Average Score Research Articles

Sleep quality of Chinese designers and the effects of digital screen time, stress, and job responsibility

The aim of this study was to describe the sleep status of Chinese designers and to identify the factors that affect their sleep quality. A total of 393 Chinese designers participated in the survey, which collected data on demographic information, digital screen time, stress, and the pittsburgh sleep quality index (PSQI). The average PSQI score of Chinese designers was 6.77, with 29% scoring equal to or higher than 9, indicating poor sleep quality. The most common sleep problem was difficulty falling asleep. Designers with management responsibilities had poorer sleep quality: the PSQI scores for those in design project management (M=8.74, SD=3.829) and design and management (M=9.05, SD=3.475) were significantly higher than for those in dedicated design (M=5.34, SD=2.908). There was a positive correlation between digital screen time for entertainment, work stress, other stress, and PSQI. The model equation was PSQI=1.160+0.276 * entertainment+0.675 * work stress+0.569 * other stress, with an r square of 0.232. Chinese designers should improve their sleep quality by reducing electronic entertainment and relieving stress. Managers in the design industry should pay special attention to their own sleep quality.

Investigating the Precise Identification of Citrullination Sites with High-Performance Score Metrics using a Powerful Computation Predicting Tool.

To elucidate the detailed mechanisms of citrullination at the molecular level and design drugs applicable to major human diseases, predicting protein citrullination sites (PCSs) is essential. Using experimental approaches to predict PCSs is time-consuming and costly. However, there is a limited scope of the current PCS predictors. In particular, most predictors are commonly used for PCS prediction and have limited performance scores. This work aims to provide an improved sophisticated predictor of citrullination sites using a benchmark dataset in a machine learning platform. This study presents a reliable citrullination site predictor based on a benchmark dataset containing a 1:1 ratio of positive and negative samples. We classified citrullination sites using the Composition of the K-Spaced Amino Acid Pairs (CKSAAP) and Support Vector Machine (SVM). We developed PCS predictors using integrated machine-learning methods that produced the highest average scores. Using 10-fold cross-validation on test datasets, the True Positive Rate (TPR) was 98.34%, the True Negative Rate (TNR) was 99.44%, the accuracy was 98.89%, the Mathew Correlation Coefficient (MCC) was 98.21%, the Area Under the ROC Curve (AUC) was 0.999, and the partial Area Under the ROC Curve (pAUC) was 0.1968. According to overall performance, our developed predictor has a significantly higher implementation in comparison with the current tools on the same benchmark dataset. Moreover, it showed better performance metrics on both test and training datasets. Our developed predictor is promising and can be implemented as a complementary technique for identifying fast and precise citrullination sites.

Exploring relational aggression and gender dynamics: a global and Indonesian perspective

Girls generally use less physical aggression than boys. However, does this mean that girls are more likely to use relational aggression. With this question in mind, our study aims to analyze the relational aggression of students in Indonesia based on gender. This type of research used is comparative research. The sample in this study was 510 respondents consisting of 19.6% male and 80.4% female. The relational aggression scale (RAS) was used to collect data on student relational aggression in various regions in Indonesia. The analysis technique used is descriptive and differences with the Welch test. The results show that there are significant differences in male and female relational aggression, with an average score difference of -0.30 logit between men and women. In addition, there are fluctuations in the level of relational aggression in the context of gender from year to year in some countries, such as the United States, China, Australia, and others. Recommendations for future research are to integrate a more in-depth social and cultural analysis in Indonesia.

Can We Differentiate a Latent Growth Curve Model from Competitors? Evidence Based on Individual Case Residuals

In latent growth curve modeling (LGCM), overall fit indices have garnered increased disputation for model selection, and model fit evaluation based on the mean structure has becoming popularity. The present study developed a versatile fit index, named Weighted Root Mean Squared Errors (WRMSE), based on individual case residuals (ICRs) with the aim of facilitating model selection. Through comparing to various fit indices, this study demonstrated that WRMSE could provide effective model fit information beyond previous indices. Due to higher sensitivity to misspecification in the mean structure, WRMSE was suggested to be utilized as a supplement for the traditional overall fit indices in the process of model selection. The limitation of WRMSE was that for complex models beyond the commonly used polynomial models, an increased number of repeated measurement occasions was required to ensure the precision of WRMSE.

