Traditional Approaches Research Articles

Determination of the Optimum Test Conditions for Measurement of Glucose Level in Liquids

Diabetes is a disease that affects more than 400 million people worldwide and currently lacks a cure. Monitoring blood sugar levels is crucial in minimizing the effects of this disease and protecting against its complications. Invasive and minimally invasive methods are commonly used traditional approaches for detecting and monitoring blood sugar levels. However, these methods bring along psychological and infectious risks. Currently, efforts are being made to develop a non-invasive method for determining blood sugar levels. Microwaves offer the possibility of non-invasive glucose measurement as they do not cause any harmful effects on human tissue. Furthermore, the complex permeability of blood is sensitive to glucose concentration in the microwave band. In literature, most of the studies are done with vector network analyzers (VNA) to detect blood sugar level noninvasively. In this study, an expensive and bulky VNA is replaced by an affordable microwave source and RMS power detector. The influence of the type and diameter of the test tube material used for non-invasive determination of sugar levels is examined with this setup. Additionally, the effect of the distance between the Vivaldi antennas used during measurements and the test tube is investigated. The results indicate that measurements performed using plastic test tubes yield better results compared to glass test tubes. Moreover, reducing the diameter of the test tube leads to improved outcomes. It has been observed that accurate results cannot be obtained if the antennas and the test tube are too close (4.5cm) from each other.

From impact to impacting: A pragmatist perspective on tackling grand challenges

Scholars have long sought to impact management practice. However, the current conceptualization of impact is grounded in dualisms, separating researchers from managers, means from ends, and thought from action. Such a dualistic understanding of impact hampers researchers’ and managers’ ability to achieve impact. Nowhere is this issue more acute than in the context of grand challenges, which require researchers and managers to work together closely. As a way forward, we propose a pragmatist perspective on impact, where impact is not seen as a one-time, unidirectional event, but rather as a relational and recursive process. By overcoming dualisms in traditional approaches to impact, pragmatist impacting can help advance progress on grand challenges and our current understanding of co-creation. In this article, we illustrate pragmatist impact ing and reflect on its opportunities and challenges through our experience at Innovation North, an innovation laboratory that brought together researchers and managers to co-create a systems innovation process.

Sulfur in Waste-Free Sustainable Synthesis: Advancing Carbon-Carbon Coupling Techniques.

This review explores the pivotal role of sulfur in advancing sustainable carbon-carbon (C-C) coupling reactions. The unique electronic properties of sulfur, as a soft Lewis base with significant mesomeric effect make it an excellent candidate for initiating radical transformations, directing C-H-activation, and facilitating cycloaddition and C-S bond dissociation reactions. These attributes are crucial for developing waste-free methodologies in green chemistry. Our mini-review is focused on existing sulfur-directed C-C coupling techniques, emphasizing their sustainability and comparing state-of-the-art methods with traditional approaches. The review highlights the importance of this research in addressing current challenges in organic synthesis and catalysis. The innovative use of sulfur in photocatalytic, electrochemical and metal-catalyzed processes not only exemplifies significant advancements in the field but also opens new avenues for environmentally friendly chemical processes. By focusing on atom economy and waste minimization, the analysis provides broad appeal and potential for future developments in sustainable organic chemistry.

Quantitative susceptibility mapping for detection of kidney stones, hemorrhage differentiation, and cyst classification in ADPKD.

The objective is to demonstrate feasibility of quantitative susceptibility mapping (QSM) in autosomal dominant polycystic kidney disease (ADPKD) patients and to compare imaging findings with traditional T1/T2w magnetic resonance imaging (MRI). Thirty-three consecutive patients (11 male, 22 female) diagnosed with ADPKD were initially selected. QSM images were reconstructed from the multiecho gradient echo data and compared to co-registered T2w, T1w, and CT images. Complex cysts were identified and classified into distinct subclasses based on their imaging features. Prevalence of each subclass was estimated. QSM visualized two renal calcifications measuring 9 and 10mm and three pelvic phleboliths measuring 2mm but missed 24 calcifications measuring 1mm or less and 1 larger calcification at the edge of the field of view. A total of 121 complex T1 hyperintense/T2 hypointense renal cysts were detected. 52 (43%) Cysts appeared hyperintense on QSM consistent with hemorrhage; 60 (49%) cysts were isointense with respect to simple cysts and normal kidney parenchyma, while the remaining 9 (7%) were hypointense. The presentation of the latter two complex cyst subtypes is likely indicative of proteinaceous composition without hemorrhage. Our results indicate that QSM of ADPKD kidneys is possible and uniquely suited to detect large renal calculi without ionizing radiation and able to identify properties of complex cysts unattainable with traditional approaches.

