Ergonomics are key in the design and application of Ming-style chairs. However, there are presently few specific design frameworks to guide Ming-style chair design. Under this background, the present study developed a questionnaire on the ergonomic design of Ming-style chairs and assessed its validity and reliability. Fifty-two respondents involved in the design and manufacture of Ming-style chairs participated in this study. The statistics of the questionnaire were analyzed and yielded a significant reliability coefficient (α > 0.70, p < 0.01). This ergonomic design framework study of Ming-style chairs analyzed the domains of Chair Form, Aesthetics, Safety, Comfort, Ease-of-use, and Productivity. To assess the importance of various design elements, we used a five-point Likert scale to score items within each domain. This scoring system enabled us to prioritize features, allowing the designers to focus on the essential elements before beginning the design process. We found that designers and manufacturers focused primarily on the Four-headed Official Chair with Armrests form. Full article

Listeria monocytogenes (L. monocytogenes) poses a significant threat to food safety due to its ability to cause severe human illness and its resistance to various antibiotics and environmental conditions. This study investigated the prevalence, serotype distribution, virulence gene profiles, and antimicrobial resistance patterns of L. monocytogenes in ready-to-eat (RTE) food products from Romania. A total of 8151 samples were analyzed, including various processed dairy, bovine, poultry, pork, and fish products. Bacterial isolation was conducted using the classical standard method, followed by confirmation through biochemical and molecular testing. Among the isolated strains, serotypes 1/2a, 1/2b, and 1/2c were identified, with a prevalence of 75% for serotype 1/2a. Additionally, virulence genes specific to listeriolysin O (hlyA) and regulatory factor A (prfA) were detected in all isolates. Antimicrobial susceptibility testing revealed varying resistance patterns among the L. monocytogenes strains. Trimethoprim-sulfamethoxazole and oxacillin showed the highest prevalence of resistance at 26.92% and 23.07%, respectively. However, all strains remained susceptible to ciprofloxacin, levofloxacin, and moxifloxacin. Notably, 23.07% of the isolates exhibited multidrug resistance, with the most common pattern being resistance to oxacillin, penicillin, and tetracycline. Analysis of antimicrobial resistance genes identified tetracycline resistance genes, particularly tet(C), tet(M), and tet(K), in a significant proportion of isolates. The presence of ampC and dfrD genes was also notable, indicating potential mechanisms of resistance. These results emphasize the necessity for ongoing surveillance of L. monocytogenes in RTE foods and emphasize the importance of thorough monitoring of antimicrobial resistance to guide public health strategies within the European Union. Full article

DNA analysis plays a crucial role in forensic investigations, helping in criminal cases, missing persons inquiries, and archaeological research. This study focuses on the DNA concentration in different skeletal elements to improve human identification efforts. Ten cases of unidentified skeletal remains brought to the Institute of Forensic Medicine in Timisoara, Romania, underwent DNA analysis between 2019 and 2023. The results showed that teeth are the best source for DNA extraction as they contain the highest concentration of genetic material, at 3.68 ng/µL, compared to the petrous temporal bone (0.936 ng/µL) and femur bone (0.633 ng/µL). These findings highlight the significance of teeth in forensic contexts due to their abundant genetic material. Combining anthropological examination with DNA analysis enhances the understanding and precision of identifying human skeletal remains, thus advancing forensic science. Selecting specific skeletal elements, such as the cochlea or teeth, emerges as crucial for reliable genetic analyses, emphasizing the importance of careful consideration in forensic identification procedures. Our study concludes that automated DNA extraction protocols without liquid nitrogen represent a significant advancement in DNA extraction technology, providing a faster, more efficient, and less labor-intensive method for extracting high-quality DNA from damaged bone and tooth samples. Full article

