Search Results (902)

Sudden cardiac death (SCD) remains a major challenge in forensic medicine, representing a leading cause of natural mortality and frequently occurring in individuals without antecedent symptoms. Although conventional autopsy and histology remain the cornerstones of investigation, up to 10–15% of cases are classified as “autopsy-negative sudden unexplained death,” underscoring the need for complementary diagnostic tools. In recent years, post-mortem biochemistry and molecular approaches have become essential to narrowing this gap. Classical protein markers of myocardial necrosis (cardiac troponins, CK-MB, H-FABP, GPBB) continue to play a fundamental role, though their interpretation is influenced by post-mortem interval and sampling site. Peptide biomarkers reflecting hemodynamic stress (BNP, NT-proBNP, copeptin, sST2) offer additional insight into cardiac dysfunction and ischemic burden, while inflammatory and immunohistochemical markers (CRP, IL-6, fibronectin, desmin, C5b-9, S100A1) assist in detecting early ischemia and myocarditis when routine histology is inconclusive. Beyond these traditional markers, molecular signatures—including cardiac-specific microRNAs, exosomal RNA, proteomic alterations, and metabolomic fingerprints—provide innovative perspectives on metabolic collapse and arrhythmic mechanisms. Molecular autopsy through next-generation sequencing has further expanded diagnostic capability by identifying pathogenic variants associated with channelopathies and cardiomyopathies, enabling both cause-of-death clarification and cascade screening in families. Emerging multi-omics and artificial intelligence frameworks promise to integrate these heterogeneous data into standardized and robust interpretive models. Pre- and post-analytical considerations, together with medico-legal implications ranging from malpractice evaluation to the management of genetic information, remain essential components of this evolving field. Overall, the incorporation of validated biomarkers into harmonized international protocols, increasingly supported by AI, represents the next frontier in forensic cardiology. Full article

Wide thermal cracks are a common form of pavement distress affecting primary state and county highways, urban residential streets, and parking lots across the Southwest climatic regions. These cracks are primarily caused by thermal fatigue, driven by diurnal temperature variations despite the lack of extremely cold events. This research aims to identify and analyze the local factors contributing to the initiation and propagation of thermal fatigue cracks. Field cores are collected from 12 sites exhibiting wide thermal cracks in the Phoenix metropolitan area in Arizona to evaluate their volumetric properties and the degree of binder aging. Advanced finite element (FE) models were developed to examine the influence of pavement structures and local climatic conditions on the development of tensile stresses due to thermal fatigue. The FE analysis indicated a high magnitude of thermal stresses due to cyclic temperature variations in Arizona compared to colder regions in the United States. Based on the forensic investigation and analysis performed, the initiation of wide cracks was shown to be primarily due to repeated localized damage from frequent thermal fatigue events on severely aged pavements. This damage is exacerbated by low air voids in mineral aggregate, an insufficient effective binder volume. and excessive binder aging, which compromise the structural integrity of the pavement. Full article

This paper investigates the flame behavior and smoke pattern characteristics of lithium-ion battery (LIB) fires using an integrated experimental and numerical simulation approach. Based on fire dynamics theory, a jet flame model for LIB thermal runaway (TR) is developed to analyze the flame height and dynamic characteristics. The results reveal two distinct regimes in LIB jet flames: momentum-controlled dominance in the early TR stage (lasting approximately 3 s) and buoyancy-controlled dominance in subsequent combustion. The jet flame shifts from a momentum-dominated regime (Fr > 5) to a buoyancy-dominated plume (Fr < 5) as the vent velocity decays below 12 m/s. The simulated flame heights align with experimental measurements and the Delichatsios model, validating the numerical approach. Furthermore, the distribution of flame components (e.g., H₂, CO, CO₂, CH₄, C₂H₄) is analyzed, highlighting the influence of multi-component gases on combustion heterogeneity. Smoke pattern analysis demonstrates that soot deposition varies significantly between momentum- and buoyancy-controlled stages, with the former producing darker, concentrated deposits and the latter yielding wider, lighter patterns. These findings provide a theoretical basis for forensic fire investigation (accident reconstruction) and targeted suppression strategies for different combustion stages. Full article

