bluuebunny/crossref_metadata_2025 huggingface dataset:
A Spatial Risk Analysis of Springtime Daily Minimum Surface Air Temperature Values for Vineyard Site Selection: Applications to Pinot noir Grapevines throughout the Willamette Valley American Viticultural Area
Authors: Skahill Brian , Berenguer Bryan , Stoll Manfred
Published: July 2024

This study introduced the application of concepts and methods from extreme value theory (EVT) to estimate the probability that daily minimum temperatures exceed springtime critical temperature thresholds for Pinot noir buds and young shoots as a function of springtime phenology. The springtime frost risk estimates were computed spatially for Pinot noir throughout the Willamette Valley (WV) American Viticultural Area (AVA) using a gridded dataset of historical daily minimum surface air temperature data. EVT-based springtime frost risk maps can inform vineyard-management operations by identifying those locations throughout a wine region with a low risk for any cold injury where remedial action is likely not necessary when there is a forecasted frost event. Frost risk estimates were computed for 1991–2021 and 1991–2022 to examine a potentially changed risk profile for springtime frost events throughout the WV AVA due to the April 2022 advective frost event. The April 2022 advective frost event influenced the risk profile throughout the AVA such that an event of its magnitude is now modelled to occur more frequently. The EVT-based risk analysis can be readily updated each year as new data become available. While spatially varying budbreak calculations facilitated computation of the spring frost risk estimates, the EVT approach profiled in this study does not necessarily depend on potentially uncertain predetermined budbreak date estimates. Gridded maps of extreme daily minimum temperature exceedances, reclassified relative to the springtime phenology critical temperature thresholds for Pinot noir, were readily combined with a ripening potential map to identify optimal areas for vineyard site selection throughout the WV AVA. When simultaneously evaluating Pinot noir ripening potential with springtime frost risk using historical data, the limiting factor for vineyard site selection throughout the WV AVA was frost risk, not ripening potential. The study approach is also applicable for other winegrape-growing regions, assessments of winter freeze risk and summertime heatwaves, and with non-gridded observed temperature datasets.
Fosfomycin-Containing Regimens for the Treatment of Central Nervous System Infections in a Neonatal Intensive Care Unit: A Case Series Study
Authors: Lenzi Angelica , Saccani Barbara , Di Gregorio Marco , Rossini Francesco , Sollima Alessio , Mulè Alice , Morucci Federica , Amadasi Silvia , Fumarola Benedetta , Lanza Paola Antonia , Lorenzotti Silvia , Van Hauwermeiren Evelyn , Cavalleri Elisa , Marzollo Roberto , Matteelli Alberto , Signorini Liana , Risso Francesco Maria
Published: July 2024

Central nervous system infections are among the most severe infectious conditions in the neonatal period and are still burdened by significant mortality, especially in preterm infants and those with a low birth weight or other comorbidities. In this study, we examined the role of fosfomycin-containing antibiotic regimens in neonates with central nervous system infections. We included six neonates over a period of five years: four with meningitis and two with cerebral abscesses. All patients underwent fosfomycin therapy after failing first-line antibiotic regimens. Of the six neonates, two died; two developed neurological and psychomotor deficits and two recovered uneventfully. None of the neonates experienced adverse reactions to fosfomycin, confirming the safety of the molecule in this population. In conclusion, the deep penetration in the central nervous system, the unique mechanism of action, the synergy with other antibiotic therapies, and the excellent safety profile all make fosfomycin an attractive drug for the treatment of neonatal central nervous system infections.
The Interplay of Protein Aggregation, Genetics, and Oxidative Stress in Alzheimer’s Disease: Role for Natural Antioxidants and Immunotherapeutics
Authors: Ali Jawad , Choe Kyonghwan , Park Jun Sung , Park Hyun Young , Kang Heeyoung , Park Tae Ju , Kim Myeong Ok
Published: July 2024

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that comprises amyloid-beta protein (Aβ) as a main component of neuritic plaques. Its deposition is considered a trigger for AD pathogenesis, progression, and the clinical symptoms of cognitive impairment. Some distinct pathological features of AD include phosphorylation of tau protein, oxidative stress, and mitochondrial dysfunction. These pathological consequences tend to produce reactive oxygen species (ROS), resulting in the dysregulation of various signaling pathways of neuroinflammation and neurodegeneration. The relationship between the Aβ cascade and oxidative stress in AD pathogenesis is like a “chicken and egg” story, with the etiology of the disease regarding these two factors remaining a question of “which comes first.” However, in this review, we have tried our best to clarify the interconnection between these two mechanisms and to show the precise cause-and-effect relationship. Based on the above hallmarks of AD, several therapeutic strategies using natural antioxidants, monoclonal antibodies, and vaccines are employed as anti-Aβ therapy to decrease ROS, Aβ burden, chronic neuroinflammation, and synaptic failure. These natural antioxidants and immunotherapeutics have demonstrated significant neuroprotective effects and symptomatic relief in various in vitro and in vivo models, as well as in clinical trials for AD. However, none of them have received final approval to enter the drug market for mitigating AD. In this review, we extensively elaborate on the pitfalls, assurances, and important crosstalk between oxidative stress and Aβ concerning current anti-Aβ therapy. Additionally, we discuss future strategies for the development of more Aβ-targeted approaches and the optimization of AD treatment and mitigation.
Study on L-Bending Springback of 45 Steel Leather Cutting Tool Coupled with Local Induction Heating
Authors: Cheng Yuan , Geng Heran , Cao Chao , Arif Abul Fazal M. , Liu Xinhua , Yuan Junfeng
Published: July 2024

