bluuebunny/crossref_metadata_2025 huggingface dataset:
Cybersecurity Risk Assessment: A Systematic Mapping Review, Proposal, and Validation
Authors: Sánchez-García Isaac Daniel , Mejía Jezreel , San Feliu Gilabert Tomás
Published: December 2022

Incorporating technologies across all sectors has meant that cybersecurity risk assessment is now a critical step in cybersecurity risk management. However, risk assessment can be a complicated process for organizations. Therefore, many authors have attempted to automate this step using qualitative and quantitative tools. The problems with the tools and the risk assessment stage in general are (1) not considering all the sub-steps of risk assessment and (2) not identifying the variables necessary for an accurate risk calculation. To address these issues, this article presents a systematic mapping review (SMR) of tools that automate the cybersecurity risk assessment stage based on studies published in the last decade. As a result, we identify and describe 35 tools from 40 primary studies. Most of the primary studies were published between 2012 and 2020, indicating an upward trend of cyber risk assessment tool publication in recent years. The main objectives of this paper are to: (I) identify the differences (reference models and applications) and coverage of the main qualitative and quantitative models, (II) identify relevant risk assessment variables, (III) propose a risk assessment model (qualitative and quantitative) that considers the main variables and sub-stages of risk assessment stage, and (IV) obtain an assessment of the proposed model by experts in the field of cybersecurity. The proposal was sent to a group of 28 cybersecurity experts who approved the proposed variables and their relevance in the cybersecurity risk assessment stage, identifying a majority use of qualitative tools but a preference of experts for quantitative tools.
Depth-Adaptive Deep Neural Network Based on Learning Layer Relevance Weights
Authors: Alturki Arwa , Bchir Ouiem , Ben Ismail Mohamed Maher
Published: December 2022

In this paper, we propose two novel Adaptive Neural Network Approaches (ANNAs), which are intended to automatically learn the optimal network depth. In particular, the proposed class-independent and class-dependent ANNAs address two main challenges faced by typical deep learning paradigms. Namely, they overcome the problems of setting the optimal network depth and improving the model interpretability. Specifically, ANNA approaches simultaneously train the network model, learn the network depth in an unsupervised manner, and assign fuzzy relevance weights to each network layer to better decipher the model behavior. In addition, two novel cost functions were designed in order to optimize the layer fuzzy relevance weights along with the model hyper-parameters. The proposed ANNA approaches were assessed using standard benchmarking datasets and performance measures. The experiments proved their effectiveness compared to typical deep learning approaches, which rely on empirical tuning and scaling of the network depth. Moreover, the experimental findings demonstrated the ability of the proposed class-independent and class-dependent ANNAs to decrease the network complexity and build lightweight models for less overfitting risk and better generalization.
Dense Semantic Forecasting with Multi-Level Feature Warping
Authors: Sović Iva , Šarić Josip , Šegvić Siniša
Published: December 2022

Anticipation of per-pixel semantics in a future unobserved frame is also known as dense semantic forecasting. State-of-the-art methods are based on single-level regression of a subsampled abstract representation of a recognition model. However, single-level regression cannot account for skip connections from the backbone to the upsampling path. We propose to address this shortcoming by warping shallow features from observed images with upsampled feature flow. Our goal is not straightforward, since warping with coarse feature flow introduces noise into the forecasted features. We therefore base our work on single-frame models that are more resistant to the noise in skip connections. To achieve this, we propose a training procedure that enables recognition models to operate reasonably well with or without skip connections. Validation experiments reveal interesting insights into the influence of particular skip connections on recognition accuracy. Our forecasting method delivers 70.2% mIoU 0.18 s into the future and 58.5% mIoU 0.54 s into the future. These experiments show 0.6 mIoU points of improved accuracy with respect to the baseline and reveal promising directions for future work.
Numerical Stability Analysis of Large Section Tunnels Using the Double-Side Heading Method: A Case Study of Xiamen Haicang Evacuate-Channel
Authors: Wang Jianxiu , Cao Ansheng , Wu Zhao , Lin Lihua , Li Zonghai , Liu Xiaotian , Li Huboqiang , Long Yanxia , Sun Yuanwei
Published: December 2022

Large section tunnels have huge excavation spans, complex excavation procedures, mutual influence among various procedures, multiple disturbances of the surrounding rock, and discontinuous connection among linings under different procedures, which bring great difficulties to their construction. The Caijianwei Mountain No. 2 tunnel of the Xiamen Haicang Evacuate-channel project in China belongs to a super large section tunnel. The tunnel’s maximum excavation section span reaches 30.52 m, and the excavation area reaches 421.73 m2. Its excavation sequence, the timing of the second primary lining support, temporary support disassembly, and secondary lining support of the double-side heading method were analyzed using numerical methods. The excavation sequence of the double side-heading method from the right pilot tunnel to the left pilot tunnel and then to the middle pilot tunnel can avoid a large horizontal displacement at the arch waist and control the deformation of the lining. The excavation sequence from the middle pilot tunnel to the two side pilot tunnels was conducive to the bearing performance of the concrete at the arch crown. The excavation sequence from the two side pilot tunnels to the middle pilot tunnel can better protect the concrete safety at the arch waist. The timing of the second primary lining support, temporary support removal, and secondary lining support have different effects on the deformation and stress of the surrounding rock and support structures. In the construction process, the timing of the second primary lining support, temporary support disassembly, and secondary lining support should be determined according to the actual deformation and stress of the site section. The research results can provide a reference for the construction design of the double-side heading method for large section tunnels.
A Novel Monocular Vision Technique for the Detection of Electric Transmission Tower Tilting Trend
Authors: Yang Yongsheng , Wang Minzhen , Wang Xinheng , Li Cheng , Shang Ziwen , Zhao Liying
Published: December 2022

