Within the context of global initiatives to address climate change, the shipping industry is facing increasingly intensified pressure to decarbonize. The industry is engaging in the exploration and implementation of greenhouse gas (GHG) emission reduction measures, including energy efficiency technologies and alternative fuels, with the objective of accelerating the progression towards greenhouse gas mitigation. The application of various GHG emission reduction measures usually requires different levels of investment costs, and economic feasibility is a key factor influencing policy formulation and investment decisions. In this regard, this paper developed a cost-effective model for energy efficiency measures and alternative fuels based on the marginal abatement cost (MAC) methodology. This model can distinguish the differences between energy efficiency measures and alternative fuels in terms of Tank-to-Wake emissions and Well-to-Wake emissions in the GHG emission evaluation system. By taking typical ship types with significant emission contributions as study cases, i.e., bulk carriers (61–63K DWT), container ships (8000 TEU), product tankers (115K DWT), crude oil tankers (315–320K DWT), and Ro-Ro passenger ferries (3500 DWT), the GHG abatement cost-effective performance of major categories of measures such as operational measures, technical measures, renewable energy sources, and alternative fuels were calculated. According to the MAC results, the marginal abatement cost curves were plotted based on the ranking of energy efficiency measures and alternative fuels, respectively. The impacts of bunker fuel prices and carbon market prices on the cost-effectiveness were analyzed. The research results provided the GHG abatement potential of the integrated application of cost-effective energy efficiency measures, the cost-effectiveness ranking of alternative fuels, and the carbon emission price expected to bridge the price gap between alternative fuels and conventional bunker fuel. The presented methodology and conclusions can be used to assist shipping companies in selecting emission reduction measures, and to support maritime authorities in developing market-based measures. Full article

Short-range elastic backscatter lidar (SR-EBL) systems are remote sensing instruments for studying low atmospheric boundary layer processes. This work presents a field campaign oriented to filling the gap between the near-surface aerosol processes regarding aerosol radiative properties and connecting them with the atmospheric boundary layer (ABL), centering attention on the residual layer and the ABL transition periods. A Colibri Aerosol Lidar (CAL) instrument, based on the short-range lidar with high spatio-temporal resolution, was used for the first time in the ACTRIS AGORA facility (Andalusian Global Observatory of the Atmosphere) in Granada (Spain). This study showed the possibility of combining lidar and in situ measurements in the lowermost 150 m. The results address, on the one hand, the characterization of the short-range lidar for developing a method to find the calibration constant of the system and to correct the incomplete overlap to further data exploitation. On the other hand, relevant radiative properties such as the temporal series of the aerosol lidar ratio and extinction coefficient were quantified. The campaign was divided in three different periods based on the vehicular emission peak in the early mornings, namely, before, during, and after the emission peak. For before and after the emission peak data classification, aerosol properties presented closer values; however, large variability was obtained after the emission peak reaching the maximum values of extinction and a lidar ratio up to 51.5 ± 11.9 ${\left(\mathrm{Mm}\right)}^{-1}$ and 36.0 ± 10.5 sr, respectively. During the emission peaks, the values reached for extinction and lidar ratio were up to 136.8 ± 26.5 ${\left(\mathrm{Mm}\right)}^{-1}$ and 119.0 ± 22.7 sr, respectively. Full article

Neuroblastoma presents with two patterns of disease: locoregional or systemic. The poor prognostic risk factors of locoregional neuroblastoma (LR-NB) include age, MYCN or MDM2-CDK4 amplification, 11q, histology, diploidy with ALK or TERT mutations, and ATRX aberrations. Anti-GD2 immunotherapy has significantly improved the outcome of high-risk (HR) NB and is mostly effective against osteomedullary minimal residual disease (MRD), but less so against soft tissue disease. The question is whether adding anti-GD2 monoclonal antibodies (mAbs) benefits patients with HR-NB compounded by only soft tissue. We reviewed 31 patients treated at SJD for HR-NB with no osteomedullary involvement at diagnosis. All tumors had molecular genetic features of HR-NB. The outcome after first-line treatment showed 25 (80.6%) patients achieving CR. Thirteen patients remain in continued CR, median follow-up 3.9 years. We analyzed whether adding anti-GD2 immunotherapy to first-line treatment had any prognostic significance. The EFS analysis using Cox models showed a HR of 0.20, p = 0.0054, and an 80% decrease in the risk of relapse in patients treated with anti-GD2 immunotherapy in the first line. Neither EFS nor OS were significantly different by CR status after first-line treatment. In conclusion, adding treatment with anti-GD2 mAbs at the stage of MRD helps prevent relapse that unequivocally portends poor survival. Full article

