Search Results (179)

Clouds modulate the net radiative flux that interacts with both shortwave (SW) and longwave (LW) radiation, but the uncertainties regarding their effect in polar regions are especially high because ground observations are lacking and evaluation through satellites is made difficult by high surface reflectance. In this work, sky conditions for six different polar stations, two in the Arctic (Ny-Ålesund and Utqiagvik [formerly Barrow]) and four in Antarctica (Neumayer, Syowa, South Pole, and Dome C) will be presented, considering the decade between 2010 and 2020. Measurements of broadband SW and LW radiation components (both downwelling and upwelling) are collected within the frame of the Baseline Surface Radiation Network (BSRN). Sky conditions—categorized as clear sky, cloudy, or overcast—were determined using cloud fraction estimates obtained through the RADFLUX method, which integrates shortwave (SW) and longwave (LW) radiative fluxes. RADFLUX was applied with daily fitting for all BSRN stations, producing two cloud fraction values: one derived from shortwave downward (SWD) measurements and the other from longwave downward (LWD) measurements. The variation in cloud fraction used to classify conditions from clear sky to overcast appeared consistent and reasonable when compared to seasonal changes in shortwave downward (SWD) and diffuse radiation (DIF), as well as longwave downward (LWD) and longwave upward (LWU) fluxes. These classifications served as labels for a machine learning-based classification task. Three algorithms were evaluated: Random Forest, K-Nearest Neighbors (KNN), and XGBoost. Input features include downward LW radiation, solar zenith angle, surface air temperature ($T_a$), relative humidity, and the ratio of water vapor pressure to $T_a$. Among these models, XGBoost achieved the highest balanced accuracy, with the best scores of 0.78 at Ny-Ålesund (Arctic) and 0.78 at Syowa (Antarctica). The evaluation employed a leave-one-year-out approach to ensure robust temporal validation. Finally, the results from cross-station models highlighted the need for deeper investigation, particularly through clustering stations with similar environmental and climatic characteristics to improve generalization and transferability across locations. Additionally, the use of feature normalization strategies proved effective in reducing inter-station variability and promoting more stable model performance across diverse settings. Full article

This study investigates the effectiveness of neural network models, particularly LSTM networks, in enhancing the accuracy of inflation forecasting. We compare LSTM models with traditional univariate time series models such as SARIMA and AR(p) models, as well as machine learning approaches like LASSO regression. To improve the standard LSTM model, we apply advanced feature selection techniques and introduce data augmentation using the MBB method. Our analysis reveals that LASSO-LSTM hybrid models generally outperform LSTM models utilizing PCA for feature selection, particularly in datasets with multiple features, as measured by RMSE. However, despite these enhancements, LSTM models tend to underperform compared to simpler models like LASSO regression, AR(p), and SARIMA in the context of inflation forecasting. These findings suggest that, for policymakers and central bankers seeking reliable inflation forecasts, traditional models such as LASSO regression, AR(p), and SARIMA may offer more practical and accurate solutions. Full article

Electric vehicles (EVs) play a critical role in the transition to sustainable transportation. Despite significant advancements in technology, EVs continue to face major challenges, particularly in terms of limited range, high costs, and insufficient charging infrastructure. This paper presents a comprehensive review that systematically categorizes the multifaceted factors influencing EV range into technical, environmental, user-related, economic, policy, and cultural dimensions. The aim is to offer a holistic view of how these elements interact to shape EV performance, adoption, and usage. Notably, advancements in battery capacity, charging time, vehicle weight, and aerodynamics are identified as key factors that significantly enhance EV range. Environmental factors such as temperature and terrain are shown to drastically impact energy consumption, with cold climates leading to up to a 50% reduction in range. Furthermore, user behaviors, driving patterns, and economic factors like battery costs, charging infrastructure availability, and electricity prices play a crucial role in determining EV efficiency. This review shows the importance of supportive policies, societal attitudes, and infrastructural developments in promoting the widespread adoption of EVs, making it an innovative and timely contribution to the field. Full article