For over three decades, the European Union’s (EU) transport policy has aimed at fostering environmental sustainability and energy efficiency. Since 2015, European policymakers have focused more on three key sustainable development goals: decarbonizing the transport system, promoting low-emission mobility solutions, and transitioning to renewable and alternative fuels. To effectively communicate priorities and engage stakeholders, EU policymakers regularly use social media platforms like Twitter (now known as X). This active discourse involves policymakers, industrial stakeholders, the media, and the public, offering insights into the role of transport policy in addressing climate change and energy transition challenges. The current research endeavors to track and analyze the evolution of user-generated content related to climate change, energy transition, and smart mobility on Twitter from 2011 to 2021. This research uses text-mining and social network analysis techniques to quantitatively and qualitatively assess the dynamics of relevant EU policies and their effects. The study’s findings can be used to establish a robust monitoring and evaluation framework at the EU and national levels. This framework will assess the effectiveness of communicating strategic priorities for sustainable transport development. It also holds potential for application in other sectors, broadening its impact. Full article

Aiming at the problem that the search efficiency of key vulnerable nodes in large-scale networks is not high and the consideration factors are not comprehensive enough, in order to improve the time and space efficiency of search and the accuracy of results, a key vulnerable node discovery method based on Bayesian attack subgraphs and improved fuzzy C-means clustering is proposed. Firstly, the attack graph is divided into Bayesian attack subgraphs, and the analysis results of the complete attack graph are quickly obtained by aggregating the information of the attack path analysis in the subgraph to improve the time and space efficiency. Then, the actual threat features of the vulnerability nodes are extracted from the analysis results, and the threat features of the vulnerability itself in the common vulnerability scoring standard are considered to form the clustering features together. Next, the optimal number of clusters is adaptively adjusted according to the variance idea, and fuzzy clustering is performed based on the extracted clustering features. Finally, the key vulnerable nodes are determined by setting the feature priority. Experiments show that the proposed method can optimize the time and space efficiency of analysis, and the fuzzy clustering considering multiple features can improve the accuracy of analysis results. Full article

An alkali–silica reaction (ASR) is a chemical process that leads to the formation of an expansive gel, potentially causing durability issues in concrete structures. This article investigates the properties and behaviour of ASR products in mortar with the addition of low-purity calcined clay as an additional material. This study includes an evaluation of the expansion and microstructural characteristics of the mortar, as well as an analysis of the formation and behaviour of ASR products with different contents of calcined clay. Expansion tests of the mortar beam specimens were conducted according to ASTM C1567, and a detailed microscopic analysis of the reaction products was performed. Additionally, their mechanical properties were determined using nanoindentation. This study reveals that with an increasing calcined clay content, the amount of the crystalline form of the ASR gel decreases, while the nanohardness increases. The Young’s modulus of the amorphous ASR products ranged from 5 to 12 GPa, while the nanohardness ranged from 0.41 to 0.67 GPa. The obtained results contribute to a better understanding of how the incorporation of low-purity calcined clay influences the ASR in mortar, providing valuable insights into developing sustainable and durable building materials for the construction industry. Full article

The Asteraceae family in Siberian Asia exhibits remarkable biodiversity and has long served as a valuable resource for domesticating various beneficial plants with medicinal, therapeutic, and industrial significance to humanity. In this work, we studied for the first time the chemical composition of six understudied or previously unexplored plant species, Artemisia jacutica (AJ), Carduus nutans subsp. leiophyllus (CL), Cirsium heterophyllum (CH), Echinops davuricus (ED), Ixeris chinensis subsp. versicolor (IV), and Lactuca sibirica (LS), which were successfully cultivated under open-field conditions as biennial or perennial crops. We profiled these species, employing a liquid chromatography–mass spectrometry approach, identifying over 100 phenolic compounds. Among these compounds were hydroxybenzoic acid glucosides, hydroxybenzoyl/p-coumaroyl/feruloyl quinic acids, hydroxycoumarin O-glucosides, caffeoyl/p-coumaroyl/feruloyl glucaric/tartaric acids, O- and C-glucosides of apigenin, acacetin, luteolin, chrysoeriol, 6-hydroxyluteolin, pectolinarigenin, kaempferol, quercetin, isorhamnetin, and tri-/tetra-O-p-coumaroyl spermines and spermidines. All examined species exhibited a significant accumulation of phenolic compounds throughout the experimental period, reaching levels comparable to or exceeding those found in wild samples (WSs), with the best total phenolic content for AJ at 26.68 mg/g (vs. 26.68 mg/g in WS; second year), CL at 50.23 mg/g (vs. 38.32 mg/g in WS; second year), CH at 51.14 mg/g (vs. 40.86 mg/g in WS; sixth year), ED at 86.12 mg/g (vs. 78.08 mg/g in WS; seventh year), IV at 102.49 mg/g (vs. 88.58 mg/g in WS; fourth year), and LS at 127.34 mg/g (vs. 110.64 mg/g in WS; fifth year). Notably, in the first year of cultivation, approximately 40–60% of the wild-level target compounds accumulated in the plants, with even higher levels detected in subsequent years, particularly in the second and third years. This study highlights the potential of cultivation to produce new Asteraceae plants rich in bioactive phenolics. Full article