With a view to maintaining the reputation of wine-producing regions among consumers, minimising economic losses caused by wine fraud, and achieving the purpose of data-driven terroir classification, the use of an absorbance–transmission and fluorescence excitation–emission matrix (A-TEEM) technique has shown great potential based on the molecular fingerprinting of a sample. The effects of changes in wine composition due to ageing and the stability of A-TEEM models over time had not been addressed, however, and the classification of wine blends required investigation. Thus, A-TEEM data were combined with an extreme gradient boosting discriminant analysis (XGBDA) algorithm to build classification models based on a range of Shiraz research wines (n = 217) from five Barossa Valley sub-regions over four vintages that had aged in bottle for several years. This spectral fingerprinting and machine learning approach revealed a 100% class prediction accuracy based on cross-validation (CV) model results for vintage year and 98.8% for unknown sample prediction accuracy when splitting the wine samples into training and test sets to obtain the classification models. The modelling and prediction of sub-regional production area showed a class CV prediction accuracy of 99.5% and an unknown sample prediction accuracy of 93.8% when modelling with the split dataset. Inputting a sub-set of the current A-TEEM data into the models generated previously for these Barossa sub-region wines yielded a 100% accurate prediction of vintage year for 2018–2020 wines, 92% accuracy for sub-region for 2018 wines, and 91% accuracy for sub-region using 2021 wine spectral data that were not included in the original modelling. Satisfactory results were also obtained from the modelling and prediction of blended samples for the vintages and sub-regions, which is of significance when considering the practice of wine blending. Full article

Recovering cobalt from waste batteries is crucial for resource recycling and environmental protection. Here, MOF-OH, a Zr-based MOF, was synthesized and merged into a polyacrylonitrile (PAN) matrix to create MOF-OH-PAN nanofibers (NFs). These NFs showed a high cobalt ion adsorption capacity of 33.1 mg/g, retaining over 90% of the capacity after six cycles. The adsorption mechanism involves Co(II) surface diffusion followed by strong bonding with functional groups. This technology enables efficient cobalt recovery from waste batteries, supporting reuse and reducing resource depletion and environmental pollution. The study provides insights into waste battery resource management, highlighting environmental and economic benefits and contributing to green resource recovery and circular economy initiatives. Full article

The utilization of Effective Microorganisms (EMs) for lake restoration represents a sustainable approach to enhancing water quality and rebalancing the ecology of aquatic ecosystems. The primary objective of this study was to evaluate the effects of two bioremediation treatment cycles employing EM-enriched biopreparations on water quality in the Siemiatycze lakes. Specifically, this research analyzed various parameters, including dissolved oxygen, transparency, chlorophyll-a, pH, chemical oxygen demand (COD), biochemical oxygen demand (BOD5), total phosphorus, total nitrogen, and suspended matter (SM), across eleven designated sampling locations. Additionally, this study employed remote sensing techniques, leveraging Sentinel-2 satellite imagery and the Maximum Chlorophyll Index (MCI), to detect and quantify algal blooms, with a particular focus on elevated chlorophyll-a concentrations. This comprehensive approach aimed to provide a holistic understanding of the impact of biotechnological reclamation on aquatic ecosystem restoration and sustainability. The study’s findings indicated a significant improvement in water quality in all lakes, with enhanced water clarity and oxygen profiles. Further, remote sensing studies indicated a reduction in algal blooms, particularly those with high chlorophyll-a concentrations. A considerable decrease in water eutrophication intensity was observed due to diminished nutrient concentrations. The improvements in water parameters are likely to enhance the living conditions of aquatic organisms. These results demonstrate the effectiveness of using EM-enriched biopreparations in the bioremediation of lakes, providing a sustainable approach to enhancing water quality and balancing aquatic ecosystems. Full article

