Vital Details Research Articles

Art Criticism in Dostoevsky’s “Grazhdanin” (“The Citizen”) 1873

Dostoevsky’s interest in art is undeniable. Painting, music, and theater were reflected in his works of art. Dostoevsky’s wife, Anna Grigorievna, regularly mentioned in her memoirs the couple’s visits to museums and galleries during their stay in Europe. Certain works of art had a strong impact on the writer. Such an effect was produced on Dostoevsky by Hans Holbein the Younger’s painting “The Dead Christ.” In “Grazhdanin” of 1873, when the writer served as the editor of the publication, notes and articles appeared devoted to reviews of exhibitions, paintings, and photographs from the Hermitage collection. The authors published in “Grazhdanin” — A. N. Maikov, V. P. Meshchersky, and I. Yu. Nekrasov — addressed painting in their publications. Dostoevsky devoted the ninth chapter of “The Writer’s Diary” to the St. Petersburg exhibition, prepared for display in Vienna. He was interested in issues related to the misunderstanding of Russian art by other cultures. Art criticism in the weekly “Grazhdanin” took various forms. The authors of “Grazhdanin” published both general reviews and topical articles. Expressing paintings and photo reproductions through words is a difficult task even for experienced art critics with specialized art history training. A. N. Maikov acted as a scrupulous reviewer, noticing small but vital details in understanding a painting. The critic turned to the historical context to expand the image. In their examination of the latest events in Moscow and St. Petersburg, I. Yu. Nekrasov and V. P. Meshchersky respectively focused on art news. In Dostoevsky’s verbal description, paintings came to life and became dynamic. Writers were often unconcerned with the image details; they sought to convey strong and vivid artistic impressions in words.

Enhancing Surgical Operative Note Standards at Doka Hospital, Sudan: A Clinical Audit.

Background Operating notes are crucial in surgical practice for post-operative care, future medical reference, and legal needs. They provide vital details about the procedure, guaranteeing patient safety and improving communication among medical staff. These notes include patient health status, surgical method, operation results, problems, and post-surgery plans. Established protocols for recording surgical operative notes are essential for medical education, research, and quality improvement efforts. The audit aimed to evaluate and enhance Doka Hospital's surgical operation notes, focusing on their quality, completeness, and potential areas for development. The audit aimed to apply necessary adjustments to raise standards and ensure high standards in medical treatment. Material and methods This retrospective-prospective study analyzed surgical postoperative notes from Doka Hospital's archives, following the implementation of a new format based on guidelines set by the Royal College of Surgeons of England. Staff training sessions were conducted to facilitate adoption. The audit population included surgical operative notes from the departments of general surgery and obstetrics and gynecology. The study collected 200 surgical operative notes, with 100 notes in the first cycle and 100 notes in the second cycle. Data was collected from the General Surgery and Obstetrics and Gynaecology departments using a predefined checklist, followed by analysis using the Microsoft Excel Sheets program (Microsoft Corporation, Redmond, USA). Ethical clearance was obtained from the ethical committee of Doka Hospital. The audit population consisted of 71 beds in a busy rural hospital with a majority of cases involving obstetrics and one-day surgical operations. Results Doka Hospital's surgical operative note review showed a significant improvement in documentation quality following a revised structure based on the Royal College of Surgeons of England's recommendations. Compliance with the criteria improved from 50.5% in the first cycle to 82.5% in the second cycle. Notable progress was made in date and time documentation, with a 96% increase in the second cycle. The proportion of anaesthetists' names also rose from 60% to 90%. The accuracy of recording surgical operation details increased from 87% to 94%. Documenting operational results, issues, and tissue removal also improved. Factors like the identity of the prosthesis or materials used remained unchanged in both cycles. Conclusion The introduction of a uniform style for recording surgical operational notes, together with focused training sessions, greatly improved the quality of surgical documentation at Doka Hospital.