Diabetes is caused by insulin deficiency or impaired biological action, and long-term hyperglycemia leads to a variety of tissue damage and dysfunction. Therefore, the early prediction of diabetes and timely intervention and treatment are crucial. This paper proposes a robust framework for the prediction and diagnosis of type 2 diabetes (T2DM) to aid in diabetes applications in clinical diagnosis. The data-preprocessing stage includes steps such as outlier removal, missing value filling, data standardization, and assigning class weights to ensure the quality and consistency of the data, thereby improving the performance and stability of the model. This experiment used the National Health and Nutrition Examination Survey (NHANES) dataset and the publicly available PIMA Indian dataset (PID). For T2DM classification, we designed a convolutional neural network (CNN) and proposed a novel attention-oriented convolutional neural network (SECNN) through the channel attention mechanism. To optimize the hyperparameters of the model, we used grid search and K-fold cross-validation methods. In addition, we also comparatively analyzed various machine learning (ML) models such as support vector machine (SVM), logistic regression (LR), decision tree (DT), random forest (RF), and artificial neural network (ANN). Finally, we evaluated the performance of the model using performance evaluation metrics such as precision, recall, F1-Score, accuracy, and AUC. Experimental results show that the SECNN model has an accuracy of 94.12% on the NHANES dataset and an accuracy of 89.47% on the PIMA Indian dataset. SECNN models and CNN models show significant improvements in diabetes prediction performance compared to traditional ML models. The comparative analysis of the SECNN model and the CNN model has significantly improved performance, further verifying the advantages of introducing the channel attention mechanism. The robust diabetes prediction framework proposed in this article establishes an effective foundation for diabetes diagnosis and prediction, and has a positive impact on the development of health management and medical industries. Full article

Objective: The Greek Society of Migraine and Headache Patients conducted its third in-line population web-based survey in 2023 to ascertain if the burden of the disease and the patients’ satisfaction with conventional and novel migraine therapies are changing compared to our previous findings from 2018 and 2020. Methods: The sampling process was based on a random call to participants to reply to a specific migraine-focused self-administered questionnaire, including 83 questions in Greek, which was distributed nationwide through the online research software SurveyMonkey. Results: We eventually enrolled 2565 patients, the majority of which were females. Our findings clearly demonstrate that migraine is still a burdensome condition. The degree of its impact on all aspects of productivity depends on the monthly frequency of migraine and the response rates to acute and prophylactic treatments. A total of 1029 (42.4%) of the patients had visited the emergency room mainly for unresponsiveness to acute treatments or aura-related symptoms. Triptans seem to be partly effective as acute therapies. OnabotulinumtoxinA seems to be effective for almost half of chronic migraine patients (43.9%) to report adequate satisfaction with this treatment (27.8% were “fairly happy”, 10.6% were “very happy”, and 5.5% were “extremely happy”). Due to their high rates of preventative effectiveness, most respondents treated with anti-CGRP Mabs expressed their optimism concerning their future while living with their migraine (88.25%), as well as towards further improvements in their quality of life (82.8%) status, mostly with fremanezumab. Conclusions: The patients recognize the usefulness of anti-CGRP Mabs in migraine prevention and consequently seem to be more optimistic than before about living with migraine. Considering the market change that is anticipated with the use of gepants and ditans, larger longitudinal population-based studies are warranted to further explore if the new era of migraine therapeutics might further lessen the burden of the disease. Full article

