Rule Violations Research Articles

Facile benzothiazole-triazole based thiazole derivatives as novel thymidine phosphorylase and α-glucosidase inhibitors: Experimental and computational approaches

The present study reports the new thiazole (A-L) derivatives based on benzothiazole fused triazole which were synthesized and assessed against thymidine phosphorylase and α-glucosidase enzymes. Several compounds with the same basic structure but different substituents were found to have high activity against the targeted enzymes, while others with the same basic skeleton but different substituents were found to have medium to low activity among the members of tested series. These analogs showed a varied range of inhibition in both case thymidine phosphorylase and alpha glucosidase, A (IC50 = 7.20 ± 0.30 µM and IC50 = 1.30 ± 0.70 µM), B (IC50 = 8.80 ± 0.10 µM and IC50 = 2.10 ± 0.30 µM), C (IC50 = 8.90 ± 0.40 µM and IC50 = 3.20 ± 0.20 µM) and thiazole containing analogs such as G (IC50 = 11.10 ± 0.20 µM and IC50 = 7.80 ± 0.20 µM) and H (IC50 = 12.30 ± 0.30 µM and IC50 = 6.30 ± 0.20 µM). When compared with standard drugs 7-Deazaxanthine, 7DX (IC50 = 10.60 ± 0.50 µM) and acarbose (IC50 = 4.30 ± 0.30 µM) respectively. These analogs were also subjected to molecular docking studies which indicated the binding interaction of molecules with active sites of the enzyme and strengthen the drug profile of these compounds. ADMET studies also predict the drug-like properties of these compounds, with no violations of drug likeness rules.

Moral distress and moral injury in the context of autism

PurposeClinically, it is often observed that autistic people may have a heightened need for rules and may find rule violations very distressing. It is clinically plausible that autistic individuals may be prone to hyper-morality and greater reactivity and adverse experiences arising from moral threats and violations. Such adverse experiences may, in some instances, lead to moral distress (MD) or in more extreme cases moral injury. Thus, this study aims to examine how MD can operate in the context of autism.Design/methodology/approachThe authors explore clinical hypotheses on how MD can intersect with different features of autism by drawing on clinical and research knowledge of autism spectrum disorder and how it contextualises experiences of morality.FindingsBased on a review of the literature and theory, the authors hypothesise that autistic individuals may be more prone to MD where they have a lower threshold for MD. As a result of this lower threshold, they may have more frequent exposure to MD, experience more immediate and intense subjective reactions to moral transgressions, for more protracted periods. Also, they may find it more difficult to over-ride and neutralise moral outrage. As a result, they may be more susceptible to mental health sequelae and impaired social and occupational functioning. Practical recommendations are made for clinicians supporting people with autism with a propensity for MD or moral injury, to improve their quality of life.Practical implicationsPractical recommendations are made for clinicians supporting people with autism with a propensity for MD or moral injury, to improve their quality of life.Originality/valueUnderstanding MD in an autistic context is important for detecting and treating the adverse impacts of MD on autistic individuals. Importantly, erroneous preconceptions about moral reasoning in autism need to be mitigated to fully understand the moral experiences and harness the many strengths of people with autism.

Survey of driver's functional state change, considering movement through mountain passes

According to traffic accident statistics, the main causes are violations of traffic rules. Based on scientific research, it is clear that the main reason for these events is the drivers' incorrect assessment of the road situation and their inadequate response. Assessing road conditions depends on drivers' qualifications, skills, work experience, reaction time, and other physiological factors. The functional state of drivers is important to consider. It reflects their ability to work reliably and affects both fatigue levels and how fatigue builds up. Factors that impact decision-making speed and quality are crucial when evaluating the risks of the transportation process, particularly those associated with human behavior. This study investigates the safety of freight transportation for drivers, road users, third parties, and the environment. The study focuses on road traffic characteristics in mountainous conditions at high altitudes. The study aims to analyze how drivers' performance is affected while navigating challenging sections of mountain roads in the Ukrainian Carpathians. Four different routes will be examined. The research will provide graphic analysis of the results. It will show the main patterns of changes in drivers' functional state based on the route's complexity. The research aims to analyze indicators of drivers' functional state and their impact on their performance reliability. It allows for a more detailed evaluation of transport routes and planning of truck drivers' work and rest schedules on suburban routes.

