Classical Formulation Research Articles

CONCEPTUAL ANALYSIS OF NASYA KARMA YOGAS IN BAHUGATA VIKARA

In Ayurveda, Pioneers have used all the possible routes of drug administration in different treatments, even from the era when the other medical sciences had not yet evolved. Among them, Nasya Karma, the non-invasive drug administration route with transmucosal nasal drug delivery, is a unique detoxification therapy in Panchakarma that aims at the alleviation of disorders that are related to the head and neck region. Bahugata Vikara is one of the most common neuromuscular ailments that hampers the day-to-day routine of an individual due to impairment of the upper limb. Even with advanced allopathic treatment modalities, patients are not getting satisfactory relief where Nasya karma is the prime treatment modality, especially for Urdhwajatrugata and Bahushirsagata Roga. It acts on higher centers of the brain, controlling different neurological, endocrinal, and circulatory functions, thus showing local and systemic effects. This article attempts to compile the various types of classical Nasya formulations specifically indicated in Bahugata Vikara by different Acharyas from Ayurveda classical texts, relevant information from previous research, and published materials from reputed sources.

Calibration of absorbing boundary layers for geoacoustic wave modeling in pseudo-spectral time-domain methods

Abstract. This paper develops a calibration methodology of the artificial absorbing techniques typically used by Fourier pseudo-spectral time-domain (PSTD) methods for geoacoustic wave simulations. Specifically, we consider the damped wave equation (DWE) that results from adding a dissipation term to the original wave equation, the sponge boundary layer (SBL) that applies an exponentially decaying factor directly to the wavefields, and finally, a classical split formulation of the perfectly matched layer (PML). These three techniques belong to the same family of absorbing boundary layers (ABLs), where outgoing waves and edge reflections are progressively damped across a grid zone of NABL consecutive layers. The ABLs used are compatible with a pure Fourier formulation of PSTD. We first characterize the three ABLs with respect to multiple sets of NABL and their respective absorption parameters for homogeneous media. Next, we validate our findings in heterogeneous media of increasing complexity, starting with a layered medium and finishing with the SEG/EAGE 3D salt model. Finally, we algorithmically compare the three PSTD–ABL methods in terms of memory demands and computational cost. An interesting result is that PML, despite outperforming the absorption of the other two ABLs for a given NABL value, requires approximately double the computational time. The parameter configurations reported in this paper can be readily used for PSTD simulations in other test cases, and the ABL calibration methodology can be applied to other wave propagation schemes.

Method of virtual sources using on-surface radiation conditions for the Helmholtz equation

We develop a novel method of virtual sources to formulate boundary integral equations for exterior wave propagation problems. However, by contrast to classical boundary integral formulations, we displace the singularity of the Green’s function by a small distance h>0. As a result, the discretization can be performed on the actual physical boundary with continuous kernels so that any naive quadrature scheme can be used to approximate integral operators. Using on-surface radiation conditions, we combine single- and double-layer potential representations of the solution to arrive at a well-conditioned system upon discretization. The virtual displacement parameter h controls the conditioning of the discrete system. We provide mathematical guidance to choose h, in terms of the wavelength and mesh refinements, in order to strike a balance between accuracy and stability. Proof-of-concept implementations are presented, including piecewise linear and isogeometric element formulations in two- and three-dimensional settings. We observe exceptionally well-behaved spectra, and solve the corresponding systems using matrix-free GMRES iterations. The method is implemented for problems with known analytical solutions for comparison. We conclude that the proposed method leads to accurate solutions and good stability for a wide range of wavelengths and mesh refinements.

