Desirable Approach Research Articles

The Evaluation of Entropy-based Algorithm towards the Production of Closed-Loop Edge

This research concerns the common problem of edge detection that produces a disjointed and incomplete edge, leading to the misdetection of visual objects. The entropy-based algorithm can potentially solve this problem by classifying the pixel belonging to which objects in an image. Hence, the paper aims to evaluate the performance of entropy-based algorithm to produce the closed-loop edge representing the formation of object boundary. The research utilizes the concept of Entropy to sense the uncertainty of pixel membership to the existing objects to classify pixels as the edge or object. Six entropy-based algorithms are evaluated, i.e., the optimum Entropy based on Shannon formula, the optimum of relative-entropy based on Kullback-Leibler divergence, the maximum of optimum entropy neighbor, the minimum of optimum relative-entropy neighbor, the thinning of optimum entropy neighbor, and the thinning of optimum relative-entropy neighbor. The experiment is held to compare the developed algorithms against Canny as a benchmark by employing five performance parameters, i.e., the average number of detected objects, the average number of detected edge pixels, the average size of detected objects, the ratio of the number of edge pixel per object, and the average of ten biggest sizes. The experiment shows that the entropy-based algorithms significantly improve the production of closed-loop edges, and the optimum of relative-entropy neighbor based on Kullback-Leibler divergence becomes the most desired approach among others due to the production of more considerable closed-loop edge in the average. This finding suggests that the entropy-based algorithm is the best choice for edge-based segmentation. The effectiveness of Entropy in the segmentation task is addressed for further research.

The Evaluation of Entropy-based Algorithm towards the Production of Closed-Loop Edge

This research concerns the common problem of edge detection that produces a disjointed and incomplete edge, leading to the misdetection of visual objects. The entropy-based algorithm can potentially solve this problem by classifying the pixel belonging to which objects in an image. Hence, the paper aims to evaluate the performance of entropy-based algorithm to produce the closed-loop edge representing the formation of object boundary. The research utilizes the concept of Entropy to sense the uncertainty of pixel membership to the existing objects to classify pixels as the edge or object. Six entropy-based algorithms are evaluated, i.e., the optimum Entropy based on Shannon formula, the optimum of relative-entropy based on Kullback-Leibler divergence, the maximum of optimum entropy neighbor, the minimum of optimum relative-entropy neighbor, the thinning of optimum entropy neighbor, and the thinning of optimum relative-entropy neighbor. The experiment is held to compare the developed algorithms against Canny as a benchmark by employing five performance parameters, i.e., the average number of detected objects, the average number of detected edge pixels, the average size of detected objects, the ratio of the number of edge pixel per object, and the average of ten biggest sizes. The experiment shows that the entropy-based algorithms significantly improve the production of closed-loop edges, and the optimum of relative-entropy neighbor based on Kullback-Leibler divergence becomes the most desired approach among others due to the production of more considerable closed-loop edge in the average. This finding suggests that the entropy-based algorithm is the best choice for edge-based segmentation. The effectiveness of Entropy in the segmentation task is addressed for further research.

The Evaluation of Entropy-based Algorithm towards the Production of Closed-Loop Edge

This research concerns the common problem of edge detection that produces a disjointed and incomplete edge, leading to the misdetection of visual objects. The entropy-based algorithm can potentially solve this problem by classifying the pixel belonging to which objects in an image. Hence, the paper aims to evaluate the performance of entropy-based algorithm to produce the closed-loop edge representing the formation of object boundary. The research utilizes the concept of Entropy to sense the uncertainty of pixel membership to the existing objects to classify pixels as the edge or object. Six entropy-based algorithms are evaluated, i.e., the optimum Entropy based on Shannon formula, the optimum of relative-entropy based on Kullback-Leibler divergence, the maximum of optimum entropy neighbor, the minimum of optimum relative-entropy neighbor, the thinning of optimum entropy neighbor, and the thinning of optimum relative-entropy neighbor. The experiment is held to compare the developed algorithms against Canny as a benchmark by employing five performance parameters, i.e., the average number of detected objects, the average number of detected edge pixels, the average size of detected objects, the ratio of the number of edge pixel per object, and the average of ten biggest sizes. The experiment shows that the entropy-based algorithms significantly improve the production of closed-loop edges, and the optimum of relative-entropy neighbor based on Kullback-Leibler divergence becomes the most desired approach among others due to the production of more considerable closed-loop edge in the average. This finding suggests that the entropy-based algorithm is the best choice for edge-based segmentation. The effectiveness of Entropy in the segmentation task is addressed for further research.

