Abstract: Despite the development of the new, modern non-metallic materials, the steel materials are largely used in various branches of industry, while in some applications they are still irreplaceable. It is expected that such a trend will remain for certain number of years. This is why the necessity is present for development of the new types of steels, which would possess even better properties. The Chromium-Molybdenum (Cr-Mo) steels, with high vanadium content, belong to the group of newer steels characterized by high values of hardness and toughness. In this research, the tests were performed on samples made from the X180CrMo12-1 steel with varying percentage of vanadium within the limits of 0.5-3%. Vanadium, as a carbide-forming alloying element, creates a carbide network of the M7C3 type around the metal matrix, and finely dispersed carbides of the V6C5 type within the metal matrix. This research was focused on determining the carbides’ composition, observing the shape of metal grains and carbide network, testing the material’s resistance to friction and wear, including the electrochemical characterization, as well. The objective was to determine the carbides microstructure and morphology, as well as to evaluate their impact on the material's characteristics. The experimental investigation was performed using the scanning electron microscopy with energy dispersive spectrometry (SEM-EDS) and X-ray diffractometric analysis (XRD). Examination of the carbide composition confirmed that it was the M7C3 carbide.

Abstract: As autonomous driving technology progresses, efficient and accurate object detectors are able to detect pedestrians, vehicles, road signs, and obstacles in real time, thereby enhancing driving safety and serving as a part of autonomous driving. However, the performance of such object detectors is limited and cannot be leveraged to satisfy a modern autonomous driving system. To address this issue, we develop an object detection network for autonomous driving scenarios, SST-YOLO, which is based on YOLOv8. Specifically, we propose a Sobel convolution & convolution (SCC) to enhance the backbone network of YOLOv8 and perform more full feature extraction. In addition, we replace the original path aggregation feature pyramid network (PAFPN) with a small object augmentation pyramid network (SOAPN) to solve the small object detection problem. For regression accuracy and classification robustness, and thereby to increase the performance in a complex driving scenario, we use a Task-Adaptive Decomposition & Alignment Head (TADAHead) to replace the old YOLOv8 detection head. Experiments on the public autonomous driving dataset KITTI also show that our proposed method outperforms the baseline YOLOv8 model. Compared with the baseline results, the detection accuracy ranges from 65.1% to 68.2%, which indicates that the proposed SST-YOLO network can achieve object detection for autonomous cars.

Abstract: The aim of this paper is to propose a sociological and interdisciplinary framework for analyzing the digitization of emotions in adolescence. This contribution aims to promote theoretical reflection and inform educational and political interventions in the digital age, framing adolescents’ digital experiences as emotionally embodied and socially integrated processes. These aspects are concepts of great importance thanks to (or because of) the rapid spread of digital technologies and artificial intelligence, which has brought about a profound transformation in the emotional, relational, and educational experiences of adolescents. The role of digital and AI-based environments in mediating communication is expanding beyond simple facilitation. These environments are increasingly involved in the production, modulation, and regulation of emotions, thus influencing developmental trajectories and identity formation processes (Lupton, 2018a; Belk, 2013). This is conceptualized as a socio-technical process through which emotions are embodied, narrated, and governed within digital environments (Auriemma, 2023a). Consequently, the article introduces the concept of digital emotional embodiment, drawing on the sociology of emotions, theories of embodiment, and critical perspectives on artificial intelligence. Specifically, the concept refers to the way adolescents experience and express emotions through avatars, images, emojis, algorithmic feedback, and AI-mediated interactions (Niedenthal, Winkielman, Mondillon & Vermeulen, 2009; Zimmermann, Wehler & Kaspar, 2023). Therefore, it is important to emphasize the transformation of empathy, which is increasingly configured as a virtualized and datafied process, moving away (transforming) from that linked to Sympathy and to which we have been accustomed since Hume. In new processes, shaped by the logic of platforms, recommendation systems, and emotionally reactive technologies, standard emotional concepts have been deconstructed, and digital constructs are slowly being restructured (Scribano & Mairano, 2021; Auriemma, 2023b). In this context, AI systems do not merely reflect adolescents’ emotions, but actively contribute to the construction of emotional narratives, influencing emotional regulation, social connection, and future orientation. Digital environments have the capacity to encourage emotional expressiveness, experimentation, and inclusivity (Gall, Roth, Stauffert, Zarges & Latoschik, 2021). Conversely, they can also promote emotional standardization, dependence, and forms of affective vulnerability, particularly during a sensitive developmental stage such as adolescence.