Revisiting a Cornuéjols-Nemhauser-Wolsey formulation for the p-median problem

We revisit a formulation for the simple plant facility location and p-median problems introduced by Cornuéjols, Nemhauser and Wolsey (1980). Despite being the smallest known formulation regarding the number of variables, this formulation is barely used or cited in the literature. Here, we reintroduce the formulation for the p-median problem from a different perspective, resulting from the intersection of a selection problem with an additional family of optimality constraints to define the costs correctly. An alternative proof that the linear relaxation of the formulation is equivalent to the linear relaxation of the well-known classical formulation is provided. By exploring the optimality constraints we discuss approaches to derive bounds for large-size instances. These approaches are based on relaxations obtained by eliminating optimality constraints and can be seen as a simple matheuristic to solve large size instances. In particular, we characterize relaxations which provide the optimal solution, and therefore, can be seen as new formulations for the p-median problem. Computational tests are reported showing that the renewed formulation can be used efficiently to solve p-median instances.

Efficient Formulation for Vendor–Buyer System Considering Optimal Allocation Fraction of Green Production

The classical joint economic lot-sizing (JELS) policy in a single-vendor single-buyer system generates an equal production quantity in all cycles, where the input parameters remain static indefinitely. In this paper, a new two-echelon supply chain inventory model is developed involving a hybrid production system. The proposed model simultaneously focuses on green and regular production methods with an optimal allocation fraction of green and regular productions. Unlike the classical mathematical formulation, cycles do not depend on each other, and consequently, each model parameter can be adjusted to be responsive to the dynamic nature of demand rate and/or price fluctuation. A rigorous heuristic approach is used to derive a global optimal solution for a joint hybrid production system. This paper accounts for carbon emissions from production and storage activities related to green and regular produced items along with transportation activity under a multi-level emission-taxing scheme. The results emphasize the significant impact of green production on emissions. That is, the higher the allocation fraction of green production, the lower the total amount of emissions generated by the system, i.e., the system becomes more sustainable. Adopting a hybrid production method not only decreases the greenhouse gas (GHG) emissions dramatically, but also reduces the minimum total cost per unit time when compared with regular production. One of the main findings is that the total system cost generated by the base closed-form formula of the proposed model is considerably lower in the first cycle (subsequent cycles) than that of the existing literature, i.e., 33.59% (16.13%) when the regular production method is assumed. Moreover, the optimal production rate generated by the proposed model is the one that minimizes the emissions production function. In addition, the system earns further revenue by utilizing a mixed transportation policy that combines the Truck Load (TL) and Less than Truck Load (LTL) services. Illustrative examples and special cases that reflect different realistic situations are compared to outline managerial insights.

Steady-state Approximations for Hodgkin-Huxley Cell Models: Towards Multi-scale Models of Uterine Smooth Muscle

The Tong, et al. 2011 publication presents a comprehensive mathematical model of electro- mechanical behaviour in a uterine smooth muscle cell. It incorporates ion channel currents into a classic Hodgkin-Huxley membrane voltage formulation as well as Ca2+ concentration dynamics with pumps and exchangers. Mechanical force generation by way of the Hai-Murphy smooth muscle cell model is computed based on the Ca2+ concentrations. The ‘full’ Tong, 2011 model (FTM) includes nineteen ordinary differential equations for the ion channel activation/inactivation variables out of a total of 105 equations overall. An effort at reducing the FTM into a smaller number of equations for computational efficiency – the ‘reduced’ Tong model (RTM) – aims at reproducing the overall behaviour of the FTM without excessive detail. We present here the CellML implementation of the RTM for simulating the FTM within the context of the reduced model’s primary publication as derived from the Tong, et al. 2011 model.

Steady-state Approximations for Hodgkin-Huxley Cell Models: Towards Multi-scale Models of Uterine Smooth Muscle

The Tong, et al. 2011 publication presents a comprehensive mathematical model of electro- mechanical behaviour in a uterine smooth muscle cell. It incorporates ion channel currents into a classic Hodgkin-Huxley membrane voltage formulation as well as Ca2+ concentration dynamics with pumps and exchangers. Mechanical force generation by way of the Hai-Murphy smooth muscle cell model is computed based on the Ca2+ concentrations. The ‘full’ Tong, 2011 model (FTM) includes nineteen ordinary differential equations for the ion channel activation/inactivation variables out of a total of 105 equations overall. An effort at reducing the FTM into a smaller number of equations for computational efficiency – the ‘reduced’ Tong model (RTM) – aims at reproducing the overall behaviour of the FTM without excessive detail. We present here the CellML implementation of the RTM for simulating the FTM within the context of the reduced model’s primary publication as derived from the Tong, et al. 2011 model.