ENGLISH LANGUAGE LEARNING PROGRAM BASED ON ISLAMIC PERSONALITY VALUES FOR EARLY CHILDREN

This research aims to explain the implementation of an English language learning program based on Islamic personality at Islamic playgroups in Kudus and to know the obstacles in practicing the English language based on Islamic personality. This research uses qualitative research. Techniques in collecting data are done by interviews, observation, and documentation. The result of this research is the program has been implemented according to the training curriculum at every stage. The trainers transfer their knowledge, practice English pronunciation, and give an example to the participants in using the right English language based on the Islamic personality. Then, the teachers practice the new English words based on Islamic personality in each playgroup. Through habituation, example, conditioning, and clarity of sanctions for violations of rules carried out professionally in a formal education environment based on Islamic values, children will be able to apply Islamic values in a pleasant atmosphere. The limited school time to practice pronunciation will cause children in Islamic playgroups to be less focused on practicing the English language based on Islamic personality. However, the efforts to overcome obstacles can be done with supportive non-verbal language by using thumbs up, applause, smile, handshake, and so on.

Halogenated Secondary Metabolites from Higher Plants: Potent Drug Candidates for Chikungunya Using in silico Approaches.

Chikungunya virus (CHIKV) causes a debilitating fever and joint pain, with no specific antiviral treatment available. Halogenated secondary metabolites from plants are a promising new class of drug candidates against chikungunya, with unique properties that make them effective against the virus. Plants produce these compounds to defend themselves against pests and pathogens, and they are effective against a wide range of viruses, including chikungunya. This study investigated the interactions of halogenated secondary metabolites with nsP2pro, a therapeutic target for CHIKV. A library of sixty-six halogenated plant metabolites screened previously for ADME properties was used. Metabolites without violation of Lipinski's rule were docked with nsP2pro using AutoDock Vina. To find the stability of the pipoxide chlorohydrin-nsP2pro complex, the GROMACS suite was used for MD simulation. The binding free energy of the ligand-protein complex was computed using MMPBSA. Molecular docking studies revealed that halogenated metabolites interact with nsP2pro, suggesting they are possible inhibitors. Pipoxide chlorohydrin showed the greatest affinity to the target. This was further confirmed by the MD simulations, surface accessible area, and MMPBSA studies. Pipoxide chlorohydrin, a halogenated metabolite, was the most potent against nsP2pro in the survey.

Anti-angiogenic Potential of Trans-chalcone in an In Vivo Chick Chorioallantoic Membrane Model: An ATP Antagonist to VEGFR with Predicted Blood-brain Barrier Permeability.

Glioblastoma multiforme (GBM) is characterized by massive tumorinduced angiogenesis aiding tumorigenesis. Vascular endothelial growth factor A (VEGF-A) via VEGF receptor 2 (VEGFR-2) constitutes majorly to drive this process. Putting a halt to tumordriven angiogenesis is a major clinical challenge, and the blood-brain barrier (BBB) is the prime bottleneck in GBM treatment. Several phytochemicals show promising antiangiogenic activity across different models, but their ability to cross BBB remains unexplored. We screened over 99 phytochemicals having anti-angiogenic properties reported in the literature and evaluated them for their BBB permeability, molecular interaction with VEGFR-2 domains, ECD2-3 (extracellular domains 2-3) and TKD (tyrosine kinase domain) at VEGF-A and ATP binding site, cell membrane permeability, and hepatotoxicity using in silico tools. Furthermore, the anti-angiogenic activity of predicted lead Trans-Chalcone (TC) was evaluated in the chick chorioallantoic membrane. Out of 99 phytochemicals, 35 showed an efficient ability to cross BBB with a probability score of > 0.8. Docking studies revealed 30 phytochemicals crossing benchmark binding affinity < -6.4 kcal/mol of TKD with the native ligand ATP alone. Out of 30 phytochemicals, 12 showed moderate to low hepatotoxicity, and 5 showed a violation of Lipinski's rule of five. Our in silico analysis predicted TC as a BBB permeable anti-angiogenic compound for use in GBM therapy. TC reduced vascularization in the CAM model, which was associated with the downregulation of VEGFR-2 transcript expression. The present study showed TC to possess anti-angiogenic potential via the inhibition of VEGFR-2. In addition, the study predicted TC to cross BBB as well as a safe alternative for GBM therapy, which needs further investigation.