Transmission lines are primarily deployed overhead, and the transmission tower, acting as the fulcrum, can be affected by the unbalanced force of the wire and extreme weather, resulting in the transmission tower tilt, deformation, or collapse. This can jeopardize the safe operation of the power grid and even cause widespread failures, resulting in significant economic losses. Given the limitations of current tower tilt detection methods, this paper proposes a tower tilt detection and analysis method based on monocular vision images. The monocular camera collects the profile and contour features of the tower, and the tower tilt model is combined to realize the calculation and analysis of the tower tilt. Through this improved monocular visual monitoring method, the perception accuracy of the tower tilt is improved by 7.5%, and the axial eccentricity is accurate to ±2 mm. The method provides real-time reliability and simple operation for detecting tower inclination, significantly reducing staff inspection intensity and ensuring the power system operates safely and efficiently.
Influence of Partial and Total Replacement of Used Foundry Sand in Self-Compacting Concrete
Authors: Del Angel Gilberto García , Aghajanian Ali , Cabrera René , Tamayo Pablo , Sainz-Aja Jose A. , Thomas Carlos
Published: December 2022

In this work, the feasibility of partially and totally replacing natural sand with used foundry sand in self-compacting concrete was studied. Natural sand was replaced in 50% and 100% vol. by used foundry sand. The fresh state properties parameters analyzed in this study were slump flow, t500, V-funnel, Japanese ring and L-box following EFNARC guidelines. Results indicated an improvement in the fresh state properties when used foundry sand was utilized for partial and total replacement. The mechanical properties compressive strength and splitting tensile strength were obtained and analyzed at 7 and 28 days. Regarding the compressive strength, used foundry sand enhanced compressive strength by up to 67% compared to control concrete. For splitting tensile strength, the self-compacting concrete with 50% vol. of used foundry sand displayed a slight decrease (2.8%) compared with the control concrete. SEM images showed that the concretes with used foundry sand had a less porous and more compacted matrix than the control concrete. It was concluded that the incorporation of used foundry sand in large volumes can be utilized as a sustainable alternative natural fine aggregate.
Study on Application of Comprehensive Geophysical Prospecting Method in Urban Geological Survey—Taking Concealed Bedrock Detection as an Example in Dingcheng District, Changde City, Hunan Province, China
Authors: Lu Yu-Long , Cao Chuang-Hua , Liu Yao-Qi , Liu Yang
Published: December 2022

In order to ascertain the concealed bedrock and its spatial distribution in an urban low-resistance coverage area of a typical lacustrine basin in Hunan Province, a multi-method comprehensive experimental study was carried out in Dingcheng District, Changde City where there are multiple sets of strata and fault structures. In this study, the wide-area electromagnetic method and microtremor survey were utilized on the basis of traditional methods, including the high-density resistivity method and controlled-source audio-frequency magnetotelluric method, to infer the concealed Cambrian limestone, fault structure, and vertical distribution of strata and the results were verified by drilling. The results indicate that the wide-area electromagnetic method is effective to explore the bedrock and concealed structure in urban geological survey. The microtremor method has an obvious effect on the detection of the Cretaceous and Quaternary silty strata within 100 m. The study may provide references for similar projects in this area.
Multi-Classifier Pipeline for Olive Groves Detection
Authors: Osa Priscilla Indira , Beck Anne-Laure , Kleverman Louis , Mangin Antoine
Published: December 2022

Pixel-based classification is a complex but well-known process widely used for satellite imagery classification. This paper presents a supervised multi-classifier pipeline that combined multiple Earth Observation (EO) data and different classification approaches to improve specific land cover type identification. The multi-classifier pipeline was tested and applied within the SCO-Live project that aims to use olive tree phenological evolution as a bio-indicator to monitor climate change. To detect and monitor olive trees, we classify satellite images to precisely locate the various olive groves. For that first step we designed a multi-classifier pipeline by the concatenation of a first classifier which uses a temporal Random-Forest model, providing an overall classification, and a second classifier which uses the result from the first classification. IOTA2 process was used in the first classifier, and we compared Multi-layer Perceptron (MLP) and One-class Support Vector Machine (OCSVM) for the second. The multi-classifier pipelines managed to reduce the false positive (FP) rate by approximately 40% using the combination RF/MLP while the RF/OCSVM combination lowered the FP rate by around 13%. Both approaches slightly raised the true positive rate reaching 83.5% and 87.1% for RF/MLP and RF/OCSVM, respectively. The overall results indicated that the combination of two classifiers pipeline improves the performance on detecting the olive groves compared to pipeline using only one classifier.
Effective Selfish Mining Defense Strategies to Improve Bitcoin Dependability
Authors: Zhou Chencheng , Xing Liudong , Liu Qisi , Wang Honggang
Published: December 2022