Identifying Student Profiles Within Online Judge Systems Using Explainable Artificial Intelligence

Online Judge (OJ) systems are typically considered within programming-related courses as they yield fast and objective assessments of the code developed by the students. Such an evaluation generally provides a single decision based on a rubric, most commonly whether the submission successfully accomplished the assignment. Nevertheless, since in an educational context such information may be deemed insufficient, it would be beneficial for both the student and the instructor to receive additional feedback about the overall development of the task. This work aims to tackle this limitation by considering the further exploitation of the information gathered by the OJ and automatically inferring feedback for both the student and the instructor. More precisely, we consider the use of learning-based schemes—particularly, Multi-Instance Learning and classical Machine Learning formulations—to model student behaviour. Besides, Explainable Artificial Intelligence is contemplated to provide human-understandable feedback. The proposal has been evaluated considering a case of study comprising 2,500 submissions from roughly 90 different students from a programming-related course in a Computer Science degree. The results obtained validate the proposal: the model is capable of significantly predicting the user outcome (either passing or failing the assignment) solely based on the behavioural pattern inferred by the submissions provided to the OJ. Moreover, the proposal is able to identify prone-to-fail student groups and profiles as well as other relevant information, which eventually serves as feedback to both the student and the instructor.

Statistical inference of travelers’ route choice preferences with system-level data

Traditional network models encapsulate travel behavior among all origin–destination pairs based on a simplified and generic travelers’ utility function. Typically, the utility function consists of travel time solely, and its coefficients are equated to estimates obtained from discrete choice models and stated preference data. While this modeling strategy is reasonable, the inherent sampling bias in individual-level experimental data may be further amplified over network flow aggregation, leading to inaccurate flow estimates. In addition, individual-level data must be collected from surveys or travel diaries, which may be labor-intensive, costly, and limited to a small time horizon. To address these limitations, this study extends classical bi-level formulations to estimate travelers’ utility functions with multiple attributes using system-level data. This data tends to be less subject to sampling bias than individual-level data, it is cheaper to collect and it has become increasingly diverse and available. To leverage system-level data, we formulate a methodology grounded on non-linear least squares to statistically infer travelers’ utility function in the network context using traffic counts, traffic speeds, the number of traffic incidents, and sociodemographic information obtained from the US Census, among other attributes. The analysis of the mathematical properties of the optimization problem and its pseudo-convexity motivates the use of normalized gradient descent, an algorithm developed in the machine learning community that is suitable for pseudo-convex programs. More importantly, we develop a hypothesis test framework to examine the statistical properties of coefficients attached to utility terms and to perform attribute selection. Experiments on synthetic data show that the travelers’ utility function coefficients can be consistently recovered and that hypothesis tests are reliable statistics to identify which attributes are determinants of travelers’ route choices. Besides, a series of Monte-Carlo experiments showed that statistical inference is robust to various levels of sensor coverage and to noises in the Origin-Destination matrix and the traffic count measurements. The methodology is also deployed at a large scale using real-world multi-source data in Fresno, CA, collected before and during the COVID-19 outbreak.