The treatment of large amounts of copper slag is an unavoidable issue resulting from the high demand for copper during the global transition to a sustainable development path. Metal-rich copper slag might serve as a potential source of metals through secondary recovery. In this study, two copper slags (CS1 and CS2) with different metallurgical properties were characterized, focusing on secondary metal recovery. The X-ray diffraction (XRD) results show that fayalite (Fe₂SiO₄) and magnetite (Fe₃O₄) were the main crystalline phases in both CS1 and CS2. In addition, CS2 exhibited a more stable amorphous silicate network than CS1, which was attributed to the differences in the content of Si-O-3NBO linkages. The sequential extraction of Zn, Cu, Fe, and Pb from the slags was also explored, with the Cu content in CS1 being substantially lower than that in CS2. All metals were distributed in the F5 residue fraction. Cu was the most mobile metal as a result of the large proportion of soluble fractions (F1–F3), followed by Zn and Fe. This study explored the chemical speciation of Zn, Cu, Fe, and Pb from copper slags, which has practical implications for secondary metal recovery from such materials. Full article

Lung diseases are the third-leading cause of mortality in the world. Due to compromised lung function, respiratory difficulties, and physiological complications, lung disease brought on by toxic substances, pollution, infections, or smoking results in millions of deaths every year. Chest X-ray images pose a challenge for classification due to their visual similarity, leading to confusion among radiologists. To imitate those issues, we created an automated system with a large data hub that contains 17 datasets of chest X-ray images for a total of 71,096, and we aim to classify ten different disease classes. For combining various resources, our large datasets contain noise and annotations, class imbalances, data redundancy, etc. We conducted several image pre-processing techniques to eliminate noise and artifacts from images, such as resizing, de-annotation, CLAHE, and filtering. The elastic deformation augmentation technique also generates a balanced dataset. Then, we developed DeepChestGNN, a novel medical image classification model utilizing a deep convolutional neural network (DCNN) to extract 100 significant deep features indicative of various lung diseases. This model, incorporating Batch Normalization, MaxPooling, and Dropout layers, achieved a remarkable 99.74% accuracy in extensive trials. By combining graph neural networks (GNNs) with feedforward layers, the architecture is very flexible when it comes to working with graph data for accurate lung disease classification. This study highlights the significant impact of combining advanced research with clinical application potential in diagnosing lung diseases, providing an optimal framework for precise and efficient disease identification and classification. Full article

Lactic acid bacteria (LAB) are commonly consumed as probiotics to improve gut barrier function and boost the immune system. This study aimed to screen LAB with high immunomodulatory activity using RAW264.7 macrophages. According to the results, Limosilactobacillus reuteri AUc2301 was selected from 84 screened strains that can stimulate RAW264.7 cell proliferation. Limosilactobacillus reuteri AUc2301 significantly enhanced the phagocytosis activity of RAW264.7 cells. In the ELISA test, Limosilactobacillus reuteri AUc2301 significantly promoted the release of interleukin-6, IL-1β, the tumor necrosis factor, and nitric oxide in RAW264.7 macrophages. In addition, Limosilactobacillus reuteri AUc2301 significantly inhibited the excessive release of IL-6, IL-1β, TNF-α, prostaglandin E2 as well as NO and the high expression of cyclooxygenase-2 in RAW264.7 macrophages induced by lipopolysaccharide. In further mechanism studies, Limosilactobacillus reuteri AUc2301 could regulate the nuclear factor-κB signaling pathway in RAW264.7 macrophages. Collectively, the screened Limosilactobacillus reuteri AUc2301 showed good immunomodulatory activity in vitro, and it has the potential to be developed as a novel probiotic. Full article