Interest in the subject of umbilical cord clamping is long-standing. New evidence reveals that placental transfusion, facilitated by delayed cord clamping (DCC), reduces death and need for blood transfusions for preterm infants without evidence of harm. Even a brief delay in clamping the cord shows improved survival and well-being, but waiting at least two minutes is even better. We propose that three major benefits from DCC contribute to reduced mortality of preterm infants: (1) benefits from the components of blood; (2) assistance from the continued circulation of blood; and (3) the essential mechanical interactions that result from the enhanced volume of blood. The enhanced blood volume generates mechanical forces within the microcirculation that support the newborn’s metabolic and cardiovascular stability and secure short- and long-term organ health. Several unique processes prime preterm and term newborns to receive the full placental transfusion, not to be misinterpreted as extra blood or over-transfusion. Disrupting cord circulation before the newborn’s lung capillary bed has been fully recruited and the lungs can replace the placenta as a respiratory, gas-exchanging organ may be harmful. Early cord clamping also denies the newborn a full quota of iron-rich red blood cells as well as valuable stem cells for regeneration, repair, and seeding of a strong immune system. We propose that delayed cord clamping and intact-cord stabilization have the potential to save lives by protecting many neonates from hypovolemia, inflammation, and ischemia. Full article

Wider availability of marine proteins for the development of food and biomedical applications has a high importance. Sea cucumber body wall proteins have specific functional properties that could be very promising for such product development. However, protein extraction from whole animals is costly and complex, whereas peptide hydrogel production using biotechnological methods can be considered an economically viable approach. Body-wall derived peptides from sea cucumber Parastichopus tremulus have been suggested as a nontraditional source of potentially edible hydrocolloids. In the current work, four peptides were produced through custom synthesis. Scanning electron microscopy (SEM) of the combined mix of the four peptides (1:1 ratio; 15 mM concentration) in a calcium ion-containing buffer confirmed untargeted self-assembly with long, thick fibrillar formations at a microscale (measured mean cross-section 2.78 µm and length sizes of 26.95 µm). The antioxidant activity of the peptides separately, and in combination (1:1 molar ratio), was studied in vitro through ORAC (values in the range from 279 to 543 µmol TE/g peptide), ABTS (from 80.4 to 1215 µmol TE/g peptide), and DPPH (from 5.2 to 19.9 µmol TE/g) assays, and confirmed for protection against oxidation in a Caco-2 cell culture model. Angiotensin-I converting enzyme inhibitory activity was also confirmed for two of the four peptides, with the highest IC 50 of 7.11 ± 0.84 mg/mL. Full article

Effective thermal management in polymer-based materials remains a critical challenge due to their inherently low thermal conductivity, driving the need for advanced nanocomposites. This study develops non-equilibrium molecular dynamics (NEMD) simulations to investigate the thermal transport properties of polyethylene (PE) reinforced with graphene functionalized by hydrogen (H), methyl (CH₃), and ethyl (C₂H₅) groups with volume fractions of 5–30%. The interfacial thermal conductance (ITC) between PE and graphene increases significantly with functionalization, reaching 2.50 × 10⁸ W/m²K with 30% ethyl coverage, a 250% enhancement compared to 8.8 × 10⁷ W/m²K for pristine graphene. The effective thermal conductivity of the PE/functionalized graphene composite peaks at 0.42 W/mK with 30% hydrogen coverage, a 17.4% improvement over the 0.36 W/mK of PE/pristine graphene, though still 6.5% below pure PE (0.45 W/mK). Analysis of the vibrational density of states reveals that ethyl groups maximize phonon coupling at the interface, explaining their superior ITC enhancement. These findings offer quantitative insights into optimizing polymer nanocomposites for thermal management applications, such as microelectronics and energy storage systems, where efficient heat dissipation is important. Full article

This study explores the integration of digitalization and circular economy (CE) principles within the maritime industry through a theoretical analysis, proposing a framework that aligns business models with Sustainable Development Goals (SDGs) and net-zero objectives. By investigating how digital servitization and circular business models can drive economic, social, and environmental outcomes, this research provides valuable insights into sustainable value creation and capture across maritime value chains. The theoretical analysis covers the evolution of business models, emphasizing their collective role in fostering sustainable transformation within the maritime sector. The central idea of this study is a sustainable value mapping approach that aligns product–service systems (PSSs) with circular economy principles, incorporating lifecycle thinking (LCT) to capture the full environmental, economic, and social impacts. This broader perspective on the economic value proposition highlights the need for a shift from selling products to offering servitized products, acknowledging the importance of sustainability across the entire product lifecycle. This framework offers actionable guidance for maritime stakeholders committed to transitioning their value chains towards sustainable, circular models, addressing both production and consumption dimensions to achieve broader environmental and social benefits. Full article