Springback error is a major obstacle in L-bending sheets via cold working. Although thermal processing can effectively reduce the springback phenomenon, it is challenging to heat the sheet globally due to the influence of working conditions. Therefore, this study applied local induction heating to reduce the springback of 45 steel sheets and investigated the effects of bending parameters on springback behavior. Initially, a thermodynamic coupling model and a static model were created utilizing the VOCE hardening model and the von Mises yield criterion in the ANSYS workbench 2022 R2 software. The springback behavior and stress distribution of the sheets were then investigated under different temperatures (room temperature and 800 °C) and bending angles (15°, 30°, 45°, 60°, 75°, and 90°). Simultaneously, the experiments were performed to investigate springback behavior and guarantee the accuracy of the model. The results indicate that the springback reached a minimum value at the 45° bending angle at room temperature while increasing with the increasing bending angle under 800 °C local heating. The springback under local heating can be decreased by 75.2% and the error of the bending angle can improve by 2–7° compared with samples processed in room temperature.
Comparing Fine-Tuning and Prompt Engineering for Multi-Class Classification in Hospitality Review Analysis
Authors: Botunac Ive , Brkić Bakarić Marija , Matetić Maja
Published: July 2024

This study compares the effectiveness of fine-tuning Transformer models, specifically BERT, RoBERTa, DeBERTa, and GPT-2, against using prompt engineering in LLMs like ChatGPT and GPT-4 for multi-class classification of hotel reviews. As the hospitality industry increasingly relies on online customer feedback to improve services and strategize marketing, accurately analyzing this feedback is crucial. Our research employs a multi-task learning framework to simultaneously conduct sentiment analysis and categorize reviews into aspects such as service quality, ambiance, and food. We assess the capabilities of fine-tuned Transformer models and LLMs with prompt engineering in processing and understanding the complex user-generated content prevalent in the hospitality industry. The results show that fine-tuned models, particularly RoBERTa, are more adept at classification tasks due to their deep contextual processing abilities and faster execution times. In contrast, while ChatGPT and GPT-4 excel in sentiment analysis by better capturing the nuances of human emotions, they require more computational power and longer processing times. Our findings support the hypothesis that fine-tuning models can achieve better results and faster execution than using prompt engineering in LLMs for multi-class classification in hospitality reviews. This study suggests that selecting the appropriate NLP model depends on the task’s specific needs, balancing computational efficiency and the depth of sentiment analysis required for actionable insights in hospitality management.
Ionic Liquid-Based Solidified Floating Organic Drop Micro-Extraction Followed by Graphite Furnace Atomic Absorption Spectrometry for the Determination of Mercury in Water Samples
Authors: Durukan Ilknur
Published: July 2024

This study aims to apply the ionic liquid-based solidified floating organic drop micro-extraction (IL-SFODME) technique to improve the preconcentration of trace mercury levels, serving as a preparatory step for analysis via graphite furnace atomic absorption spectrometry (GFAAS) without using chelating agents. The process involves separating and preconcentrating mercury ions using IL-SFODME, followed by GFAAS analysis. This study systematically examined several factors affecting micro-extraction efficiency, including pH, the amount of ionic liquid 1-Hexyl-3-methylimidazolium hexafluorophosphate (HMIMPF6), extraction time, stirring rate, and temperature, to determine the optimal conditions. Under these optimized conditions, an enhancement factor of 171 was achieved for a 75 mL sample solution. The calibration curve was linear within the 0.5–10 µg/L range, with a detection limit (3σ) of 0.12 µg/L and a quantification limit (10σ) of 0.40 µg/L. The relative standard deviation (RSD) for 7 replicate measurements of 1 µg/L mercury was ±2.78%. The combination of the ionic liquid and SFODME methods without using a chelating agent is used for the first time for metal extraction. The method was successfully applied to extract and determine mercury in reference water samples and various real water samples, demonstrating notably satisfactory extraction efficiency.
A Cost-Effective Approach for the Integrated Optimization of Line Planning and Timetabling in an Urban Rail Transit Line
Authors: Gao Yi , Jia Chuanjun , Wang Zhipeng , Hu Zhiyuan
Published: July 2024

