bluuebunny/crossref_metadata_2025 huggingface dataset:
Robust Fixed-Time Adaptive Fault-Tolerant Control for Dynamic Positioning of Ships with Thruster Faults
Authors: Zhang Yuanyuan , Zhang Jianqiang , Sui Bowen
Published: July 2024

The ship dynamic positioning system has long been a subject of great interest due to the intricate and unpredictable nature of the maritime environment, which affects its dependability and timeliness. This study focuses on the issue of developing a robust, efficient, and adaptable fault-tolerant control system for ship dynamic positioning systems that experience thruster malfunctions. Firstly, we build the motion model of the ship’s dynamic positioning system with three degrees of freedom, as well as the model for faults in the thruster. A second-order fast fixed-time nonsingular sliding mode surface is formulated, and a robust adaptive control technique is introduced to counteract external environmental disturbances and model errors. The objective is to ensure rapid convergence of the system within a predetermined time frame. Simultaneously, combined with the fault-tolerant control technique, it is ensured that the dynamic positioning ship can still achieve system stability when thruster faults occur. In addition, the analysis of the Lyapunov stability criterion proves that the proposed fixed-time adaptive fault-tolerant control scheme can make the system tracking error converge within a fixed time. Finally, the effectiveness of the proposed control scheme is verified by numerical simulation results.
Deep Learning and Face Recognition: Face Recognition Approach Based on the DS-CDCN Algorithm
Authors: Deng Nan , Xu Zhengguang , Li Xiuyun , Gao Chenxuan , Wang Xue
Published: July 2024

To enhance the performance and reliability of the face recognition algorithm that is based on deep learning technology, this study utilizes a density-based noise-applied spatial clustering algorithm to cluster a large-scale face image dataset, resulting in a self-constructed dataset. A deep separable center differential convolutional network algorithm is utilized for face recognition. The impact of convolutional parameters on the algorithm’s performance is verified through experiments with ablated convolutional parameters. The study found that the density-based noise-applied spatial clustering algorithm resulted in time savings of 43.66% and 51.22% compared to the K-means clustering algorithm and the hierarchical clustering algorithm, respectively, when analyzing 8000 images. Additionally, the depth-separable center difference convolutional network algorithm had a lower average classification error rate compared to the other two algorithms, with reductions of 2.49% and 17.01%, respectively. The depth-separable center difference convolutional network technique is an advanced method for identifying the faces of people of different races, according to the experimental investigation. It can provide efficient and accurate services for the face recognition needs of various races.
Compositional and Microstructural Investigations of Prehistoric Ceramics from Southern Romania (Middle Neolithic Pottery)
Authors: Ion Rodica-Mariana , Pungoi Ancuta-Elena , Iancu Lorena , Grigorescu Ramona Marina , Vasilievici Gabriel , Gheboianu Anca Irina , Slamnoiu-Teodorescu Sofia , Alexandrescu Elvira
Published: July 2024

In this paper, based on our previous expertise on ceramic artifacts, several archaeometric methods applied to some samples collected from the Dudești archaeological site (Oltenia region, Romania) are reported for the first time in the literature. The chemical composition, and microstructural and morphological characterization of these samples offer important conclusions about the processing conditions. Some specific techniques such as X-ray diffraction (XRD), wavelength-dispersive X-ray fluorescence (WDXRF), optical microscopy (OM), stereomicroscopy, environmental scanning electron microscopy (ESEM), Fourier-transform infrared spectroscopy (FTIR), and Raman spectroscopy provide compositional information about composition and the decay processes. Additionally, the Brunauer–Emmett–Teller (BET) method helps to estimate pore sizes and specific surface areas. A thermogravimetric analysis (TGA/TDG) was used to establish details regarding the production technology and also the raw materials source used to make the ceramics. The obtained results indicated that the ceramics are based on a paste of muscovite and feldspar, with high plasticity, together with quartz and hematite/goethite and calcite, the latter in very low concentrations. According to the obtained results, we could assume that clays from the investigated samples had a low concentration of calcium. Gypsum is present as paste in a very low concentration, identified by the presence of a sulphate group in WDXRF. In the same context, iron oxides have a significant impact on the firing atmosphere of iron-rich clay, resulting in blackening under reducing conditions and a reddish coloration under oxidative conditions. The use of hematite and gypsum as pigments further contributes to the color variations in the pottery. The consistent firing temperature range of 200–600 °C in Dudești pottery implies a standardized production process, the variation in color being dependent on the specific reducing/oxidative regime conditions (reducing atmosphere followed by rapid oxidation). This relationship between clay composition and local sources suggests a connection to Neolithic pottery production in the region and their color depending on the reducing/oxidative regime conditions.
Subject-Independent Model for Reconstructing Electrocardiography Signals from Photoplethysmography Signals
Authors: Guo Yanke , Li Shiyong , Chen Zhencheng , Tang Qunfeng
Published: July 2024

