Method For Quantitative Analysis Research Articles

Pengaruh Pengetahuan Lingkungan terhadap Sikap dan Perilaku Konsumen dalam Memilih Produk Ramah Lingkungan: Studi Kasus Mahasiswa Universitas Negeri Medan

This study aims to uncover the impact of environmental knowledge on consumer attitudes and behaviors in selecting environmentally friendly products, especially among students at Universitas Negeri Medan. A quantitative analysis method was applied by collecting data through online surveys using Google Forms, involving 35 randomly selected respondents from various faculties. The analysis was conducted using the Statistical Package for the Social Sciences (SPSS) software, confirming that environmental knowledge significantly influences consumer attitudes and behaviors. These findings emphasize the importance of environmental knowledge in shaping environmentally conscious attitudes and behaviors among consumers, particularly among students. Furthermore, this research identifies consumer attitudes as a mediator in the relationship between environmental knowledge and consumer behavior. Based on these results, it is recommended to develop comprehensive environmental education programs, conduct further research on other influencing factors, collaborate with industries, develop supportive campus policies, and launch environmental awareness campaigns. These steps are expected to enhance awareness and consumer behavior regarding environmentally friendly products among students and provide directions for further research in this field.

Pengaruh Konten Pemasaran dan Kualitas Produk Terhadap Minat Beli Ulang pada Toko Aladoy di Shopee

Technological progress is inevitable in life following the progress of the times and science, especially in the women's clothing sector, marketing content and product quality play an important role in attracting consumers' buying interest again. The phenomenon of increasing progress in innovation is influencing people's shopping behavior: consumers who usually shop offline are now shopping online. The Aladoy store on Shopee offers an interesting marketing content feature that the company calls live streaming as live online shopping. The aim of this research is to examine how marketing content and product quality influence consumers' willingness to repurchase. The sample selection amounted to 180 respondents using the purposive sampling method. Data was collected by distributing questionnaires. This research uses quantitative and technical data analysis methods in the form of descriptive analysis, data processing uses Partial Least Square (PLS). The results of this research show that marketing content and product quality influence the interest in repurchasing women's clothing products at the Aladoy store on Shopee.

Influence of Micro, Small & Medium Enterprises Taxpayer Compliance Factors in Bandung

The Indonesian State Revenue and Expenditure Budget (APBN) is primarily funded by taxes. There are 65.5 million micro, small, and medium-sized enterprises (MSMEs) in Indonesia, making them a sector with a significant potential to assist with tax payments. In order for MSMEs to actively participate in taxation, the government continues to provide various facilities. The Indonesian tax self-assessment system, on the other hand, grants the taxpayer authority to determine the amount of tax owed, which necessitates the taxpayer's awareness and complete compliance. As a result, research was conducted on the factors that influence MSME taxpayer compliance, using tax knowledge, taxpayer awareness, and taxpayer motivation as the variables. Using a purposive sampling strategy, this study selected 100 MSMEs from Bandung with diverse industrial backgrounds and characteristics as the sample population. This study employs quantitative and explanatory analysis methods, as well as a questionnaire as the research medium. PLS-SEM was also used for analysis, and two assessments, the measurement model (validity) and the structural model assessment, were performed. This study found that taxpayer awareness has no significant impact on tax compliance, while tax literacy and taxpayer motivation have a significant impact. Accurate tax reporting and payment are supported by solid literacy. Because taxpayers are unaware of government losses, their awareness has less of an impact. Compliance is largely driven by internal and external motivation

Pengaruh Efektivitas implementasi e-Parkir pada Area Pasar Induk Among Tani Batu Menggunakan Pendekatan Metode TAM

This study aims to examine the influence of the effectiveness of the E-Parking system at Pasar Induk Among Tani Batu. The research intends to determine whether the implementation of the information system is effective in managing parking and addressing congestion issues and whether it is well-received by the surrounding community. The sampling technique used is purposive sampling with a sample size of 43 market visitors. Using a descriptive quantitative analysis method, data were collected through questionnaires with a Likert scale. The results of this study indicate that the perceived usefulness variable (X1) has a significant effect on effectiveness (Y). The perceived ease of use variable (X2) has a partially positive effect on the effectiveness variable (Y). The actual use variable (X3) partially affects the effectiveness variable (Y). The E-Parking system is well-received by users and effective in addressing parking issues at Pasar Induk Among Tani Batu.