Aiming at the problems of uneven distribution of dislodged material on the screen surface of longitudinal axial flow grain combine harvester, a large difference in material clearing time, and large clearing loss, a dislodged material homogenizing device that can realize dislodged material return and homogenization at the rear of longitudinal axial flow was developed. (1) The structure and motion parameters of the reflux plate were determined, and simulation tests were carried out to verify them; (2) A test bench was set up, and the Box-Behnken test method was adopted to determine the influence law of each factor on the operating effect and the optimal parameter combination, and the results showed that the tilt angle of the return plate, motor speed, and amplitude had a significant influence on the distribution uniformity of the material on the screen surface; it was determined that the optimal combination of the angle of the return plate configuration was 28.7°, the speed of the motor was 247 r/min, the amplitude of the return plate was 18.3 mm, and the seed contamination rate was 0.48%. The optimum combination was determined to be 28.7°, 247 r/min, 18.3 mm, and 0.48% impurity rate; (3) under the conditions of the field test validation, the validation error is less than 5%, proving that it can effectively improve the performance of the clearing and reduce the rate of impurity content. Full article

The use of dental implants for prosthetic rehabilitation in dentistry is based on the concept of osteointegration. This concept enables the clinical stability of the implants and a total absence of inflammatory tissue between the implant surface and the bone tissue. For this reason, it is essential to understand the role of the titanium surface in promoting and maintaining or not maintaining contact between the bone matrix and the surface of the titanium implant. Materials and Methods: Five types of titanium discs placed in contact with osteoblast cultures of osteosarcomas were studied. The materials had different roughness. Scanning electron microscopy (SEM) photos were taken before the in vitro culture to analyze the surfaces, and at the end of the culturing time, the different gene expressions of a broad pattern of proteins were evaluated to analyze the osteoblast response, as indicated in the scientific literature. Results: It was demonstrated that the responses of the osteoblasts were different in the five cultures in contact with the five titanium discs with different surfaces; in particular, the response in the production of some proteins was statistically significant. Discussion: The key role of titanium surfaces underlines how it is still possible to carry out increasingly accurate and targeted studies in the search for new surfaces capable of stimulating a better osteoblastic response and the long-term maintenance of osteointegration. Full article

The utilization of radiative transfer models for interpreting remotely sensed data to evaluate forest disturbances is a cost-effective approach. However, the current radiative transfer modeling approaches are either too abstract (e.g., 1D models) or too complex (detailed 3D models). This study introduces a novel multilayer heterogeneous 3D radiative transfer framework with medium complexity, termed MART3D, for characterizing forest disturbances. MART3D generates 3D canopy structures accounting for the within-crown clumping by clustering leaves, which is modeled as a turbid medium, around branches, applicable for forests of medium complexity, such as temperate forests. It then automatically generates a multilayer forest with grass, shrub and several layers of trees using statistical parameters, such as the leaf area index and fraction of canopy cover. By employing the ray-tracing module within the well-established LargE-Scale remote sensing data and image Simulation model (LESS) as the computation backend, MART3D achieves a high accuracy (RMSE = 0.0022 and 0.018 for red and Near-Infrared bands) in terms of the bidirectional reflectance factor (BRF) over two RAMI forest scenes, even though the individual structures of MART3D are generated solely from statistical parameters. Furthermore, we demonstrated the versatility and user-friendliness of MART3D by evaluating the band selection strategy for computing the normalized burn ratio (NBR) to assess the composite burn index over a forest fire scene. The proposed MART3D is a flexible and easy-to-use tool for studying the remote sensing response under varying vegetation conditions. Full article

