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Częstochowa is a city known as the “spiritual capital of Poland”. The reason behind this name is the sanctuary of Our Lady—”Jasna Góra” monastery—around which a settlement was established, giving rise to the city today. The first parish was established in the 14th century. The next two parishes in Częstochowa were only established at the turn of the 19th and 20th centuries: St Barbara’s in 1891 and St Joseph’s in 1910. Today, Częstochowa has a population of almost 200,000, is an industrial and academic center, and has 53 parish churches. In comparison, Warsaw, Poland’s capital, with a population of 1,861,599, has 86 parishes. The vast majority of Częstochowa’s churches were built in the 1980s and 1990s. This paper presents a short history of the city’s development with particular reference to its sacred architecture. It also attempts to present the influence of church buildings on the urban layout and religious atmosphere of the city. The paper is an attempt to present the phenomenon of Częstochowa, which attracts hundreds of thousands of pilgrims from Poland and all over the world every year. Full article

Due to severe global energy issues and the widespread demand for high-quality winter heating, this study designed a new type of graphene-based electrically heated solid wood composite floor. This flooring maintains the convenience of a traditional floor installation while providing users with a more comfortable living experience. Additionally, the low-temperature heating and temperature regulation system further reduces energy consumption, offering a new perspective for green home living. This paper introduces the overall structure and temperature control system of the graphene-heated solid wood composite flooring. Based on the above reasons, the working mechanism and heat transfer process of the graphene-heated flooring were analyzed, and a mathematical model was established. Furthermore, simulations of flooring with different thicknesses were conducted to determine temperature rise curves and corresponding times. Finally, a comparative experimental verification was conducted on the thermodynamic performance of the solid wood composite graphene flooring. The results showed that in the case of a floor with an 18 mm thickness, the time for the surface layer of the floor to reach 22 °C is 27 min; the time to reach 26 °C is 56 min; and that the time to reach 28 °C is 109 min. The time required to return to 22 °C after the power has been switched off is 25 min. The results also showed that one hour after the power was turned off, the surface temperature of the floor was still above 20 °C. The study shows that the graphene-heated flooring can be used to achieve high-quality heating. Full article

In UAV path-planning research, it is often difficult to achieve optimal performance for conflicting objectives. Therefore, the more promising approach is to find a balanced solution that mitigates the effects of subjective weighting, utilizing a multi-objective optimization algorithm to address the complex planning issues that involve multiple machines. Here, we introduce an advanced mathematical model for cooperative path planning among multiple UAVs in urban logistics scenarios, employing the non-dominated sorting black-winged kite algorithm (NSBKA) to address this multi-objective optimization challenge. To evaluate the efficacy of NSBKA, it was benchmarked against other algorithms using the Zitzler, Deb, and Thiele (ZDT) test problems, Deb, Thiele, Laumanns, and Zitzler (DTLZ) test problems, and test functions from the conference on evolutionary computation 2009 (CEC2009) for three types of multi-objective problems. Comparative analyses and statistical results indicate that the proposed algorithm outperforms on all 22 test functions. To verify the capability of NSBKA in addressing the multi-UAV cooperative problem model, the algorithm is applied to solve the problem. Simulation experiments for three UAVs and five UAVs show that the proposed algorithm can obtain a more reasonable collaborative path solution set for UAVs. Moreover, path planning based on NSBKA is generally superior to other algorithms in terms of energy saving, safety, and computing efficiency during planning. This affirms the effectiveness of the meta-heuristic algorithm in dealing with multiple objective multi-UAV cooperation problems and further enhances the robustness and competitiveness of NSBKA. Full article

Diabetic retinopathy (DR) is a severe microvascular complication of diabetes that affects the eyes, leading to progressive damage to the retina and potential vision loss. Timely intervention and detection are crucial for preventing irreversible damage. With the advancement of technology, deep learning (DL) has emerged as a powerful tool in medical diagnostics, offering a promising solution for the early prediction of DR. This study compares four convolutional neural network architectures, DenseNet201, ResNet50, VGG19, and MobileNetV2, for predicting DR. The evaluation is based on both accuracy and training time data. MobileNetV2 outperforms other models, with a validation accuracy of 78.22%, and ResNet50 has the shortest training time (15.37 s). These findings emphasize the trade-off between model accuracy and computational efficiency, stressing MobileNetV2’s potential applicability for DR prediction due to its balance of high accuracy and a reasonable training time. Performing a 5-fold cross-validation with 100 repetitions, the ensemble of MobileNetV2 and a Graph Convolution Network exhibits a validation accuracy of 82.5%, significantly outperforming MobileNetV2 alone, which shows a 5-fold validation accuracy of 77.4%. This superior performance is further validated by the area under the receiver operating characteristic curve (ROC) metric, demonstrating the enhanced capability of the ensemble method in accurately detecting diabetic retinopathy. This suggests its competence in effectively classifying data and highlights its robustness across multiple validation scenarios. Moreover, the proposed clustering approach can find damaged locations in the retina using the developed Isolate Regions of Interest method, which achieves almost a 90% accuracy. These findings are useful for researchers and healthcare practitioners looking to investigate efficient and effective powerful models for predictive analytics to diagnose diabetic retinopathy. Full article