Adverse events during general anesthesia at the university hospital center (CHU) “Hôpital du Mali”

Objective: To evaluate the incidence of adverse events during general anesthesia and to describe their progression. Methodology: Cross sectional study with retrospective collection over 6 months at the Mali Hospital University Hospital. It included all patients who underwent elective or urgent surgeries under general anesthesia (GA) with tracheal intubation. Vital parameters and details of incidents and accidents were collected and analyzed. Results: During the study period, 723 anesthesia procedures were performed, of which 339 were GA, a prevalence of 46.9%. Among these patients, 149 (43.9%) experienced at least one AE. The mean age was 28 ± 19.9 years old [3 days old; 85 years old], with a sex ratio ~1. AEs occurred during elective anesthesia in 68.4%. The primary surgical specialties were thoracic/cardiovascular surgery (40.4%) and neurosurgery (37.8%). Patients were classified as American Society of Anesthesiologists-2 (ASA 2) in 49.5%. Pharmacological premedication was administered in 84.7%. The intravenous hypnotics used included propofol (85.5%), thiopental (8%), and ketamine (6.5%). Inhalation agents used were isoflurane (99.4%) and halothane (0.6%). Vecuronium (95.5%) was the most commonly used muscle relaxant, while suxamethonium (4.5%) was used for intubation. AEs predominantly occurred during induction and maintenance of anesthesia, with hypotension (84.6%), desaturation, hypertension, and cardiac arrest being the most common. Delayed awakening was observed in 5.6%, including one instance of unrecovered cardiac arrest (0.6%). The overall mortality rate due to AEs was 0.6%. Conclusion: Adverse events are common during GA, particularly during induction and entretien phases, and are associated with a mortality rate of 0.6%. Keywords: Adverse events, GA, Incidence, Mali.

Ghost Readers of the Nile: Decrypting Password Sharing Habits in Chatting Applications among Egyptian Women

Password sharing is a convenient means to access shared resources, save on subscription costs, provide emergency access, and avoid forgetting vital account details. However, it also raises significant privacy concerns, especially in digital communication contexts where content may be inadvertently exposed to unintended recipients. In this paper, we investigate this duality, using a survey of 86 Egyptian women to understand their sharing behavior and the design and evaluation of a chat application used by 60 participants. This application issues warnings based on content sensitivity, leading to increased user awareness about privacy risks. Our findings indicate that, while many participants initially shared passwords, they were surprised to discover others doing the same. Furthermore, our application effectively reduced password sharing, reflecting improved awareness of associated risks. This research acknowledges the cultural aspects of password sharing while striving to enhance the experience, enabling participants to make informed choices that enhance their information control.

The emerging view on the origin and early evolution of eukaryotic cells.

The origin of the eukaryotic cell, with its compartmentalized nature and generally large size compared with bacterial and archaeal cells, represents a cornerstone event in the evolution of complex life on Earth. In a process referred to as eukaryogenesis, the eukaryotic cell is believed to have evolved between approximately 1.8 and 2.7 billion years ago from its archaeal ancestors, with a symbiosis with a bacterial (proto-mitochondrial) partner being a key event. In the tree of life, the branch separating the first from the last common ancestor of all eukaryotes is long and lacks evolutionary intermediates. As a result, the timing and driving forces of the emergence of complex eukaryotic features remain poorly understood. During the past decade, environmental and comparative genomic studies have revealed vital details about the identity and nature of the host cell and the proto-mitochondrial endosymbiont, enabling a critical reappraisal of hypotheses underlying the symbiotic origin of the eukaryotic cell. Here we outline our current understanding of the key players and events underlying the emergence of cellular complexity during the prokaryote-to-eukaryote transition and discuss potential avenues of future research that might provide new insights into the enigmatic origin of the eukaryotic cell.

Precision in Preservation: Mastering Cadaver Embalming with the Femoral Artery Approach - a technical note.

Human anatomy is a fundamental aspect of a physician's knowledge. While novel technologies offer innovative ways to teach anatomy, cadavers remain an essential component of anatomical education. The quality of specimens begins with well-preserved cadavers, and the chosen vascular access for injection plays a crucial role. Unfortunately, there is a lack of literature regarding embalming procedures, as discourse on such practices could enhance the quality, safety, and effectiveness of anatomical instruction. In this study, a femoral artery approach is described for embalming, which entails a meticulous process of cutting through the skin, navigating through fascias and adipose tissue by means of blunt dissection, ultimately reaching the artery for embalming injection. Tips and techniques pertaining to this technique are provided, including vital details for convenient accessibility and minimal impairment of tissue. The objective of this study is to facilitate anatomists and technicians in the adoption of the femoral artery approach, and to encourage further exploration of alternative embalming methods, thus contributing to the continuous advancement of anatomical sciences.