Throughout the smart city literature, there are mentions of capacities, the application of which is claimed to result in the sustainable achievement of objectives. Because of the often desperate need for smart city objectives to be met, we sought to understand which were the capacities and whether the components of these capacities are explained sufficiently for them to be effective in practice. We applied a four-stage methodology commencing with a search of multiple databases for smart city capacity knowledge. We next assembled the evidence from the items identified in that search using a thematic analysis that identified the capacity to exploit technology, innovate, collaborate, and orchestrate. Next, we followed the threads of knowledge, iteratively allocating the knowledge to each of the four capacities to a typology of what, why, and who. The fourth stage was a cross-capacity analysis that generated further refinement and identified important factors. We identified that capacities are not sufficiently explained. In addition to the need for more levels of detail as to practical implementation, we identified significant underdevelopment of the literature as to the impact of institutional complexity and the influence of stakeholders. We propose research directed at increasing the effectiveness of capacities, define the concept of smart city capacities, propose a framework of the components of capacities, and draw on established stakeholder theory to create a stakeholder influence research framework. Full article

Vitis vinifera L. subsp. sylvestris (sylvestris) is the only native wild grapevine in Eurasia (Europe and western Asia) and is the existing ancestor of the grapevine varieties (for wine and table grape production) belonging to the subsp. sativa. In Slovenia, the prevailing opinion has been that there are no Slovenian sylvestris habitats. This study describes sylvestris in Slovenia for the first time and aims to present an overview of the locations of the wild grapevine in the country. In this project, a sample set of 89 accessions were examined using 24 SSR and 2 SSR markers plus APT3 markers to determine flower sex. The accessions were found in forests on the left bank of the Sava River in Slovenia, on the border between alluvial soils and limestone and dolomite soils, five different sites, some of which are described for the first time. The proportion of female to male accessions differed between sites. At two sites, female plants dominated; at others, the ratio was balanced. The plants’ genetic diversity and structure were compared with autochthonous and unique varieties of subsp. sativa from old vineyards in Slovenia and with rootstocks escaped from nature from abandoned vineyards. Sylvestris was clearly distinguishable from vinifera and the rootstocks. Based on genetic analyses, it was confirmed that Slovenian sylvestris is closest to the Balkan and German sylvestris groups. Meanwhile, a safety duplication of the wild grapevine accessions has been established at the University Centre of Viticulture and Enology Meranovo, Faculty of Agriculture and Life Sciences at the University of Maribor. Full article

Machine safety is not only a prerequisite for successful production but also the foundation for the sustainability and growth of any manufacturing organization. The latest approaches in this rapidly developing field integrate effective risk management tools and strategies into occupational health and safety (OHS) management systems. The study, through a real example from practice, describes the use of the analytic hierarchy process (AHP) method for machine safety improvement, considering the possible types of losses. Classification and Regression Tree Analysis (CART) was applied to assess the efficiency, cost-effectiveness, and, therefore, the overall sustainability level of the relevant safety measures. These were proposed risk reduction measures that typically raised uncertainty among managers regarding the estimation of cost-effectiveness. The advantage of the application decision tree approach is the possibility to identify and establish relatively homogeneous groups of undesirable events and their impact on the organization’s objectives. A comprehensive model has been developed to support management decision making in manufacturing organizations towards implementing and improving safety measures in line with manufacturing sustainability goals. Full article

