Journal Of Engineering Research Articles

An Isolated High Gain Push Pull Type DC to DC Converter for Fuel Cell Power Generation Systems

Due to their high efficiency and low environmental impact, fuel cell power generation systems have emerged as promising alternatives to traditional energy sources. However, the integration of fuel cells into practical applications necessitates the development of efficient power conversion systems. dc-dc converters play a crucial role in optimizing the power flow between the fuel cell stack and the load. In this paper, a push-pull type high gain dc-dc converter is proposed which consists of a push-pull inverter, a rectifier circuit, and C-filter. The push pull inverter produces high frequency square wave output while the full bridge rectifier and C-filter convert this square wave output into desired dc voltage. The converter boosts the 12V dc input supply to 326 V, representing a gain factor of more than 27. The isolated coupling inductor used in push pull inverter produces high gain and provides electrical isolation. Besides, the proposed topology eliminates the use of bulky grid-connected power transformers that decreases the system size and cost. To evaluate the performance of the proposed topology, a number of simulations under varying loads were carried out in the MATLAB Simulink framework. Further-more, a scaled-down prototype of 160W at 12/326 V was developed to confirm the simulation results. The findings indicate that the suggested converter can be a viable option for the hydrogen fuel cell-based power conversion system. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 10(1), 2023 P 37-52

COVID-19 Identification System from X-Ray Images of Chest using Deep Neural Network with Transfer Learning

Recently, the impact of COVID-19 has significantly diminished; however, it has not been completely eradicated. There are still instances where individuals are experiencing suffering due to this life-threatening virus which has a significant impact on health care as well as lifestyles throughout the world. So, early discovery is important to controlling case extension and the death rate. The RT-PCR is known as the true leading diagnosis test; nevertheless, the expense and result times of these tests are long, thus additional quick and accessible diagnostic techniques are required. However, most countries are suffering due to limited testing resources and kits. The unavailability of testing resources, kits, and a rising amount of regular occurrences, caused us to develop a model on Deep Learning which may benefit radiologists as well as doctors for detecting COVID-19 instances using images of chest X-rays. For developing a representation of modality-specific features, a convolutions neural network and a variety of ImageNet pre-trained models are trained and evaluated at the patient level by using different available CXR datasets. We choose 5000 images in total from the dataset collected from Kaggle where we kept 4000 images in case of training and validation, and the remaining 1000 in case of testing. We use four Pre-train Deep CNN Models which are very popular for image calcification. VVG16, VGG19, InceptionV3, and Resnet50 CNN Models we choose to analyze the performance and find the best one among them. In our testing, we get 88.5% testing accuracy on ResNet and 95.10% on InceptionV3 models while VGG19 gives 90.22% accuracy and VGG16 gives the highest 96.10% accuracy. To increase performance accuracy, Transfer Learning knowledge is transmitted and fine-tuned. After applying Transfer Learning in the modified VGG16 we got an accuracy of 97% which is clearly an improvement over the previous VGG16 model. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 10(1), 2023 P 53-67

Evaluating CNN Models for Gait Recognition: A Study on the CASIA-B Dataset

Gait recognition, a form of biometric identification, has garnered considerable interest for its ability to identify individuals from a distance. Yet, its effectiveness depends significantly on the precision of the underlying categorization models. This study thoroughly evaluates many Convolutional Neural Network (CNN) models to determine their effectiveness in properly identifying and categorizing gait patterns. We train and test several CNN architectures using advanced deep learning techniques on the widely known CASIA-B dataset, which is a benchmark dataset for gait recognition research. The analyses involve models like as VGG16, VGG19, NASNetLarge, NASNetMobile, EfficientNetB0, and Xception, each well-known for their effectiveness in picture categorization tasks. We evaluate the performance of these models by conducting thorough experiments and detailed analysis, focusing on accuracy, validation loss, and other pertinent metrics. The Xception model demonstrated the highest accuracy of 97.17% of the models assessed. This model frequently surpasses similar models, demonstrating its strength and effectiveness in precisely recognizing gait patterns. On the other hand, the accuracy of other models varied from 9.78% to 93.85%, demonstrating the variable levels of efficacy among various designs. The findings have significant implications for the creation and implementation of gait recognition systems. The high precision demonstrated by the Xception model highlights the promise of CNN-based methods in progressing gait identification technology. Our work highlights the significance of choosing suitable model architectures for achieving the best performance in gait detection tasks. In the future, research may focus on using larger datasets and exploring other CNN architectures to improve the accuracy and reliability of gait detection systems. Our goal is to enhance gait recognition technology to improve biometric identification for security, surveillance, and healthcare applications. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 10(1), 2023 P 17-26