Selfish mining is a typical malicious attack targeting the blockchain-based bitcoin system, an emerging crypto asset. Because of the non-incentive compatibility of the bitcoin mining protocol, the attackers are able to collect unfair mining rewards by intentionally withholding blocks. The existing works on selfish mining mostly focused on cryptography design, and malicious behavior detection based on different approaches, such as machine learning or timestamp. Most defense strategies show their effectiveness in the perspective of reward reduced. No work has been performed to design a defense strategy that aims to improve bitcoin dependability and provide a framework for quantitively evaluating the improvement. In this paper, we contribute by proposing two network-wide defensive strategies: the dynamic difficulty adjustment algorithm (DDAA) and the acceptance limitation policy (ALP). The DDAA increases the mining difficulty dynamically once a selfish mining behavior is detected, while the ALP incorporates a limitation to the acceptance rate when multiple blocks are broadcast at the same time. Both strategies are designed to disincentivize dishonest selfish miners and increase the system’s resilience to the selfish mining attack. A continuous-time Markov chain model is used to quantify the improvement in bitcoin dependability made by the proposed defense strategies. Statistical analysis is applied to evaluate the feasibility of the proposed strategies. The proposed DDAA and ALP methods are also compared to an existing timestamp-based defense strategy, revealing that the DDAA is the most effective in improving bitcoin’s dependability.
High-Throughput Virtual Screening of Quinones for Aqueous Redox Flow Batteries: Status and Perspectives
Authors: Khetan Abhishek
Published: December 2022

Quinones are one of the most promising and widely investigated classes of redox active materials for organic aqueous redox flow batteries. However, quinone-based flow batteries still lack the necessary performance in terms of metrics, such as specific capacity, power density, and long-term stability, to achieve mass market adoption. These performance metrics are directly related to the physicochemical properties of the quinone molecules, including their equilibrium redox potential, aqueous solubility, and chemical stability. Given the enormous chemical and configurational space of possible quinones and the high tunability of their properties, there has been a recent surge in the use of high-throughput virtual screening (HTVS) for the rational design and discovery of new high-performing molecules. In this review article, HTVS efforts for the computational design and discovery of quinones are reviewed with a special focus on the enumerated space of core quinone motif, the methods and approximations used for the estimation of performance descriptors, and the emergent structure-property relationships. The knowledge and methodological gaps in conventional HTVS efforts are discussed, and strategies for improvement are suggested.
Stabilizing a Zn Anode by an Ionic Amphiphilic Copolymer Electrolyte Additive for Long-Life Aqueous Zn-Ion Batteries
Authors: Liu Yu-E , Wang Xin
Published: December 2022

The rampant growth of zinc dendrites and severe uncontrollable reactions have largely limited the industrialization of aqueous Zn-ion batteries. Electrolyte additive engineering was found to be a facile yet effective strategy in addressing these issues; however, traditional organic small molecule additives raise additional safety and health risks and thus compromise the intrinsic advantage of aqueous batteries. In this study, we report a polyacrylonitrile-co-poly(2-acrylamido-2-methylpropanesulfonic acid) (PAN-co-PAMPS) copolymer with ionic and hydrophilicity PAMPS and non-ionic PAN, which acts as an electrolyte additive to regulate the Zn deposition in aqueous Zn-ion batteries. The hydrophilicity of PAMPS is designed to meet water solubility. Moreover, ionic PAMPS reacts with a Zn anode surface, chemically peels the surface, leaves a pre-polished anode surface, and removes heterogeneity and impurity of the metal surface. All these effects are beneficial for homogeneous zinc ion deposition and long-life battery. The PAN segments act as a water-shielding layer on a Zn anode to prevent its direct contact with H2O. Consequently, the Zn|Zn symmetric cells with additive-containing electrolytes have a much longer life than those without additives (up to eight times) at a current density of 1 mA cm−2 and a capacity of 1 mA h cm−2. The assembled Zn|Cu cells and the Zn|V2O5 full batteries also display prominent electrochemical reversibility. The reactively acidic amphiphilic polymer provides not only an alternative strategy for the design of multi-functional electrolyte additives, but also constitutes an easy-to-operate way for advancing commercialization of aqueous zinc-storage devices.
Limitations of Reconstructing Pentacam Rabbit Corneal Tomography by Zernike Polynomials
Authors: Baraya Mohamed , Moore Jessica , Lopes Bernardo T. , Wu Richard , Bao FangJun , Zheng XiaoBo , Consejo Alejandra , Abass Ahmed
Published: December 2022

