Journals
2026 EN
Rai Philips Kumar · Kumar Pradeep
ABSTRACT 2 NO • ${\rm NO bullet }$ +O 2 ${\rm O}_{2}$→ $$ 2 NO 2 • ${\rm NO}_{2 bullet }$ is an experimentally well‐explored reaction. The first measurement of the rate constant was carried out in 1918, and the latest rate constant measurement was done in 2020. Similarly, the first computational study of the rate constant was performed in 1935. In spite of such a long history of experimental and computational results, there are still unanswered questions for this reaction. For example, a long‐standing question regarding this reaction is whether it is a true termolecular reaction or a sequential bimolecular one. In addition, it is known that the rate constant value for this reaction decreases rapidly up to 600 K and remains almost constant with a further increase in temperature. In the present work, using CCSDT(Q)/CBS//CCSD(T)/aug‐cc‐pVDZ level of theory combined with Rice–Ramsperger–Kassel–Marcus theory/master equation kinetics calculations, we have tried to shed light on this almost century old problem.
Journals
2026 EN
Chaubey Mrityunjay · Pathak Abhay Kumar · Gupta Manjari
ABSTRACT In recent years, artificial intelligence (AI) has become an automated tool for detecting cardiovascular diseases using ECG images. Activation functions are the core of neural network models, ranging from shallow to deep convolutional neural networks (CNN). In ECG image‐based cardiovascular disease detection, activation functions enable the network to capture non‐linear patterns like irregular heartbeats and subtle anomalies. The proposed CNN architecture in this paper comprised convolutional layers for feature extraction, followed by custom activation functions to introduce non‐linearity and enhanced learning. These features are downsampled using max pooling and aggregated through global average pooling. Fully connected layers, with a suitable dropout regularization, map the features to the final classification output, which is probabilistically determined using a softmax activation function. This paper used a public dataset of ECG images of cardiac patients to analyze the significance of activation functions in predicting the four main cardiac abnormalities: irregular heartbeat, myocardial infarction, history of myocardial infarction, and normal person classes. We have analyzed 19 different activation functions for their detection performance on the same dataset. The detection performance is compared with the existing state‐of‐the‐art studies. A set of activation functions is suggested for robust and accurate detection of cardiovascular disease using ECG images.
Journals
2026 EN
Olabi Abdul Ghani · Alashkar Adnan · Mahmoud Montaser
+5 more
Concentrated photovoltaic (CPV) systems offer a promising approach to enhancing solar energy conversion efficiency by utilizing optical concentrators and advanced solar cell technologies. This paper provides a comprehensive overview of the history, evolution, and fundamental characteristics of CPV systems. The paper also explores the strengths and limitations of CPV technologies, with particular attention to the materials used in the optical and solar cell components, as well as the manufacturing challenges that affect scalability and performance. A detailed analysis of cooling techniques, such as radiative, phase change material, liquid immersion, microchannel, and jet impingement, is presented to address thermal management in high‐concentration environments. Accordingly, the differences among these cooling techniques in terms of energy consumption, reliability, and adaptability have been investigated. Furthermore, the paper examines the integration of CPV systems into advanced applications, including solar‐powered desalination, thermoelectric generators, light‐splitting configurations, and building‐integrated structures. Moreover, digital integration in CPVs has recently been considered a promising approach to maximizing the electrical output of these systems, mainly through the enhancement of solar tracking systems and input/output predictions. Through this multidimensional review, the study highlights the potential of CPV technologies to contribute significantly to sustainable energy solutions, while also addressing the technical and practical challenges that remain.
Journals
2026 EN
PeránQuesada Rosa · CamachoRomero Miguel · SesmeroCarrasco Rafael
Abstract In recent years, the invasive algae Rugulopteryx okamurae has spread along the Mediterranean and Atlantic coasts, causing ecological and economic damage. However, upwelling algae could provide a valuable source of carbon biomass for circular economy applications. Marine algae, particularly brown algae, have a long history of use in agriculture as biostimulants and biofertilizers, demonstrating their effectiveness on various crops and underscoring their potential as a valuable resource for sustainable agricultural practices. This study aimed to evaluate the agronomic effects of aqueous extracts from R. okamurae on radish growth using a rapid, cost‐effective method, with the goal of exploring potential applications for upwelling biomass. Two groups of seaweed were used: one washed with distilled water and the other unwashed. Both groups were macerated in water for 10 days, with and without a mixture of activators (chickpea flour, poultry manure, brown sugar, and fertile soil). Four liquid extracts, along with a water control, were tested on Raphanus sativus . The results showed a significantly higher biostimulant effect on germination index and plant growth (root and shoot length, fresh and dry weight) compared to the control. The greatest increase in shoot and root length was obtained with non‐washed seaweed (NWS), with improvements of + 40.6% and +68.2%, respectively. The best performance in fresh and dry plant weight was achieved with non‐washed seaweed + activators (NWS + A), which increased root fresh weight by + 160.9% and root dry weight by + 146.2%. These findings highlight the potential agronomic use of low‐cost aqueous extracts from R. okamurae as biostimulants.
