquisition from multiple ground user equipments (UEs) towards a destination. We assume that the UAV-mounted relay may act, besides providing relaying services, as a potential eavesdropper called the untrusted UAV-relay (UUR). To safeguard end-to-end communications, we present a secure two-phase transmission strategy with cooperative jamming. Then, we devise an optimization framework in terms of a new measure $-$ secrecy energy efficiency (SEE), defined as the ratio of achievable average secrecy rate to average system power consumption, which enables us to obtain the best possible security level while taking UUR's inherent flight power limitation into account. For the sake of quality of service fairness amongst all the UEs, we aim to maximize the minimum SEE (MSEE) performance via the joint design of key system parameters, including UUR's trajectory and velocity, communication scheduling, and network power allocation. Since the formulated problem is a mixed-integer nonconvex optimization and computationally intractable, we decouple it into four subproblems and propose alternative algorithms to solve it efficiently via greedy/sequential block successive convex approximation and non-linear fractional programming techniques. Numerical results demonstrate significant MSEE performance improvement of our designs compared to other known benchmarks.

A Many-Objective Evolutionary Algorithm Based on Two-Phase Selection

Aims: The main purpose of this paper is to achieve good convergence and distribution in different Pareto fronts. Background: Research in recent decades has shown that evolutionary multi-objective optimization can effectively solve multi-objective optimization problems with no more than 3 targets. However, when solving MaOPs, the traditional evolutionary multi-objective optimization algorithm is difficult to effectively balance convergence and diversity. In order to solve these problems, many algorithms have emerged, which can be roughly divided into the following three types: decomposition-based, indexbased, and dominance relationship-based. In addition, there are many algorithms that introduce the idea of clustering into the environment. However, there are some disadvantages to solving different types of MaOPs. In order to take advantage of the above algorithms, this paper proposes a manyobjective optimization algorithm based on two-phase evolutionary selection. Objective: In order to verify the comprehensive performance of the algorithm on the testing problem of different Pareto front, 18 examples of regular PF problems and irregular PF problems are used to test the performance of the algorithm proposed in this paper. Method: This paper proposes a two-phase evolutionary selection strategy. The evolution process is divided into two phases to select individuals with good quality. In the first phase, the convergence area is constructed by indicators to accelerate the convergence of the algorithm. In the second phase, the parallel distance is used to map the individuals to the hyperplane, and the individuals are clustered according to the distance on the hyperplane, and then the smallest fitness in each category is selected. Result: For regular Pareto front testing problems, MaOEA/TPS performed better than RVEA, PREA, CAMOEA and One by one EA in 19, 21, 30, 26 cases, respectively, while it was only outperformed by RVEA, PREA, CAMOEA and One by one EA in 8, 5, 1, and 6 cases. For the irregular front testing problem, MaOEA/TPS performed better than RVEA, PREA, CAMOEA and One by one EA in 20, 17, 25, and 21 cases, respectively, while it was only outperformed by RVEA, PREA, CAMOEA and One by one EA in 6, 8, 1, and 6 cases. Conclusion: The paper proposes a many-objective evolutionary algorithm based on two phase selection, termed MaOEA/TPS, for solving MaOPs with different shapes of Pareto fronts. The results show that MaOEA/TPS has quite a competitive performance compared with the several algorithms on most test problems. Other: Although the algorithm in this paper has achieved good results, the optimization problem in the real environment is more difficult, therefore, applying the algorithm proposed in this paper to real problems will be the next research direction.

One-step model agnostic meta-learning using two-phase switching optimization strategy

Conventional training mechanisms often encounter limited classification performance due to the need of large training samples. To counter such an issue, the field of meta-learning has shown great potential in fine tuning and generalizing to new tasks using mini dataset. As a variant derived from the concept of Model Agnostic Meta-Learning (MAML), an one-step MAML incorporated with the two-phase switching optimization strategy is proposed in this paper to improve performance using less iterations. One-step MAML uses two loops to conduct the training, known as the inner and the outer loop. During the inner loop, gradient update is performed only once per task. At the outer loop, gradient is updated based on losses accumulated by the evaluation set during each inner loop. Several experiments using the BERT-Tiny model are conducted to analyze and compare the performance of the one-step MAML with five benchmark datasets. The performance of evaluation shows that the best loss and accuracy can be achieved using one-step MAML that is coupled with the two-phase switching optimizer. It is also observed that this combination reaches its peak accuracy with the fewest number of steps.

Dietary supplementation with lysine (protein) stimulates mammary development in late pregnant gilts.