WITHDRAWAL – Administrative Duplicate Publication: Vs-0 Correction Factors for Input Ground Motions used in Seismic Site Response Analyses

Input motions used in seismic site response analyses are commonly selected based on similarities between the shear wave velocity (Vs) at the recording station, and the reference depth at the site of interest (among other aspects such as the intensity of the expected ground motion). This traditional approach disregards the influence of the attenuation in the shallow crust on site response. Given that this attenuation (damping) can be characterized by the distance-independent high-frequency attenuation parameter 0, a Vs-0 correction framework for input motions is proposed to render them compatible with the assumed properties of the reference depth at the site. The proposed correction factors were applied to a subset of recordings from the KiK-net database, and compared to traditional deconvolution. Results indicate that Vs-0 corrected motions outperform deconvolved motions in the characterization of the spectral energy in the high-frequency range. However, motions recorded at sites with soft deposits are not good candidates for the Vs-0 correction approach. Vs-0 corrections also affect amplification functions which are important in the assessment of site-specific seismic hazards.

ANALYSIS OF THE FUNCTIONS OF PROJECT MANAGEMENT INFORMATION SYSTEMS – TRADITIONAL APPROACH

Project management information systems in enterprises help managers to manage, improve and track the progress in the implementation of projects from their conception to the achievement of results. The article reviews the literature and presents the most commonly used applications for storing, organizing and controlling project information in accordance with the traditional approach to project management. The authors analyzed the possibility of implementing the processes and achieving the results defined for them, presented in the Project Management Body of Knowledge, by means of five selected project management information systems. On the basis of the obtained results of research conducted on a group of Project Managers, conclusions were formulated, also indicating directions for further research.

Abstract 3147: DXC025, a novel anti-MUC1/EGFR bispecific antibody-tubulysin conjugate with a function linker, exhibits potent anti-tumor efficacy

Abstract Antibody-drug conjugates (ADCs) have become a promised drug class in cancer therapy. Armed with three main components-an antibody, a linker molecule, and a cytotoxic agent (“payload”), plus sites of the antibody conjugation and the distribution of tumor-associate antigens, nowadays ADCs have proved the unique ability of the precision of targeted therapy, a great leap forward from traditional chemotherapeutic approaches that cause widespread effects without specificity. Both epidermal growth factor receptor (EGFR) and mucin 1 (MUC1) are tumor-associated antigens (TAA) that are co-expressed in many solid tumors, such as, colon cancer, non-small cell lung cancer (NSCLC), esophageal cancer, epidermal cancer, and pancreatic cancer. In particular, MUC1, a glycoprotein essential for the formation of the epithelial mucous barrier, is hypoglycosylated and dimerizes with EGFR, a potent oncoprotein, in transformed cells. For our anti-Muc1 ADC clinical program, for treatment of colon and pancreatic cancers, it was found out that the highly glycosylated transmembrane Muc1 had much more turning over rates on the luminal surface of epithelial cells. Here, we first designed and generated bispecific Fab/scFv antibodies targeting both MUC1 and EGFR, using the knob-into-hole technology. The addition of the affinity- and internalization-optimized anti-EGFR arm to the other single Muc1 arm was aimed to broaden tumor selectivity and to reduce on-target toxicity, for the treatment of gastrointestinal tumors in comparison to individual full IgG1-ADC, either EGFR or Muc1-ADC. Indeed, many of generated anti- MUC1/EGFR bispecific antibodies (BsAbs) showed strong binding affinity in MUC1modetateEGFRmodetate cells, in addition to both MUC1 and EGFR highly expressed cells. Internalization assays demonstrated that the BsAbs were endocytosed in tumor cells co-expressing EGFR and MUC1 more efficiently than monoclonal antibodies targeting MUC1. The selected BsAbs were subsequently conjugated with tubulysin B analogs with varieties of Tubulysin B analog payloads and function peptidyl spacer/linkers to generate bispecific ADC (DXC025) candidates. DXC025 candidates exhibited very potent cytotoxicity in vitro against epidermal cancer, lung adenocarcinoma, and pancreatic cancer cell lines with IC50 of single or tens digital pM. In in vivo cell-derived xenograft (CDX) models, DXC025 candidates demonstrated much superior efficacies of tumor growth inhibition in comparison with parental (monoclonal), either Muc1- or EGFR- ADC for the treatment of gastrointestinal tumors. These results indicated that the MUC1/EGFR-ADC (DXC025) would be a promising ADC candidate for targeted treatment of either Muc1- or EGFR-expressing gastrointestinal tumors. Citation Format: Xingyan Jiang, Junxiang Jia, Huihui Guo, Xiangfei Kong, Yongfang Xu, Sishi Ye, Yong Du, Zhicang Ye, Qikuang Cen, Lingli Zhang, Yongxiang Chen, Gaituo Chen, Lu Bai, Yunxia Zheng, Wei Zheng, Jun Zheng, Juan Wang, Wenjun Li, Yuanyuan Huang, Linyao Zhao, Yifang Xu, Mengmeng Liu, Binbin Chen, Meng Dai, Miaomiao Chen, Zhixiang Guo, Qingliang Yang, Robert Y. Zhao. DXC025, a novel anti-MUC1/EGFR bispecific antibody-tubulysin conjugate with a function linker, exhibits potent anti-tumor efficacy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3147.

