Parkinson’s disease (PD) is a neurodegenerative movement disorder associated with a loss of dopamine neurons in the substantia nigra. The diagnosis of PD is sensitive since it shows clinical features that are common with other neurodegenerative diseases. In addition, most symptoms arise at the late stage of the disease, where most dopaminergic neurons are already damaged. Several studies reported that oxidative stress is a key modulator in the development of PD. This condition occurs due to excess reactive oxygen species (ROS) production in the cellular system and the incapability of antioxidants to neutralize it. In this study, we focused on the pathology of PD by measuring serum xanthine oxidase (XO) activity, which is an enzyme that generates ROS. Interestingly, the serum XO activity of patients with PD was markedly upregulated compared to patients with other neurological diseases (ONDs) as a control. Moreover, serum XO activity in patients with PD showed a significant correlation with the disease severity based on the Hoehn and Yahr (HY) stages. The investigation of antioxidant status also revealed that serum uric acid levels were significantly lower in the severe group (HY ≥ 3) than in the ONDs group. Together, these results suggest that XO activity may contribute to the development of PD and might potentially be a biomarker for determining disease severity in patients with PD. Full article

The safety condition assessment of prestressed concrete bridges is currently subject to great uncertainty due to the subjectivity of data collection and data types. This study proposes an improved evidence fusion method, improving the conventional Dempster–Shafer fusion method to reduce assessment inaccuracies caused by data uncertainty. Firstly, the uncertain analytic hierarchy process was applied to construct a three-level safety assessment model for 15 different indicators with their initial weights. Secondly, the fuzzy matter element theory was proposed to obtain basic probability assignments required for the evidence fusion. Finally, an improved evidence fusion method was proposed based on the evidence credibility and preprocessing corrections for highly conflicting evidence. In this study, a prestressed concrete bridge in eastern China was used as a case study to perform a comprehensive safety assessment and verify the effectiveness and practicality of the proposed method. The assessment results demonstrate that the improved fusion method in this study can deal with conflicting evidence better than existing fusion methods. Compared with conventional fuzzy AHP method, it has greater sensitivity to certain indicators with severe damages, which prevents those indicators from being overshadowed by other well-performing ones in the overall assessment. Full article

This study explores the historical occurrence of wetland ecosystems in Greece by using recurring Phragmites australis (common reed) burnings as an indicator. Phragmites australis, a plant closely associated with wetlands, provides excellent insights into wetland distribution. We establish a substantial association between reed fires and historical wetland existence in Greece using geographical and statistical analysis, with these fires exhibiting remarkable constancy across time. Using Corine land-cover (CLC) data, we extend our analysis into land-use dynamics, demonstrating that places with the highest reed-bed-fire rates were originally wetlands, particularly those converted into permanent irrigated land and areas with complex agriculture patterns. We find spatial commonalities between reed fires and past wetland existence by analyzing fire occurrence across three main categories: reed fires, agricultural land fires, and grassland fires. Historical records of wetland conversion into agricultural land (or land reclamation works) in locations such as Yianitsa and Kopaida give context to our findings. Visualizations confirm the clustering of reed fires around these converted agricultural regions. In summary, our study offers a unique indicator based on Phragmites australis burnings that can be used to identify previous wetland-type ecosystems, with Mediterranean-wide implications. Despite data constraints, this study adds to the conversation about wetland preservation and sustainable land-use management. Full article

Knowledge tracing plays a crucial role in effectively representing learners’ understanding and predicting their future learning progress. However, existing deep knowledge tracing methods, reliant on the forgetting model and Rasch model, often fail to account for the varying rates at which learners forget different knowledge concepts and the variations in question embedding covering the same concept. To address these limitations, this paper introduces an enhanced deep knowledge tracing model that combines the transformer network model with two innovative components. The first component is a multiband attention mechanism, which comprehensively summarizes a learner’s past response history across various temporal scales. By computing attention weights using different decay rates, this mechanism adaptively captures both long-term and short-term interactions for different knowledge concepts. The second component utilizes a quantized question embedding module to effectively capture variations among questions addressing the same knowledge concept. This module represents these differences in a rich embedding space, avoiding overparameterization or overfitting issues. The proposed model is evaluated on popular benchmark datasets, demonstrating its superiority over existing knowledge tracing methods in accuracy. This enhancement holds potential for improving personalized learning systems by providing more precise insights into learners’ progress. Full article

