Search Results (766)

There is a long tradition of studying river sharing problems. A central question frequently examined and addressed is how common benefits or costs can be distributed fairly. In this context, axiomatic approaches of cooperative game theory often use contradictory principles of international water law, which are strictly rejected in practice. That leads to the question: Are these methods suitable for a real-world application? First, we conduct a systematic literature review based on the PRISMA approach to categorise the river sharing problems. We identified several articles describing a variety of methods and real-world applications, highlighting interdisciplinary interest. Second, we evaluate the identified axiomatic literature related to TU games with regard to their suitability for real-world applications. We exclude those “standalone” methods that exclusively follow extreme principles and/or do not describe cooperative behaviour. This is essential for a fair distribution. Third, we propose to use the traditional game-theoretical approach of airport games in the context of river protection measures to ensure a better economic interpretation and to enforce future cooperation in the joint implementation of protective measures. Full article

The specific features of the phase diagrams of aqueous Pluronic systems, and particularly those of reverse Pluronics, are critically important for their broad range of applications, notably as nanocarriers for anticancer molecules. This work aims to investigate the effect of increasing hydrophobicity, achieved by varying the PPO/PEO ratio and the molecular weight, on the phase behavior of three reverse Pluronics: 10R5 [(PPO)₈–(PEO)₂₂–(PPO)₈], 17R4 [(PPO)₁₄–(PEO)₂₄–(PPO)₁₄] and 31R1 [(PPO)₂₆–(PEO)₇–(PPO)₂₆]. A broad set of physical measurements, including density, sound velocity, viscosity, and surface tension, was used to characterize the physical properties of the solutions. These data were complemented by additional techniques such as direct observation, dynamic light scattering, and rheological measurements. Based on the primary measurements, molar volume, apparent adiabatic compressibility, and hydration profiles were subsequently derived. Phase diagrams were constructed for each system over concentration ranges of 0.1–90 wt.% and temperatures between 6 and 70 °C, identifying distinct regions corresponding to random networks, flower-like micelles, and micellar networks. Notably, the 31R1/water system does not form flower-like micelles, whereas both the 17R4/water and 10R5/water systems display such structures, albeit in a narrow interval, that shift toward higher concentrations and temperatures with increasing PPO/PEO ratio. Altogether, the present study provides new insights into the physicochemical behavior of reverse Pluronic systems, offering a foundation for their rational design as hydrophobic nanocarriers, either as standalone entities or in conjunction with other copolymers. Full article

The shikimate pathway is the fundamental metabolic route for aromatic amino acid biosynthesis in bacteria, plants, and fungi, but is absent in mammals. This review explores how multi-enzyme synergy and allosteric regulation coordinate metabolic flux through this pathway by focusing on three key enzymes: 3-deoxy-d-arabino-heptulosonate-7-phosphate synthase, chorismate mutase, and tryptophan synthase. We examine the structural diversity and distribution of these enzymes across evolutionary domains, highlighting conserved catalytic mechanisms alongside species-specific regulatory adaptations. The review covers directed evolution strategies that have transformed naturally regulated enzymes into standalone biocatalysts with enhanced activity and expanded substrate scope, enabling synthesis of non-canonical amino acids and complex organic molecules. Industrial applications demonstrate the pathway’s potential for sustainable production of pharmaceuticals, polymer precursors, and specialty chemicals through engineered microbial platforms. Additionally, we discuss the therapeutic potential of inhibitors targeting pathogenic organisms, particularly their mechanisms of action and antimicrobial efficacy. This comprehensive review establishes the shikimate pathway as a paradigmatic system where understanding allosteric networks enables the rational design of biocatalytic platforms, providing blueprints for biotechnological innovation and demonstrating how evolutionary constraints can be overcome through protein engineering to create superior industrial biocatalysts. Full article