A High-Speed Floating Point Matrix Multiplier Implemented in Reconfigurable Architecture

Matrix multiplication is a fundamental operation in computational applications across various domains. This paper introduces a novel reconfigurable co-processor that enhances the efficiency of matrix multiplication by concurrently executing addition and multiplication operations upon matrix elements of different sizes. The proposed design aims to reduce computation time and improve efficiency for matrix multiplication equations. Experimental evaluations were conducted on matrices of different sizes to demonstrate the effectiveness of the processor. The results reveal substantial improvements in both time and efficiency when compared to traditional approaches. The reconfigurable transformation processor harnesses parallel processing capabilities, enabling the simultaneous execution of addition and multiplication operations by partitioning input matrices into smaller submatrices and performing parallel computations, thus the processor achieves faster results. Additionally, the design incorporates configurable arithmetic units that dynamically adapt to matrix characteristics, further optimizing performance. The experimental evaluations provide evidence of reduction in computation time and improvement in efficiency. present significant benefits over traditional sequential methods. This makes this co-processor ideally fit for domains that require intensive linear algebra computations such as computer vision, machine learning, and signal processing.

Reweighted Extreme Learning Machine-Based Clutter Suppression and Range Compensation Algorithm for Non-Side-Looking Airborne Radar

Non-side-looking airborne radar provides important applications on account of its all-round multi-angle airspace coverage. However, it suffers clutter range dependence that makes the samples fail to satisfy the condition of being independent and identically distributed (IID), and it severely degrades traditional approaches to clutter suppression and target detection. In this paper, a novel reweighted extreme learning machine (ELM)-based clutter suppression and range compensation algorithm is proposed for non-side-looking airborne radar. The proposed method involves first designing the pre-processing stage, the special reweighted complex-valued activation function containing an unknown range compensation matrix, and two new objective outputs for constructing an initial reweighted ELM-based network with its training. Then, two other objective outputs, a new loss function, and a reverse feedback framework driven by the specifically designed objectives are proposed for the unknown range compensation matrix. Finally, aiming to estimate and reconstruct the unknown compensation matrix, special processes of the complex-valued structures and the theoretical derivations are designed and analyzed in detail. Consequently, with the updated and compensated samples, further processing including space–time adaptive processing (STAP) can be performed for clutter suppression and target detection. Compared with the classic relevant methods, the proposed algorithm achieves significantly superior performance with reasonable computation time. It provides an obviously higher detection probability and better improvement factor (IF). The simulation results verify that the proposed algorithm is effective and has many advantages.

