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Aim/Purpose: This paper aims to address the gap in comprehensive, real-world applications of Generative Artificial Intelligence (GenAI) in education, particularly in higher education settings. Despite the evident potential of GenAI in transforming educational practices, there is a lack of consolidated knowledge about its practical effectiveness and real-world impact. Background: This study addresses this gap by conducting a systematic literature review to collate and analyze real-life instances of GenAI applications in higher education, thus providing a nuanced understanding of its practical implementations and measurable outcomes. Methodology: The paper utilizes a systematic literature review methodology, adopting the PRISMA approach complemented by a thematic analysis procedure to ensure a comprehensive and in-depth evaluation of the literature. It synthesizes information from relevant articles from 2022 to 2024, focusing on the applications of GenAI in higher education. This analysis covers various aspects, including research settings, analysis scales, data types, collection tools, and analytical methods. Contribution: The paper contributes to the academic community by offering a comprehensive review of GenAI applications in education, highlighting the current precision level of these tools, and providing strategic recommendations for their effective use in academia. Furthermore, the research defines seven specific cases where Gen AI can be utilized as a reference for educational institutions in their adoption strategies. Findings: Key findings include the versatility of GenAI in generating teaching materials, enhancing skill development, supporting student tasks, academic performance evaluation, feedback delivery, and its role as a virtual assistant and in research support. Recommendations for Practitioners: Practitioners are advised to explore the integration of GenAI for diverse educational purposes, from content creation to student assessment, while being cognizant of its limitations and ethical considerations. Recommendation for Researchers: Future research should focus on addressing the gaps identified, such as the implications of GenAI in research roles, its application in various disciplines, and the exploration of newly developed AI tools tailored to specific educational needs. Impact on Society: The findings of this paper highlight the potential of GenAI in revolutionizing the educational sector, offering personalized learning experiences, and significantly influencing teaching methodologies and student engagement, but it also reveals significant deficiencies of Generative AI, known as hallucinations, which can impact the expected results. Future Research: Subsequent research should explore the evolving capabilities of GenAI models, their impact on various academic disciplines, and the development of pedagogical strategies to optimize their use in education.

Background: Hodgkin lymphoma (HL) is a rare malignancy characterized by a malignant proliferation of cells in the reticuloendothelial system, mainly lymph node, and the presence of Reed-Sternberg cells with a relatively good prognosis compared to other pediatric malignancies. This study aimed to produce epidemiologic and clinical data on HL for Albania, aiming for a better understanding of the disease presentation to diagnose it at an earlier stage with the result of a better outcome. Methods and Results: This single-center, retrospective study performed in the Mother Theresa University Hospital Center (Tirana, Albania) analyzed epidemiological and clinical data of pediatric patients under 14 years of age diagnosed with HL during a 10-year study period from 2012 to 2022. During the last 10 years, 25 children (ages 0-14) were diagnosed with HL at UHC "Mother Theresa," Tirana. From the demographic data of our study, most patients (68%) were in the age group of 10-14. The male-to-female ratio was 2.12:1. The first clinical presentation was mainly because of lymphadenopathy in 92% of patients, with the presence of B symptoms in 68%. In most cases, the CBC was not affected, yet 24% of patients had high platelets, and 12% had low RBCs, while WBCs increased in 16% of patients and decreased in 12%. Lymphopenia and monocytosis were found in more than half of cases. Around 68% of patients had high levels of LDH and CRP. ESR and ALP were high in 64% of patients, Ferritin was high in 32%, and fibrinogen level was high in 28%. According to the Ann Arbor system, most of our patients were at stage II (32%), followed closely by stage I (28%) and stage III (24%), and only 16% were at stage IV upon presentation. The most common histopathologic type was nodular sclerosis classical HL, presented in 44% of cases. Conclusion: HL is a relatively frequent pediatric malignancy in young adults, affecting mainly males, and is diagnosed at a relatively early stage in our country.