Balanced opioid-free anesthesia with lidocaine and esketamine versus balanced anesthesia with sufentanil for gynecological endoscopic surgery: a randomized controlled trial

In this randomized controlled trial, 74 patients scheduled for gynecological laparoscopic surgery (American Society of Anesthesiologists grade I/II) were enrolled and randomly divided into two study groups: (i) Group C (control), received sufentanil (0.3 μg/kg) and saline, followed by sufentanil (0.1 μg/kg∙h) and saline; and (ii) Group F (OFA), received esketamine (0.15 mg/kg) and lidocaine (2 mg/kg), followed by esketamine (0.1 mg/kg∙h) and lidocaine (1.5 mg/kg∙h). The primary outcome was the 48-h time-weighted average (TWA) of postoperative pain scores. Secondary outcomes included time to extubation, adverse effects, and postoperative sedation score, pain scores at different time points, analgesic consumption at 48 h, and gastrointestinal functional recovery. The 48-h TWAs of pain scores were 1.32 (0.78) (95% CI 1.06–1.58) and 1.09 (0.70) (95% CI 0.87–1.33) for Groups F and C, respectively. The estimated difference between Groups F and C was − 0.23 (95% CI − 0.58 − 0.12; P = 0.195). No differences were found in any of the secondary outcomes and no severe adverse effects were observed in either group. Balanced OFA with lidocaine and esketamine achieved similar effects to balanced anesthesia with sufentanil in patients undergoing elective gynecological laparoscopic surgery, without severe adverse effects.Clinical Trial Registration: ChiCTR2300067951, www.chictr.org.cn 01 February, 2023.

#1419 A single, easy-to-use question to assess pruritus intensity in daily dialysis practice

Abstract Background and Aims Chronic kidney disease-associated pruritus (CKD-aP) has been shown to negatively impact certain clinical outcomes and quality of life (QoL) in patients with kidney failure. However, regular screening for CKD-aP is not yet routine dialysis practice and no consensus on the preferred instrument exists. To facilitate implementation of regular screening for CKD-aP we assessed the performance of two different instruments validated in patients with CKD-aP. Method The Worst Itching Intensity Numerical Rating Scale (WI-NRS) asks patients to indicate the worst itch intensity in the past 24 hours (range 0 [no itch] to 10 [worst itch imaginable]). The 5-D Itch scale assesses itch-related QoL (range 5-25 where higher values indicate worse QoL) and includes one question on itch intensity over the past 2 weeks (5-D ‘degree’ domain, range 1 [not present] to 5 [unbearable]). Both instruments were included in two phase-3 clinical trials, KALM-1 and KALM-2, that established the favourable benefit-risk profile of difelikefalin over placebo in patients on haemodialysis with moderate to severe CKD-aP. WI-NRS was collected daily during the trial and 5-D Itch at the first dialysis visit in weeks 5, 9, 11 and 13. We assessed the correlations between both the 5-D total score and degree domain and a) the single WI-NRS measurement collected on the same day and b) the average of daily WI-NRS scores collected over the previous 2 weeks in all patients independent of treatment exposure. Results The two instruments demonstrated moderate to strong correlations (range 0.5635-0.7039, p < 0.0001) independent of whether the total 5-D Itch score or the 5-D itch degree question were compared with a single, same-day WI-NRS measurement or with the mean WI-NRS scores over the 2-week recall period of the 5-D Itch (Table). In addition, correlations remained consistent over time, supporting the reliability of the findings. Conclusion Using the WI-NRS to ask patients about their worst itch in the past 24 hours is a simple and swift approach for reliably identifying those with bothersome pruritus that also affects their itch-related QoL.

