A method of traffic engineering (TE) based on the method of multi-path routing is proposed in the study. Today, one of the main challenges in networking is to organize an efficient TE system that will provide such parameters of quality of service (QoS) as the allowable value of packet loss and time for traffic re-routing. Traditional one-way routing facilities do not provide the required quality of service (QoS) parameters for TE. Modern computer networks use static and dynamic routing algorithms, which are characterized by big time complexity and a large amount of service information. This negatively affects the overall state of the network, namely: leads to network congestion, device failure, loss of information during routing and increases the time for traffic re-routing. Research has shown that the most promising way to solve the TE problem in computer networks is a comprehensive approach, which consists of multi-path routing, SDN technology and monitoring of the overall situation of the network. This paper proposes a method of traffic engineering in a software-defined network with specified quality of service parameters, which has reduced the time of traffic re-routing and the percentage of packet loss due to the combination of the centralized TE method and multi-path routing. From a practical point of view, the obtained method, will improve the quality of service in computer networks in comparison with the known method of traffic construction.

Sentiment analysis on Twitter provides organizations and persons with quick and effective instrument to observe the public's perceptions of them and their competition. A modest number of assessment datasets have been produced in recent years to check the efficiency of sentiment analysis algorithms on Twitter. Researchers offer a review of eight publicly accessible as well as manually annotated assessment datasets for analyzing Twitter sentiment in this research. As a result of this evaluation, we demonstrate that is a widespread weakness of many when using these datasets performing at sentiment analysis the objective (entity) level is indeed the absence of different sentiment classifications across tweets as well as the objects contained in them.[1], As an example all of that "I love my iPhone but I despise my iPad." Could be marked with a made-by-mixing classify however the object iPhone contained within this Twitter post should be annotated with just a label with an optimism. To get around this restriction and enhance existing assessment We have datasets that provide STS-Gold a novel assessment of datasets in which tweets or objects (entities) remain tagged separately hence might show alternative opinion labels. Though research furthermore compares the various datasets on multiple characteristics such as an entire quantity of posts as well as vocabulary size and sparsity.[2] In addition, look at pair by pair relationships between these variables and how they relate to sentiment classifier performance on various data. In this study we used five different classifiers and compared them and, in our experiment, we found that the bagging ensemble classifier performed best among them and have an accuracy level of 94.2% for the GASP dataset and 91.3% for the STS-Gold dataset.

Markers of white matter (WM) injury on brain MRI are important indicators of brain health. Different patterns of WM atrophy, WM hyperintensities (WMHs), and microstructural integrity could reflect distinct pathologies and disease risks, but large-scale imaging studies investigating WM signatures are lacking. This study aims to identify distinct WM signatures using brain MRI in community-dwelling adults, determine underlying risk factor profiles, and assess risks of dementia, stroke, and mortality associated with each signature. Between 2005 and 2016, we measured WMH volume, WM volume, fractional anisotropy (FA), and mean diffusivity (MD) using automated pipelines on structural and diffusion MRI in community-dwelling adults aged older than 45 years of the Rotterdam study. Continuous surveillance was conducted for dementia, stroke, and mortality. We applied hierarchical clustering to identify separate WM injury clusters and Cox proportional hazard models to determine their risk of dementia, stroke, and mortality. We included 5,279 participants (mean age 65.0 years, 56.0% women) and identified 4 distinct data-driven WM signatures: (1) above-average microstructural integrity and little WM atrophy and WMH; (2) above-average microstructural integrity and little WMH, but substantial WM atrophy; (3) poor microstructural integrity and substantial WMH, but little WM atrophy; and (4) poor microstructural integrity with substantial WMH and WM atrophy. Prevalence of cardiovascular risk factors, lacunes, and cerebral microbleeds was higher in clusters 3 and 4 than in clusters 1 and 2. During a median 10.7 years of follow-up, 291 participants developed dementia, 220 had a stroke, and 910 died. Compared with cluster 1, dementia risk was increased for all clusters, notably cluster 3 (hazard ratio [HR] 3.06, 95% CI 2.12-4.42), followed by cluster 4 (HR 2.31, 95% CI 1.58-3.37) and cluster 2 (HR 1.67, 95% CI 1.17-2.38). Compared with cluster 1, risk of stroke was higher only for clusters 3 (HR 1.55, 95% CI 1.02-2.37) and 4 (HR 1.94, 95% CI 1.30-2.89), whereas mortality risk was increased in all clusters (cluster 2: HR 1.27, 95% CI 1.06-1.53, cluster 3: HR 1.65, 95% CI 1.35-2.03, cluster 4: HR 1.76, 95% CI 1.44-2.15), compared with cluster 1. Models including clusters instead of an individual imaging marker showed a superior goodness of fit for dementia and mortality, but not for stroke. Clustering can derive WM signatures that are differentially associated with dementia, stroke, and mortality risk. Future research should incorporate spatial information of imaging markers.

