Results Of Evaluation Research Articles

Towards understanding the reaction network in the hydrogenation of CO2-derived ethylene carbonate

The selective hydrogenation of ethylene carbonate (EC) to produce methanol (MeOH) and ethylene glycol (EG) is a crucial route to achieve indirect and efficient utilization of CO2 under mild reaction conditions. Nonetheless, due to the cyclic carbonate structure of EC, the intricate reaction network involved in the hydrogenation process has not been systematically reported yet. Here, a series of Cu/SiO2 catalysts were designed to investigate the related reaction pathways. The catalytic evaluation results, coupled with thermodynamic calculations, reveal that there are several competitive side-reactions other than EC hydrogenation to MeOH and EG, including decarboxylation, hydrolysis and decarbonylation reactions to produce CO2 and CO as byproducts. Moreover, the distribution of products is significantly influenced by evaluation conditions. Furthermore, by doping metal oxides to adjust the surface properties of Cu/SiO2 catalyst, it is found that acidic sites could promote EC hydrolysis, whereas basic sites predominantly facilitate EC decarboxylation and decarbonylation reactions. Overall, a comprehensive reaction network of EC hydrogenation is systematically proposed to provide appropriate evaluation conditions and guidance for designing new catalysts to minimize unwanted side reactions, which is of great significance to enhance the indirect utilization of CO2.

An evaluation model for in-situ bioremediation technology of petroleum hydrocarbon contaminated soil

Considering the interference of the complexity of underground environment to the bioremediation scheme, an evaluation model for bioremediation technology in the soil source area of oil contaminated sites was established. On the basis of traditional CDE model, a compartment model was coupled to express the adsorption and degradation process, and the spatial expression of biodegradation was enriched through environment-dependent factors. The visualization of the model was achieved based on COMSOL Multiphysics software platform. Two sets of indoor sandbox experiments on natural attenuation and bioaugmentation were carried out for 120 days to verify the prediction function of the model. The results showed that bioaugmentation greatly improved the remediation effect. Petroleum hydrocarbons with different occurrence states exhibited different spatial distributions under the influence of environmental factors. The prediction accuracy evaluation results of total petroleum hydrocarbons, bio available hydrocarbons and non extractable hydrocarbons showed excellent fitting degree, and the model had a good prediction function for petroleum hydrocarbon in soil under different bioremediation scenarios. This model can be used to screen bioremediation technical schemes, prevent pollution and assess risk of petroleum hydrocarbon contaminated sites.

Enhanced industrial text classification via hyper variational graph-guided global context integration.

Joint local context that is primarily processed by pre-trained models has emerged as a prevailing technique for text classification. Nevertheless, there are relatively few classification applications on small sample of industrial text datasets. In this study, an approach of employing global enhanced context representation of the pre-trained model to classify industrial domain text is proposed. To achieve the application of the proposed technique, we extract primary text representations and local context information as embeddings by leveraging the BERT pre-trained model. Moreover, we create a text information entropy matrix through statistical computation, which fuses features to construct the matrix. Subsequently, we adopt BERT embedding and hyper variational graph to guide the updating of the existing text information entropy matrix. This process is subjected to iteration three times. It produces a hypergraph primary text representation that includes global context information. Additionally, we feed the primary BERT text feature representation into capsule networks for purification and expansion as well. Finally, the above two representations are fused to obtain the final text representation and apply it to text classification through feature fusion module. The effectiveness of this method is validated through experiments on multiple datasets. Specifically, on the CHIP-CTC dataset, it achieves an accuracy of 86.82% and an F1 score of 82.87%. On the CLUEEmotion2020 dataset, the proposed model obtains an accuracy of 61.22% and an F1 score of 51.56%. On the N15News dataset, the accuracy and F1 score are 72.21% and 69.06% respectively. Furthermore, when applied to an industrial patent dataset, the model produced promising results with an accuracy of 91.84% and F1 score of 79.71%. All four datasets are significantly improved by using the proposed model compared to the baselines. The evaluation result of the four dataset indicates that our proposed model effectively solves the classification problem.