Состояние опьянения как признак транспортных преступлений

One of the most difficult issues of the qualification of transport crimes in investigative and judicial practice is still the issue of assessing violations of traffic rules and (or) operation of vehicles, as well as other requirements of transport safety, committed while intoxicated, and associated with the abandonment of the place of a traffic accident by the culprit. These signs have long been in the area of close attention not only of the legislator and law enforcement officer, but also of the entire scientific community, nevertheless, they have not yet received a proper exhaustive interpretation both in theory and in the norms of the law themselves. The article attempts to resolve the accumulated contradictions, formulates proposals for improving the criminal law mechanism for countering criminal accidents in transport caused by persons who are intoxicated.

Network Intrusion Detection and Anomaly Classification

A software application known as an intrusion detection system (IDS) is utilized to monitor networks for any unauthorized or malicious activities that may violate security policies related to system confidentiality, integrity, and availability. Our thesis involved conducting extensive literature reviews on various types of IDS, anomaly detection methods, and machine learning algorithms that can be utilized for detection and classification. IDS are crucial for safeguarding advanced communication networks and were initially designed to identify specific patterns, signatures, and rule violations. In recent years, machine learning and deep learning approaches have been employed in the field of network intrusion detection, providing promising alternatives. Our proposed system involved identifying noisy features through causal intervention, preserving only those features that had a causality with cyberattacks. The ML algorithm was then utilized to make a preliminary classification to select the most relevant types of cyberattacks, ultimately leading to the detection of unique labeled cyberattacks through the counterfactual detection algorithm. Keywords—Intrusion Detection System (IDS), anomaly detection, machine learning algorithms, deep learning, causal intervention, cyberattack classification, and counterfactual detection.

Very Low Level Flight Rules for Manned and Unmanned Aircraft Operations

An analysis of the development of legal regulations regarding unmanned civil aviation leads to the conclusion that the current air traffic rules are among the key issues that require amending. Are drones allowed to fly at any height? Can drones fly freely over a person’s house or garden, several meters above the ground? What is the minimum allowable height for drone flights? The method of study consisted of content analysis of the existing legislation. Current doctrines were confronted with the existing regulations, documents, materials, safety reports, and statistics. The results of the study show that the existing air traffic rules, precisely in the case of aircraft operations performed by manned and unmanned aviation at very low heights, are definitely practical in nature. First, in most countries violations of air traffic rules are prohibited acts subject to criminal penalty. Second, determining the principles of air traffic for air operations is of crucial importance for determining legally permissible interference in property ownership. The urban air mobility is outside the scope of this research.

Further on the Choice of Space Group for Scapolite Group Members and Genetic Considerations about the Si-Al Ordering in Their Framework Construction

This paper poses a major question regarding the choice of space group for scapolite mineral group members. An artificial boundary is typically drawn between space groups I4/m and P42/n when solving the structures of scapolites within the marialite–meionite series. The authors debate if solving the crystal structure in lower symmetries is justified. The choice of space group here is attributed to Si-Al ordering of the framework, and it is shown that the interstitial framework cations and anions have an accompanying role in that decision. Some answers on the ranges and limits of distribution of space groups of scapolite members in the marialite–meionite series, and the manifestations of violation of the Lowenstein rule or the so-called aluminum avoidance rule are presented. Modern physical methods (SEM-EDS and SXDA) are employed in the study to properly analyze the solid solution series in detail. New crystal–chemical data are reported for scapolite samples from different localities. An analysis was made for the types of possible Al-O-Al bonds that can occur in the structures at different Al:Si ratios and their influence on Al-Si ordering. Finally, genetic considerations about Al-Si ordering in the framework construction during the mineral formation processes are proposed.

