Objective Basis Research Articles

Nomogram model for the diagnosis of solitary nodular pulmonary mucinous adenocarcinoma

The objective of this study was to develop a nomogram model based on the natural progression of tumor and other radiological features to discriminate between solitary nodular pulmonary mucinous adenocarcinoma and non-mucinous adenocarcinomas. A retrospective analysis was conducted on 15,655 cases of lung adenocarcinoma diagnosed at our institution between January 2010 and June 2023. Primary nodular invasive mucinous adenocarcinomas and non-mucinous adenocarcinomas with at least two preoperative CT scans were included. These patients were randomly assigned to training and validation sets. Univariate and multivariate analyses were employed to compare tumor growth rates and clinical radiological characteristics between the two groups in the training set. A nomogram model was constructed based on the results of multivariate analysis. The diagnostic value of the model was evaluated in both the training and validation sets using calibration curves and receiver operating characteristic curves (ROC). The study included 174 patients, with 58 cases of mucinous adenocarcinoma and 116 cases of non-mucinous adenocarcinoma. The nomogram model incorporated the maximum tumor diameter, the consolidation/tumor ratio (CTR), and the specific growth rate (SGR) to generate individual scores for each patient, which were then accumulated to obtain a total score indicative of the likelihood of developing mucinous or non-mucinous adenocarcinoma. The model demonstrated excellent discriminative ability with an area under the receiver operating characteristic curve of 0.784 for the training set and 0.833 for the testing set. The nomogram model developed in this study, integrating SGR with other radiological and clinical parameters, provides a valuable and accurate tool for differentiating between solitary nodular pulmonary mucinous adenocarcinoma and non-mucinous adenocarcinomas. This prognostic model offers a robust and objective basis for personalized management of patients with pulmonary adenocarcinomas.

RGB Color Model: Effect of Color Change on a User in a VR Art Gallery Using Polygraph.

This paper presents computer and color vision research focusing on human color perception in VR environments. A VR art gallery with digital twins of original artworks is created for this experiment. In this research, the field of colorimetry and the application of the L*a*b* and RGB color models are applied. The inter-relationships of the two color models are applied to create a color modification of the VR art gallery environment using C# Script procedures. This color-edited VR environment works with a smooth change in color tone in a given time interval. At the same time, a sudden change in the color of the RGB environment is defined in this interval. This experiment aims to record a user's reaction embedded in a VR environment and the effect of color changes on human perception in a VR environment. This research uses lie detector sensors that record the physiological changes of the user embedded in VR. Five sensors are used to record the signal. An experiment on the influence of the user's color perception in a VR environment using lie detector sensors has never been conducted. This research defines the basic methodology for analyzing and evaluating the recorded signals from the lie detector. The presented text thus provides a basis for further research in the field of colors and human color vision in a VR environment and lays an objective basis for use in many scientific and commercial areas.

A fourfold-objective-based cloud privacy preservation model with proposed association rule hiding and deep learning assisted optimal key generation

ABSTRACT Numerous studies have been conducted in an attempt to preserve cloud privacy, yet the majority of cutting-edge solutions fall short when it comes to handling sensitive data. This research proposes a “privacy preservation model in the cloud environment”. The four stages of recommended security preservation methodology are “identification of sensitive data, generation of an optimal tuned key, suggested data sanitization, and data restoration”. Initially, owner’s data enters the Sensitive data identification process. The sensitive information in the input (owner’s data) is identified via Augmented Dynamic Itemset Counting (ADIC) based Associative Rule Mining Model. Subsequently, the identified sensitive data are sanitized via the newly created tuned key. The generated tuned key is formulated with new fourfold objective-hybrid optimization approach-based deep learning approach. The optimally tuned key is generated with LSTM on the basis of fourfold objectives and the new hybrid MUAOA. The created keys, as well as generated sensitive rules, are fed into the deep learning model. The MUAOA technique is a conceptual blend of standard AOA and CMBO, respectively. As a result, unauthorized people will be unable to access information. Finally, comparative evaluation is undergone and proposed LSTM+MUAOA has achieved higher values on privacy about 5.21 compared to other existing models.