Optimization, Formulation, and Stability of Topical Rapamycin Used for Rare Tuberous Sclerosis Disease: from Ointment to Liposomes

IntroductionTopical rapamycin has been established as an effective and safe therapy for facial angiofibromas in tuberous sclerosis. Different formulations have been tested for this skin disease, most using an ointment as a vehicle.PurposeTo improve the classical formulation of topical rapamycin and to determine the validity period of the proposed options based on chemical, physical, and microbiological stability studies.MethodsFour different 0.4% rapamycin formulations were prepared (ointment, emulsion, gel, and liposomes). The stability studies for each formulation over 56 days were as follows: (1) chemical: extraction with different solvents and high-performance liquid chromatography assay; (2) physical: pH, uniformity, extensibility, absence of crystals, absence of phase separation, and only for liposomal formulation, particle size, zeta potential, and encapsulation efficiency were determined; and (3) microbiological: culture samples in blood-agar media.ResultsOnly liposomes were chemically, physically, and microbiologically stable after 8 weeks. Ointment, emulsion, and gel formulations lost their chemical or physical stability before 56 days.ConclusionsThe authors describe four new formulations to improve the previous treatment for facial angiofibromas in tuberous sclerosis. The liposome-based formulation was the most appropriate according to chemical, physical, and microbiological stability studies. However, it would be necessary to carry out clinical studies to ensure the effectiveness and safety of this formulation and also guarantee an improvement in the quality of life of patients.

Antimicrobial evaluation of Pathyashadangam kwath, a classical Ayurvedic polyherbal formulation

Pathyashadangam kwath, a polyherbal formulation prepared from pathya, aksa, dhatri, bhunimba, nisa, nimba and amrita has been prescribed by Ayurveda physicians from time immemorial for the treatment of multitude of diseases including upper respiratory tract infections. A number of secondary metabolites isolated from ingredients of the kwath have been reported to have antimicrobial properties. But no reports on antimicrobial evaluation of the formulation is available as on date. Hence an effort has been made to evaluate the antimicrobial effectiveness of Pathyashadangam kwath against specific microbes.Three batches of Pathyashadangam kwath were prepared, following the standard procedures described in the kwatha vidhi of Sarngadhara Samhita, an ancient Ayurvedic text. Pure cultures of Gram-positive bacteria namely Bacillus subtilis, Staphylococcus aureus and Staphylococcus epidermidis, Gram - negative bacteria like Pseudomonas aeruginosa, Klebsiella pneumoniae subsp.pneumoniae, Salmonella enterica typhimurium and fungal strain Candida albicans were procured from MTCC Chandigarh. Antimicrobial evaluation of the formulation was done by agar well diffusion assay and minimum inhibitory concentration by Resazurin based micro titer dilution assay.Antimicrobial assays revealed that the kwath has substantial antimicrobial potential against all the three Gram positive bacteria and yeast strain, Candida albicans and moderate activity against Gram negative bacterium, Pseudomonas aeruginosa. Gram negative bacteria, Klebsiella pneumoniae subsp.pneumoniae and Salmonella enterica typhimurium were found to be resistant to the tested dose of kwath. Resazurin based microtitre dilution assay revealed that low concentrations of Pathyashadangam kwath could inhibit the growth of Gram-positive bacteria and fungi, but higher concentrations were required to inhibit Gram-negative bacteria.From the study it was concluded that Pathyashadangam kwath has significant antimicrobial potential which contributes to its therapeutic efficacy.

The generalized E-DVA method: a new approach for multi-modal pushover analysis under multi-component earthquakes with local variables maximization

Pushover analysis is a common nonlinear static procedure, performed to evaluate seismic response of civil structures. This approach allows considering the structural nonlinear behavior under earthquake loading in a simplified yet effective way; however, in its classic formulation, it is limited by restrictive and unrealistic assumptions. Thus, the basic method is inadequate for the accurate analysis of buildings without a vertical plane of symmetry and/or for multi-component earthquakes, where torsion effects cannot be neglected. Furthermore, standard pushover analysis only comprises one dominant mode of vibration; higher modes, and therefore their effects, are not accounted for. For all these reasons, the standard technique is generally not considered suitable for the assessment of buildings at high risk of natural-technological (NaTech) events, such as nuclear facilities in areas of high seismic activity. To overcome these limitations, a multi-modal pushover analysis procedure is here tested and validated on the data from a non-symmetric structure (the SMART case study) under multi-component earthquakes. In detail, the procedure applies a linear combination of modal load patterns, defined according to the well-established Direct Vectorial Addition (DVA) method. With respect to other existing multi-modal pushover analysis techniques, elliptical response envelopes are employed to calculate the corresponding combination factors. Innovatively, the identification of the dominant modes for the load pattern is not limited to the classic maximization of the forces at the basis of a structure but rather generalized to shear and displacement maximization at different heights and locations throughout the whole structural frame.

