Oligomeric alpha-synuclein (α-syn) in saliva and phosphorylated α-syn deposits in the skin have emerged as promising diagnostic biomarkers for Parkinson’s disease (PD). This study aimed to assess and compare the diagnostic value of these biomarkers in discriminating between 38 PD patients and 24 healthy subjects (HSs) using easily accessible biological samples. Additionally, the study sought to determine the diagnostic potential of combining these biomarkers and to explore their correlations with clinical features. Salivary oligomeric α-syn levels were quantified using competitive ELISA, while skin biopsies were analyzed through immunofluorescence to detect phosphorylated α-syn at Ser129 (p-S129). Both biomarkers individually were accurate in discriminating PD patients from HSs, with a modest agreement between them. The combined positivity of salivary α-syn oligomers and skin p-S129 aggregates differentiated PD patients from HSs with an excellent discriminative ability with an AUC of 0.9095. The modest agreement observed between salivary and skin biomarkers individually suggests that they may reflect different aspects of PD pathology, thus providing complementary information when combined. This study’s results highlight the potential of utilizing a multimodal biomarker approach to enhance diagnostic accuracy in PD. Full article

Future index prices are viewed as a critical issue for any trader and investor. In the literature, various models have been developed for forecasting index prices. For example, the geometric Brownian motion (GBM) model is one of the most popular tools. This work examined four types of GBM models in terms of the presence of memory and the kind of volatility estimations. These models include the classical GBM model with memoryless and constant volatility assumptions, the SVGBM model with memoryless and stochastic volatility assumptions, the GFBM model with memory and constant volatility assumptions, and the SVGFBM model with memory and stochastic volatility assumptions. In this study, these models were utilized in an empirical study to forecast the future index price of the energy sector in the Saudi Stock Exchange Market. The assessment was led by utilizing two error standards, the mean square error (MSE) and mean absolute percentage error (MAPE). The results show that the SVGFBM model demonstrates the highest accuracy, resulting in the lowest MSE and MAPE, while the GBM model was the least accurate of all the models under study. These results affirm the benefits of combining memory and stochastic volatility assumptions into the GBM model, which is also supported by the findings of numerous earlier studies. Furthermore, the findings of this study show that GFBM models are more accurate than GBM models, regardless of the type of volatility. Furthermore, under the same type of memory, the models with a stochastic volatility assumption are more accurate than the corresponding models with a constant volatility assumption. In general, all models considered in this work showed a high accuracy, with MAPE ≤ 10%. This indicates that these models can be applied in real financial environments. Based on the results of this empirical study, the future of the energy sector in Saudi Arabia is forecast to be predictable and stable, and we urge financial investors and stockholders to trade and invest in this sector. Full article

The shikimate pathway is crucial for the biosynthesis of aromatic amino acids in plants and represents a promising target for developing new herbicides. This work aimed to identify inhibitors of shikimate dehydrogenase (SDH), a key enzyme of the shikimate pathway that catalyzes the conversion of 3-dehydroshikimate to shikimate. Virtual screening and molecular dynamic simulations were performed on the SDH active site of Arabidopsis thaliana (AtSDH), and 6-nitroquinazoline-2,4-diol (NQD) was identified as a potential inhibitor. In vitro assays showed that NQD decreased the activity of AtSDH by reducing V_max while keeping K_M unchanged, indicating non-competitive inhibition. In vivo, hydroponic experiments revealed that NQD reduced the root length of soybean and maize. Additionally, NQD increased the total protein content and certain amino acids. Soybean roots uptake NQD more efficiently than maize roots. Furthermore, NQD reduced shikimate accumulation in glyphosate-treated soybean roots, suggesting its potential to restrict the flow of metabolites along the shikimate pathway in soybean. The simultaneous treatment of maize seedlings with glyphosate and NQD accumulated gallic acid in the roots, indicating that NQD inhibits SDH in vivo. Overall, the data indicate that NQD inhibits SDH both in vitro and in vivo, providing valuable insights into the potential development of herbicides targeting SDH. Full article