Predicting TCM patterns in PCOS patients: An exploration of feature selection methods and multi-label machine learning models

BackgroundTraditional Chinese Medicine (TCM) offers individualized treatment for Polycystic Ovary Syndrome (PCOS) through pattern differentiation, but the subjectivity of TCM diagnoses can lead to inconsistent outcomes. Integrating machine learning (ML) offers an objective basis to support TCM diagnoses. This study aims to evaluate various feature selection techniques and multi-label ML algorithms to develop an effective predictive model for classifying TCM patterns in PCOS patients, thereby enhancing diagnostic standardization and treatment personalization. MethodsThe study utilized a dataset comprising 432 patients with PCOS, exhibiting one or more of five TCM patterns. Feature selection began with Variance Thresholding (VT), followed by a comparison of five advanced techniques: Statistical Analysis Test, Recursive Feature Elimination with Cross-Validation (RFECV), Least Absolute Shrinkage and Selection Operator Regression, BorutaShap, and ReliefF. To ascertain the most effective model for predicting PCOS TCM patterns, four ML algorithms—Support Vector Machine, Logistic Regression, Extreme Gradient Boosting (XGBoost), and Artificial Neural Networks—were evaluated against the identified feature set. ResultsVT reduced the feature count from 224 to 174. RFECV emerged as the most effective feature selection method, identifying 67 key features. XGBoost emerged as the top-performing model, demonstrating superior testing accuracy (0.7870), F1 score (0.9519), and Hamming loss (0.0481) with RFECV-optimized features. ConclusionsThe RFECV-XGBoost model proved effective for classifying TCM patterns in PCOS. It emphasizes the necessity of precise feature selection and the significant capabilities of ML in advancing TCM pattern diagnostics, marking a significant step toward enhancing precise and personalized healthcare in biomedical studies.

Analysis of the Multi Objective Optimization by Ratio Analysis (MOORA) Method in Determining Pilot Areas at PT. XYZ

This research analyzes the application of the Multi Objective Optimization by Ratio Analysis (MOORA) method model in determining the Pilot Area at PT XYZ. This method is used to evaluate various performance criteria, including customer satisfaction, productivity, service quality, and operational efficiency. Currently, the Pilot Area assessment and selection process at PT XYZ is still done manually, which causes a lack of accuracy and efficiency. MOORA was chosen for its ability to handle multi-criteria decision-making problems more systematically and objectively. The analysis results showed that Alternative Area 7 obtained the highest final score of 0.39, placing it as an area with superior performance. The application of MOORA is proven to improve accuracy and efficiency in the Pilot Area determination process, providing a more objective basis for decision-making. By using MOORA, PT XYZ can evaluate area performance more comprehensively and accountably. This research recommends that PT XYZ implement the MOORA method thoroughly and conduct periodic evaluations of the methods used. For theory development, PT XYZ can add specific evaluation criteria according to company needs. The implementation of these suggestions is expected to improve the quality of service and competitiveness of PT XYZ in the global market. Further research is expected to compare MOORA with other methods to strengthen the validity of the results. Thus, this research not only provides a practical contribution to PT XYZ but also adds academic insight into the application of multi-criteria optimization methods in the context of performance management and service improvement.