Multi-Objective Optimization in WEDM Process of Titanium Alloy (Ti-3Al-2.5V-2.0WC) using Desirability Approach

Recently, Titanium alloy (Ti-3Al-2.5V) reinforced with Tungsten Carbide (Ti-3Al-2.5V-2.0WC) developed using the microwave sintering process has plays a major role in mining industry. It has very good mechanical property and corrosion resistance. Due to its better strength and high melting point, it is difficult to machine in conventional machining process. Therefore, WEDM process give the solution to cut this high strength material in an effective manner. In this present work, the WEDM input variables such as Servo Voltage (SV), Pulse Time - On (PT-ON), Pulse Time – Off (PT-OFF) and Cutting Speed Percentage (CSP) are considered. L09 Orthogonal Array Matrix (OAM) is considered for experimental work. The output variables like Material Removal Rate (MRR) and Surface Roughness (SR) are considered to obtained the quality machining process. Multi objective optimization technique like Taguchi-Desirability Approach method is implemented to find the optimum input variables. The Improvement of MRR from 0.05978 g/min to 0.06420 g/min and SR from 3.812 μm to 3.452 μm are obtained using Desirability Approach.

EXPERIMENTAL INVESTIGATION AND OPTIMIZATION OF WIRE EDM PROCESS TO CREATE DIFFERENT GEOMETRICAL PROFILES IN METAL MATRIX COMPOSITES

Machining of accurate geometrical profiles like triangles, squares or circles with good surface finish and high productivity in metal matrix composites is required for various industrial applications. Thermoelectic principle-based wire electric discharge machine (WEDM) can be conventionally used to overcome the difficulty which occurs during the machining of composites due to the presence of hard abrasive particles. Therefore, in this investigation, three geometrical profiles possessing equal perimeters were produced in 5 mm thick aluminum metal matrix composites (AMMCs) by WEDM. In order to analyze the effect of reinforcements three different AMMCs containing Al6061 as a matrix were produced by stir casting method. The fabricated composites, respectively, contain 10% alumina ([Formula: see text]), 10% silicon carbide (SiC) and a mixture of 5% [Formula: see text] with 5% SiC. The individual effect of various input parameters like pulse-on time ([Formula: see text] = 30-50 [Formula: see text]s), pulse-off time ([Formula: see text] = 6–12 [Formula: see text]s), current ([Formula: see text] = 1–5 A) and geometrical shapes (circle, triangle, and square) on cutting velocity (CV) and surface roughness (SR) were investigated. Furthermore, parametric analysis using regression models was also performed to evaluate the simultaneous effect of two interacting parameters on CV and SR. The study reveals that in the case of hybrid composite, CV decreases with [Formula: see text] and SR is always higher for triangular profiles when [Formula: see text] and [Formula: see text] change. The regression plots indicate that the interaction effect of [Formula: see text] × [Formula: see text] plays a major role in CV for all three composites. Multiobjective optimization using a composite desirability approach reveals a marginal increase in CV (8.58%) and a significant reduction (57.72%) in SR at optimal input parameters for Al6061–10% SiC. The morphological analysis at optimal input parameters indicates a significant reduction in resolidified metal drops and microcraters.

Flux optimization using multiple promoters in Halomonas bluephagenesis as a model chassis of the next generation industrial biotechnology

Predictability and robustness are challenges for bioproduction because of the unstable intracellular synthetic activities. With the deeper understanding of the gene expression process, fine-tuning has become a meaningful tool for biosynthesis optimization. This study characterized several gene expression elements and constructed a multiple inducible system that responds to ten different small chemical inducers in halophile bacterium Halomonas bluephagenesis. Genome insertion of regulators was conducted for the purpose of gene cluster stabilization and regulatory plasmid simplification. Additionally, dynamic ranges of the multiple inducible systems were tuned by promoter sequence mutations to achieve diverse scopes for high-resolution gene expression control. The multiple inducible system was successfully employed to precisely control chromoprotein expression, lycopene and poly-3-hydroxybutyrate (PHB) biosynthesis, resulting in colorful bacterial pictures, optimized cell growth, lycopene and PHB accumulation. This study demonstrates a desirable approach for fine-tuning of rational and efficient gene expressions, displaying the significance for metabolic pathway optimization.