Corporate sustainability performance becomes the central element in many current business developments: the total value of ESG investment grows, more and more clients want to invest in projects that deliver more than the pure financial return, companies are innovating and transforming their business models, and adopting the B-Corp values. Environmental and wider societal impact becomes the central focus of the businesses that want to become the force for good. This article adopts an empirical approach and builds an ESG index of corporate performance based on eight critical metrics representing the economic, social, and environmental dimensions under varying policy priorities. Furthermore, it investigates correlations between these aggregate ESG indices and share prices as well as return on investment, or ROE, of companies. The article goes on to investigate empirically the correlation between employees/turnover, diversity, CO₂ emissions, waste generation, and water use of companies and share prices and ROE metrics, respectively. The reasons for divergence between correlations of sustainability performance indicators with share prices, ROE, and profits/turnover are discussed in detail, with particular attention drawn to the reasons why diversity might matter more for the share prices than ROE and why waste generation, water use, and CO₂ emissions might still be poorly reflected in the ROE while some of these metrics have a significant connection with the profit/turnover ratio. The article will undoubtedly be of interest to ESG fund managers, investors, corporate sustainability officers, and policymakers. Full article

Thermal spray techniques allow coatings to be deposited from a wide range of materials with controlled thicknesses, from micrometres to millimetres. For this reason, thermal spraying can optimize performance for diverse applications across industries, ensuring strong adhesion and the durability of coated surfaces. In this work, composite ethylene chlorotrifluoroethylene/ceramic (ECTFE/Al₂O₃) coatings with different ceramic ratios were deposited by plasma spraying. Four coatings were produced by spraying blended powders consisting of pure ECTFE and ECTFE with 5%, 10%, and 15 wt.% Al₂O₃. The effect of varying the ceramic ratio on the coatings’ microstructure and properties was investigated. Morphology and particle size distributions were determined for the raw powders. The microstructural examination of the coatings showed proportional increases in Al₂O₃ content. An improvement in adhesion was achieved with ceramic in the coatings from 5 wt.% Al₂O₃. Enhanced friction coefficients were obtained with ceramic, except for 15 wt.% Al₂O₃. Taber abrasion tests showed a minimal influence on ceramic content. Full article

One of the significant challenges during wellbore drilling is accurately predicting frictional pressure losses in symmetrical drill pipes. In this work, a Bayesian regularized neural network (BRANN) and multivariate adaptive regression splines (MARS) are employed to develop accurate and interpretable models for predicting frictional pressure losses during drilling. Utilizing data of frictional pressure loss collected through experimentation, the models are created. The model inputs include mud flow rate, mud density, pipe diameter (inside and outside diameters), and viscometer dial readings, while pressure loss is the output. Statistical comparisons between the model predictions and the actual values demonstrate the models’ ability to reasonably forecast frictional pressure losses in wells. The performance of the models, as measured by error metrics, is as follows: BRANN (0.999, 0.076, 16.76, and 11.67) and MARS (0.998, 0.0989, 21.32, and 16.499) with respect to the coefficient of determination, average absolute percentage error, root mean square error, and mean absolute error, respectively. Additionally, a parametric importance study reveals that, among the input variables, internal and external pipe diameters are the top predictors, with a relevancy factor of −0.784 for each, followed by the mud flow rate, with a relevancy factor of 0.553. The trend analysis further confirms the physical validity of the proposed models. The explicit nature of the models, together with their physical validation through trend analysis and interpretability via a sensitivity analysis, adds to the novelty of this study. The precise and robust estimations provided by the models make them valuable virtual tools for the development of drilling hydraulics simulators for frictional pressure loss estimations in the field. Full article