The study aims to investigate the likelihood of Zernike polynomial being used for reconstructing rabbit corneal surfaces as scanned by the Pentacam segment tomographer, and hence evaluate the accuracy of corneal power maps calculated from such Zernike fitted surfaces. The study utilised a data set of both eyes of 21 rabbits using a reverse engineering approach for deductive reasoning. Pentacam raw elevation data were fitted to Zernike polynomials of orders 2 to 20. The surface fitting process to Zernike polynomials was carried out using randomly selected 80% of the corneal surface data points, and the root means squared fitting error (RMS) was determined for the other 20% of the surface data following the Pareto principle. The process was carried out for both the anterior and posterior surfaces of the corneal surfaces that were measured via Pentacam scans. Raw elevation data and the fitted corneal surfaces were then used to determine corneal axial and tangential curvature maps. For reconstructed surfaces calculated using the Zernike fitted surfaces, the mean and standard deviation of the error incurred by the fitting were calculated. For power maps computed using the raw elevation data, different levels of discrete cosine transform (DCT) smoothing were employed to infer the smoothing level utilised by the Pentacam device. The RMS error was not significantly improved for Zernike polynomial orders above 12 and 10 when fitting the anterior and posterior surfaces of the cornea, respectively. This was noted by the statistically non-significant increase in accuracy when the order was increased beyond these values. The corneal curvature calculations suggest that a smoothing process is employed in the corneal curvature maps outputted by the Pentacam device; however, the exact smoothing method is unknown. Additionally, the results suggest that fitting corneal surfaces to high-order Zernike polynomials will incur a clinical error in the calculation of axial and tangential corneal curvature of at least 0.16 ± 01 D and 0.36 ± 0.02 D, respectively. Rabbit corneal anterior and posterior surfaces scanned via the Pentacam were optimally fitted to orders 12 and 10 Zernike polynomials. This is essential to get stable values of high-order aberrations that are not affected by Zernike polynomial fittings, such as comas for Intracorneal Ring Segments (ICRS) adjustments or spherical aberration for pre-cataract operations. Smoothing was necessary to replicate the corneal curvature maps outputted by the Pentacam tomographer, and fitting corneal surfaces to Zernike polynomials introduces errors in the calculation of both the axial and tangential corneal curvatures.
Silica-Based Nanomaterials for Diabetes Mellitus Treatment
Authors: Marinheiro Diogo , Martel Fátima , Ferreira Bárbara J. M. L. , Daniel-da-Silva Ana L.
Published: December 2022

Diabetes mellitus, a chronic metabolic disease with an alarming global prevalence, is associated with several serious health threats, including cardiovascular diseases. Current diabetes treatments have several limitations and disadvantages, creating the need for new effective formulations to combat this disease and its associated complications. This motivated the development of therapeutic strategies to overcome some of these limitations, such as low therapeutic drug bioavailability or poor compliance of patients with current therapeutic methodologies. Taking advantage of silica nanoparticle characteristics such as tuneable particle and pore size, surface chemistry and biocompatibility, silica-based nanocarriers have been developed with the potential to treat diabetes and regulate blood glucose concentration. This review discusses the main topics in the field, such as oral administration of insulin, glucose-responsive devices and innovative administration routes.
Making Visible the Invisible: Automatically Measured Global and Regional Brain Volume Is Associated with Cognitive Impairment and Fatigue in Multiple Sclerosis
Authors: Ziccardi Stefano , Pizzini Francesca Benedetta , Guandalini Maddalena , Tamanti Agnese , Cristofori Cecilia , Calabrese Massimiliano
Published: December 2022

In multiple sclerosis (MS), the transition from relapsing-remitting to the secondary-progressive phase is characterized by a progression independent of relapse activity (PIRA), resulting in physical disability accumulation and invisible symptoms, i.e., fatigue and cognitive impairment (CI). These symptoms are related to neurodegenerative processes and have been correlated with MRI measures of brain atrophy only at a group level; however, the application in clinical practice of atrophy-based measurements for single-patient evaluation is yet to be fully investigated. In the present study, we aimed to evaluate the association between brain atrophy, measured with easy-to-use automatic software, and the “invisible” MS symptoms of cognition and fatigue. A total of 69 MS patients were included in the study; cognitive impairment and fatigue (FSS) (in addition to neurological disability, EDSS) were assessed and correlated with brain volumes calculated using the automated software QyScore® which is validated for single-patient use in the clinical setting. Results showed that the cognitive status was accurately reflected by measures of atrophy, with a sensitivity of up to 90%. CI patients showed a lower volume compared to cognitively normal patients in the whole brain (p = 0.017), gray matter (p = 0.042), insula (p = 0.035), cerebellum (p = 0.008), and limbic lobe (p = 0.049). FSS was associated with temporal lobe (r = −0.37, p = 0.013) and insular (r = −0.36, p = 0.019) volumes. The volumes of the same regions were also associated with EDSS. The global/regional atrophy results, assessed with automatic and easy-to-use software, correlated with cognitive and fatigue symptoms, thus supporting the clinical application in routine patient management.
FIB-4 Index and Neutrophil-to-Lymphocyte-Ratio as Death Predictor in Coronary Artery Disease Patients
Authors: Gaggini Melania , Minichilli Fabrizio , Gorini Francesca , Del Turco Serena , Landi Patrizia , Pingitore Alessandro , Vassalle Cristina
Published: December 2022

Nonalcoholic fatty liver disease (NAFLD)-associated liver fibrosis is likely related to coronary artery disease (CAD) by the mediation of systemic inflammation. This study aimed at evaluating the predictive value of neutrophil-to-lymphocyte-ratio (NLR) and fibrosis-4 index (FIB-4), indices of inflammation and fibrosis, respectively, on CAD mortality. Data from 1460 CAD patients (1151 males, age: 68 ± 10 years, mean ± SD) were retrospectively analyzed. Over a median follow-up of 26 months (interquartile range (IQR) 12–45), 94 deaths were recorded. Kaplan–Meier survival analysis revealed worse outcomes in patients with elevation of one or both biomarkers (FIB-4 > 3.25 or/and NLR > 2.04, log-rank p-value < 0.001). In multivariate Cox regression analysis, the elevation of one biomarker (NLR or FIB-4) still confers a significant independent risk for mortality (hazard ratio (HR) = 1.7, 95% confidence interval (95% CI): 1.1–2.7, p = 0.023), whereas an increase in both biomarkers confers a risk corresponding to HR = 3.5 (95% CI: 1.6–7.8, p = 0.002). Categorization of patients with elevated FIB-4/NLR could provide valuable information for risk stratification and reduction of residual risk in CAD patients.
FT3 to FT4 Conversion Ratio May Be an Independent Prognostic Factor in Pancreatic Cancer Patients
Authors: Majos Alicja , Sewerynek Ewa , Grząsiak Oliwia , Ciesielski Wojciech , Hogendorf Piotr , Hołyński Jarosław , Strzelczyk Janusz , Durczyński Adam
Published: December 2022

