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This study investigated the stroke and slope characteristics in left‐handed and right‐handed handwriting. Stroke (letters t, f, đ, and H) and slope (letters t, f, l, d, and g) directions were analyzed on in‐house samples (n = 64), revealing statistically significant differences (p ≤ 0.05) between the groups. Right‐handers predominantly exhibited left‐to‐right strokes (98%–100%), while left‐handers showed greater variability. Although statistically significant for most letters analyzed, slope direction did not demonstrate consistent patterns. A logistic regression model was developed and validated on the same sample to classify handedness based on the averaged strokes of the letter "t." The model was further tested on samples (n = 252) from a publicly available handwriting database. If the model classified the sample as produced by left hand, it was correct in 100% of cases. In contrast, when the model classified writing as right‐handed, it was correct in 73%–97% of cases, depending on the validation sample. The model classified writing as of left‐handed origin if more than 36% of the letters "t" had a stroke from right to left, while otherwise, writing was classified as of right‐handed origin. The developed method showed great potential for classifying the handedness of the author of disputed handwriting, thus eliminating individuals as text authors or narrowing down the pool of potential authors. [ABSTRACT FROM AUTHOR]

Reconstructing the face from the skull is important not only for forensic identification but also as a tool that can provide insight into the appearance of individuals from past populations. It requires a multidisciplinary approach that combines anthropological knowledge, advanced imaging methods, and artistic skills. In the present study, we demonstrate this process on the skull of an early medieval warrior from Croatia. The skeletal remains were prepared and scanned using multi-slice computed tomography (MSCT) and examined using standard anthropological and radiological methods. The analysis revealed that the remains belonged to a 35–45-year-old male individual who had suffered severe cranial trauma, probably causing his death. From MSCT images, we reconstructed a three-dimensional (3D) model of the skull, on which we digitally positioned cylinders demarking the soft tissue thickness and created the face with a realistic texture. A 3D model of the face was then optimized, printed, and used to produce a clay model. Sculpturing techniques added skin textures and facial features with scars and trauma manifestations. Finally, after constructing a plaster model, the model was painted and refined by adding fine details like eyes and hair, and it was prepared for presentation in the form of a sculpture. [ABSTRACT FROM AUTHOR]

The determination of sex is one of the first steps in anthropological analysis. When skeletal remains are fragmented, the most useful approach is application of osteometric methods. The methods are population specific, and therefore require development of discriminant functions for each population group.

ChatGPT-3.5, an AI language model capable of text generation, translation, summarization, and question-answering, has recently been released for public use. Studies have shown it can generate abstracts, research papers, and dissertations, and create quality essays on different topics. This led to ethical issues in using ChatGPT in academic writing, AI authorship, and evaluating students' essays. However, it is still unknown how ChatGPT performs in students' environments as a writing assistant tool and if it enhances students' essay-writing performance. In the present study, we examined students' essay-writing performances with or without ChatGPT as an essay-writing assistance tool. The average essay grade was C for both control (traditional essay-writing, n = 9) and experimental (ChatGPT-assisted essay-writing, n = 9) groups. None of the predictors affected essay scores: group, writing duration, study module, and GPA. The text unauthenticity was slightly higher in the experimental group, but the similarity among essays was generally low in the overall sample. In the experimental group, the AI classifier recognized more potential AI-generated texts. Our results demonstrate that the ChatGPT group did not perform better in either of the indicators; the students did not deliver higher quality content, did not write faster, nor had a higher degree of authentic text. We anticipate that these results can relieve some concerns about this tool's usage in academic writing. ChatGPT-assisted writing could depend on the previous knowledge and skills of the user, which might, in certain instances, lead to confusion in inexperienced users and result in poorer essay writing performance. [ABSTRACT FROM AUTHOR]