Due to the high pollution of the transportation sector, nowadays the role of electric vehicles has been noticed more and more by governments, organizations, and environmentally friendly people. On the other hand, the problem of electric vehicle routing (EVRP) has been widely studied in recent years. This paper deals with an extended version of EVRP, in which electric vehicles (EVs) deliver goods to customers. The limited battery capacity of EVs causes their operational domains to be less than those of gasoline vehicles. For this purpose, several charging stations are considered in this study for EVs. In addition, depending on the operational domain, a full charge may not be needed, which reduces the operation time. Therefore, partial recharging is also taken into account in the present research. This problem is formulated as a multi-objective integer linear programming model, whose objective functions include economic, environmental, and social aspects. Then, the preemptive fuzzy goal programming method (PFGP) is exploited as an exact method to solve small-sized problems. Also, two hybrid meta-heuristic algorithms inspired by nature, including MOSA, MOGWO, MOPSO, and NSGAII_TLBO, are utilized to solve large-sized problems. The results obtained from solving the numerous test problems demonstrate that the hybrid meta-heuristic algorithm can provide efficient solutions in terms of quality and non-dominated solutions in all test problems. In addition, the performance of the algorithms was compared in terms of four indexes: time, MID, MOCV, and HV. Moreover, statistical analysis is performed to investigate whether there is a significant difference between the performance of the algorithms. The results indicate that the MOSA algorithm performs better in terms of the time index. On the other hand, the NSGA-II-TLBO algorithm outperforms in terms of the MID, MOCV, and HV indexes. Full article

Spectral Photon-Counting Computed Tomography (SPCCT) represents a groundbreaking advancement in X-ray imaging technology. The core innovation of SPCCT lies in its photon-counting detectors, which can count the exact number of incoming x-ray photons and individually measure their energy. The first part of this review summarizes the key elements of SPCCT technology, such as energy binning, energy weighting, and material decomposition. Its energy-discriminating ability represents the key to the increase in the contrast between different tissues, the elimination of the electronic noise, and the correction of beam-hardening artifacts. Material decomposition provides valuable insights into specific elements’ composition, concentration, and distribution. The capability of SPCCT to operate in three or more energy regimes allows for the differentiation of several contrast agents, facilitating quantitative assessments of elements with specific energy thresholds within the diagnostic energy range. The second part of this review provides a brief overview of the applications of SPCCT in the assessment of various cardiovascular disease processes. SPCCT can support the study of myocardial blood perfusion and enable enhanced tissue characterization and the identification of contrast agents, in a manner that was previously unattainable. Full article

Precise Point Positioning (PPP), coupled with the ambiguity resolution (AR) method, has demonstrated substantial potential in fields like agricultural navigation and airborne mapping. However, there remains a notable deficiency in the comprehensive comparative evaluation of its performance when using rapid and real-time satellite products, especially for mass low-cost receivers. Stations equipped with geodetic and low-cost receivers are analyzed in kinematic and static mode. In the kinematic mode, the GPS+Galileo-combined PPP, employing ambiguity fixing with “WHU” rapid products, achieves the highest positioning accuracy of 0.9 cm, 0.9 cm, and 2.6 cm in the North, East, and Up components, respectively. Real-time PPP using “CNT” products attains accuracies of 2.1 cm, 2.7 cm, and 4.8 cm for these components, utilizing GPS ambiguity-fixed PPP. BDS positioning accuracy is inferior to standalone GPS, but improves when the number of visible BDS satellites exceeds 10. Convergence time analysis shows that approximately 38.2 min are required for single GPS or BDS PPP using the “WHU” products and geodetic receivers, which can be reduced to 18.5 min for dual-system combinations and further to 14.8 min for triple-system combinations. The time can be further reduced by ambiguity fixing. In the static mode, multi-GNSS combination does not significantly impact convergence times, which are more influenced by the precision of the products used. Real-time products require approximately 22 min to achieve horizontal accuracy below 0.1 m, while rapid products reach this accuracy within 10 min. For PPP using low-cost GNSS receivers, more than two hours are necessary to achieve an accuracy better than 0.1 m for kinematic PPP, which is considerably longer than the convergence time observed at MGEX stations. However, the accuracy achieved after convergence, as well as the performance of static PPP, is comparable to that of MGEX stations. Full article