Journals
2026 EN
Zhang Ming · Li Tao · Aarsland Dag
+5 more
ABSTRACT Objectives Given that DLB is the second most common neurodegenerative dementia and early detection is crucial, this study sought to delineate the pre‐diagnostic symptom evolution in patients from a Chinese memory clinic. Methods Prospective patients diagnosed with probable DLB ( n = 47, mean age at first symptom 71 years) registered at Dementia Care and Research Center, Peking University Institute of Mental Health were included. A dementia specialist performed data collection, medical history, and examination. We used the unified data form to prospectively collect the data at examinations every 3–6 months. In addition, retrospective data were extracted from medical records to identify the evolution of symptoms before diagnosis, including the first‐onset symptom(s) and the time elapsed before diagnosis. Results Most informants ( n = 36, 76.6%) reported only one initial symptom. The most frequently reported initial symptom was memory decline (57.4%). Throughout the journey to a diagnosis, the most common symptom was visual hallucination ( n = 29, 61.7%), followed by sleep problems and systematized delusions (both n = 20, 42.6%). Anxiety was the earliest recognizable individual psychiatric symptom of DLB, occurring an average of 73 months before diagnosis, followed by depression, memory decline (34 months before diagnosis), RBD, hallucinations and delusions, and motor symptoms of about 15 months before diagnosis. Fluctuating cognition, delirium, and impulsive aggressive behavior were documented relatively shortly before the diagnosis. Conclusions Cognitive and emotional symptoms were the most common early symptoms before a diagnosis of DLB. The findings are informative for the early detection of DLB.
Journals
2026 EN
Fukuyo Ryosuke · Yamamoto Hiroyuki · Tokunaga Masanori
+3 more
ABSTRACT Background Radical gastrectomy with lymph node dissection is the primary treatment for gastric cancer. However, the overall complication rate remains approximately 10%–20%, with a postoperative mortality rate of 2.3%. Therefore, preoperative stratification of patients based on their expected surgical risks is important. This study aimed to develop a deep learning prediction model using big data from the National Clinical Database (NCD) to predict operative mortality after total gastrectomy. Methods Patients aged 18 years or older who underwent total gastrectomy for gastric cancer and were registered in the NCD between January 2018 and December 2019 were included. A total of 62 variables, including age, sex, past medical history, preoperative blood test results, and tumor characteristics, were used as covariates, with operative mortality as the outcome variable. Deep learning models were developed using Python, TensorFlow and Keras. Hyperparameters were adjusted using the k‐fold method with the training data. The model was evaluated using validation data. Results Of the 14 980 eligible cases, 11 980 were used for training and 3000 for validation. The event rate was 1.2%. A four‐layer, 5217‐variable model was developed. The final C‐statistic was 0.79 (95% confidence intervals: 0.74–0.83) for the training data and 0.74 (95% confidence intervals: 0.62–0.85) for the validation data. Conclusion We developed a deep learning model to predict operative mortality using big data from the NCD. To improve the accuracy, it is necessary to introduce new variables related to postoperative complications or factors that cannot be analyzed using conventional methods.
Journals
2026 EN
Ikegami Toru · Tsunematsu Masashi · Onda Shinji
+4 more
ABSTRACT Although living‐donor liver transplantation (LDLT) has become the standard treatment for end‐stage liver disease, one of its major challenges is small‐for‐size syndrome (SFSS). SFSS is characterized by severe icterus and intractable ascites, although the severity of the condition can vary. Some cases are managed with medical treatment alone, others require interventions such as splenic embolization, and some may result in graft loss or necessitate re‐transplantation. A recent area of interest in this field is the new grading system introduced through collaboration between the International Liver Transplantation Society, the International Living‐Donor Liver Transplantation Study Group, and the Liver Transplant Society of India in 2003. This grading system has helped define SFSS in the context of LDLT. Recent trends also include right lobe graft selection with V5/V8 reconstruction and optimal outflow to manage the high venous pressure associated with end‐stage liver disease. To develop effective strategies for transplanting small‐for‐size grafts and preventing SFSS, it is crucial to have a comprehensive understanding of how to evaluate graft quality and volume, alongside portal pressure management, during LDLT. We review the latest literature on the pathogenesis of SFSS and the strategies for overcoming it after LDLT.