This study examined if the cranial measurements from Data Collection Procedures for Forensic Skeletal Material 2.0 are repeatable when measured in dry bones and MSCT images and if the virtual measurements correspond to the physical ones. The sample included 33 dry crania imaged by MSCT. Two observers measured dry bones, two placed landmarks on 2D and 3D MSCT reconstructions, and one conducted measurements/landmarking on both media. One of the observers for each media repeated the measurements. Technical and relative technical error of measurement (TEM and rTEM) and percentage differences were calculated to examine the repeatability of measurements and compare measuring modalities. Intraobserver rTEM was above 1.5% for six bone measurements: FOB, ZOB, OBB, NLH, DKB, MDH (1.51%–4.87%) and for seven MSCT measurements: OBH, FOB, OBB, MDH, NLB, ZOB, DKB (1.57%–5.55%). The interobserver rTEM was above the acceptable level (>2%) for 11 measurements: PAC, NLH, OBB, EKB, MAL, FOB, NLB, OBH, ZOB, DKB, and MDH (2.01%–9.34%). The percentage differences were not systematically larger for measurements taken by the same user on both modalities than those obtained by different users on the same modality. When physical and MSCT measurements were tested on sex classification standards, the proportion of crania classified as male or female did not significantly differ (p > 0.05). The study showed that physical and virtual cranial measurements could be interchangeable for developing or applying sex estimation standards. However, clarifications and adaptations are necessary for measurements of mastoid, nasal, and orbital regions that did not meet the standard criteria. [ABSTRACT FROM AUTHOR]

The introduction of interdisciplinarity and industry-academia collaborations (IAC) into higher education institutions (HEIs) and curricula as tools for promoting sustainable development has been debated both in academic and non-academic contexts. While overall rising trends in the acceptance of interdisciplinarity and IAC exist, research has stressed difficulty in implementation and practices. We conducted eight focus groups at six European Universities (members of the SEA-EU alliance) and analysed the transcripts using Braun and Clarke's reflexive thematic approach to qualitative analysis in order to develop themes on barriers and facilitators to both conducting interdisciplinarity and IAC, as well as the inclusion of university students in interdisciplinary research. We observed that the main barriers to IR and IAC and the inclusion of students in such activities include traditional HEI structures focused on single-discipline approaches, a lack of joint platforms for IR and IAC, and academic differences (publication outcome differences, academic background). Likewise, a lack of funding (especially for early career researchers), employability (for students willing to do a research career), and a lack of validation by HEIs for researchers conducting IR and IAC are major barriers. To IDR- and IAC-related activities, a top-down approach is needed to restructure HEIs and make them more accommodating to both students and staff willing to conduct IR and IAC activities, thus refocusing them towards sustainability. [ABSTRACT FROM AUTHOR]

Simple Summary: This study adopts a dynamic methodology to explore challenges to the practical application of the adjusted binary classification (ABC) approach, which are related to the unmodifiable characteristics of data used in its development, such as intrasexual variation (sexual dimorphism) of variables and methodological factors such as the selected classification algorithm and sample size. The adequacy of a training dataset's size was judged relative to the classification performance in an independent test set. Finding an optimal classifier was also addressed in this study, wherein the results demonstrate that both statistical modeling and machine learning techniques perform almost equally in the univariate models; however, differences are evident in the multivariate model due to the different number of variables included via the feature selection process, as well as the effect of inadequate training sample size relative to the test set. This approach is particularly useful when quick classification/prediction is required for making real-time forensic decisions. The adjusted binary classification (ABC) approach was proposed to assure that the binary classification model reaches a particular accuracy level. The present study evaluated the ABC for osteometric sex classification using multiple machine learning (ML) techniques: linear discriminant analysis (LDA), boosted generalized linear model (GLMB), support vector machine (SVM), and logistic regression (LR). We used 13 femoral measurements of 300 individuals from a modern Turkish population sample and split data into two sets: training (n = 240) and testing (n = 60). Then, the five best-performing measurements were selected for training univariate models, while pools of these variables were used for the multivariable models. ML classifier type did not affect the performance of unadjusted models. The accuracy of univariate models was 82–87%, while that of multivariate models was 89–90%. After applying ABC to the crossvalidation set, the accuracy and the positive and negative predictive values for uni- and multivariate models were ≥95%. Sex could be estimated for 28–75% of individuals using univariate models but with an obvious sexing bias, likely caused by different degrees of sexual dimorphism and between-group overlap. However, using multivariate models, we minimized the bias and properly classified 81–87% of individuals. A similar performance was also noted in the testing sample (except for FEB), with accuracies of 96–100%, and a proportion of classified individuals between 30% and 82% in univariate models, and between 90% and 91% in multivariate models. When considering different training sample sizes, we demonstrated that LR was the most sensitive with limited sample sizes (n < 150), while GLMB was the most stable classifier. [ABSTRACT FROM AUTHOR]