Line planning and timetabling play important roles in the design of urban rail transportation services. Due to the complexity of the integrated optimization of entire transportation plans, previous studies have generally considered line planning and timetabling design independently, which cannot ensure the global optimality of transportation services. In this study, the integrated design problem of line planning and timetabling was characterized as an equilibrium space–time network design problem and solved with a bi-objective nonlinear integer programming model. The model, in which train overtaking and passenger path choice behavior were considered, adjusted the network topology and link attributes (time and capacity) of the travel space–time network by optimizing the train service frequency, operation zone, stopping pattern, train formation, and train order to minimize the system life cycle cost and total passenger travel time perception. An algorithm was constructed using the non-dominated sorting genetic algorithm II combined with the self-adaptive gradient projection algorithm to solve the model. A real-world case was considered to evaluate the effectiveness of the proposed model and algorithm. The results showed that the model not only performed well in the trade-off between system cost and passenger travel efficiency, but it could also reduce the imbalance of train and station loads. Pareto front analysis of the model with different parameters showed that more types of trains did not correlate with a better performance, some line-planning strategies had a combination effect, and multi-strategy line planning was more suitable for scenarios with a high imbalance in the temporal and spatial distributions of passenger flow.
Optimization of Parametric Equalizer Filters in In-Vehicle Audio Systems with a Genetic Algorithm
Authors: Başay Volkan , Coşkun Oğuzhan , Yılmaz Güneş
Published: July 2024

This study aims to automate the optimization of a full-range speaker in an SUV’s audio system according to the equal-loudness contours principle. The input signal and frequency responses of the amplifier and speaker were transferred to Matlab. Using ideal filter parameters, ten parametric equalizer models were created, and the speaker output was obtained using the convolution technique. The same filter settings were applied to the vehicle multimedia system, and the experimental results were obtained. The simulation and experimental results were compared, showing high similarity, with a Pearson correlation of 0.9295 and a root mean square error (RMSE) of 2.29. The results were compared with the ideal contour. The Pearson correlation coefficients for the simulation and experimental results were 0.6341 and 0.6715, with RMSE values of 4.88 and 2.57, showing low similarity. Consequently, each parametric equalizer filter’s parameters were optimized using a genetic algorithm. The genetic algorithm was executed thirteen times for robustness, and the best parameters were selected. The optimized parameters were applied to the multimedia system, and the results were compared with the ideal contour. The correlation coefficients for the simulation and experimental results were 0.9692 and 0.9675, with RMSE values of 1.17 and 1.34. These results indicate that optimization aligns the speaker output closer to the ideal contour, enhancing in-vehicle audio system performance and increasing users’ satisfaction.
On the Need of Compressive Regularization in Damage Models for Concrete: Demonstration on a Modified Mazars Model
Authors: Debuisne Martin , Davenne Luc , Jason Ludovic
Published: July 2024

Due to its significant non-linear softening characteristics and its wide variety of use cases, concrete has received considerable attention for the modeling of its mechanical behavior. The non-linear simulation of linear concrete structures is often associated with mesh dependency, the resolution of which requires some form of regularization. While most of the past research has focused on tension energy regularization for better mesh-objectivity, the compression behavior has been partly left out, even though it may have a significant impact for particular applications. By starting from the failed attempt to simulate a pushout test from the literature, this paper focuses on the enhancements brought by the energetic regularization in compression to an isotropic damage model based on Mazars’ equivalent strain. The resulting model is applied in three representative case studies where the enhanced mesh-objectivity is shown relative to the load–displacement behaviors and the damage patterns that are produced, and compared to those obtained by the classical model.
Understanding the Foreign Body Response via Single-Cell Meta-Analysis
Authors: Liang Norah E. , Parker Jennifer B. , Lu John M. , Januszyk Michael , Wan Derrick C. , Griffin Michelle , Longaker Michael T.
Published: July 2024

Foreign body response (FBR) is a universal reaction to implanted biomaterial that can affect the function and longevity of the implant. A few studies have attempted to identify targets for treating FBR through the use of single-cell RNA sequencing (scRNA-seq), though the generalizability of these findings from an individual study may be limited. In our study, we perform a meta-analysis of scRNA-seq data from all available FBR mouse studies and integrate these data to identify gene signatures specific to FBR across different models and anatomic locations. We identify subclusters of fibroblasts and macrophages that emerge in response to foreign bodies and characterize their signaling pathways, gene ontology terms, and downstream mediators. The fibroblast subpopulations enriched in the setting of FBR demonstrated significant signaling interactions in the transforming growth factor-beta (TGF-β) signaling pathway, with known pro-fibrotic mediators identified as top expressed genes in these FBR-derived fibroblasts. In contrast, FBR-enriched macrophage subclusters highly expressed pro-fibrotic and pro-inflammatory mediators downstream of tumor necrosis factor (TNF) signaling. Cell–cell interactions were additionally interrogated using CellChat, with identification of key signaling interactions enriched between fibroblasts and macrophages in FBR. By combining multiple FBR datasets, our meta-analysis study identifies common cell-specific gene signatures enriched in foreign body reactions, providing potential therapeutic targets for patients requiring medical implants across a myriad of devices and indications.
The Emerging Role of Ferroptosis in EBV-Associated Cancer: Implications for Cancer Therapy
Authors: He Shan , Luo Cheng , Shi Feng , Zhou Jianhua , Shang Li
Published: July 2024