Electrocardiography (ECG) is the gold standard for monitoring vital signs and for diagnosing, controlling, and preventing cardiovascular diseases (CVDs). However, ECG requires continuous user participation, and cannot be used for continuous cardiac monitoring. In contrast to ECG, photoplethysmography (PPG) devices do not require continued user involvement, and can offer ongoing and long-term detection capabilities. However, from a medical perspective, ECG can provide more information about the heart. Currently, most existing work contains different signals recorded from the same subject in training and test sets. This study proposes a neural network model based on a 1D convolutional neural network (CNN) and a bidirectional long short-term memory (BiLSTM) network. This neural network model can directly reconstruct ECG signals from PPG signals. The learned features are captured from the CNN model and fed into the BiLSTM model. In order to verify the validity of the model, it is evaluated using the MIMIC II dataset in the completely subject-independent model (records are placed in a training set, and a test set appears once, but the test signal belongs to a record that is not in the training set). The Pearson’s correlation coefficient between the reconstructed ECG and the reference ECG of the proposed model is 0.963 in the completely subject-independence model. The results of the proposed model are better than those of several cited state-of-the-art models. The results of our trained model indicate that we can obtain reconstructed ECGs that are highly similar to reference ECGs in the completely subject-independent model.
Low-Cost IoT Air Quality Monitoring Station Using Cloud Platform and Blockchain Technology
Authors: Hassan Ahmed K. , Saraya Mohamed S. , Ali-Eldin Amr M. T. , Abdelsalam Mohamed M.
Published: July 2024

Air pollution is a growing concern due to severe threats to public health and the environment. The need for reliable air quality monitoring solutions has never been more critical. This research paper introduces an innovative approach to addressing this challenge by deploying a low-cost Internet of Things (IoT) air monitoring station and providing a blockchain technology solution to enhance environmental data transparency, reliability, and accessibility. Our paper adopts a concept of merging IoT and blockchain technologies and collecting some parameters that help to assess air quality by using three sensors, DHT11, MQ7, and MQ135, to collect temperature, humidity, carbon monoxide, and carbon dioxide parameters, respectively, to measure the gases and thus indicate the air quality within the surrounding area. Collecting and sharing these types of valuable data will be very important for various stakeholders, such as governmental bodies, researchers, and the public. This approach is consistent with the principles of sustainable development, facilitating informed decision-making and promoting eco-friendly policies. This research explores the technical architecture of the IoT air monitoring stations, offering a promising solution for addressing air pollution concerns while promoting sustainable development goals. The proposed system is a model for leveraging emerging technologies to advance environmental monitoring and create smarter, livable cities. This approach aligns with the principles of sustainable development and eco-friendly initiatives. This research offers a promising model for enhancing environmental monitoring efforts and advancing the creation of smarter, more sustainable urban environments. The proposed IoT, cloud platform and blockchain-based system not only addresses pressing air pollution challenges but also sets a benchmark for leveraging emerging technologies in environmental science.
Enhancing Air Quality Prediction with an Adaptive PSO-Optimized CNN-Bi-LSTM Model
Authors: Zhu Xuguang , Zou Feifei , Li Shanghai
Published: July 2024

Effective air quality prediction models are crucial for the timely prevention and control of air pollution. However, previous models often fail to fully consider air quality’s temporal and spatial distribution characteristics. In this study, Xi’an City is used as the study area. Data from 1 January 2019 to 31 October 2020 are used as the training set, while data from 1 November 2020 to 31 December 2020 are used as the test set. This paper proposes a multi-time and multi-site air quality prediction model for Xi’an, leveraging a deep learning network model based on APSO-CNN-Bi-LSTM. The CNN model extracts the spatial features of the input data, the Bi-LSTM model extracts the time series features, and the PSO algorithm with adaptive inertia weight (APSO) optimizes the model’s hyperparameters. The results show that the model achieves the best results in terms of MAE and RMSE. Compared to the PSO-SVR, BPTT, CNN-LSTM, and GA-ACO-BP models, the MAE improved by 9.375%, 6.667%, 2.276%, and 4.975%, while the RMSE improved by 8.371%, 8.217%, 6.327%, and 5.293%. These significant improvements highlight the model’s accuracy and its promising application prospects.
Advanced Servo Control and AI Integration in 3-DoF Platforms for Enhanced Simulation Interactivity
Authors: Wei Ming-Yen , Yuan Hsin-Chuan
Published: July 2024