Aim To develop discriminant functions for sex estimation on medieval Croatian population and test their application on contemporary Croatian population. Methods From a total of 519 skeletons, we chose 84 adult excellently preserved skeletons free of antemortem or postmortem changes and took all standard measurements. Sex was estimated/determined using standard anthropological procedures and ancient DNA (amelogenin analysis) where pelvis was insufficiently preserved or where sex morphological indicators were not consistent. We explored which measurements showed sexual dimorphism and used them for developing univariate and multivariate discriminant functions for sex estimation. We included only those functions that reached accuracy rate =80%. We tested the applicability of developed functions on modern Croatian sample (n = 37). Results From 69 standard skeletal measurements used in this study, 56 of them showed statistically significant sexual dimorphism (74.7%). We developed five univariate discriminant functions with classification rate 80.6%-85.2% and seven multivariate discriminant functions with an accuracy rate of 81.8%-93.0%. When tested on the modern population functions showed classification rates 74.1%-100%, and ten of them reached aimed accuracy rate. Females showed higher classified in the mediaeval populations, whereas males were better classification rates in the modern populations. Conclusion Developed discriminant functions are sufficiently accurate for reliable sex estimation in both medieval Croatian population and modern Croatian samples and may be used in forensic settings. The methodological issues that emerged regarding the importance of considering external factors in development and application of discriminant functions for sex estimation should be further explored. [ABSTRACT FROM AUTHOR]

In a Sidonian sarcophagus, from the Late Antique/early Christian period, skeletal remains of two persons were found. One of them, male, 30-50 years old, was found almost completely ankylosed, with highly osteoporotic bones and prominent erosion of joint surfaces. We diagnosed rheumatoid arthritis based on the eroded odontoid process, mandibular condyles, distal humerus, proximal and distal ulna, as well ankylosed hand and foot bones. Despite the fact that ankyloses of vertebrae and sacroiliac joint could point towards ankylosing spondylitis, the lack of typical vertebral ankyloses and new bone formation led to exclusion. In a practical sense, due to the advanced stage of the disease, the man was fixed in the supine position, on the left, with his head turned to the right. Apparently, he could not move and had problems with chewing and breathing. But, the high standard of provided healthcare probably enabled him to survive in advanced stages of the disease. This case shed light on the antiquity of the disease, its medical, and social context and provided the example of most extreme osteological changes reported in the paleopathological and medical literature. [ABSTRACT FROM AUTHOR]

Sister Marija Krucifiksa Kozulić (1852–1922) was a Croatian nun who is in consideration for beatification by the Vatican, which is facilitated by the identification of her 20th-century remains. Sister Marija was buried in a tomb in Rijeka, Croatia, along with other nuns including her biological sister, Tereza Kozulić (1861–1933). When the remains were exhumed in 2011, they were found in a deteriorated state and commingled with several other sets of remains. Thus, mitochondrial genome sequencing of the long bones was performed to sort the remains by mitochondrial haplotype. Two similar but unique haplotypes belonging to haplogroup H1bu were identified, and samples from these bones were subjected to autosomal short tandem repeat (STR) and single nucleotide polymorphism (SNP) sequencing. Although only partial profiles were obtained, the data were sufficient for kinship analysis with the profile of a paternal niece of Sister Marija (Fides Kozulić). The data indicate that it is 574,195-fold more likely that the two sets of skeletal remains represent 2nd-degree relatives of Fides than sisters who are unrelated to Fides. Although it is impossible to discern which set of remains belongs to Marija and which belongs to Tereza, forensic genomics methods have enabled identification of the sisters. [ABSTRACT FROM AUTHOR]