Influenza A virus (IAV) continues to pose serious threats to the global animal industry and public health security. Identification of critical host factors engaged in the life cycle of IAV and elucidation of the underlying mechanisms of their action are particularly important for the discovery of potential new targets for the development of anti-influenza drugs. Herein, we identified Hydroxyacyl-CoA Dehydratase 3 (HACD3) as a new host factor that supports the replication of IAV. Downregulating the expression of HACD3 reduced the level of viral PB1 protein in IAV-infected cells and in cells that were transiently transfected to express PB1. Silencing HACD3 expression had no effect on the level of PB1 mRNA but could promote the lysosome-mediated autophagic degradation of PB1 protein. Further investigation revealed that HACD3 interacted with PB1 and selective autophagic receptor SQSTM1/p62, and HACD3 competed with SQSTM1/p62 for the interaction with PB1, which prevented PB1 from SQSTM1/p62-mediated autophagic degradation. Collectively, these findings establish that HACD3 plays a positive regulatory role in IAV replication by stabilizing the viral PB1 protein. Full article

In civil aviation, severe weather conditions such as strong wind shear, crosswinds, and thunderstorms near airport runways often compel pilots to abort landings to ensure flight safety. While aborted landings due to wind shear are not common, they occur under specific environmental and situational circumstances. This research aims to accurately predict aircraft aborted landings using three advanced deep learning techniques: the conventional deep neural network (DNN), the deep and cross network (DCN), and the wide and deep network (WDN). These models are supplemented by various data augmentation methods, including the Synthetic Minority Over-Sampling Technique (SMOTE), KMeans-SMOTE, and Borderline-SMOTE, to correct the imbalance in pilot report data. Bayesian optimization was utilized to fine-tune the models for optimal predictive accuracy. The effectiveness of these models was assessed through metrics including sensitivity, precision, F1-score, and the Matthew Correlation Coefficient. The Shapley Additive Explanations (SHAP) algorithm was then applied to the most effective models to interpret their results and identify key factors, revealing that the intensity of wind shear, specific runways like 07R, and the vertical distance of wind shear from the runway (within 700 feet above runway level) were significant factors. The results of this research provide valuable insights to civil aviation experts, potentially revolutionizing safety protocols for managing aborted landings under adverse weather conditions, thereby improving overall airport efficiency and safety. Full article

To investigate the impact of Al and Nb elements on the formation of a protective oxide layer on the surface of Fe-35Ni-20Cr-xAl-yNb (x = 0, 2, 4, 6 wt.%; y = 0, 1, 2 wt.%) alloys, their oxidation behavior was examined at 1000 °C, 10⁻¹⁷ atm. and 10⁻²⁵ atm. oxygen pressure, and the oxidation mechanism was analyzed by Factsage and Pandat calculations. Enhancing the Al content at 10⁻¹⁷ atm. inhibited the generation of FeCr₂O₄ on the alloy surface and increased the Al content in the M₂O₃ layer. When the Al content exceeded 6 wt.%, the oxide film partially peeled off. It was found that the addition of Nb increased the activity of Cr and Al and decreased the activity of Ni and Fe and promoted the formation of Al₂O₃, and the appearance of Nb₂O₅ in the subsurface layer increased the density of the oxide film. In addition, under an oxygen pressure of 10⁻²⁵ atm., the only protective layer on the surface of the alloy comprised of Al₂O₃. The experimental results demonstrated that the Fe-35Ni-20Cr-4Al-2Nb alloy generated a continuous and dense Al₂O₃ protective film, and the reduction in oxygen pressure and the addition of Nb elements were favorable for selective external oxidation of Al₂O₃. Full article

Fractional integro-differential equations (FIDEs) of both Volterra and Fredholm types present considerable challenges in numerical analysis and scientific computing due to their complex structures. This paper introduces a novel approach to address such equations by employing a Cubic B-spline collocation method. This method offers a robust and systematic framework for approximating solutions to the FIDEs, facilitating precise representations of complex phenomena. Within this research, we establish the mathematical foundations of the proposed scheme, elucidate its advantages over existing methods, and demonstrate its practical utility through numerical examples. We adopt the Caputo definition for fractional derivatives and conduct a stability analysis to validate the accuracy of the method. The findings showcase the precision and efficiency of the scheme in solving FIDEs, highlighting its potential as a valuable tool for addressing a wide array of practical problems. Full article