There is a clear need to characterize and validate molecular biomarkers of cerebral amyloid angiopathy (CAA), in an effort to improve diagnostics, especially in the context of patients with Alzheimer disease (AD) receiving immunotherapies (for whom underlying CAA is the driver of amyloid-related imaging abnormalities). We performed an updated meta-analysis of 5 core CSF biomarkers (Aβ42, Aβ40, Aβ438, total tau [T-tau], and phosphorylated tau [P-tau]) to assess which of these are most altered in sporadic CAA. We systematically searched PubMed for eligible studies reporting data on CSF biomarkers reflecting APP metabolism (Aβ42, Aβ40, Aβ38), neurodegeneration (T-tau), and tangle pathology (P-tau), in symptomatic sporadic CAA cohorts (based on the Boston criteria) vs control groups and/or vs patients with AD. Biomarker performance was assessed in random-effects meta-analysis based on ratio of mean (RoM) biomarker concentrations in (1) patients with CAA to controls and (2) CAA to patients with AD. RoM >1 indicates higher biomarker concentration in CAA vs comparison population, and RoM <1 indicates higher concentration in comparison groups. 8 studies met inclusion criteria: a total of 11 CAA cohorts (n = 289), 9 control cohorts (n = 310), and 8 AD cohorts (n = 339). Overall included studies were of medium quality based on our assessment tools. CAA to controls had lower mean level of all amyloid markers with CSF Aβ42, Aβ40, and Aβ38 RoMs of 0.46 (95% CI 0.38-0.55, p < 0.0001), 0.70 (95% CI 0.63-0.78, p < 0.0001), and 0.71 (95% CI 0.56-0.89, p = 0.003), respectively. CSF T-tau and P-tau RoMs of patients with CAA to controls were both greater than 1: 1.56 (95% CI 1.32-1.84, p < 0.0001) and 1.31 (95% CI 1.13-1.51, p < 0.0001), respectively. Differentiation between CAA and AD was strong for CSF Aβ40 (RoM 0.76, 95% CI 0.69-0.83, p < 0.0001) and Aβ38 (RoM 0.55, 95% CI 0.38-0.81, p < 0.0001), but not Aβ42 (RoM 1.00; 95% CI 0.81-1.23, p = 0.970). For T-tau and P-tau, average CSF ratios in patients with CAA vs AD were 0.64 (95% CI 0.58-0.71, p < 0.0001) and 0.64 (95% CI 0.58-0.71, p < 0.0001), respectively. Specific CSF patterns of Aβ42, Aβ40, Aβ38, T-tau, and P-tau might serve as molecular biomarkers of CAA, in research and clinical settings, offering the potential to improve the clinical diagnostic approach pathway in specific scenarios.

In recent times, there has been a growing emphasis on adjusting communication strategies to foster strong customer relationships. This shift is driven by intensified competition, market maturation, and swift advancements in business technology. Consequently, companies have established call centers to efficiently handle customer support and fulfil customer inquiries. A pivotal aspect of enhancing service quality within these call centers involves accurately identifying customers during their interactions. The primary objective of this study is to introduce a methodology for identifying customers within call centers by analyzing their voice characteristics. Voice authentication (VA) has gained prominence in critical security operations, including banking transactions and conversations within call centers. The susceptibility of automatic speaker verification systems (ASVs) to deceptive spoofing attacks has prompted the development of countermeasures (CMs). These countermeasures are designed to differentiate between authentic and fabricated speech. ASVs and CMs collectively constitute contemporary VA systems, positioned as robust access control mechanisms. To achieve this goal, various customer identification systems within call centers have been examined, along with an analysis of audio signal attributes. Ultimately, the manuscript presents a novel approach to customer identification through voice biometrics. Notably, this method excels in recognizing customers even when provided with limited voice data. Empirical findings demonstrate that the suggested speaker identity confirmation method outperforms alternative techniques utilizing different algorithms, exhibiting a higher recognition rate. The present research work is based on two important perspectives of the call centres: a. call center agents experience and b. customer experience. The data collected separately from customers and agents for understanding the effective usage of voice biometric system in call centres. The data represented and satisfies the effectiveness of voice biometric system from both the perspectives. From the data it is also cleared that, the implementation of voice biometric system in call centres still have long way to go but will be a major technological change for the industries worldwide.

Python is widely used in artificial intelligence (AI) and machine learning (ML) because of its flexibility, adaptability, rich libraries, active community, and broad environment, which makes it a popular choice for AI development. Python compatibility has already been examined with Java using TCP socket programming on both non-graphical and graphical user interfaces, which is highly essential to implement in the Jakarta Faces web application to grab potential competitive advantages. Python data analysis library modules such as numpy, pandas, and scipy, as well as visualization library modules such as Matplotlib and Seaborn, and machine-learning module Scikit-learn, are intended to be integrated into the Jakarta Faces web application. The research method uses similar TCP socket programming for the enhancement process, which allows instruction and data exchange between Python and Jakarta Faces web applications. The outcome of the findings emphasizes the significance of modernizing data science and machine learning (ML) workflows for Jakarta Faces web developers to take advantage of Python modules without using any third-party libraries. Moreover, this research provides a well-defined research design for an execution model, incorporating practical implementation procedures and highlighting the results of the innovative fusion of AI from Python into Jakarta Faces.