Background: Scholarly literature indicates a slow pace at which maritime ports fully embrace digital transformation (DT). The reasons to this are largely anecdotal and lack solid empirical grounding. This inhibits an overall understanding of DT’s tenets and the development of evidence-based policies and targeted actions. Methods: This study deployed a qualitative case study strategy to unpack the challenges of undertaking DT through the lens of principal-agent theory (PAT). Results: Analysis of data collected through 13 semi-structured interviews from a port’s value chain stakeholders revealed five thematic challenges that contradict successful implementation of DT. These included interagency constraints and system ownership tussles; system sabotage and prevalent corruption; prevalent human agency in port operations; cultural constraints; and political influence on port governance. Conclusions: To address these challenges, the study proposes a four-stage empirically grounded DT strategy framework that guides both practitioners and policymakers through DT endeavors. The framework includes: (1) the port’s value chain mapping, (2) stakeholder engagement, (3) resource mobilization, and (4) effective monitoring. For scholars, we provide an avenue for testing statistical significance of association and causality among the identified challenges. Full article

Pain is the most frequent symptom of disease. In treating pain, a lower incidence of adverse effects is found for paracetamol versus other non-steroidal anti-inflammatory drugs. Nevertheless, paracetamol can trigger side effects when taken regularly. Combined therapy is a common way of lowering the dose of a drug and thus of reducing adverse reactions. Since β-caryophyllene oxide (a natural bicyclic sesquiterpene) is known to produce an analgesic effect, this study aimed to determine the anti-nociceptive and gastroprotective activity of administering the combination of paracetamol plus β-caryophyllene oxide to CD1 mice. Anti-nociception was evaluated with the formalin model and gastroprotection with the model of ethanol-induced gastric lesions. According to the isobolographic analysis, the anti-nociceptive interaction of paracetamol and β-caryophyllene oxide was synergistic. Various pain-related pathways were explored for their possible participation in the mechanism of action of the anti-nociceptive effect of β-caryophyllene oxide, finding that NO, opioid receptors, serotonin receptors, and K⁺_ATP channels are not involved. The combined treatment showed gastroprotective activity against ethanol-induced gastric damage. Hence, the synergistic anti-nociceptive effect of combining paracetamol with β-caryophyllene oxide could be advantageous for the management of inflammatory pain, and the gastroprotective activity should help to protect against the adverse effects of chronic use. Full article

The living vegetation volume (LVV) can accurately describe the spatial structure of greening trees and quantitatively represent the relationship between this greening and its environment. Because of the mostly line shape distribution and the complex species of street trees, as well as interference from artificial objects, current LVV survey methods are normally limited in their efficiency and accuracy. In this study, we propose an improved methodology based on vehicle-mounted LiDAR data to estimate the LVV of urban street trees. First, a point-cloud-based CSP (comparative shortest-path) algorithm was used to segment the individual tree point clouds, and an artificial objects and low shrubs identification algorithm was developed to extract the street trees. Second, a DBSCAN (density-based spatial clustering of applications with noise) algorithm was utilized to remove the branch point clouds, and a bottom-up slicing method combined with the random sampling consistency iterative method algorithm (RANSAC) was employed to calculate the diameters of the tree trunks and obtain the canopy by comparing the variation in trunk diameters in the vertical direction. Finally, an envelope was fitted to the canopy point cloud using the adaptive AlphaShape algorithm to calculate the LVVs and their ecological benefits (e.g., O₂ production and CO₂ absorption). The results show that the CSP algorithm had a relatively high overall accuracy in segmenting individual trees (overall accuracy = 95.8%). The accuracies of the tree height and DBH extraction based on vehicle-mounted LiDAR point clouds were 1.66~3.92% (rRMSE) and 4.23~15.37% (rRMSE), respectively. For the plots on Zijin Mountain, the LVV contribution by the maple poplar was the highest (1049.667 m³), followed by the sycamore tree species (557.907 m³), and privet’s was the lowest (16.681 m³). Full article