Robust kernel extreme learning machines with weighted mean of vectors and variational mode decomposition for forecasting total dissolved solids

A stable and accurate forecast of water quality parameters is crucial for planning and managing future investment programs. A well-known water quality indicator is the total dissolved solids (TDS), which measures the number of metals, minerals, and salts dissolved in a particular volume of water. This paper introduces an innovative approach, which combines the kernel extreme learning machine (KELM) with the robust weight mean of vectors (INFO) algorithm. The proposed technique stands out for its strategic integration of Boruta-XGBoost (B-XGB) as an exceptional feature selection method, along with variational mode decomposition (VMD) to decompose the input variables and enhance the accuracy of forecasting. The integrated model is termed V-KELM-INFO. INFO is an essential component that incorporates a complex exploration process. It utilizes a weighted mean of vectors, a vector combining operator to enhance population variety, and a localized search operator to expedite convergence. INFO, a potent tool, is included in the V-KELM training stage. It efficiently extracts optimum parameters and dramatically improves the accuracy of monthly TDS predictions at the Idenak station in southwest Iran. Also, the multi-criteria decision-making (MCDM) approach of weighted aggregated sum product assessment (WASPAS) is used to rank models. The V-KELM-INFO model was chosen based on the WASPAS results and statistical metrics (R = 0.962, RMSE = 57.84, WHD = 7.01, and U95 = 160.74) as the best model to forecast the TDS. In addition, the superiority of the proposed model was assessed against the metaheuristic-based V-KELM models, comprising the V-KELM grey wolf optimizer (V-KELM-GWO), V-KELM slime mold algorithm (V-KELM-SMA), and V-KELM equilibrium optimizer (V-KELM-EO). This work provided insight into improving the accuracy of modeling-based methodologies and spurred water quality modeling technology to develop sustainable and clean practices.

History of periodontitis as a risk factor for implant failure and incidence of peri-implantitis: A systematic review, meta-analysis, and trial sequential analysis of prospective cohort studies.

Dental implants are widely employed as dependable replacements for lost teeth. However, it is crucial to establish, solely through prospective cohort studies, whether a history of periodontitis indeed constitutes a significant risk factor for implant failure. A systematic literature search was conducted in October 2022 in several electronic databases with subsequent manual updates. Only original prospective cohort studies evaluating the implant (loss) rate ≥1 year after implant loading were included. Logarithmic risk ratio and weighted mean differences were calculated. Study results were summarized using random effects meta-analyses evaluated by trial sequential analyses. The Newcastle-Ottawa scale evaluated study bias and the GRADE approach assessed the certainty/quality of the evidence. A total of 14 publications reporting on 12 prospective cohort studies were included. Low evidence certainty/quality evidence due to the absence of randomized clinical trials revealed significantly greater odds of failure in patients with a history of periodontitis at follow-ups both after ≤5 years (OR = 1.65; 95% CI: 1.12-2.44; p = 0.012) and >5 years (OR = 2.36; 95% CI: 1.13-4.95; p = 0.023). The incidence of peri-implantitis (OR = 5.93; 95% CI: 2.75-12.8; p < 0.001) and the weighted mean (WM) of marginal bone loss (WM difference = 0.75 mm; 95% CI: 0.18-1 0.3; p < 0.05) were statistically significantly greater in the periodontally compromised group, whereas there was no significant difference between the two groups for peri-implant probing depth. A history of periodontitis can be considered a significant risk factor for incident implant failure, peri-implantitis, and greater marginal bone loss.

Strawberry Gel with a Concentration of 83% and 75% as an Extrinsic Stain Remover

Strawberries have many benefits, where the malic acid contained in strawberries acts as a substance that will erode and remove several stains on the surface of teeth, including cleaning extrinsic stains. Gel preparations are widely used in medicines, cosmetics and food products, including toothpaste and teeth whitening agents. The purpose of this study was to determine the effectiveness of strawberry gel with a concentration of 83% and 75% in cleaning teeth with extrinsic stain on academic member of the Health Polytechnic Jakarta I. This study was an Experiment on 30 people who had upper and lower anterior teeth with extrinsic stain as samples. Data collection was done by applying strawberry gel on the surface of the anterior teeth, allowed to stand for 5 minutes, then brushed and rinsed with water. This procedures was done 2 times a day for 5 consecutive days. Changes in extrinsic stain are measured using the Lobene Stain Index which measures the intensity and area of the stain, as well as a combined score of both. The average of combined score with measurements using the Lobene Stain Index for extrinsic stain before being treated with strawberries. Hopefully the score will decreased every day starting from day 1 to day 5 of treatment.