Ferroptosis is a novel and iron-dependent form of programmed cell death, which has been implicated in the pathogenesis of various human cancers. EBV is a well-recognized oncogenic virus that controls multiple signaling pathways within the host cell, including ferroptosis signaling. Recent studies show that inducing ferroptosis could be an efficient therapeutic strategy for EBV-associated tumors. This review will firstly describe the mechanism of ferroptosis, then summarize EBV infection and EBV-associated tumors, as well as the crosstalk between EBV infection and the ferroptosis signaling pathway, and finally discuss the role and potential application of ferroptosis-related reagents in EBV-associated tumors.
Anti-B-Cell-Activating Factor (BAFF) Therapy: A Novel Addition to Autoimmune Disease Management and Potential for Immunomodulatory Therapy in Warm Autoimmune Hemolytic Anemia
Authors: Cheekati Mahija , Murakhovskaya Irina
Published: July 2024

Although rituximab is not specifically approved for the treatment of warm autoimmune hemolytic anemia (WAIHA), the First International Consensus Group recommends considering its use as part of the initial therapy for patients with severe disease and as a second-line therapy for primary WAIHA. Some patients do not respond to rituximab, and relapses are common. These relapses are associated with elevated B-cell-activating factor (BAFF) levels and the presence of quiescent long-lived plasma cells (LLPCs) in the spleen. A new group of immunomodulatory drugs, B-cell-activating factor inhibitors (BAFF-i), demonstrated efficacy in multiple autoimmune diseases and have the potential to improve WAIHA treatment outcomes by targeting B-cells and LLPCs. This article reviews the role of BAFF in autoimmune disorders and the currently available literature on the use of BAFF-directed therapies in various immunologic disorders, including WAIHA. Collectively, the clinical data thus far shows robust potential for targeting BAFF in WAIHA therapy.
Diagnoses and Treatment Recommendations—Interrater Reliability of Uroflowmetry in People with Multiple Sclerosis
Authors: Jaekel Anke K. , Rieger Julia , Butscher Anna-Lena , Möhr Sandra , Schindler Oliver , Queissert Fabian , Hofmann Aybike , Schmidt Paul , Kirschner-Hermanns Ruth , Knüpfer Stephanie C.
Published: July 2024

Background: Uroflowmetry (UF) is an established procedure in urology and is recommended before further investigations of neurogenic lower urinary tract dysfunction (NLUTD). Some authors even consider using UF instead of urodynamics (UD). Studies on the interrater reliability of UF regarding treatment recommendations are rare, and there are no relevant data on people with multiple sclerosis (PwMS). The aim of this study was to investigate the interrater reliability (IRR) of UF concerning diagnosis and therapy in PwMS prospectively. Methods: UF of 92 PwMS were assessed by 4 raters. The diagnostic criteria were normal findings (NFs), detrusor overactivity (DO), detrusor underactivity (DU), detrusor–sphincter dyssynergia (DSD) and bladder outlet obstruction (BOO). The possible treatment criteria were as follows: no treatment (NO), catheter placement (CAT), alpha-blockers, detrusor-attenuating medication, botulinum toxin (BTX), neuromodulation (NM), and physiotherapy/biofeedback (P/BF). IRR was assessed by kappa (κ). Results: κ of diagnoses were NFs = 0.22; DO = 0.17; DU = 0.07; DSD = 0.14; and BOO = 0.18. For therapies, the highest κ was BTX = 0.71, NO = 0.38 and CAT = 0.44. Conclusions: There is a high influence of the individual rater. UD should be subject to the same analysis and a comparison should be made between UD and UF. This may have implications for the value of UF in the neuro-urological management of PwMS, although at present UD remains the gold standard for the diagnostics of NLUTD in PwMS.
Innovation through Artificial Intelligence in Triage Systems for Resource Optimization in Future Pandemics
Authors: Garrido Nicolás J. , González-Martínez Félix , Losada Susana , Plaza Adrián , del Olmo Eneida , Mateo Jorge
Published: July 2024

Artificial intelligence (AI) systems are already being used in various healthcare areas. Similarly, they can offer many advantages in hospital emergency services. The objective of this work is to demonstrate that through the novel use of AI, a trained system can be developed to detect patients at potential risk of infection in a new pandemic more quickly than standardized triage systems. This identification would occur in the emergency department, thus allowing for the early implementation of organizational preventive measures to block the chain of transmission. Materials and Methods: In this study, we propose the use of a machine learning system in emergency department triage during pandemics to detect patients at the highest risk of death and infection using the COVID-19 era as an example, where rapid decision making and comprehensive support have becoming increasingly crucial. All patients who consecutively presented to the emergency department were included, and more than 89 variables were automatically analyzed using the extreme gradient boosting (XGB) algorithm. Results: The XGB system demonstrated the highest balanced accuracy at 91.61%. Additionally, it obtained results more quickly than traditional triage systems. The variables that most influenced mortality prediction were procalcitonin level, age, and oxygen saturation, followed by lactate dehydrogenase (LDH) level, C-reactive protein, the presence of interstitial infiltrates on chest X-ray, and D-dimer. Our system also identified the importance of oxygen therapy in these patients. Conclusions: These results highlight that XGB is a useful and novel tool in triage systems for guiding the care pathway in future pandemics, thus following the example set by the well-known COVID-19 pandemic.
The Roles of Proactive Personality and Career Satisfaction in the Impact of Employer Brand Attributes Congruence on Creativity
Authors: Zhang Jiexuan , Zhu Fei
Published: July 2024

