bluuebunny/crossref_metadata_2025_split huggingface dataset:
Movement‐evoked pain is not associated with pain at rest or physical function in knee osteoarthritis
Authors: Lozano‐Meca José Antonio , Gacto‐Sánchez Mariano , Montilla‐Herrador Joaquina 
Published: January 2024

AbstractIntroductionKnee Osteoarthritis (KOA) is mainly characterized by pain. The assessment of KOA‐related pain frequently focuses on different constructs subject to sources of bias or drawbacks, as the classical Pain at Rest (PAR). Movement‐evoked pain (MEP), recently defined as ‘pain during walking’, emerges as a differential concept, since PAR and MEP are driven by different underlying mechanisms. Given the novelty of the MEP approach, its association with PAR or with different performance‐based tests has not been studied in KOA yet.Materials and MethodsA cross sectional study was conducted. PAR was measured, alongside the performance of four mobility tests and their corresponding MEP: Timed Up and Go Test, 10‐metre Walk Test, 2‐Minute Walk Test, and 6‐Minute Walk Test. Association and agreement were explored for MEP versus PAR, while the correlation of the tests versus each corresponding MEP‐measure was assessed.ResultsNeither association nor agreement were found in the duality MEP versus PAR. Also, the lack of association between the performance of a mobility test and the perceived level of pain during the development of the test was stated.ConclusionMovement‐evoked pain is neither related to pain at rest nor to functional performance in subjects affected by knee osteoarthritis. The results from our study suggest that MEP and pain at rest measure and refer to different constructs in knee osteoarthritis. The implementation of MEP as an outcome in exercise‐therapy could enhance the tracking of results, as well as the development of tailored interventions under different conditions.SignificanceThis research elucidates the relevance of MEP, recently defined as ‘pain during walking’, through the analysis of its association with PAR and with functional performance (measured through four mobility tests) in knee osteoarthritis. The results from our study highlight the absence of either association or agreement between MEP and PAR, fact that supports and endorses the idea that both concepts measure and refer to different constructs in knee osteoarthritis.
Exogenous γ‐aminobutyric acid (GABA) improves the cadmium phytoremediation capacity of Solanum nigrum var. humile under cadmium stress
Authors: Li Wanzhi , Li Xiufen , Zhou Kexuan , Jin Xin , Huang Chengyi , Hu Rongping , Lin Lijin , Wang Jin 
Published: January 2024

Abstractγ‐aminobutyric acid (GABA) has shown promise in enhancing crop tolerance to heavy metal stress. To improve the cadmium (Cd) phytoremediation capacity of hyperaccumulator Solanum nigrum var. humile, the effects of exogenous GABA on the growth and Cd accumulation of S. nigrum var. humile were studied. When exposed to Cd stress (0.1 mg/L) through irrigation, S. nigrum var. humile exhibited inhibited growth, characterized by decreased biomass and contents of photosynthetic pigments (chlorophyll a, chlorophyll b, total chlorophyll, and carotenoid). However, the application of exogenous GABA at 1 and 2 g/L increased various organs biomass and photosynthetic pigment content of S. nigrum var. humile to certain degree under Cd stress. Compared with the Cd treatment, the concentrations of 1 and 2 g/L GABA increased the shoot biomass by 8.24% and 16.62%, respectively. GABA also increased the Cd contents, bioconcentration factors, and Cd extractions in various organs of S. nigrum var. humile. Compared with the Cd treatment, the concentrations of 1 and 2 g/L GABA increased the shoot Cd extraction by 17.66% and 24.47%, respectively. Moreover, correlation and gray relational analyses showed that the shoot biomass, chlorophyll b content, and total chlorophyll content were the top three parameters closely related to shoot Cd extraction. These findings suggest that GABA can alleviate Cd‐induced stress in S. nigrum var. humile, thus enhancing its phytoremediation capacity for Cd‐contaminated soils.
A diagram‐based design procedure for Intermediate Isolation System in existing buildings with inelastic behavior
Authors: Esposito Francesco , Faiella Diana , Mele Elena 
Published: January 2024

AbstractVertical extensions of existing buildings can be realized through Intermediate Isolation System (IIS): the extension, equipped with a base isolation system on the rooftop of the existing building, can work as a mass damper, thus reducing the seismic demand on the old structure. The idea proposed in this paper is to predict the elastic or inelastic response of the existing structure in the IIS configuration by means of the results of simple linear analyses. Parametric response spectrum analyses are performed on simplified two degree‐of‐freedom models by varying the mass ratio and the periods of both the existing building and the new isolated vertical extension. So‐called IIS design spectra are derived, and the results are provided as design charts. Given the period of the existing building and the mass ratio, the period of the new isolated vertical extension is selected to obtain the required/desired response of the existing building. For existing buildings working in the elastic field, the designer can derive the response of the existing building by utilizing the IIS design spectra as design charts. For existing building working in the inelastic field, the design charts can still be adopted, though within a more complex procedure, which accounts for two limit behaviors that the extended building exhibits in the inelastic field. The outlined design procedure is applied to some case studies and validated through the comparison with the results of nonlinear time history analyses.
An efficient and secure wearable sensor based remote healthcare monitoring system using adaptive dilated transformer Bi‐LSTM with gated recurrent unit
Authors: Vishnupriya G. , Anusha S. , Kayikci Safak 
Published: January 2024

