Differentiating suicide from homicide in sharp-force fatalities with stab and/or incised wounds: A scoping review.

The majority of sharp-force fatalities with stab and/or incised wounds are homicides. However, suicidal sharp-force fatalities with stab and/or incised wounds are also reported. Thus, distinguishing suicidal stab and/or incised wounds from homicidal stab and/or incised wounds is significant from the forensic perspective. This scoping review primarily summarizes the existing research findings on the differentiation of suicide from homicide in sharp-force fatalities with stab and/or incised wounds. The literature was systematically searched on February 28, 2023, using the PubMed database. A search string formed by a combination of keywords related to suicide, homicide, and stab and incised wounds yielded 23 records. After applying the eligibility criteria, six records/studies met the inclusion criteria and were included in the present scoping review. Results showed that the predictive strength of various parameters, either individually or collectively, in diagnosing the manner of sharp-force fatality as suicide or homicide is not always hundred percent accurate. Some of the important predictors of the homicidal manner of death in sharp-force fatalities include clothing damage, presence of defense injuries, presence of injuries caused by another type of violence other than sharp-force, vertically oriented chest stabs, and sharp-force injuries in the head and back anatomical sites. Some of the important predictors of the suicidal manner of death in sharp-force fatalities include the presence of tentative injuries, sharp-force injuries to the wrist, and the presence of a suicide note.

Phishing URLs Detection Using Sequential and Parallel ML Techniques: Comparative Analysis.

In today's digitalized era, the world wide web services are a vital aspect of each individual's daily life and are accessible to the users via uniform resource locators (URLs). Cybercriminals constantly adapt to new security technologies and use URLs to exploit vulnerabilities for illicit benefits such as stealing users' personal and sensitive data, which can lead to financial loss, discredit, ransomware, or the spread of malicious infections and catastrophic cyber-attacks such as phishing attacks. Phishing attacks are being recognized as the leading source of data breaches and the most prevalent deceitful scam of cyber-attacks. Artificial intelligence (AI)-based techniques such as machine learning (ML) and deep learning (DL) have proven to be infallible in detecting phishing attacks. Nevertheless, sequential ML can be time intensive and not highly efficient in real-time detection. It can also be incapable of handling vast amounts of data. However, utilizing parallel computing techniques in ML can help build precise, robust, and effective models for detecting phishing attacks with less computation time. Therefore, in this proposed study, we utilized various multiprocessing and multithreading techniques in Python to train ML and DL models. The dataset used comprised 54 K records for training and 12 K for testing. Five experiments were carried out, the first one based on sequential execution followed by the next four based on parallel execution techniques (threading using Python parallel backend, threading using Python parallel backend and number of jobs, threading manually, and multiprocessing using Python parallel backend). Four models, namely, random forest (RF), naïve bayes (NB), convolutional neural network (CNN), and long short-term memory (LSTM) were deployed to carry out the experiments. Overall, the experiments yielded excellent results and speedup. Lastly, to consolidate, a comprehensive comparative analysis was performed.

Android Ransomware Detection Using Supervised Machine Learning Techniques Based on Traffic Analysis.

