The Kielce University of Technology (Polish: Politechnika Świętokrzyska) is a relatively young institution, although the traditions of higher education in Kielce go back to the beginning of the 19th century. It was here that Stanisław Staszic founded the Mining Academy, one of the first higher schools in Poland, which operated in the years 1816–1826 and provided qualified personnel to meet the needs of the Old Polish Industrial Basin. Higher education became available in Kielce again in 1965 when Kielce-Radom Evening Higher Engineering School was established. It was transformed into the Kielce University of Technology in 1974.The University has five faculties:At present, over 9,400 students take courses in seven fields of studies: Civil Engineering, Environmental Engineering, Electrical Engineering, Computer Science, Mechanics and Machinery Design, Management and Marketing, Management and Production Engineering. The University is entitled to award a Doctor's degree in five academic disciplines: civil engineering, environmental engineering, electrical engineering, machine building and operation, mechanics, and a degree of Doctor Habilitated in machine building and operation. In the last discipline doctoral courses are also run.Staff of 403 academic teachers, including 81 Professors and Doctors Habilitated and 153 PhDs together with laboratories (e.g. those of acoustic emission, laser technologies, soil mechanics, cracking mechanics, geometrical quantities measurement or materials strength) provide education in all fields of studies and specializations.27 bilateral agreements provide basis for collaboration in research and teaching with 50 universities from 27 countries. The University is currently running 10 projects being a part of international programmes and also research tasks, one of which belongs to the Fifth EU Framework Programme.
Vehicle crush boxes are one of the safety elements used in vehicles to minimize damage that may occur during an accident. The task of crush boxes is to absorb the energy which is generated during an accident. In this study, peak force, energy absorption and specific energy absorption values of cylindrical composite crush boxes, to which 0.25% and 0.50% graphene was added, were experimentally investigated with hydrothermal aging. The composite crush boxes were produced with vacuum infusion method. Glass, aramid and carbon fibers and their hybridizations were used as fibers. During hybridization, the winding order of the fibers was changed from inside to outside. The parameters for hydrothermal aging were selected as 500 h and 1000 h at 60 °C. The highest energy absorption value was obtained in the carbon fiber-reinforced sample CFRPG1H2 with 0.25% graphene-added epoxy resin matrix, aged for 1000 h. The lowest peak strength was observed in the aramid fiber-reinforced sample AFRPG2H2 with 0.50% graphene-added epoxy resin matrix, hydrothermally aged for 1000 h. It was observed that increasing the graphene addition rate reduced the negative effects on aging. It was determined that increasing the graphene ratio by 0.25% had an effect on aging.
Reliable tool-wear monitoring is essential for maintaining machining quality and preventing unscheduled downtime in manufacturing. This investigation presents a sound-based classification framework for identifying wear states in the turning of AISI 316L stainless steel using advanced gradient-boosting models. Acoustic signals were recorded under constant cutting parameters to eliminate process-induced variability, and each recording was divided into standardized 2 s segments. A total of 540 multidomain features-including RMS, ZCR, spectral descriptors, Mel-spectrogram statistics, MFCCs and their derivatives, and discrete wavelet energies-were extracted to capture both stationary and transient characteristics of tool-workpiece interactions. Feature selection was performed using a three-stage pipeline comprising Boruta, LASSO, and SHAP analysis, resulting in a compact subset of highly informative descriptors. LightGBM, XGBoost, and CatBoost classifiers were trained using stratified 10-fold cross-validation across three wear states: Unworn, Slight wear, and Severe wear. LightGBM and XGBoost achieved the best performance, with mean accuracies above 0.96 and strong PRC-AUC and ROC-AUC values (0.98-1.00). Although Slight wear remained the most difficult class due to its transitional acoustic characteristics, all models showed clear separability for Unworn and Severe wear conditions. The results confirm that boosted decision-tree methods combined with SHAP-enhanced feature selection provide an effective, low-cost, and non-contact solution for tool-wear classification in 316L turning.