AbstractInnovative research works in the healthcare sector keep on advancing every day. As the “Internet of Things (IoT)” keeps on evolving, the application of IoT in the medical field is prominent these days. Utilizing IoT devices, alert messages can be sent directly to medical professionals in case of an emergency. So, monitoring the health condition of an individual using IoT technology has become a popular and beneficial method in today's contemporary medical field. With the help of mobile IoT medical equipment, the technology of smart Healthcare Monitoring System (HMS) is proliferating. By utilizing deep learning and IoT technology, the medical diagnosis system has evolved from direct face‐to‐face visits to the hospital to remote telemedicine method. Most of the data generated by the IoT wearable sensors are highly correlated and may consist of outliers. The extraction of the essential attributes from these data is a complicated task. So, “deep learning and machine learning” techniques are adapted to determine the most relevant and appropriate feature required for efficient diagnosis from the unstructured data produced by the IoT devices and thus help in minimizing the redundancy of unnecessary data. Fusing deep learning methods with healthcare IoT made only the essential details to be available for diagnosis. Therefore, a deep learning‐oriented IoT‐based HMS is executed in this work. With the support of several wearable healthcare devices, the required data are acquired. The encryption of the data acquired from standard sources using Optimal Key‐based Advanced Encryption Standard (OK‐AES) is carried out next to assure the security of the sensitive medical data. The keys for AES encryption are optimally chosen with the aid of the Enhanced Heap‐Based Optimizer Algorithm (EHBOA). The encrypted data is transferred to the “cloud platform” for data storage. Once there is a need for the data, then the encrypted data is initially downloaded from the cloud platform. Then using the same AES scheme, the decryption of the data to attain the original data is carried out. From the retrieved data, the extraction of the crucial attributes is carried out. The extracted features are chosen in an optimized manner and are concatenated with the tuned weights to form the weighted feature matrix. This formulated weighted feature matrix is provided as input to the “Adaptive Dilated Transformer Bidirectional Long Short‐Term Memory (Bi‐LSTM) with Gated Recurrent Unit (GRU) (ADTBi‐LSTM‐GRU) model.” The variables in the ADTBG are optimized using the EHBOA for providing an accurate classification outcome. The classified disease outcome is obtained from the deployed ADTBi‐LSTM‐GRU model. Simulations are done to verify the efficiency and reliability of the implemented deep learning and IoT‐based HMS.
Scalability of nanopore osmotic energy conversion
Authors: Tsutsui Makusu , Hsu Wei‐Lun , Yokota Kazumichi , Leong Iat Wai , Daiguji Hirofumi , Kawai Tomoji 
Published: January 2024

AbstractArtificial nanofluidic networks are emerging systems for blue energy conversion that leverages surface charge‐derived permselectivity to induce voltage from diffusive ion transport under salinity difference. Here the pivotal significance of electrostatic inter‐channel couplings in multi‐nanopore membranes, which impose constraints on porosity and subsequently influence the generation of large osmotic power outputs, is illustrated. Constructive interference is observed between two 20 nm nanopores of 30 nm spacing that renders enhanced permselectivity to osmotic power output via the recovered electroneutrality. On contrary, the interference is revealed as destructive in two‐dimensional arrays causing significant deteriorations of the ion selectivity even for the nanopores sparsely distributed at an order of magnitude larger spacing than the Dukhin length. Most importantly, a scaling law is provided for deducing the maximal membrane area and porosity to avoid the selectivity loss via the inter‐pore electrostatic coupling. As the electric crosstalk is inevitable in any fluidic network, the present findings can be a useful guide to design nanoporous membranes for scalable osmotic power generations.
Bioaccessibility and antioxidant capacity of kefir‐based smoothies fortified with kale and spinach after in vitro gastrointestinal digestion
Authors: Yilmaz‐Ersan Lutfiye , Ozcan Tulay , Usta‐Gorgun Buse , Ciniviz Melike , Keser Gokce , Bengu Ilay , Keser Raziye Asli 
Published: January 2024