Establishing a user demand hierarchy model driven by a mental model for complex operating systems

In complex human-computer interactions, issues such as task failures, system malfunctions, and frequent accidents caused by user errors are common. Therefore, it is necessary to study complex system interactions to enhance overall efficiency. This study focuses on the task interface of a digital twin system for computer numerical control (CNC) machine tools and examines the relationship between the user mental model and interface design elements. Key mental information is obtained through questionnaires and interviews, forming the basis for establishing a user mental model. High-frequency information words are extracted, experimental samples are designed, and importance rating surveys are conducted. Quantitative analysis methods, including factor analysis and weight calculation, are utilized to analyze the needs of target users. Consequently, a user demand hierarchy model is constructed. This approach aims to effectively reduce user errors in the human-computer interaction process within complex systems and enhance cognitive efficiency.

The Impact of the National Export Strategy on the Plastics and Packaging Industry in Malawi

ABSTRACT: Export-led industrialization is crucial for economic growth, emphasizing the shift from primary production to manufacturing. Malawi aims for industrialization through its National Export Strategy (NES), focusing on oilseed and sugarcane products and manufacturing. However, research gaps exist on export promotion programs’ impact, especially in less developed countries like Malawi and specific sectors like plastics and packaging. A mixed-method approach was employed, combining qualitative and quantitative methods for robust analysis. All 13 firms in Malawi’s plastics and packaging industry were surveyed in Blantyre and Lilongwe to assess the impact of the National Export Strategy (NES). A questionnaire with 37 questions across six sections, adapted from prior research, was used to gather comprehensive data on various aspects such as employment, production, and investment. Ethical clearance was obtained, and completed questionnaires underwent thorough review before data analysis. Results are presented in subsequent sections. The impact of Malawi’s National Export Strategy (NES) on its plastics and packaging industry highlights vital challenges. Employment suffers from a lack of skilled labor, stemming from a mismatch with training institutions. Firms struggle with limited access to finance and high borrowing costs, hindering investment. High taxes and duties hamper export competitiveness, while unreliable energy supply increases operational costs. Additionally, transportation expenses impede raw material imports, exports, and local distribution. These findings underscore the need to address skill mismatches, enhance financial access, reduce trade barriers, and improve infrastructure to bolster the industry’s resilience and competitiveness under the NES. Policy implications include collaborating with financial institutions to enhance access to grants and affordable credit, imposing higher import taxes on finished products to protect local producers, improving energy supply, enhancing transportation infrastructure, introducing incentives for investment in support services, raising awareness of NES benefits, and establishing industry-specific meetings to address sector-specific issues. These measures aim to bolster the plastics and packaging industry’s competitiveness and support its growth under the NES.

Identification study of soil types based on feature factors of XRF spectrum combining with machine learning

Soil type significantly influences the detection accuracy of heavy metals using X-ray fluorescence (XRF) technology. Rapid and accurate soil type identification is crucial for selecting appropriate XRF quantitative analysis methods for soil heavy metals, thereby enhancing analysis accuracy. This study utilized 26 soil samples from 10 distinct soil types, extracting 13 feature factors for soil type identification by analyzing XRF spectral variability. These factors were then integrated with three machine learning methods: Random Forest (RF), Support Vector Machine (SVM), and Backpropagation Neural Network (BPNN). The effectiveness of these methods in soil type identification was compared, highlighting the importance of XRF spectral feature factor extraction. The results demonstrate that identification based on feature factor extraction from XRF spectral variability markedly improves identification accuracy, stability and speed compared to full-spectrum XRF analysis. When identifying soil types by the gross area of spectral peaks of XRF feature factors, the accuracies of three machine learning methods—RF, SVM, and BPNN—were 99.62%, 99.04%, and 98.85%, respectively. Random Forest achieved the highest accuracy (99.62%) and fastest operation speed (0.179 s). Therefore, by extracting the differential features of XRF spectra and combining them with machine learning methods, it is possible to quickly and accurately recognize and judge soil types. This study demonstrates the successful and accurate identification of soil types using machine learning combined with XRF spectroscopy.It establishes an important methodological foundation for the future development of fast and accurate field testing equipment for soil heavy metals using XRF technology.