Unveiling the Potent Antiviral and Antioxidant Activities of an Aqueous Extract from Caesalpinia mimosoides Lamk: Cheminformatics and Molecular Docking Approaches.

Our group previously demonstrated that Caesalpinia mimosoides Lamk exhibits many profound biological properties, including anticancer, antibacterial, and antioxidant activities. However, its antiviral activity has not yet been investigated. Here, the aqueous extract of C. mimosoides was prepared from the aerial parts (leaves, stalks, and trunks) to see whether it exerts anti-influenza (H1N1) effects and to reduce the organic solvents consumed during extraction, making it a desirable approach for the large-scale production for medical uses. Our plant extract was quantified to contain 7 g of gallic acid (GA) per 100 g of a dry sample, as determined using HPLC analysis. It also exerts potent antioxidant activities comparable to those of authentic GA. According to untargeted metabolomics (UPLC-ESI(-)-QTOF-MS/MS) with the aid of cheminformatics tools (MetFrag (version 2.1), SIRIUS (version 5.8.3), CSI:FingerID (version 4.8), and CANOPUS), the major metabolite was best annotated as "gallic acid", phenolics (e.g., quinic acid, shikimic acid, and protocatechuic acid), sugar derivatives, and dicarboxylic acids were deduced from this plant species for the first time. The aqueous plant extract efficiently inhibited an influenza A (H1N1) virus infection of MDCK cells with an IC50 of 5.14 µg/mL. Of equal importance, hemolytic activity was absent for this plant extract, signifying its applicability as a safe antiviral agent. Molecular docking suggested that GA interacts with conserved residues (e.g., Arg152 and Asp151) located in the catalytic inner shell of the viral neuraminidase (NA), sharing the same pocket as those of anti-neuraminidase drugs, such as laninamivir and oseltamivir. Additionally, other metabolites were also found to potentially interact with the active site and the hydrophobic 430-cavity of the viral surface protein, suggesting a possibly synergistic effect of various phytochemicals. Therefore, the C. mimosoides aqueous extract may be a good candidate for coping with increasing influenza virus resistance to existing antivirals.

Fast nonlinear cross-sparse filtering for rolling bearings compound fault diagnosis

The investigation of faults in rotating machinery has been thoroughly examined. Among the different methods under exploration, sparse optimization-based techniques have arisen as a highly desirable approach. However, in real industrial environments, the collected bearing signals often contain a random impact component resulting from changes in working conditions and load mutations. When a machine malfunctions, it can readily induce and generate new faults, resulting in composite faults. To address this challenge, this paper proposes a novel multidimensional blind deconvolution method named fast nonlinear cross-sparse filtering (FNCr-SF). The FNCr-SF aims to separate weak compound faults under random impact interference. Various preprocessing techniques, including Z-score normalization and nonlinear sigmoid activation function, are employed to amplify the faint characteristics of compound faults and minimize the influence of random interference. Furthermore, the FNCr-SF method enables adaptive decomposition of fault components without the need for prior knowledge or pre-processing. This approach effectively reduces random interference and accurately detects compound faults in bearings. Experimental and simulation signals validate the effectiveness of the FNCr-SF method in compound fault detection, demonstrating its high accuracy and robustness.

Strategies for Direct, Transition Metal‐Free Addition of Nitrogen Synthons to Alkenes

AbstractThe incorporation of nitrogen into small molecules is one of the highest‐demanded transformations in modern synthetic chemistry. Alkene difunctionalization and aziridination offer desirable approaches to the incorporation of these amino synthons from feedstock precursors. Although amino groups can be added to alkenes following alkene reaction with an initiation reagent, the most desirable approach involves direct addition of the nitrogen group to the alkene, allowing for a range of possible secondary functionalization outcomes. Although many of these strategies have been accomplished through the use of transition metals, the utility of a transition metal‐free approach is still at the forefront of synthetic chemistry, both in development and demand. This review aims to present a comprehensive review of the history and current state‐of‐the‐art in the transition metal‐free, direct addition of nitrogen to alkenes.