This study presents a hybrid method for state-of-charge (SOC) estimation of lithium-ion batteries using LSTM neural networks optimized with genetic algorithms (GA), combined with Coulomb Counting (CC) as an initial estimator. Experimental tests were conducted using medium-voltage (48–72 V) lithium-ion battery packs under standardized driving cycles (NEDC and WLTP). The proposed method enhances prediction accuracy under dynamic conditions by recalibrating the LSTM output with CC estimates through a dynamic fusion parameter $\alpha$. The novelty of this approach lies in the integration of machine learning and physical modeling, optimized via evolutionary algorithms, to address limitations of standalone methods in real-time applications. The hybrid model achieved a mean absolute error (MAE) of 0.181%, outperforming conventional estimation strategies. These findings contribute to more reliable battery management systems (BMS) for electric vehicles and second-life applications. Full article

This paper presents a standalone system that utilizes a capacitive isolated series resonant converter using an extremum-seeking control algorithm to extract the maximum power from PV panels. While resonant converters have been used for battery charging applications, series resonant converters that utilize capacitive galvanic isolation have not been sufficiently explored, and their design considerations for battery charging have not been established. In addition, extremum-seeking control algorithms have been explored for maximum power point tracking using PWM converters, but not using PFM converters such as resonant converters. This paper lays out the advantages of using an extremum-seeking-based control algorithm with resonant converters, specifically series resonant converters, and it presents simulation results of a 200 W standalone battery charging system to validate the stated benefits. Full article

Background: Wound healing and scar management remain significant challenges in dermatology and aesthetic medicine. Recent advances in regenerative medicine have introduced plant-derived exosome-like nanoparticles (PDENs) as potential therapeutic agents due to their bioactive properties. This study examines the clinical application of rose stem cell exosomes (RSCEs) in combination with established treatments for managing different types of scars. Methods: A case series of four patients with different scar etiologies (dog bite, hot oil burn, forehead trauma, and facial laser treatment complications) was treated with RSCEs in combination with microneedling (Dermapen 4.0, 0.2–0.4 mm depth) and/or thulium laser therapy (Lutronic Ultra MD, 8–14 J), or as a standalone topical treatment. All cases underwent sequential treatments over periods ranging from two to four months, with comprehensive photographic documentation of the progression. The efficacy was assessed through clinical photography and objective evaluation using the modified Vancouver Scar Scale (mVSS) and the Patient and Observer Scar Assessment Scale (POSAS), along with assessment of scar appearance, texture, and coloration. Results: All cases demonstrated progressive improvement throughout the treatment course. The dog bite scar showed significant objective improvement, with a 71% reduction in modified Vancouver Scar Scale score (from 7/13 to 2/13) and a 61% improvement in Patient and Observer Scar Assessment Scale scores after four combined treatments. The forehead trauma case exhibited similar outcomes, with a 71% improvement in mVSS score and 55–57% improvement in POSAS scores. The hot oil burn case displayed the most dramatic improvement, with a 78% reduction in mVSS score and over 70% improvement in POSAS scores, resulting in near-complete resolution without visible scarring. The facial laser complication case showed a 75% reduction in mVSS score and ~70% improvement in POSAS scores using only topical exosome application without device-based treatments. Clinical improvements across all cases included reduction in elevation, improved texture, decreased erythema, and better integration with surrounding skin. No adverse effects were reported in any of the cases. Conclusions: This preliminary case series suggests that plant-derived exosome-like nanoparticles, specifically rose stem cell exosomes (RSCEs), may enhance scar treatment outcomes when combined with microneedling and laser therapy, or even as a standalone topical treatment. The documented objective improvements, measured by standardized scar assessment scales, along with clinical enhancements in scar appearance, texture, and coloration across different scar etiologies—dog bite, burn, traumatic injury, and iatrogenic laser damage—suggest that this approach may offer a valuable addition to the current armamentarium of scar management strategies. Notably, the successful treatment of laser-induced complications using only topical exosome application demonstrates the versatility and potential of this therapeutic modality. Full article

