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Social life cycle sustainability assessment of dried tomato products based on material and process selection through multi-criteria decision making
No full text<p>BACKGROUNDTomatoes are a significant product of the Mediterranean region and a crucial component of the Mediterranean diet. The formulation of dried tomato products enriched with proteins and bioactive compounds could be a strategic approach to promote adherence to the Mediterranean diet. Six different novel tomato products were analyzed using different protein enrichment sources (pea proteins and leaf proteins) and drying technologies (hot-air dryer, microwave vacuum dryer, and conventional dryer). The novelty of this approach lies in combining product-specific criteria with global societal factors across their life cycles. Using 21 criteria and an analytic hierarchy process (AHP) survey of experts, the social sustainability score for each product was determined through a multi-criteria assessment.RESULTSThe tomato product's life cycles have minimal regional impacts on unemployment, access to drinking water, sanitation, or excessive working hours. However, they affect discrimination, migrant labor, children's education, and access to hospital beds significantly. The study identified nutritional quality as the top criterion, with the most sustainable design being a tomato bar enriched with pea protein and processed using microwave vacuum drying.CONCLUSIONThe study revealed that integrating sensory and nutrient compounds into social sustainability assessments improves food sustainability and provides a practical roadmap for social life cycle assessments of food products. It emphasized the importance of considering global social issues when reformulating Mediterranean products to ensure long-term adherence to the Mediterranean diet. Incorporating social factors into sustainability scores can also enhance the effectiveness of product information for conscious customers. (c) 2024 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.</p>
Effectiveness of Social Learning Theory Based Ecological Footprint Awareness Program in 60-72-Month-Old Children: A Randomized Controlled Study
No full text<p>Early childhood is a pivotal period for developing environmental awareness and sustainable behaviors, During this age period, social learning theory contributes to understanding how young children form behavioral patterns. The aim of this study was to evaluate the effectiveness of the Ecological Footprint Awareness Program based on social cognitive learning theory in 60-72-month-old children. This study was conducted using a cluster randomized controlled pretest-posttest experimental design. It was carried out in four preschools located in a city center between April and June 2023. Two of the preschools were assigned to the intervention group while the other two were assigned to the control group. Data was collected using the Ecological Footprint Awareness Scale for Children (EFAS-C). The Social learning theory based on Ecological Footprint Awareness Program was carried out with the children in the intervention group for a period of six weeks, with one session of 40 min per week. The program covered waste management, water and energy use, food consumption, and transportation, with one topic each week. The data was analyzed using descriptive statistics, paired groups t-test, and independent groups t-test, with a 95% confidence interval and p < .05 significance level. At the end of the study, it was found that the ecological footprint awareness of the children who participated in the training program was higher than those who did not participate in the training program. This randomized controlled trial provides strong evidence for the impact of environmental education programs based on social cognitive learning on young children's understanding and actions regarding their ecological footprint.</p>
Surrogate Model-Driven Estimation of Adiabatic Surface Temperature of Fire Exposed Suspension Bridge Towers
No full text<p>Evaluating adiabatic surface temperature (AST) as the thermal response of fire-exposed bridge elements is a complex and time-consuming task. Correspondingly, this study streamlined fire dynamic simulator (FDS) and machine learning (ML) in a surrogate model to predict the AST of suspension bridge tower. For this, various FDS simulations were conducted for suspension bridge tower exposed to different vehicular fire conditions incorporating factors such as vehicle type, exposure duration, and wind conditions to generate a diverse bridge fire dataset for training of ML algorithms. Eight ML models were evaluated using performance metrics, whereby the random forest model demonstrated exceptional consistency and reliability in a fivefold cross-validation, maintaining a high R2 value of 0.99 across all tests and showing stable MAE and MSE metrics, confirming its superior performance and robustness in predictive accuracy. The proposed surrogate model offers a robust and efficient tool for enhancing the resilience of bridge fire evaluations by providing a time-efficient solution that adapts quickly to a range of fire conditions.</p>
Central treatment of neuropeptide-S attenuates cognitive dysfunction and hippocampal synaptic plasticity impairment by increasing CaMKII/GluR1 in hemiparkinsonian rats