Deterioration of Hearing due to Hearing Aids.

In the large population with sensorineural hearing loss, one of the main reasons for insufficient adoption rate of hearing aids (HA) has been the social concern that the devices may negatively affect remaining hearing ability. To date, there have been few studies with controversial results on the issue. This study examined the long-term effects of HA on standard audiometric changes in sensorineural hearing loss. The study retrospectively analyzed patients who had newly acquired unilateral HA between 2015 and 2017 and showed consistent use for five years. Demographics; medical comorbidities; audiometric data; questionnaire results of the Hearing Handicap Inventory for the Elderly (HHIE) and International Outcome Inventory for Hearing Aids (IOI-HA); history of noise exposure and previous HA use were examined. Fifty-five patients used unilateral HA for a mean follow up period of 5.32 years. Thirty-one patients (56.4%) used HA on the right side. Audiometric data of the aided side did not show significant difference compared to the unaided side, in prefit pure-tone average of air conduction (AC) and word recognition score (WRS) (P= 0.73, 0.11, respectively), in fiveyear follow-up audiometry of AC and WRS (P=0.98, 0.07) or in the change from last to prefit audiometry of AC and WRS (P=0.58, 0.70). When comparing patients who showed 5dB or more deterioration of hearing on the aided side compared with the unaided side, 11 patients (20%) had worsened AC on the aided side and 23 (53.5%) presented with further deterioration of WRS on the aided side. There were no significant factors contributing to the difference in hearing deterioration in either group for AC or WRS. After long-term use of HA, there were no significant factors contributing to the hearing deterioration and, overall, the use of HA did not negatively affect residual hearing.

Metacognitive self regulation integrated with science technology society to improving problem solving ability in microbiology courses

The purpose of this study was to improve students’ problem-solving ability and metacognitive self-regulation (MSR) by applying science technology society (STS) learning model integrated with metacognitive self-regulation. The participants were 13 students from Department of Biology Education who took microbiology courses. A quasi-experimental method with one-group pre and posttest design was used in this study. Data were collected through pre and posttest with eight open ended questions to measure students’ problem-solving ability on four microbiology topics (food, pathogen, waste and water microbiology). These topics were studied sequentially in two months. The instrument used to measure MSR is a questionnaire with open-ended questions. This questionnaire was developed based on three aspects of MSR namely, planning, monitoring and evaluation. MSR questionnaire was administered to students at the end of each topic. The N-gain test was used to analyze the improvement of students' problem-solving ability on each topic. The average score of all aspects in MSR questionnaire was used to explore students’ metacognitive self-regulation. The correlation between MSR and problem-solving ability was analyzed using Pearson correlation. The results revealed that the N gain score of problem-solving ability was increased from 0.56 in the first topic to 0.7 in the next three topics. The average of MSR score also increased from 66.15 in the first topic to 87.23 in fourth topic. There was a positive correlation between students’ MSR and problem-solving ability. These results indicated that application of Science Technology Society integrated with MSR is an effective strategy in improving students’ problem-solving ability and MSR. The implementation of this research is to develop a lecture program as an effort to improve the learning process

Acute Ankle Diastasis Injuries Treated with Dynamic, Static Fixation or Anatomic Repair: A Meta-Analysis and Systematic Review of Comparison Studies.