Preclinical evidence suggests that T4 can promote tumor growth while T3 can act conversely; therefore, the fT3 and fT4 concentrations should affect overall survival (OS) in cancer patients. The objective of the study was to look for an association between thyroid hormone concentrations in peripheral blood and OS in the pancreatic adenocarcinoma (PDAC) patients group. We included, retrospectively, 15 PDAC patients, without thyroid dysfunction under treatment, who underwent radical surgery, with no prior history of anticancer therapy. TSH, fT3, and fT4 concentrations were determined in blood samples taken preoperatively. We found that the fT3/fT4 ratio categorized into two groups (<0.22 vs. ≥0.22) dichotomized the study population into poor and good prognosis subgroups (log-rank p = 0.03; OS medians, respectively: 3 and 14 months), being a statistically significant predictor both in uni- and multivariate Cox regression analysis. We conclude that the importance of fT4 into fT3 conversion means not just its standard metabolic effects as the final products of thyroid gland activity. We hypothesize that it is linked to the progression of pancreatic malignancies, either via thyroid hormone receptors or indirectly, by interaction with cancer cells product.
Production and Functional Evaluation of Anti-Loxosceles Sera Raised by Immunizations of Rabbits with Mutated Recombinant Phospholipases-D
Authors: Antunes Bruno Cesar , Polli Nayanne Louise Costacurta , Schluga Pedro Henrique de Caires , Silva Thais Pereira da , Wille Ana Carolina Martins , Locatelli-Dittrich Rosangela , Souza Giovana Scuissiatto de , Matsubara Fernando Hitomi , Minozzo João Carlos , Senff-Ribeiro Andrea , Gremski Luiza Helena , Veiga Silvio Sanches
Published: December 2022

Loxoscelism is the clinical condition triggered after the bite of spiders of the genus Loxosceles. The main species involved in accidents in South America are L. intermedia, L. laeta, and L. gaucho. The only specific treatment is the anti-Loxosceles serum produced with crude venoms. As phospholipases D (PLDs) trigger most of the effects observed in accidents, we developed and evaluated second-generation sera using mutated PLDs as antigens. Three isoforms of PLDs with site-directed mutations without biological activities were used for rabbit immunizations: D32A-E34A (L. gaucho), W230A (L. intermedia), and H12A-H47A (L. laeta). Sera were produced using crude venoms of three species of Loxosceles enriched with mutated recombinant PLDs (MIX) or using only mutated PLDs (REC). Immunizations stimulated the immune system from the second immunization with higher antibody production in the REC group. In vivo neutralization assays demonstrated that both sera reduced edema and dermonecrosis caused by Loxosceles intermedia crude venom. Follow-up of animals during the immunization protocols and in the neutralization assays demonstrated that the mutated proteins and the sera are safe. Results demonstrate the potential of using mutated recombinant PLDs in total or partial replacement of Loxosceles venoms in animal immunizations to produce anti-Loxosceles sera for treatments of Loxoscelism.
Onset of Action of Selected Second-Generation Antipsychotics (Pines)–A Systematic Review and Meta-Analyses
Authors: Meyer Rikke , Skov Kenneth , Dhillon Inderjeet Kaur , Olsson Emilie , Graudal Niels Albert , Baandrup Lone , Jürgens Gesche
Published: December 2022

Recommendations for duration of treatment with antipsychotics before considering a switch vary from 2 to 8 weeks, although several studies suggest a rapid onset of action. The objective of this review was to estimate time to onset of action and time to maximum antipsychotic effect of asenapine, olanzapine, quetiapine, and zotepine (pines). We searched bibliographic databases for randomized, placebo-controlled trials in adults with schizophrenia estimating the antipsychotic effect of pines over time. Thirty-five studies including 6331 patients diagnosed with chronic schizophrenia were included. We estimated the standardized mean differences (SMD) of changes in symptom score from baseline to follow-up between intervention and placebo groups across studies using meta-analysis techniques. The summarized effect across all included pines administered as immediate-release formulations showed a statistically significant effect at week 1 (SMD, −0.20 [CI95% −0.28, −0.13]), which increased until week 3 (SMD, −0.42 [CI95% −0.50, −0.34]), after which the effect leveled off (week 6: SMD, −0.53 [CI95% −0.62, −0.44]). The sensitivity analyses of the individual pines confirm this finding, although data sparsity increases variability and limits conclusiveness of these analyses.
Gross, Fine and Visual-Motor Skills in Children with Language Disorder, Speech Sound Disorder and Their Combination
Authors: Varuzza Cristiana , D’Aiello Barbara , Lazzaro Giulia , Quarin Fabio , De Rose Paola , Bergonzini Paola , Menghini Deny , Marini Andrea , Vicari Stefano
Published: December 2022