No full text<p>Neuropeptide-S (NPS) has been demonstrated to mitigate learning and memory deficits in experimental models of Parkinson's Disease (PD). Despite this, the precise mechanisms through which NPS exerts its influence on cognitive functions remain to be fully unknown. This study aims to elucidate the effects of central administration of NPS on learning and memory deficits associated with an experimental rat hemiparkinsonian model, examining both electrophysiological and molecular parameters. The hemiparkinsonian model was established via stereotactic injection of 6-hydroxydopamine (6-OHDA) into the right medial forebrain bundle. Central NPS (1 nmol, icv) was administered into the lateral ventricle via a cannula for seven consecutive days following the 6-OHDA lesion. The Morris water maze and object recognition tests were used to evaluate the rat's learning and memory abilities. Long-term potentiation (LTP) recordings were conducted to assess hippocampal synaptic plasticity. Immunohistochemistry was employed to determine the expression levels of phosphorylated CaMKII (pCaMKII), GluR1, and GluR2 in the hippocampus. The 6-OHDA-induced decline in cognitive performance was significantly (p < 0.05) improved in rats that received central NPS. In 6-OHDA-lesioned rats, NPS treatment significantly (p < 0.05) enhanced the amplitude of LTP at the dentate gyrus/perforant path synapses. Furthermore, NPS significantly (p < 0.05) increased the number of pCaMKII and GluR1 immunoreactive cells in the hippocampus, which had been diminished due to 6-OHDA, except for GluR2 levels. These findings provide insight into the mechanisms by which central NPS administration enhances cognitive functions in an experimental model of PD, highlighting its potential therapeutic benefits for addressing cognitive deficits in PD.</p>
Navigating the storm: the impact of the Russia–Ukraine war on EU's quest for strategic autonomy
No full text<p>This article investigates how the Russia–Ukraine war has reshaped the European Union's defence priorities and its pursuit of strategic autonomy. Using advanced text analytics and machine learning methods of over 26,000 European External Action Service (EEAS) documents, we examine shifts in topic prevalence across the Strategic Compass while considering broader geopolitical dynamics, including Brexit, China's positioning and US-NATO relations. Using EEAS documents spanning from one year before to two years after the Russian invasion, we employ a two-stage topic modelling approach. First, we classify EEAS documents under the Strategic Compass dimensions through majority voting, utilising text embedding and supervised learning models, followed by detailed mapping of documents to specific subtopics derived from the Strategic Compass framework by topic modelling. Our findings reveal a significant reorientation from a pre-war emphasis on long-term capability development towards an increased focus on immediate crisis response and strengthened international partnerships postinvasion. While the EU demonstrated enhanced operational capacity and partnership-building, persistent challenges remain in achieving comprehensive strategic autonomy and becoming an international actor. These results suggest that external crises can accelerate strategic autonomy in discourse. However, achieving genuine independence requires more effective crisis response, long-term capability development, and strong partnership management. </p>
Comparative study of two MIP-based electrochemical sensors for selective detection and quantification of the antiretroviral drug lopinavir in human serum
No full text<p>Thermal polymerization (TP) and electropolymerization (EP) are the two methods used in this study to explore the molecular imprinting process. To detect the antiviral medication lopinavir (LPV), an inhibitor of enzyme HIV-1 protease that is co-formulated with ritonavir (RTV) to extend its half-life in the body, with greater precision, these methods were merged with an electrochemical sensor. The sensors were created on glassy carbon electrodes (GCE) based on molecularly imprinted polymers (MIP) using TP with methacrylic acid (MAA) functional monomer and EP with p-aminobenzoic acid (PABA) functional monomer. Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), and electrochemical methods were utilized to examine the technical features of the suggested sensors. For both approaches, the necessary optimization investigations were carried out. Different LPV concentrations, ranging from 1.0 pM to 17.5 pM in drug solution and commercial human serum samples, were used to validate the analytical efficiency of the two sensors and compare their electroanalytical behaviour. For TP-LPV@MIP/GCE and EP-LPV@MIP/GCE, the corresponding limit of detection (LOD) was 2.68 x 10(-13) M (0.169 pg mL(-1)) and 1.79 x 10(-13) M (0.113 pg mL(-1)) in standard solutions, and 2.87 x 10(-13) M (0.180 pg mL(-1)) and 2.91 x 10(-13) M (0.183 pg mL(-1)) in serum samples. For the measurement of LPV in tablet form and serum samples, the proposed TP-LPV@MIP/GCE and EP-LPV@MIP/GCE sensors provide good recovery, demonstrating 99.85-101.16 % and 100.36-100.97 % recovery, respectively. The imprinting factor was utilized to demonstrate the selectivity of the suggested sensors by utilizing several anti-viral drugs that are structurally comparable to LPV. Additionally, the constructed sensors were examined for the potential impacts of interferences and the stability during the storage.</p>
Novel functional copolymer: Design, synthesis, click chemistry modification, and evaluation of dielectric and thermal properties