Cnn-trans model: A parallel dual-branch network for fundus image classification

The existence of fundus diseases not only endangers people’s vision, but also brings serious economic burden to the society. Fundus images are an objective and standard basis for the diagnosis of fundus diseases. With the continuous advancement of computer science, deep learning methods dominated by convolutional neural networks (CNN) have been widely used in fundus image classification. However, the current CNN-based fundus image classification research still has a lot of room for improvement: CNN cannot effectively avoid the interference of repeated background information and has limited ability to model the whole world. In response to the above findings, this paper proposes the CNN-Trans model. The CNN-Trans model is a parallel dual-branch network, which is the two branches of CNN-LSTM and Vision Transform (ViT). The CNN-LSTM branch uses Xception after transfer learning. As the original feature extractor, LSTM is responsible for dealing with the gradient disappearance problem in neural network iterations before the classification head, and then introduces a new type of lightweight attention mechanism between Xception and LSTM: Coordinate Attention, so as to emphasize the key information related to classification and suppress the less useful repeated background information; while the self-attention mechanism in the ViT branch is not limited by local interactions, it can establish long-distance dependence on the target and extract global features. Finally, the concatenation (Concat) operation is used to fuse the features of the two branches. The local features extracted by the CNN-LSTM branch and the global features extracted by the ViT branch form complementary advantages. After feature fusion, more comprehensive image feature information is sent to the to the classification layer. Finally, after a large number of experimental tests and comparisons, the results show that: the CNN-Trans model achieved an accuracy of 80.68% on the fundus image classification task, and the CNN-Trans model has a classification that is comparable to the state-of-the-art methods. performance..

Association between neural prosody discrimination and language abilities in toddlers: a functional near-infrared spectroscopy study

BackgroundLanguage delay affects near- and long-term social communication and learning in toddlers, and, an increasing number of experts pay attention to it. The development of prosody discrimination is one of the earliest stages of language development in which key skills for later stages are mastered. Therefore, analyzing the relationship between brain discrimination of speech prosody and language abilities may provide an objective basis for the diagnosis and intervention of language delay.MethodsIn this study, all cases(n = 241) were enrolled from a tertiary women’s hospital, from 2021 to 2022. We used functional near-infrared spectroscopy (fNIRS) to assess children’s neural prosody discrimination abilities, and a Chinese communicative development inventory (CCDI) were used to evaluate their language abilities.ResultsNinety-eight full-term and 108 preterm toddlers were included in the final analysis in phase I and II studies, respectively. The total CCDI screening abnormality rate was 9.2% for full-term and 34.3% for preterm toddlers. Full-term toddlers showed prosody discrimination ability in all channels except channel 5, while preterm toddlers showed prosody discrimination ability in channel 6 only. Multifactorial logistic regression analyses showed that prosody discrimination of the right angular gyrus (channel 3) had a statistically significant effect on language delay (odd ratio = 0.301, P < 0.05) in full-term toddlers. Random forest (RF) regression model presented that prosody discrimination reflected by channels and brain regions based on fNIRS data was an important parameter for predicting language delay in preterm toddlers, among which the prosody discrimination reflected by the right angular gyrus (channel 4) was the most important parameter. The area under the model Receiver operating characteristic (ROC) curve was 0.687.ConclusionsNeural prosody discrimination ability is positively associated with language development, assessment of brain prosody discrimination abilities through fNIRS could be used as an objective indicator for early identification of children with language delay in the future clinical application.

Towards automatic forecasting of lung nodule diameter with tabular data and CT imaging

Objective:This study aims to forecast the progression of lung cancer by estimating the future diameter of lung nodules. Methods:This approach uses as input the tabular data, axial images from tomography scans, and both data types, employing a ResNet50 model for image feature extraction and direct analysis of patient information for tabular data. The data are processed through a neural network before prediction. In the training phase, class weights are assigned based on the rarity of different types of nodules within the dataset, in alignment with nodule management guidelines. Results:Tabular data alone yielded the most accurate results, with a mean absolute deviation of 0.99 mm. For malignant nodules, the best performance, marked by a deviation of 2.82 mm, was achieved using tabular data applying Lung-RADS class weights during training. The tabular data results highlight the influence of using the initial nodule size as an input feature. These results surpass the literature reference of 348-day volume doubling time for malignant nodules. Conclusion:The developed predictive model is optimized for integration into a clinical workflow after detecting, segmenting, and classifying nodules. It provides accurate growth forecasts, establishing a more objective basis for determining follow-up intervals. Significance:With lung cancer’s low survival rates, the capacity for precise nodule growth prediction represents a significant breakthrough. This methodology promises to revolutionize patient care and management, enhancing the chances for early risk assessment and effective intervention.