Optimizing PV Module Efficiency: Investigating the impact of Dry and Wet Dust on Solar Panel Output Power

Solar photovoltaic modules convert sunlight into electricity through the photoelectric effect. Dust accumulation on PV modules can significantly reduce their output electrical characteristics such as voltage, current, and power. The power reduction is attributed to the decrease in the amount of light reaching the solar cells due to the absorption and scattering of light by the dust layer. The magnitude of the power reduction depends on the amount and type of dust component of the solar panel. In this work, four dust components such as red soil, sand, wood power, and bird drooping are considered to test the PV performance. An indoor experimental setup is developed to test the dust impact on the PV modules using an artificial sun simulator. Experimental shows that the effect of dust accumulation on the voltage and current characteristics of solar panels is complex and depends on various factors. The percentage of degradation of output power is measured using dust under dry and wet conditions. Moreover, different amounts of dust such as 1gm, 3gm, 5gm, 7gm, and 9gm are considered for dry conditions, and 3gm and 5gm are considered for wet conditions. Bird drop causes a maximum degradation of 24.5% in dry conditions and it reduces by 17.2% in wet conditions. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 10(1), 2023 P 68-74

Electrochemical sensing of nitrate ions at Cu-immobilized gold electrode in neutral medium

The Cu-immobilized Au surface was electro-catalytically employed for nitrate ions reduction in a neutral solution. According to cyclic voltammetric studies, the Cu/Au electrode has an improved electro-catalytic impact on converting nitrate ions into nitrite ions, governed by a two-electron transfer system. This modified electrode demonstrated good performance in real sample analysis along with good sensitivity (3.2518 $ \mu A \mu M^{-1} cm^{-2}$), long linear detection range (1.2 to 111.4 $\mu M$), a very lower limit of detection (0.85 $\mu M$), an excellent reproducibility. Thus, to determine nitrate ions in an aqueous media, an electrochemical sensor based on a Cu/Au electrode is suggested. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 10(1), 2023 P 75-85

Editorial for Vol.32, No.1

The March 2024 (Vol. 32, No. 1) issue of CIT. Journal of Computing and Information Technology brings four papers from the areas of power load forecasting, optimization of production supply, optimization of financial market trading, and large language models.

Effectiveness of eHealth Interventions in Improving Medication Adherence Among Patients With Cardiovascular Disease: Systematic Review and Meta-Analysis.