Although the relationship between employer brand and employee creativity has become a popular theme, this nexus is indirect and ambiguous. Additionally, most studies ignore the consistency of instrumental and symbolic attributes when discussing the consequences of employer brand. This study explored the mechanism of employer brand attributes congruence on employee creativity through career satisfaction, and further revealed the moderating role of proactive personality. Based on the cue consistency theory and the social information processing theory, a polynomial regression model was created and a response surface analysis was conducted using 488 paired questionnaires. The results showed that employer brand attributes congruence impacted employee creativity via career satisfaction. A consistent employer brand strategy is more effective for the creativity of less proactive individuals, while a high-level proactive personality can compensate for the deficiencies of employer brand attributes incongruence. The results complemented employer brand research from the perspective of the instrumental–symbolic attribute configuration and provided supportive empirical evidence of employer brand practices aiming at improving employee creativity. This study has certain practical implications for HR practitioners.
“We Can Do Better”: Developing Attitudinal Scales Relevant to LGBTQ2S+ Issues—A Primer on Best Practice Recommendations for Beginners in Scale Development
Authors: Bishop CJ , Morrison Todd Graham
Published: July 2024

In this primer, following best practice recommendations and drawing upon their own expertise in psychometrics, the authors provide a step-by-step guide for developing measures relevant to sexual- and gender-marginalized persons (SGMPs). To ensure that readers operate from a uniform understanding, definitions for central elements of psychometric testing (e.g., reliability and validity) are provided. Then, detailed information is given about developing and refining scale items. Strategies designed to reduce a pool of items to a manageable number are also highlighted. The authors conclude this primer by discussing various forms of validation (e.g., convergent, discriminant, and known groups). To further readers’ understanding, illustrative examples from measures designed for SGMPs are brought into focus throughout.
Training and Technology Acceptance of ChatGPT in University Students of Social Sciences: A Netcoincidental Analysis
Authors: García-Alonso Elena María , León-Mejía Ana Cristina , Sánchez-Cabrero Roberto , Guzmán-Ordaz Raquel
Published: July 2024

This study analyzes the perception and usage of ChatGPT based on the technology acceptance model (TAM). Conducting reticular analysis of coincidences (RAC) on a convenience survey among university students in the social sciences, this research delves into the perception and utilization of this artificial intelligence tool. The analysis considers variables such as gender, academic year, prior experience with ChatGPT, and the training provided by university faculty. The networks created with the statistical tool “CARING” highlight the role of perceived utility, credibility, and prior experience in shaping attitudes and behaviors toward this emerging technology. Previous experience, familiarity with video games, and programming knowledge were related to more favorable attitudes towards ChatGPT. Students who received specific training showed lower confidence in the tool. These findings underscore the importance of implementing training strategies that raise awareness among students about both the potential strengths and weaknesses of artificial intelligence in educational contexts.
Evaluating Machine Learning Models for HVAC Demand Response: The Impact of Prediction Accuracy on Model Predictive Control Performance
Authors: Wang Huilong , Mai Daran , Li Qian , Ding Zhikun
Published: July 2024

Heating, ventilation, and air-conditioning systems (HVAC) have significant potential to support demand response programs within power grids. Model Predictive Control (MPC) is an effective technique for utilizing the flexibility of HVAC systems to achieve this support. In this study, to identify a proper prediction model in the MPC controller, four machine learning models (i.e., SVM, ANN, XGBoost, LightGBM) are compared in terms of prediction accuracy, prediction time, and training time. The impact of model prediction accuracy on the performance of MPC for HVAC demand response is also systematically studied. The research is carried out using a co-simulation test platform integrating TRNSYS and Python. Results show that the XGBoost model achieves the highest prediction accuracy. LightGBM model’s accuracy is marginally lower but requires significantly less time for both prediction and training. In this research, the proposed control strategy decreases the economic cost by 21.61% compared to the baseline case under traditional control, with the weighted indoor temperature rising by only 0.10 K. The result also suggests that it is worth exploring advanced prediction models to increase prediction accuracy, even within the high prediction accuracy range. Furthermore, implementing MPC control for demand response remains beneficial even when the model prediction accuracy is relatively low.
Using Machine Learning Technologies to Design Modular Buildings
Authors: Tusnin Alexander Romanovich , Alekseytsev Anatoly Victorovich , Tusnina Olga
Published: July 2024