Background/Objectives: Forensic DNA phenotyping (FDP) enables the prediction of externally visible characteristics (EVCs) such as eye, hair, and skin color, ancestry, and age from biological traces. However, low template DNA (LT-DNA), often derived from degraded or trace samples, poses significant challenges due to allelic dropout, contamination, and incomplete profiles. This review evaluates recent advances in FDP from LT-DNA, focusing on the integration of machine learning (ML) models to improve predictive accuracy and operational readiness, while addressing ethical and population-related considerations. Methods: A comprehensive literature review was conducted on FDP and ML applications in forensic genomics. Key areas examined include SNP-based trait modeling, genotype imputation, epigenetic age estimation, and probabilistic inference. Comparative performance of ML algorithms (Random Forests, Support Vector Machines, Gradient Boosting, and deep learning) was assessed using datasets such as the 1000 Genomes Project, UK Biobank, and forensic casework samples. Ethical frameworks and validation standards were also analyzed. Results: ML approaches significantly enhance phenotype prediction from LT-DNA, achieving AUC > 0.9 for eye color and improving SNP recovery by up to 15% through imputation. Tools like HIrisPlex-S and VISAGE panels remain robust for eye and hair color, with moderate accuracy for skin tone and emerging capabilities for age and facial morphology. Limitations persist in admixed populations and traits with polygenic complexity. Interpretability and bias mitigation remain critical for forensic admissibility. Conclusions: L integration strengthens FDP from LT-DNA, offering valuable investigative leads in challenging scenarios. Future directions include multi-omics integration, portable sequencing platforms, inclusive reference datasets, and explainable AI to ensure accuracy, transparency, and ethical compliance in forensic applications. Full article

Phishing websites continue to evolve in sophistication, making them increasingly difficult to distinguish from legitimate platforms and challenging the effectiveness of current detection systems. In this study, we investigate the role of subtle deceptive behavioral cues such as mouse-over effects, pop-up triggers, right-click restrictions, and hidden iframes in enhancing phishing detection beyond traditional structural and domain-based indicators. We propose a hierarchical hybrid detection framework that integrates dimensionality reduction through Principal Component Analysis (PCA), phishing campaign profiling using K Means clustering, and a stacked ensemble classifier for final prediction. Using a public phishing dataset, we evaluate multiple feature configurations to quantify the added value of behavioral indicators. The results demonstrate that behavioral indicators, while weak predictors in isolation, significantly improve performance when combined with conventional features, achieving a macro F1 score of 97 percent. Explainable AI analysis using SHAP confirms the contribution of specific behavioral characteristics to model decisions and reveals interpretable patterns in attacker manipulation strategies. This study shows that behavioral interactions leave measurable forensic signatures and provides evidence that combining structural, domain, and behavioral features offers a more comprehensive and reliable approach to phishing intrusion detection. Full article

The 1992 cold case homicide of “Ina Jane Doe” illustrates how an interdisciplinary team worked to identify the decedent using a combined approach of skeletal re-analysis, updated forensic art informed by anthropologists’ input, archival research, and forensic investigative genetic genealogy. The original forensic art for “Ina Jane Doe” showed an over-pathologization of skeletal features and an inaccurate hairstyle; however, the case gained notoriety on internet true crime forums leading to speculation about the decedent’s intellectual capacity and physical appearance. The “Ina Jane Doe” case demonstrates the importance of advocating for skeletal re-analysis as more robust methods and technologies emerge in forensic science, as well as the impact of sustained public interest in cold cases. In this case, continuous public interest and online speculation led to anthropologists constructing a team of experts to correct and revise narratives about the decedent. Forensic anthropologists’ role in cold cases may include offering skeletal re-analysis, recognizing and correcting errors in the original estimations of the biological profile, searching for missing person matches, and/or working collaboratively with subject matter experts in forensic art, odontology and forensic investigative genetic genealogy. Full article