Nonadherence to medication among patients with cardiovascular diseases undermines the desired therapeutic outcomes. eHealth interventions emerge as promising strategies to effectively tackle this issue. The aim of this study was to conduct a network meta-analysis (NMA) to compare and rank the efficacy of various eHealth interventions in improving medication adherence among patients with cardiovascular diseases (CVDs). A systematic search strategy was conducted in PubMed, Embase, Web of Science, Cochrane, China National Knowledge Infrastructure Library (CNKI), China Science and Technology Journal Database (Weipu), and WanFang databases to search for randomized controlled trials (RCTs) published from their inception on January 15, 2024. We carried out a frequentist NMA to compare the efficacy of various eHealth interventions. The quality of the literature was assessed using the risk of bias tool from the Cochrane Handbook (version 2.0), and extracted data were analyzed using Stata16.0 (StataCorp LLC) and RevMan5.4 software (Cochrane Collaboration). The certainty of evidence was evaluated using the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) approach. A total of 21 RCTs involving 3904 patients were enrolled. The NMA revealed that combined interventions (standardized mean difference [SMD] 0.89, 95% CI 0.22-1.57), telephone support (SMD 0.68, 95% CI 0.02-1.33), telemonitoring interventions (SMD 0.70, 95% CI 0.02-1.39), and mobile phone app interventions (SMD 0.65, 95% CI 0.01-1.30) were statistically superior to usual care. However, SMS compared to usual care showed no statistical difference. Notably, the combined intervention, with a surface under the cumulative ranking curve of 79.3%, appeared to be the most effective option for patients with CVDs. Regarding systolic blood pressure and diastolic blood pressure outcomes, the combined intervention also had the highest probability of being the best intervention. The research indicates that the combined intervention (SMS text messaging and telephone support) has the greatest likelihood of being the most effective eHealth intervention to improve medication adherence in patients with CVDs, followed by telemonitoring, telephone support, and app interventions. The results of these network meta-analyses can provide crucial evidence-based support for health care providers to enhance patients' medication adherence. Given the differences in the design and implementation of eHealth interventions, further large-scale, well-designed multicenter trials are needed. INPLASY 2023120063; https://inplasy.com/inplasy-2023-12-0063/.

Removal of Azo Dye from water By jute (Corchorus Capsularis) stick activated Carbon: a cost-effective ecofriendly approach

The textile industry consumes a lot of water during production and generates a massive amount of effluents, including synthetic dyes, into the environment, which raises public concern. Azo dyes that are predominantly utilized in the textile sector are regarded to offer serious health hazards. Jute sticks are a typical fuel source in Bangladeshi villages. After using jute sticks as fuel, we turn them into highly cost effective activated charcoals which are capable of removing Levafix Amber (most used azo dye) from textile effluent. In this article we reported for the first time a cost effective, ecofriendly jute stick extracted adsorbent for the removal of Levafix Amber dye from textile effluents. The most notable benefits of our approach are its low cost, simplicity, and lack of the need for costly instruments. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 9(1), 2022 P 30-41

Short-Term Solar Power Prediction using Machine Learning Algorithms : A High Performing approach

The use of solar energy is growing in popularity across the globe as a clean and sustainable energy source. Nevertheless, integrating solar power into the grid and guaranteeing a steady supply of electricity is made more difficult by the weather patterns’ tendency to cause unpredictability in solar power generation. One potential solution to these issues is the use of machine learning (ML) techniques for short term solar photovoltaic (PV) power forecasting. This research looks into how well various machine learning algorithms work for short-term PV power forecasting. It also offers a summary of the variables that affect time-series data performance and suggests high-performance methods. Long Short-term Memory (LSTM), Support Vector Machines (SVM), and Artificial Neural Networks (ANN) were tested on an 88,494-item dataset with 20 features at a 15-minute resolution. As accuracy metrics, root mean square error (RMSE) and mean square error (MSE) show that ANNs and LSTM perform better than SVM. The superiority of the suggested methods is demonstrated by a careful comparison with recently published results. The reason for the effectiveness of ANNs and LSTMs is their capacity to represent the complex and non-linear relationships found in the data. Short-term solar power forecasting is complex, and SVMs are better equipped to handle linear relationships. These findings will be a useful guidance for those who intend to work in the field of PV power forecasting, even though further research is required to generalize this observation. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 9(1), 2022 P 82-95

IoT-Based Rainfall Monitoring System to Detect Rainy Flood for Coastal Areas of Bangladesh