Development and Validation of an ICH-Compliant Optimized RP-HPLC Method for Quantitative Analysis of Favipiravir

Favipiravir (FAV) has emerged as a promising antiviral agent. It is particularly effective against influenza and other RNA virus infections. The aim and objective of the present study was developing and optimizing chromatographic conditions for faster analysis of FAV and demonstrating its applicability for tablet assay. The chromatographic separation was obtained using mobile phase with 90:10 % v/v ratio of ammonium acetate buffer (pH 4.5) and methanol on a Waters C18 column (250 x 4.6 mm, 5 µ). A flow rate of 1.4 mL/min was optimized. Chromatographic detection was perform at a wavelength of 323 nm. The FAV retention time was 6.46 min, indicating the efficiency and speed of the method. The developed method was rigorously validated according to ICH guidelines. A strong linear correlation (r2 = 0.9995) was established in the 5–60 μg/mL range, indicating adequacy for quantitative analysis. The method demonstrated high accuracy, with FAV recovery ranging from 98.77 % to 100.89 %. The % RSD results of less than 2% for intermediate precision and repeatability showed that the method exhibited high precision. The developed HPLC method reduces the Rt. Notably, this method complies with regulatory standards, establishing it as a valuable tool for quality assurance, pharmacological evaluation, and clinical monitoring of FAV. Thus, this validated RP-HPLC method provides a robust and sensitive method for routine FAV quantification, with high accuracy, precision, and compliance results, supporting its potential use in the clinical pharmaceutical industry.

Novel simultaneous analysis of 18 types of glycosaminoglycan-derived disaccharides using 4-aminobenzoic acid ethyl ester derivatization by HPLC with fluorescence detection.

Glycosaminoglycans (GAGs), including hyaluronic acid (HA), chondroitin sulfate (CS)/dermatan sulfate (DS), heparan sulfate (HS)/heparin (HP), and keratan sulfate (KS), play pivotal roles in living organisms. Generally, GAGs are analyzed after enzymatic digestion into unsaturated or saturated disaccharides. Due to high structural similarity between disaccharides, however, separation during analysis is challenging. Additionally, little is known about the structures of GAGs and their functional relationships. Elucidating the function of GAGs requires highly sensitive quantitative analytical methods. We developed a method for the simultaneous analysis of 18 types of disaccharides derived from HA (1 type), CS/DS (7 types), HS/HP (8 types), and KS (2 types) potentially detectable in analyses of human urine. The simple method involves HPLC separation with fluorescence detection following derivatization of GAG-derived disaccharides using 4-aminobenzoic acid ethyl ester (ABEE) as a pre-labeling agent and 2-picoline borane as a reductant. The ABEE derivatization reaction can be performed under aqueous conditions, and excess derivatization reagents can be easily, rapidly, and safely removed. This method enables highly sensitive simultaneous analysis of the 18 abovementioned types of GAG-derived disaccharides using HPLC with fluorescence detection in small amounts of urine (1mL) in a single run. The versatile method described here could be applied to the analysis of GAGs in other biological samples.

Incidence of trust in corporate value generated by ICT in commerce and services microbusinesses

The main objective of this study is to identify the impact of trust on the construction of corporate value in commerce and services microbusinesses. This work is based on identifying the challenges faced by SMEs (Small and Medium-sized Enterprises), which are conditioned by the type of business and the regulatory and incentive variables that exist in the territory, affecting their permanence and stability in the market and their financial and commercial development. A local study is carried out in Bogotá, Colombia, through a descriptive research project, using a quantitative analysis method (SPSS) to process data obtained from local microbusinesses. As a result, it was observed that trust has a discrete impact on the creation of corporate value, which is created from the use of ICT (Information and Communication Technology). This leads to the recognition that it is necessary to strengthen horizontal networks with suppliers, clients, and similar businesses, as well as vertical networks with entities and public associations, to generate lasting and strong links that increase the competitiveness of these business units in the face of exogenous risks shaped by the social, economic, and cultural characteristics of the territory, which are increasingly conditioned to the use of communication technology.

Application of SWOT Analysis and Porter’s Five Forces As a Foundation for Formulating Marketing Strategies

Abstract Introduction: This study develops previous research by combining SWOT and Porter's Five Forces Models as a basis for determining tourism marketing strategies at the Patuha Geothermal Destination in Bandung Regency. The Patuha Geothermal Destination in Bandung Regency has become one of the geothermal tourism areas that has great potential, so this research is expected to contribute to the development of sustainable tourism. Methodology: The analytical method used is quantitative data analysis method and case studies using two well-known strategic analysis frameworks, namely SWOT Analysis (Strengths, Weaknesses, Opportunities, Threats) and Porter's Five Forces as a basis for formulating marketing strategies. Findings: The results of this research show that the SWOT results are in quadrant II so that in developing and planning the marketing of the Geothermal Patuha destination, it is necessary to implement diverse and innovative strategies as an effort to utilize internal resources and anticipate competitor threats. Apart from that, using the Porter's Five Forces approach, the appropriate strategy model for the Patuha Geothermal destination marketing strategy is to develop market penetration. Conclusion: Based on the results of the analysis using Porter's Five Forces approach, the recommended strategy for maintaining Patuha Geothermal tourism potential is through an intensive approach with market development strategies that involve more active promotional activities and offering more competitive prices to visitors. Keywords: SWOT Analysis, Porter's Five Forces Model, Marketing Strategy