Accurate post-mortem diagnosis of sepsis remains a critical challenge in forensic pathology, as conventional morphological findings often lack specificity. Circulating microRNAs (miRNAs) have been proposed as stable molecular biomarkers, yet their diagnostic value in cadaveric samples is still unclear. This exploratory study investigated the expression of three candidate miRNAs (miR-122-5p, miR-125b-5p, and miR-27a-3p) in post-mortem peripheral whole blood to assess their association with sepsis-related death versus non-infective controls. Out of 58 cases, 45 met quality-control criteria (26 sepsis-related deaths and 19 controls). miRNA expression was quantified by qRT-PCR, normalized to miR-320, and analyzed using ΔCt values. Group differences were evaluated using linear regression models with adjustment for age, sex, and post-mortem interval, with Benjamini–Hochberg correction for multiple testing. In adjusted models, miR-125b-5p and miR-27a-3p showed evidence of association with sepsis status, whereas miR-122-5p did not. These results support the feasibility of miRNA quantification in post-mortem samples and motivate validation in larger, independent cohorts and within multimodal post-mortem diagnostic frameworks. Full article

The flourishing of the Internet of Things (IoT) has not only improved our lives in smart homes and healthcare but also made us more susceptible to cyberattacks. Legacy intrusion detection systems are simply overwhelmed by the scale and diversity of IoT traffic, which is why there is a need for more intelligent forensic solutions. In this paper, we present a statistical technique, the Averaging Detection Method (ADM), for detecting attack traffic. Furthermore, the five deep learning models SimpleRNN, LSTM, GRU, BLSTM, and BGRU are compared for malicious traffic detection in IoT network forensics. A smart home dataset with a simulated DoS attack was used for performance analysis of accuracy, precision, recall, F1-score, and training time. The results indicate that all models achieve high accuracy, above 97%. BiGRU achieves the best performance, 99% accuracy, precision, recall, and F1-score, at the cost of high training time. GRU achieves perfect precision and recall (100%) with faster training, which can be considered for resource-constrained scenarios. SimpleRNN trains faster with comparable accuracy, while LSTMs and their bidirectional counterparts are better at capturing long-term dependencies but are computationally more expensive. In summary, deep learning, especially BiGRU and GRU, holds great promise for boosting IoT forensic investigation by enabling real-time DoS detection and reliable evidence collection. Meanwhile, the proposed ADM is simpler and more efficient at classifying DoS traffic than deep learning models. Full article

Background/Objectives: Many studies in the literature are increasingly focusing on how genes influence the development of individual behaviors and personality traits through genome sequencing. Most research indicates that complex behaviors and their characteristics are influenced by multiple genes, highlighting the crucial role of genetic studies in this field. Behavioral genetics, as a scientific discipline, investigates how genetic factors shape individuals’ behaviors and personality traits. The concepts of violence and aggression, observable in various contexts, have been extensively studied, with a particular focus on the underlying causes of these behaviors. In sports, where physical strength plays a significant role, regulations designed to prevent violent behaviors and aggressive attitudes contribute to the establishment of appropriate behavior patterns and discipline. Methods: This study aims to identify correlations between polymorphisms found in athletes and their responses to questionnaires, focusing on candidate genes known to influence personality and behavior traits, such as catechol-O-methyltransferase (COMT), serotonin transporter (5-HTT), monoamine oxidase (MAO-A), and serotonin 1A transporter (5-HT1A). A total of twenty licensed athletes participated in the study. Participants completed three standardized instruments: the Sportsmanship Behavior Scale (27 items), the Sports Emotion Scale (22 items), and the Anger-Control Scale (34 items). Following the acquisition of informed consent, buccal swab samples were collected for single nucleotide polymorphism (SNP) analysis targeting the COMT, MAO-A, 5- HT1A, and 5-HTT genes. Subsequent to sample collection and questionnaire administration, statistical analyses were conducted to evaluate the relationships among behavioral measures and genetic variants. Results: Overall, the findings point to gene-specific patterns in 5-HTT, MAO-A, and COMT, while no clear pattern emerged for 5-HT1A. Conclusions: Ultimately, this study provides an early exploration of aggression-related genetic patterns within the context of forensic sciences, highlighting preliminary trends and potential associations that may inform the design of future research. Full article