Flood disasters in Bangladesh’s coastal regions have been a major source of worry due to their damaging effects on both the natural world and people. For this natural disaster to be tracked and efficiently controlled, rainfall measurement must be precise and trustworthy. The rain gauge, which has acquired global acceptability, is the most commonly used instrument for measuring rainfall. This study presents the design and experimental evaluation of a cloud-based sensor-based rain gauge for the real determination of total rainfall and rate. An ultrasonic sensor is connected to a round water container, a round rainwater acquisition, a Bluetooth module, and a data processing unit for data collecting and processing in the proposed recommendation sensor-based rain gauge. Improved rainfall evaluation and detection methods have been created using cutting-edge sensor data processing techniques. Throughout a local rainfall event, the performance of the electronic sensor-based rain gauge is assessed and contrasted with that of a hand-made rain gauge. The experimental results show that the suggested system offers an accurate reading of rainfall quantity and rate per unit of time and area, real-time rainfall monitoring, and reliable and consistent analysis. The created method is made to maintain rainy floods in Bangladesh’s coastal regions by storing rainfall data. The system is anticipated to help the region establish an efficient rainy flood monitoring system. Overall, the suggested digital sensor-based rain gauge offers a useful and trustworthy instrument for managing and monitoring rainfall in Bangladesh’s coastal regions in real-time. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 9(1), 2022 P 52-57

A Densely Connected Convolutional Network Towards Efficient Recognition of Intracranial Hemorrhage Using CT Images

Intracranial Bleeding also known as Intracranial Hemorrhage (ICH) is a severe issue for human beings and the most common cause of it is trauma. Majorly ICH occurs due to hypertension and around 2.5 per 10,000 people get affected by one of its subtypes. The human brain consists of lots of soft tissue and nerves, that’s why it is so tough to find affected areas within the shortest period and to apply proper treatment or medication for a radiologist by analyzing the Computed Tomography (CT) images. In addition to ICH treatment being expensive, a densely connected convolutional network (DenseNet-169) model is recommended to accurately detect the damaged region quickly and affordably to facilitate the treatment of the patient. All were private and inaccessible, with the exception of the CQ500 and the Radiological Society of North America (RSNA) datasets about ICH. In our study, we employed stage-2 of the RSNA dataset, which comprises 121,232 test images and 752,803 training images. Phase-1 and phase-2 are the two stages of the dataset. Among the various preprocessing techniques, image type conversion, resizing, and normalization were performed on the dataset. During the learning phase of our model, for hyper-tuning, a portion (30%) of training data was utilized as validation data. The test data was then used to evaluate the model’s efficacy, and it was found that the ICH recognition accuracy of our developed model was 98%. Index Terms— Intracranial Hemorrhage, CT images, DenseNet, Training, Testing, Recognition, RSNA. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 9(1), 2022 P 13-22

An Electronic Economic Controller for automatically adjusting the intensity of Solar-Powered LED Street Lightning Systems in rural areas

Using photovoltaic energy for street lighting systems is increasing rapidly in rural areas. An economic and smart controller design for automatic intensity control of LED-based street lights is the main challenge. In this work, an electronic-based controller is proposed for controlling the LED light intensity automatically, considering vehicle movement and atmospheric conditions. A HC-SR501 PIR sensor is used for detection of vehicle movements and to assist the controller in making decisions regarding the brightness of the LED. The proposed LED is operated at three different energy levels, such as 0 W, 10 W, and 30 W. In the daytime, the load will remain off, and at night, the LED will operate at two power levels, 33% and 100%, based on the vehicle occupancy rate. In addition, the proposed controller will enhance the battery lifetime. Furthermore, the proposed work ensures significant savings in energy and operating costs and also favors environmental responsibility. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 9(1), 2022 P 58-65

Task Offloading and Execution in Edge-Cloud Collaborative Network Using Genetic Algorithm