Increasing evidence shows that children with Communication Disorders (CDs) may show gross, fine, and visual-motor difficulties compared to children with typical development. Accordingly, the present study aims to characterize gross, fine and visual-motor skills in children with CDs, distinguishing children with CDs into three subgroups, i.e., with Language Disorders (LD), Speech Sound Disorders (SSD), and LD + SSD. In Experiment 1, around 60% of children with CDs (4 to 7 years; 21 with LD, 36 with SSD, and 90 with LD + SSD) showed clinical/borderline scores in balance skills, regardless of the type of communication deficit. However, children with LD, SSD, and LD + SSD did not differ in gross and fine motor skills. In Experiment 2, a higher percentage of children with CDs (4 to 7 years; 34 with LD, 62 with SSD, 148 with LD + SSD) obtained clinical/borderline scores in Visual Perception skills. Moreover, children with LD + SSD performed significantly worsen in Visual Perception and Fine Motor Coordination skills compared to children with SSD only. Our results underlined that CDs are generally associated with gross motor difficulties and that visual-motor difficulties are related to the type of communication deficit. Paying earlier attention to the motor skills of children with CDs could help clinicians design effective interventions.
Tumor Response, Disease Control, and Progression-Free Survival as Surrogate Endpoints in Trials Evaluating Immune Checkpoint Inhibitors in Advanced Non-Small Cell Lung Cancer: Study- and Patient-Level Analyses
Authors: Horita Nobuyuki
Published: December 2022

Background: To assess the usefulness of tumor response and progression-free survival (PFS) as surrogates for overall survival (OS) in non-small cell lung cancer (NSCLC) trials with immune checkpoint inhibitors (ICI), which have not been confirmed. Methods: Patient- and trial-level analyses were performed. The Response Evaluation Criteria in Solid Tumors was preferred for image assessment. For trial-level analysis, surrogacy was assessed using the weighted rank correlation coefficient (r) following “reciprocal duplication.” This method duplicates all plots as if the experimental and the reference arms were switched. Monte Carlo simulations were performed for evaluating this method. Results: A total of 3312 cases were included in the patient-level analysis. Patients without response (first line (1L): hazard ratio (HR) 1.95, 95% confidence interval (CI) 1.71–2.23; second or later line (2L-): HR 4.22, 95% CI 3.22–5.53), without disease control (1L: HR 4.34, 95% CI 3.82–4.94; 2L-: HR 3.36, 95% CI 2.96–3.81), or with progression during the first year (1L: HR 3.42, 95% CI 2.60–4.50; 2L-: HR 3.33, 95% CI 2.64–4.20), had a higher risk of death. Systematic searches identified 38 RCTs including 17,515 patients for the study-level analysis. Odds ratio in the objective response rate (N = 38 × 2, r = −0.87) and HR in PFS (N = 38 × 2, r = 0.85) showed an excellent association with HR in overall survival, while this effect was not observed in the disease control rate (N = 26 × 2, r = −0.03). Conclusions: Objective response rate and PFS are reasonable surrogates for OS in NSCLC trials with ICI.
Calcium Channels, OST1 and Stomatal Defence: Current Status and Beyond
Authors: Upadhyay Santosh Kumar
Published: December 2022

Stomatal immunity is regulated by pathogen-associated molecular patterns (PAMPs)- and abscisic acid (ABA)-triggered signalling in different ways. Cytoplasmic Ca2+ signature in the guard cells plays a vital function in stomatal immunity, but the mechanism of Ca2+ import is unknown. It has been very recently established that the hyperosmolality-gated calcium-permeable channels (OSCAs) and cyclic nucleotide-gated channels (CNGCs) are responsible for the influx of Ca2+ in the cytoplasm, which are activated after BIK1-mediated phosphorylation and ABA interaction during PAMPs- and ABA-triggered stomatal immunity in plants, respectively. Further, ABA-triggered OPEN STOMATA1 (OST1) causes the disassembly of microtubules in the guard cells besides activation of S-type anion channels (SLAC1) for the efflux of cytoplasmic anions that leads to stomata closure.
Rhinovirus Suppresses TGF-β-GARP Presentation by Peripheral NK Cells
Authors: Krammer Susanne , Yang Zuqin , Mitländer Hannah , Grund Janina C. , Trump Sonja , Mittler Susanne , Zirlik Sabine , Finotto Susetta
Published: December 2022

Asthma is a chronic airway disease whose exacerbations are often triggered by rhinovirus infection. TGF-β1 induces rhinovirus replication in infected cells. Moreover, TGF-β1 is a pleiotropic mediator that is produced by many immune cells in the latent, inactive form bound to the latency-associated peptide (LAP) and to the transmembrane protein glycoprotein A repetitions predominant (GARP). In this study we wanted to investigate the effect of rhinovirus infection on the TGF-β secretion and the downstream signaling via TGF-βRI/RII in peripheral blood mononuclear cells from control and asthmatic patients after rhinovirus infection ex vivo. Here, we found a significant upregulation of TGF-βRII in untouched PBMCs of asthmatics as well as a suppression of TGF-β release in the rhinovirus-infected PBMC condition. Moreover, consistent with an effect of TGF-β on Tregs, PBMCs infected with RV induced Tregs, and TGF-βRII directly correlated with RV1b mRNA. Finally, we found via flow cytometry that NK cells expressed less GARP surface-bound TGF-β, while cytokine-producing NKbright cells were induced. In summary, we show that rhinovirus infection inhibits TGF-β release in PBMCs, which results in the activation of both Treg and NK cells.
Molecular Modeling Based on Time-Dependent Density Functional Theory (TD-DFT) Applied to the UV-Vis Spectra of Natural Compounds
Authors: Anhaia-Machado João Otávio , Soares Artur Caminero Gomes , de Oliveira Pinto Claudinéia Aparecida Sales , Barrera Andres Ignacio Ávila , Baby André Rolim , Trossini Gustavo Henrique Goulart
Published: December 2022