The typical Integrated Circuit (IC) development process commences with formulating specifications in natural language and subsequently proceeds to Register Transfer Level (RTL) implementation. RTL code is traditionally generated through manual efforts, using Hardware Description Languages (HDL) such as VHDL or Verilog. High-Level Synthesis (HLS), on the other hand, converts programming languages to HDL; these methods aim to streamline the engineering process, minimizing human effort and errors. Currently, Electronic Design Automation (EDA) algorithms have been improved with the use of AI, with new advancements in commercial (such as ChatGPT, Bard, among others) Large Language Models (LLM) and open-source tools presenting an opportunity to automate the chip design process. This paper centers on the creation of AIbyAI, a Convolutional Neural Network (CNN) IC entirely developed by an LLM (ChatGPT-4), and its manufacturing with the first fabricable open-source Process Design Kit (PDK), SKY130A. The challenges, opportunities, advantages, disadvantages, conversation flow, and workflow involved in CNN IC development are presented in this work, culminating in the manufacturing process of AIbyAI using a 130 nm technology, marking a groundbreaking achievement as possibly the world’s first CNN entirely written by AI for its IC manufacturing with a free PDK, being a benchmark for systems that can be generated today with LLMs. Full article

Surface tension is an important characteristic of materials. In particular at high temperatures, surface tension values are often unknown. However, for metals, these values are highly relevant in order to enable efficient industrial processing or simulation of material behavior. Plasma, electron or laser beam processes can induce such high energy inputs, which increase the metal temperatures to, and even above, boiling temperatures, e.g., during deep penetration welding or remote cutting. Unfortunately, both theoretical and experimental methods experience challenges in deriving surface tension values at high temperatures. Material models of metals have limitations in explaining complex ion interactions, and experimentally measuring temperature and surface tension at high temperatures is a challenge for methods and equipment. Therefore, surface wave analysis was conducted in this work to derive surface tension values around the boiling temperature of steel and identify trends. In addition, a simple ion interaction calculation was used to simulate the impacting parameters that define the surface tension. Since both the experimental values and simulation results indicate an increasing trend in surface tension above the boiling temperature, it is concluded that the dominating attractive forces above this temperature should increase with increasing temperature and lead to increasing surface tension forces in the surface layers of liquid metal. Full article

Salinity stress is a major factor affecting the nutritional and metabolic profiles of crops, thus hindering optimal yield and productivity. Recent advances in nanotechnology propose an avenue for the use of nano-fertilizers as a potential solution for better nutrient management and stress mitigation. This study aimed to evaluate the benefits of conventional and nano-fertilizers (nano-Zn/nano-Si) on maize and subcellular level changes in its ionomic and metabolic profiles under salt stress conditions. Zinc and silicon were applied both in conventional and nano-fertilizer-using farms under stress (100 mM NaCl) and normal conditions. Different ions, sugars, and organic acids (OAs) were determined using ion chromatography and inductively coupled plasma mass spectroscopy (ICP-MS). The results revealed significant improvements in different ions, sugars, OAs, and other metabolic profiles of maize. Nanoparticles boosted sugar metabolism, as evidenced by increased glucose, fructose, and sucrose concentrations, and improved nutrient uptake, indicated by higher nitrate, sulfate, and phosphate levels. Particularly, nano-fertilizers effectively limited Na accumulation under saline conditions and enhanced maize’s salt stress tolerance. Furthermore, nano-treatments optimized the potassium-to-sodium ratio, a critical factor in maintaining ionic homeostasis under stress conditions. With the growing threat of salinity stress on global food security, these findings highlight the urgent need for further development and implementation of effective solutions like the application of nano-fertilizers in mitigating the negative impact of salinity on plant growth and productivity. However, this controlled environment limits the direct applicability to field conditions and needs future research, particularly long-term field trials, to confirm such results of nano-fertilizers against salinity stress and their economic viability towards sustainable agriculture. Full article