The Influence of Human Capital and Spiritual Capital on Employee Performance in the District Head Office

This research was conducted with the aim of knowing and analyzing the influence of human capital on employee performance at the Central City District Head Office, Gorontalo City; knowing and analyzing the influence of spiritual capital on employee performance at the Central City Subdistrict Office, Gorontalo City; determine and analyze the influence of human capital and spiritual capital together on employee performance at the Central City District Head Office, Gorontalo City. The analytical method used is a quantitative data analysis method, using a multiple linear regression model. The research population was all employees of the Central City District Head Office, 38 people using purposive sampling, 38 people were obtained using census techniques. The results of the research show that in partial testing, Human Capital and Spiritual Capital have a significant effect on employee performance at the Central City District Head Office. The results of simultaneous calculations show that the Fcount value of 327.063 is greater than the Ftable value of 3.259 with a significance level of F of 0.000 which is a value smaller than 0.05, so it can be concluded that together Human Capital and Spiritual Capital have a significant influence on employee performance at the City District Head Office. Middle. For the Adjusted R Square value of 0.944, it means that 94.4% of the two independent variables Human Capital and Spiritual Capital together can explain the model of this research and the remaining 5.6% is explained by other variables or factors that were not studied.

Establishment of a UPLC-MS method for quantitative analysis of tryptophan-kynurenine metabolism in IBS-D model rats

Background and Aims Abnormalities in tryptophan (TRP) metabolism induce abdominal pain and intestinal motility disorders. The study of TRP metabolism in diarrhea-predominant-irritable bowel syndrome (IBS-D) is important for the prevention, diagnosis, and treatment of this disease. In this study, a rapid and reliable ultra performance liquid chromatography-mass spectrometry (UPLC-MS) method was established to quantify tryptophan-kynurenine (TRP-Kyn) metabolism in the colon of a rat model with IBS-D. Methods The proteins were precipitated by methanol, chromatographically separated on a Welch Ultimate® Polar RP column with a gradient elution for 12 min, and detected by high-resolution tandem mass spectrometry. Pure water were used as an alternative mechanism for standard calibration, and the stable structural analog 2-Cl-Phe was used as an internal standard. Results Within a certain range, the r of TRP, kynurenine (Kyn) and quinolinic acid (QA), kynurenic acid (KA) are greater than 0.99, were found to be accurate and precise. The metabolism of TRP was significantly up-regulated along the Kyn pathway in the IBS-D model rats and normalized after treatment with pivacurium bromide. Conclusion This study investigates the mechanisms of IBS-D gastrointestinal dysfunction from the perspective of colonic TRP metabolism, and also provides new directions for the diagnosis and therapeutic approach of this disease.

A Low-Frequency Oscillation Suppression Method for Regional Interconnected Power Systems with High-Permeability Wind Power.

With the integration of large-scale wind power into the power grid, the impact on system stability, especially the issue of low-frequency oscillations caused by small disturbances, is becoming increasingly prominent. Therefore, this paper proposes a damping quantitative analysis method for regional interconnected power systems incorporating large-scale wind power. Using the cross-entropy particle swarm optimization (CE-PSO) algorithm, the control parameters of wind turbines are optimized to suppress low-frequency oscillations in interconnected systems. The method begins with the state equation of the interconnected power system in two regions; it deduces the characteristic polynomial of the interconnected system, including wind farms, and takes into account the influence of wind power integration on the electrical connectivity of the system. Subsequently, the influence of wind turbine control parameters on the system is quantified, and a quantitative analysis model of the impact of wind power integration on system damping characteristics is constructed. Based on this, an optimization model for wind turbine control parameters is established, and the CE-PSO algorithm is utilized to achieve suppression of low-frequency oscillations in interconnected power grids with wind power integration. Finally, the accuracy and effectiveness of the proposed method are verified through a typical electromagnetic transient simulation model of the two-region interconnected power system.