Acute ankle diastasis injuries are complex and debilitating. These injuries occur when the syndesmotic complex becomes compromised. Treatments of acute syndesmotic injuries include static fixation with screws, dynamic fixation with an elastic device, or anatomic repair of the damaged ligament. However, there is disagreement over which method is most effective. The primary purpose of this study was to compare the 3 treatment methods for acute syndesmotic injuries. A systematic literature search was conducted on Embase and PubMed. Studies that compared at least 2 groups with relevant American Orthopaedic Foot & Ankle Society (AOFAS), Visual Analog Scale (VAS), reoperation rate, and complication (implant failure, implant irritation, and infection) data were analyzed. Statistical analysis for this study was performed using Review Manager 5.4, with a standard p-value of ≤0.05 for statistical significance. Twenty-one studies including a total of 1,059 patients (452 dynamic, 529 static, and 78 anatomic) were included for analysis. Dynamic fixation had significantly higher mean AOFAS scores at 3 months postoperation by 5.12 points (95% confidence interval [CI], 0.29-9.96, p = 0.04) as well as at 1 year postoperation by 4.64 points (95% CI, 1.74-7.55, p = 0.002) than static fixation. Anatomic repair had significantly higher AOFAS scores at 6 months postoperation by 3.20 points (95% CI, 1.06-5.34, p = 0.003) and 1 year postoperation by 1.86 points (95% CI, 0.59-3.14, p = 0.004) than static fixation. Dynamic fixation had significantly higher AOFAS scores at 6 months postoperation by 2.81 points (95% CI, 0.76-4.86, p = 0.007), 12 months postoperation by 3.17 points (95% CI, 0.76-5.58, p = 0.01), and at 2 years postoperation by 5.56 points (95% CI, 3.80-7.32, p < 0.001) than anatomic repair. Dynamic fixation also had a lower VAS score average (favorable), only significant at 12 months postoperation, than static fixation by 0.7 points (95% CI -0.99 to -0.40, p < 0.001). Anatomic repair did not have significant difference in VAS scores compared with static fixation. Anatomic repair had significantly lower VAS scores at 12 months postoperation by 0.32 points (95% CI -0.59 to -0.05, p = 0.02) than dynamic fixation. Dynamic fixation had significantly less implant failures (odds ratio [OR], 0.13, 95% CI, 0.05-0.32, p < 0.001) than static fixation. Anatomic repair was not significantly different from static fixation in the complication metrics. Dynamic fixation and anatomic repair were not significantly different in the complication metrics either. Dynamic fixation had a significantly lower reoperation rate than static fixation (OR, 0.23, 95% CI, 0.09-0.54, p < 0.001). Anatomic repair did not have a significantly different reoperation rate compared with static fixation. However, dynamic fixation had a significantly lower reoperation rate than anatomic repair (OR, 4.65, 95% CI, 1.10-19.76, p = 0.04). Dynamic fixation seems to demonstrate superior early clinical outcomes. However, these advantages become negligible in the long term when compared with alternative options. Dynamic fixation is associated with a lower risk for complications, specifically seen with the decrease in implant failures. This method also presents a significantly lower reoperation rate compared with the other treatment approaches. Apart from showing improved early clinical outcomes in comparison with static fixation, anatomic repair did not have significant distinctions in other metrics, including complications or reoperation rate. Level III. See Instructions for Authors for a complete description of levels of evidence.

The Advantages of the OWA (Ordered Weighted Averaging) Method in Decision Making and Reliability Testing of Spatial Multi-Criteria Site Selection (SMCSS) Model

The Advantages of the OWA (Ordered Weighted Averaging) Method in Decision Making and Reliability Testing of Spatial Multi-Criteria Site Selection (SMCSS) Model

A longitudinal investigation of the impact of code-switching versus target language only on Saudi EFL students' written receptive vocabulary knowledge development in terms of breadth

This longitudinal research was conducted to investigate the effect of code-switching versus target language only on Saudi students' written receptive vocabulary knowledge development in terms of breadth. A total of sixty male samples were randomly chosen from the population of Saudi EFL students at Jouf University in Saudi Arabia who enrolled in extensive English courses. This course is provided for those who scored less than the direct admission score of 2.5 out of 5 in the English replacement test. Samples were equally divided and allocated into two classrooms. In the first class (i.e., C1), thirty students were instructed by a non-native English speaker who employed code-switching as a pedagogical strategy. Students in the second class (i.e., C2) were instructed by non-native English speakers who employed the target language only as a pedagogical strategy due to the different language backgrounds between the instructor and participants. Data were collected from samples of both classes using the Vocabulary Size Test developed by Nation and Beglar (2007) at four different intervals and over four months (i.e., one academic semester). Results revealed that Saudi students' vocabulary knowledge in terms of breadth had developed significantly at four different intervals in both classes. However, the development in C2 where the instructor employed the target language only, was significantly higher than in C1 where code-switching is employed. Moreover, results also indicated that samples in C1 where code-switching is employed experienced a newly coined linguistic status called Receptive Vocabulary Recission (RVR) which indicates the act of avoiding the acquisition of new English vocabulary since they were habituated to be given an additional explanation for new vocabulary using their first language knowledge. This rescission was very tangible from the mean difference between C1 and C2 score averages.