: Gas concentration monitoring is an effective method for predicting gas disasters in mines. In response to the shortcomings of low efficiency and accuracy in conventional gas concentration prediction, a new method for gas concentration prediction based on Particle Swarm Optimization and Long Short-Term Memory Network (PSO-LSTM) is proposed. First, the principle of the PSO-LSTM fusion model is analyzed, and the PSO-LSTM gas concentration analysis and prediction model is constructed. Second, the gas concentration data are normalized and preprocessed. The PSO algorithm is utilized to optimize the training set of the LSTM model, facilitating the selection of the training data set for the LSTM model. Finally, the MAE, RMSE, and coefficient of determination R² evaluation indicators are proposed to verify and analyze the prediction results. Gas concentration prediction comparison and verification research was conducted using gas concentration data measured in a mine as the sample data. The experimental results show that: (1) The maximum RMSE predicted using the PSO-LSTM model is 0.0029, and the minimum RMSE is 0.0010 when the sample size changes. This verifies the reliability of the prediction effect of the PSO-LSTM model. (2) The predictive performance of all models ranks as follows: PSO-LSTM > SVR-LSTM > LSTM > PSO-GRU. Comparative analysis with the LSTM model demonstrates that the PSO-LSTM model is more effective in predicting gas concentration, further confirming the superiority of this model in gas concentration prediction. Full article

In general, when using infrared thermography (IRT) techniques to excite a heat source on the surface of an inspection object, the heat source is focused on the center of the image of the infrared (IR) camera. If the object to be inspected is small, uniform excitation of the heat source is possible, but if the area is large, the heat source is concentrated locally, resulting in uneven heat distribution. Therefore, in this study, heat distribution was analyzed after inducing a non-uniform heat source by exciting the heat source at different locations. Additionally, the fast Fourier transform (FFT) algorithm with Gaussian filtering was applied to resolve the non-uniform distribution of the heat sources. Excellent results were obtained from the amplitude image, and the effectiveness of the FFT algorithm was verified using the Otsu algorithm. Finally, the signal-to-noise ratio (SNR) was calculated, and the detection ability according to each thinning rate was analyzed. Full article

This article explores the punching shear behavior of GFRP-RC interior slab–column connections. The parameters tested included the column–aspect ratio (1.0, 2.0, 3.0, 4.0, and 5.0), perimeter-to-depth ratio for square column stubs with side lengths of 0.3, 0.4, 0.5, 0.6, and 0.7 meters, and span-to-depth ratios of 4, 6, 8, 10, and 12. A review of the literature revealed that no previous study has investigated the effect of these parameters or their interactions on this type of connection. Numerically, twenty-five slabs were created using finite element (FE) software (V3), each with square dimensions of 2.5 meters and a constant thickness of 0.2 meters. The central column extended 0.3 meters from the top and bottom of the slab. All four sides of the slabs were supported, and the specimens underwent pure static shear load testing. The test results demonstrated that all slabs failed due to punching shear. Increasing any parameter value reduced the punching shear stresses. Additionally, the results indicated that Canadian (CSA-S806-12) and Japanese (JSCE-97) standards for FRP-RC materials generally provided the closest predictions of punching shear capacity compared to the American guideline, ACI 440.1R-22. However, all standards exhibited shortcomings and require enhancement and modifications, particularly to consider the impact of the span-to-depth ratio. Therefore, three equations were developed to predict the shear strength of the connections, yielding better results than those prescribed by the North American and Japanese standards. Full article