A holistic approach to assessing REDD+ forest loss baselines through ex post analysis

Abstract The implementation of REDD+ (Reducing Emissions from Deforestation and Forest Degradation) projects has become a key Nature Based Solutions (NBS) strategy to protect at-risk forests using the sale of verified emission reductions (carbon credits) as financing, generated by reducing forest loss against counterfactual baseline scenarios. Controversy over the reasonableness of such baseline scenarios has thrown this nascent market mechanism into disarray. While new technical approaches to baseline-setting that promise wider market acceptance are set to roll out in the coming years, existing projects are becoming unviable, as carbon credit buyers reduce investment due to lost confidence in the integrity of emissions reduction claims. Transparent, reproducible methods to assess existing REDD+ project baselines are needed in order to provide a clearer picture of the real impact of projects, and provide an objective basis on which investment decisions can be made today. Here we introduce such a method. .. In contrast to existing studies which utilize only one method to create a single “control,” we integrate actual forest loss rates from a variety of control sites to establish a “zone of reasonable accuracy (or ZORA)”. Application of our method in Cambodia, using two geospatial datasets (one global and one locally calibrated), shows that all three project baselines fall within or below ZORA. This approach is fully reproducible, and provides a transparent way for analysts to assess REDD+ baselines during this critical time when investment in forest protection must increase dramatically and without delay.

Analysis of green infrastructure network (GIN) based on morphological spatial-temporal pattern analysis (MSTPA): A case study of Shijingshan district in Beijing

Abstract Rapid urbanization results in habitat fragmentation. GIN analysis provides an objective basis for the complete construction of ecological networks. Although there is currently no uniform standard for GIN analysis, this study found that the core region and the connectivity between the core became two key elements of GIN analysis. By analyzing the morphological recognition of GIN, Morphological Spatial Pattern Analysis (MSPA) is targeted in the core area and connectivity analysis, so it has become the mainstream of GIN analysis methods. At the same time, this study finds that there are commonalities in MSPA analysis methods of different scholars. However, it should be noted that GIN analysis by MSPA method in China has two main research gaps: First, it lacks attention to the space scale of urban administrative district level; Second, there is a lack of research on GIN that considers time as a factor. In order to fill these two research gaps, the Shijingshan District of Beijing was selected as the research site to conduct GIN analysis based on Morphological Spatial-Temporal Pattern Analysis (MSTPA). At the same time, the analysis method and data of this study can provide a reference for future scholars to carry out the same type of research.

Multimodal imaging evaluation of early neurological deterioration following acute ischemic stroke.

Early neurologic deterioration occurs in up to one-third of patients with acute ischemic stroke (IS), often leading to poor functional outcomes. At present, few studies have applied amide proton transfer (APT) imaging to the evaluation of early neurological deterioration (END). This study analyzed the value of computed tomography perfusion (CTP) combined with multimodal magnetic resonance imaging (MRI) in patients with acute IS with END. This retrospective study included patients with acute IS who were admitted to the neurology inpatient department in a tertiary hospital from October 2021 to June 2023. Patients with acute IS underwent CTP within 24 hours of stroke onset and MRI [arterial spin labeling (ASL), susceptibility-weighted imaging (SWI), and APT] within 7 days. END was defined as an elevation of ≥2 points on the National Institute of Health Stroke Scale (NIHSS) within 7 days of stroke onset. Univariable and multivariable analyses were used to compare clinical and imaging biomarkers in patients with acute IS with and without END. The performance of potential biomarkers in distinguishing between the two groups was evaluated using receiver operating characteristic (ROC) curve analysis. Among the 70 patients with acute IS, 20 (29%) had END. After conducting univariable analysis, variables were selected for entry into a binary logistic regression analysis based on our univariable analysis results, previous research findings, clinical experience, and methodological standards. The results indicated that relative cerebral blood volume (CBV) on CTP, relative cerebral blood flow (CBF) on ASL, and relative signal intensity on amide proton transfer-weighted (APTw) imaging were independent risk factors for END. The areas under the ROC curves for these risk factors were 0.710 [95% confidence interval (CI): 0.559-0.861, P=0.006], 0.839 (95% CI: 0.744-0.933, P<0.001), and 0.804 (95% CI: 0.676-0.932, P<0.001), respectively. The combined area under the curve (AUC), sensitivity, and specificity of the four indices (0.941, 100%, and 78%, respectively) were higher than those of the four indices alone. CTP combined with multi-modal MRI better evaluated hemodynamics, tissue metabolism, and other relevant patient information, providing an objective basis for the clinical assessment of patients with acute IS with END and facilitating the development of accurate and personalized treatment plans.