Mobile Edge Computing (MEC) system is now one of the most emerging sectors in wireless communication. It provides computation capability near the edge users and reduces the dependency on the Internet Cloud (IC). The edge users offload the task to the MEC server due to the lack of computing resources for executing resource-hungry applications. MEC servers can serve faster as they are quite close to the edge users but have limited computation resources compared to IC. On the other hand, IC has abundant computation resources but offloading to IC will add extra latency to complete tasks execution. In this scenario, an MEC has to decide intelligently which tasks should be executed by itself and which should be sent to IC. In this paper, we addressed this issue of task assignment and execution either on MEC or on IC and formulated an optimization problem to reduce the task processing latency. The problem is Integer Linear and NP-Complete, thus having higher time complexity. In this regard, we proposed a Genetic algorithm-based Meta-Heuristic method to solve the task completion problem considering the delay constraint of the user. Simulation results of the proposed method indicated improvement in terms of successful task execution, task turnaround time, and better Quality of experience (QoE) compared to the state-of-the-art works. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 9(1), 2022 P 42-51

Enhanced SOC estimation in Lithium-ion Batteries using GRU Neural Networks with GSA

State of charge (SOC) is one of the most important metrics to evaluate the performance of the battery management system (BMS) in electric vehicles (EVs). Lithium-ion batteries have been utilized often for SOC estimates in EV applications because of its attractive characteristics such as extended lifespans, high voltages, energy, and capacities. However, dynamic charging and discharging profiles, material degradation, battery aging, chemical reaction, and temperature fluctuations would diverge the accuracy of SOC estimation. Deep learning has proven to become an efficient method for SOC estimation in EVs due to its strong computational capabilities, enhanced generalization performance, and excellent accuracy under dynamic loading profiles. Therefore, this study proposes the use of the gated recurrent unit neural network (GRUNN) for estimating SOC in lithium-ion batteries. The best hyperparameters of GRUNN that enable improving SOC accuracy are found using gravitational search optimization (GSA). To evaluate the functional sustainability of the suggested method, a full battery test bench model is created and accordingly, robustness of the proposed approach is verified under diverse EV drive cycles and aging impacts. The effectiveness of the proposed approach is evaluated by comparing with several existing approaches. The results demonstrate that the suggested method archives SOC error and RMSE in EV driving cycles and ageing effects below 5% and 2%, respectively. The excellent outcomes obtained by the proposed method would certainly enhance the battery charging and discharging profile and EV performance. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 9(1), 2022 P 1-12

Iodine and goitrogens distribution in drinking water of some secondary schools of Kebbi, Nigeria

The objective of this paper is to carry out an evaluation of iodine, calcium, magnesium, and other goitrogenic factors in drinking water obtained from three schools in Kebbi Local Government Area, Kebbi State, Nigeria. Standard methods and procedures including atomic absorption spectroscopy were utilized to attain the objective of the study. One-way analysis of variance (ANOVA) test was carried out and results are significant at (p¡0.05). The amounts of iodine, magnesium, phosphate, and calcium were in the ranges of 0.250 ± 0.02 - 0.120 ± 0.03 ppm, 2.2 ± 0.01- 10.0 ± 0.01 ppm, 0.03 ± 0.0001 -0.07 ± 0.0001 ppm, 3.4 ± 0.01 - 8.5 ± 0.01 ppm. There were diverse levels of the studied goitrogens. Fluoride, bromide, chloride, cyanide, perchlorate, and nitrate are in respective ranges as follows: 1.12 ± 0.02 - 3.14 ± 0.02 ppm, 0.02 ± 0.002 - 0.15 ± 0.002 ppm, 0.61 ± 0.002 - 1.21 ± 0.02 ppm, 0.32 ± 0.001 - 0.51 ± 0.001 ppm, 0.120 ±0.0001- 0.250±0.02 ppm, and 2.2±0.02 – 10.0±0.01 ppm respectively. The result has revealed that the water used in the studied schools contains goitrogens that can contribute to the interference in iodine metabolism and leading to consequent harmful effects on youngsters. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 9(1), 2022 P 23-29