The accurate prediction of crops’ water requirements is an important reference for real-time irrigation decisions on farmland. In order to achieve precise control of irrigation and improve irrigation water utilization, a real-time crop water requirement prediction model combining convolutional neural networks (CNNs) and the Informer model is presented in this paper, taking the real-time water demand of winter wheat–summer maize from 2017 to 2021 as the research object. The CNN model was used to extract the depth features of the day-by-day meteorological data of the crops, and the extracted feature values were inputted into the Informer model according to the time series for training and prediction to obtain the predicted water demand of winter wheat and summer maize. The results showed that the prediction accuracy of the constructed CNN–Informer combination model was higher compared to CNN, BP, and LSTM models, with an improvement of 1.2%, 25.1%, and 21.9% for winter wheat and 0.4%, 37.4%, and 20.3% for summer maize; based on the good performance of the model in capturing the long-term dependency relationship, the irrigation analysis using the model prediction data showed a significant water-saving effect compared with the traditional irrigation mode, with an average annual water saving of about 1004.3 m³/hm², or 18.4%, which verified the validity of the model, and it can provide a basis for the prediction of crops’ water demand and sustainable agricultural development. Full article

Tillage modes and straw returning influence soil aggregate stability and the distribution of organic carbon (C) and nitrogen (N) in aggregates of different particle sizes. In the typical hilly regions of southwest China, the predominant soil type is purple soil, characterized by heavy texture and high stickiness, with relatively lower soil fertility compared to other soil types. The improper use of fertilizers and field management practices further exacerbates soil compaction. However, abundant straw resources in the region provide an opportunity for comprehensive straw utilization. The effective utilization of straw resources is of significant importance for stabilizing agricultural ecological balance, improving resource utilization efficiency, and alleviating ecological pressure. Previously, most studies have focused on the impact of different mechanized tillage systems on the physical and chemical properties of soil in hilly areas, while research on the preservation of water-stable aggregates’ organic C and N content remains limited. In this study, the soil properties of fields under a winter pea–summer corn rotation for two years were studied with regards to the effects of straw returning on its water-stable aggregate distribution, macroaggregate content (R_0.25), mean weight diameter (MWD), geometric mean diameter (GMD), and the organic C and N content in soil aggregates of different particle sizes and at different depths. The effects of five different tillage modes were assessed, namely rotary tillage with straw mixed retention (RTM), conventional tillage with straw burial retention (CTB), no-tillage with straw covered retention (NTC), subsoiling with straw covered retention (STC), and no-tillage without straw retention (NT). Based on the study results, under different tillage modes, straw returning effectively enhanced the soil organic carbon (SOC) and total nitrogen (TN) reserves at the plow layer (0–30 cm), SOC increased by 17.2% to 88%, and TN increased by 8.6% to 85.9%. At the same time, the content of 0.25–2 mm aggregates increased under the straw-return treatments under different tillage patterns. The NT treatment had the lowest R_0.25 and MWD and GMD values for soil aggregates at different depths, which were significantly different (p < 0.05) from the other treatment modes. The correlation coefficients between SOC and soil aggregate stability indices ranged from 0.68 to 0.90, with most of them showing highly significant positive correlations (p < 0.01). In conclusion, straw returning under different tillage systems has improved soil aggregate stability and promoted soil structure stability. Specifically, the STC treatment has shown more pronounced effects on soil improvement in the upper soil layer of the hilly regions in southwest China, while the RTM treatment is beneficial for improving the lower soil layer. Therefore, the comprehensive experimental results indicate that the combination of STC and RTM treatments represents the most promising mechanized tillage and straw returning practices for the hilly regions in southwest China. Full article

Microbial-driven N turnover is important in regulating N fertilizer use efficiency through the secretion of metabolites like glycolipids. Currently, our understanding of the potential of glycolipids to partially reduce N fertilizer use and the effects of glycolipids on crop yield and N use efficiency is still limited. Here, a three-year in situ field experiment was conducted with seven treatments: no fertilization (CK); chemical N, phosphorus and potassium (NPK); NPK plus glycolipids (N+PKT); and PK plus glycolipids with 10% (0.9 N+PKT), 20% (0.8 N+PKT), 30% (0.7 N+PKT), and 100% (PKT) N reduction. Compared with NPK, glycolipids with 0–20% N reduction did not significantly reduce maize yields, and also increased N uptake by 6.26–11.07%, but no significant changes in grain or straw N uptake. The N resorption efficiency under 0.9 N+PKT was significantly greater than that under NPK, while the apparent utilization rates of N fertilizer and partial factor productivity of N under 0.9 N+PKT were significantly greater than those under NPK. Although 0.9 N+PKT led to additional labor and input costs, compared with NPK, it had a greater net economic benefit. Our study demonstrates the potential for using glycolipids in agroecosystem management and provides theoretical support for optimizing fertilization strategies. Full article