MENINGKATKAN MINAT BERWIRAUSAHA ANGGOTA KARANGTARUNA KECAMATAN TANJUNGANOM

Entrepreneurial activities are activities that are one solution to creating jobs and earning additional income. The partner for this service activity is the government of Sonobekel Village, Getas Village, Warujayeng Village, Tanjunganom District, Nganjuk Regency. One of the partners' problems is that most of the people are farmers and farm workers, so they sometimes experience financial problems. Providing entrepreneurship outreach to members of youth organizations in the three villages is expected to motivate and increase understanding of entrepreneurship. The method of implementing service is by means of lectures held for one day in each village, with three terms. In the first term, the service implementation team introduced the concept of entrepreneurship to the participants, including business planning and various types of business opportunities. In the second term, material and simulations regarding financial management for small businesses are provided, and in the third term there are discussions and questions and answers. The activity participants consisted of 20 members of youth organizations in each village. Evaluation activities were carried out to assess the effectiveness of training in increasing the interest and motivation of youth organization members to become entrepreneurs. Evaluation activities are also used to assess the success of community service activities. The measuring tool is a questionnaire that has been filled in by members of the youth organization. The results of the activity evaluation showed an increase in the interest of youth organizations in entrepreneurship, after being given material and motivation regarding entrepreneurship.

Controlling Product Distribution of Polyethylene Hydrogenolysis Using Bimetallic RuM3 (M = Fe, Co, Ni) Catalysts.

Plastic hydrogenolysis is an attractive approach for producing value-added chemicals due to its mild reaction conditions, but controlling product distribution is challenging due to the formation of undesired CH4. This work reports several bimetallic RuM3/CeO2 (M = Fe, Co, Ni) catalysts that shift the product of low-density polyethylene hydrogenolysis toward longer-chain hydrocarbons. These catalysts were characterized by using X-ray absorption fine structure spectroscopy, electron microscopy imaging, and H2 temperature-programmed reduction. The combined catalytic evaluation and characterization results revealed that the product distribution was regulated by the formation of bimetallic alloys. A model compound, n-hexadecane, was selected to further understand the differences in hydrogenolysis over the Ru-based catalysts. Although a longer reaction time shifted the product toward smaller molecules, the bimetallic (RuCo3/CeO2) catalyst limited the further conversion of C2-C5 into CH4. This work highlights the role of bimetallic alloys in tailoring the interaction with hydrocarbons, thereby controlling the product distribution of polymer hydrogenolysis.

ATURAN ASOSIASI UNTUK REKOMENDASI STRATEGI PEMASARAN PRODUK KERAJINAN MENGGUNAKAN FREQUENT PATTERN GROWTH

Besides selling equipments and spareparts for two-wheeled vehicles, NTN Racing Store also sells handicraft products made from water hyacinth. Lately, NTN Racing Store facing challenges in selling its products, as evidence by the unpredictable monthly revenue. The reason is insufficiency of customer purchasing patterns understanding. The impacts are the declining of the marketing and sales performance for creative water hyacinth handicrafts products since 2020. The main purpose of this studi is to identify which products are purchase together by customers in one transaction when shopping for water hyacinth products. Market Basket Analysis is a rule that determines associations between various attributes and is a key application in the retail industry. This method is use to analyze past consumer purchasing behavior to determine items that are frequently purchase together. Its purpose is to uncover relationships between products purchased by customers in a single transaction. On the other hand, Frequent Pattern Growth is an alternative algorithm use to identify the most frequently occurring datasets within a collection of data. The testing results of 435 transactional data points from NTN Racing Store, using 1% minimum support value and 25% minimum confidence value, resulting four association rules within lift ratios above 1 which indicates significant relationships among itemsets. From these evaluation results, customer purchasing patterns can be identifying trough the association analysis rules. This information can assist the store owner in determining bundling packages to sell and to devise store sales strategies.