As diseases caused by solar radiation have gained great prominence, several methods to prevent them have been developed. Among the most common, the use of sunscreens is customary and accessible. The application of theoretical methods has helped to design new compounds with therapeutic and protective functions. Natural compounds with described photoprotective potential properties (3-O-methylquercetin, gallic acid, aloin, catechin, quercetin, and resveratrol) were selected to perform theoretical studies. Computational methods were applied to predict their absorption spectra, using DFT and TD-DFT methods with functional B3LYP/6−311+g(d,p) basis sets and methanol (IEFPCM) as a solvent. The main electronic transitions of the compounds were evaluated by observing whether the differences in HOMO and LUMO energies that absorb in the UV range are UVA (320–400 nm), UVB (290–320 nm), or UVC (100–290 nm). Experimental validation was carried out for EMC, quercetin, and resveratrol, demonstrating the consistency of the computational method. Results obtained suggest that resveratrol is a candidate for use in sunscreens. The study provided relevant information about the in silico predictive power of natural molecules with the potential for use as photoprotective adjuvants, which may result in fewer time and resource expenditures in the search for photoprotective compounds.
Comparative Investigation of the Undercooling Capacity and Single-Crystal Castability of Some Ni-Based Superalloys
Authors: Ma Dexin , Zhao Yunxing , Xu Weitai , Xu Fuze , Wei Jianhui , Zhang Haijie
Published: December 2022

The undercooling capacity of a superalloy is an essential physical property to determine its single-crystal (SC) castability, because stray grains (SGs) will be formed if the geometrical undercooling established at the platform extremities exceeds the undercooling capacity of the applied alloy. In the present work, both the undercooling capacity of eight Ni-based superalloys and their SC castability were experimentally investigated. The liquidus temperature, the critical temperature for grain nucleation, and hence the undercooling capacities of the investigated alloys were evaluated based on the temperature evolution during the heating and cooling processes. The current experimental study revealed a significant difference in undercooling capacity for the superalloys. In the production of SC blade castings, the tendency to form SG defects was found to be highly related to the alloy’s undercooling capacity. The alloys having a low undercooling capacity of around 10 K were very prone to the formation of SGs. In comparison, the alloys with a moderate undercooling capacity from 20 K to 30 K could be easily cast into SC blades without SGs, exhibiting the best SC castability. Other factors influencing the SG formation were also analyzed. As a result, a criterion for the formation of geometry-related SG defects was proposed, in which the influence of the alloy undercooling capacity, casting geometry, and solidification condition are involved.
Machine Learning for Online Automatic Prediction of Common Disease Attributes Using Never-Ending Image Learner
Authors: Rajesh E. , Basheer Shajahan , Dhanaraj Rajesh Kumar , Yadav Soni , Kadry Seifedine , Khan Muhammad Attique , Kim Ye Jin , Cha Jae-Hyuk
Published: December 2022

The rapid increase in Internet technology and machine-learning devices has opened up new avenues for online healthcare systems. Sometimes, getting medical assistance or healthcare advice online is easier to understand than getting it in person. For mild symptoms, people frequently feel reluctant to visit the hospital or a doctor; instead, they express their questions on numerous healthcare forums. However, predictions may not always be accurate, and there is no assurance that users will always receive a reply to their posts. In addition, some posts are made up, which can misdirect the patient. To address these issues, automatic online prediction (OAP) is proposed. OAP clarifies the idea of employing machine learning to predict the common attributes of disease using Never-Ending Image Learner with an intelligent analysis of disease factors. Never-Ending Image Learner predicts disease factors by selecting from finite data images with minimum structural risk and efficiently predicting efficient real-time images via machine-learning-enabled M-theory. The proposed multi-access edge computing platform works with the machine-learning-assisted automatic prediction from multiple images using multiple-instance learning. Using a Never-Ending Image Learner based on Machine Learning, common disease attributes may be predicted online automatically. This method has deeper storage of images, and their data are stored per the isotropic positioning. The proposed method was compared with existing approaches, such as Multiple-Instance Learning for automated image indexing and hyper-spectrum image classification. Regarding the machine learning of multiple images with the application of isotropic positioning, the operating efficiency is improved, and the results are predicted with better accuracy. In this paper, machine-learning performance metrics for online automatic prediction tools are compiled and compared, and through this survey, the proposed method is shown to achieve higher accuracy, proving its efficiency compared to the existing methods.
“It Enables Us to Reflect More on Nutrition”: A Mixed Methods Cross-Sectional Study on Preclinical Digital Training in Nurse Education
Authors: Almendingen Kari , Knutsen Ingrid Ruud , Hjerpaasen Kari Jonsbu , Henjum Sigrun , Hakestad Kari Anne
Published: December 2022