The article discusses a solution to the relevant task of analyzing and designing modular buildings made of blocks to be used in industrial and civil engineering. A block that represents a container is a combination of plate and beam systems. The criteria for its failure include both the strength of the individual elements and the loss of stability in a corrugated web. Methods of engineering analysis are hardly applicable to this system. Numerical analysis based on the finite element method is time-consuming, and this fact limits the number of design options for modular buildings made of blocks. Adjustable machine learning models are proposed as a solution to these problems. Decision trees are made and clustered into a single ensemble depending on the values of the design parameters. Key parameters determining the structures of decision trees include design steel resistance values, types of loads and the number of loadings, and ranges of rolled sheet thickness values. An ensemble of such models is used to take into account the nonlinear strain of elements. Piecewise approximation of the dependencies between components of the stress–strain state is used for this purpose. Linear regression equations are subjected to feature binarization to improve the efficiency of nonlinearity projections. The identification of weight coefficients without laborious search optimization methods is a distinguishing characteristic of the proposed models of steel blocks for modular buildings. A modular building block is used to illustrate the effectiveness of the proposed models. Its purpose is to accommodate a gas compressor of a gas turbine power plant. These machine learning models can accurately spot the stress–strain state for different design parameters, in particular for different corrugated web thickness values. As a result, ensemble models predict the stress–strain state with the coefficient of determination equaling 0.88–0.92.
Clinical and Imaging Follow-Up for High-Risk Cutaneous Melanoma: Current Evidence and Guidelines
Authors: Vetto John T.
Published: July 2024

The most recent (eighth) edition of the American Joint Committee on Cancer (AJCC) staging system divides invasive cutaneous melanoma into two broad groups: “low-risk” (stage IA–IIA) and “high-risk” (stage IIB–IV). While surveillance imaging for high-risk melanoma patients makes intuitive sense, supporting data are limited in that they are mostly respective and used varying methods, schedules, and endpoints. As a result, there is a lack of uniformity across different dermatologic and oncologic organizations regarding recommendations for follow-up, especially regarding imaging. That said, the bulk of retrospective and prospective data support imaging follow-up for high-risk patients. Currently, it seems that either positron emission tomography (PET) or whole-body computerized tomography (CT) are reasonable options for follow-up, with brain magnetic resonance imaging (MRI) preferred for the detection of brain metastases in patients who can undergo it. The current era of effective systemic therapies (ESTs), which can improve disease-free survival (DFS) and overall survival (OS) beyond lead-time bias, has emphasized the role of imaging in detecting various patterns of EST response and treatment relapse, as well as the importance of radiologic tumor burden.
Nutritional Deficiencies and Associated Oral Health in Adolescents: A Comprehensive Scoping Review
Authors: Hung Man , Blazejewski Amy , Lee Samantha , Lu Johanna , Soto Andres , Schwartz Connor , Mohajeri Amir
Published: July 2024

Introduction: The shift to processed foods in American diets has increased vitamin and mineral deficiencies among adolescents, impacting growth and health, often manifesting as oral lesions. This review study aimed to explore the link between nutritional deficiencies and adolescent oral health to improve guidance and prevent long-term issues. Methods: A systematic review of literature from 2013 to 2023 was conducted on adolescents aged 10–19 years, using PRISMA guidelines. Searches in PubMed, Web of Science, Dentistry & Oral Sciences Source—Ebscohost, and Scopus included peer-reviewed articles, excluding reviews and non-empirical studies. Data were screened and extracted with independent reviews for accuracy. Results: Malnutrition strongly correlates with poor oral health. Undernourished children have a 60% increase in dental caries, exacerbated by high sugar intake. Early malnutrition delays dental eruption, temporarily protecting against caries, while stunting and infections cause enamel defects. Chronic conditions like cerebral palsy and celiac disease worsen oral health, with risks persisting into adulthood. Conclusions: Nutritional deficiencies and oral health are interconnected, requiring integrated healthcare. Early interventions and holistic strategies can improve outcomes and reduce long-term burdens. Comprehensive health education and routine dental evaluations are essential for prevention and treatment, enhancing health across all demographics.
Antibiotics, Analgesic Sedatives, and Antiseizure Medications Frequently Used in Critically Ill Neonates: A Narrative Review
Authors: Kontou Angeliki , Agakidou Eleni , Chatziioannidis Ilias , Chotas William , Thomaidou Evanthia , Sarafidis Kosmas
Published: July 2024

Antibiotic, analgesic sedative, and antiseizure medications are among the most commonly used medications in preterm/sick neonates, who are at high risk of nosocomial infections, central nervous system complications, and are exposed to numerous painful/stressful procedures. These severe and potentially life-threatening complications may have serious short- and long-term consequences and should be prevented and/or promptly treated. The reported variability in the medications used in neonates indicates the lack of adequate neonatal studies regarding their effectiveness and safety. Important obstacles contributing to inadequate studies in preterm/sick infants include difficulties in obtaining parental consent, physicians’ unwillingness to recruit preterm infants, the off-label use of many medications in neonates, and other scientific and ethical concerns. This review is an update on the use of antimicrobials (antifungals), analgesics (sedatives), and antiseizure medications in neonates, focusing on current evidence or knowledge gaps regarding their pharmacokinetics, indications, safety, dosage, and evidence-based guidelines for their optimal use in neonates. We also address the effects of early antibiotic use on the intestinal microbiome and its association with long-term immune-related diseases, obesity, and neurodevelopment (ND). Recommendations for empirical treatment and the emergence of pathogen resistance to antimicrobials and antifungals are also presented. Finally, future perspectives on the prevention, modification, or reversal of antibiotic resistance are discussed.
Developing an Audit Framework for Local Flood Risk Management Strategies: Is Increasing Surface Water Flood Risk in England Being Adequately Managed?
Authors: Russell Andrew , McCue Adam James , Patel Aakash Dipak
Published: July 2024