Normalization factor database for life cycle impact assessment in China

Normalization is applied to calculate the magnitude of an impact relative to the total impact of a given reference in Life Cycle Assessment (LCA). Normalized profiles of environmental impacts depend on geographical areas due to differences in the industrial and economic activities. So far, Normalization factors for China are not developed to conduct life cycle assessment analysis. The purpose of this research is to propose a database of normalization factors (NFs) covering 18 environmental impact categories in a Chinese context to fill the research gap. To establish the local database, we collected the domestic inventory of emission and resources extraction in China with necessary data extrapolation and then calculated the normalization factors based on the international model ReCiPe2016. Furthermore, normalization factors for each impact category are differentiated into individual and national levels, and the substance contribution proportion of the 18 environmental impact categories is quantified. In addition, we used the Data Quality Indicator (DQI) model to evaluate the data source of impact categories, while analyzing the uncertainty of normalization factors quantitatively based on the evaluation result. The results of the quantitative analyses showed uncertainty intervals for the normalization factors. It was found that ionizing radiation and toxicity got the largest uncertainty intervals among the normalization factors for all impact categories, owing to the low completeness of the inventory of substances in those two impact categories. According to the data quality indicators of impact categories, we located the data gap in domestic inventory and proposed measures to promote comprehensiveness of inventory. This China Normalization Factors Database provides the LCA researchers with a starting point for a subsequent weighing step and improves the accuracy of LCA study at the Chinese territory.

Phytochemical Composition and Toxicological Profiling of Curcuma longa (Turmeric) Root Extract in Rats

Background: Curcuma longa Linn (turmeric) is a common medicinal plant used in Africa for the treatment of diseases. In this study, its extract was evaluated to ascertain its phytochemical composition and toxicity in wistar rats.
 Methods: The prepared rhizome ethanol extract was first subjected to preliminary phytochemical evaluation and then lethal dose (LD50) test. In the sub-acute toxicity test, 20 adult wistar rats assigned to 4 groups of 5 rats each were administered oral graded doses of the extract. Group 1 served as control while groups 2, 3 and 4 received 250, 500 and 1000 mg/kg body weight of the extract over a period of 28 days, after which blood samples were collected for haematological and serum biochemical analyses.
 Results: Results obtained from the phytochemical analysis revealed the presence of saponins, tannins, phenolics, flavonoids, steroids, terpenoids, cardiac glycosides and alkaloids in extract, with alkaloids being most abundant (41.20+0.53 mg/100g) and terpenoids, the least (3.98+0.03 mg/100g). Lethal dose value for the extract was found to be >5000mg/kg body weight. Results of sub-acute toxicity evaluation of the extract showed no deleterious effect on liver and kidney function parameters with these parameters being not significantly different from their control values (p<0.05). Treatment with the extract also caused significant hypolipidemia (p<0.05) but increased the values of high-density lipoprotein cholesterol. The activities of antioxidant enzymes and red blood cell counts were also significantly increased following treatment with the extract, especially at higher dose levels.
 Conclusion: Therefore, we conclude that C. longa extract may be safe for use in the treatment of diseases when administered via the oral route.

Public Private Partnership to Brownfield Remediation Projects in China: A Combined Risk Evaluation Approach

Brownfields have been receiving significant attention all over the world because of their potential threats to the environment and public health. However, a funding shortage constitutes the main obstacle to the brownfield remediation (BR). In China, to ease financial dilemmas, the governments seek collaborations with private-sector companies, i.e., the Public Private Partnership (PPP) mode. Despite all the benefits, BR and PPP contain high risks, making stakeholders extremely cautious about investing in such projects. To support the decision-making process of the public and private parties, this paper designs a comprehensive approach to evaluate the risks of BR PPP projects in China. In more detail, several commonly used risk methods, such as TOPSIS, GRE, and FSE, are employed to construct a combined risk evaluation process, which applies multiple combined evaluation techniques to iteratively integrate individual results from those methods until a valid common result is achieved. To show the practical implementation procedure of the proposed combined approach, a hypothetical case study is performed to assess the risks of seven BR PPP projects. The analytical process also verifies that the consistency and reliability of the risk evaluation result can be achieved effectively and efficiently by jointly deploying multiple risk methods through combined techniques. The proposed decision framework facilitates a novel research idea in evaluating complicated risk situations, and can be applied to other similar scenarios where uncertainties and inconsistencies are inevitable.