Adequate nutrition is a basic human right, yet older adults are at high risk of malnutrition. Nutrition is not a part of most nursing curricula. Limited research has been done on digital case-based learning (CBL) in breakout rooms (digital groups); therefore, the aim was to develop and evaluate a preclinical digital CBL unit (3-h synchronously and with an asynchronously flipped classrooms approach) for prevention and treatment of malnutrition and to explore nursing students’ experiences and learning outcomes. Different scenarios for two fictive cases were created in which malnutrition-related challenges were included (such as terminal care) and embedded on the ThingLink platform. In an explorative mixed methods cross-sectional study design, students (n = 78) completed an online evaluation. The results revealed that students wanted more synchronous interaction with educators and less time alone in breakout rooms due to their peers being unprepared, passive and unfamiliar and not turning on their cameras or logging on too late. The learning outcome from quizzes and word clouds were high, but the added pedagogical value of ThingLink seemed low. This explorative study sheds light on central issues related to the use of technology in nurse education, resistance against student active methods and digital pedagogy.
Fast Supraharmonic Estimation Algorithm Based on Simplified Compressed Sensing Model
Authors: Gui Zesen , Zhou Qun , Zhou Hui , Liao Zheng , Wang Ziyi
Published: December 2022

The computational time of compressed sensing algorithms applied to supraharmonic needs to be improved in online applications. In this paper, a simplified supraharmonic compressive sensing model is proposed. The model first detects the supraharmonic raw spectral array to obtain the estimated sparsity and the index of supraharmonic emissions, which simplifies the sensing matrix in the iteration according to the index and then shortens the whole iteration time of compressed sensing. The simulation verifies that the model can reduce the computation time to less than half of the original compressed sensing model and does not affect the computation accuracy. Finally, the online application effect of the algorithm is verified by experiments.
Design of a New Capacitive Load Cross-Coupled Cavity Filter
Authors: Wang Jingchao , Wang Rui , Li Lezhong , Guo Junlin , Zhang Jian
Published: December 2022

This paper presents a new design method for a capacitor-loaded cross-coupled cavity filter, which significantly increases the power capacity of the cavity filter while improving out-of-band rejection. The filter uses a coaxial resonator at the open end of the donut capacitive-load. A triangular form of cavity structure is used for the full-cavity arrangement to reduce the size of the filter. In order to validate the design, a filter with a center frequency of 2.08 GHz and bandwidth of 80 MHz was designed, processed, and tested. The measured insertion loss was ≤0.97 dB, and the return loss was >20 dB in the range of 2040 MHz to 2120 MHz; the rejection was as high as 80 dB, and in-band ripple was 0.06 dB in the upper and lower stopband-range, which was only 30 MHz away from the sideband. After a simulation of the model using HFSS software (HFSS 2021, Canonsburg, PA, USA), the measured results matched the simulation results, which verified the correctness and practicality of the design. Compared with the ordinary-disc-load resonant cavity, the new donut capacitively loaded cavity-bandpass-filter has a 13.2% reduction in height and a 29.7% increase in power capacity.
Simulation Analysis of Heat Pumps Application for the Purposes of the Silesian Botanical Garden Facilities in Poland
Authors: Ciuman Piotr , Kaczmarczyk Jan , Jastrzębska Małgorzata
Published: December 2022

Generating energy from renewable sources is becoming more and more popular and widespread. Air source heat pumps are one of the most popular types of heat pumps due to the general availability of a heat source and relatively low investment costs. The aim of the paper was to investigate the impact of the use of air heat pumps on the improvement of energy efficiency of Silesian Botanical Garden facilities in Poland. Year-round numerical analyses of energy consumption were carried out with the use of IDA ICE software, based on data received from the facility administrator. Different variants of the boiler room configuration including air-to-water heat pumps were analysed. The results showed that implementation of heat pumps led to significant decrease of energy consumption in the building and reduction of values of Primary Energy and CO2 emission.
An Analysis of the Potential of Hydrogen Energy Technology on Demand Side Based on a Carbon Tax: A Case Study in Japan
Authors: Qian Fanyue , Gao Weijun , Yu Dan , Yang Yongwen , Ruan Yingjun
Published: December 2022

Hydrogen energy is considered one of the main measures of zero carbonization in energy systems, but high equipment and hydrogen costs hinder the development of hydrogen energy technology. The objectives of this study are to quantify the environmental advantages of hydrogen energy through a carbon tax and study the application potential of hydrogen energy technology in a regional distributed energy system (RDES). In this study, various building types in the smart community covered by Japan’s first hydrogen energy pipeline are used as an example. First, ten buildings of five types are selected as the research objectives. Subsequently, two comparative system models of a regional distributed hydrogen energy system (RDHES) and an RDES were established. Then, by studying the optimal RDHES and RDES configuration and combining the prediction of future downward trends of fuel cell (FC) costs and energy carbon emissions, the application effect of FC and hydrogen storage (HS) technologies on the demand side was analyzed. Finally, the adaptability of the demand-side hydrogen energy system was studied by analyzing the load characteristics of different types of buildings. The results show that, when the FC price is reduced to 1.5 times that of the internal combustion engine (ICE), the existing carbon tax system can sufficiently support the RDHES in gaining economic advantages in some regions. Notably, when the carbon emissions of the urban energy system are reduced, the RDHES demonstrates stronger anti-risk ability and has greater suitability for promotion in museums and shopping malls. The conclusions obtained in this study provide quantitative support for hydrogen energy promotion policies on the regional demand side and serve as a theoretical reference for the design and adaptability research of RDHESs.