Targeting Epidermal Growth Factor Receptor with Ficus virens Metabolites to Manage Cancer Progression: Molecular Docking and ADME Study

Introduction: Targeting epidermal growth factor receptor (EGFR) has been used in the treatment of several cancer types where EGFR has a notable involvement in signalling pathways. Medicinal plants are the oldest and healthiest source of medication used in modern pharmacological therapy. Previous research indicates that the compounds found in Ficus virens stem bark have significant therapeutic properties against a range of illnesses, including cancer. Therefore, in this study, molecular docking research was conducted to determine the binding interactions and affinity of secondary metabolites predicted in F. virens methanolic extract with the target protein, EGFR. Materials and Methods: Three-dimensional (3D) conformers of the secondary metabolism products and adenosine triphosphate (ATP) were collected from PubChem on 5 January 2024. The 3D structure of EGFR and its inhibitor was retrieved from the Protein Data Bank (PDB) databank and ligands and proteins were converted to AutoDock-compatible format and then the energy minimisation was performed by the Open Babel in PyRx. Finally, using PyRx-Python 0.8, molecular docking was done, and using Discovery Studio, visualisation was done as well. The grid box dimensions were specified at 30 Å ×30 Å ×25 Å, and the physiochemical property evaluation was done by the SwissADME online server. Results: In this study, molecular docking assessed 14 compounds, including phytochemicals and ATP, for their binding with EGFR (PDB Id: 1XKK). Redocking of lapatinib validated the results. Natural chemicals showed binding energies from −4.0 to −7.8 Kcal/mol, with oleic acid and iso-caryophyllene demonstrating promising interactions, sourced from F. virens. Despite some limitations, these compounds exhibit potential for EGFR-targeted drug development, despite one Lipinski’s rule violation. Conclusions: EGFR inhibitory activities of iso-caryophyllene and oleic acid, which were identified in the F. virens methanol extract, were remarkable and higher than that of the substrate; their potential opens up exciting new possibilities for moving forward with cancer treatment. It is encouraged to further investigate the in vivo effectiveness of iso-caryophyllene, by means of animal models and cell lines.

HELMET RULE VIOLATION DETECTION USING DEEP LEARNING

The widespread non-compliance of helmet-wearing regulations among motorcyclists in India poses a significant threat to road safety. This paper introduces a new approach to address this issue through the developing a Helmet Rule Violation Detection system. Given the challenges faced by traffic police in enforcing helmet regulations, the proposedsystem leverages computer vision techniques to automatically identify motorcyclists without helmets. The methodology involves the detection of moving vehicles through thresholding, followed by a classification process based on area and aspect ratio to distinguish between motorcyclists and non-motorcyclists. The subsequent identification of helmet violation is achieved by isolating relevant blobs and employing a classification mechanism. By automating this process, the system aims to reduce the burden on traffic police and enhance the overall safety of road users. The proposed approach not only contributes to the mitigation of helmet rule violations but also promotes responsible road behavior, fostering a safer and more compliant traffic environment in India.

An efficient intrusion detection technique for traffic pattern learning

Efficient intrusion detection algorithms are required for network traffic learning patterns in order to protect advanced network communication channels. These systems can be used to detect normal and unusual patterns, signatures, and rule violations. In recent years, conventional and deep machine learning algorithms have been utilized in the field of network intrusion detection for network traffic learning systems. The use of machine learning opens up new attack surfaces that are very intriguing to investigate. Attackers can introduce noisy data into training data to influence testing patterns in computer networks. The goal of this work is to create an efficient intrusion detection solution for network traffic learning patterns using a supervised and unsupervised technique. We developed an effective intrusion detection system (IDs) using an appropriate NSLKDD dataset for network traffic patterns. The model was trained and evaluated using the Genetic Optimization Algorithm (GOA) and the Niave Bayesian technique to recognize usual and unexpected network traffic patterns. We created a strategy that begins with a random population and subsequent iterates through the fitness function, returning the best parents with high detection accuracy. The best parents were determined using the n-parameters iterated by the crossover and mutation procedures. A cross over function was created to combine genes from two fitness parents by randomly selecting portions from each parent. The individual components of the crossover offsprings are randomly flipped to achieve the mutation. The fitness of the previous generation was obtained to generate a new generation, and this process was repeated n times. This was created to detect network intrusions using Nave Bayes' binary categorization problem and evolutionary algorithms. We accomplished this task by aggregating noise into training set before broadcasting the average number, and it is critical not to have that public average too frequently. The experimental results reveal that our proposed GA fared better than the NB technique, with a detection accuracy of 95.0% versus a recommendable detection accuracy of 53.0%.