The goal of this project was to determine if standardized ileal digestible (SID) lysine provided at 40% above estimated requirements, with the concomitant increase in protein intake, from days 90 to 110 of gestation would stimulate mammary development in gilts. From day 90 of gestation, Yorkshire × Landrace gilts were fed 2.65 kg of either a conventional diet (CTL, control, n = 19) providing 18.6 g/d of SID Lys or a diet providing 26.0 g/d of SID Lys via additional soybean meal (HILYS, n = 19). Both diets were isoenergetic. Jugular blood samples obtained on days 90 and 110 of gestation were used to measure concentrations of insulin-like growth factor-1 (IGF-1), metabolites, and amino acids (AA). Gilts were necropsied on day 110 ± 1 of gestation to obtain mammary glands for compositional analyses, immunohistochemistry, and analysis of mRNA abundance for AA transporters and markers of cell proliferation and differentiation. The HILYS gilts gained more body weight (P < 0.01) during the experimental period compared with CTL gilts, and had greater fetal weights (1.29 vs. 1.21 ± 0.03 kg, P < 0.05). There was no difference in circulating IGF-1, glucose, or albumin (P > 0.10) between HILYS and CTL gilts on day 110 of gestation, whereas concentrations of urea and free fatty acids were greater (P < 0.01), and those of Trp and Ala were lower (P < 0.05), in HILYS than CTL gilts. The provision of lysine at 40% above estimated requirements increased total mammary parenchymal mass by 44%, as well as total parenchymal fat, protein, DNA, and RNA (P < 0.01). The mRNA abundance of ACACA was greater (P < 0.05) in HILYS than CTL gilts, while only the AA transporter SLC6A14 tended (P < 0.10) to be greater. Results demonstrate that providing dietary Lys above current National Research Council recommendations in late gestation increases mammary development in gilts. Results also indicate that Lys may have been limiting for protein retention. These data suggest that the use of a two-phase feeding strategy during gestation, whereby dietary Lys is increased from day 90, could benefit potential sow milk yield in the subsequent lactation.

Hand-eye calibration for 2D laser profile scanners using straight edges of common objects

This paper presents a method for calibrating a 2D profile laser scanner mounted on an industrial articulated robot; a task also known as the hand-eye calibration problem. The challenge of recovering the transformation matrix, from the robot's flange coordinate system to the scanner's coordinate system, lies in the lack of sufficient 3D information, as only 2D data is available. The task is typically performed using precision calibration specimens such as spheres, disks, and planes or using additional external devices such as cameras and 3D sensors. Here, we present an approach based on detecting straight edges found in common objects. Points extracted from the same edge, under various robot poses, are used to solve the calibration problem using a two-phase least-squares strategy, where rotation is recovered first, followed by translation. The process is semi-autonomous, requires minimal laborious and error-prone manual operations; its setup effort is small, because common objects can be used instead of costly precision gauges or external devices; it does not require large number of samples and it is simple to reason about, implement and compute.

Multimodal Sentiment Analysis With Two-Phase Multi-Task Learning

Multimodal Sentiment Analysis (MSA) is a challenging research area that studies sentiment expressed from multiple heterogeneous modalities. Given those pre-trained language models such as BERT have shown state-of-the-art (SOTA) performance in multiple NLP disciplines, existing models tend to integrate these modalities into BERT and treat the MSA as a single prediction task. However, we find that simply fusing the multimodal features into BERT cannot well establish the power of a strong pre-trained model. Besides, the classification ability of each modality is also suppressed by single-task learning. In this paper, we propose a multimodal framework named Two-Phase Multi-task Sentiment Analysis (TPMSA), which applies a two-phase training strategy to make the most of the pre-trained model and a novel multi-task learning strategy to investigate the classification ability of each representation. We conduct experiments on two multimodal benchmark datasets CMU-MOSI and CMU-MOSEI, the results show that our TPMSA model significantly outperforms the current SOTA method on both datasets across all the metrics, which clearly show the effectiveness of our proposed method. Our code is available at \url{https://github.com/TPMSA/TPMSA}.

Scheduling with a processing time oracle

In this paper we study a single machine scheduling problem with the objective of minimizing the sum of completion times. Each of the given jobs is either short or long. However the processing times are initially hidden to the algorithm, but can be tested. This is done by executing a processing time oracle, which reveals the processing time of a given job. Each test occupies a time unit in the schedule, therefore the algorithm must decide for which jobs it will call the processing time oracle. The objective value of the resulting schedule is compared with the objective value of an optimal schedule, which is computed using full information. The resulting competitive ratio measures the price of hidden processing times, and the goal is to design an algorithm with minimal competitive ratio. Two models are studied in this paper. In the non-adaptive model, the algorithm needs to decide beforehand which jobs to test, and which jobs to execute untested. However in the adaptive model, the algorithm can make these decisions adaptively depending on the outcomes of the job tests. In both models we provide optimal polynomial time algorithms following a two-phase strategy, which consist of a first phase where jobs are tested, and a second phase where jobs are executed obliviously. Experiments give strong evidence that optimal algorithms have this structure. Proving this property is left as an open problem.