Robots are increasingly emerging as effective platforms to overcome a wide range of challenges in agriculture. Beyond functioning as standalone systems, agricultural robots are proving valuable as collaborative platforms, capable of supporting and integrating with humans and other technologies and agricultural activities. In this study, we designed and implemented an automated system embedded in a ground-based robotic platform to support spraying drone operations. The system consists of a robotic platform that carries the spraying drone along with all necessary support devices, including a water tank, chemical reservoirs, a mixer, generators for drone battery charging, and a top landing pad. The system is controlled with a mobile app that calculates the total amount of water and chemicals required and sends commands to the platform to prepare the application mixture. The input information in the app includes the field area, application rate, and up to three chemical dosages simultaneously. Additionally, the platform allows the drone to take off from and land on it, enhancing both safety and operability. A set of pumps was used to deliver water and chemicals as specified in the mobile app. To automate pump control, we used Arduino technology, including both the microcontroller and a programming environment for coding and designing the mobile app. To validate the system’s effectiveness, we individually measured the amount of water and chemical delivered to the mixer tank and compared it with conventional manual methods for calculating chemical quantities and preparation time. The system demonstrated consistent results, achieving high precision and accuracy in delivering the correct amount. This study advances the field of agricultural robotics by highlighting the role of collaborative platforms. Particularly, the system presents a valuable and low-cost solution for small farms and experimental research. Full article

Crataegus monogyna, commonly known as hawthorn, is a valuable plant in pharmaceutical production. Its flowers, leaves, and fruits are rich in antioxidants. This study explores the application of pulsed electric field (PEF) for enhanced extraction of bioactive compounds from C. monogyna leaves. The liquid-to-solid ratio, solvent composition (ethanol, water, and 50% v/v aqueous ethanol), and key PEF parameters—including pulse duration, pulse period, electric field intensity, and treatment duration—were investigated during the optimization process. To determine the optimal extraction conditions and their impact on antioxidant activity, response surface methodology (RSM) with a six-factor design was employed. The total polyphenol content in the optimized extract was 244 mg gallic acid equivalents/g dry weight, while individual polyphenols were analyzed using high-performance liquid chromatography coupled with a diode array detector (HPLC-DAD). Furthermore, antioxidant activity was assessed using ferric-reducing antioxidant power (FRAP) and DPPH radical scavenging assays, yielding values of 3235 and 1850 μmol ascorbic acid equivalents/g dry weight, respectively. Additionally, correlation analyses were conducted to evaluate the interactions between bioactive compounds and antioxidant capacity. Compared to other extraction techniques, PEF stands out as an eco-friendly, non-thermal standalone method, offering a sustainable approach for the rapid production of health-promoting extracts from C. monogyna leaves. Full article

To address the challenges of low recognition accuracy and substantial model complexity in crop disease identification models operating in complex field environments, this study proposed a novel hybrid model named ConvTransNet-S, which integrates Convolutional Neural Networks (CNNs) and transformers for crop disease identification tasks. Unlike existing hybrid approaches, ConvTransNet-S uniquely introduces three key innovations: First, a Local Perception Unit (LPU) and Lightweight Multi-Head Self-Attention (LMHSA) modules were introduced to synergistically enhance the extraction of fine-grained plant disease details and model global dependency relationships, respectively. Second, an Inverted Residual Feed-Forward Network (IRFFN) was employed to optimize the feature propagation path, thereby enhancing the model’s robustness against interferences such as lighting variations and leaf occlusions. This novel combination of a LPU, LMHSA, and an IRFFN achieves a dynamic equilibrium between local texture perception and global context modeling—effectively resolving the trade-offs inherent in standalone CNNs or transformers. Finally, through a phased architecture design, efficient fusion of multi-scale disease features is achieved, which enhances feature discriminability while reducing model complexity. The experimental results indicated that ConvTransNet-S achieved a recognition accuracy of 98.85% on the PlantVillage public dataset. This model operates with only 25.14 million parameters, a computational load of 3.762 GFLOPs, and an inference time of 7.56 ms. Testing on a self-built in-field complex scene dataset comprising 10,441 images revealed that ConvTransNet-S achieved an accuracy of 88.53%, which represents improvements of 14.22%, 2.75%, and 0.34% over EfficientNetV2, Vision Transformer, and Swin Transformer, respectively. Furthermore, the ConvTransNet-S model achieved up to 14.22% higher disease recognition accuracy under complex background conditions while reducing the parameter count by 46.8%. This confirms that its unique multi-scale feature mechanism can effectively distinguish disease from background features, providing a novel technical approach for disease diagnosis in complex agricultural scenarios and demonstrating significant application value for intelligent agricultural management. Full article