In the Republic of Korea, fire outbreaks caused by electrical devices are one of the most frequent accidents, causing severe damage to human lives and infrastructure. The metropolitan police, The National Institute of Scientific Investigation, and the National Fire Research Institute conduct fire root-cause inspections to determine whether these fires are external or internal infrastructure fires. However, obtaining results is a complex process. In addition, the situation has been hampered by the lack of sufficient digital forensics and relevant programs. Apart from electrical devices, multi-sockets are among the main fire instigators. In this study, we aim to verify the feasibility of utilizing YOLO-based deep-learning object detection models for fire-cause inspection systems for multi-sockets. Particularly, we have created a novel image dataset of multi-socket fire causes with 3300 images categorized into the three classes of socket, both burnt-in and burnt-out. This data was used to train various models, including YOLOv4-csp, YOLOv5n, YOLOR-csp, YOLOv6, and YOLOv7-Tiny. In addition, we have proposed an improved YOLOv5n-SE by adding a squeeze-and-excitation network (SE) into the backbone of the conventional YOLOv5 network and deploying it into a two-stage detector framework with a first stage of socket detection and a second stage of burnt-in/burnt-out classification. From the experiment, the performance of these models was evaluated, revealing that our work outperforms other models, with an accuracy of 91.3% mAP@0.5. Also, the improved YOLOv5-SE model was deployed in a web browser application. Full article

Background/Objectives: Immunohistochemistry (IHC) is a critical diagnostic tool in forensic pathology, enabling molecular-level assessment of wound vitality, post-mortem interval, and cause of death. However, IHC interpretation is subject to variability due to its reliance on human expertise. This study investigates whether artificial intelligence (AI), specifically a generative model, can assist in the diagnostic evaluation of IHC slides and replicate expert-level scoring, thereby improving consistency and reproducibility. Methods: A total of 225 high-resolution IHC images were classified into five immunoreactivity categories. The AI model (ChatGPT-4V) was trained on 150 labeled images and tested blindly on 75 unseen slides. Performance was assessed using confusion matrices, per-class precision/recall/F1, overall accuracy, Cohen’s κ (unweighted and weighted), and binary metrics (sensitivity, specificity, MCC). Results: Overall accuracy was 81.3% (95% CI: 71.1–88.5%), with substantial agreement (κ = 0.767 unweighted; 0.805 linear-weighted; 0.848 quadratic-weighted). Binary classification achieved a sensitivity of 98.3%, specificity of 93.3%, MCC of 0.92. Accuracy was highest in extreme categories (− and +++, 93.3%), while intermediate classes (+ and ++) showed reduced performance (error rates up to 33%). Evaluation was rapid and consistent but lacked interpretative reasoning and struggled with borderline cases. Conclusions: AI-assisted diagnostic evaluation of IHC slides demonstrates promising accuracy and consistency, particularly in well-defined staining patterns. While not a replacement for human expertise, AI can serve as a valuable adjunct in forensic pathology, supporting rapid and standardized assessments. Ethical and legal considerations must guide its implementation in medico-legal contexts. Full article

Sodium hydroxide (NaOH) and hydrochloric acid (HCl) are household chemicals used to disfigure victims in forensic contexts due to their high availability and apparent effects, which alter both the structural integrity and composition of skeletal elements. NaOH dissolves soft tissues and produces violent, exothermic reactions but, ostensibly, fails to alter the structure and color of bones and teeth. HCl is considered one of the most destructive chemical agents utilized, causing rapid demineralization of hard tissues. Current works focus on total dissolution times, rather than on discrete changes and the potential for personal identification. This research aims to comprehensively assess the intervallic micro- and macroscopic changes occurring in chemically altered bones and teeth. Analyses were conducted to investigate how morphological shape and surface area-to-volume ratios may affect the degree of alteration and to evaluate the feasibility of DNA isolation and profiling. The relationships between these factors were not linear, and the results show a variable pattern of alteration and DNA yields depending on the treatment and duration of exposure. Teeth were found to be better sources for obtaining higher quality and yield of DNA compared to bones, and complete STR profiles were obtained from all tooth samples. Overall, this pilot study highlights the challenges of analyzing taphonomically altered remains and underscores the need for effective identification methods. Full article