Blockchain-driven framework for financing credit in small and medium-sized real estate enterprises

PurposeThis study aimed to improve the financing credit evaluation for small and medium-sized real estate enterprises (SMREEs). A financing credit evaluation model was proposed, and a blockchain-driven financing credit evaluation framework was designed to improve the transparency, credibility and applicability of the financing credit evaluation process.Design/methodology/approachThe design science research methodology was adopted to identify the main steps in constructing the financing credit model and blockchain-driven framework. The fuzzy analytic hierarchy process (FAHP)–entropy weighting method (EWM)–set pair analysis (SPA) method was used to design a financing credit evaluation model. Moreover, the proposed framework was validated using data acquired from actual cases.FindingsThe results indicate that: (1) the proposed blockchain-driven financing credit evaluation framework can effectively realize a transparent evaluation process compared to the traditional financing credit evaluation system. (2) The proposed model has high effectiveness and can achieve efficient credit ranking, reflect SMREEs' credit status and help improve credit rating.Originality/valueThis study proposes a financing credit evaluation model of SMREEs based on the FAHP–EWM–SPA method. All credit rating data and evaluation process data are immediately stored in the proposed blockchain framework, and the immutable and traceable nature of blockchain enhances trust between nodes, improving the reliability of the financing credit evaluation process and results. In addition, this study partially fulfills the lack of investigations on blockchain adoption for SMREEs' financing credit.

Guide to the preparation of speech reports for implanted children: opinion of specialists.

To develop a guide for the preparation of speech-language reports of implanted children to be shared among speech-language pathologists of cochlear implant (CI) services and rehabilitation professionals. The Delphi method was used to select the relevant and fundamental items that should be included in the two versions proposed for the guide: Guide 1 - Speech-language reports provided by the CI services to rehabilitators, and Guide 2 - Speech-language reports provided by the rehabilitators to CI services. Twenty-one speech therapists specialized and with experience in cochlear implants and auditory rehabilitation participated in the discussion and judgment of the items during the selection rounds. Consensus was considered when the item reached agreement equal to or greater than 80% among participants, being selected to compose the two guides. After the two rounds, 21 items from Guide 1 reached consensus among therapists, that is, more than 80% of them agreed that these items should be present in the report sent by the CI service. For Guide 2, 22 items analyzed by speech therapists working in CI services in the postoperative sector were selected in the second round. Based on the analysis of the two rounds, the "Guide for the preparation of speech-language pathology reports: intersection between CI service and rehabilitators" was developed. This material can be applied in the follow-up of implanted children, standardizing the information shared about the electronic device, evaluation results, monitoring of results and therapeutic process of this population.

Guia para a elaboração de relatórios fonoaudiológicos de crianças implantadas: opinião de especialistas

ABSTRACT Purpose To develop a guide for the preparation of speech-language reports of implanted children to be shared among speech-language pathologists of cochlear implant (CI) services and rehabilitation professionals. Methods The Delphi method was used to select the relevant and fundamental items that should be included in the two versions proposed for the guide: Guide 1 - Speech-language reports provided by the CI services to rehabilitators, and Guide 2 - Speech-language reports provided by the rehabilitators to CI services. Twenty-one speech therapists specialized and with experience in cochlear implants and auditory rehabilitation participated in the discussion and judgment of the items during the selection rounds. Consensus was considered when the item reached agreement equal to or greater than 80% among participants, being selected to compose the two guides. Results After the two rounds, 21 items from Guide 1 reached consensus among therapists, that is, more than 80% of them agreed that these items should be present in the report sent by the CI service. For Guide 2, 22 items analyzed by speech therapists working in CI services in the postoperative sector were selected in the second round. Conclusion Based on the analysis of the two rounds, the “Guide for the preparation of speech-language pathology reports: intersection between CI service and rehabilitators” was developed. This material can be applied in the follow-up of implanted children, standardizing the information shared about the electronic device, evaluation results, monitoring of results and therapeutic process of this population.