Titanium alloys have been widely used in aerospace and other fields due to their excellent properties such as light weight and high strength. However, the extremely poor tribological properties of titanium alloys limit their applications in certain special working conditions. In order to improve the tribological properties of titanium alloys, the zirconia coatings were prepared on the surface of a TC4 titanium alloy using the discharge plasma sintering method in this article. The influence of sintering parameters on properties such as density, adhesion, hardness, and phase composition, as well as tribological properties (friction coefficient, wear rate) were investigated, and the influence mechanism of the coating structure on its mechanical and frictional properties was analyzed. The results showed that, with the increase in sintering temperature, the density, bonding strength, and hardness of the zirconia coating were significantly improved. The zirconia coating prepared at a sintering temperature of 1500 °C and a sintering time of 20 min had the lowest friction coefficient and wear rate, which are 0.33 and 6.2 × 10⁻⁸ cm³·N⁻¹·m⁻¹, respectively. Numerical analysis showed that the increase in temperature and the extension of time contributed to the extension of the diffusion distance between zirconia and titanium, thereby improving the interfacial adhesion. The influence mechanism of different sintering temperatures and sintering times on the wear performance of zirconia coatings was explained through Hertz contact theory. Full article

Greenhouse-gas (GHG) reporting schemes for companies are increasingly part of climate-mitigation policies worldwide. Notably, the European Green Deal (2019) boosts new public regulations that oblige companies to compile GHG emission inventories, i.e., account for their emissions in a given system boundary. Along with this boost, the workload for companies increases; at the same time, the quality of reporting is questioned. Given the overarching goal to improve companies’ climate-mitigation performance, the quality of reporting is inseparably connected to the quality of the respective accounting. However, the literature discusses carbon accounting as a universal umbrella term focusing on managerial issues, thus disregarding the crucial role of accounting methodologies in the sense of calculation approaches. In this publication, we apply an analytical approach introducing a clear differentiation between the task of quantitatively accounting for GHG inventories and the task of reporting results from calculated inventories in response to stakeholder or policy expectations. We use this approach to investigate European GHG reporting schemes and related GHG accounting methodologies in detail. Our findings indicate that the current phase of the European Green Deal depicts a quantitative growth in reporting schemes and a significant qualitative change by shifting from formerly voluntary to mandatory reporting schemes, along with the application of accounting methodologies originally not intended for politically compulsory purposes. We analyze the consequences of this shift, which poses new challenges for companies and policymakers, i.e., data-management concepts and refined methodological frameworks. Full article

The Dutch housing market comprises three sectors: social-rented, private-rented, and owner-occupied. The contemporary market is marked by a shortage of supply and a large subsidised social sector. Waiting lists for social housing are growing, whereas households with incomes above the limit do not or cannot leave the social sector. Government policy and market regulations change frequently, not least for political reasons. In view of commonly recognised problems in the housing market, this article considers the ‘internal demand’ of those households that are dissatisfied with their current residence. We examine the effects of regulatory policy by means of an exploratory agent-based simulation. The results provide perspectives on how internal demand is impacted by regulations in a housing market that is suffering from a shortage, and allow decision makers to weigh the pros and cons of policy measures. Full article