AbstractThe kefir‐based smoothies with kale and spinach were designed as a ready‐to‐drink and innovative functional snack. Microbiological, physicochemical, as well as pre‐ and postgastrointestinal total antioxidant capacity (TAC; CUPRAC, DPPH, and FRAP) analyses were conducted. It was determined that the kefir‐based smoothies with vegetables had higher ash, carbohydrate, and dietary fiber values. Fructose and glucose contents of smoothy with kale were high, while smoothy with spinach included high sucrose and maltose. The microbiology results revealed that kefir‐based vegetable smoothies had minimum Lactobacillaceae viability (>log 7 cfu g−1) for the required functional effect after 14‐day storage. Moreover, the addition of kale significantly increased (p < .01) the level of initial TAC (CUPRAC, DPPH, and FRAP) and total phenolic content (TPC) values. After in vitro gastric digestion analysis, smoothie with spinach demonstrated higher TAC and TPC values and the control sample had higher TAC and TPC values compared with a predigestion step. It was found that in vitro intestinal DPPH values were higher for the sample with spinach samples, while the sample with kale had the highest FRAP values. It was also found that the bioaccessibility indexes of plain kefir were determined to be the highest in both in vitro gastric and intestinal procedures. The present study provided novel insights into the in vitro digestion properties of kefir fortified with vegetables. Nevertheless, further studies are needed to identify the functional properties of the milk and plant matrices mixture using in vitro and in vivo trials.
Assessing the relationship of maternal short stature with coexisting forms of malnutrition among neonates, infants, and young children of Pakistan
Authors: Khaliq Asif , Nambiar Smita , Miller Yvette D. , Wraith Darren 
Published: January 2024

AbstractEvidence from previous studies suggests a strong association between pediatric undernutrition and maternal stature. However, there's a scarcity of evidence regarding the relationship between maternal stature and pediatric coexisting forms of malnutrition (CFM). This study examined the prevalence and trends of CFM at the individual, household, and community levels, using data from the Demographic & Health Surveys (DHS) of Pakistan. Furthermore, this study assessed the association between pediatric CFM and short maternal stature while adjusting for multiple covariates. A panel cross‐sectional analysis was conducted using data from the 2012–2013 and 2017–2018 Pakistan Demographic & Health Survey (PDHS). We included data from 6194 mother–child dyads aged 15–49 years and 0–59 months, respectively, while excluding data from pregnant mothers and dyads with incomplete anthropometric variables and anthropometric outliers. Across the two survey periods, our findings reveal a significant decline in pediatric malnutrition, including CFM, alongside a concurrent increase in maternal overweight/obesity. Three out of four households had either a malnourished mother, and/or a malnourished child, and/or both. Our study demonstrates that short maternal stature increased the odds of various forms of pediatric undernutrition by two‐to‐threefolds (p < .041), but we did not find an association with wasting, overweight/obesity, and nutritional paradox. This underscores the heightened vulnerability of children born to short‐stature mothers to various forms of pediatric undernutrition. Addressing the high prevalence of pediatric undernutrition among children of short‐stature mothers necessitates a comprehensive approach that considers an individual's nutritional status throughout their entire life cycle.
Investigation of high‐entropy alloy derived spinel‐based layer for SOFC cathode‐side contact application
Authors: Yu Yutian , Lin Youchen , Li Ruizhu , Hao Jingxuan , Lu Yue , Guan Chengzhi , Xiao Guoping , Wang Jian‐Qiang 
Published: January 2024

AbstractA simulated interconnect/contact/cathode/cathode support test cell is fabricated to investigate the effectiveness of the high‐entropy alloy as the contact material on the microstructure and the performance of the converted spinel‐based layer. Although the CuMnNiFeCo alloy powder is selected as the contact precursor, it showed good sinterability after sintering at 900°C for 2 h in air. The electrical performance of the contact layer is evaluated by the area‐specific resistance measurement, including isothermal exposure and thermal cycling. The compatibility of the contact layer with adjacent components is investigated through observing the cross‐sectional view of the test cell. Furthermore, the effectiveness of the contact layer in inhibiting Cr migration is also assessed.
CARDPSoML: Comparative approach to analyze and predict cardiovascular disease based on medical report data and feature fusion approach
Authors: Sinha Anurag , Narula Dev , Pandey Saroj Kumar , Kumar Ankit , Hassan Md. Mehedi , Jha Pooja , Kumar Biresh , Tiwari Manish Kumar 
Published: January 2024