Here, we investigate whether England’s 152 local flood risk management strategies (LFRMSs) satisfy minimal legislative criteria and address the growing surface water flood (SWF) risk caused by climate change. A systematic audit was used to assess the alignment of the LFRMSs with national climate change legislation and other relevant national strategies. An objective method to identify inclusion of a range of factors that good strategies should include was applied. LFRMSs are mostly meeting their minimum statutory requirements. However, there is a widespread issue across most LFRMSs regarding inadequate consideration of increasing SWF risk from climate changes, which highlights the need for enhanced LFRMSs by improved planning and climate change adaptation plans. There is some evidence of good practice within the LFRMS portfolio, which is discussed in the context of the ongoing LFRMS update process. Beyond England, there are implications for developing FRM processes at a local level that can be objectively assessed against national requirements. Communities in England face inadequately managed SWF risk in the future because of the range in plan quality across the LFRMSs. This research contributes to the ongoing examination of the full suite of 152 LFRMSs and, therefore, builds towards a complete assessment of the SWF management approach in England. This will help inform local climate change adaptation strategies that cater to the escalating threat of SWF due to climate change.
Diagnostics Increases Visibility
Authors: Kjaer Andreas
Published: July 2024

It is with great pleasure that we announce that our journal has recently received the 2023 CiteScore of 4 [...]
Local versus Global Time in Early Relativity Theory
Authors: Dieks Dennis
Published: July 2024

In his groundbreaking 1905 paper on special relativity, Einstein distinguished between local and global time in inertial systems, introducing his famous definition of distant simultaneity to give physical content to the notion of global time. Over the following decade, Einstein attempted to generalize this analysis of relativistic time to include accelerated frames of reference, which, according to the principle of equivalence, should also account for time in the presence of gravity. Characteristically, Einstein’s methodology during this period focused on simple, intuitively accessible physical situations, exhibiting a high degree of symmetry. However, in the final general theory of relativity, the a priori existence of such global symmetries cannot be assumed. Despite this, Einstein repeated some of his early reasoning patterns even in his 1916 review paper on general relativity and in later writings. Modern commentators have criticized these arguments as confused, invalid, and inconsistent. Here, we defend Einstein in the specific context of his use of global time and his derivations of the gravitational redshift formula. We argue that a detailed examination of Einstein’s early work clarifies his later reasoning and demonstrates its consistency and validity.
Influence of Pulsed Reverse Electrodeposition on Mechanical Properties of Ni–W Alloys
Authors: Gu Zeyu , Wu Jhen-Yang , Jiang Yiming , Kurioka Tomoyuki , Chen Chun-Yi , Lin Hwai-En , Luo Xun , Yamane Daisuke , Sone Masato , Chang Tso-Fu Mark
Published: July 2024

Ni–W alloys have received considerable interest as a promising structural material for microelectromechanical systems (MEMS) due to their exceptional properties, including hardness, ductility, corrosion resistance, and thermal stability. However, the electrodeposition of Ni–W alloys in the MEMS fabrication process to achieve intact structures with a thickness of several tens of micrometers is challenging due to the occurrence of cracking caused by side reactions and internal stresses during the electrodeposition process. To address this issue, our focus was on pulsed reverse electrodeposition (PRE) as a potential solution. The utilization of the PRE technique allows for a high concentration of reactive species on the electrode surface, thereby mitigating side reactions such as hydrogen generation. In this study, we examined the effects of the PRE method on the morphological characteristics, average crystal grain size, Vickers hardness, and micro-mechanical properties of Ni–W alloys.
Research on Boost-Type Cascaded H-Bridge Inverter and Its Power Control in Photovoltaic Power Generation System
Authors: Kang Jiayu , Yu Miao , Sun Boyang , Xiao Yongchao , Liu Baoquan
Published: July 2024