Abstract: Background: Leptospirosis is a globally prevalent zoonosis with significant morbidity and mortality, especially in tropical regions like South Asia. In its severe form, the disease often leads to multiorgan dysfunction, with pulmonary haemorrhage being a significant cause of death. Evidence supporting specific treatments for severe leptospirosis with pulmonary involvement remains limited. Recent studies suggest that immunomodulatory therapies, such as therapeutic plasma exchange (TPE), may offer survival benefits. This case series examines the application and outcomes of TPE in patients with severe leptospirosis at a tertiary-care hospital in Sri Lanka. Methods: We studied a case series involving 10 patients with confirmed severe leptospirosis and multiorgan involvement, from September 2021 to October 2022. All patients received intravenous antibiotics and methylprednisolone. TPE was initiated in nine patients based on clinical severity, particularly in the presence of pulmonary haemorrhage. Clinical, laboratory, and radiological data were collected from patient records and follow-up. ELISA IgM confirmed Leptospirosis. A multidisciplinary team made TPE decisions. Results: Of the nine patients who received TPE, seven survived (77.8%). Pulmonary haemorrhage was the primary indication for TPE in all the cases. All patients had multiorgan involvement: renal failure (90%), hepatic dysfunction (60%), and myocarditis (70%). Mortality was associated with inotropic-dependent myocarditis and mechanical ventilation at TPE initiation. Patients requiring intubation had a 50% mortality rate, compared to 14.3% in those who were not intubated. Non-survivors also had elevated lactate levels (>4 mmol/L) and worsening acid-base status. Four patients required dialysis: three survived. Clinical severity scores increased from admission (mean 3.78) to TPE initiation (mean 4.44), and no patient showed improvement before TPE. Conclusion: Early initiation of TPE may be beneficial in severe leptospirosis, particularly when performed before irreversible cardiopulmonary failure. The combined use of TPE and corticosteroids may provide additional benefit by mitigating immune-mediated tissue injury. These findings support further investigation into the timing and patient selection criteria for TPE in severe leptospirosis, particularly in resource-limited settings.

Abstract: The detection of SF₆ decomposition products is essential for diagnosing insulation faults in gas-insulated switchgear. Using first-principles density functional theory, this study investigates the adsorption behavior of five characteristic gases (H₂S, SO₂, SOF₂, SO₂F₂, and SF₆) on pristine and vanadium-doped graphene/MoS₂ (GMV) heterostructures to evaluate their potential for gas sensing applications. Pristine graphene/MoS₂ exhibits weak physisorption toward all target molecules, with low adsorption energies and negligible charge transfer, indicating insufficient sensitivity for practical use. To address this limitation, a V-doped graphene/MoS₂ heterostructure is proposed, wherein vanadium atoms are incorporated into the graphene lattice to introduce active centers and modulate interfacial charge transfer. The results demonstrate that H₂S, SO₂, and SOF₂ preferentially adsorb atop the V site via local covalent interactions, with significantly enhanced adsorption energies (up to −0.388 eV for SO₂) and shortened distances. In contrast, SO₂F₂ and SF₆ adsorb near electron-depleted carbon regions driven by electrostatic attraction. Charge density difference and Bader charge analyses reveal pronounced charge redistribution upon SO₂ and SF₆ adsorption, while density of states analysis confirms orbital hybridization near the Fermi level, suggesting possible chemical bond formation. Notably, adsorption of SO₂ and SF₆ substantially reduces the density of states at near Fermi level, indicating a measurable modulation of surface conductivity. These findings establish V-doped graphene/MoS₂ as a promising sensing material for selective detection of SF₆ decomposition products, offering a viable strategy for advancing online monitoring technologies in power systems.