The purpose of this review is to present reasons for pregnancy loss in dogs. Termination of pregnancy can occur at any stage of gestation, sometimes even before pregnancy is diagnosed. If embryonic death occurs before day 35 of gestation, the embryo is absorbed. Fetal death in the second half of gestation leads to abortion or fetal emphysema, maceration, or mummification. Abnormal embryonic development, competition between placental sites, or genetic abnormalities of the embryo(s) can lead to termination of pregnancy. Maternal factors that alter ovarian function or the environment in the oviducts and uterus, such as cystic endometrial hyperplasia or inflammation, can lead to pregnancy loss. Abnormalities of the placenta, uterine torsion, metrocoele, environmental stress, and malnutrition can have detrimental effects on fetal development. Several drugs or medications can also have embryotoxic/teratogenic effect. Endocrinological disorders such as hypoluteodism, gestational diabetes, and hypothyroidism can be reasons for pregnancy failure. Several bacteria, protozoa, and viruses can cause pregnancy loss in dogs. Etiological diagnoses of pregnancy loss in dogs are challenging as the exact causes are not always obvious; so, a multi-faceted approach is required. Full article

Background/Objectives: As reported by the World Health Organization, Mpox (monkeypox) is an important disease present in 110 countries, mostly in South Asia and Africa. The number of Mpox cases has increased rapidly, and the medical world is worried about the emergence of a new pandemic. Detection of Mpox by traditional methods (using test kits) is a costly and slow process. For this reason, there is a need for methods that have high success rates and can diagnose Mpox disease from skin images with a deep-learning-based autonomous method. Methods: In this work, we propose a multi-class, fast, and reliable autonomous disease diagnosis model using transformer-based deep learning architectures and skin lesion images, including for Mpox disease. Our other aim is to investigate the effects of self-supervised learning, self-distillation, and shifted window techniques on classification success when multi-class skin lesion images are trained with transformer-based deep learning architectures. The Mpox Skin Lesion Dataset, Version 2.0, which was publicly released in 2024, was used in the training, validation, and testing processes of the study. Results: The SwinTransformer architecture we proposed in our study achieved about 8% higher accuracy evaluation metric classification success compared to its closest competitor in the literature. ViT, MAE, DINO, and SwinTransformer architectures achieved 93.10%, 84.60%, 90.40%, and 93.71% accuracy classification success, respectively. Conclusions: The results obtained in the study showed that Mpox disease and other skin lesion images can be diagnosed with high success and can support doctors in decision-making. In addition, the study provides important results that can be used in other medical fields where the number of images is low in terms of transformer-based architecture and technique to use. Full article

Today, the global trend toward plant-based beverages has grown for sustainability, health-related, lifestyle, and dietary reasons. Among them, drinks produced from almonds have been recognized as a concentrated nutrient source. Commercial almond milk was fermented under the same processing conditions using water and milk kefir grains to determine the starter culture leading to the beverage with the better nutritional profile. The resulting fermented beverages were investigated for protein, phenolic, and flavonoid content, fatty acid profile, and antioxidant activity, determined by DPPH, ABTS, and FRAP assays. Comparing the results, it was found that the almond beverage from milk kefir grains had the highest protein. The lipid profile of both beverages was characterized by a high content of monounsaturated fatty acids and a lower saturated fatty acid concentration compared to almond milk. Despite the higher phenolic content of the almond beverage from milk kefir grains, the ABTS and DPPH tests showed increased antioxidant activity in both fermented beverages, but with no significant difference between them, while the FRAP test showed a pronounced predominance of iron-reducing ability in the beverage from water kefir grains. The evidence from this study suggested that both types of grains can be used as starter cultures to enhance the nutritional and bioactive properties of almond milk. Full article

Studies on disability and unpaid domestic and care work (UDCW) in low- and middle-income countries are rare. This paper contributes to a nascent body of literature by exploring how children’s unpaid workloads at home are affected by the disability status of their primary caregiver and of younger children within the household in 34 low- and middle-income countries and areas. We constructed ordinary least-squares regression models for the pooled data using country-fixed effects and for each country and area separately. Random effects models are included as a test of robustness. Our analysis of the pooled data revealed that when a household included a child aged 2–4 with a disability, the time dedicated to UDCW by children aged 5–17 rose by approximately 10 percent, on average. While we did not find an association between the disability status of the caregiver and the time children dedicated to UDCW in the pooled regressions, in the country-specific regressions, having a caregiver with a disability was associated with changes in the time spent on UDCW in certain countries, signaling the heterogeneity of country experience. As such, there is reason to devote additional attention to understanding the implications of disability on the amount of time children spend on UDCW and to consider what types of support might be needed in households with diverse disability profiles. Full article