The Diagnosis-Effective Sampling of Application Traces

Distributed tracing is cutting-edge technology used for monitoring, managing, and troubleshooting native cloud applications. It offers a more comprehensive and continuous observability, surpassing traditional logging methods, and is indispensable for navigating modern complex software architectures. However, the sheer volume of generated traces is staggering in distributed applications, and the direct storage and utilization of every trace is impractical due to associated operational costs. This entails a sampling strategy to select which traces warrant storage and analysis. Historically, sampling methods have included a rate-based approach, often relying heavily on a manual configuration. There is a need for a more intelligent approach, and we propose a hierarchical sampling methodology to address multiple requirements concurrently. Initial rate-based sampling mitigates the overwhelming volume of traces, as no further analysis can be performed on this level. In the next stage, more nuanced analysis is facilitated based on the previous foundation, incorporating information regarding trace properties and ensuring the preservation of vital process details even under extreme conditions. This comprehensive approach not only aids in the visualization and conceptualization of applications but also enables more targeted analysis in later stages. As we delve deeper into the sampling hierarchy, the technique becomes tailored to specific purposes, such as the simplification of application troubleshooting. In this context, the sampling strategy prioritizes the retention of erroneous traces from dominant processes, thus facilitating the identification and resolution of underlying issues. The focus of this paper is to reveal the impact of sampling on troubleshooting efficiency. Leveraging intelligent and explainable artificial intelligence solutions enables the detection of malfunctioning microservices and provides transparent insights into root causes. We advocate for using rule-induction systems, which offer explainability and efficacy in decision-making processes. By integrating advanced sampling techniques with machine-learning-driven intelligence, we empower organizations to navigate the complexities of large-scale distributed cloud environments effectively.

A Prior-Guided Dual Branch Multi-Feature Fusion Network for Building Segmentation in Remote Sensing Images

The domain of remote sensing image processing has witnessed remarkable advancements in recent years, with deep convolutional neural networks (CNNs) establishing themselves as a prominent approach for building segmentation. Despite the progress, traditional CNNs, which rely on convolution and pooling for feature extraction during the encoding phase, often fail to precisely delineate global pixel interactions, potentially leading to the loss of vital semantic details. Moreover, conventional CNN-based segmentation models frequently neglect the nuanced semantic differences between shallow and deep features during the decoding phase, which can result in subpar feature integration through rudimentary addition or concatenation techniques. Additionally, the unique boundary characteristics of buildings in remote sensing images, which offer a rich vein of prior information, have not been fully harnessed by traditional CNNs. This paper introduces an innovative approach to building segmentation in remote sensing images through a prior-guided dual branch multi-feature fusion network (PDBMFN). The network is composed of a prior-guided branch network (PBN) in the encoding process, a parallel dilated convolution module (PDCM) designed to incorporate prior information, and a multi-feature aggregation module (MAM) in the decoding process. The PBN leverages prior region and edge information derived from superpixels and edge maps to enhance edge detection accuracy during the encoding phase. The PDCM integrates features from both branches and applies dilated convolution across various scales to expand the receptive field and capture a more comprehensive semantic context. During the decoding phase, the MAM utilizes deep semantic information to direct the fusion of features, thereby optimizing segmentation efficacy. Through a sequence of aggregations, the MAM gradually merges deep and shallow semantic information, culminating in a more enriched and holistic feature representation. Extensive experiments are conducted across diverse datasets, such as WHU, Inria Aerial, and Massachusetts, revealing that PDBMFN outperforms other sophisticated methods in terms of segmentation accuracy. In the key segmentation metrics, including mIoU, precision, recall, and F1 score, PDBMFN shows a marked superiority over contemporary techniques. The ablation studies further substantiate the performance improvements conferred by the PBN’s prior information guidance and the efficacy of the PDCM and MAM modules.