Abstract: This paper consolidates our previous work and articulates Scheme C, a constitutional architecture designed to resolve deadlocks and instability in fragmented democracies by synthesising previous findings into a self-contained theoretical framework. The scheme's centrepiece is a game-based investiture rule that guarantees the appointment of a prime minister through a strategic nomination process, eliminating the risk of investiture-related collapse. Central to this system is also a bifurcated confidence structure -- assigning the prime minister either type I (majority) or type II (minority) status -- managed by a dynamic no-confidence mechanism. Stability is reinforced by a synchronised electoral rhythm and a Westminster-style dissolution mechanism that protects cohesive assemblies while resulting in contingent, quasi-midterm elections. To ensure continuity, a novel hybrid caretaker office bridges Westminster and Presidential traditions by automatically converting a departing prime minister into a tenure-secured, though authority-attenuated, caretaker. This "converted" logic is balanced by a presidential-style "acting" appointment mode for vacancies, ensuring administrative resilience throughout the electoral cycle. Ultimately, Scheme C provides a resilient architecture that ensures unyielding governmental functionality and rigorous legislative oversight regardless of the underlying electoral system or party landscape.

Abstract: Physical inactivity remains a major modifiable risk factor for non-communicable diseases and continues to exhibit marked socioeconomic and gender disparities in Latin America. Identifying robust and interpretable predictors of inactivity in nationally representative datasets is essential for informing public health strategies. This study compared a survey-weighted logistic regression model and an explainable machine learning approach (XGBoost) to predict physical inactivity among Chilean adults using data from the 2024 National Physical Activity and Sports Survey (ENAFyD; n = 5,248). Models were evaluated on a stratified held-out test set (n = 1,050) using weighted and unweighted area under the ROC curve (AUC), Brier scores, and calibration curves. Survey-weighted logistic regression achieved a weighted AUC of 0.801, while XGBoost achieved 0.797, demonstrating comparable discrimination. XGBoost showed marginally lower Brier scores, indicating slightly improved probabilistic calibration. Low socioeconomic status, female sex, lower monthly physical activity expenditure, limited facility access, and lower engagement with digital resources were consistently associated with higher inactivity risk. SHAP-style contribution analysis provided additional insight into feature-level influence within the machine learning framework. Overall, both approaches demonstrated similar predictive capacity, supporting the complementary use of classical regression and explainable machine learning for population-level physical inactivity research.

Abstract: Peripartum cardiomyopathy (PPCM) is a distinct condition that presents as heart failure (HF) in a woman who was previously healthy and has no prior cardiovascular issues. It results from idiopathic left ventricular (LV) dysfunction, characterized by a reduced LV ejection fraction below 45%. PPCM is a life-threatening condition with a high mortality rate (MR) that demands urgent treatment. Methods: This narrative review article aims to define PPCM and its pathophysiology, and to conduct a scoping review of the latest data on managing patients with peripartum cardiomyopathy during pregnancy and the postpartum period. Results: Currently, treatment follows standard HF protocols for reduced ejection fraction, with the possible addition of bromocriptine and, during pregnancy, medications that do not harm the fetus. Conclusions: Early, aggressive therapy is essential for a better prognosis, but managing PPCM can be challenging. Treatment of PPCM patients should be led by a team of highly qualified specialists, also known as the Obstetric and Cardiac Care Team, consisting of an obstetrician-perinatologist, an anaesthesiologist, a cardiologist, and a cardiac intensive care specialist. Baseline left ventricular end-diastolic diameter (LVEDD) and left ventricular ejection fraction (LVEF) are the main prognostic factors. LVEF less than 30%, significant LV dilatation, LVEDD ≥ 6.0 cm, and right ventricular involvement are factors indicative of a poor prognosis. While pregnancy after PPCM is possible, it should be discouraged due to the significant risk of complications and even death. The most common causes of death in patients with PPCM are thromboembolic complications, severe HF, serious ventricular arrhythmias, cardiogenic shock, and sudden cardiac arrest.

Abstract: Achieving meaningful reductions in residential heating demand requires design strategies that can respond to climate-specific solar availability and envelope performance. Although passive solar principles are well established, their effectiveness remains highly context-dependent, and simplified prescriptive approaches may not capture interactions across different climates. This study presents an AI-guided evolutionary optimization framework for passive solar residential design, focusing exclusively on the reduction in annual space heating demand under standardized assumptions. A standardized single-story residential prototype is simulated across three climatic contexts: hot–dry (Riyadh), temperate (Barcelona), and cold (Toronto). Dynamic building performance simulations are conducted using EnergyPlus, coupled with DesignBuilder’s built-in NSGA-II evolutionary optimization engine. Envelope-related variables, including the window-to-wall ratio, orientation, glazing configuration, and thermal mass, are optimized using a single objective: minimizing the annual heating load under idealized heating conditions. The results demonstrate substantial climate-dependent reductions in heating demand. In Toronto, the annual heating demand is reduced from approximately 16,900 kWh to 9600 kWh (≈43%). In Barcelona, a reduction from approximately 5650 kWh to 1990 kWh (≈65%) is achieved, while in Riyadh, heating demand is reduced from approximately 990 kWh to 39 kWh (>95%). The optimized solutions reveal distinct climate-specific design logic rather than universal passive rules. The results demonstrate that evolutionary optimization can support early-stage envelope design by revealing climate-specific heating strategies under clearly defined and comparable assumptions.