Background: In chronic diseases characterized by persistent inflammation, anemia of chronic disease (ACD) and iron deficiency anemia (IDA) are commonly encountered forms of anemia and can often co-occur. In such situations, the conventional iron status tests used for differential diagnosis are influenced by inflammation, reducing their diagnostic accuracy. The primary objective of this study was to assess the significance of hepcidin as a crucial diagnostic marker for ACD and to investigate the correlation between hepcidin and inflammation-related indicators. Furthermore, a significant secondary aim of this research was to ascertain the diagnostic role of novel biochemical markers, specifically soluble transferrin receptor (sTfR) and the derived sTfR/log ferritin index (sTfR-F index). Methods and Results: This study was conducted at the Laboratory Service of the University Hospital Center "Mother Teresa" and included a cohort of 187 subjects, comprising 156 patients (83 females and 73 males) who were admitted to and received treatment at the Rheumatology and Cardiology Departments of the "Mother Teresa" University Hospital Center in Tirana. Additionally, 31 individuals without anemia and inflammatory conditions were included as a control group. All subjects incorporated into the study were classified into five distinct groups based on a comprehensive analysis of their complete blood profiles, iron status, and inflammation-related biomarkers: IDA, ACD, ACD+IDA, RA/CHF patients without anemia, and the control group without anemia. The comparison between groups showed that the mean values of pro-hepcidin, TNFα, IL6, Hs-PCR, and ferritin are significantly decreased in IDA vs. ACD, while sTFR and sTfR-F index are significantly increased. In comparing ACD vs. ACD+IDA groups, ferritin increased significantly in the ACD group, while sTFR and sTfR-F index decreased significantly in ACD, compared to the ACD+IDA group. The ROC curves analysis of the biochemical parameters selected for the comparison of ACD vs IDA showed that the pro-hepcidin test is a perfect test for the differential diagnosis of ACD vs. IDA. The suggested cut-off value for pro-hepcidin was ≥153 ng/ml, yielding a sensitivity of 100% and a specificity of 100% for the diagnosis of ACD. As regards sTFR, the suggested cut-off value for the diagnosis of IDA vs. ACD was ≥ 4.9 µg/ml resulting in 84% sensitivity and 100% specificity. The sTfR-F index was also very useful for the diagnosis of IDA vs. ACD: for a cut-off value of ≥2.06, the sensitivity was 90% and the specificity was 96%. sTfR resulted in a good test for the diagnosis of ACD+IDA vs. ACD: the cut-off value was ≥ 3.7µ/ml, the sensitivity was 92.3% and the specificity was 93.2%. Similarly, the parameter sTfR-F index resulted in a very good test for the diagnosis of ACD+IDA vs. ACD: for the suggested cut-off value of ≥2.3, the sensitivity was 92.3% and the specificity was 100%. Conclusion: As a result of the study, it was found that the pro-hepcidin test was a highly accurate test for distinguishing between ACD and IDA. Meanwhile, sTfR and the sTfR-F index proved to be excellent indicators for the differential diagnosis of IDA in chronic inflammatory conditions.

We prototyped a true analog current computing multilayer neural network (NN) chip, where multiple analog in-memory computing (AiMC) circuit blocks are connected to each other via simple analog non-linear operation circuits. The true analog current computing is achieved with the invention of an analog current rectified linear unit (ReLU) circuit of a three-stage current mirror. With the prototyped NN chip, we demonstrated that the true analog computing (1) achieves process variation compensation utilizing current driving, (2) eliminates digital-analog or analog-digital data conversion between NNs, and (3) realizes low power inference, not only in multiply-accumulate (MAC) but in ReLU operation. Through classification of Mixed National Institute of Standards and Technology dataset, the chip exhibits a low energy of 1.1 nJ/classification and an accuracy of 91.6%, achieves weight retention of five hours, much longer than dynamic random access memory, and enables 68% power reduction compared with serially connected two single-layer NN chips with analog-digital converters and digital-analog converters in between. Although periodic refresh from an external storage class memory is necessary for applications that require continuous operation exceeding five hours, our AiMC capable of MAC and non-linear operations with low power is effective in applications such as edge artificial intelligence terminals with limited power sources.