This study aims to assess the operational capability of disaster and emergency management resources (DEMRs), which is not only critical for effective loss reduction and resilience, but also facilitates the sharing and optimal use of resources for the more effective achievement of sustainable development. This study constructs an evaluation index system of the operational capability of DEMRs, encompassing four key aspects: resource planning, organizational management capability, resource support capability, and information processing capability. It focuses on identifying the factors that influence the operational capability of DEMRs in China and Korea, comparing and analyzing the relative importance and priority of each evaluation domain and indicator within these countries. The results show that the organizational management capability is most significant in China, whereas the resource support capability is prioritized in Korea. A comparative analysis of the local weight of indicators within each domain revealed the largest discrepancy between China and Korea in the information processing capability domain. This study concludes by calculating global weights, identifying the fast response capability and resource allocation capability as the most impactful factors on the operational capability of DEMRs, and highlighting their critical role in disaster and emergency management. Full article

This research proposes a strategy to diagnose open-phase faults (OPF) and open-switching faults (OSF) in dual three-phase permanent magnet synchronous motor (DTP-PMSM) inverters. The method is based on the dual d–q predictive current model and involves establishing a mathematical model and utilizing the finite control set model predictive current extraction technique to predict the motor current. It then analyzes the characteristics of the switching-tube current under both normal and fault conditions. Finally, a fault predictive current model is introduced and the residual is calculated based on the predicted fault current value and the actual measured current value to diagnose the inverter fault. The proposed method effectively overcomes misjudgment issues encountered in traditional open-circuit fault diagnosis of inverters. It enhances the system’s response speed during dynamic processes and strengthens the robustness of diagnosis algorithm parameters. The experimental results demonstrate that the proposed method can rapidly, effectively, and accurately diagnose open-circuit faults presented in this paper fastest within one-fifth of a current cycle. It achieves a diagnostic accuracy rate of 97% in the dual three-phase permanent magnet synchronous motor drive system. Full article

The increasing use of convolutional neural networks (CNNs) has brought about a significant transformation in numerous fields, such as image categorization and identification. In the development of a CNN model to classify images of sericea lespedeza [SL; Lespedeza cuneata (Dum-Cours) G. Don] from weed images, four architectures were explored: CNN model variant 1, CNN model variant 2, the Visual Geometry Group (VGG16) model, and ResNet50. CNN model variant 1 (batch normalization with adjusted dropout method) demonstrated 100% validation accuracy, while variant 2 (RMSprop optimization with adjusted learning rate) achieved 90.78% validation accuracy. Pre-trained models, like VGG16 and ResNet50, were also analyzed. In contrast, ResNet50’s steady learning pattern indicated the potential for better generalization. A detailed evaluation of these models revealed that variant 1 achieved a perfect score in precision, recall, and F1 score, indicating superior optimization and feature utilization. Variant 2 presented a balanced performance, with metrics between 86% and 93%. VGG16 mirrored the behavior of variant 2, both maintaining around 90% accuracy. In contrast, ResNet50’s results revealed a conservative approach for class 0 predictions. Overall, variant 1 stood out in performance, while both variant 2 and VGG16 showed balanced results. The reliability of CNN model variant 1 was highlighted by the significant accuracy percentages, suggesting potential for practical implementation in agriculture. In addition to the above, a smartphone application for the identification of SL in a field-based trial showed promising results with an accuracy of 98–99%. The conclusion from the above is that a CNN model with batch normalization has the potential to play a crucial role in the future in redefining and optimizing the management of undesirable vegetation. Full article