Monotonic circuits are a class of input–output mode (IOM) asynchronous circuits that are relaxed compared to quasi-delay-insensitive (QDI) IOM asynchronous circuits in terms of signaling the completion of internal processing. Some recent works have demonstrated the superiority of monotonic logic over QDI logic for arithmetic circuits such as adders and multipliers. This paper presents a new monotonic asynchronous two-bit full adder (TFA) that can be duplicated and cascaded to form a ripple-carry adder (RCA). While an RCA is a slow adder with respect to synchronous design, with respect to IOM asynchronous design an RCA is a noteworthy adder since it has perhaps the least reverse latency that is not attainable through other IOM asynchronous adders. Conventionally, an RCA is constructed via a cascade of one-bit full adders (OFAs). An OFA adds two input bits along with any carry input and produces a sum bit and any carry output. On the other hand, a TFA simultaneously adds two pairs of input bits along with any carry input and produces two sum bits and any carry output. Using our proposed monotonic TFA, we realized an RCA to compare its performance with RCAs constructed using different asynchronous OFAs, and RCAs constructed using existing TFAs. We considered the popular delay-insensitive dual-rail scheme for encoding the adder inputs and outputs, and two 4-phase handshake protocols, namely return-to-zero handshaking (R0H) and return-to-one handshaking (R1H) for communication separately. We used a 28 nm CMOS process for implementation and considered a 32-bit addition as an example. Based on the design metrics estimated, the following inferences were derived: (i) compared to the RCA using the state-of-the-art monotonic OFA, the RCA incorporating the proposed TFA achieved a 26% reduction in cycle time for R0H and a 28.5% reduction in cycle time for R1H while dissipating almost the same power; the cycle time governs the data application rate in an IOM asynchronous circuit, and (ii) compared to the RCA comprising an early output QDI TFA, the RCA incorporating the proposed TFA achieved a 22.3% reduction in cycle time for R0H and a 25.4% reduction in cycle time for R1H while dissipating moderately less power. Also, compared to the existing early output QDI TFA, the proposed TFA occupies 40.9% less area for R0H and 42% less area for R1H. Full article

In this paper, we introduce Wide-TSNet, a novel hybrid approach for predicting Bitcoin prices using time-series data transformed into images. The method involves converting time-series data into Markov transition fields (MTFs), enhancing them using histogram equalization, and classifying them using Wide ResNets, a type of convolutional neural network (CNN). We propose a tripartite classification system to accurately represent Bitcoin price trends. In addition, we demonstrate the effectiveness of Wide-TSNet through various experiments, in which it achieves an Accuracy of approximately 94% and an F1 score of 90%. It is also shown that lightweight CNN models, such as SqueezeNet and EfficientNet, can be as effective as complex models under certain conditions. Furthermore, we investigate the efficacy of other image transformation methods, such as Gramian angular fields, in capturing the trends and volatility of Bitcoin prices and revealing patterns that are not visible in the raw data. Moreover, we assess the effect of image resolution on model performance, emphasizing the importance of this factor in image-based time-series classification. Our findings explore the intersection between finance, image processing, and deep learning, providing a robust methodology for financial time-series classification. Full article