Abstract: Calcium silicate boards are widely used in passive fire protection systems due to their high structural stability and excellent thermal insulation performance under extreme temperatures. The fireproofing efficiency of such materials strongly depends on their thermophysical properties and structural configuration. In this study, the effects of density, thickness, and thermal conductivity on the fire insulation performance of three commercially available calcium silicate boards were systematically investigated by fire testing. Temperature evolution on the unexposed surface was monitored and validated through numerical simulations to predict how the fireboards behave under various heating conditions. In addition, the microstructural and physical properties of the boards were characterized and correlated with their fire performance. The simulation results show good agreement with experimental data, revealing that density, thickness, and thermal conductivity play a critical role in determining the fireproofing efficiency of calcium silicate boards.

Abstract: Background and Objectives: The present study aimed to evaluate the association between dental caries, periodontal pockets, and radiologically detected periapical lesions with serum levels of Dickkopf-1 (Dkk-1) and tartrate-resistant acid phosphatase 5 (TRAP-5) in oncological patients with ear, nose, and throat (ENT) cancer compared with healthy controls. Materials and Methods: The study included 63 subjects divided into a study group of 33 patients diagnosed with ENT cancer and a control group of 30 healthy individuals. Blood samples were collected to determine serum Dkk-1 levels using a sandwich enzyme immunoassay and TRAP-5 levels. Dental radiological evaluation consisted of orthopantomography (OPT) and cone-beam computed tomography (CBCT) to assess the number and depth of dental caries and the presence of periapical lesions. Periodontal pockets were recorded through clinical examination. Results: OPT assessment showed a maximum of eight carious sites in both groups. The mean number of carious lesions was higher in healthy subjects (2.97 ± 2.48) than in cancer patients (2.06 ± 2.29). CBCT evaluation revealed 0–8 carious lesions in healthy individuals (maximum = 8) and 0–6 lesions in cancer patients (maximum = 6). The mean number of caries detected by CBCT was significantly higher in the control group (2.97 ± 2.48) compared with the cancer group (1.85 ± 1.89). Periodontal pockets were more frequent in cancer patients (mean: 0.67 ± 1.32) than in controls (0.37 ± 0.81). OPT evaluation showed a higher mean number of periapical lesions in cancer patients (0.82 ± 1.29) compared with controls (0.37 ± 0.67). CBCT examination demonstrated that the mean number of periapical lesions in cancer patients was more than twice that of the control group; however, this difference did not reach statistical significance. Conclusions: Patients with ENT cancer presented a higher prevalence of periodontal pockets and periapical lesions compared with healthy controls, while carious lesions were more frequently detected in healthy individuals. Radiological findings, together with systemic biomarkers such as Dkk-1 and TRAP-5, may contribute to a better understanding of oral–systemic interactions in oncological patients.

Abstract: One of common human disease nowadays is the well known the viral Hepatitis C (HCV). Untreated corectlly, viral hepatitis could be one of the most causes of morbidity and mortality. Actually, the incidence rates of chronic viral hepatitis C in the Republic of Kazakhstan have increased. Using in the laboratory, specific tests, the proposed aim is to identify the viral genome. We present data related to the screening of risk groups for hepatitis C and also the diagnostic significance and finally show their results, for disease diagnosis.

Abstract: Background/Objectives: Mathematical modelling and computational simulation are important tools in the analysis of transport phenomena and surface reactions. These include surface hybridization that takes place in a microfluidic channel, creating a DNA microarray. Methods: In this work, the surface hybridization with multiple DNA probe spots immobilized on the bottom of a microfluidic channel was investigated using computational modelling. The model incorporates the 2D transport process, coupled with convective/diffusive mass transport (infinite dilution) and surface binding models for both non-specific adsorption and specific hybridization. Results: Hybridization signals obtained at different reaction times, various flow rates, and two different channel depths were analyzed, targeting enhanced surface hybridization while minimizing assay time and sample usage. It was found that a high aspect ratio (width/height) channel geometry with electrodes placed on the top and bottom walls would enable significantly improved fuel utilization and reduced inter-diffusional mixing width. Conclusions: The numerical study also suggested the implementation of a tapered electrode design that accommodates the growth of the co-laminar mixing zone in the downstream direction.

