In this study, we propose an eco-friendly method for synthesizing cerium tungstate nanoparticles using hydrothermal techniques. We used scanning, transmission electron microscopy, and X-ray diffraction to analyze the morphology of the synthesized nanoparticles. The results showed that the synthesized nanoparticles were uniform and highly crystalline, with a particle size of about 50 nm. The electrocatalytic properties of the nanoparticles were then investigated using cyclic voltammetry and electrochemical impedance spectroscopy. We further used the synthesized nanoparticles to develop an electrochemical sensor based on a carbon paste electrode that can detect hydroquinone. By optimizing the differential pulse voltammetric method, a wide linearity range of 0.4 to 45 µM and a low detection limit of 0.06 µM were obtained. The developed sensor also expressed excellent repeatability (RSD up to 3.8%) and reproducibility (RSD below 5%). Interferences had an insignificant impact on the determination of analytes, making it possible to use this method for monitoring hydroquinone concentrations in tap water. This study introduces a new approach to the chemistry of materials and the environment and demonstrates that a careful selection of components can lead to new horizons in analytical chemistry.

Does climate change influence if societies will be better or worse equipped to reduce climatic risks in the future? A society’s adaptive capacity determines whether the potential of adaptation to reduce risks will be realized. Assumptions about the level of adaptive capacity are inherently made when the potential for adaptation to reduce risks in the future and resultant levels of risk are estimated. In this review, we look at the literature on human impacts of climate change through the lens of adaptive capacity. Building on evidence of impacts on financial resources as presented in the Working Group 2 (WG2) report of the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6), we here present the methodology behind this review and complement it with an analysis of climatic risks to human resources. Based on our review, we argue that climate change itself adds to adaptation constraints and limits. We show that for more realistic assessments of sectoral climate risks, assumed levels of future adaptive capacity should — and can — be usefully constrained in assessments that rely on expert judgment, and propose avenues for doing so.

This article proposes a new framework of aerial reconfigurable intelligent surface (ARIS) enhancing the nonorthogonal multiple access (NOMA) system. The base station (BS) transmits superimposed signals to multiple users with different channel gains through ARIS which can flexibly change channel conditions and perform intelligent NOMA operations. It ensures that our system can perform well in providing services to multiple users simultaneously. In this system, the placement of the unmanned aerial vehicle (UAV) is jointly optimized along with the AIRS passive beam and the multiuser power allocation in order to maximize the communication sum rate. Since the joint optimization problem is nonconvex and coupled, it is hence disintegrated into three subproblems and it is solved alternately through the successive convex approximation (SCA). Moreover, semi definite programming (SDP) is used to deal with the rank one constraint of RIS reflection matrix and comparisons are made using particle swarm optimization (PSO). The numerical results show that the proposed ARIS-NOMA framework can achieve better sum rate performance than traditional NOMA with fixed RIS and OMA-ARIS.

Abstract Lipofibromatosis (LF) is a rare benign fibrofatty tumor of infancy and childhood with a predilection for distal extremities, poor margination, and a high local recurrence rate. We report a toddler who presented with an LF involving her right labiocrural fold. Imaging showed a soft tissue mass extending through the right labiocrural fold with possible infiltration into the underlying muscles. The mass was excised entirely, preserving adjacent structures. The histopathologic report revealed the mass to be LF. A 3-year follow-up revealed no disease recurrence. No other cases of LF in this localization have been presented in the literature. Despite its rarity, LF should be considered in diagnosing soft tissue neoplasms in children. Accurate diagnosis and proper surgical management with complete resection are essential to reduce the postoperative recurrence risk.