Shipping carries over 80% of global trade volumes and emits 3% of global greenhouse gas emissions, but it is hard to abate due to the simple fact that ships require a lot of energy and move around. Therefore, a large amount of research and development is poured into understanding the choices of alternative fuels and developing new technologies. Unfortunately, much of the work and policies derived, therefore, seem to rest on a hidden assumption that a relevant amount of green alternative fuel will be available, but that assumption does not stand up to scrutiny on a global level. For example, the results show that decarbonizing global shipping using green ammonia produced from renewable energy sources will require 3.7 times the total EU-27 power production in 2022. The purpose and novelty of this paper are to offer a clear rationale for the correct contextualization of research and development on curbing greenhouse gas emissions from global shipping and individual shipping segments to avoid overpromising and underdelivering. Full article

The use of fish rest raw material for the production of fish protein hydrolysates (FPH) through enzymatic hydrolysis has received significant interest in recent decades. Peptides derived from fish proteins are known for their enhanced bioactivity which is mainly influenced by their molecular weight. Studies have shown that novel technologies, such as high-pressure processing (HPP), can effectively modify protein structures leading to increased biological activity. This study investigated the effect of various HPP conditions on the molecular weight distribution, antioxidant activity, and dipeptidyl-peptidase IV (DPP-IV) inhibitory effect of FPH derived from a mixture of rainbow trout (Oncorhynchus mykiss) and Atlantic salmon (Salmo salar) rest raw material. Six different treatments were applied to the samples before enzymatic hydrolysis; 200 MPa × 4 min, 200 MPa × 8 min, 400 MPa × 4 min, 400 MPa × 8 min, 600 MPa × 4 min, and 600 MPa × 8 min. The antioxidant and DPP-IV inhibitory effects of the extracted FPH were measured both in vitro and at cellular level utilizing human intestinal Caco-2 cells. The results indicated that low and moderate pressures (200 and 400 MPa) increased the proportion of larger peptides (2–5 kDa) in the obtained FPH, while treatment at 600 MPa × 4 min resulted in a higher proportion of smaller peptides (1–2 kDa). Furthermore, HPP led to the formation of peptides that demonstrated increased antioxidant activity in Caco-2 cells compared to the control, whereas their potential antidiabetic activity remained unaffected. Full article

The shorthorn sculpin (Myoxocephalus scorpius) is considered a suitable sentinel species for marine pollution in the Arctic due to its ecology and stationary habits. To evaluate its role as a bioindicator for potential natural and anthropic impacts on the marine ecosystem of the Kongsfjorden (Svalbard, Norwegian Arctic), 33 female and male specimens of shorthorn sculpins were collected in July 2018 in proximity of the Ny-Ålesund international research facility and analyzed for the content of 25 major and trace elements and methylmercury (MeHg) in the muscle, liver, gonads, and gills by using spectroscopic techniques. Most elements had their maximum average concentrations in the gills (Al, Cr, Fe, Mn, Na, Ni, Pb, Se, Si, Sr, and V), while the livers featured higher contents of some toxic and heavy metals (As, Cd, Cu, Mo, and Zn). The muscle was characterized by high contents of Ca, K, and Mg, while Ba, Co, and P were mostly concentrated in the gonads. The gonads presented higher concentrations of Cr, K, Mg, Ni, P, and V for the males and Co, Cu, Fe, Mn, and Se for the females. Both the total Hg and MeHg concentrations in the muscle correlated with the fish size, indicating bioaccumulation, although high Se/Hg molar ratios (11.0 ± 2.2) suggested a low toxic potential of mercury. Full article

A microgrid (MG) is a unique area of a power distribution network that combines distributed generators (conventional as well as renewable power sources) and energy storage systems. Due to the integration of renewable generation sources, microgrids have become more unpredictable. MGs can operate in two different modes, namely, grid-connected and islanded modes. MGs face various challenges of voltage variations, frequency deviations, harmonics, unbalances, etc., due to the uncertain behavior of renewable sources. To study the impact of these issues, it is necessary to analyze the behavior of the MG system under normal and abnormal operating conditions. Two different tools are used for the analysis of microgrids under normal and abnormal conditions, namely, power flow and short-circuit analysis, respectively. Power flow analysis is used to determine the voltages, currents, and real and reactive power flow in the MG system under normal operating conditions. Short-circuit analysis is carried out to analyze the behavior of MGs under faulty conditions. In this paper, a review of power flow and short-circuit analysis algorithms for MG systems under two different modes of operation, grid-connected and islanded, is presented. This paper also presents a comparison of various power flow as well as short-circuit analysis techniques for MGs in tabular form. The modeling of different components of MGs is also discussed in this paper. Full article