Palm oil is a highly versatile natural resource that has gathered significant attention due to its bioactive properties, particularly its antimicrobial and antioxidant effects. Rich in tocotrienols, tocopherols, and carotenoids, palm oil exhibits potent antioxidant activity, while its fatty acid content and other bioactive molecules contribute to its antimicrobial efficacy against various pathogens. The underlying mechanisms of action driving these bioactivities involve intricate molecular interactions, biochemical pathways, and redox processes, which influence microbial cell function and oxidative stress reduction. This review provides a critical analysis of the current mechanistic understanding of palm oil’s biofunctional properties, emphasizing its potential incorporation into engineered biomaterials. Particular focus is given to the chemical composition, reaction pathways, and synergistic potential of palm oil derivatives in material-based formulations. Furthermore, the potential applications of palm oil as a standalone or synergistic agent in novel therapeutic and industrial formulations are explored. By elucidating the mechanistic basis of its bioactivity within material contexts, this review highlights palm oil’s promising role in the development of advanced functional materials for pharmaceutical and dental technologies. Full article

Background/Objectives: With the rapid progression of technology, applications have been proposed as a promising alternative to conventional psychotherapeutic treatment. Nonetheless, research on unguided self-help applications for binge eating remains scarce, with most existing studies utilizing cognitive behavioral therapy (CBT) principles. Therefore, this paper presents the protocol for a randomized controlled trial designed to evaluate the efficacy and acceptability of eMOTE, a standalone application designed specifically for women in Portugal who binge eat. eMOTE, adapted from dialectical behavior therapy (DBT), is unique in that it focuses on teaching emotion regulation skills while also integrating core CBT strategies. Methods: At least 68 females who self-report binge eating episodes will be randomized into an intervention group with access to eMOTE for eight weeks or a delayed waitlist, which will have access to eMOTE after the T1 assessment. Assessments will be conducted at baseline (T0), post-intervention (T1), and at 2-month follow-up (T2). The primary outcomes will include objective and subjective binge eating frequency and binge eating symptomatology, while secondary outcomes will assess global levels of ED psychopathology, shape concern, weight concern, eating concern, dietary restraint, compensatory behaviors, mindfulness, emotion regulation difficulties, intuitive eating, psychological distress, and body mass index. Conclusions: This study will contribute to the limited literature on the use of smartphone technology as an alternative to traditional psychotherapy. Furthermore, this standalone application will offer insights into the use of emotion regulation and food monitoring components designed for adult females experiencing binge eating episodes. Full article

Rapid global urbanization poses complex challenges that demand advanced data-driven forecasting solutions for smart cities. Traditional statistical and standalone Long Short-Term Memory (LSTM) models often struggle to capture non-linear dynamics and long-term dependencies in urban time-series data. This review critically examines hybrid LSTM models that integrate LSTM with complementary algorithms, including CNN, GRU, ARIMA, and SVM. These hybrid architectures aim to enhance prediction accuracy, integrate diverse data sources, and improve computational efficiency. This study systematically reviews principles, trends, and real-world applications, quantitatively evaluating hybrid LSTM models using performance metrics such as mean absolute error (MAE), root mean square error (RMSE), and the coefficient of determination (R²), while identifying key study limitations. The case studies considered include traffic management, environmental monitoring, energy forecasting, public health, infrastructure assessment, and urban waste management. For example, hybrid models have achieved substantial accuracy improvements in traffic congestion forecasting, reducing their mean absolute error by up to 29%. Despite the inherent challenges related to structural complexity, interpretability, and data requirements, ongoing research on attention mechanisms, model compression, and explainable AI has significantly mitigated these limitations. Thus, hybrid LSTM models have emerged as vital analytical tools capable of robust spatiotemporal prediction, effectively supporting sustainable urban development and data-driven decision-making in evolving smart city environments. Full article