The cascaded H-bridge (CHB) inverter has become pivotal in grid-connected photovoltaic (PV) systems owing to its numerous benefits. Typically, DC–DC converters are employed to boost the input voltage in grid-connected systems to meet the grid’s higher voltage requirements, but this approach increases equipment size and cost. To enhance inverter efficiency, this paper proposes a boost-type, three-phase CHB PV grid-connected inverter. This design can raise the input voltage and satisfy grid requirements with only a few additional components. Additionally, PV environmental fluctuations can cause variations in PV power generation, leading to a power imbalance in the inverter and potentially affecting the stability of the PV system. Based on this, we consider grid voltage fluctuations induced by unbalanced power output from the inverter and propose an improved control method based on the superposition of zero-sequence components. Finally, we construct a simulation model and conduct experimental verification using the MATLAB/Simulink platform. The validation results demonstrate that this topology reduces equipment volume and effectively enhances the efficiency of PV power generation systems. Furthermore, the designed control method ensures system stability while effectively mitigating power imbalances caused by PV module and grid voltage fluctuations.
A ±0.15 °C (3σ) Inaccuracy CMOS Smart Temperature Sensor from −40 °C to 125 °C with a 10 ms Conversion Time-Leveraging an Adaptative Decimation Filter in 65 nm CMOS Technology
Authors: Passos Fábio , Santos Gabriel , dos Santos Marcelino
Published: July 2024

This paper presents the design and implementation of a highly accurate smart temperature sensor designed in 65 nm CMOS technology. The sensor exhibits a ±0.15 °C (3σ) error across a wide temperature range from −40 °C to 125 °C, catering to diverse application needs. Leveraging advanced CMOS technology, the sensor employs an adaptive decimation filter that allows us to control the conversion time, ensuring that the accuracy of the conversion is maintained even in challenging conditions. The proposed sensor architecture integrates advanced techniques for temperature sensing for improved accuracy and reliability. Through meticulous circuit design and the usage of dynamic element matching, chopping, and calibration/trimming, the sensor demonstrates exceptional performance characteristics, making it suitable for various industrial, automotive, and consumer electronics applications demanding high precision temperature monitoring.
CuFP: An HLS Library for Customized Floating-Point Operators
Authors: Hajizadeh Fahimeh , Ould-Bachir Tarek , David Jean Pierre
Published: July 2024

High-Level Synthesis (HLS) tools have revolutionized FPGA application development by providing a more efficient and streamlined approach, significantly impacting digital design methodologies. Despite the capability of FPGAs to customize numerical representations in data paths, most HLS projects have focused on fixed-point precision, while floating-point representations remain limited to vendor-provided single, double, and half-precision formats. This paper proposes a customized floating-point library compatible with HLS to address these limitations. This library allows programmers to define the number of exponent and mantissa bits at compile time, providing greater flexibility and enabling the use of mixed precision. Moreover, this library includes optimized implementations of common components such as vector summation (VSUM), dot-product (DP), and matrix-vector multiplication (MVM). Results demonstrate that the proposed library reduces latency and resource utilization compared to vendor IP blocks, particularly in VSUM, DP, and MVM operations. For example, the mvm operation involving a 32 × 32 matrix, using vendor IP requires 22 clock cycles, whereas CuFP completes the same task in just 7 clock cycles, using approximately 60% fewer DSPs, 10% fewer LUTs, and 60% fewer FFs.
Energy-Saving Evaluation and Comprehensive Benefit Analysis of Power Transmission/Distribution System Based on Cloud Model
Authors: Liu Xuan , Tang Lei , Lu Yan , Xiang Jing , Qin Huifang , Zhou Geqian , Liu Chengwei , Qin Bo
Published: July 2024

The current situation of the energy crisis requires various industries around the world to seek technological upgrades to achieve energy conservation. The renovation of power grid lines, transformers, and substations significantly affects the comprehensive benefits of power transmission/distribution systems, and the optimization of specific parameters of these elements plays a critical role in their operational performance. In order to determine the effects of these parameters and realize their accurate optimization, herein, we establish a method combining an analytic hierarchy process and an entropy weight method to calculate the weightings of the indexes, and then evaluate the comprehensive benefits through a cloud model. Based on the evaluation system above, a case study was utilized to analyze the power transmission/distribution system of a designated area in Wuhan City, and the results showed that the comprehensive benefits increased by around 5.4% compared to 2022, and could be upgraded to level IV in the cloud model with the technical transformation. This exhibits the effectiveness and the application feasibility of this evaluation system.
Study on the Energy Consumption Characteristics and the Self-Sufficiency Rate of Rooftop Photovoltaic of University Campus Buildings
Authors: Gao Lizhen , Wang Shidong , Mao Mingqiang , Liu Chunhui , Li Tao
Published: July 2024

Campus buildings often face issues with high energy consumption, low efficiency, and significant carbon emissions, making the creation of a green, low-carbon campus urgent. Utilizing solar photovoltaics on rooftops can provide an effective power solution to address high energy consumption. This study focuses on a university campus, employing the DeST energy consumption simulation software to model the HVAC systems, electrical devices, and hot water loads of five typical buildings. It combines this with calculations of available rooftop areas to assess the potential for rooftop solar photovoltaics. The results confirm varying annual electricity consumption among the different buildings, which directly correlates with building size and operational schedules. Among the five building types, sports facilities and academic buildings have relatively high rooftop photovoltaic self-sufficiency rates, exceeding 60%, while the library has the lowest, under 20%. The entire university campus has an annual rooftop photovoltaic self-sufficiency rate of 35%, significantly addressing the issue of high energy consumption in university campuses. This research provides a theoretical basis for implementing rooftop photovoltaic systems to achieve campus energy savings.