Benefits of customizing dust hazard controls to meet your needs

AbstractThe OSHA Combustible Dust National Emphasis Program (NEP), recent studies commissioned by the National Fire Protection Association (NFPA) Research Foundation and U.S. Chemical Safety and Hazard Investigation Board (CSB), as well as emphasis by insurance companies have brought more urgency to combustible dust safety. NFPA standards are being revised for consistency and clarity of standards for combustible dust safety. While that is a significant improvement from conditions of past decades, the situation can be improved even more by implementing risk‐based methods to help develop application‐specific explosion protection. Many facilities that handle combustible particulate solids follow prescriptive requirements of applicable NFPA standards, especially for new installations. Decisions are often driven primarily by meeting codes and standards with minimum capital cost. Legacy installations that do not meet current standards typically claim “grandfather” status as the reason for not implementing the current standard requirement or good engineering practice. The goal of this paper is to present a method of adapting the familiar process safety risk matrix to assess likelihood of a deflagration event rather than the overall the risk of a consequence. This method provides an objective basis to assist staff of a legacy facility to make decisions to prioritize capital improvement projects, to follow current good engineering practice, and to stop relying on “grandfather” status. This paper makes the broad, practical assumption that any deflagration event has serious consequences. The magnitude of those consequences may vary based on facility siting, deflagration area of effect, population of personnel in the affected area, or business criticality of affected equipment and structures. Decisions based solely on the likelihood matrix may inadvertently prioritize a more likely, but less consequential, concern over an event that may put multiple people at risk, or have serious commercial implications. Methods such as Hazard and Operability Study (HAZOP), Failure Modes and Effects Analysis (FMEA), Fault Tree Analysis (FTA) and Layer of Protection Analysis (LOPA) are useful to more comprehensively understand risks. Flexible, outside‐the‐box thinking in applying safeguards, Independent Protection Layers (IPLs), administrative controls, and facility siting may provide a better solution than merely following prescriptive code without making the effort to fully understand the hazard.

Predictive Value of Thromboelastography for Postoperative Blood Loss in Infants Undergoing Cardiac Surgery

Thromboelastography (TEG) has emerged as a critical tool in promptly identifying the risk of postoperative bleeding, especially in infants and young children undergoing cardiac surgery. By meticulously analyzing TEG parameters, clinicians gain a profound understanding of patients&amp;apos; coagulation status, enabling timely intervention to minimize and manage postoperative bleeding risks. The present study adopts a rigorous, prospective, and observational approach to explore the relationship between intraoperative TEG test results and postoperative blood loss. Furthermore, it aims to assess the potential of intraoperative TEG testing as an early predictor of postoperative bleeding in high-risk pediatric patients. In this process, a key focus is placed on perioperative red blood cell suspension and platelet transfusion volume, as well as their association with TEG testing outcomes. Our findings reveal a significant correlation between the volume of red blood cell suspension and platelet transfusion and the MA value derived from TEG testing. The MA value, a pivotal indicator in TEG test results, represents the maximum clotting strength. Notably, during perioperative red blood cell suspension and platelet transfusion, the MA value often exhibits corresponding changes. These changes not only reflect improvements in coagulation function but also provide clinicians with an objective basis for evaluating the effectiveness of blood transfusion. However, despite the notable impact of blood transfusion on MA values, our study did not detect statistically significant associations between blood transfusion and other TEG parameters, including TEG-ACT values, α-Angle, and LY30. This absence of significant associations may be attributed to the differential effects of various transfusion components on the coagulation process, as well as the influence of confounding factors such as patient age, comorbidities, and surgical procedure type.