Recent research progress in tetrodotoxin detection and quantitative analysis methods.

Tetrodotoxin (TTX) is a highly potent and widely distributed ion-channel marine neurotoxin; it has no specific antidote and poses a great risk to human health. Therefore, detecting and quantifying TTX to effectively implement prevention strategies is important for food safety. The development of novel and highly sensitive, highly specific, rapid, and simple techniques for trace TTX detection has attracted widespread attention. This review summarizes the latest advances in the detection and quantitative analysis of TTX, covering detection methods based on biological and cellular sensors, immunoassays and immunosensors, aptamers, and liquid chromatography-mass spectrometry. It further discusses the advantages and applications of various detection technologies developed for TTX and focuses on the frontier areas and development directions of TTX detection, providing relevant information for further investigations.

Quantitative analysis of multicomponent mixtures of rubber and additives based on terahertz time-domain spectroscopy and Krawtchouk moments

This paper presents a quantitative analytical method based on terahertz time-domain spectroscopy and chemometrics, which was used to detect the content of diphenyl-p-phenylenediamine in a five-component mixture comprising tire rubber and additives. We separately extracted the features of the spectra by using principal component analysis and Krawtchouk moments, and then used partial least squares regression and support vector regression to develop quantitative analysis models. The experimental results show that support vector regression outperforms partial least squares regression in the quantitative analysis of multicomponent mixtures, and the Krawtchouk moments method effectively enhances the prediction accuracy of the model. The Krawtchouk moment combined with support vector regression model shows excellent predictive ability. The correlation coefficient and root mean square error of Krawtchouk moment combined with support vector regression model for the calibration set were 0.9749 and 1.7256%, respectively, and for the prediction set were 0.9669 and 1.9777%, respectively. The results demonstrate that terahertz time-domain spectroscopy combined with Krawtchouk moments and support vector regression is an effective method for determining the antioxidant content in rubber and additive compounds.

New screen-printed electrodes for Raman spectroelectrochemistry. Determination of p-aminosalicylic acid

BackgroundThe availability of new surface enhanced Raman scattering (SERS) substrates is essential to develop quantitative analytical methods. Electrochemistry is an easy, fast and reproducible methodology to prepare SERS substrates on screen-printed electrodes (SPEs). ResultsThis work proposes new SPEs based on a three-electrode system all made of silver. Using the same ink for the whole electrode system facilitates the fabrication process, reduces production costs, and leads to excellent analytical performance. The results showed that Raman enhancement depends strongly on the type of silver ink. To demonstrate the capabilities of the new electrodes developed, 4-aminosalicylic acid was determined in complex matrices and in the presence of strong interfering compounds such as salicylic acid and acetylsalicylic acid. The proposed analytical method is based on the electrochemical surface oxidation enhanced Raman scattering (EC-SOERS) strategy. AgCl nanocrystals are generated on the working electrode surface, which amplify the Raman signal of 4-aminosalicylic acid. Good figures of merit were obtained both in the absence and in the presence of the interfering compounds, achieving a correct estimation of a 4-aminosalicylic test sample in complex matrices. SignificanceThe new SPEs have been demonstrated to be very sensitive and reproducible which, together to the high specificity of the Raman signal, makes this methodology very attractive for chemical analysis.

Quantitative Three-Dimensional Imaging Analysis of HfO2 Nanoparticles in Single Cells via Deep Learning Aided X-ray Nano-Computed Tomography.

It is crucial for understanding mechanisms of drug action to quantify the three-dimensional (3D) drug distribution within a single cell at nanoscale resolution. Yet it remains a great challenge due to limited lateral resolution, detection sensitivities, and reconstruction problems. The preferable method is using X-ray nano-computed tomography (Nano-CT) to observe and analyze drug distribution within cells, but it is time-consuming, requiring specialized expertise, and often subjective, particularly with ultrasmall metal nanoparticles (NPs). Furthermore, the accuracy of batch data analysis through conventional processing methods remains uncertain. In this study, we used radioenhancer ultrasmall HfO2 nanoparticles as a model to develop a modular and automated deep learning aided Nano-CT method for the localization quantitative analysis of ultrasmall metal NPs uptake in cancer cells. We have established an ultrasmall objects segmentation method for 3D Nano-CT images in single cells, which can highly sensitively analyze minute NPs and even ultrasmall NPs in single cells. We also constructed a localization quantitative analysis method, which may accurately segment the intracellularly bioavailable particles from those of the extracellular space and intracellular components and NPs. The high bioavailability of HfO2 NPs in tumor cells from deeper penetration in tumor tissue and higher tumor intracellular uptake provide mechanistic insight into HfO2 NPs as advanced radioenhancers in the combination of quantitative subcellular image analysis with the therapeutic effects of NPs on 3D tumor spheroids and breast cancer. Our findings unveil the substantial uptake rate and subcellular quantification of HfO2 NPs by the human breast cancer cell line (MCF-7). This revelation explicates the notable efficacy and safety profile of HfO2 NPs in tumor treatment. These findings demonstrate that this 3D imaging technique promoted by the deep learning algorithm has the potential to provide localization quantitative information about the 3D distributions of specific molecules at the nanoscale level. This study provides an approach for exploring the subcellular quantitative analysis of NPs in single cells, offering a valuable quantitative imaging tool for minute amounts or ultrasmall NPs.