Exploring cocrystals and polymorphism in pharmaceutical science: A comprehensive review

The pharmaceutical industry has been working hard in recent years to find ways to control polymorphism, which involves many solid drug forms due to their various physical and chemical characteristics. Creating cocrystals and salts of active pharmaceutical ingredients (APIs) is one potential research direction. These substances have drawn a lot of attention because they can improve crucial pharmacological features without reducing therapeutic efficacy. It is significant to note that not all compounds having multiple hydrogen bond donor/acceptor groups and polymorphism properties will form cocrystals; this behavior varies. Pharmaceutical firms and researchers alter APIs, frequently by generating cocrystals, salts, or polymorphs. This review discusses analytical approaches for solids drug forms such as diffraction, spectroscopy, thermal analysis, and pharmaceutical characterization. These techniques enable us to investigate solid APIs thoroughly, from their bulk characteristics to their molecular composition. They offer vital details regarding the composition, attributes, purity, and probable changes of the substance. These technologies are also useful for continuous monitoring and analysis of drug development physical procedures.

Comparative Analysis of Machine and Deep Learning Techniques for Text Classification with Emphasis on Data Preprocessing

Physician-written discharge medical notes include vital details regarding their patients' health. Numerous deep learning algorithms have shown effective in gleaning crucial insights from unstructured medical notes data, leading to potentially useful outcomes in the medical field. The goal of this research is to determine how different deep learning algorithms perform as models for text classification issues in long short term memory (LSTM). Titanic Disaster Dataset has been used for pre-processing is essential since there is a lot of unnecessary information in textual data. Next, clean up the data by eliminating duplicate rows and filling in the blanks. Besides traditional machine learning algorithms such as naive bayes (NB), gradient boosting (GB), and support vector machine (SVM), we use deep learning algorithms to classify data, including bidirectional – LSTM using Conditional Random Fields (CRFs). BiLSTM is the most precise model compared to other models and baseline research, with a classification accuracy of 98.5%.

NN scattering with NΔ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N\\varDelta $$\\end{document} coupling: dibaryon resonances without “dibaryons”?

It is known that at their threshold intermediate two-baryon NΔ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N\\varDelta $$\\end{document} states can produce resonance-like structures in some isospin one states, often interpreted as more exotic manifest six-quark states. This paper applies the coupled-channel method to study vital details of the NΔ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N\\varDelta $$\\end{document} effect in isospin one NN scattering with interactions constrained to exclude the influence of such extraneous hypothetical particles in two respects. Firstly, the calculated phase parameters are fitted only below pion inelasticity, i.e. far below the region of “dibaryons”. Secondly, the strength of the NN→NΔ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$NN \\rightarrow N\\varDelta $$\\end{document} transition is constrained to agree with the pion production reaction NN→dπ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$NN\\rightarrow d\\pi $$\\end{document}, fairly independent of the details of the pure “diagonal” NN potential and its effect practically as strong as the NN itself. The strongly angular momentum dependent ingredients and results may be considered as a necessary background for searches of any additional dibaryon effects. Optimal conditions for resonant NΔ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N\\varDelta $$\\end{document} appearances involve a decrease of the orbital angular momentum in the transition NN→NΔ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$NN\\rightarrow N\\varDelta $$\\end{document}, as is the case in the 1D2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^1D_2$$\\end{document} and 3F3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^3F_3$$\\end{document} waves. Detailed complex phase shift results are further presented also for isospin one peripheral partial waves up to J=6\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$J=6$$\\end{document}, where, contrary to common prejudice, NΔ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N\\varDelta $$\\end{document} excitation is still found to be comparable to or even larger than one pion exchange.

Criminal Investigation System

The Criminal Investigation System with represents a significant advancement in the field of law enforcement technology. By leveraging the power of data and predictive analytics, it empowers investigators to solve cases faster, identify suspects more accurately, and ultimately, enhance public safe. Based on the historical crime data suggest that Hashing algorithm. The digital system functions by gathering and classifying different kinds of information, such as crime scene reports, witness statements, forensic evidence, and suspect profiles. It facilitates the work of an officer by allowing him to compile new, vital case details on demand and to monitor the case's progress online. Keywords: Criminal Investigation, System Law, enforcement technology ,Data Predictive, analytics, Solve cases, faster, Identify suspects public safety, Historical crime , data Hashing algorithm , Gathering information Classifying , information Crime ,scene reports, Witness statements ,Forensic evidence ,Suspect profiles , Officer.