Biomechanical analysis of trunk and lower limbs during stair activity in patients with scoliosis

Staircases are a frequently encountered obstacle in daily life, requiring individuals to navigate ascending and descending movements that place additional demands on the trunk and lower limbs compared to walking on level surfaces. Therefore, it is crucial to examine the biomechanical characteristics of the trunk and lower limbs in individuals with scoliosis during stair activity. The aim of this study was to investigate the biomechanical differences in trunk and lower limbs during daily stair activities between patients with scoliosis and a healthy population. Additionally, the study aimed to explore the relationship between trunk abnormalities and lower limb biomechanics, providing a clinical and objective assessment basis for scoliosis. The Qualisys system, based in Gothenburg, Sweden, was employed for data collection in this study, with a sampling frequency of 150 Hz. It captured the kinematics of the trunk and lower limbs, as well as the kinetics of the lower limbs during stair ascent and descent for both the 28 individuals with scoliosis and the 28 control participants. The results indicate that scoliosis patients demonstrated significantly higher asymmetry compared to the control group in various measures during ascent and decent. These include different parts of kinematics and kinetics. Scoliosis patients demonstrate noticeable variations in their movement patterns compared to the healthy population when engaging in stair activities. Specifically, during stair ascent, scoliosis patients exhibit a seemingly more rigid movement pattern, whereas descent is characterized by an unstable pattern.

The descriptive analysis of paratextual elements in the case of poetry translation

Paratextual elements are significant for the construction of meaning in textual analysis. In this context, the role of paratextual elements in poetry translation, which are believed to be challenging or even impossible, is worth analyzing. Delving into the functionality and efficacy of paratextual elements, this study aims to unravel the translation choices within the light of paratextual uses in the field of poetry translation. Through the case study of Cem Yavuz’s translation of T. S. Eliot’s poetry, the study qualitatively analyzes the types of paratextual elements that are mainly provided at the notes section, the preface and a YouTube interview. Commenting on T. S. Eliot's poetic profile, Cem Yavuz emphasizes the concepts of dramatic monologue and objective correlative and bases his translation approach on these concepts. The findings acquired from the poem The Love Song of J. Alfred Prufrock reveal that paratextual elements such as religious, mythological, historical and technical notes are presented to enlighten the intertextual references, enhance reader engagement and awareness, and discover the authentic linguistic and emotional richness in the source. The examples extracted from the study show that the translator not only uses elegant language to sound poetic and natural, but also gives the paratextual elements a teaching role.

Effect of acupuncture on tic disorder: a randomized controlled clinical trial based on energy metabolomics and infrared thermography

BackgroundAcupuncture is a method for treating tic disorder. However, there is a lack of sufficient clinical objective basis in regards of its treatment efficacy. Indeed, there are structural abnormalities present in energy metabolism and infrared thermography in children with tic disorder. Therefore, this study proposes a clinical trial scheme to explore the possible mechanism of acupuncture in treating tic disorder.MethodsThis randomized controlled trial will recruit a total of 90 children, in which they will be divided into non-intervention group and intervention group. The non-intervention group consists of 30 healthy children while the intervention group consists of 60 children with tic disorder. The intervention group will be randomly allocated into either the treatment group or the control group, with 30 children randomly assigned in each group. Children either received acupuncture treatment and behavioral therapy (treatment group) or sham acupuncture treatment and behavioral therapy (control group), 3 treatment sessions per week for a period of 12 weeks, with a total of 36 treatment sessions. Outcome measures include YGTSS, urinary and fecal metabolomics, infrared thermography of body surface including governor vessel. For the intervention group, these outcome measures will be collected at the baseline and 90th day prior to intervention. Whereas for the non-intervention group, outcome measures (excluding YGTSS) will be collected at the baseline.DiscussionThe main outcome will be to observe the changes of the severity of tic condition, the secondary outcome will be to observe the changes of structural characteristic of infrared thermography of body surface/acupoints along the governor vessel and to evaluate the changes of urinary and fecal metabolomics at the end of the treatment, so as to analyze the relationship between them and to provide further knowledge in understanding the possible mechanism of acupuncture in improving the clinical symptoms via regulating and restoring the body metabolomics network, which in future it can develop as a set of clinical guideline (diagnosis, treatment, assessment, prognosis) in treating tic disorder. ChiCTR2300075188(Chinese Clinical Trial Registry, http://www.chictr.org.cn, registered on 29 August 2023).