Introduction Inflammation can arise as a consequence of both extracorporeal shock wave lithotripsy (ESWL) and ureteroscopy (URS) treatments. Alterations in inflammatory parameters may serve as indicators of kidney injuries and the ensuing inflammation. This study aims to investigate the effects of ESWL and URS procedures on inflammatory parameters for proximal ureteral stone treatment. Materials and methods A prospective interventional study comprised 120 patients with confirmed stones measuring less than 10 mm in the upper half of the proximal ureter. These patients were randomly assigned to either the ESWL or URS treatment groups. Laboratory analyses encompassed interleukin-6 (IL-6), leukocyte count, fibrinogen levels, and erythrocyte sedimentation rate (ESR), which were assessed prior to the intervention, on the first postoperative day, and six months later. IL-6 levels in the serum were determined using a chemiluminescence immunoassay (CLIA). Results There was no significant difference in IL-6 levels between pre-intervention and the first post-intervention day in patients treated with ESWL (1.8 (1.4-2.59) pg/mL vs. 2.33 (1.22-3.19) pg/mL). However, for patients treated with URS, the pre-intervention IL-6 value was 2.9 (1.9-3.34) pg/mL, and it increased significantly to 7.1 (3.85-28.07) pg/mL on the first post-intervention day (p<0.001). On the first post-intervention day, levels of IL-6, CRP, leukocyte count, and ESR were significantly higher in patients treated with URS compared to ESWL (p<0.001; p<0.001; p=0.03; p=0.03, respectively). Conclusion Our research findings suggest that monitoring IL-6 levels can offer valuable insights into the degree of inflammation and tissue damage during and following observed procedures, particularly among patients undergoing URS, even within the initial days post-procedure.

The cryptocurrency market has attracted considerable attention from investors and researchers alike. This paper examines the volatility patterns of two major cryptocurrencies utilizing GARCH modeling: Bitcoin, based on a proof-of-work mechanism, and Cardano, operating on a proof-of-stake mechanism. Our findings reveal differences in the volatility structures of the two cryptocurrencies, with Cardano demonstrating a reduced long-term volatility compared to Bitcoin. This study suggests that transitioning from proof-of-work to proof-of-stake mechanisms might lead to a decrease in market volatility.

The increasing prevalence of colon and lung cancer presents a considerable challenge to healthcare systems worldwide, emphasizing the critical necessity for early and accurate diagnosis to enhance patient outcomes. The precision of diagnosis heavily relies on the expertise of histopathologists, constituting a demanding task. The health and well‐being of patients are jeopardized in the absence of adequately trained histopathologists, potentially leading to misdiagnoses, unnecessary treatments, and tests, resulting in the inefficient utilization of healthcare resources. However, with substantial technological advancements, deep learning (DL) has emerged as a potent tool in clinical settings, particularly in the realm of medical imaging. This study leveraged the LC25000 dataset, encompassing 25,000 images of lung and colon tissue, introducing an innovative approach by employing a self‐organized operational neural network (Self‐ONN) to accurately detect lung and colon cancer in histopathology images. Subsequently, our novel model underwent comparison with five pretrained convolutional neural network (CNN) models: MobileNetV2‐SelfMLP, Resnet18‐SelfMLP, DenseNet201‐SelfMLP, InceptionV3‐SelfMLP, and MobileViTv2_200‐SelfMLP, where each multilayer perceptron (MLP) was replaced with Self‐MLP. The models’ performance was meticulously assessed using key metrics such as precision, recall, F1 score, accuracy, and area under the receiver operating characteristic (ROC) curve. The proposed model demonstrated exceptional overall accuracy, precision, sensitivity, F1 score, and specificity, achieving 99.74%, 99.74%, 99.74%, 99.74%, and 99.94%, respectively. This underscores the potential of artificial intelligence (AI) to significantly enhance diagnostic precision within clinical settings, portraying a promising avenue for improving patient care and outcomes. The synopsis of the literature provides a thorough examination of several DL and digital image processing methods used in the identification of cancer, with a primary emphasis on lung and colon cancer. The experiments use the LC25000 dataset, which consists of 25,000 photos, for the purposes of training and testing. Various techniques, such as CNNs, transfer learning, ensemble models, and lightweight DL architectures, have been used to accomplish accurate categorization of cancer tissue. Various investigations regularly show exceptional performance, with accuracy rates ranging from 96.19% to 99.97%. DL models such as EfficientNetV2, DHS‐CapsNet, and CNN‐based architectures such as VGG16 and GoogleNet variations have shown remarkable performance in obtaining high levels of accuracy. In addition, methods such as SSL and lightweight DL models provide encouraging outcomes in effectively managing large datasets. In general, the research emphasizes the efficacy of DL methods in successfully diagnosing cancer from histopathological pictures. It therefore indicates that DL has the potential to greatly improve medical diagnostic techniques.