Augmenting cybersecurity in smart urban energy systems through IoT and blockchain technology within the Digital Twin framework

This research presents a comprehensive framework to enhance the cybersecurity and efficiency of smart urban energy systems by integrating cutting-edge technologies. Leveraging advanced evolutionary optimization techniques, specifically Covariance Matrix Adaptation Evolution Strategy (CMA-ES), the proposed system optimizes energy management and trading within urban infrastructure. Simultaneously, the integration of Internet of Things (IoT) devices enables real-time monitoring and decision-making, while blockchain technology ensures secure and transparent transactions. The study evaluates system performance by comparing it with traditional approaches, assessing key metrics such as convergence speed, exploration, exploitation, and cybersecurity. Results indicate that the proposed framework outperforms traditional systems in energy efficiency, cost-effectiveness, and cybersecurity metrics. This research offers valuable insights into integrating emerging technologies to address evolving challenges in smart urban energy systems. The findings highlight the potential of this innovative framework to revolutionize urban energy management, providing a foundation for more resilient, secure, and efficient smart cities. As the global urban landscape continues to evolve, this framework represents a strategic approach to meet the increasing demands for sustainable and secure urban energy solutions.

Impact-Rubbing Dynamics of Rotor with Hollow Shaft and Offset Discs Based on MDOF Timoshenko Beam Theory

The impact-rubbing dynamic characteristics of the power turbine rotor with the hollow shaft and offset discs for aircraft engine are investigated, and the impact-rubbing analytical method for the complex rotor based on MDOP Timoshenko beam theory is proposed in this paper. Compared with the traditional approach, the novel method can obtain more data to satisfy the need of engineering. The Lagrange equation is adopted to derive the equations of motion for the rotor system, and the Newmark-β method is applied to solve the equations. The diagrams such as the bifurcation, axis trajectory, spectrum, and Poincaré map are obtained to research on the effect of the rotating speed, gap, and eccentricity on the vibration response. The finite element analysis was carried out to validate the correctness of the theoretical modeling method. The research results indicate that the power turbine rotor with the hollow shaft on operation shows the various nonlinear dynamic behaviors including the multiperiod, quasi-period, jumping phenomenon, and chaotic motions; there exists an optimal gap between the rotor and the stator from the perspective of the efficiency and the dynamics; the optimal gap should make system avoid the resulting chaos or the quasi-period motion for the stability and safety of the machinery.

Power quality improvement using a 31-level multi-level inverter with bio-inspired optimization approach

In recent years, the power quality (PQ) improvements have been explored through various approaches. The employment of electronic devices with renewable energy sources has expanded the harmonics level of voltage and current. Due to harmonics, the PQ of a specific electrical system gets affected. At critical load conditions, the traditional PQ mitigation approaches fail to develop the performance of the system. Therefore, in this work, the Spider Monkey Optimization convolutional neural network (SM-CNN)-based 31-level multilevel inverter (MLI) is used. This method balances the reactive power demands and enhances real power in the grid-tied photovoltaic (PV) system. The maximum power point tracking (MPPT) algorithm depending on radial basis function neural networks (RBFNNs) is used to maximize PV power. For strengthening the voltage level of the PV and to generate higher DC voltage with a minimized switching loss, an integrated boost fly back converter (IBFC) is introduced. The presented technique is implemented in the MATLAB/Simulink platform to figure out the estimation of PQ issues. The suggested MLI lessens the total harmonic distortion (THD) value to 2.45% with an improved power factor.

Detection of Image Tampering Using Deep Learning, Error Levels and Noise Residuals

Images once were considered a reliable source of information. However, when photo-editing software started to get noticed it gave rise to illegal activities which is called image tampering. These days we can come across innumerable tampered images across the internet. Software such as Photoshop, GNU Image Manipulation Program, etc. are applied to form tampered images from real ones in just a few minutes. To discover hidden signs of tampering in an image deep learning models are an effective tool than any other methods. Models used in deep learning are capable of extracting intricate features from an image automatically. Here we proposed a combination of traditional handcrafted features along with a deep learning model to differentiate between authentic and tampered images. We have presented a dual-branch Convolutional Neural Network in conjunction with Error Level Analysis and noise residuals from Spatial Rich Model. For our experiment, we utilized the freely accessible CASIA dataset. After training the dual-branch network for 16 epochs, it generated an accuracy of 98.55%. We have also provided a comparative analysis with other previously proposed work in the field of image forgery detection. This hybrid approach proves that deep learning models along with some well-known traditional approaches can provide better results for detecting tampered images.