Early detection methods for cognitive impairment are crucial for its effective treatment. Dual-task-based pipelines that rely on skeleton sequences can detect cognitive impairment reliably. Although such pipelines achieve state-of-the-art results by analyzing skeleton sequences of periodic stepping motion, we propose that their performance can be improved by decomposing the skeleton sequence into representative phase-aligned periods and focusing on them instead of the entire sequence. We present the phase-aligned periodic graph convolutional network, which is capable of processing phase-aligned periodic skeleton sequences. We trained it with a cross-modality feature fusion loss using a representative dataset of 392 samples annotated by medical professionals. As part of a dual-task cognitive impairment detection pipeline that relies on two-dimensional skeleton sequences extracted from RGB images to improve its general usability, our proposed method outperformed existing approaches and achieved a mean sensitivity of 0.9231 and specificity of 0.9398 in a four-fold cross-validation setup.

Optimizing the regeneration process of surgically created anastomoses (blood vessels, intestines, nerves) is an important topic in surgical research. One of the most interesting parameter groups is related to the biomechanical properties of the anastomoses. Depending on the regeneration process and its influencing factors, tensile strength and other biomechanical features may change during the healing process. Related to the optimal specimen size, the range and accuracy of measurements, and applicability, we have developed a custom-tailored microcontroller-based device. In this paper, we describe the hardware and software configuration of the latest version of the device, including experiences and comparative measurements of tensile strength and elasticity of artificial materials and biopreparate tissue samples. The machine we developed was made up of easily obtainable parts and can be easily reproduced on a low budget. The basic device can apply a force of up to 40 newtons, and can grasp a 0.05–1 cm wide, 0.05–1 cm thick tissue. The length of the test piece on the rail should be between 0.3 and 5 cm. Low production cost, ease of use, and detailed data recording make it a useful tool for experimental surgical research.

Objective To assess the predictive value of four cardiovascular (CV) risk algorithms for identifying high-risk psoriatic arthritis (PsA) patients.Methods Evaluation of patients with PsA enrolled in the Spanish prospective project CARdiovascular in RheuMAtology. Baseline data of 669 PsA patients with no history of CV events at the baseline visit, who were followed in rheumatology outpatient clinics at tertiary centres for 7.5 years, were retrospectively analysed to test the performance of the Systematic Coronary Risk Assessment (SCORE), the modified version (mSCORE) European Alliance of Rheumatology Associations (EULAR) 2015/2016, the SCORE2 algorithm (the updated and improved version of SCORE) and the QRESEARCH risk estimator version 3 (QRISK3).Results Over 4790 years of follow-up, there were 34 CV events, resulting in a linearised rate of 7.10 per 1000 person-years (95% CI 4.92 to 9.92). The four CV risk scales showed strong correlations and all showed significant associations with CV events (p

Résumé : le théâtre de Brecht s’est révélé être, plus de soixante ans après sa mort, une rupture esthétique et idéologique dans la pratique de l’art dramatique. Marquée par les troubles socio-politiques du XXe siècle, son écriture se veut la traduction de ses convictions esthétiques notamment avec le principe de la disjonction. La présente réflexion montre que par une déstructuration à la fois actantielle et spatio-temporelle du texte théâtral, la logique disjonctive apparait comme l’assise textuelle de la distanciation. Mots-clés : Disjonction – Antigone – Théâtre épique – Distanciation.

The estimation of aerial forest carbon storage and capacity (CCS) is a fundamental instrument to evaluate the application of forest management measures and soil recovery under the approach of use in new ecosystem services (CCUS). The research has aimed to evaluate the capture and storage of carbon in the dry forests of Pomac (Lambayeque, Peru) to improve the approach of reforestation in new ecosystem services. For this, the non-destructive method was used, 15 random plots were selected, the tree species were identified, counted and measured, taking into account the diameter at breast height (DBH), the results indicated a total accumulation of aerial biomass ( dry) of 75.09 t/ha and a mass of stored carbon of 37,545 t/ha. The highest contribution of aerial biomass was dominated by three species: Prosopis pallida H.B.K. for Arnata ferreyra (carob tree) (90.99%) > Capparis ovalifolia (Vichayo) (6.13 %) > Capparis scabrida (Sapote) (2.02 %). The quadratic models to estimate the aerial biomass (AGB) of each species and of the plots studied based on the DBH were robust (R2>0.7) and significant and demonstrated a great payment potential in relation to the carbon credit in the Voluntary Market of Carbon. These results can be used to improve forest management and the recovery of degraded soils under the approach of new ecosystem uses that include the CCUS approach.