BeiDou navigation satellite system (BDS) precise point positioning (PPP)-B2b has significant potential for application in meteorological fields, such as standalone water vapor monitoring in depopulated area without Internet. In this study, the practical ability of water vapor monitoring using the BDS PPP-B2b service is illustrated through a continuously operated water vapor monitoring system in Wuhan, China, with a 25-day experiment in 2025. Original observations from the Global Positioning System (GPS) and BDS are collected and processed in the near real-time (NRT) mode using ephemeris from the PPP-B2b service. Precipitable water vapor PWV monitored with B2b ephemeris are evaluated with radiosonde and ERA5 reanalysis, respectively. Taking PWV from radiosonde observations as the reference, RMS of PWV based on B2b ephemeris varies from 3.71 to 4.66 mm for different satellite combinations. While those values are with a range from 3.95 to 4.55 mm when compared with ERA5 reanalysis. These values are similar to those processed with the real-time ephemeris from the China Academy of Science (CAS). In general, this study demonstrates that the practical accuracy of water vapor monitored based on the BDS PPP-B2b service can meet the basic demand for operational meteorology for the first time. This will provide a scientific reference for its wide promotion to meteorological applications in the near future. Full article

With the increasing application of sodium-ion batteries in energy storage systems, accurate state of charge (SOC) estimation plays a vital role in ensuring both available battery capacity and operational safety. Traditional Kalman-filter-based methods often suffer from limited model expressiveness or oversimplified physical assumptions, making it difficult to balance accuracy and robustness under complex operating conditions, which may lead to unreliable estimation results. To address these challenges, this paper proposes a hybrid framework that combines an unscented Kalman filter (UKF) with a long short-term memory (LSTM) neural network for SOC estimation. Under various driving conditions, the UKF—based on a second-order equivalent circuit model with online parameter identification—provides physically interpretable estimates, while LSTM effectively captures complex temporal dependencies. Experimental results under CLTC, NEDC, and WLTC cycles demonstrate that the proposed LSTM-UKF approach reduces the mean absolute error (MAE) by an average of 2% and the root mean square error (RMSE) by an average of 3% compared to standalone methods. The proposed framework exhibits excellent adaptability across different scenarios, offering a precise, stable, and robust solution for SOC estimation in sodium-ion batteries. Full article

Nowadays, the Metaverse is facing many challenges. In this context, Virtual Reality (VR) applications allowing voice-based human–3D object interactions are limited due to the current hardware/software limitations. In fact, adopting Automated Speech Recognition (ASR) systems to interact with 3D objects in VR applications through users’ voice commands presents significant challenges due to the hardware and software limitations of headset devices. This paper aims to bridge this gap by proposing a methodology to address these issues. In particular, starting from a Mel-Frequency Cepstral Coefficient (MFCC) extraction algorithm able to capture the unique characteristics of the user’s voice, we pass it as input to a Convolutional Neural Network (CNN) model. After that, in order to integrate the CNN model with a VR application running on a standalone headset, such as Oculus Quest, we converted it into an Open Neural Network Exchange (ONNX) format, i.e., a Machine Learning (ML) interoperability open standard format. The proposed system demonstrates good performance and represents a foundation for the development of user-centric, effective computing systems, enhancing accessibility to VR environments through voice-based commands. Experiments demonstrate that a native CNN model developed through TensorFlow presents comparable performances with respect to the corresponding CNN model converted into the ONNX format, paving the way towards the development of VR applications running in headsets controlled through the user’s voice. Full article