Abstract Objective. Studies that have evaluated correlation between body mass index (BMI) and novel lipid indices such as triglycerides (TG)/high-density lipoprotein-cholesterol (HDL-C), total cholesterol (TC)/HDL-C, and low-density lipoprotein cholesterol (LDL-C)/HDL-C in type 2 diabetes mellitus (T2DM) are scarce. Hence, the aim of the present study was to explore the correlation between BMI and novel lipid indices in Bosnian patients with T2DM. Methods. Present study included 117 patients with T2DM (mean age: 66.51 years) and 68 controls (mean age: 68.37 years). BMI was calculated as weight/height². Lipids were measured by standard methods. TG/HDL-C, TC/HDL-C, and LDL-C/HDL-C ratios were separately calculated. The differences between the groups were assessed by Student’s t-test or Man Whitney U test. Correlations were determined by Spearman’s test. Results. In a total sample of T2DM patients, 41.0% were overweight and 44.4% were obese. In the control group, 51.5% of subjects were overweight and 25.0% were obese. In T2DM group, a significant correlation was observed between BMI and HDL-C, LDL-C, TG/HDL, TC/HDL-C, and LDL-C/HDL-C ratios. In the control group, there was a significant correlation found between BMI and HDL-C, TG, TG/HDL, TC/HDL-C, and LDL-C/HDL-C-ratios. Correlation between BMI and other lipid parameters in T2DM and the control group was not determined. Conclusion. The present study showed significant correlation between BMI and novel lipid indices in both T2DM patients and the control group of subjects. Possible explanation for the observed results might be prevalence of overweight and obese participants in this study sample. Since novel lipid indices are used in the prediction of cardiometabolic risk, results obtained in the present study have valuable clinical implications.

Abstract The impact of logistics performance in the era of sustainable mobility on the overall economic development of a country is inevitable. It can even be said to represent an extremely important component in identifying economic conditions and provides the possibility of defining adequate strategies. In this article, the evaluation of the member countries of the European Union was carried out on the basis of the logistics performance index (LPI) according to the latest report of the World Bank (WB). A unique and original Multiple-Criteria Decision Making (MCDM) approach has been created, and it involves the application of four methods: Criteria Importance Through Intercriteria Correlation, Method based on the Removal Effects of Criteria, and Entropy and Fuzzy ROV (Range of Value). The weighting coefficients of six factors were obtained with the first three methods in crisp form, so they were converted into Triangular Fuzzy Number. The Fuzzy ROV method has been created for the first time in the literature and represents a great contribution from the methodological aspect. The results of the developed model and the applied steps show that there are certain differences in the rankings compared to the World Bank report, with a note that the best-ranked countries have maintained their positions. In addition, verification tests of the originally obtained results were created, with an emphasis on the importance of evaluation parameter values and their impact on the LPI ranking.