Efficient grain size evaluation based on single direction measurement of ultrasonic backscattering coefficient

<sec>GH4742 nickel-based superalloy exhibits excellent mechanical properties, and grain size is a key factor affecting its performance. A physical model-based ultrasonic backscattering method makes grain size measurement accurate and efficient. Nevertheless, it is constrained by complex models or multiple measurements taken from various beam angles. As a result, a backscattering coefficient method that requires only a single measurement for grain size evaluation is proposed. In contrast to the existing methods, the proposed method solely focuses on the backscattering coefficient component of the backscattering signal. It effectively eliminates the influence of unrelated factors, such as the measurement system and the acoustic field, through the utilization of reference signals.</sec><sec>The independent scattering model is employed to derive the backscattering coefficient, which solely pertains to the material itself. The relationship between grain size and backscattering coefficient is described by using a spatial correlation function. To consider the irrelevant factors, an experimental measurement method is developed by using the reference signals. Through numerical calculation and analysis, it has been observed that the backscattering coefficient is closely related to the frequency. When the product of the wavenumber and the grain size is significantly greater than 1 (<inline-formula><tex-math id="M1">\begin{document}$ ka\gg 1 $\end{document}</tex-math><alternatives><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="7-20231959_M1.jpg"/><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="7-20231959_M1.png"/></alternatives></inline-formula>), a Stochastic scattering limit is reached. Conversely, when <inline-formula><tex-math id="M2">\begin{document}$ ka\ll 1 $\end{document}</tex-math><alternatives><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="7-20231959_M2.jpg"/><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="7-20231959_M2.png"/></alternatives></inline-formula>, a Rayleigh scattering limit is observed. Furthermore, the backscattering coefficient is directly proportional to the grain size. As a general trend, larger grain sizes result in higher backscattering coefficient.</sec><sec>Three sets of GH4742 specimens with different grain sizes are prepared for phased array ultrasound experiments. It can be observed that the experimental backscattering coefficients, root mean square (RMS) values, and the amplitude trend of time domain signal are consistent. To perform grain size inversion, the backscattering coefficients in the effective bandwidth range of the probe are selected. By utilizing the least-square method, the theoretical backscattering coefficient is employed to fit the curves of the experimental backscattering coefficients. The evaluation results are compared with those obtained by metallographic analysis. The results show that the grain sizes obtained by the proposed method have a maximum relative error of –22.7% and a minimum relative error of –3.7%.</sec>

PageRank talent mining algorithm of power system based on cognitive load and DPCNN

AbstractPageRank talent mining in power system is an effective means for enterprises to recruit talents, which can correctly recommend talents in practical applications. At present, the mining evaluation index system is not perfect, and the consistency coefficient between the evaluation results and the actual situation is low in practical applications. Therefore, PageRank talent mining algorithm in power system based on cognitive load and dilated convolutional neural network (DPCNN) is proposed. The cognitive load and DPCNN are used to establish a talent capability evaluation system, calculate the index weight value, construct the PageRank talent capability evaluation model of the power system according to the corresponding weight of the index, determine the membership range of the index, calculate the comprehensive score of the appraiser's ability, and determine the ability level of the appraiser, thus realizing the PageRank talent mining algorithm of the power system. The experimental results show that the algorithm has high accuracy and objectivity, good encryption effect, cannot crack the attack node, the prediction error and the prediction relative error are closest to the standard value, the maximum error is 0.51, the maximum relative error is 0.82, and can achieve the accurate prediction of talent demand.

Sentiment Analysis on Twitter Using Deep Belief Network Optimized with Particle Swarm Optimization

Deep Belief Network is a type of artificial neural network that is widely used in machine learning and deep learning tasks that allows it to learn hierarchical representations of the input data. However, Deep Belief Network has a drawback of being sensitive to hyperparameters. DBN has several hyperparameters that need to be chosen appropriately for the network to function effectively. Poor hyperparameter choices can lead to unstable training or poor performance. Therefore, the Particle Swarm Optimization algorithm is used to search for the best hyperparameters, which can lead to stable training and improved performance. The purpose of this study is to analyze public sentiment on Twitter using the Deep Belief Network method and to optimize it using Particle Swarm Optimization. The evaluation results obtained in this study are 71.4% accuracy, 71.7% precision, 71.4% recall, and 71.2% F1-score in the Deep Belief Network model which is optimized by the Particle Swarm Optimization algorithm, whereas when compared with the Deep Belief Network model alone gets evaluation results of 68.3% Accuracy, 69.0% Precision, 68.3% Recall and 68.0% F1-score. These results indicate that the use of the Particle Swarm Optimization algorithm is quite influential in analyzing sentiment in text.