This study presents a Two-Layer Deep Deterministic Policy Gradient (TL-DDPG) energy management strategy for Hydrogen fuel cell hybrid train, that aims to solve the problem that traditional reinforcement learning strategies require high initial values and are difficult to optimize global variables. Augmenting the optimization capabilities of the inner layer, a frequency decoupling algorithm integrates into the outer layer, furnishing a fitting initial value for strategy optimization. This addition aims to bolster the stability of fuel cell output, thereby enhancing the overall efficiency of the hybrid power system. In comparison with the traditional reinforcement learning algorithm, the proposed approach demonstrates notable improvements: a reduction in hydrogen consumption per 100 km by 16.3 kg, a 9.7% increase in the output power stability of the fuel cell, and a 1.8% enhancement in its efficiency. Full article

Teaching gesture recognition is a technique used to recognize the hand movements of teachers in classroom teaching scenarios. This technology is widely used in education, including for classroom teaching evaluation, enhancing online teaching, and assisting special education. However, current research on gesture recognition in teaching mainly focuses on detecting the static gestures of individual students and analyzing their classroom behavior. To analyze the teacher’s gestures and mitigate the difficulty of single-target dynamic gesture recognition in multi-person teaching scenarios, this paper proposes skeleton-based teaching gesture recognition (ST-TGR), which learns through spatio-temporal representation. This method mainly uses the human pose estimation technique RTMPose to extract the coordinates of the keypoints of the teacher’s skeleton and then inputs the recognized sequence of the teacher’s skeleton into the MoGRU action recognition network for classifying gesture actions. The MoGRU action recognition module mainly learns the spatio-temporal representation of target actions by stacking a multi-scale bidirectional gated recurrent unit (BiGRU) and using improved attention mechanism modules. To validate the generalization of the action recognition network model, we conducted comparative experiments on datasets including NTU RGB+D 60, UT-Kinect Action3D, SBU Kinect Interaction, and Florence 3D. The results indicate that, compared with most existing baseline models, the model proposed in this article exhibits better performance in recognition accuracy and speed. Full article

The process of lipid crystallization influences the characteristics of lipid. By changing the chemical composition of the lipid system, the crystallization behavior could be controlled. This review elucidates the internal factors affecting lipid crystallization, including triacylglycerol (TAG) structure, TAG composition, and minor components. The influence of these factors on the TAG crystal polymorphic form, nanostructure, microstructure, and physical properties is discussed. The interplay of these factors collectively influences crystallization across various scales. Variations in fatty acid chain length, double bonds, and branching, along with their arrangement on the glycerol backbone, dictate molecular interactions within and between TAG molecules. High-melting-point TAG dominates crystallization, while liquid oil hinders the process but facilitates polymorphic transitions. Unique molecular interactions arise from specific TAG combinations, yielding molecular compounds with distinctive properties. Nanoscale crystallization is significantly impacted by liquid oil and minor components. The interaction between the TAG and minor components determines the influence of minor components on the crystallization process. In addition, future perspectives on better design and control of lipid crystallization are also presented. Full article

The era of Industry 4.0 is gradually transforming our society into a data-driven one, which can help us uncover valuable information from accumulated data, thereby improving the level of social governance. The detection of anomalies, is crucial for maintaining societal trust and fairness, yet it poses significant challenges due to the ubiquity of anomalies and the difficulty in identifying them accurately. This paper aims to enhance the performance of the current Graph Convolutional Network (GCN)-based Graph Anomaly Detection (GAD) algorithm on datasets with extremely low proportions of anomalous labels. This goal is achieved through modifying the GCN network structure and conducting feature extraction, thus fully utilizing three types of information in the graph: node label information, node feature information, and edge information. Firstly, we theoretically demonstrate the relationship between label propagation and feature convolution, indicating that the Label Propagation Algorithm (LPA) can serve as a regularization penalty term for GCN, aiding in training and enabling learnable edge weights, providing a basis for incorporating node label information into GCN networks. Secondly, we introduce a method to aggregate node and edge features, thereby incorporating edge information into GCN networks. Finally, we design different GCN trainable weights for node features and co-embedding features. This design allows different features to be projected into different spaces, greatly enhancing model expressiveness. Experimental results on the DGraph dataset demonstrate superior AUC performance compared to baseline models, highlighting the feasibility and efficacy of the proposed approach in addressing GAD tasks in the scene with extremely low proportions of anomalous data. Full article