AbstractBackground and AimsDiabetes patients are at high risk for cardiovascular disease (CVD), which makes early identification and prompt management essential. To diagnose CVD in diabetic patients, this work attempts to provide a feature‐fusion strategy employing supervised learning classifiers.MethodsPreprocessing patient data is part of the method, and it includes important characteristics connected to diabetes including insulin resistance and blood glucose levels. Principal component analysis and wavelet transformations are two examples of feature extraction techniques that are used to extract pertinent characteristics. The supervised learning classifiers, such as neural networks, decision trees, and support vector machines, are then trained and assessed using these characteristics.ResultsBased on the area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy, these classifiers' performance is closely evaluated. The assessment findings show that the classifiers have a good accuracy and area under the receiver operating characteristic curve value, suggesting that the suggested strategy may be useful in diagnosing CVD in patients with diabetes.ConclusionThe recommended method shows potential as a useful tool for developing clinical decision support systems and for the early detection of CVD in diabetes patients. To further improve diagnostic skills, future research projects may examine the use of bigger and more varied datasets as well as different machine learning approaches. Using an organized strategy is a crucial first step in tackling the serious problem of CVD in people with diabetes.
Quantum‐inspired hybrid algorithm for image classification and segmentation: Q‐Means++ max‐cut method
Authors: Roy Suman Kumar , Rudra Bhawana 
Published: January 2024

AbstractFinding brain tumors is a crucial step in medical diagnosis that can have a big impact on how patients turn out. Conventional detection techniques can be laborious and demand a lot of computing power. Brain tumor detection could be made more effective and precise, thanks to the quickly developing field of quantum computing. In this article, we propose a quantum machine learning (QML)‐based method for brain tumor extraction and detection based on quantum computing. To implement our strategy, we use a Hybrid Quantum‐Classical Convolutional Neural Network (HQC‐CNN) that has been trained using a collection of brain MRI images. Additionally, we employ Batchwise Q‐Means++ Clustering for segmenting the images and a Max‐cut approach with Adiabatic Quantum Computation (AQC) to extract the tumor region from the segmented MRI image. Our results highlight the strength of Quanvolutional Layer in Neural Network and reduced time complexity exponentially or quadratically in clustering and max‐cut algorithms respectively and see the potential of quantum computing for improving the accuracy and speed of medical diagnosis and have implications for the future of healthcare technology.
Protecting health monitoring privacy in fitness training: A federated learning framework based on personalized differential privacy
Authors: Shao Lifang 
Published: January 2024

AbstractThe rapid advancement of health monitoring technologies has led to increased adoption of fitness training applications that collect and analyze personal health data. This paper presents a personalized differential privacy‐based federated learning (PDP‐FL) algorithm with two stages. Classifying the user's privacy according to their preferences is the first stage in achieving personalized privacy protection with the addition of noise. The privacy preference and the related privacy level are sent to the central aggregation server simultaneously. In the second stage, noise is added that conforms to the global differential privacy threshold based on the privacy level that users uploaded; this allows the global privacy protection level to be quantified while still adhering to the local and central protection strategies simultaneously adopted to realize the complete protection of global data. The results demonstrate the excellent classification accuracy of the proposed PDP‐FL algorithm. The proposed PDP‐FL algorithm addresses the critical issue of health monitoring privacy in fitness training applications. It ensures that sensitive data is handled responsibly and provides users the necessary tools to control their privacy settings. By achieving high classification accuracy while preserving privacy, the framework balances data utility and protection, thus positively impacting health monitoring ecosystem and medical systems.
MARVEL analysis of high‐resolution rovibrational spectra of  13C 16O 2
Authors: Ibrahim Mohammad Taha I. , Alatoom Dunia , Furtenbacher Tibor , Császár Attila G. , Yurchenko Sergei N. , Azzam Ala'a A. A. , Tennyson Jonathan 
Published: January 2024

AbstractA set of empirical rovibrational energy levels, obtained through the MARVEL (measured active rotational‐vibrational energy levels) procedure, is presented for the CO isotopologue of carbon dioxide. This procedure begins with the collection and analysis of experimental rovibrational transitions from the literature, allowing for a comprehensive review of the literature on the high‐resolution spectroscopy of CO, which is also presented. A total of 60 sources out of more than 750 checked provided 14,101 uniquely measured and assigned rovibrational transitions in the wavenumber range of 579–13,735 cm. This is followed by a weighted least‐squares refinement yielding the energy levels of the states involved in the measured transitions. Altogether 6318 empirical rovibrational energies have been determined for CO. Finally, estimates have been given for the uncertainties of the empirical energies, based on the experimental uncertainties of the transitions. The detailed analysis of the lines and the spectroscopic network built from them, as well as the uncertainty estimates, all serve to pinpoint possible errors in the experimental data, such as typos, misassignment of quantum numbers, and misidentifications. Errors found in the literature data were corrected before including them in the final MARVEL dataset and analysis.
A novel approach for designing hands‐on training programs using Quick Reference code‐linked videos
Authors: Manton Jesse W. , Nix Ned L. , Mashkoor Fatima , Lam David K. 
Published: January 2024

AbstractProviding training on the proper use of technology in the context of a large number of learners and limited faculty availability is a constant challenge to dental schools. We found the implementation of a QR‐code‐based autonomous program design to be efficient and effective at providing trainees hands‐on training with newly installed perioperative equipment.