Aim To determine the incidence of metopism in the modern and archaeological Croatian population. Methods A total of 800 specimens (454 modern multislice computed tomography [MSCT] scans and 346 dry archaeological skulls) were visually examined for metopic suture presence. The metopic suture was deemed complete when aligned nasion to bregma. Results In the overall sample, the metopic suture was observed in 36 of 800 subjects (4.5%): 19 of 424 (4.5%) men and 17 of 370 (4.6%) women. A significant difference was not observed between modern and archaeological samples (Χ² = 3.219, P = 0.359) or between the sexes (Χ² = 0.006, P = 0.939). The frequency of metopism varied from 3.5% in the modern population to 7.04% in the samples from the Roman period. Conclusion There are no visible secular changes on metopic suture in the Croatian population through time. Some variations can be the result of differences in sample size in different time periods. [ABSTRACT FROM AUTHOR]

Aim To analyze an additional set of ˝Y-chromosome genetic markers to acquire a more detailed insight into the diversity of the Croatian population. Methods A total of 518 Yfiler Plus profiles were genotyped. Allele frequencies, haplotype frequencies, and haplotype diversity were calculated by using the STRAF software v. 2.0.4. Genetic distances were quantified by Rst with AMOVA online tool from the YHRD. The evolutionary history was inferred with the neighbor-joining method of phylogenetic tree construction in the MEGAX software. Whit Athey’s Haplogroup Predictor v. 5 was used for additional comparison with regional and other European populations. Results A total of 507 haplotypes were used for genetic STR analysis. An interpopulation study on 17 Y-STR markers showed the lowest genetic diversity between the Croatian and Bosnian-Herzegovinian populations and the highest between the Croatian and Irish populations. Additional interpopulation comparison with the original 27 Y-STR markers (for the population with available data) was also performed. A total of 518 haplotypes were used in the determination of haplogroup diversity. Haplogroup I with its sublineage I2a expressed the highest prevalence. The second most prevalent haplogroup was R, with its major sublineage R1a, except for the subpopulation of Hvar, where E1b1b was the second most prevalent haplogroup. Rare haplogroups also confirmed in this study were L, T, and Q. G1 was detected for the first time in the Croatian population. Conclusion We obtained a new insight into the differences between examined subpopulations of Croatia and their possible (dis)similarities with neighboring and distant populations. [ABSTRACT FROM AUTHOR]

Binary classification techniques are commonly used in forensic examination to test if a specimen belongs to a particular group and base the expert opinion on the questioned evidence. However, most of the currently used methods do not achieve sufficient accuracy due to the ignoring of the specimens classified in the overlapping area. To address the issue, we proposed a novel Adjusted binary classification (ABC) algorithm that automatically adjusts posterior probabilities to reach classification accuracy and positive/negative predicted values (PPV, NPV) of 95 %. In the presented example, we used three handprint measurements from 160 participants (80 males and 80 females) to develop models that would classify sex from their dimensions. The sample was split into the training/cross-validated (70 %) and testing sample (30 %). We developed four classification models using linear discriminant analysis (LDA) by employing traditional single cut-off values and ABC approach that for each group provides a specific posterior probability cut-off threshold. In the cross-validated sample, the accuracy of traditional models was 78.7-92.5 %, while PPVs/NPVs ranged between 78.2 and 93 %. ABC models provided 95 % accuracy, PPV, and NPV, and could classify 35.5-88.1 % of specimens. In the testing sample, ABC models achieved accuracy of 97.3-100 %, PPV/NPV 95.4-100 %, and could be applied to 29.1-87.5 % of specimens. The study demonstrated that the ABC approach could adjust classification models to reach predefined values of accuracy, PPV, and NPV. Therefore, it could be an efficient tool for conducting a binary classification in forensic settings and minimizing the possibilities of incorrect classifications. [ABSTRACT FROM AUTHOR]