Background: Accurate detection of axillary lymph node (ALN) metastases in breast cancer is crucial for clinical staging and treatment planning. This study aims to develop a deep learning model using clinical implication-applied preprocessed computed tomography (CT) images to enhance the prediction of ALN metastasis in breast cancer patients. Methods: A total of 1128 axial CT images of ALN (538 malignant and 590 benign lymph nodes) were collected from 523 breast cancer patients who underwent preoperative CT scans between January 2012 and July 2022 at Hallym University Medical Center. To develop an optimal deep learning model for distinguishing metastatic ALN from benign ALN, a CT image preprocessing protocol with clinical implications and two different cropping methods (fixed size crop [FSC] method and adjustable square crop [ASC] method) were employed. The images were analyzed using three different convolutional neural network (CNN) architectures (ResNet, DenseNet, and EfficientNet). Ensemble methods involving and combining the selection of the two best-performing CNN architectures from each cropping method were applied to generate the final result. Results: For the two different cropping methods, DenseNet consistently outperformed ResNet and EfficientNet. The area under the receiver operating characteristic curve (AUROC) for DenseNet, using the FSC and ASC methods, was 0.934 and 0.939, respectively. The ensemble model, which combines the performance of the DenseNet121 architecture for both cropping methods, delivered outstanding results with an AUROC of 0.968, an accuracy of 0.938, a sensitivity of 0.980, and a specificity of 0.903. Furthermore, distinct trends observed in gradient-weighted class activation mapping images with the two cropping methods suggest that our deep learning model not only evaluates the lymph node itself, but also distinguishes subtler changes in lymph node margin and adjacent soft tissue, which often elude human interpretation. Conclusions: This research demonstrates the promising performance of a deep learning model in accurately detecting malignant ALNs in breast cancer patients using CT images. The integration of clinical considerations into image processing and the utilization of ensemble methods further improved diagnostic precision. Full article

Non-Representational Theory (NRT) emphasizes the significance of routine experience in shaping human geography. In doing so, the theory largely eschews traditional approaches that have offered area-based, longitudinal, and synoptic formalisms for geographic inquiry. Instead, NRT prioritizes the roles of individualized and often dynamic lived geographies as they unfold in the moment. To date, NRT has drawn significant inspiration from the synergies that it shares with philosophy, critical geography, and self-referential ethnography. These activities have been tremendous in advancing NRT as a concept, but the theory’s strong ties to encounter and experience invariably call for practical exposition. Alas, applications of NRT to concrete examples at scales beyond small case studies often prove challenging, which we argue artificially constrains further development of the theory. In this paper, we examine some of the thorny problems that present in applying NRT in practical terms. Specifically, we identify ten traps that NRT can fall into when moving from theory to actuality. These traps include conundrums of small geographies, circularity in representation, cognitive traps, issues of mustering and grappling with detail, access issues, limitations with empiricism, problems of subjectivity, methodological challenges, thorny issues of translation, and the unwieldy nature of process dynamics. We briefly demonstrate a novel observational instrument that can sidestep some, but not all, of these traps. Full article

Megaproject Social Responsibility (MSR) is widely acknowledged as contributing to project performance. However, the effect of Megaproject Social Responsibility Behavior (MSRB) implemented by organizations participating in construction on project performance remains a subject of considerable debate, and the intrinsic mechanism of MSRB’s effect on the performance of megaprojects has not been elucidated. Therefore, this study employs resource-based theory to investigate the mechanism underlying MSRB’s effect on project performance, taking into account both internal and external social capital as well as resource integration capacity as pivotal influences. Drawing on sample data from 206 experienced project managers across the various parties involved, this study develops a Bayesian network model to elucidate the MSRB effect mechanism. Through inference and sensitivity analysis, this study discovers variations in the enhancement effects across the four dimensions of MSRB on project performance. Notably, a combination strategy yields superior enhancement effects. Furthermore, when project performance is suboptimal, resource integration capacity emerges as a significant mediator between MSRB and project performance. Conversely, at high levels of project performance, MSRB directly contributes to enhancing project outcomes. The findings of this study offer valuable insights for the governance of MSR and the enhancement of project performance in megaprojects. Full article