Osteosarcopenia, the concurrent presence of sarcopenia and osteopenia/osteoporosis, poses a significant health risk to older adults, yet its impact on clinical outcomes is not fully understood. The aim of this prospective, longitudinal multicentre study was to examine the impact of osteosarcopenia on 3-year mortality and unplanned hospitalizations among 572 older hospitalized patients (mean age 75.1 ± 10.8 years, 78% female). Sarcopenia and low bone mineral density (BMD) were evaluated using Dual Energy X-ray Absorptiometry and the European Working Group on Sarcopenia in Older People (EWGSOP2) and WHO criteria, respectively. Among participants, 76% had low BMD, 9% were sarcopenic, and 8% had osteosarcopenia. Individuals with osteosarcopenia experienced a significantly higher rate of mortality (46%, p < 001) and unplanned hospitalization (86%, p < 001) compared to those without this condition. Moreover, “healthy” subjects—those without sarcopenia or low BMD—showed markedly lower 3-year mortality (9%, p < 001) and less unplanned hospitalization (53%, p < 001). The presence of osteosarcopenia (p = 0.009) increased the 3-year mortality risk by 30% over sarcopenia alone and by 8% over low BMD alone, underscoring the severe health implications of concurrent muscle and bone deterioration. This study highlights the substantial impact of osteosarcopenia on mortality among older adults, emphasizing the need for targeted diagnostic and therapeutic strategies. Full article

Microscopic traffic simulations have become increasingly important for research targeting connected vehicles. They are especially appreciated for enabling investigations targeting large areas, which would be practically impossible or too expensive in the real world. However, such large-scale simulation scenarios often lack validation with real-world measurements since these data are often not available. To overcome this issue, this work integrates probe counts from floating car data as reference counts to model a large-scale microscopic traffic scenario with high-resolution detector data. To integrate the frequent probe counts, a road network matching is required. Thus, a novel road network matching method based on a decision tree classifier is proposed. The classifier automatically adjusts its cosine similarity and Hausdorff distance-based similarity metrics to match the network’s requirements. The approach performs well with an F₁-score of 95.6%. However, post-processing steps are required to produce a sufficiently consistent detector dataset for the subsequent traffic simulation. The finally modeled traffic shows a good agreement of 95.1%. with upscaled probe counts and no unrealistic traffic jams, teleports, or collisions in the simulation. We conclude that probe counts can lead to consistent traffic simulations and, especially with increasing and consistent penetration rates in the future, help to accurately model large-scale microscopic traffic simulations. Full article

Exploring the Moon and Mars are crucial steps in advancing space exploration. Numerous missions aim to land and research in various lunar locations, some of which possess challenging surfaces with unchanging features. Some of these areas are cataloged as lunar light plains. Their main characteristics are that they are almost featureless and reflect more light than other lunar surfaces. This poses a challenge during navigation and landing. This paper compares traditional feature matching techniques, specifically scale-invariant feature transform and the oriented FAST and rotated BRIEF, and novel machine learning approaches for dense feature matching in challenging, unstructured scenarios, focusing on lunar light plains. Traditional feature detection methods often need help in environments characterized by uniform terrain and unique lighting conditions, where unique, distinguishable features are rare. Our study addresses these challenges and underscores the robustness of machine learning. The methodology involves an experimental analysis using images that mimic lunar-like landscapes, representing these light plains, to generate and compare feature maps derived from traditional and learning-based methods. These maps are evaluated based on their density and accuracy, which are critical for effective structure-from-motion reconstruction commonly utilized in navigation for landing. The results demonstrate that machine learning techniques enhance feature detection and matching, providing more intricate representations of environments with sparse features. This improvement indicates a significant potential for machine learning to boost hazard detection and avoidance in space exploration and other complex applications. Full article

This study addresses the challenge of recycling adhesive bonds, as their disassembly is irreversible and damages the substrates. It explores the use of thermally expandable particles (TEPs), which, when heated, expand and weaken the bond. The magnetization of TEPs allows us to control their distribution using a magnetic field. The work aims to obtain magnetized TEPs, study their influence on resin curing, mechanical performance, and durability, test their mobility in graded bonds, and analyze the temperature-induced debonding process. TEPs are characterized using various techniques, including differential scanning calorimetry, nuclear magnetic resonance, and scanning electron microscopy. Additionally, the impact of 25 wt.% TEPs on epoxy resin curing is examined using the Kamal model. Adhesion and disassembly assessments were conducted through tensile shear tests using single-lap-joint specimens, while the bond durability was determined via wedge testing. It was found that magnetization reduces the debonding time, though it decreases shear strength while increasing bond durability. The crack formation energy is higher with magnetic TEPs, and total crack length is lower in long-term wedge tests. Once debonded, the substrates are sanded and reused as raw material. Full article