Mushroom consumption and wild mushroom gathering are increasing in our country and in the world. Mushroom poisoning has an important place in food poisoning cases. Mushroom poisoning accounts for approximately 7% of poisoning cases in adults. Mushroom collection and consumption is common in many regions of our country. In this study, a deep learning based mobile application was developed to reduce the incidence of mushroom poisoning by taking a photo of a mushroom and determining the type and toxicity of the mushroom from the photo. This mobile application is called MushAPP. In the first phase of the study, 5150 mushroom images of 20 mushroom species were used to create the dataset. The dataset was then pre-processed and converted into a format that can be used by the deep learning algorithm. The mobile application side of the project was developed in Android Studio IDE environment. An artificial intelligence model was integrated into the designed mobile application. In the application, the type and toxicity status of the mushroom viewed from the mobile device camera are determined and presented to the user. The research findings were analyzed and it was determined that the accuracy rate of the application in detecting the mushroom species was 99.8%.

The pelvic examination is a fundamental tool for the evaluation and diagnosis of women's health conditions and an important skill for all medical students to learn as future physicians for the early detection of treatable conditions such as infection or cancer. Although the American College of Obstetricians and Gynecologists (ACOG) asserts that performing pelvic examinations under anesthesia for educational purposes should only occur if the patient provides explicit and informed consent, there still have been reports of medical students performing pelvic examinations on anesthetized patients across the country, and many states are now starting to pass bills requiring informed patient consents to conduct pelvic examinations under anesthesia. The objectives of this study are to evaluate the prevalence of pelvic examinations performed by osteopathic medical students on anesthetized patients without consent while fulfilling their third-year OB-GYN clerkship requirements. The survey was administered and distributed to all osteopathic medical schools in the country via the Student Osteopathic Medical Association's (SOMA's) chapter emails, outreach emails, and SOMA's social media accounts to collect data. Inclusion criteria included third- or fourth-year osteopathic medical students who completed their OB-GYN clerkship rotations when taking the survey. The exclusion criteria included any osteopathic medical student who had not completed their OB-GYN clerkship rotation. We utilized descriptive analysis to summarize the final data. We received 310 responses. The final number of responses was 291 after meeting the exclusion criteria. Most osteopathic medical students (94.2 %, n=274) considered the practice of performing pelvic examinations on anesthetized patients without their explicit consent unethical. Among theparticipants, 40.9 % (n=119) admitted to performing pelvic examinations on patients under anesthesia while on OB-GYN rotations, but most of them (57.1 %, n=68) did so without obtaining prior consent from the patients. Notably, the number of pelvic examinations performed by medical students on patients under anesthesia ranged widely from 1 to 25 with a median number of 10. Moreover, 58.9 % (n=70) indicated that they had not been properly educated to obtain specific consent before performing pelvic examinations under anesthesia. Many participants cited efficiency of practice, lack of policy awareness and personal education by medical students, andfailure to refuse to perform pelvic examinations on anesthetized patients as trainees when asked by their seniors or preceptors. This study demonstrates that although most osteopathic medical students consider performing pelvic examinations on anesthetized patients unethical, many still admit to practicing pelvic examinations on patients under anesthesia, while on OB-GYN rotations for efficiency of practice, lack of policy awareness and personal education, and being in unique positions in which grades are determined by seniors and preceptors for their willingness to do what is asked even if the practice does not align with their conviction. This study highlights the importance of ongoing research and implementation of policies at institutional and state levels that will procure the value of pelvic examinations while protecting and upholding the ethics of patients' rights and autonomy of medical students.

PyCI is a free and open-source Python library for setting up and running arbitrary determinant-driven configuration interaction (CI) computations, as well as their generalizations to cases where the coefficients of the determinant are nonlinear functions of optimizable parameters. PyCI also includes functionality for computing the residual correlation energy, along with the ability to compute spin-polarized one- and two-electron (transition) reduced density matrices. PyCI was originally intended to replace the abinitio quantum chemistry functionality in the HORTON library but emerged as a standalone research tool, primarily intended to aid in method development, while maintaining high performance so that it is suitable for practical calculations. To this end, PyCI is written in Python, adopting principles of modern software development, including comprehensive documentation, extensive testing, continuous integration/delivery protocols, and package management. Computationally intensive steps, notably operations related to generating Slater determinants and computing their expectation values, are delegated to low-level C++ code. This article marks the official release of the PyCI library, showcasing its functionality and scope.