Marine-Derived Cytosine Arabinoside (Ara-C) Inhibits Biofilm Formation by Inhibiting PEL Operon Proteins (Pel A and Pel B) of Pseudomonas aeruginosa: An In Silico Approach.

Pseudomonas aeruginosa (P. aeruginosa) is a gram-negative biofilm-forming opportunistic human pathogen whose vital mechanism is biofilm formation for better survival. PelA and PelB proteins of the PEL operon are essential for bacterial-synthesized pellicle polysaccharide (PEL), which is a vital structural component of the biofilm. It helps in adherence of biofilm on the surface and maintenance of cell-to-cell interactions and with other matrix components. Here, in-silico molecular docking and simulation studies were performed against PelA and PelB using ten natural bioactive compounds, individually [podocarpic acids, ferruginol, scopadulcic acid B, pisiferic acid, metachromin A, Cytarabine (cytosine arabinoside; Ara-C), ursolic acid, oleanolic acid, maslinic acid, and betulinic acid], those have already been established as anti-infectious compounds. The results obtained from AutoDock and Glide-Schordinger stated that a marine-derived cytosine arabinoside(Ara-C) among the ten compounds binds active sites of PelA and PelB, exhibiting strong binding affinity [Trp224 (hydrogen), Ser219 (polar), Val234 (hydrophobic) for PelA; Leu365 and Glu389 (hydrogen), Gln366 (polar) for PelB] with high negative binding energy -5.518kcal/mol and -6.056kcal/mol, respectively. The molecular dynamic and simulation studies for 100ns showed the MMGBSA binding energy scores are -16.4kcal/mol (Ara-C with PelA), and -22.25kcal/mol (Ara-C with PelB). Further, ADME/T studies indicate the IC50 values of AraC are 6.10mM for PelA and 18.78mM for PelB, which is a comparatively very low dose. The zero violation of Lipinski's Rule of Five further established that Ara-C is a good candidate for drug development. Thus, Ara-C could be considered a potent anti-biofilm compound against PEL operon-dependent biofilm formation of P. aeruginosa.

Combating violations of customs rules in Ukraine and prospects for administrative and procedural settlement

Combating violations of customs rules in Ukraine and prospects for administrative and procedural settlement

Normative data for the Tower of London (Drexel version) in the Quebec-French population aged between 50 and 88 years

The Tower of London (ToL) is a neuropsychological test used to assess several executive functions such as strategical reasoning, mental planning, and problem-solving. Like other cognitive tests, performance on the ToL can vary according to age, level of education, sex, and cultural background of individuals. The present study aimed to establish normative data for the Drexel version of the ToL among French-Quebec people aged 50 years and over. The normative sample consisted of 174 healthy individuals aged 50–88 years, all from the province of Quebec, Canada. Analyses were performed to estimate the associations between age, sex, and education level on one hand, and ToL performance, on the other hand. Results indicated that Total Execution Time was associated with age, whereas the Total Type II Errors and Total Rule Violation score (Type I + II Errors) were associated with both age and education level. All other scores were not significantly associated with the demographic characteristics of the participants. Since the distributions of the data were all skewed, the normative data are presented in the form of percentile ranks. To conclude, the present norms will ease the detection of executive impairments in French-Quebec middle-aged and older adults.