Abstract: We assess associations between emergency room (ER) visits, scaled to per 105 population per year, for asthma and chronic obstructive pulmonary disease (COPD), two of the most common respiratory diseases, and zip-code level exposure to criteria air pollutants (CAPs) coming from point sources in New York State (NYS) from 2010 to 2018. Exposure data on point source CAPs was retrieved from the United States Environmental Protection Agency (USEPA) National Emission Inventory (NEI) database and ER visits for asthma and COPD were acquired from the New York State Department of Health (NYSDOH) Statewide Planning and Research Cooperative System (SPARCS). To account for within-county variability we used log-linear mixed effects models, adjusted for year, sex, age category, county level poverty, smoking, PM2.5, volatile organic compounds (VOCs) and CAPs sources within the study period. Results show significant associations between ER visits for asthma and COPD and most of the pollutants in the study, even after adjusting for the effects of poverty and smoking. The findings suggest that zip-code level point source criteria air pollutants, while comprising a small portion of total air pollution, poses a modest but significant contribution to the risk of respiratory disease-related ER visits.

Abstract: Invasive aspergillosis (IA) is a disease with a complex diagnosis due to its non-specific clinical manifestations, which delays treatment and consequently leads to a worsening of the disease. The Aspergillus galactomannan (GM) lateral flow assay was evaluated in pretreated sputum (PS) and serum samples for the diagnosis of IA. Clinic epidemiological data were collected from 76 patients at the Instituto Médico La Floresta between January 1, 2022, and April 30, 2024. The GM lateral flow assay was performed on serum, BAL (bronchoalveolar lavage), and PS samples for subsequent classification according to the criteria established by the EORTC/MSG into possible, probable, proven IA, and no IA. Only 28 patients were presented with possible IA, 28 with probable IA, and 20 did not present with IA. A descriptive analysis was applied to 32 patients with galactomannan results ≥ 0.7 in serum and ≥ 0.8 in sputum or BAL, yielding statistically significant values for sputum and the associated serum (p=0.00), and for BAL (p=0.041) and its accompanying serum (p=0.05). Furthermore, the means obtained were within the 95% confidence intervals, being consistent and reliable. A correlated t-test was performed on 3 patients who provided all three sample types, revealing p (0.453) > 0.05, which indicates no significant difference between pretreated sputum and BAL. PS is an alternative sample to BAL for high-risk patients. The use of respiratory samples (BAL/PS) increases the probability of galactomannan detection in non-neutropenic patients compared to the use of serum and can be used for IA classification; however, it is considered that EORTC/MSG criteria have limited applicability in this population.

Abstract: Background: Rabies remains a major zoonotic disease worldwide, particularly in regions with large populations of free-roaming animals. In urban settings, animal-related injuries constitute a substantial healthcare burden and frequently result in the administration of rabies post-exposure prophylaxis (PEP). This study aimed to evaluate the epidemiological characteristics of animal exposures and real-world PEP practices in a metropolitan emergency department. Methods: This retrospective descriptive study included 1,960 patients presenting to a tertiary metropolitan emergency department between March 1 and September 1, 2025, due to suspected animal exposure. Demographic data, animal species involved, exposure mechanisms, animal ownership and vaccination status, time to presentation, and PEP practices were analyzed using descriptive statistics. Results: Most exposures were cat-related (86.3%) and resulted from scratching (81.5%). Nearly all injuries were superficial (99.8%), while deep injuries were rare (0.2%). The majority of animals were classified as stray (90.1%), and vaccination status was unknown in 81.2% of cases. Rabies immunoglobulin was administered in only 0.6% of patients; however, rabies vaccination was initiated in 98.8%. Approximately 74.5% of patients presented within 24 hours. Post-exposure animal observation was documented in only 20.2% of cases. Conclusions: Urban animal exposures in this metropolitan setting were predominantly superficial and cat-related, yet rabies vaccination was administered to nearly all patients. Limited animal observation and incomplete vaccination documentation appear to constrain risk stratification and may contribute to the use of precautionary PEP. Strengthening surveillance systems, improving documentation, and implementing evidence-based risk-stratification strategies are essential to optimizing rabies prophylaxis practices in urban environments.