Oil palm cultivation thrives as a prominent agricultural endeavor within the southern region of Thailand, where the country ranks third globally in production, following Malaysia and Indonesia. The assessment of oil palm bunch ripeness serves various purposes, notably in determining purchasing prices, pre-harvest evaluations, and evaluating the impacts of disasters or low market prices. Presently, two predominant methods are employed for this assessment, namely human evaluation, and machine learning for ripeness classification. Human assessment, while boasting high accuracy, necessitates the involvement of farmers or experts, resulting in prolonged processing times, especially when dealing with extensive datasets or dispersed fields. Conversely, machine learning, although capable of accurately classifying harvested oil palm bunches, faces limitations concerning its inability to process images of oil palm bunches on trees and the absence of a platform for on-tree ripeness classification. Considering these challenges, this study introduces the development of a classification platform leveraging machine learning (deep learning) in conjunction with geospatial analysis and visualization to ascertain the ripeness of oil palm bunches while they are still on the tree. The research outcomes demonstrate that oil palm bunch ripeness can be accurately and efficiently classified using a mobile device, achieving an impressive accuracy rate of 99.89% with a training dataset comprising 8779 images and a validation accuracy of 96.12% with 1160 images. Furthermore, the proposed platform facilitates the management and processing of spatial data by comparing coordinates derived from images with oil palm plantation data obtained through crowdsourcing and the analysis of cloud or satellite images of oil palm plantations. This comprehensive platform not only provides a robust model for ripeness assessment but also offers potential applications in government management contexts, particularly in scenarios necessitating real-time information on harvesting status and oil palm plantation conditions. Full article

Organic photovoltaics (OPVs) have attracted widespread attention and became an important member of clean energy. Recently, their power conversion efficiency (PCE) has surpassed 19%. As is well known, the morphology of the active layer in OPVs crucially influences the PCE. In consideration of the intricate interactions between the donor molecules and acceptor molecules, the precise control of the morphology of the active layer is extremely challenging. Hence, it is urgent to develop effective methods to fabricate the hierarchical structure of the active layer. One significant driving force for the morphological evolution of the active layer is crystallization. Therefore, regulating the crystallization kinetics is an effective strategy for morphology control. In this review, we present the kinetic strategies recently developed to highlight their significance and effectiveness in morphology control. By applying these kinetic strategies, the hierarchical structure, including phase separation, domain size, crystallinity, and molecular orientation of the active layer can be optimized in different blend systems, leading to an improved PCE of OPVs. The outcomes set the stage for future advancements in device performance. Full article

Representing the second most common skin cancer, the incidence and disease burden of cutaneous squamous cell carcinoma (cSCC) continues to increase. Surgical excision of the primary site effectively cures the majority of cSCC cases. However, an aggressive subset of cSCC persists with clinicopathological features that are indicative of higher recurrence, metastasis, and mortality risks. Acceleration of these features is driven by a combination of genetic and environmental factors. The past several years have seen remarkable progress in shaping the treatment landscape for advanced cSCC. Risk stratification and clinical management is a top priority. This review provides an overview of the current perspectives on cSCC with a focus on staging, treatment, and maintenance strategies, along with future research directions. Full article

Transcriptomics and metabolomics offer distinct advantages in investigating the differentially expressed genes and cellular entities that have the greatest influence on end-phenotype, making them crucial techniques for studying plant quality and environmental responses. While numerous relevant articles have been published, a comprehensive summary is currently lacking. This review aimed to understand the global and longitudinal research trends of transcriptomics and metabolomics in plant quality and environmental response (TMPQE). Utilizing bibliometric methods, we presented a comprehensive science mapping of the social structure, conceptual framework, and intellectual foundation of TMPQE. We uncovered that TMPQE research has been categorized into three distinct stages since 2020. A citation analysis of the 29 most cited articles, coupled with a content analysis of recent works (2020–2023), highlight five potential research streams in plant quality and environmental responses: (1) biosynthetic pathways, (2) abiotic stress, (3) biotic stress, (4) development and ripening, and (5) methodologies and tools. Current trends and future directions are shaped by technological advancements, species diversity, evolving research themes, and an environmental ecology focus. Overall, this review provides a novel and comprehensive perspective to understand the longitudinal trend on TMPQE. Full article