Background: Ipilimumab and nivolumab (ipi/nivo) improved overall survival (OS) compared to sunitinib in the pivotal Checkmate 214 trial of metastatic renal cell carcinoma (mRCC) with International Metastatic RCC Database Consortium (IMDC) intermediate/poor risk disease. We evaluated the efficacy and toxicity of ipi/nivo in older and frailer populations in a real-world mRCC cohort. Methods: Analysis was conducted on a real-world cohort with mRCC (N = 551) treated with first-line ipi/nivo from the Canadian Kidney Cancer information system (CKCis) database from January 2014 to December 2021. A comparison was made between outcomes and toxicity in patients 1. <70 versus (vs.) ≥70 yo, 2. <75 vs. ≥75 yo, and 3. KPS ≥70 vs. <70 yo. OS, progression-free survival (PFS), and time to treatment failure (TTF) were calculated by Kaplan–Meier analysis. Log-rank tests were used for comparison between groups. Results: Ipi/nivo treatment had no impact on survival outcomes or toxicity for patients >70 yo and >75 yo when controlled for IMDC. However, when comparing patients with KPS > 70 vs. KPS < 70, patients with a poor performance status had decreased median OS at 54.5 m vs. 10.8 m (p-value < 0.0001) and PFS at 11.6 vs. 3.1 m (p-value < 0.0001). Conclusions: The use of ipi/nivo in mRCC demonstrated similar survival outcomes and toxicity in an older patient population. In patients with a poor performance status, it was associated with inferior OS and PFS. We believe that ipi/nivo is a reasonable treatment option for these patient populations, particularly in older patients. Full article

Fully harnessing the inherent flexible adjustment potential of steel enterprises and fostering their interaction with the power grid is a crucial pathway to advancing green transformation. However, traditional research usually takes reducing energy consumption as the optimization goal, which limits the adjustment response capability, or ignores the storage and conversion constraints of secondary energy sources such as gas, steam, and electricity, making it difficult to fully explore and reasonably utilize the potential of multi-energy coordination. This study considers the production constraints of the surplus energy recovery and utilization system, establishes a collaborative scheduling model for a gas–steam–power system (GSPS) in an iron and steel enterprise, and proposes a demand response strategy that considers internal production constraints. Considering the time-of-use (TOU) tariff, iron and steel enterprises achieve a dynamic optimization adjustment range of electricity demand response through the conversion and storage process of gas, steam, and power. The adjustment capability of the GSPS reaches 26.94% of the initial electricity load, while reducing the total system energy cost by 2.24%. There is vast development potential of iron and steel enterprises participating in electricity demand response for promoting cost reduction and efficiency improvement, as well as enhancing the power grid flexibility. Full article

Universities hold and process vast amounts of financial, user, and research data, which makes them prime targets for cybercriminals. In addition to the usual external threat actors, universities face a unique insider threat from students, who—alongside staff—may lack adequate cyber security training despite having access to various sensitive systems. This paper provides a focused assessment of the current cyber security threats facing UK universities, based on a comprehensive review of available information. A chronological timeline of notable cyber attacks against universities is produced, with incidents classified according to the CIA triad (Confidentiality, Integrity, Availability) and incident type. Several issues have been identified. Limited disclosure of attack details is a major concern, as full information is often withheld for security reasons, hindering institutions’ abilities to assess vulnerabilities thoroughly and respond effectively. Additionally, universities increasingly rely on third-party service providers for critical services, meaning that an attack on these external providers can directly impact university operations and data security. While SQL injection attacks, previously a significant issue, appear to have declined in frequency—perhaps reflecting improvements in defences—other threats continue to persist. Universities report lower levels of concern regarding DDoS attacks, potentially due to enhanced resilience and mitigation strategies; however, ransomware and phishing attacks remain prevalent. Insider threats, especially from students with varied IT skills, exacerbate these risks, as insiders may unknowingly or maliciously facilitate cyber attacks, posing ongoing challenges for university IT teams. This study recommends that universities leverage these insights, along with other available data, to refine their cyber security strategies. Developing targeted policies, strengthening training, and implementing international standards will allow universities to enhance their security posture and mitigate the complex and evolving threats they face. Full article

In modern marine warfare, unmanned underwater vehicles (UUVs) have fast and efficient attack capabilities. However, existing research on UUV attack strategies is relatively limited, often ignoring the requirement for the effective allocation of different strategic value areas, which restricts its performance in the marine combat environment. To this end, this paper proposes an innovative UUV task allocation and saturation attack strategy. The strategy first divides the area according to the distribution density of enemy UUVs, and then reasonably allocates tasks according to the enemy’s regional value and the attack capability of our UUVs. Our UUVs then sail to the enemy area and are evenly distributed in the encirclement to ensure accurate saturation attacks. In the task allocation link, the grey wolf optimizer is improved by introducing Logistic chaos mapping and differential evolution mechanism, which improves the search efficiency and allocation accuracy. At the same time, the combination of the optimal matching algorithm and Bezier curve dynamic path control ensures the accuracy and flexibility of a coordinated attack. The simulation experimental results show that the strategy shows high attack efficiency and practicality in marine combat scenarios, providing an effective solution for UUV attack tasks in complex marine environments. Full article