The toxicity of emerging organic pollutants to photosystems of aquatic plants is still not well clarified. This study aimed to develop a novel ecotoxicological experimental protocol based on nanoscale electrochemical mapping of photosynthetic oxygen evolution of aquatic plants by scanning electrochemical microscopy (SECM). The protocol was also checked by confocal laser scanning microscopy (CLSM), the traditional Clark oxygen electrode method, and the chlorophyll fluorescence technique. The typical persistent organic pollutant 2,4-dichlorophenol (2,4-DCP) in a water environment and the common aquatic Microsorium pteropus (M. pteropus) were chosen as the model organic pollutant and tested plant, respectively. It was found that the SECM method could discriminate the responses of stoma micromorphology and spatial pattens of photosynthetic oxygen evolution on single stoma well. The shape of stoma blurred with increasing 2,4-DCP concentration, which was in good agreement with the CLSM images. The dose–response curves and IC₅₀ values obtained from the SECM data were verified by the data measured by the traditional Clark oxygen electrode method and chlorophyll fluorescence test. The IC₅₀ value of single-stoma oxygen emission of plant leaves exposed for 24 h, which was derived from the SECM current data (32,535 μg L⁻¹), was close to those calculated from the maximum photosynthetic efficiency (Fv/Fm) measured by the chlorophyll fluorescence test (33,963 μg L⁻¹) and the Clark oxygen electrode method photosynthetic oxygen evolution rate (32,375 μg L⁻¹). The 72 h and 96 h 2,4-DCP exposure data further confirmed the reliability of the nanoscale stoma oxygen emission mapping methodology for ecotoxicological assessment. In this protocol, the procedures for how to collect effective electrochemical data and how to extract useful information from the single-stoma oxygen emission pattern were well established. This study showed that SECM is a feasible and reliable ecotoxicological tool for evaluation of toxicity of organic pollutants to higher plants with a unique nanoscale visualization advantage over the conventional methods. Full article

Ovule abortion significantly contributes to a reduction in chestnut yield. Therefore, an examination of the mechanisms underlying ovule abortion is crucial for increasing chestnut yield. In our previous study, we conducted a comprehensive multiomic analysis of fertile and abortive ovules and found that ACS genes in chestnuts (CmACS) play a crucial role in ovule development. Therefore, to further study the function of ACS genes, a total of seven CmACS members were identified, their gene structures, conserved structural domains, evolutionary trees, chromosomal localization, and promoter cis-acting elements were analyzed, and their subcellular localization was predicted and verified. The spatiotemporal specificity of the expression of the seven CmACS genes was confirmed via qRT–PCR analysis. Notably, CmACS7 was exclusively expressed in the floral organs, and its expression peaked during fertilization and decreased after fertilization. The ACC levels remained consistently greater in fertile ovules than in abortive ovules. The ACSase activity of CmACS7 was identified using the genetic transformation of chestnut healing tissue. Micro Solanum lycopersicum plants overexpressing CmACS7 had a significantly greater rate of seed failure than did wild-type plants. Our results suggest that ovule fertilization activates CmACS7 and increases ACC levels, whereas an overexpression of CmACS7 leads to an increase in ACC content in the ovule prior to fertilization, which can lead to abortion. In conclusion, the present study demonstrated that chestnut ovule abortion is caused by poor fertilization and not by nutritional competition. Optimization of the pollination and fertilization of female flowers is essential for increasing chestnut yield and reducing ovule abortion. Full article

The complex and multi-stage processes of carcinogenesis are accompanied by a number of phenomena related to the potential involvement of various chemopreventive factors, which include, among others, compounds of natural origin such as flavonols. The use of flavonols is not only promising but also a recognized strategy for cancer treatment. The chemopreventive impact of flavonols on cancer arises from their ability to act as antioxidants, impede proliferation, promote cell death, inhibit angiogenesis, and regulate the immune system through involvement in diverse forms of cellular death. So far, the molecular mechanisms underlying the regulation of apoptosis, autophagy, necroptosis, pyroptosis, ferroptosis, and cuproptosis occurring with the participation of flavonols have remained incompletely elucidated, and the results of the studies carried out so far are ambiguous. For this reason, one of the therapeutic goals is to initiate the death of altered cells through the use of quercetin, kaempferol, myricetin, isorhamnetin, galangin, fisetin, and morin. This article offers an extensive overview of recent research on these compounds, focusing particularly on their role in combating cancer and elucidating the molecular mechanisms governing apoptosis, autophagy, necroptosis, pyroptosis, ferroptosis, and cuproptosis. Assessment of the mechanisms underlying the anticancer effects of compounds in therapy targeting various types of cell death pathways may prove useful in developing new therapeutic regimens and counteracting resistance to previously used treatments. Full article