Financial performance analysis is of vital importance those involved in a business (e.g., shareholders, creditors, partners, and company managers). An accurate and appropriate performance measurement is critical for decision-makers to achieve efficient results. Integrated performance measurement, by its nature, consists of multiple criteria with different levels of importance. Multiple Criteria Decision Analysis (MCDA) methods have become increasingly popular for solving complex problems, especially over the last two decades. There are different evaluation methodologies in the literature for selecting the most appropriate one among over 200 MCDA methods. This study comprehensively analyzed 41 companies traded on the Borsa Istanbul Corporate Governance Index for 10 quarters using SWARA, CRITIC, and SD integrated with eight different MCDA method algorithms to determine the position of Turkey's most transparent companies in terms of financial performance. In this study, we propose "stock returns" as a benchmark in comparing and evaluating MCDA methods. Moreover, we calculate the "rank reversal performance of MCDA methods". Finally, we performed a "standard deviation" analysis to identify the objective and characteristic trends for each method. Interestingly, all these innovative comparison procedures suggest that PROMETHEE II (preference ranking organization method for enrichment of evaluations II) and FUCA (Faire Un Choix Adéquat) are the most suitable MCDA methods. In other words, these methods produce a higher correlation with share price; they have fewer rank reversal problems, the distribution of scores they produce is wider, and the amount of information is higher. Thus, it can be said that these advantages make them preferable. The results show that this innovative methodological procedure based on 'knowledge discovery' is verifiable, robust and efficient when choosing the MCDA method.

In the public safety sector, 5G offers immense opportunities for enhancing mission-critical services by provisioning virtualized service functions at the network edge, which enables achieving high reliability and low-latency. One of these mission-critical services is Back Situation Awareness (BSA) that supports Emergency Vehicles (EmVs) by increasing awareness about them on the roads. In this article, we introduce an on-demand BSA application service, which has been developed for multi-domain Multi-Access Edge Computing (MEC) systems, enabling early notification for vehicles on the Estimated Time of Arrival (ETA) of an approaching EmV. The state-of-the-art approaches inform civilian vehicles about EmVs only when they are in a close proximity (up to 300 m). However, in some situations (e.g., in congested areas), this may not be enough for the civilian vehicles to safely and timely maneuver out of the lane of an EmV. Our approach is, to the best of our knowledge, a unique way to significantly extend this awareness by creating an orchestrated 5G-based MEC deployment of BSA application service on optimally selected edges, thereby stretching over multiple edge domains and even countries. While consuming the real-time location, speed, and heading of an EmV, such application service affords the drivers with sufficient time to create a clear corridor, allowing the EmV to pass through unhindered in a safe manner thereby increasing the mission success. The detailed design and the performance analysis of the BSA application service that has been created following modern cloud-native principles based on Docker and Kubernetes, is presented in terms of the impact of emergency scale on the MEC system resources and service response time. Moreover, we also introduce a metric called panic indicator, which depicts how the proposed BSA service can potentially help in enabling drivers to calmly maneuver out of the path of an EmV, thereby increasing road safety.

Introduction: There is considerable concern with rising rates of antimicrobial resistance (AMR) with its subsequent impact on morbidity, mortality and costs. In low- and middle-income countries, a key driver of AMR is the appreciable misuse of antibiotics in ambulatory care, which can account for up to 95% of human utilisation. A principal area is the selling of antibiotics without a prescription. There is conflicting evidence in South Africa regarding this practice alongside rising AMR rates. Consequently, there is a need to explore this further, especially in more rural areas of South Africa. A pilot study was undertaken to address this. Materials and Methods: A two-step descriptive approach involving a self-administered questionnaire amongst pharmacists and their assistants followed by cognitive interviews with some of the participants. Results: Twenty-one responses were obtained from nine of the 11 community pharmacies invited to participate. Participating pharmacies were all independently owned. Ten of the 21 participants admitted dispensing antibiotics without a prescription, including both adults and children, representing five of the nine participating pharmacies. A minority dispensed antibiotics before recommending suitable over-the-counter medicines. These high rates were exacerbated by patient pressure. There were issues with the length of the questionnaire and some of the phraseology, which will be addressed in the main study. Conclusion: There were concerns with the extent of purchasing antibiotics without a prescription in this pilot in South Africa study. Key issues will be explored further in the main study.