Constant-Round Private Decision Tree Evaluation for Secret Shared Data

Decision tree evaluation is extensively used in machine learning to construct accurate classification models. Often in the cloud-assisted communication paradigm cloud servers execute remote evaluations of classification models using clients' data. In this setting, the need for private decision tree evaluation (PDTE) has emerged to guarantee no leakage of information for the client's input nor the service provider's trained model i.e., decision tree. In this paper, we propose a private decision tree evaluation protocol based on the three-party replicated secret sharing (RSS) scheme. This enables us to securely classify inputs without any leakage of the provided input or the trained decision tree model. Our protocol only requires constant rounds of communication among servers, which is useful in a network with longer delays.Ma et al. (NDSS 2021) presented a lightweight PDTE protocol with sublinear communication cost with linear round complexity in the size of the input data. This protocol works well in the low latency network such as LAN while its total execution time is unfavourably increased in the WAN setting. In contrast, Tsuchida et al. (ProvSec 2020) constructed a constant round PDTE protocol at the cost of communication complexity, which works well in the WAN setting. Although their construction still requires 25 rounds, it showed a possible direction on how to make constant round PDTE protocols. Ji et al. (IEEE Transactions on Dependable and Secure Computing) presented a simplified PDTE with constant rounds using the function secret sharing (FSS) at the cost of communication complexity. Our proposed protocol only requires five rounds among the employed three servers executing secret sharing schemes, which is comparable to previously proposed protocols that are based on garbled circuits and homomorphic encryption. To further demonstrate the efficiency of our protocol, we evaluated it using real-world classification datasets. The evaluation results indicate that our protocol provides better concrete performance in the WAN setting that has a large network delay.

Scoping Review of Technological Solutions for Community Dwelling Older Adults and Implications for Instrumental Activities of Daily Living.

Aging in place is not without its challenges, with physical, psychological, social, and economic burdens on caregivers and seniors. To address these challenges and promote active aging, technological advancements offer a range of digital tools, applications, and devices, enabling community dwelling older adults to live independently and safely. Despite these opportunities, the acceptance of technology among the older adults remains low, often due to a mismatch between technology development and the actual needs and goals of seniors. The aim of this review is to identify recent technological solutions that monitor the health and well-being of aging adults, particularly within the context of instrumental activities of daily living (IADL). A scoping review identified 52 studies that meet specific inclusion criteria. The outcomes were classified based on social connectedness, autonomy, mental health, physical health, and safety. Our review revealed that a predominant majority (82%) of the studies were observational in design and primarily focused on health-related IADLs (59%) and communication-related IADLs (31%). Additionally, the study highlighted the crucial role of involving older adults in study design processes, with only 8 out of the 52 studies incorporating this approach. Our review also established the interview method as the most favoured technology evaluation tool for older adults' studies. The metrics of 'usability' and 'acceptance' emerged as the most frequently employed measures for technology assessment. This study contributes to the existing literature by shedding light on the implications of technological solutions for community dwelling older adults, emphasizing the types of technologies employed and their evaluation results.