Best practices for portfolio optimization by quantum computing, experimented on real quantum devices

In finance, portfolio optimization aims at finding optimal investments maximizing a trade-off between return and risks, given some constraints. Classical formulations of this quadratic optimization problem have exact or heuristic solutions, but the complexity scales up as the market dimension increases. Recently, researchers are evaluating the possibility of facing the complexity scaling issue by employing quantum computing. In this paper, the problem is solved using the Variational Quantum Eigensolver (VQE), which in principle is very efficient. The main outcome of this work consists of the definition of the best hyperparameters to set, in order to perform Portfolio Optimization by VQE on real quantum computers. In particular, a quite general formulation of the constrained quadratic problem is considered, which is translated into Quadratic Unconstrained Binary Optimization by the binary encoding of variables and by including constraints in the objective function. This is converted into a set of quantum operators (Ising Hamiltonian), whose minimum eigenvalue is found by VQE and corresponds to the optimal solution. In this work, different hyperparameters of the procedure are analyzed, including different ansatzes and optimization methods by means of experiments on both simulators and real quantum computers. Experiments show that there is a strong dependence of solutions quality on the sufficiently sized quantum computer and correct hyperparameters, and with the best choices, the quantum algorithm run on real quantum devices reaches solutions very close to the exact one, with a strong convergence rate towards the classical solution, even without error-mitigation techniques. Moreover, results obtained on different real quantum devices, for a small-sized example, show the relation between the quality of the solution and the dimension of the quantum processor. Evidences allow concluding which are the best ways to solve real Portfolio Optimization problems by VQE on quantum devices, and confirm the possibility to solve them with higher efficiency, with respect to existing methods, as soon as the size of quantum hardware will be sufficiently high.

A peridynamics approach to flexible multibody dynamics for fracture analysis of mechanical systems

The classical theory of continuum mechanics is formulated using partial differential equations (PDEs) that fail to describe structural discontinuities, such as cracks. This limitation motivated the development of peridynamics, reformulating the classical PDEs into integral-differential equations. In this theory, each material point interacts with its neighbours inside a characteristic length-scale through bond-interaction forces. However, while peridynamics can simulate complex multi-physics phenomena, its integration in the study of mechanical systems is still limited. This work presents a methodology that incorporates a peridynamics formulation into a planar multibody dynamics (MBD) formulation to allow the integration of flexible structures described by peridynamics into mechanical systems. A flexible body is described by a collection of point masses, in analogy with the meshless collocation scheme commonly used for peridynamics discretisations. Each point mass interacts with other point masses through nonlinear forces governed by a bond-based peridynamics (BBPD) formulation. The virtual bodies methodology enables the definition of kinematic joints connecting the flexible body with the neighbouring bodies. The implementation of the methodology proposed is illustrated using various mechanisms with different levels of complexity. Notched plates subjected to different loading conditions are compared with the results presented in the literature of the peridynamics field. The deformations of a flexible slider-crank mechanism compare well with the results obtained using a classical flexible MBD formulation. Additionally, three scenarios involving a rotating pendulum illustrate how the methodology proposed allows simulating impact scenarios. The results demonstrate how this methodology is capable to successfully simulate highly nonlinear phenomena, including crack propagation, in a multibody framework.