This study investigated the spatiotemporal characteristics and influencing factors of PM2.5 concentrations at the provincial scale in China. The findings indicate significant spatial autocorrelation, with notable high–high agglomerations in East and North China and mixed patterns in the northwest. The spatial Durbin model (SDM) with fixed effects, validated through comprehensive tests, was utilized to analyze data on 31 provincial scale regions from 2000 to 2020, addressing spatial autocorrelation and ensuring model reliability. The research delved into the effects of 21 variables on PM2.5 concentrations, identifying synergistic and trade-off effects among environmental and socioeconomic indicators. Environmental measures like vegetation protection and sulfur dioxide emission reduction correlate with lower PM2.5 levels, whereas economic growth and transport volume often align with increased pollution. The analysis reveals regional variances in these effects, suggesting the need for region-specific policies. The study underscores the intricate relationship between environmental policies, economic development, and air quality, advocating for an integrated approach to air quality improvement. It highlights the necessity of balancing industrial growth with environmental sustainability and suggests targeted, region-specific strategies to combat PM2.5 pollution effectively. This study offers crucial insights for policymakers, emphasizing that enhancing air quality requires comprehensive strategies that encompass environmental, economic, and technological dimensions to foster sustainable development. Full article

While total knee arthroplasties (TKAs) are performed with the intent to reduce pain, chronic postsurgical pain (CPSP) is one of the most well-documented complications that can occur following surgery. This study aimed to assess whether perioperative factors, focusing on acute postsurgical pain and perioperative opioid consumption, were associated with the development of chronic postsurgical pain. Under general anesthesia, 108 patients underwent TKA and were treated postoperatively with a multimodal analgesia approach. Numeric Rating Scale (NRS) pain scores at rest and with movement were recorded on postoperative days 0–3, 7, 14, and 30. Patients were sent a survey to assess chronic pain at months 22–66, which was examined as a single-group post hoc analysis. Based on the responses, patients were either classified into the CPSP or non-CPSP patient group. Chronic postsurgical pain was defined as an NRS score ≥ 4 with movement and the presence of resting pain. The primary outcome was a change in NRS. There were no differences in NRS pain scores with movement in the first 30 days postoperatively between patients with CPSP and without CPSP. Each unit increase in resting pain on postoperative days 3 and 14 was associated with significantly greater odds of CPSP presence (OR = 1.52; OR = 1.61, respectively), with a trend towards greater odds of CPSP at days 7 and 30 (OR = 1.33; OR = 1.43, respectively). We found that very intense pain in the initial phase seems to be related to the development of CPSP after TKA. Full article

In the world of human–robot coexistence, ensuring safe interactions is crucial. Traditional logic-based methods often lack the intuition required for robots, particularly in complex environments where these methods fail to account for all possible scenarios. Reinforcement learning has shown promise in robotics due to its superior adaptability over traditional logic. However, the exploratory nature of reinforcement learning can jeopardize safety. This paper addresses the challenges in planning trajectories for robotic arm manipulators in dynamic environments. In addition, this paper highlights the pitfalls of multiple reward compositions that are susceptible to reward hacking. A novel method with a simplified reward and constraint formulation is proposed. This enables the robot arm to avoid a nonstationary obstacle that never resets, enhancing operational safety. The proposed approach combines scalarized expected returns with a constrained Markov decision process through a Lagrange multiplier, resulting in better performance. The scalarization component uses the indicator cost function value, directly sampled from the replay buffer, as an additional scaling factor. This method is particularly effective in dynamic environments where conditions change continually, as opposed to approaches relying solely on the expected cost scaled by a Lagrange multiplier. Full article

In this study, a series of four surface-active compounds—N-alkyl betaine ethyl ester chlorides, C_nBetC₂Cl—were synthesized and characterized in aqueous solutions. As with other alkyl betaines, these amphiphiles can be practically used, for example, as co-surfactants and/or solubility enhancers acting according to hydrotropic or micellar mechanisms, depending on the alkyl chain length in the amine. We focused on the representatives of the medium alkyl chain length (C₆–C₁₂) to find the dependence between the alkyl chain length in N-alkyl betaine ethyl ester chlorides and the surface, volumetric, acoustic, and viscometric properties of their solutions. Ethyl esters, the derivatives of amino acids, were chosen to increase functionality and take advantage of possible hydrolysis in solutions at higher pH, which is also a key parameter in biodegradability. The micellization parameters were calculated based on the physicochemical characteristics. We focused our interest on the ester with a dodecyl substituent since we can compare and discuss its properties with some other C₁₂ representatives that are available in literature. Surprisingly, its micellization characteristic is almost temperature-independent in the investigated temperature range, t = (15–45) °C. Particularly interesting are the results of dynamic light scattering (DLS), which show that the changes in physicochemical parameters of the C₁₂ homolog around the CMC are caused by the two types of micelles of different sizes present in solutions. Full article