Enforcing Traffic Safety: A Deep Learning Approach for Detecting Motorcyclists’ Helmet Violations Using YOLOv8 and Deep Convolutional Generative Adversarial Network-Generated Images

In this study, we introduce an innovative methodology for the detection of helmet usage violations among motorcyclists, integrating the YOLOv8 object detection algorithm with deep convolutional generative adversarial networks (DCGANs). The objective of this research is to enhance the precision of existing helmet violation detection techniques, which are typically reliant on manual inspection and susceptible to inaccuracies. The proposed methodology involves model training on an extensive dataset comprising both authentic and synthetic images, and demonstrates high accuracy in identifying helmet violations, including scenarios with multiple riders. Data augmentation, in conjunction with synthetic images produced by DCGANs, is utilized to expand the training data volume, particularly focusing on imbalanced classes, thereby facilitating superior model generalization to real-world circumstances. The stand-alone YOLOv8 model exhibited an F1 score of 0.91 for all classes at a confidence level of 0.617, whereas the DCGANs + YOLOv8 model demonstrated an F1 score of 0.96 for all classes at a reduced confidence level of 0.334. These findings highlight the potential of DCGANs in enhancing the accuracy of helmet rule violation detection, thus fostering safer motorcycling practices.

Prevention regulatory focus, desired cultural tightness, civic moral disengagement, and emotional reactions to normative daily transgressions: a serial mediation model among adults in Italy.

Why do people have positive or indifferent reactions to norm violations? The present research hypothesized that individuals who focus on the avoidance of negative outcomes, for example punishments for rule violations, (i.e., a prevention focus) are hypothesized to also have a desire for rigid and clear norms (i.e., desired cultural tightness) as well as punishments for norm violations. Such norms and punishments narrow accepted behavior and, if clearly communicated, can limit rule violations. Consequently, individuals who desire higher levels of cultural tightness should be less likely to justify poor citizenship behavior (i.e., civic moral disengagement) as this behavior is antithetical to desired cultural tightness. Finally, such individuals should also be more likely to react negatively to norm violations. Data for the present study was conducted in Italy. A total of 1,181 participants were included in the analysis; participation requirements were that they be adults who were residents of Italy. Participants completed self-report measures of the prevention focus, desired cultural tightness, civic moral disengagement, and reactions to general norm violations (e.g., exceeding the speed limit, vandalism). The hypothesized serial mediation model was supported. This model can help explain why individuals can fail to react to "everyday" norm violations, as well societal-level violations (e.g., failing to respect hygienic standards during the COVID pandemic). It also calls on the need to develop mass communication approaches that can influence individuals' prevention focus on a large scale, as this can have downstream effects of reactions to such violations.

Об уголовной ответственности владельца за вред, причиненный домашним животным (собакой) третьим лицам

The laws of Russia and some foreign states have no special criminal law provisions that establish the liability of the owner for damage caused by his/her pet (dog) to third parties. The article reviews issues of qualification of such acts under the laws of Russia and foreign countries. A comparative analysis of laws of some foreign states and Russia that regulate the bases and procedure for bringing pet dog owners to the legal liability for damage caused by them to health of third parties has shown the unity of approaches towards strict legal liability including criminal liability of such owners in cases of gross violation of rules for handling their pets. At the same time, countries apply different approaches within their jurisdiction to solving the issue of specification of criminal laws applicable to the cases under consideration by singling out or refusing to single out special elements of crimes for violation of pet handling rules. The task of determination of legally relevant attributes of the link between the crime subject (dog owner), behavior of his/her pet and actions/inactivity of the victim seems relevant for the Russian criminal law practice.

Implementation of Vehicle Detection and Tracking Model Using YOLO and DeepSORT for Controlling Traffic Rule Violation

The field of computer vision has advanced significantly in the last several years, especially in the areas of object tracking and detection. This research offers a thorough analysis of the use of a vehicle tracking and detection model that makes use of DeepSORT (Deep Simple Online and Realtime Tracking) for tracking and YOLOv8 (You Only Look Once version 8) for detection. By combining these state-of-the-art methods, it will be possible to track and identify vehicles in a variety of situations in real-time, improving surveillance and traffic management as well as autonomous vehicle navigation. This project intends to develop computer vision applications in the transportation and surveillance sectors by means of a thorough examination and experimentation.