Shoutai Pill Enhances Endometrial Receptivity in Controlled Ovarian Hyperstimulation Mice by Improving the In-Vivo Immune Environment.

The Shoutai pill (STP) is a classic formulation in traditional Chinese medicine. Preliminary experimental observations from our study suggest that it is effective in enhancing endometrial receptivity. However, the underlying mechanisms by which STP influences endometrial receptivity remain to be elucidated. The objective of this study is to investigate the effects and mechanisms of the STP formulation in enhancing endometrial receptivity in controlled ovarian hyperstimulation (COH) model mice. The network pharmacology analysis identified target proteins associated with the reduction of endometrial receptivity by STP. The COH mouse model was established using the GnRHa+PMSG+HCG protocol. The levels of MHC-1 and MHC-2 in mouse serum were measured using the ELISA method, while the levels of IL-1B, IL-4, IL-10, IP-10, IL-1a, IL-2, IL-17, TNF-a, and IFN-y were measured using liquid chip technology. STP exhibited a significant improvement in the immune environment of COH model mice. The major active components of STP were identified as beta-sitosterol and quercetin, among others. Furthermore, AKT1, VEGFA, and several immune factors, such as TNF, IFN, IL1B, and IL10, were identified as key targets for regulating endometrial receptivity. STP enhanced the expression of IL-10, IL-4, and IP-10 in the mice while reducing the expression levels of IL-2, IL-17, TNF-α, and IFN-γ in COH mice. These effects led to the modulation of early high expression of IL-1B and an improvement in endometrial receptivity. This study demonstrates that STP can modulate in-vivo immune factors throughout the COH process, subsequently restoring the immune equilibrium within the endometrium, thereby enhancing the endometrial receptivity in the COH model mice.

A Phytochemical study of Bodhivriksha Twaka

Abstract: Bodhivriksha is an important drug described in detail in Ayurvedic classical texts. Ashwatha, a classical Ayurvedic polyherbal formulation is used for the treatment of Rakatpitta Vikaras, Neelameha, Kaphavikaras, Vatrakta and Yoni Doshas. Ashwatha is used in the form of both single drug and compound formulations. The phytochemical analysis showed the presence of alkaloids, saponins, tannins, terpenoids, steroid, flavonoids and carbohydrates. The present study was to evaluate the phytochemical screening of extracts of Twaka of Bodhivriksha. Study has been shown that this medicinal plant can be used as pharmaceutical adjuvants in the formulation of various dosage forms.

An Improved Method for Swing State Estimation in Multirotor Slung Load Applications

A method is proposed to estimate the swing state of a suspended payload in multirotor drone delivery scenarios. Starting from the equations of motion of the coupled slung load system, defined by two point masses interconnected by a rigid link, a recursive algorithm is developed to estimate cable swing angle and rate from acceleration measurements available from an onboard Inertial Measurement Unit, without the need for extra sensors. The estimation problem is addressed according to the Extended Kalman Filter structure. With respect to the classical linear formulation, the proposed approach allows for improved estimation accuracy in both stationary and maneuvering flight. As an additional contribution, filter performance is enhanced by accounting for aerodynamic disturbance force, which largely affects the estimation accuracy in windy flight conditions. The validity of the proposed methodology is demonstrated as follows. First, it is applied to an octarotor platform where propellers are modeled according to blade element theory and the load is suspended by an elastic cable. Numerical simulations show that estimated swing angle and rate represent suitable feedback variables for payload stabilization, with benefits on flying qualities and energy demand. The algorithm is finally implemented on a small-scale quadrotor and is investigated through an outdoor experimental campaign, thus proving the effectiveness of the approach in a real application scenario.