No full text<p>In this study, the modification of new polymers carrying dipeptide side groups with chalcone and their electrical properties were investigated. Therefore, initially, the phenylalanine dipeptide (Tyr(Boc)-Phe-OCH3) was synthesized. The ester group in the structure of this compound was converted to carboxylic acid to obtain the BocTyrosine Phenylalanine-OH (Tyr(Boc)-Phe-OH) compound. This compound reacted with propargyl amine (PA) to synthesize (Tyr(Boc)-Phe-PA), which subsequently reacted with methacryloyl chloride to produce the methacrylate monomer (MA-Tyr(Boc)-Phe-PA). The polymerization of the prepared methacrylate monomer was carried out using the free radical polymerization method. In the final step, the active alkyne-terminated homopolymer reacted with the azide-terminated (4-Cl-CF3 N3-Chalcone) under copper catalysis through a click reaction, resulting in the synthesis of poly(MA-Tyr(Boc)-Phe-click-chalcone). The thermal behaviors of the polymers were determined using DSC and TGA thermal analysis methods. The initial decomposition temperature of the polymer was found to be 207.05 degrees C, whereas this value was 188.53 degrees C for the P(MA-Tyr(Boc)-Phe-click-chalcone) polymer. Additionally, the temperatures corresponding to a 50 % mass loss were 383.79 degrees C for the P(MA-Tyr (Boc)-Phe-PA) polymer and 394.78 degrees C for the P(MA-Tyr(Boc)-Phe-click-chalcone) polymer. The dielectric constant of the P(MA-Tyr(Boc)-Phe-PA) polymer was calculated to be 8.98, while the dielectric constant of the modified polymer obtained after click modification was calculated to be 13.87. The increase in the dielectric constant is thought to be due to the greater polarization of the triazole ring formed by the click reaction under an electric field. Furthermore, the AC conductivities of the compounds were calculated to be 5.00 x 10-9 S/cm and 1.11 x 10-9 S/cm at 1 kHz. When the dielectric properties of these two compounds are compared, the second polymer exhibits better polarization in an electric field, indicating its potential use as an electronic circuit element.</p>
Assessing the Impact of RPL Attacks in Challenging Environments: An Evolution-assisted Study
No full text<p>The integration of IoT-enabled smart technologies into our daily lives offers numerous benefits in many ways. This, however, requires well-founded security concerns because the protocols designed for IoT networks exhibit numerous vulnerabilities today. One such protocol is the IPv6 Routing Protocol for Low Power Lossy Networks (RPL) which is frequently used in IoT networks to enable routing between the heterogeneous devices. RPL has exhibited significant shortcomings and has become a target for various attacks up to now. Evaluating the performance of RPL attacks is a non-trivial task for securing IoT network effectively. Although performance analysis studies are numerous in literature, all of them rely on 'human - crafted' attack environments. In contrast, this study considers the most challenging malicious environments for performance evaluation. To achieve such environments, the use of genetic algorithm is explored in this study. The findings reveal that the impact of the attack is greatly influenced by the position as well as the density of the attackers in the network.</p>
Optimization of high efficiency blue emissive N-, S-doped graphene quantum dots
No full text<p>Graphene quantum dots (GQDs) with bright emission at short wavelengths have attracted much attention due to their importance in various applications such as light-emitting diodes. During or after synthesis, several parameters can significantly improve the optical properties of GQDs. This study presents a facile solvothermal method with low-cost precursors using glutamic acid as the carbon source to realize blue emitting GQDs. The positive effects of urea and 1-octanethiol as nitrogen and sulfur dopants on the photoluminescence quantum yield (PLQY) of the prepared GQDs were demonstrated and optimized. The results confirmed the formation of 2.2 nm nanoparticles with a bright emission around 381 nm with a full width at half maximum of 58 nm and a PLQY approaching 70 %. The decay lifetime of the emission also showed a tri-exponential profile with an average lifetime of 2.4 ns. The simplicity of the preparation method without any post-treatment process, together with a high PLQY of 70 % at short wavelengths, nominates the prepared GQDs for optoelectronics and UV light-driven biological purposes.</p>
Structural health monitoring in aviation: a comprehensive review and future directions for machine learning
No full text<p>Aircraft structures are exposed to a variety of operational and environmental loads that can cause structural deformation and fractures. Structural Health Monitoring (SHM) has emerged as a promising solution for in-situ monitoring of structural components. This article presents a state-of-the-art review of SHM in aviation, current regulations, data acquisition sensors and equipment, and damage detection and identification methods. The article discusses in detail the regulations SHM specific to both civil and military aviation. A comprehensive review of conventional electrical resistance sensors, fiber optic, piezoelectric sensors and smart materials used for SHM monitoring in aircraft structures is then presented. The pros and cons of each data acquisition approach were discussed individually. The damage detection and identification section begins by describing the traditional knowledge-based methods that are combined with expert knowledge and theory, then focuses on the applicability in aircraft SHM systems of spectral or frequency domain models. The last part investigates the new paradigm, machine learning and deep learning methods such as CNN and LSTM on different types of aircraft structures through the existing literature. Furthermore, it covers an emerging approach called physics-informed neural networks (PINN), which combines physics and machine learning, and explore its potential for SHM applications.</p>