HHO optimized support vector machine classifier for traditional Chinese medicine syndrome differentiation of diabetic retinopathy.

To develop a classifier for traditional Chinese medicine (TCM) syndrome differentiation of diabetic retinopathy (DR), using optimized machine learning algorithms, which can provide the basis for TCM objective and intelligent syndrome differentiation. Collated data on real-world DR cases were collected. A variety of machine learning methods were used to construct TCM syndrome classification model, and the best performance was selected as the basic model. Genetic Algorithm (GA) was used for feature selection to obtain the optimal feature combination. Harris Hawk Optimization (HHO) was used for parameter optimization, and a classification model based on feature selection and parameter optimization was constructed. The performance of the model was compared with other optimization algorithms. The models were evaluated with accuracy, precision, recall, and F1 score as indicators. Data on 970 cases that met screening requirements were collected. Support Vector Machine (SVM) was the best basic classification model. The accuracy rate of the model was 82.05%, the precision rate was 82.34%, the recall rate was 81.81%, and the F1 value was 81.76%. After GA screening, the optimal feature combination contained 37 feature values, which was consistent with TCM clinical practice. The model based on optimal combination and SVM (GA_SVM) had an accuracy improvement of 1.92% compared to the basic classifier. SVM model based on HHO and GA optimization (HHO_GA_SVM) had the best performance and convergence speed compared with other optimization algorithms. Compared with the basic classification model, the accuracy was improved by 3.51%. HHO and GA optimization can improve the model performance of SVM in TCM syndrome differentiation of DR. It provides a new method and research idea for TCM intelligent assisted syndrome differentiation.

Shopping Mall Site Selection Based on Consumer Behavior Changes in the New Retail Era

As a product of the development of e-commerce over a specific period of time, the “new retail model” breaks the barriers between the traditional retail industry and e-commerce. Supported by Internet technology, it builds a new business model of “physical store + e-commerce + logistics” through the integration of online, offline, and logistics, which also leads to a great change in consumer behavior. Therefore, in order to meet consumer demand and achieve the long-term development of shopping malls, while taking into account the fair allocation of urban space resources, the indicators and methods of shopping mall site selection evaluation in the new retail era will be significantly different from traditional shopping mall site selection decisions. In this paper, the Wuhan East Lake Hi-Tech Zone is selected as the research object, and a comprehensive AHP-GIS assessment model is proposed. By investigating the impact of consumers’ behavioral changes on shopping mall location in the new retail era, a suitability evaluation system containing eight evaluation indicators is constructed, and the weights of each factor are determined using hierarchical analysis. At the same time, GIS is used to process the spatial analysis of the indicators, and combined with the weights of the factors, superposition analysis and quantitative research are carried out. Finally, based on the correlation analysis between ratings and customer flow, the suitability evaluation results are further supported in order to provide a more objective and scientific basis for the location of shopping malls from the perspective of the change in consumer behavior under the new retail model, and to put forward universal suggestions for the construction and development of shopping malls in the future.

Evaluation of the correlation between trigeminal nerve atrophy and trigeminal neuralgia using multimodal image fusion: A single-center retrospective study