MBRs (Membrane bioreactors) have been increasingly employed for municipal and industrial wastewater treatment in the last decades for their small footprint and excellent effluent quality. However, microorganisms are often detected in the permeates of microfiltration (MF) membranes even with small pore sizes. Coliform bacteria are known for indicating the potential presence of pathogenic bacteria that cause infectious disease such as bacteremia, respiratory tract infections, and urinary tract infections. Thus, the retention of coliform bacteria by membrane processes is important when the membrane process is utilized in water reclamation. In this study, a microbial community of coliform bacteria in the permeates of MF membranes with different pore sizes (0.2, 0.4, and 0.8 µm) was identified. The results showed that the dominant coliform bacteria changed from Enterobacter spp. and Citrobacter spp. in the activated sludge to Enterobacter spp. and Klebsiella spp. in the permeate of MF membranes, while some pieces of membranes showed complete retention. The bacterial regrowth on the surface of the piping system on the permeate side could be a significant factor contributing to the frequent and exclusive detection of Enterobacter spp. and Klebsiella spp. in the case of membranes with small pore size (0.2 and 0.4 µm) after a long continuous filtration time. To indicate the public health-related risk of treated wastewater by MF, Escherichia coli may not be a suitable indicator species because E. coli is relatively retentive in MF compared to other coliforms. Full article

The charge carrier formation and transport in the pristine polymers as well as in the polymer–fullerene blend is still a hot topic of discussion for the scientific community. In the present work, the carrier generation in some prominent organic molecules has been studied through ultrafast transient absorption spectroscopy. The identification of the exciton and polaron lifetimes of these polymers has led to device performance-related understanding. In the Energy Gap Law, the slope of the linear fit gradient (γ) of lifetimes vs. bandgap are subjected to the geometrical rearrangements experienced by the polymers during the non-radiative decay from the excited state to the ground state. The value of gradient (γ) for excitons and polarons is found to be −1.1 eV⁻¹ and 1.14 eV⁻¹, respectively. It suggests that the exciton decay to the ground state is likely to involve a high distortion in polymer equilibrium geometry. This observation supports the basis of Stokes shift found in the conjugated polymers due to the high disorder. It provides the possible reasons for the substantial variation in the exciton lifetime. As the bandgap becomes larger, exciton decay rate tends to reduce due to the weak attraction between the holes in the HUMO and electron in the LUMO. The precise inverse action is observed for the polymer–fullerene blend, as the decay of polaron tends to increase as the bandgap of polymer increases. Full article

“Managing Undernutrition in Pediatric Oncology” is a collaborative consensus statement of the Polish Society for Clinical Nutrition of Children and the Polish Society of Pediatric Oncology and Hematology. The early identification and accurate management of malnutrition in children receiving anticancer treatment are crucial components to integrate into comprehensive medical care. Given the scarcity of high-quality literature on this topic, a consensus statement process was chosen over other approaches, such as guidelines, to provide comprehensive recommendations. Nevertheless, an extensive literature review using the PubMed database was conducted. The following terms, namely pediatric, childhood, cancer, pediatric oncology, malnutrition, undernutrition, refeeding syndrome, nutritional support, and nutrition, were used. The consensus was reached through the Delphi method. Comprehensive recommendations aim to identify malnutrition early in children with cancer and optimize nutritional interventions in this group. The statement underscores the importance of baseline and ongoing assessments of nutritional status and the identification of the risk factors for malnutrition development, and it presents tools that can be used to achieve these goals. This consensus statement establishes a standardized approach to nutritional support, aiming to optimize outcomes in pediatric cancer patients. Full article