A computational DFT study of Imidazolium based Ionic liquids: binding energy calculation with different anions using Solvation Model

Basic knowledge of chemical events in the gaseous or liquid phase, as well as on surfaces, is required to determine the macroscopic process. The structure of ionic liquids were investigated using quantum chemistry. Cation and anion can bond together, according to the findings. Their binding and interaction energies reveal that they are produced by hydrogen bonds. The mechanism of a room temperature ionic liquid, such as [BMIM][Cl] (1-butyl-3-Methylimidazolium chloride) monomer, was studied using Density functional theory (DFT) computations using the modern continum solvation model (IEFPCM-SMD). Ionic liquid [IL] contains both anions and cations, allowing for greater intermolecular interactions. By analyzing the relative minimum energy of [BMIM][Cl] in the presence and absence of water, we were able to establish the minimum energy structures and probable Cl anion binding sites in [BMIM][Cl] ionic liquid. We have found that all 1-butyl-3-methylimidazolium halide ILs investigated had trans conformations due to the butyl group’s dihedral angles. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 9(1), 2022 P 66-70

Prediction models for skin tears in the elderly: A systematic review and meta-analysis

BackgroundThe prevalence of risk prediction models for skin tears in the elderly is growing; however, there is still debate regarding the usefulness and suitability of these models for clinical use and additional study. ObjectiveThe purpose of this work is to perform a systematic review and meta-analysis of published research on skin tear risk prediction models in the elderly. MethodsWe conducted a comprehensive search of various databases, including Cumulative Index to Nursing and Allied Health Literature (CINAHL), Embase, PubMed, Web of Science, MEDLINE, Scopus, The Cochrane Library, Wanfang Database, China Science and Technology Journal Database (VIP), and China National Knowledge Infrastructure (CNKI), from the beginning until November 27, 2023. Data extraction from the chosen studies encompassed various elements, such as study design, sample size, outcome definition, data source, predictors, model development, and performance. The assessment of bias and applicability was conducted using the Prediction Model Risk of Bias Assessment Tool (PROBAST) checklist. The Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) checklist was utilized to assess the transparency in reporting the prediction models—a meta-analysis of the most common predictors to assess predictor reliability. In addition, a narrative synthesis was carried out to provide an overview of the qualities, bias risk, and effectiveness of the current models. The reporting procedures of this meta-analysis conformed to the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) statement. ResultsOut of the initially retrieved 1499 studies, this review included eight prediction models from eight selected studies. All the studies employed logistic regression to develop prediction models for skin tears. The prevalence of skin tears in the elderly varied from 3.0% to 33.3%. Senile purpura and a history of previous skin tears were the most commonly utilized predictors. The reported values for the area under the curve (AUC) ranged from 0.765 to 0.854. All the studies exhibited a high risk of bias, primarily due to inadequate reporting in the outcome and analysis domains. Furthermore, serious questions concerning their applicability were highlighted by four studies. ConclusionBased on the PROBAST checklist, the current models for predicting skin tears in the elderly showed a high risk of bias. The development of new prediction models with bigger sample sizes, appropriate study designs, and external validation from multiple sources ought to be the primary focus of future research. Patient or public contributionThere was no patient or public contribution to this systematic review. RegistrationPROSPERO registration number: CRD42023494387.

Identifying design criteria for implementing inherent safety in chemical process industries part 1: Design reasoning