Applications of artificial intelligence algorithms in artificial lift systems: A critical review

In the oil and gas industry, the implementation of an artificial lift system is utilized to enhance the extraction of hydrocarbons from a reservoir that has depleted its natural energy. This can be achieved by employing pumps such as positive-displacement or centrifugal downhole pumps, or by injecting gas into the well tubing to reduce liquid hydrostatic pressure and improve fluid flow towards the surface. Recently, artificial intelligence-based algorithms have emerged as the leading approach for modelling various artificial lift parameters and problems. Consequently, there has been a renewed interest in understanding the role and effectiveness of different computational algorithms used in the artificial lift system domain. While there is an extensive body of literature on this subject, no prior research has effectively consolidated the findings from various artificial lift system domains, underscoring the need for an all-encompassing review. The purpose of this review is to create a guide that serves as a framework that helps scholars identify knowledge gaps and suggest novel ideas that would enhance the workings of artificial lift systems in the oil and gas industry. This article presents the first comprehensive analysis of artificial intelligence applications in five key areas of artificial lift systems in the oil and gas industry namely: prediction of oil and gas flow rates in artificial lift wells, prediction of artificial lift system failures, selection of artificial lift systems, parameter optimization, and the application of soft sensing technologies in artificial lift systems. The research studies pertaining to each of these areas were systematically identified from the existing literature, and their main features were highlighted. To ensure the uniqueness and novelty of this work, a critical evaluation of the artificial lift articles in each broad area was conducted. The review outcomes were then presented in clear tables that highlighted the main features of each study. Findings from the study indicate that artificial intelligence models are prevalent in almost every aspect of artificial lift systems, and the number of articles on this topic continues to grow. Some of these models have even been field tested and proven effective in improving artificial lift system operations and reducing costs. However, there is need to enhance the explicitness and interpretability of some of the predictive models by including vital details, to enhance seamless implementation and easy deployment in the field. This work will be valuable for industry operators, researchers, and students who seek a guide or reference material on artificial intelligence-based models for artificial lift systems.

Anatomy of Maxillary Sinus: Focus on Vascularization and Underwood Septa via 3D Imaging.

The study of the maxillary sinus anatomy should consider the presence of two features of clinical importance. The arterial supply course and the presence of the so-called Underwood septa are two important factors to consider when planning surgical treatment to reduce the risk of surgical complications such as excessive bleeding and Schneiderian membrane perforations. This study aimed to investigate the above-mentioned anatomical structures to improve the management of eventual vascular and surgical complications in this area. This study included a total of 200 cone-beam computed topographies (CBCTs) divided into two groups of 100 CBCTs to evaluate the arterial supply (AAa) course through the lateral sinus wall and Underwood's septa, respectively. The main parameters considered on 3D imaging were the presence of the AAa in the antral wall, the length of the arterial pathway, the height of the maxillary bone crest, the branch sizes of the artery in the first group, and the position of the septa, the length of the septa, and their gender associations in the second group. The CBCT analysis showed the presence of the arterial supply through the bone wall in 100% of the examined patients, with an average size of 1.07 mm. With regard to the septa, 19% of patients presented variations, and no gender difference was found to be statistically significant. The findings add to the current understanding of the clinical structure of the maxillary sinus, equipping medical professionals with vital details for surgical preparation and prevention of possible complications.

SpatialPPI: Three-dimensional space protein-protein interaction prediction with AlphaFold Multimer

Rapid advancements in protein sequencing technology have resulted in gaps between proteins with identified sequences and those with mapped structures. Although sequence-based predictions offer insights, they can be incomplete due to the absence of structural details. Conversely, structure-based methods face challenges with respect to newly sequenced proteins. The AlphaFold Multimer has remarkable accuracy in predicting the structure of protein complexes. However, it cannot distinguish whether the input protein sequences can interact. Nonetheless, by analyzing the information in the models predicted by the AlphaFold Multimer, we propose a highly accurate method for predicting protein interactions. This study focuses on the use of deep neural networks, specifically to analyze protein complex structures predicted by the AlphaFold Multimer. By transforming atomic coordinates and utilizing sophisticated image-processing techniques, vital 3D structural details were extracted from protein complexes. Recognizing the significance of evaluating residue distances in protein interactions, this study leveraged image recognition approaches by integrating Densely Connected Convolutional Networks (DenseNet) and Deep Residual Network (ResNet) within 3D convolutional networks for protein 3D structure analysis. When benchmarked against leading protein-protein interaction prediction methods, such as SpeedPPI, D-script, DeepTrio, and PEPPI, our proposed method, named SpatialPPI, exhibited notable efficacy, emphasizing the promising role of 3D spatial processing in advancing the realm of structural biology. The SpatialPPI code is available at: https://github.com/ohuelab/SpatialPPI.