Identification of cable tension through physical models and non-contact measurements

Determining the static and dynamic response of structural cables is a mechanical problem of theoretical and practical interest in many aspects of civil engineering. In particular, the cable tension is a fundamental variable to be assigned in direct design problems, as well as a fundamental unknown to be assessed in inverse identification problems. The present paper outlines an original analytical strategy for identifying the axial tension of inclined sagged cables in cable-stayed structures, based on static measurements. Starting from the classic formulation of an inextensible, perfectly flexible model of catenary suspended cables, a perturbation-based low-order polynomial approximation of the static configuration under self-weight is achieved (direct problem). The quadratic and cubic coefficients of the configurational function are tension-dependent quantities, whose analytical expressions can be mathematically inverted to define consistent parametric formulas for tension identification (inverse problem). The experimental assessment of the static cable configuration, which serves as input for the inverse problem, is provided by the geometric data acquired by modern and noninvasive techniques of non-contact measurement. The methodological advantage is that the laser scanner acquisition of point cloud models can efficiently and economically provide precise and redundant three-dimensional geometric descriptions of the cable configuration, which is suitable for a highly accurate and statistically robust data treatment. Lastly, the identification strategy is successfully applied to a real-scale structure, for tension identification in the stays of a cable-stayed footbridge. The results are discussed from a qualitative and quantitative viewpoint, and finally validated through a comparison with the outcomes of a dynamic identification procedure based on high-speed camera acquisitions.

ANALYSIS OF TRADITIONAL CHINESE MEDICINE PRESCRIPTIONS FOR CEREBRAL STROKE-RELATED DISEASES

Cerebral stroke, a type of cerebrovascular disease, has become the second leading cause of death globally. It is closely related to many diseases, including hypertension, diabetes, senile dementia, and so forth. As traditional Chinese medicine formulas are increasingly used to treat stroke and its associated diseases, people have begun to use machine learning methods to analyze Chinese medicine prescriptions and summarize their laws. In this study, we collected the data from classic Chinese formulations. Using the Jaccard similarity coefficient method, we calculated the similarity between different prescriptions. We then employed average linkage clustering, categorizing medicine prescriptions for chronic diseases such as diabetes, hypertension, and coronary heart disease into 12 groups. Some of these included “Heart Failure and Warm Kidney Soup”, “Sinus Chamber Junction Syndrome, Quadruple One Depression, Yi Qi, Activating Blood and Feeding Heart Soup”, “Nerves One, Depression One, Tian Ma Hook and Vine Drink”, and “Bawei Antihypertensive Decoction, Anemia Decoction, Hypotension Decoction, Myocardial Live Drink”. We observed that similar prescriptions had more meaningful mutual references. Subsequently, a correlation algorithm was used to analyze the “indications” and “prescription composition”, revealing 11 effective correlation rules. Among these, palpitations were strongly correlated with Astragalus membranaceus, Angelica sinensis, and cassia twig; weakness with Salvia miltiorrhiza, A. membranaceus, and A. sinensis; headaches with Ligusticum wallichii; and vertigo with A. membranaceus. These findings provided a theoretical reference for using traditional Chinese medicine in treating cerebral stroke and associated illnesses.

Causal decision theory, context, and determinism

AbstractThe classic formulation of causal decision theory (CDT) appeals to counterfactuals. It says that you should aim to choose an option that would have a good outcome, were you to choose it. However, this version of CDT faces trouble if the laws of nature are deterministic. After all, the standard theory of counterfactuals says that, if the laws are deterministic, then if anything—including the choice you make—were different in the present, either the laws would be violated or the distant past would be changed. And as several authors have shown, it's easy to transform this upshot of the standard theory of counterfactuals into full‐blown counterexamples to CDT. In response to these counterexamples, I argue here that the problem lies, not so much with CDT's guiding idea—that it's the expected causal consequences of your actions that matter for rational decision‐making—but with the fact that the classic formulation of CDT doesn't pay sufficient attention to the context‐sensitivity of counterfactuals. I develop a contextualist version of CDT which better accounts for this context‐sensitivity. And I show that my theory avoids the problems faced by the classic formulation of CDT in determinstic worlds.