The Relationship between Parental Educational Involvement and Learning Engagement among Chinese Middle School Students: The Mediating Effect of Gratitude and Hope.

Despite the extensive body of literature on the correlation between family dynamics and academic achievement among students, there remains a notable gap in research investigating the influence of parental educational involvement on student learning engagement. Based on the developmental-ecological model of student engagement and relevant theoretical frameworks, this study used the quantitative analysis method to construct a chain mediation model to test the relationship between parental educational involvement and the learning engagement of middle school students, as well as the potential mediating role of gratitude and hope. This study employed a cross-sectional study using whole-cluster random sampling to measure middle school students aged 11-18 years old in two schools over a one-week period. Through the use of self-reporting surveys, this study assessed the levels of parental educational involvement, gratitude, hope, and learning engagement among 754 middle school students (48% female; Mage = 14.83, SD = 1.65) in Hubei Province, China. The mediation effect was analyzed using regression analysis and the chained mediation model and tested via the Bootstrap method. The findings suggested that parental educational involvement significantly positively related to learning engagement among middle school students, while gratitude and hope serve as partial mediators in the relationship between parental educational involvement and learning engagement. These findings revealed the psychological mechanisms underlying the relationship between parental educational involvement and learning engagement among middle school students, providing valuable insights for enhancing their level of learning engagement.

Five commonly used traditional Chinese medicine formulas in the treatment of ulcerative colitis: A network meta-analysis.

Currently, traditional Chinese medicine (TCM) formulas are commonly being used as adjunctive therapy for ulcerative colitis in China. Network meta-analysis, a quantitative and comprehensive analytical method, can systematically compare the effects of different adjunctive treatment options for ulcerative colitis, providing scientific evidence for clinical decision-making. To evaluate the clinical efficacy and safety of commonly used TCM for the treatment of ulcerative colitis (UC) in clinical practice through a network meta-analysis. Clinical randomized controlled trials of these TCM formulas used for the adjuvant treatment of UC were searched from the establishment of the databases to July 1, 2022. Studies that met the inclusion criteria were screened and evaluated for literature quality and risk of bias according to the Cochrane 5.1 standard. The methodological quality of the studies was assessed using ReviewManager (RevMan) 5.4, and a funnel plot was constructed to test for publication bias. ADDIS 1.16 statistical software was used to perform statistical analysis of the treatment measures and derive the network relationship and ranking diagrams of the various intervention measures. A total of 64 randomized controlled trials involving 5456 patients with UC were included in this study. The adjuvant treatment of UC using five TCM formulations was able to improve the clinical outcome of the patients. Adjuvant treatment with Baitouweng decoction (BTWT) showed a significant effect [mean difference = 36.22, 95% confidence interval (CI): 7.63 to 65.76]. For the reduction of tumor necrosis factor in patients with UC, adjunctive therapy with BTWT (mean difference = -9.55, 95%CI: -17.89 to -1.41), Shenlingbaizhu powder [SLBZS; odds ratio (OR) = 0.19, 95%CI: 0.08 to 0.39], and Shaoyao decoction (OR = -23.02, 95%CI: -33.64 to -13.14) was effective. Shaoyao decoction was more effective than BTWT (OR = 0.12, 95%CI: 0.03 to 0.39), SLBZS (OR = 0.19, 95%CI: 0.08 to 0. 39), and Xi Lei powder (OR = 0.34, 95%CI: 0.13 to 0.81) in reducing tumor necrosis factor and the recurrence rate of UC. TCM combined with mesalazine is more effective than mesalazine alone in the treatment of UC.