Edible honeysuckle as a source of fruits promising for new functional food products development

The possibilities of introducing honeysuckle berries into the plant-based functional product being developed as a component that enriches its composition are discussed basing on initial results of an experimental evaluation of the introduction of original lozenges based into the diet of laboratory animals (rats). The lozenges were prepared using apple pulp with the addition of mixed berries of five honeysuckle varieties (Berel, Sinichka, Moskovskaya M103 (L. caеrulea varieties), Kamchadalka and Chelyabinka (L. kamtschatica varieties). the most variable for used berries was the sugar content in the juice (from 8 to 11%), somewhat less the content of the ash component and the amount of phenolic compounds differed, but dry matter content (about 12% for all varieties) was similar. The highest polyphenols accumulation was detected for Sinichka variety (8.3 mg/g fresh fruit weight). The two-month consumption of functional lozenges with honeysuckle in a volume of 50% of the daily diet led to a decrease in systolic pressure in the experimental group of rats by 19% compared to the control. Also this diet resulted in less weight gain in experimental animals, their body weight was 30% less than that of control rats after a two-month exposure.

Resective Epilepsy Surgery after Corpus Callosotomy in Children with Lennox-Gastaut Syndrome

Purpose: This study examined the characteristics and outcomes of resective epilepsy surgery following corpus callosotomy (CC) in children with Lennox-Gastaut syndrome (LGS).Methods: We retrospectively analyzed 17 children with LGS who underwent resective surgery (RS) after CC over a span of 10 years, with a minimum of 2 years of follow-up, at a single tertiary epilepsy center in Korea.Results: Of the 17 patients, 13 (73.5%) demonstrated favorable surgical outcomes (Engel class I or II) at 1 year after RS, and eight (47.1%) were ultimately free of seizures 2 years after surgery. A significantly larger decrease in the number of anti-seizure medications taken from before to 2 years after the final surgical procedure was observed in the group that became seizure-free than in the group with persistent seizures (<i>P</i>=0.062). Furthermore, a significantly greater decline in daily adaptive function was found in the persistent seizure group (<i>P</i>=0.059). The baseline characteristics, results of presurgical evaluation, and treatment-related factors assessed prior to surgery showed no significant differences between the seizure-free group and the group with persistent seizures.Conclusion: In conclusion, RS may be a viable option for patients with LGS who exhibit lateralization and/or localization on presurgical evaluation after CC, as the procedure may reveal a concealed primary focus. The proactive implementation of two-stage epilepsy surgery could provide significant seizure reduction and preservation of cognitive function in carefully selected patients with LGS.

Quality evaluation of salt-fired Eucommiae Cortex based on HPLC fingerprint, multi-component content determination, and chemometrics

This study established an HPLC fingerprint and multi-component content determination method for salt-fired Eucommiae Cortex, and evaluated the quality of salt-fired Eucommiae Cortex from different sources using fingerprint similarity evaluation, cluster analysis(CA), principal component analysis(PCA), and orthogonal partial least square discriminate analysis(OPLS-DA). HPLC was launched on a Cosmosil 5C_(18)-MS-Ⅱ column(4.6 mm×250 mm, 5 μm) by gradient elution with a mobile phase of methanol-0.2% phosphoric acid aqueous solution at a flow rate of 1.0 mL·min~(-1), detection wavelength of 238 nm, column temperature of 30 ℃, and an injection volume of 10 μL. The results of fingerprint similarity evaluation for 20 batches of salt-fired Eucommiae Cortex indicated that, except for batch S3 with a similarity of 0.893, the similarity of the other 19 batches was of ≥ 0.919, suggesting good similarity. Fourteen common peaks were calibrated and seven common peaks were identified including geniposidic acid. The mass fractions of geniposidic acid, chlorogenic acid, geniposide, genipin, pinoresinol diglucoside, liriodendrin, and pinoresinol-4-O-β-D-glucopyranoside were 0.062 0%-0.426 9%, 0.024 9%-0.116 5%, 0.009 5%-0.052 9%, 0.005 5%-0.034 8%, 0.115 9%-0.317 8%, 0.016 4%-0.108 8%, and 0.026 4%-0.039 8%, respectively. Using CA, PCA, and OPLS-DA, the 20 batches of salt-fired Eucommiae Cortex were classified into three categories. Additionally, through the analysis of variable importance in projection(VIP) under OPLS-DA, two differential quality markers, geniposidic acid and chlorogenic acid, were identified. The established HPLC fingerprint and multi-component content determination method is stable and reliable, providing a reference for quality control of salt-fired Eucommiae Cortex.