Norwegian spring spawning (NVG) herring milt is a raw material with high nutritional and functional values. However, its incorporation into food presents physicochemical and sensory challenges. Its high DNA content, the presence of TMA/TMAO and possibly heavy metal and/or environmental pollutants, and its bitter taste due to amino acids or peptides requires a careful approach to food development. Hydrolysis with food-grade enzymes enable an improvement in both the functional and sensory properties of the substrate and the increased stability of the raw materials and end products. HLPC, GC-MS, and in vitro protocols were used for the characterisation of manually extracted material (sample code: HMC) and milt from a fish-filleting line from early spring/late autumn catches. Three different food-grade protein hydrolysates were prepared from these raw materials (sample codes: H1, H2, and H3) as a means to estimate their functional food development potential. Combinations of three commercial enzymatic preparations were applied, targeting specific sensory properties. Parameters related to consumer safety (e.g., the presence of heavy metals and TMA/TMAO); beneficial health effects, such as antioxidant or antihypertensive bioactivities (measured using in vitro TAC, ORAC, DPPH, and ACE I inhibitory activity assays); the presence of beneficial fatty acids and micronutrients; and the protein quality were studied. On the basis of their total amino acid compositions, freeze-dried herring milt and hydrolysates could provide high-quality protein with most of the essential amino acids and taurine. Powdered milt has a particularly high fatty acid profile of bioavailable omega-3 fatty acids (2024.06 mg/100 g docosahexaenoic acid (DHA; 22:6n-3) and 884 mg/100 g eicosapentaenoic acid (EPA; 20:5n-3)). The experimentally measured levels of arsenic (3.9 ± 1.2 mg/kg) and cadmium (0.15 ± 0.05 mg/kg) were higher than the levels of the other two heavy metals (mercury and lead). The bioactivity is concentration-dependent. Overall, this work presents complementary information for the future utilisation of C. harengus powdered milt (possibly obtained directly from a fish-filleting line) and some of its protein hydrolysates as food ingredients. Full article

This study examines the in vitro effects of a soluble protein hydrolysate (SPH) derived from Atlantic salmon (Salmo salar) on incretin receptor activity and pancreatic islet cell protection to explore the mechanisms underlying SPH’s observed benefits on weight loss and metabolic health in overweight individuals. SPH demonstrated a dose-dependent enhancement of glucagon-like peptide-1 (GLP-1) and gastric inhibitory polypeptide (GIP) receptor activity, with significant increases of 2.4-fold (p < 0.05) and 2.6-fold (p < 0.01) at 10 mg/mL, respectively, compared to the control. Pancreatic islet cell assays showed a substantial proliferation effect, with up to a 57% increase at 50 µL/well, indicating potential protective properties against inflammation-induced cell loss. Notably, the smallest SPH peptide fraction (<1000 Da) exhibited GLP-1 agonist activity comparable to semaglutide, a widely used therapeutic agent, underscoring SPH’s potential efficacy in modulating metabolic pathways. These results suggest that SPH not only enhances key incretin signaling but also promotes islet cell health, positioning it as a promising dietary intervention to improve age-related metabolic health, including the weight gain and underlying adverse metabolic changes frequently encountered through the menopause. Full article

Line-to-line (LL) faults are one of the most frequent short-circuit conditions in photovoltaic (PV) arrays which are conventionally detected and cleared by overcurrent protection devices (OCPDs). However, OCPDs are shown to face challenges when detecting LL faults under critical detection conditions, i.e., low mismatch levels and/or high fault impedance values. This occurs due to insufficient fault current, thus leaving the LL faults undetected and leading to power losses and even catastrophic fire hazards. To compensate for OCPD deficiencies, recent studies have proposed modern artificial intelligence (AI)-based methods. However, various limitations can still be witnessed even in AI-based methods, such as (i) most of the models requiring a massive training dataset, (ii) critical fault detection conditions not being taken into consideration, (iii) models not being accurate enough when dealing with critical conditions, etc. To this end, the present paper proposes a learning vector quantization (LVQ)-based ensemble learning model in which three LVQs are individually trained to detect and classify LL faults in PV arrays. The initial LVQ vectors are determined using the k-means clustering method, and the learning rate is optimized by the adaptive gradient (AdaGrad) optimizer. The training and testing datasets are collected according to the PV array’s current–voltage (I–V) characteristic curve, and several features are extracted based on the Canberra and chi-squared distance techniques. The model utilizes a small training dataset, considers various critical detection conditions for LL faults—such as different mismatch levels and fault impedance values—and the final experimental results show that the model achieves an impressive average accuracy of 99.26%. Full article