Estimating soil erodible fraction based on basic soil properties in arid lands is a valuable research topic in the field of soil science and land management. The Proximal Sensing (PS) technique offers a non-destructive and efficient method to assess wind erosion potential in arid regions. By using Partial Least Squares Regression (PLSR) and Support Vector Machine (SVM) models and combining soil texture and chemical properties, determined through Visible-Near Infrared (vis-NIR) spectroscopy in 96 soil samples, this study aims to predict soil erodibility, soil organic matter (SOM), and calcium carbonate equivalent (CaCO₃) in arid lands located in Elkobaneyya Valley, Aswan Governorate, Egypt. Results showed that the soil erodibility fraction (EF-Factor) had the highest values and possessed a strong relationship between slope and SOM of 0.01% in determining soil erodibility. The PLSR model performed better than SVM for estimating SOM, CaCO₃, and EF-Factor. Furthermore, the results showed that the spectral responses of CaCO₃ were observed in separate places in the wavelengths of 570, 649, 802, 1161, 1421, 1854, and 2362 nm, and the wavelengths with SOM parameter were 496, 658, 779, 1089, 1417, 1871, and 2423 nm. The EF-factor shows the highest significant correlation with spectral reflectance values at 526, 688, 744, 1418, 1442, 2292, and 2374 nm. The accuracy and performance of the PLSR model in estimating the EF-Factor using spectral reflectance data and the distribution of data points for both the calibration and validation data-sets indicate a good accuracy of the PLSR model, with RMSE values of 0.0921 and 0.0836 Mg h MJ⁻¹ mm⁻¹, coefficient of determination (R²) values of 0.931 and 0.76, and RPD values of 2.168 and 2.147, respectively. Full article

Electric vehicle (EV) adoption grows steadily on a global scale, yet there is no consistent experience for EV drivers to charge their vehicles, which hinders the important EV mass market adoption. The Open Charge Point Protocol (OCPP) is the solution to this challenge, as it provides standardization and open communication between EV infrastructure components. The interplay of the OCPP with open cross-functional communication standards boosters driver experience on the one hand, while the charging station itself is integrated into a renewable energy ecosystem. This paper presents a deep dive into the combination of the OCPP with the OpenADR protocol, the Open Smart Charging Protocol (OSCP), the ISO 15118, and eRoaming protocols to explore possibilities and limitations. Furthermore, we suggest LoRa communication as an alternative to IP-based communication for deep-in building applications. Hence, this paper reveals the next important steps towards a successful EV mass market transition powered by user-friendliness and green energy. Full article

This study focuses on the development of a body for an electric racecar, utilizing CAD software for the design. A simplified full-vehicle geometric model was constructed. Based on fundamental theories of computational fluid dynamics and using CAE software platforms, the shear stress transport (SST) k-ω physical model was chosen to establish a three-dimensional computational model of the racecar’s external flow field. Simulations were conducted to analyze the pressure, airflow streamlines, and velocity distribution around the body and its surrounding flow field, elucidating the impact of body shape structure on aerodynamic characteristics. Finally, a manufacturing process for the body was designed, and a prototype was produced and integrated into the complete vehicle for road testing. The results indicate that the designed electric racecar body maintained consistent airflow over its surface, meeting the basic requirements of aerodynamics. Full article

This paper presents a novel ultrasonic-assisted electrodeposition process of Mn-doped NiCo₂O₄ onto a commercial nickel foam in a neutral electroplating bath (pH = 7.0) under an ultrasonic power of 1.2 V and 100 W. Different sample properties were studied based on their crystallinity through X-ray diffraction (XRD), morphology was studied through scanning electron microscopy (SEM), and photodegradation was studied through ultraviolet–visible (UV–Vis) spectrophotometry. Based on the XRD results, the dominant crystallite phase obtained was shown to be a pure single NiCo₂O₄ phase. The optical properties of the photocatalytic film showed a range of energy band gaps between 1.72 and 1.73 eV from the absorption spectrum. The surface hydroxyl groups on the catalytic surface of the Mn-doped NiCo₂O₄ thin films showed significant improvements in removing methyl red via photodegradation, achieving 88% degradation in 60 min, which was approximately 1.6 times higher than that of pure NiCo₂O₄ thin films. The maximum hydrogen rate of the composite films under 100 mW/cm² illumination was 38 μmol/cm² with a +3.5 V external potential. The electrochemical performance test also showed a high capacity retention rate (96% after 5000 charge–discharge cycles), high capacity (260 Fg⁻¹), and low intrinsic resistance (0.8 Ω). This work concludes that the Mn-doped NiCo₂O₄ hybrid with oxygen-poor conditions (oxygen vacancies) is a promising composite electrode candidate for methyl red removal, hydrogen evolution, and high-performance hybrid supercapacitor applications. Full article