Electric Vehicle Battery State of Charge and Charging Station Distance Estimation Using IoT

Background: The global transition to green energy and the rapid development of Electric Vehicle (EV) technology, along with falling component costs, have fueled the growing popularity of electric vehicles. To support the widespread adoption of EVs, an efficient and userfriendly charging infrastructure is crucial. Objective: This work aims to propose a comprehensive EV charging system that addresses the rising demand for charging stations, streamlines the charging process, and empowers EV drivers with essential information. The primary focus is on an economical and effective booking system, enabling users to locate nearby charging stations and make informed choices about their charging preferences. Method: We suggest developing a EVs Charging Finder App, serving as a central platform for EV users to find nearby charging stations. The app will provide vital details, including ratings, reviews, available time slots, charging duration estimates, and more. Users can also contribute new charging station data, fostering app growth. Additionally, an alert system will notify users when nearby charging slots become available, enhancing convenience for EV drivers. Results: The EVs Charging Finder App is anticipated to significantly enhance the accessibility and convenience of EV charging. Users can effortlessly locate charging stations, assess quality through reviews and ratings, and plan charging sessions based on real-time availability. The battery voltage of 45.2 V is a critical parameter for monitoring the health and performance of the battery, influencing the accuracy of state of charge (SoC) estimations and potentially impacting the efficiency of the electric vehicle. The 47.7 km driven is a key factor in assessing energy consumption and vehicle efficiency, which can affect the remaining state of charge in the battery. The battery's state of charge (SOC) is at 85%, indicating a relatively high charge level. Knowing that the charging station is available is crucial for planning charging activities, allowing users to proceed without concerns about station availability. The booking time at 10:00 AM is essential for efficiently managing charging infrastructure, especially in scenarios with high demand for charging services. These data points collectively contribute to optimizing the charging experience and ensuring the effective utilization of electric vehicle resources. Conclusion: The proposed EVs Charging Finder App offers a practical and efficient solution to address the surging demand for charging stations. By providing comprehensive information and real-time alerts, this system aims to make EV charging more accessible, user-friendly, and environmentally sustainable.

Environmental management for car accident precaution and remote notification

Cars have been utilized as reliable means of transportation beyond the capacity of motorcycles. However, the misuse and abuse of these vehicles through unsafe practices like speeding and reckless driving poses a great risk that results in accidents – and sometimes – with casualties. In such unfortunate circumstances, the importance of prompt accident notification is extremely important for the driver. This notification should encompass vital accident details and precise location information. Thus, the implementation of a comprehensive system for accident warnings and the remote transmission of accident data emerges as an imperative safety measure. In this system, accidents are assessed based on the vehicle's slope relative to the road and speed. The vehicle's slope is measured using ultrasonic sensor, which calculates the ground clearance distance at two different locations of the vehicles. The two sensors will provide warning when there is a difference of more than 3 cm. Speed is measured using GPS, with the limit set at 90 km/h. If crashed occurs, Airbag Sensor gives logic ‘1’, then GPS will take current coordinates and date of the accident, then sends it via SIM800L in SMS format to registered number. Thus, the dangerous condition signed by 4,7 cm difference marked by Red LED and formed 7o angles which caused by excessive maneuver while GPS has 95% accuracy with speedometer. Thus, this voice output sure be great while quiet environment which output quality is 45dB at conversation environment.

STRUCTURAL ANALYSIS OF UNPUBLISHED CHAGATAI-PERSIAN DICTIONARIES OF THE 16th–18th CENTURIES: LEXICOGRAPHIC AND SOCIOCULTURAL ASPECTS