The magnetoresistance of three Pd–Co(1)/Cu/Pd–Co(3) trilayers with different Pd–Co(3) thicknesses were evaluated in a H2–N2 gas mixture. The resistance of Pd–Co/Cu/Pd–Co increased with increasing hydrogen concentration in the gas phase, which is similar to the behavior observed in the Pd metal. The peak structure was shown in the magnetoresistance under a nitrogen atmosphere and was associated with magnetic scattering at the interface between the Pd–Co and Cu layers. In the H2–N2 gas mixture, the peak position and width in the resistance profiles were independent of the hydrogen concentration for all the prepared trilayers, and the peak intensity decreased with increasing hydrogen concentration for the trilayer containing a 4.2 nm thick Pd–Co(3) layer. The decrease in intensity was due to the reduction in magnetization in the Pd–Co layer after hydrogen absorption. The change in the resistance ratio by hydrogen absorption in the trilayer was larger under a high external magnetic field of 170 mT than under a low magnetic field of 10 mT. These results confirmed that the multilayered structure of the Pd–Co/Cu enhances the resistance response to hydrogen under a high magnetic field.

Forests are capable of sequestering carbon through tree biomass and it is important to predict their storage potential given the increase in CO2 from deforestation and illegal mining. The objective of this research was to evaluate the storage of carbon and calorific value based on the aerial biomass of the forest diversity located between 2358 to 2450 masl of the Montane Humid Forest (Bh mo) in the buffer zone of San Alberto-Quilla in the Biosphere Reserve. The forest was located on the eastern Andean flank of Oxapampa in Cerro de Pasco (Peru). Four forest plots with an area of 900 m2 each were evaluated, using the destructive method for trees with a diameter at breast height (DBH) equal to or greater than 10 cm. Species richness, density, diameter at breast height, and aerial biomass (AGB) were determined.

Using ear tags, farmers can track specific data for individual lambs such as age, medical records, body condition scores, genetic abnormalities; to make data-based decisions. However, automatic reading of ear tags using Radio Frequency Identification requires (a) an antenna, (b) a reader, (c) comparable reading standards; consequently, such a system can be expensive and impractical for a large group of lambs, especially in situations where animals are not required to have a compulsory Electronic identification, contrary to the case in Europe, where it is mandatory. Therefore, this paper proposes a machine vision system for indoor animals to identify individual lambs using existing ear tags. Using a camera that is installed such that the trough is visible, the drinking behaviour of the lambs can be automatically monitored. Data from different lamb groups in two different pens were collected. The identification algorithm includes a number of steps: (1) Detecting the lambs' face, and its ear tags in each image; (2) Cropping each ear tag image and discerning the digits on it to obtain the tag number; (3) Tracking each lamb throughout the visit using a tracking algorithm; (4) Recovering the ear tag number using an algorithm that incorporates a list of the ear tag numbers of the lambs in each pen, and the predictions for each lamb in each frame. The You Only Look Once deep learning object detection algorithm was applied to locate and localise the lamb's face and the digits in an image. The models' datasets contained 1 160 and 2 165 images for the training set, and 325 and 616 images for the validation set, respectively. The algorithm output includes the identity of each lamb that came to drink, and its duration. The identification system resulted in a total accuracy of 93% for the data tested, which consisted of approximately 900 visits to the drinking stations, and was collected in real time in a natural environment. The ground truth of each video of a visit was obtained by human observation by studying the video. We checked if there was indeed a visit to the water trough and if so we registered the ear tag number of each lamb whose head was above the water trough. Thus, identifying lambs in a commercial pen using a relatively inexpensive and easily installed system consisting of a RGB camera and a computer vision-based algorithm has potential for farm management.