Ambient grain orientation imaging on complex surfaces

Crystal orientation imaging is generally confined to the laboratory, typically following destructive sectioning, with most current techniques reliant on electron-material interactions that require a vacuum. This information is gathered in a manner that requires careful planning, however a more desirable approach would allow the manufacturer to acquire this data non-destructively at the point of manufacture, with little or no time penalty. We show that coupling a numerically controlled etching method to topographical data processing can be used to spatially map grain orientations over planar and non-planar surfaces. Our method allows the construction of large area orientation maps (≈400 mm2) in agreement with electron backscatter diffraction datasets. We have characterized spatial and angular resolution limits for the technique, which are correlated to length scales of microscale etch surfaces and our ability to measure their geometries. This approach has the potential to augment materials processing technologies, where resultant microstructures require strict control in order to guarantee through-life integrity.

Development of Self-Assembled Protein Nanocage Spatially Functionalized with HA Stalk as a Broadly Cross-Reactive Influenza Vaccine Platform.

There remains a need for the development of a universal influenza vaccine, as current seasonal influenza vaccines exhibit limited protection against mismatched, mutated, or pandemic influenza viruses. A desirable approach to developing an effective universal influenza vaccine is the incorporation of highly conserved antigens in a multivalent scaffold that enhances their immunogenicity. Here, we develop a broadly cross-reactive influenza vaccine by functionalizing self-assembled protein nanocages (SAPNs) with multiple copies of the hemagglutinin stalk on the outer surface and matrix protein 2 ectodomain on the inner surface. SAPNs were generated by engineering short coiled coils, and the design was simulated by MD GROMACS. Due to the short sequences, off-target immune responses against empty SAPN scaffolds were not seen in immunized mice. Vaccination with the multivalent SAPNs induces high levels of broadly cross-reactive antibodies of only external antigens, demonstrating tight spatial control over the designed antigen placement. This work demonstrates the use of SAPNs as a potential influenza vaccine.

Development, optimization and in-vivo pharmacokinetic evaluation of flubiprofen nanocrystal tablets for efficient chronotherapy against rheumatoid arthritis

In an effort to enhance drug solubility for buccal mucosal delivery, various strategies such as nanoemulsions, cyclodextrin inclusions, and solid dispersions have been explored, but these methods often face limitations. An emerging solubilization technology, drug nanocrystals, offers a promising alternative due to their high drug loading capacity and scalability for large-scale production. The current study aimed to create solid nanocrystals of flurbiprofen (FP-NC) through a wet milling process to significantly improve water solubility, regardless of pH, and consequently enhance bioavailability. FP-NC was further used to formulate tablets and compressed coatings with carboxymethyl xanthan gum (CXG) and sodium alginate (SA). The compression coating process has been optimized using Quality by Design principles. Utilizing the desirability approach, we identified an optimal formulation with 56.05 mg of CXG, 74.96 mg of SA, and a coating material weight of 249.98 mg. This formulation achieves desired chronotherapeutic characteristics with a lag time (LT) of 5.99 h and T90% of 11.48 h. We conducted in-vitro and ex-vivo drug release studies and compared them with in-vivo pharmacokinetic parameters, employing level A in-vitro in-vivo correlation (IVIVC). Notably, the continuous ex-vivo method exhibited a stronger correlation compared to traditional in-vitro drug release assessments. This suggests that our novel drug release system using the NC compressed coating technique holds promise for achieving chronotherapeutic effects with improved bioavailability.

Application of response surface methodology and desirability approach to optimize the performance of an ultra-low temperature cascade refrigeration system

In this paper, multi-objective optimization of an ultra-low temperature cascade refrigeration system is presented using response surface methodology and desirability approach. Evaporation temperature, high-temperature circuit and low-temperature circuit condensation temperature, cascade temperature difference, and superheat degrees of the compressor suction are chosen as the variables, whereas COP, total exergy destruction and exergy efficiency are adopted as the responses that simultaneously maximizing COP and exergy efficiency while minimizing the exergy destruction. The quadratic models for three responses are obtained after the analysis of variance. The interactive influences of the variables on the responses are indicated. The results present that the maximum deviations between the predicted and actual values for the COP and exergy efficiency are 0.84% and 0.96%, whereas the deviations for the exergy destruction are within ± 2% accounting for 96% of the samples. Evaporator temperature, condenser temperature, cascade temperature difference, ect. are found to be important parameters which cause great conflicts among the objective functions. A group of optimum values for the input variables are selected and validated. The derivations between predicted modeling and simulation values for the COP, total exergy destruction, and exergy efficiency are −0.604%, 2.886%, and 0.0452%, respectively, indicating that the method is efficient to optimize the performance.