AbstractRecurrence of similar accidents is evidenced by past accidents, which show that accidents are not decreasing globally. It is recommended to implement the concept of inherent safety design (ISD) in the chemical process industry as a component of accident prevention strategies. This study aims to identify potential indicators of ISD for each inherent safety (IS) keyword, which will be referred to later as design reasoning (DR), based on 529 selected cases that suggest design changes that enhance the safety of processes, materials, or equipment. The cases were collected from accident cases, IS handbooks, chemical engineering journals, new product brochures, and chemical engineering magazines. The cases have to demonstrate that the new design is safer than the previous design. The design changes were evaluated to extract the corrective action and determine the common strategy employed. This information was then interpreted as DR. The collected DR were subsequently categorized and measured in terms of frequency according to IS keywords. From the statistical analysis, the highest percentage of DR is improve mixing (18%) which accounted from 95 out of 529 cases. The second is loose proximity (15.5%) and the third is fewer equipment (14.9%). For the IS keyword ranking, it can be summarized that: moderation (40%) >minimization (27%) >simplification (21%) >substitution (12%). Results showed that moderation inherent safety is a popular strategy used to implement IS. The findings serve as a valuable reference for designers or engineer attempting to implement ISD in their design task.

Protective effects of ginsenosides on ulcerative colitis: a meta-analysis and systematic review to reveal the mechanisms of action.

Ulcerative colitis (UC) is a chronic inflammatory disease of the colon. Ginsenoside may be an ideal agent for UC treatment. However, its efficacy and safety are unknown. We aim to conduct a systematic evaluation to assess the effects and potential mechanisms of ginsenosides in animal models of UC. Six electronic databases will be searched (PubMed, Embase, Web of Science, China Knowledge Network (CNKI), China Science and Technology Journal Database (CQVIP), and Wanfang Data Knowledge). SYRCLE list will be used to assess the quality of literature, and STATA 15.1 for data analysis. Time-dose effects analysis will be used to reveal the time-dosage response relations between ginsenosides and UC. Ultimately, fifteen studies involving 300 animals were included. Preliminary evidence was shown that ginsenosides could reduce Disease Activity Index (DAI) scores, weight loss, histological colitis score (HCS), spleen weight, Malondialdehyde (MDA), Myeloperoxidase (MPO) activity, interleukin-1β (IL-1β), interleukin 6 (IL-6), tumor necrosis factor α (TNF-α) and increase colon length (CL), myeloperoxidase (GSH), interleukin 4 (IL-4), interleukin 10 (IL-10), Zonula Occludens-1 (ZO-1) and occludin. Results of time-dose interval analysis indicated that ginsenosides at a dosage of 5-200mg/kg with an intervention time of 7-28days were relatively effective. Preclinical evidence suggests that ginsenoside is a novel treatment for UC. And the mechanisms of ginsenosides in treating UC may involve anti-inflammatory, antioxidant, barrier protection, intestinal flora regulation, and immune regulation. Although, due to the high heterogeneity, further large-scale and high-quality preclinical studies are needed to examine the protection of ginsenosides against UC.

Effects of Intrathecal Versus General Anesthesia on Postoperative Cognitive Function in Elderly Hip Fracture Patients: a System Review and Meta Analysis Protocol

Randomized controlled studies have shown that the incidence of postoperative cognitive function in elderly patients undergoing hip fracture surgery varies significantly by anesthesia type. The aim of this study was to assess the effects of intrathecal anesthesia and general anesthesia on postoperative cognitive function in elderly hip fracture by system review and meta-analysis. This study followed the guidance of the Preferred Reporting Items for Systematic Reviews and Meta-analyses. First, the Cochrane Library, Web of Science, PubMed, EMBASE, Chinese Biomedical Literature Database, China National Knowledge Infrastructure, China Science and Technology Journal Database, Wanfang Database, and Clinical Trials Database were searched. Second, the literature was screened and identified for inclusion step by step according to the established inclusion and exclusion criteria using Endnote. Further, Excel 2010 was used to record the basic characteristics and relevant data of the included literature, and the Cochrane Risk of Bias Assessment Tool was used to rate the quality of each included study. Subsequently, meta-analysis was performed using RevMan 5.3 software to assess the effects of intrathecal anesthesia and general anesthesia on postoperative cognitive function of hip fracture in the elderly, and funnel plots were drawn to assess publication bias. The results of this systematic evaluation and meta-analysis will be published in a peer-reviewed journal.