Determination of the Knowledge Level of Surgical Intensive Care Nurses in Medical Device Induced Pressure Injury

Aim: The purpose of this study was to measure the level of knowledge and attitudes of nurses in surgical intensive care units (ICUs) about medical device-induced pressure injury. Method: The cross-sectional study was conducted with 227 surgical intensive care nurses in Turkey between November 2022 and June 2023. “Nurse Introduction Form” and “Pressure Ulcer Knowledge Level Assessment Tool Scale” were used as data collection tools. Since the scale score averages did not comply with normal distribution, Mann Whitney U and Kruskal Wallis tests were used in the statistical analysis of the data. Results: It was found that the majority of participants (n=227) were in the age range of 26-33 years (n=115), and 66.1% (n=113) had been working in the ICU for 1-5 years. 51.5% (n=117) of the participants reported having received training in medical device-related pressure injury. In the pressure ulcer knowledge level assessment tool, the mean of the "prevention of pressure ulcers" sub-dimension (mean: 4.74) was found to be the highest, while the total knowledge level score was 14.87±5.6 and it was found that the nurses had a moderate level of knowledge. Conclusion: The findings of the study indicated that nurses' knowledge of medical device-related pressure injuries was inadequate, but that their knowledge of pressure injury prevention increased with increasing levels of education.

A Comparison of the Smile Esthetic Understanding of Periodontists, Orthodontists, General Dentists, and Dental Students

Objective: The aim of this study was to evaluate smile esthetics among periodontists, orthodontists, general dentists, and dental students. Methods: The study consisted of five groups: periodontists, orthodontists, general dentists, fifth-year dental students, and fourth-year dental students. The evulaters used the Smile Esthetics Index (SEI) consisting of 10 items to analyze the natural smile photographs of 15 different individuals. One-way ANOVA was performed for the comparison of group means. The Duncan multiple comparison test was also used to identify different groups. Non-linear principal component analysis was performed to determine the configuration of the relationship between categories of variables in two-dimensional space. Results: The group with the highest reliability coefficient among the groups was the fourth-year dental students (Cronbach's Alpha = 0.89). This was followed by general dentists, periodontists, fifth-year dental students, and orthodontists. According to the evaluation of the total score averages of the fifteen pictures, a statistically significant difference was observed between the groups (p = 0.041). Accordingly, the fourth-year dental students (5.78 ±1 .13) had a lower total score than the fifth-year dental students (6.56 ± 0.88), and this difference was statistically significant. However, no significant difference was observed between the fourth-year dental students and the general and specialized dentists and between the fifth-year dental students and graduated and specialized dentists in terms of the total score given to the pictures. Conclusion: It was observed that the reliability coefficient was high in all groups evaluated with SEI. It can be concluded that esthetic perception is formed by actively performing the profession of dentistry, and this situation does not change according to specialization.

Elucidating the phenotypic basis of multi-environment stability for fiber yield and quality traits of cotton (Gossypium hirsutum L.) using 498 recombinant inbred lines