Double-layer multi-criteria group decision-making approach using neutralized possibility degree-based decision matrix with fuzzy information

Multi-criteria group decision-making (MCGDM) problems are fundamentally characterized by uncertainty and ambiguity. The quantification of such data is vital for determining the most optimal course of action. In this paper, a double-layer possibility degree-based interval-valued q-rung orthopair fuzzy sets (IVq-ROFSs) Vlsekriterijumska Optimizacija i Kompromisno Resenje (VIKOR) approach is presented. First, the possibility degrees of IVq-ROFSs are calculated and processed to establish a new neutralized decision matrix. Second, the components of the new decision matrix are taken into account for obtaining a new pair of positive and negative ideal solutions. The utility and regret measures in the VIKOR approach are ultimately determined by combining criteria weights and cumulative possibility degrees to rank the alternatives. The results of the study show that the proposed approach contributes to acquiring accurate alternative rankings in situations where traditional approaches have had difficulty generating ranks. Also, by eliminating the early normalization stage, which has a direct impact on the final ranking, the proposed approach concludes a unique ranking for each decision-making problem. Several comparative and sensitivity analysis are also conducted to exhibit robustness and applicability of the proposed approach in real world.

A different causal perspective with Necessary Condition Analysis

This article delves deeper into the causal perspective of Necessary Condition Analysis (NCA). In contrast to traditional probabilistic sufficiency approaches in quantitative social science research about what will happen on average in a group of cases, NCA is interested in what will not occur in almost every case if a necessary condition is absent. Rooted in David Hume's theory of causation NCA explores factors that act as ‘must-haves’ or bottlenecks for the outcome. Operating from this necessity perspective, NCA functions deterministically (without exceptions) or non-deterministically (allowing exceptions). NCA can enrich theories and models in social science research by identifying essential factors. The article contributes to the literature by precisely describing the necessity causal perspective that is used in NCA, and by explaining how this is different from causal perspectives that are commonly used in social science research.

Analisis Perbandingan Penggunaan Pengkodean Low-Code dan Konvensional Pada Pengembangan Sistem Informasi Kepegawaian

The creation of a web-based application management system is essential to assist organisations in maintaining their operational efficiency in the rapidly growing digital era. The purpose of this research is to apply the waterfall development method approach to the development of a web-based application management system. We compared the time efficiency and features of a low-code platform such as OutSystems and traditional coding using native PHP. By streamlining procedures for recruitment, leave, warning or dismissal data, and human resource management, the system can improve overall operational effectiveness and efficiency. This research can provide in-depth information on the efficiency of development time and overall features. Our contribution is to clarify the performance comparison in the context of application management system development between low-code platforms and traditional development approaches.

MeshPointNet: 3D Surface Classification Using Graph Neural Networks and Conformal Predictions on Mesh-Based Representations

Abstract In many design automation applications, accurate segmentation and classification of 3D surfaces and extraction of geometric insight from 3D models can be pivotal. This paper primarily introduces a machine learning-based scheme that leverages graph neural networks for handling 3D geometries, specifically for surface classification. Our model demonstrates superior performance against two state-of-the-art models, PointNet + + and PointMLP, in terms of surface classification accuracy, beating both models. Central to our contribution is the novel incorporation of conformal predictions, a method that offers robust uncertainty quantification and handling with marginal statistical guarantees. Unlike traditional approaches, conformal predictions enable our model to ensure precision, especially in challenging scenarios where mistakes can be highly costly. This robustness proves invaluable in design applications, and as a case in point, we showcase its utility in automating the computational fluid dynamics meshing process for aircraft models based on expert guidance. Our results reveal that our automatically generated mesh, guided by the proposed rules by experts enabled through the segmentation model, is not only efficient but matches the quality of expert-generated meshes, leading to accurate simulations.