The investigation delves into Chagatai-Persian dictionaries from the 16th to the 18th centuries, preserved in the Central Library of Punjab University. The article aims to study the structural features of these dictionaries in the context of their sociocultural significance and influence on the further development of Turkic languages. These invaluable medieval artifacts underwent detailed scrutiny using multiple methods, encompassing structural, lexical, codicological, lexicographic and sociocultural analyses. Within a single manuscript cover, there exist six treatises on the Turkic-Persian lexicon alongside a textbook elucidating Turkic language grammar. These are inscribed in the Perso-Arabic script, a writing system employed for the Chagatai language. The chief lexicographic design integrates headwords and their explanations in couplets, avoiding any typographic or grammatical demarcation. The artful use of rhyme schemes is evident in the systematic arrangement of headwords. A thorough scrutiny of these medieval Chagatai-Persian dictionaries brings to light their distinct lexicographical framework, crafted meticulously to support linguistic conservation. These works capture a wide spectrum of vocabulary from different Turkic territories, underlining the endeavor to encapsulate a broad cultural legacy. Elements such as rhyming masnavi-inspired explanatory sentences, thematic clusters, and dedicated segments on commands and equine terminology are observed. Notably, the integration of Persian elements, especially in the form of auxiliary verbs, symbolizes a fusion of languages. The dictionaries showcase an emphasis on practical vocabulary acquisition through the harmonious pairing of headwords with poetic contexts, devoid of typographic or grammatical deviations. Despite the headword’s seamless integration, there is meticulous attention to maintaining rhyming continuity. The rarity of prefaces is counterbalanced by the richness of colophons, which offer vital details about the manuscript. Though the Chagatai language receded over time, the indelible mark left by these dictionaries endures, with remnants identifiable in modern Turkic vocabularies. A more exhaustive comparative exploration of these works could further deepen insights into Chagatai linguistics and lexicographic practices during the medieval period. This study also triggers broader reflections on the pivotal role of bilingual lexicons in safeguarding vulnerable linguistic treasures. By methodically presenting and analyzing these linguistic gems, the investigation facilitates their appreciation within scholarly circles, underscoring the Turkic cultural legacy. This also solidifies their recognition as prime examples of the mutual literary interplay between Turkic and Persian traditions, a relationship rooted in the medieval timeframe.

Understanding the Mechanism of the Structure-Dependent Mechanical Performance of Carbon-Nanotube-Based Hierarchical Networks from a Deformation Mode Perspective.

Carbon nanotube (CNT)-based networks have wide applications, in which structural design and control are important to achieve the desired performance. This paper focuses on the mechanism behind the structure-dependent mechanical performance of a CNT-based hierarchical network, named a super carbon nanotube (SCNT), which can provide valuable guidance for the structural design of CNT-based networks. Through molecular dynamic (MD) simulations, the mechanical properties of the SCNTs were found to be affected by the arrangement, length and chirality of the CNTs. Different CNT arrangements cause variations of up to 15% in the ultimate tensile strains of the SCNTs. The CNT length determines the tangent elastic modulus of the SCNTs at the early stage. Changing the CNT chirality could transform the fracture modes of the SCNT from brittle to ductile. The underlying mechanisms were found to be associated with the deformation mode of the SCNTs. All the SCNTs undergo a top-down hierarchical deformation process from the network-level angle variations to the CNT-level elongations, but some vital details vary, such as the geometrical parameters. The CNT arrangement induces different deformation contributors of the SCNTs. The CNT length affects the beginning point of the CNT elongation deformation. The CNT chirality plays a crucial role in the stability of the junction's atomic topology, where the crack propagation commences.

QRxPERT: Automated Medicine Dispensing Machine with QR Code-Enabled Medication Counselling

QRxPERT aims to tackle chronic illnesses and medication non-compliance by integrating smart medication counseling and AI-enabled drug dispensers, with the help of a Smart Health Card. Patients can effortlessly scan QR codes on medication labels, unlocking a wealth of information instantly available at their fingertips. This includes vital details such as dosage instructions, potential side effects, and other pertinent information crucial for informed and effective self-management. QRxPERT’s AI-enabled drug Dispenser uses advanced machine learning and an RFID chip to accurately dispense medication, reducing human error and preventing medication non-compliance. This dispenser is designed to significantly reduce the margin of error in medication administration, ensuring that patients receive the precise dosage required. This not only enhances the efficacy of treatment but also serves as a robust preventative measure against medication non-compliance. The SMART HEALTH CARD also sends Reminder Messages to Mobile Phones, Regarding Tablets/Medication to take according to dose Interval given by DOCTORS. In conclusion, QRxPERT is a revolutionary healthcare innovation that integrates advanced technology to improve medication management and adherence, by offering smart medication counseling and AI-enabled drug dispensers.