Background/Objectives: The further application of high-coverage whole genome sequencing in fields such as paleogenomics, forensic investigations, and conservation genomics is impeded by two major barriers: extremely high costs and stringent sample requirements. Utilizing low-coverage sequencing offers a practical solution to these constraints; however, this approach introduces a primary challenge—the necessity to reconstruct distorted genomic information for downstream analysis. Methods: Analytical experiments conducted on low- to medium-coverage sequencing data confirmed the accuracy of several existing methods for inferring relationships up to the third degree and distinguishing unrelated individuals. Subsequently, efforts were made to evaluate allele-frequency-independent methods within animal genomics, where analyses are likely to encounter challenges such as uncertain allele frequencies, diverse sample types, and suboptimal sample quality. Kinship inference was performed on a total of 33 pairs of animal samples across three species, comprising nine parent–offspring pairs and four full-sibling pairs. Results: The analysis revealed that two efficient algorithm implementations (READ and KIN) successfully identified all unrelated pairs. Notably, among the various algorithms utilized, only KIN exhibited confusion between first- and second-degree relationships when subjected to. Conclusions: This study has filled a critical gap in the existing literature by conducting a comprehensive evaluation of various algorithms on low-coverage sequencing data derived from authentic human and animal samples, accompanied by detailed ground truth—a vital task that has been overlooked. Full article

Biological samples of non-human origin, commonly encountered in wildlife crime investigations, present distinct challenges regarding forensic DNA analysis efforts. Although the types of samples encountered in human identity testing can vary to some degree, analyzing DNA from one species is facilitated by unified processes, common genetic marker systems, and national DNA databases. In contrast, non-human animal species identification is confounded by a diverse range of target species and a variety of sampling materials, such as feathers, processed animal parts in traditional medicine, and taxidermy specimens, which often contain degraded DNA in low quantities, are contaminated with chemical inhibitors, and may be comingled with other species. These complexities require specialized analytical approaches. Compounding these issues is a lack of validated non-human species forensic sampling and typing kits, and the risk of human DNA contamination during evidence collection. Markers residing on the mitochondrial genome (mtDNA) are routinely sought because of the large datasets available for comparison and their greater sensitivity of detection. However, the barcoding results can be complicated at times for achieving species-level resolution, the presence of nuclear inserts of mitochondrial DNA (NUMTs), and the limitation of mtDNA analysis alone to detect hybrids. Species-specific genetic markers for identification have been developed for a few high-profile species; however, many CITES (Convention on International Trade in Endangered Species of Wild Fauna and Flora)-listed organisms lack specific, validated forensic analytical tools, creating a significant gap in investigative enforcement capabilities. This deficiency stems in part from the low commercial nature of wildlife forensics efforts, a government research-driven field, the difficulty of obtaining sufficient reference samples from wild populations, limited training and education infrastructure, and inadequate funding support. Full article

Introduction: Paternal filicide is a rare and complex form of intrafamilial homicide, frequently associated with underlying psychopathology, interpersonal conflict, and psychosocial stressors. While maternal filicide has been more extensively studied, cases involving fathers—especially those employing multiple homicidal methods—remain significantly underrepresented in the forensic literature. This paper presents an unusual case of paternal filicide involving combined lethal methods, contextualized through a narrative review of comparable cases. Methods: A comprehensive forensic-pathological and psychiatric investigation was conducted following the homicide of an 8-year-old boy, killed by his father through a combination of asphyxiation and stabbing. A narrative literature review was performed using PubMed, Scopus, and Google Scholar, focusing on case reports and case series concerning paternal filicide. Particular attention was paid to homicidal methods, motivational dynamics, psychiatric comorbidities, and post-crime behavior. Results: The child’s body was found concealed in a building, in a bed storage drawer, with packing tape tightly wrapped around the mouth and nose and a kitchen knife embedded in the neck. No defensive wounds were observed, suggesting a sudden and unopposed assault, likely facilitated by the victim’s trust in the perpetrator. Autopsy findings revealed signs of asphyxiation and three stab wounds to the chin, neck, and thorax, involving vital structures such as the thyroid cartilage and heart. The father was found in a state of acute alcohol intoxication and subsequently convicted of intentional homicide. The motive appeared to be revenge-related, stemming from a highly conflictual marital separation. The literature review confirmed the predominance of retaliatory motives, frequent substance use, and post-crime suicidal behavior. However, the use of combined homicidal methods and the concealment of the body were found to be exceedingly rare. Conclusions: This case, combined with the literature review, highlights the need for deeper scientific exploration of paternal filicide. Comprehensive forensic and psychiatric assessments are essential to identify recurring situational patterns, motivational profiles, sociocultural contexts, and psychiatric vulnerabilities. These findings are critical not only for post-crime evaluations but also for the development of interdisciplinary prevention strategies targeting early warning signs and high-risk family dynamics. Full article