Understanding genotype-environment interactions and trait relationships is pivotal for guiding breeding efforts aimed at stabilizing cotton varieties across diverse environments while boosting yield and fiber quality. The study aimed to assess genotypic variability and stability in 10 agronomic and fiber quality traits across four environments (Anyang in 2020 and 2021 and Weixian in 2020 and 2021) for three parental lines (ZR014121, CCRI60, and EZ60) and their corresponding 498 recombinant inbred lines (RILs) populations. The results indicated that the analysis of variance showed significant genotype and genotype-by-environment effects for all measured traits. Analysis of correlation revealed highly significant positive correlations between seed cotton yield and each of boll weight (r = 0.95), lint yield (r = 0.90), and between boll weight and lint yield (r = 0.90), while highly significant negative correlations were noticed between fiber maturity and fiber elongation (r = −0.52) and between fiber length and lint percentage (r = −0.38). Furthermore, the weighted average of absolute scores (WAASB) was calculated for each trait, revealing moderate to high stability for some yield traits and fiber quality parameters. Also, the multi-trait stability index (MTSI) applied to these RILs populations identified G13 as the most stable line (MTSI = 5.5) and S23 as the least stable (MTSI = 12.1). Selecting 25 RILs with the lowest MTSI values (5.5–6.47) revealed elite stable lines with favorable trait values, providing a valuable genetic resource for developing high-performance cotton cultivars in diverse environments.

Urban Road Network Connectivity Analysis Based on GIS for Aurangabad City, Maharashtra

Urban road network plays an important role in urban local network. The urban road network plays a key role in the urban spatial structure. It is the main city social-economy activities and transportation carrier. An important event of the cultural landscape is that transportation serves as a powerful factor in regional as well as national development. In connectivity network analysis, the term connectivity is known as the quantity of connections between nodes in a given network, so it derives the structure of the road network. Road network, major junctions, retail junctions, major corridors, road density and connectivity can be analyzed using the network connectivity indicators on the GIS platform. Road network dataset is created in ArcGIS which provides the number of transport lines and number of Junctions in study region. One of the most basic features of a transportation network is the network index, such as the alpha, beta, gamma index, etc. There are various indices, and each index has its individual equation for different purposes. It requires road network (line), junction (nodes) to draw connectivity index. Roads density is highest in most of the zones in Aurangabad city and lowest road density recorded in zone A in the north and E is south-west direction. he averages score of the gamma index has been increased from 0.50 in 2001 to 0.53 in 2011 where the average value also increased from 0.44 to 0.48 during this period.

LogCSS: Log anomaly detection based on BERT-CNN with context-semantics-statistics features

With the development of artificial intelligence, deep-learning-based log anomaly detection proves to be an important research topic. In this paper, we propose LogCSS, a novel log anomaly detection framework based on the Context-Semantics-Statistics Convolutional Neural Network (CSSCNN). It is the first model that uses BERT (Bidirectional Encoder Representation from Transformers) and CNN (Convolutional Neural Network) to extract the semantic, temporal, and correlational features of the logs. We combine the features with the statistic information of log templates for the classification model to improve the accuracy. We also propose a technique, DOOT (Deals with the Out-Of-Templates), for online template matching. The experimental research shows that our framework improves the average F1 score of the six best algorithms in the industry by more than 5% on the open-source dataset HDFS, and improves the average F1 score of the six best algorithms in the industry by more than 8% on the BGL dataset, LogCSS also performs better than other similar methods on our own constructed dataset.

MSegResRF-SPECT: A Novel Joint Classification Model of Whole Body Bone Scan Images for Bone Metastasis Diagnosis.

Whole-body bone scanning is a nuclear medicine technique with high sensitivity used for the diagnosis of bone-related diseases [e.g., bone metastases] that can be obtained by positron emission tomography[PET] or single-photon emission computed tomography[SPECT] imaging, depending on the different radiopharmaceuticals used. In contrast to the high sensitivity of the bone scan, it has low specificity, which leads to misinterpretation, causing adverse effects of unwarranted intervention or interruption to timely treatment. To address this problem, this paper proposes a joint model called mSegResRF-SPECT, which accomplishes for the first time the task of classifying whole-body bone scan images on a public SPECT dataset [BS-80K] for the diagnosis of bone metastases. The mSegResRF-SPECT adopts a multi-bone region segmentation algorithm to segment the whole body image into 13 regions, ResNet34 as an extractor to extract the regional features, and a random forest algorithm as a classifier. The experimental results of the proposed model show that the average accuracy, sensitivity, and F1 score of the model on the BS-80K dataset reached SOTA. The proposed method presents a promising solution for better bone scan classification methods.