In today's digitalized era, the usage of Android devices is being extensively witnessed in various sectors. Cybercriminals inevitably adapt to new security technologies and utilize these platforms to exploit vulnerabilities for nefarious purposes, such as stealing users' sensitive and personal data. This may result in financial losses, discredit, ransomware, or the spreading of infectious malware and other catastrophic cyber-attacks. Due to the fact that ransomware encrypts user data and requests a ransom payment in exchange for the decryption key, it is one of the most devastating types of malicious software. The implications of ransomware attacks can range from a loss of essential data to a disruption of business operations and significant monetary damage. Artificial intelligence (AI)-based techniques, namely machine learning (ML), have proven to be notable in the detection of Android ransomware attacks. However, ensemble models and deep learning (DL) models have not been sufficiently explored. Therefore, in this study, we utilized ML- and DL-based techniques to build efficient, precise, and robust models for binary classification. A publicly available dataset from Kaggle consisting of 392,035 records with benign traffic and 10 different types of Android ransomware attacks was used to train and test the models. Two experiments were carried out. In experiment 1, all the features of the dataset were used. In experiment 2, only the best 19 features were used. The deployed models included a decision tree (DT), support vector machine (SVM), k-nearest neighbor (KNN), ensemble of (DT, SVM, and KNN), feedforward neural network (FNN), and tabular attention network (TabNet). Overall, the experiments yielded excellent results. DT outperformed the others, with an accuracy of 97.24%, precision of 98.50%, and F1-score of 98.45%. Whereas, in terms of the highest recall, SVM achieved 100%. The acquired results were thoroughly discussed, in addition to addressing limitations and exploring potential directions for future work.

Artificial Intelligence and Patient Autonomy in Obesity Treatment Decisions: An Empirical Study of the Challenges.

Background This study aims to explore the factors associated with artificial intelligence (AI) and patient autonomy in obesity treatment decision-making. Methodology A cross-sectional, online, descriptive survey design was adopted in this study. The survey instrument incorporated the Ideal Patient Autonomy Scale (IPAS) and other factors affecting patient autonomy in the AI-patient relationship. The study participants included 74 physicians, 55 dieticians, and 273 obese patients. Results Different views were expressed in the scales AI knows the best (µ = 2.95-3.15) and the patient should decide (µ = 2.95-3.16). Ethical concerns (µ = 3.24) and perceived privacy risks (µ = 3.58) were identified as having a more negative influence on patient autonomy compared to personal innovativeness (µ = 2.41) and trust (µ = 2.85). Physicians and dieticians expressed significantly higher trust in AI compared to patients (p < 0.05). Conclusions Patient autonomy in the AI-patient relationship is significantly affected by privacy, trust, and ethical issues. As trust is a multifaceted factor and AI is a novel technology in healthcare, it is essential to fully explore the various factors influencing trust and patient autonomy.

Graphene Nanoflake Uptake Mediated by Scavenger Receptors.

The biological interactions of graphene have been extensively investigated over the last 10 years. However, very little is known about graphene interactions with the cell surface and how the graphene internalization process is driven and mediated by specific recognition sites at the interface with the cell. In this work, we propose a methodology to investigate direct molecular correlations between the biomolecular corona of graphene and specific cell receptors, showing that key protein recognition motifs, presented on the nanomaterial surface, can engage selectively with specific cell receptors. We consider the case of apolipoprotein A-I, found to be very abundant in the graphene protein corona, and observe that the uptake of graphene nanoflakes is somewhat increased in cells with greatly elevated expression of scavenger receptors B1, suggesting a possible mechanism of endogenous interaction. The uptake results, obtained by flow cytometry, have been confirmed using Raman microspectroscopic mapping, exploiting the strong Raman signature of graphene.

Identification of Receptor Binding to the Biomolecular Corona of Nanoparticles.

Biomolecules adsorbed on nanoparticles are known to confer a biological identity to nanoparticles, mediating the interactions with cells and biological barriers. However, how these molecules are presented on the particle surface in biological milieu remains unclear. The central aim of this study is to identify key protein recognition motifs and link them to specific cell-receptor interactions. Here, we employed an immuno-mapping technique to quantify epitope presentations of two major proteins in the serum corona, low-density lipoprotein and immunoglobulin G. Combining with a purpose-built receptor expression system, we show that both proteins present functional motifs to allow simultaneous recognition by low-density lipoprotein receptor and Fc-gamma receptor I of the corona. Our results suggest that the "labeling" of nanoparticles by biomolecular adsorption processes allows for multiple pathways in biological processes in which they may be "mistaken" for endogenous objects, such as lipoproteins, and exogenous ones, such as viral infections.