Total Syntheses of Galanthamine and Lycoramine via a Palladium-Catalyzed Cascade Cyclization and Late-Stage Reorganization of the Cyclized Skeleton.

Herein, we report the highly efficient total syntheses of galanthamine and lycoramine from a common tetracyclic intermediate. This concise synthetic route features a two-phase strategy, which includes the early-stage rapid construction of a tetracyclic skeleton followed by the late-stage selective reorganization of the tetracyclic skeleton. Key to the success of this strategy are a palladium-catalyzed carbonylative cascade annulation, a DDQ-mediated intramolecular regioselective oxidative lactamization, as well as a BF3·Et2O-promoted reorganization of the bridged tetracyclic skeleton.

A novel TMGWO–SLBNC‐based multidimensional feature subset selection and classification framework for frequent diagnosis of breast lesion abnormalities

The selection of optimal subset of features from high-dimensional data sets still remains a major challenge during breast cancer detection and categorization. There exist several research works regarding optimal feature subset selection from high-dimensional data sets, but the obtained results are not satisfying when multidimensional data sets (MDDs) are employed in large amount during disease analysis. In this article, an effective feature subset selection and classification method suitable for MDD is proposed. At first, the important and distinct features are extracted from the mammogram images using a Deep Neural Network with wrapper-based extraction technique. Then, a novel two-phase mutation strategy integrated with grey wolf optimizer algorithm is employed for selecting the most relevant feature subsets. Finally, a learning-based semilazy Bayesian network classifier with parallel implementation is proposed for the precise categorization of the breast cancer stages. The proposed method is executed in MATLAB platform and analyzed using mammogram images taken from MAMMOSET database. The proposed method is likened with three state-of-the-art existing feature subset selection and classification approaches for validating the efficiency of the proposed approach. For Data set 1, the proposed method shows an accuracy of 90%, 92% and 98%, which is better than the existing methods taken for comparison. Also for Data set 2, the proposed method shows an accuracy of 97%, 91% and 94%, which is much better than the accuracy achieved by the existing approaches. Thus, the proposed approach outperforms the compared existing approaches by providing better precision, recall, F-measure, specificity and accuracy.

Two-Phase Multidocument Summarization Through Content-Attention-Based Subtopic Detection

Multidocument summarization problem deals with extracting main information and ideas from a set of related documents. Solution to this problem is to find an extraction strategy that aims at finding a small subset of sentences that is able to cover the most important information about the whole document set. Although a large number of machine-learning-based methods have shown great promise, the lack of high-quality training data poses an inherent obstacle to them. Furthermore, because of the proliferation of low-quality documents on the Internet, the existing summarization strategies, which are merely based on statistical features, get poor performance. In this article, we propose a new two-phase multidocument summarization strategy using content attention-based subtopic detection. First, inspired by distance dynamics-based community detection mechanism, we extract subtopics from the set of documents by having insight into their own content attention and also underlying semantic relations. Instead of complicated neural attention mechanisms, we propose a simple iteration-based content attention method to complete the subtopic detection task. Second, we formulate summarization from different subtopics as a combinatorial optimization problem of minimizing sentence distance and maximizing topic diversity. We prove the submodularity of the above optimization problem, which allows us to propose a new multidocument summarization algorithm based on the greedy mechanism. Finally, we experimentally validate our new algorithms on BBC news summary and wikiHow data. The results show our new algorithms outperform the state-of-the-art methods.

Total Synthesis and Late-Stage C-H Oxidations of ent-Trachylobane Natural Products.

Herein, we present our studies to construct seven ent-trachylobane diterpenoids by employing a bioinspired two-phase synthetic strategy. The first phase provided enantioselective and scalable access to five ent-trachylobanes, of which methyl ent-trachyloban-19-oate was produced on a 300 mg scale. During the second phase, chemical C-H oxidation methods were employed to enable selective conversion to two naturally occurring higher functionalized ent-trachylobanes. The formation of regioisomeric analogs, which are currently inaccessible via enzymatic methods, reveals the potential as well as limitations of established chemical C-H oxidation protocols for complex molecule synthesis.