A comprehensive machine learning-coupled response surface methodology approach for predictive modeling and optimization of biogas potential in anaerobic Co-digestion of organic waste

The increasing demand for renewable energy sources has driven the research and development of biogas production as a sustainable and efficient solution. Biogas production depends on various process parameters, which are complicated and highly non-linear processes that need to be controlled at optimal levels to ensure high productivity. The current study seeks to predict and optimize the biogas production yield in an anaerobic digester using co-digestion. Using the central composite design (CCD), L30 orthogonal arrays were developed for experimentation at four factor five levels of parameters: solid concentrations (5–25 %), pH levels (4–8), temperatures (30–50 °C), and co-digestions (0–40 %). Furthermore, three different gradient boosting algorithms (AdaBoost, Light Gradient Boosting Machine, and Extreme Gradient Boosting) were used to develop an ML-based prognostic model using experimental data. The developed prognostic model was used to predict the biogas yield. Three performance indicators (R2, RMSE, and MAE) were used to evaluate the robustness of the algorithms. Based on the coefficient of determination (R2 = 0.999), RMSE (0.6265), and MAE (0.4669), the XGB model has the most accurate predictions, followed by LGBM (R2 = 0.996, RMSE = 1.488, MAE = 1.1316), and then AdaBoost (R2 = 0.988, RMSE = 8.8943, MAE = 7.265). To optimize the parameters of biogas production, the Response Surface Methodology (RSM)-based desirability approach was employed. The finding showed that all of the considered parameters significantly affected the biogas yield. The optimal combination of the findings included a solid concentration of 11.44 %, a pH of 6.96, a temperature of 38.94 °C, and a co-digestion rate of 39.0 %, which led to a biogas yield of 6029.28 ml. The RSM finding was confirmed by an experimental investigation, and the error rate was within an acceptable limit. It was observed that RSM coupled with machine learning is an effective hybrid technique for modeling, predicting, and optimizing biogas production yield.

Optimization and comparison of machining characteristics of SKD61 steel in powder-mixed EDM process by TOPSIS and desirability approach

Optimization and comparison of machining characteristics of SKD61 steel in powder-mixed EDM process by TOPSIS and desirability approach

A review of functionalizing plasma electrolytic oxidation (PEO) coatings on titanium substrates with laser surface treatments

Nowadays, hybrid surface modification techniques are desirable approaches that are widely used to enhance the corrosion and wear behavior and biocompatibility of titanium dental and orthopedic implants. Laser processing is an effective, facile method with high flexibility, accuracy, and controllability for surface modification of titanium implant materials. Plasma electrolytic oxidation (PEO) is a cost-effective and eco-friendly process that can enhance the surface of valve metals like titanium, zirconium, magnesium, and aluminum, as well as non-valve metals such as zinc and niobium. This is achieved by creating thick, hard coatings that are resistant to both corrosion and wear, similar to ceramics. This review focuses on the improvement of anti-corrosion and anti-wear behavior, as well as the biocompatibility of titanium and its alloys through laser surface pre- or post-treatments involving laser surface melting (LSM) and laser surface texturing (LST), combined with PEO technology.

Development of combustion control map for flex fuel operation in methanol powered direct injection SI engine

The current study proposes a model-based calibration strategy for developing an optimum combustion control map in furtherance of flex-fuel operation with methanol-gasoline blends. Furthermore, a model is developed through the response surface methodology (RSM) for the engine parameters and responses based on the experimental results. In addition, a multi-objective optimization is enacted that relies on a desirability approach keeping equal attention to engine performance and exhaust emission characteristics. The established RSM model exhibits a reliable prediction ability across the operating realms of the engine. Thereby, it has been explored the tendency of parameter calibration and its effects on the engine responses for gasoline and methanol-gasoline blends are studied. From the optimally calibrated map, the test fuel of M30 shows the highest value of brake thermal efficiency (BTE) of 30.76 % and the lowest level of brake specific fuel consumption (BSFC) is recorded for M0 about 147 g/kWh. Besides, the maximal value of 1.70 %vol, 13.96 %vol, 268 ppm, and 535 ppm is observed in the engine exhaust for carbon monoxide (CO), carbon dioxide (CO2), hydrocarbon (HC), and nitrogen oxides (NOx) respectively despite of fuel used. Moreover, the validation results assure and reveal identical outcomes with predicted values within the allowable errors.