Journals
2026 EN
Wu YiHua · Huang PoJu · Lai HsiangChun
+3 more
Abstract Coronavirus disease 2019 (COVID‐19) vaccines are effective in preventing severe disease in patients with inflammatory bowel disease (IBD), but the risk of infection following vaccination in ulcerative colitis (UC) versus Crohn's disease (CD) patients remains unclear. This study compares the risk of COVID‐19 infection between UC and CD patients postvaccination. A retrospective cohort study was conducted on IBD patients who received at least two doses of the COVID‐19 vaccine at China Medical University Hospital between January 1, 2020, and October 31, 2024. The study included 169 IBD patients (96 UC, 73 CD). Demographic data, vaccination history, and medical records were analyzed. Logistic regression assessed the odds of COVID‐19 infection, adjusting for potential confounders. UC patients were significantly older than CD patients (44.92 ± 13.72 years vs. 37.27 ± 15.27 years, p = .0008). The overall prevalence of COVID‐19 infection was 49.11%, with UC patients having a significantly higher infection rate (56.25%) compared to CD patients (39.73%, p = .0333). Logistic regression showed UC patients had an odds ratio of 1.95 (95% confidence interval [CI] = 1.05–3.62) and an adjusted odds ratio of 2.78 (95% CI = 1.20–6.44), indicating a 1.95‐ to 2.78‐fold higher risk of infection. A trend toward reduced infection risk with more vaccine doses was observed, with medication use not significantly affecting infection risk. Our study showed that vaccinated UC patients had a significantly higher risk of COVID‐19 infection compared to CD patients, highlighting the need for tailored monitoring and care strategies for UC patients, even postvaccination.
Wiley Publishing Asia Pty Ltd
Journals
2026 EN
Zhang Naifeng · SaliliJames Arianna · Poon Sanson T. S.
+2 more
The Natural History Museum, UK (NHM), is at the forefront of digitizing vast natural history collections, with over six million of its 80 million specimens already digitized. Extensive, high‐quality, digital specimen datasets are crucial for the integration, and analysis of biological information, providing global accessibility and digital preservation. However, at current rates, it could take centuries to digitize entire collections. To accelerate this, researchers at NHM are exploring the use of collaborative robots (cobots) for digitization. Here, the focus is on the development of artificial intelligence (AI) pipelines for the digitization of one of the largest NHM collections: pinned insects. Aa proof‐of‐concept workflow is presented that leverages AI to assist in precise identification, handling, and digitization of insect specimens and labels. The pipeline is designed to be adaptable across different museum specimen datasets, and to one day integrate seamlessly with the newly introduced cobot at NHM. Experimental results achieved accuracies of 0.95 for specimen identification, 0.79 for pinheads, and 0.92 for specimen labels, in independent image and video test sets. These results demonstrate the potential of this workflow in accelerating digitization efforts whilst prototyping novel cobot‐integrated digitization systems and advancing the biodiversity informatics for data creation and accessibility.
Journals
2026 EN
Roe Brian E.
Abstract Popular society increasingly questions preferences that drive many resource allocations and production decisions, with many groups actively seeking to alter those preferences to achieve changes to resource use. Agricultural and applied economists, who are already equipped with excellent technical skills to undertake consumer preference and valuation studies, must also be challenged to understand post‐Beckerian consumer theories that can help guide emerging requests placed upon economists as multi‐disciplinary collaborators as non‐academic groups press us to join in work involving interventions that work from the implicit assumption that preferences are malleable and potentially endogenous. I call association members to follow our best traditions of studying production dynamics and incorporating emerging theories drawn from or inspired by other disciplines so that we may better interact with the broader scientific community who, as many suggest, finds our insistence on stable and static preferences to limit the usefulness of economists in handling a raft of modern dilemmas. In addition to setting out the history of economists' reticence in considering endogenous preferences, I will outline several threads of emerging literature that can provide structure to professional inquiry in this domain and sketch some emergent cases with implications for the agricultural and resource sectors.