Implementation of geometric morphometry in the study of shapes of Bronze Age sickles from the Volga-Ural region

This article aims at evaluating the potential of geometric morphometry by means of an example of analysis of shapes of the Bronze Age sickles from the Volga-Ural region, as compared with the traditional morphometric approach. For the study, cast bronze sickles with hooks, categorized by V.A. Dergachev and V.S. Bochkarev into the Ibrakaevo, Derbeden, Perelyub and Yavlenka types using the traditional morphometric approach, have been selected. The analysis was applied to only full drawings of the items, including reconstructed ones, while fragmented items were not considered. The sample constitutes 167 objects: 86 Ibrakaevo, 49 Derbeden, 24 Perelyub, and 8 of the Yavlenka type. Application of geometric morphometry tools shows that, within the sample, three main forms can be easily identified, with the exception of the sickles earlier attributed to the Yavlenka type, probably due to their small number. Prepara-tion of primary files for recording landmark coordinates and processing of files with recorded coordinates were carried out in the tpsUtil program. The analysis of characteristics of changes of the forms was carried out using the principal component method in the MorphoJ program. Summarizing the obtained results, we can say that the principal components method has been able to identify three main variations of the objects. Most clear are the differences between the Ibrakaevo and Derbeden types, which show virtually no overlap. An intermediate position between them is taken by the Perelyub type, which is also distinctively grouped in the graph being close to the Ibrakaev group. In terms of identifying individual types, the results of the study are rather consistent with the data obtained by the tradi-tional morphometry. At the same time, it is possible to trace the vectors of shape variability for all types of tools based on three main components. Overall, it can be concluded that the method of geometric morphometry demonstrates its efficiency for the analysis of shapes of metal sickles and in future it could be applied to wider sample groups.

Research progress of nanomedicine for tumor immunotherapy

<p class="MsoNormal" style="text-align: justify;"><span lang="EN-US">Cancer, a pervasive threat to human health, presents formidable challenges to traditional treatment approaches. Tumor immunotherapy has emerged as a promising strategy for combating malignancies by bolstering the body's immune response to thwart tumor metastasis and recurrence. Nonetheless, the intricacies of tumors, patient heterogeneity, and the presence of tumor-immunosuppressive microenvironments have limited the overall efficacy of immunotherapy, achieving only approximately a 20% success rate. In recent years, nanomaterials have garnered increasing attention in the realm of tumor immunotherapy due to their inherent advantages, such as excellent biocompatibility, precise targeting, and controlled drug release. Nanomaterials empower immunostimulatory molecules and therapeutic agents with the ability to specifically target tumors, amplify drug accumulation at tumor sites, facilitate local immune modulation, alleviate immunosuppressive microenvironments, and thereby enhance the effectiveness of tumor immunotherapy. This review provides an overview of the current state of immunotherapy and offers insights into the ongoing research progress surrounding various nanomaterials aimed at augmenting the efficacy of immunotherapy.</span></p>

Development and effectiveness of a BOPPPS teaching model-based workshop for community pharmacists training.

With the pharmaceutical innovation and clinical knowledge updating, the continuing education and on-the-job training are extremely important for improving community pharmacists' professional competence. Previous training often adopted traditional lecture-based teaching, and the efficacy was limited. The aim of this study is to develop a new strategy for community pharmacist training. Based on the BOPPPS (Bridge-in, Objective, Pre-assessment, Participatory Learning, Post-assessment and Summary) teaching model and workshop method, a continuing on-the-job training program was constructed. Participates were randomly and evenly divided into two groups by random number table method. Twenty-four community pharmacists in total completed all training contents and evaluation components in this study. Twelve pharmacists in experimental group were trained via this new BOPPPS-based workshop, while others still adopted traditional didactic lecture-based approaches. After training, quantitative examination combined with clinical pharmacy practice tests were carried out to evaluate the effectiveness and outcomes of two training groups. For written exam, the total scores from the BOPPPS-based workshop group (82.67 ± 4.70) was higher than that of traditional lectured-base group (73.75 ± 6.15) (P < 0.001). Encouragingly, compared with the results of practical ability assessment from traditional training group (71.75 ± 4.75), the pharmacists receiving BOPPPS-based workshop training presented more excellent performance (78.25 ± 5.03), which displayed statistically significant differences (P < 0.01). In addition, an anonymous questionnaire was used to survey trainees' feelings after completing this continuing education program. The results revealed that the BOPPPS-based workshop can bring a better learning experience than traditional lecture-based training, and the percentages of positive response to each item were more than 91.7%. Through multi-dimensional evaluation, it was suggested that our BOPPPS-based workshop achieved desired training effects. Moreover, our research also demonstrated that this strategy had advantages of stimulating inspiration, autonomous learning, team-work spirit and pharmacy practice improvement. It may provide a reference of innovative training method for community pharmacists.