Experimental study on combustion, performance, and emission characteristics of a homogeneous charge compression ignition engine fuelled with multiple biofuel-gasoline blends

For sustainable future mobility and power generation applications, there is a compelling need to develop flex-fuel engines that operate with multiple regional or season-specific biofuels. The present investigation focussed on mitigating agricultural diesel engines’ pollutant emission and performance problems. A systematic experimental study is carried out in a modified agricultural diesel engine to compare and evaluate the engine characteristics of the biofuel-gasoline blends fuelled Homogeneous Charge Compression Ignition (HCCI) mode. Seven different oxygenated biofuels: acetone, ethanol, methanol, isopropanol, isobutanol, ethyl acetate, and diisopropyl ether blended in gasoline, were investigated. 2-Ethylhexyl nitrate was used to improve the reactivity of fuel blends to the extent that lower engine loads could be reached. All the investigated biofuel-gasoline blends could produce permissible emission levels by suitably tuning the fuel proportions. The total nitrogen oxides and smoke emissions were below 2.8 and 0.0007 g/kWh, respectively, for all the investigated biofuel-gasoline blends at all the HCCI engine load conditions. The highest indicated thermal efficiency of 42% was attained using 60% ethanol/34% gasoline/6% 2-EHN blend in the HCCI engine at 4.63 bar BMEP (86% rated load), a notable 25% improvement compared to the conventional diesel engine. A detailed investigation was done to elucidate the HCCI engine performance (indicated thermal efficiency) trend variations with biofuel type and engine load. The Response Surface Method was adopted to correlate the maximum and the minimum engine load with the fuel composition and properties. The Desirability Approach provided the optimum fuel composition and properties to maximize the engine load range. The optimum solution achieved the engine load range of 20%–89% rated load. Overall, a compact, efficient, and clean HCCI combustion-based engine technology was developed that could be operated using multiple biofuels over an expanded load range.

A multi-objective optimization approach for the production of polyhydroxybutyrate via Chlorogloea fritschii under diurnal light with single-stage cultivation

The present study aims to optimize the nutrients for maximization of cyanobacterial biomass with high content of polyhydroxybutyrate (PHB), a bioplastic, and recovery of biomass by auto-sedimentation under diurnal light mimic to sunlight. The multi-objective optimization with desirability approach was used to improve dry cell weight (DCW), PHB content (% w/w), and auto-sedimentation concentration factor (SCF) of biomass. Initially, NaNO3, K2HPO4, TRACE (micronutrient solution), Na2EDTA, and MgSO4.7H2O were screened as important media compositions. Screening was followed by the application of response surface methodology for the development of a model used in multi-objective optimization. The optimized media selected from many optimal solutions, a set of Pareto solutions generated by multi-objective optimization was validated in a flat panel photobioreactor. Using a single-stage cultivation strategy under diurnal light, Chlorogloea fritschii TISTR 8527 has shown capability to produce DCW of 1.23 g/l with PHB content of 31.78 % and SCF of 93.63 with optimal media. This leads to the enhancement of both PHB content (2.72 fold) and SCF (1.64 fold) were observed when compared to the non-optimal medium. This is the first multi-objective optimization study for media optimization using cyanobacteria reported till now under diurnal light mimic to sunlight for bioplastic production.

Shinmi (親身): a Distinctive Japanese Medical Virtue?

In Western countries, the ideal professional and ethical attributes of healthcare providers and the ideal patient-doctor relationship have been analysed in detail. Other cultures, however, may have different norms, arising in response to diverse healthcare needs, cultural values and offering alternative perspectives. In this paper, drawing a case study, we introduce the concept of Shinmi, used in Japan to describe a desirable approach to medical care. Shinmi means kind or cordial in Japanese. In the medical context, it refers to doctors treating patients with a degree of emotional closeness as if they were the doctors’ own family. We analyse the concept of Shinmi, drawing on virtue ethics. We distinguish two different elements to a Shinmi-na attitude. As illustrated in our example, excessive Shinmi can be problematic for patients and doctors. Furthermore, elements of Shinmi may conflict with existing Western values (for example, norms that encourage emotional detachment and discourage doctors’ treatment of family members). However, if pursued appropriately, we argue that a balanced Shinmi-na approach can be conducive to the goals of medicine. The concept of Shinmi may be valuable for medical students, in Japanese and potentially other health care systems, and help them to cultivate a virtuous approach to meeting the emotional needs of patients.