Abstract: Graph classification is dominated by permutation-invariant graph neural networks. We revisit this problem from a different perspective: can small language models (SLMs) act as graph classifiers when graphs are serialized as text? Unlike GNNs, sequence-based transformers do not encode permutation invariance by construction, raising a fundamental question about structural stability under node relabeling.We provide the first systematic study of permutation robustness in small graph-as-text models. We introduce an evaluation protocol based on Flip Rate and KL-to-Mean divergence to quantify prediction instability across random node permutations. To enforce structural consistency, we propose Permutation-Invariant Training (PIT), a multi-view regularization scheme that aligns predictions across relabeled graph views, and examine its interaction with degree-aware token embeddings as a minimal inductive bias.Across benchmark datasets using parameter-efficient fine-tuning, we show that SLMs achieve competitive classification accuracy, yet standard fine-tuning exhibits non-trivial permutation sensitivity. PIT consistently reduces instability and in most evaluated settings improves accuracy, demonstrating that structural invariance in sequence-based graph models can emerge through explicit regularization.

Abstract: Wheat straw is an abundant agricultural residue with high potential for carbohydrate-based bioconversion, yet its efficient utilization is limited by lignocellulosic recalcitrance. This study systematically investigated Organosolv extraction of German wheat straw with the goal of achieving near-complete enzymatic hydrolysis at minimized process severity and energy demand. Process severity was evaluated using the P-Factor concept. In preliminary screening, acid catalysts and liquor ratios were assessed. Strong acids clearly outperformed weak acids: at comparable severity, 5% (w/w, DM) H2SO4 or p-toluenesulfonic acid (PTSA) yielded glucose yields of 83 ± 2.4% and 81 ± 6.2%, respectively, whereas weak acids (phosphoric, lactic, acetic) and a catalyst-free control resulted in only ~20–41% glucose yield. Liquor ratio strongly affected extraction performance; a ratio of 1:19 provided the highest glucose yield (85 ± 1.4%) and robust mixing compared to 1:12–1:15 (67–68%). Two novel pretreatment strategies applied prior to Organosolv extraction, namely hot-water pretreatment (HWP) and water pretreatment (WP), significantly increased hydrolysability compared to untreated straw (58 ± 3%), reaching 79 ± 2% for HWP and 86 ± 5% for WP. DOE-based experiments (135–170 °C; P-Factor 3.0–4.0) showed that increasing temperature from 135 to 150 °C markedly improved hydrolysability (e.g., WP: 74 ± 3% to 96 ± 3%), while further increase to 170 °C provided no additional benefit. Response-surface modeling predicted a maximum hydrolysability of approximately 88% for HWP but complete hydrolysis for WP within 152–170 °C, indicating a broad operational window. Overall, combining simple pretreatment with severity-optimized Organosolv extraction enables energy-efficient, near-complete enzymatic hydrolysis of wheat straw.

Abstract: The article explores the current state of circular economy adoption in Romanian small and medium-sized companies and their conceptualization regarding organizational excellence through the lens of environmental concerns. A survey of 52 companies from diverse sectors is conducted by means of a questionnaire and the results obtained are analyzed using descriptive and inferential statistics (correlation analysis, ANOVA, and variance testing). The results obtained indicate a growing body of knowledge and best practices that at company level are starting to evolve past the material and pro-cess efficiency preoccupations and towards an incipient understanding of environ-mental excellence, with positive interest ranging between 40% and 86%. Also, re-spondents recognize strong complementarities between circularity, sustainability, and organizational excellence. The need for an advanced systemic strategy, support policies, resource management, and stakeholder engagement is highlighted as the path towards achieving sustainability goals in conjunction with market success and not in spite of it.

Abstract: Electron collision broadening parameters calculations were performed for 33 singly ionized yttrium spectral lines, 27 with special relevance in astrophysical applications (can be found in the Atmospheres of FGK Stars). Calculations were made in the semi-empirical Griem approach using the Gaunt factors proposed by Van Regemorter and those proposed by Douglas H. Sampson. Furthermore, to test our calculations, the electron collision broadening of 4 well isolated spectral lines of Y II was experimentally measured in laser- induced breakdown experiments using a Q-switched Nd:YAG laser focused on Pb-Y alloy samples.