Human activity impacts the natural environment. One example of such an impact is energy production, including energy from renewable sources. The aim of this study was to analyse and assess changes in the state of the environment in 2008, 2015 and 2023, resulting from the development and structure of renewable energy sources in EU countries. Three research questions were formulated: Question 1 (Q1). Is the state of the environment in most EU countries characterised by variability in terms of the level of renewable energy development? Question 2 (Q2). Has the composition of the group of EU countries with the highest environmental status changed? Question 3 (Q3). Is the group of EU countries with the highest environmental status characterised by a diverse structure of renewable energy sources used? The study covers three key periods: 2008, 2015 and 2023. This approach allows for the identification of the impact of crisis factors on the relationship between the energy transition and environmental status. The evaluation applied the TOPSIS, EDAS and Ward’s methods. Based on a substantive and formal analysis, diagnostic variables were selected: 18 describing the structure and level of RES development, 7 economic indicators and 11 reflecting the environmental status of EU countries. The selection criterion was data availability, with sources drawn from the EUROSTAT, IRENA and World Bank Group databases. The results show that the main leaders were Italy, Sweden, France and Germany, with Austria and Denmark maintaining high positions only in 2008. Italy took the lead in 2015 and retained it in 2023 thanks to extensive emission reductions, while Finland joined the top group. Poland and Lithuania ranked last in 2015 and 2023. A growing gap was also observed between the leaders and the lowest-performing countries. Among the highest-ranked countries, hydropower was the dominant RES, while in Germany and Denmark, wind energy and biofuels also played a key role. Cluster analysis using Ward’s method confirmed the diversity of environmental and energy profiles, as well as Belgium’s distinct position. The study confirms the instability of most EU countries’ positions, the persistence of a small group of leaders and widening disparities in sustainable environmental development within the EU.
This study evaluated the performance of four temperate wetland macrophytes (Phragmites australis, Iris pseudacorus, Typha latifolia, and Alisma plantago-aquatica) in floating treatment wetlands (FTWs) exposed to a Cu-Pb-Cd-As mixture. Mesoscale (35 L) batch reactors were operated for 84 days under low (1 mg/L) and high (5 mg/L) metal(loid) loadings, with nutrient-only treatments as controls, enabling assessment of early plant development, nutrient removal, metal(loid) fate, and microbial community responses. Metal(loid) exposure inhibited nitrification and slowed phosphorus removal. Under low loading, FTWs with Typha and Alisma achieved the highest removal efficiencies for Cu (75% and 61%) and Pb (89% and 88%), whereas Phragmites and Iris were more effective for Cd (similar to 30%) and As (similar to 15%). At high loading, removal from solution increased to 86-92% (Cu), 90-95% (Pb), 73-89% (Cd), and 29-34% (As), driven mainly by abiotic and biogeochemical immobilization rather than direct accumulation in biomass or sediments. Metals accumulated predominantly in roots. Mass balance analysis showed that under low loading, plant biomass and sediments together accounted for up to 14.44 mg Cu (48%, Alisma), 8.23 mg Pb (27%, Alisma), and 9.89 mg Cd (33%, Iris), while As was mainly sediment-bound. Under high loading, plant-associated percentages decreased but absolute retention increased (up to 24.45 mg Cu, 37.32 mg Cd, and 14.49 mg As), indicating phytostabilization. Analysis of the developed microbiome revealed that high loading enriched Pseudomonas and Flavobacterium, while Sphingopyxis and Sphingorhabdus persisted under low exposure, indicating their utility as bioindicators.
Ensuring robust railway safety is paramount for efficient and reliable transportation systems, a challenge increasingly addressed through advancements in artificial intelligence (AI). This review paper comprehensively explores the burgeoning role of AI in enhancing the safety of railway operations, focusing on key contributions from machine learning, neural networks, and computer vision. We synthesize current research that leverages these sophisticated AI methodologies to mitigate risks associated with railroad accidents and optimize railroad tracks management. The scope of this review encompasses diverse applications, including real-time monitoring of track conditions, predictive maintenance for infrastructure components, automated defect detection, and intelligent systems for obstacle and intrusion detection. Furthermore, it delves into the use of AI in assessing human factors, improving signaling systems, and analyzing accident/incident reports for proactive risk management. By examining the integration of advanced analytical techniques into various facets of railway operations, this paper highlights how AI is transforming traditional safety paradigms, paving the way for more resilient, efficient, and secure railway networks worldwide.