• The first facial reconstruction of the Catholic mummified saint's remains. • The reconstructed skull was compared with MSCT scanned mummified remains. • Only non-destructive techniques were used. • Our approach confirmed the value of this method for cultural heritage presentation. In addition to forensic science, facial reconstruction also plays an outstanding role in the historical and archaeological context. Precisely, it the most exact method to reconstruct the appearance of the people that lived in the past and a popular way of presenting results not only to scientific community but also to a broader public. The main purpose of this study was to reconstruct the appearance of Saint-Nicolosa Bursa, a Christian saint whose mummified remains are kept in Church of Saint-Blaise in Vodnjan (Croatia). The additional aims were to validate the methodology and to provide a realistic model of the Saint's face that could be used for exhibition and presentation of the research results to a broader public. The mummified remains of the saint were scanned with multislice computed tomography (MSCT). The MSCT images were used to develop a skull model which was 3D printed and used for facial reconstruction. The facial appearance recovery was accomplished using the Manchester method by the progressive layering of clay on the ABS model according to the anthropometric data, specific for sex, age, and ancestry. After the clay model was done, it was used to create a realistic gypsum sculpture appropriate for the presentation of results on exhibition. When the model was compared to the photographs of the mummified remains of the Saint, it showed to be consistent in all facial traits that could be considered. Unfortunately, additional data were not available due to the preservation degree of soft tissue remains as well as the lack of pictorial or written sources about the appearance of the Saint. However, this research showed, that although the facial reconstruction is always only an approximation of the actual person's appearance, it is essential in historical context as an only existing method that can show us how a certain historical figure could look. In the present study, it enabled us to conduct the first reconstruction of the appearance of the Saint from the mummified remains and develop sculpture that will be used for exhibition and presentation of results. [ABSTRACT FROM AUTHOR]

Aim To determine the sexual dimorphism of the sternum with standard measurements in a contemporary Croatian population sample using multi-slice computed tomography (MSCT) and to compare the data obtained by an automatic with those obtained by a manual approach. Methods Five sternal measurements were obtained from MSCT images of 73 men and 55 women and three sternal indices were calculated. Custom image analysis software was developed for automatic segmentation and calculation of sternal measurements. Measurements of sexual dimorphism were automatically calculated and compared with manual measurements. Results All of the sternal measurements exhibited signifi- cant differences between men and women. The discrepancies between manual and automatic measurements ranged from 2.8% to 3.6% of the mean average values obtained with the automatic approach. The most accurate single-variable discriminant function was sternal body length (82.8%), the most accurate index was sternal area (89.1%), and the discriminant function using three variables was manubrium width, sternal body length, and sternal body width (90.6%). Conclusion Sternal measurements are a reliable sex indicator and can be used in forensic casework. Computer-aided measurement methods can accelerate sex estimation and improve its precision and accuracy. [ABSTRACT FROM AUTHOR]

The osteometric methods are the most reliable way to estimate the sex of skeletons when DNA analysis is not used. However, as osteometric studies usually ignore the overlap in male and female skeletal dimensions, they rarely achieve accuracy sufficient for forensic application. To resolve this issue, recent studies suggest sex estimation only when posterior probability (pp) is greater than 0.95, but that approach does not always provide sufficient accuracy and creates a large proportion of unsexed skeleton. Thus, our study aimed to explore whether it is possible to adjust pp on skeletal measurements with pronounced sexual dimorphism to meet 95% accuracy and to enable sex estimation on a reasonable proportion of individuals. From 207 skeletons, we included 65 postcranial measurements and selected 10% of variables with the highest sexual dimorphism. We computed univariate and bivariate discriminant functions using pp threshold of 0.5, 0.95, and the threshold required to achieve accuracy of ≥ 95%. Discriminant functions with pp=0.5 obtained accuracy of 85%-93%, while those with pp≥0.95 and adjusted posterior probabilities obtained 94%-99%. However, we showed that by selecting a particular threshold, sex could be estimated on a greater proportion of individuals than for pp≥0.95: 42%-86% vs. 24%-62% for univariate and 69%-95% vs. 49%-78% for bivariate functions. Therefore, when developing sex estimation models, we suggest not to use fixed pp level, but to adjust pp to achieve 95% accuracy and to minimize the percentage of unsexed skeletons. [ABSTRACT FROM AUTHOR]