Background3D-Slicer is an open-source medical image processing and visualization software. In the surgical treatment of trigeminal neuralgia, it is commonly used to predict the responsible vessels. However, there are few reports on the use of 3D-Slicer software to quantitatively measure the bilateral trigeminal nerve volume in patients with primary trigeminal neuralgia (PTN) based on the three-dimensional images. Therefore, this study aims to explore the role of three-dimensional fused images processed by 3D-Slicer in the evaluation of trigeminal nerve atrophy, providing an objective basis for the diagnosis of PTN. Methods57 PTN patients who underwent microvascular decompression (MVD) or percutaneous balloon compression (PBC) surgery in Hebei general hospital between January 2020 and April 2023 were included. Additionally, 30 patients with facial spasms(HFS) were included as a control group. All patients underwent 3D-TOF-MRA and 3D-FIESTA sequence examinations. Comparisons of bilateral trigeminal nerve volumes within and between groups were conducted by performing image fusion using 3D-slicer. ResultsThe volume of the affected trigeminal nerve in the MVD group (33.96 mm³±12.61 mm³) and PBC group (23.05 mm³±7.71 mm³) was smaller than that of the unaffected trigeminal nerve in the MVD group (39.61 mm³±12.83 mm³) and PBC group (26.14 mm³±6.42 mm³), as well as the average volume of the trigeminal nerve in the control group (40.27 mm³±10.25 mm³) (P<0.05). The differences in bilateral trigeminal ganglion volume (∆V) was significant between the MVD group (∆V=23.59 %±14.32 %) and the control group (∆V=14.64 %±10.00 %) (P<0.05). There was no statistical difference in the trigeminal nerve volume difference between the MVD group (∆V=23.59 %±14.32 %) and the PBC group (∆V=26.52 %±15.00 %) (P>0.05). ConclusionTrigeminal nerve atrophy is correlated with primary trigeminal neuralgia. 3D-slicer software can quantitatively measure trigeminal nerve volume and assist in the diagnosis of primary trigeminal neuralgia based on the difference in bilateral trigeminal nerve volumes. However, trigeminal nerve atrophy is not associated with postoperative pain recurrence in patients.

CONCEPTUAL POLYCOMPONENT MODEL OF AN INNOVATIVE MECHANISM FOR IMPROVING THE COMPETITIVENESS OF AGRO-INDUSTRIAL COMPLEX ENTERPRISES

The modern agricultural sector is going through a period of intense transformation caused by both internal and external factors. In the context of active globalisation, business entities are forced to withstand fierce competition and therefore need to constantly adapt to the environment. This creates an urgent need for agribusiness enterprises to constantly improve their approaches and mechanisms of operation. Continuous adaptation requires flexibility, innovation and strategic thinking. Changes in the economic, political and technological landscape require businesses to respond quickly to new opportunities and challenges to ensure their competitiveness and sustainability. This constant process of adaptation is becoming a prerequisite not only for success, but also for survival in today's global business environment. The purpose of the article is to develop an innovative mechanism for improving the competitiveness of agribusiness enterprises. Methodology. The methodological basis of the study, which objectively reveals and substantiates the problem of increasing the competitiveness of agro-industrial enterprises using the model of the innovation mechanism, is based on such special and general scientific methods as: monographic, logical-theoretical, statistical and economic-mathematical, visualisation, system analysis, neural network modelling, logical generalisation, abstraction and conclusion formation. The information base of the study was formed by the materials of the State Statistics Service of Ukraine, statistical yearbooks, scientific developments of scientists on the defined topic, and financial statements of the studied enterprises. The subject and object basis of the study is the entrepreneurial activity of agricultural enterprises in Vinnytsia Oblast: Agricultural complex "Zelena Dolyna", PJSC "Dashkivtsi", LLC "Selyshchanske", FE "Yavir", FE "IRYNA – O.T". The results of the study have shown that the innovation mechanism, which consists in ensuring the management of innovative development of agribusiness enterprises, involves adjusting the business environment of an economic entity to achieve a cumulative effect from the efficient use of innovative and energy potentials. The paper emphasises that the cumulative effect means that the effect of the interaction of innovation and energy resources exceeds the simple effect of the impact of each of them separately. Practical implications. The authors calculated the indicators of competitiveness of the studied agricultural enterprises of Vinnytsia Oblast; assessed the level of innovation potential of these enterprises; determined the indicators of energy potential. On the basis of the obtained diagnostic results, a fuzzy knowledge base has been formed and a multicomponent decision-making system has been built for various combinations of values of the components of the innovation mechanism.