This paper proposes a new approach to enhance the realism and interactivity of shooting simulation systems by integrating a three-degree–of–freedom (3-DoF) platform with sensory and interactive elements, as well as digital content. The system employs visual effects computers and servo controls, utilizing network packet messages for communication based on different scene definitions. When the control handle sends commands, the visual effects computer transmits control parameters to the image generator. Additionally, AI-controlled aircrafts act as enemy planes, autonomously determining flight paths, tracking targets, and engaging in combat, thereby enhancing realism in interactive mechanisms. An iterative learning control (ILC) is designed to provide the platform with good dynamic response, load capacity, and tracking ability when operated by a manual control handle. The core control uses a TMS320F28377D digital signal processor from Texas Instruments, integrated with visual effects computers for three-axis control, controller computation, finite state machines, and network communication operations. Experimental results demonstrate the feasibility and effectiveness of the developed three-axis shooting platform, achieving immersion and coordination with AI enemy aircrafts.
Tropospheric Ozone: A Critical Review of the Literature on Emissions, Exposure, and Health Effects
Authors: Donzelli Gabriele , Suarez-Varela Maria Morales
Published: July 2024

Tropospheric ozone is a significant air pollutant with severe adverse effects on human health. The complex dynamics of ozone formation, distribution, and health impacts underscore the need for a comprehensive understanding of this pollutant. Despite well-documented health risks, including an estimated 423,100 deaths annually due to ozone exposure, millions of people in major countries continue to be exposed to unhealthy levels. Notably, the epidemiological evidence linking long-term ozone exposure to health outcomes is limited compared to short-term exposure studies, leaving some findings incomplete. Regulatory standards vary globally, with the implementation of the World Health Organization recommendation for an 8-h average limit of 50 ppb to protect public health remaining heterogeneous, leading to significant disparities in adoption across countries, and often significantly higher. Emissions from diesel and gasoline vehicles are major sources of VOCs and NOx in urban areas, and their reduction is a key strategy. Additionally, climate change may exacerbate ozone pollution through increased natural precursor emissions, leading to higher ground-level ozone in polluted regions, like the eastern US, southern Europe, and parts of Asia. Addressing tropospheric ozone effectively requires an integrated approach that considers both natural and anthropogenic sources to reduce concentrations and mitigate health impacts.
Fractional-Order Sequential Linear Differential Equations with Nabla Derivatives on Time Scales
Authors: Zhu Cheng-Cheng , Zhu Jiang
Published: July 2024

In this paper, we present a general theory for fractional-order sequential differential equations with Riemann–Liouville nabla derivatives and Caputo nabla derivatives on time scales. The explicit solution, in the case of constant coefficients, for both the homogeneous and the non-homogeneous problems, are given using the ∇-Mittag-Leffler function, Laplace transform method, operational method and operational decomposition method. In addition, we also provide some results about a solution to a new class of fractional-order sequential differential equations with convolutional-type variable coefficients using the Laplace transform method.
Suitable Heel Height, a Potential Method for Musculoskeletal Problems during the Third Trimester: A Pilot Study
Authors: Wei Linjuan , Wang Yan , Peng Yinghu , Zhang Guoxin , Tan Qitao , Gu Yaodong , Zhang Ming
Published: July 2024

Background: The treatment options for third-trimester musculoskeletal issues are limited. This study aims to examine how heel height affects gait biomechanics and provides heel height recommendations for various musculoskeletal problems. Methods: Five third-trimester gravidas were recruited wearing uniform footwear with four heel heights (0 mm, 15 mm, 30 mm, and 45 mm). Lower-limb muscle forces, joint angles, joint torques, joint contact forces, and ground reaction forces (GRF) at specific moments (the first peak, valley, and second peak of GRF) were collected for one-way analysis of variance with repeated measures. Results: The soleus, gastrocnemius, tibialis posterior, plantaris, obturator externus, gluteus maximus, gemellus superior, and obturator internus were the smallest at heel heights of 45 mm and 15 mm at the valley of GRF. Hip extension and knee flexion displayed the smallest joint angle and joint torques at a height of 15 mm. Ankle joint contact force decreased with increased heel height. Conclusions: The height of the heel significantly impacts muscle force, joint angles, joint torques, and joint contact force. A heel of 15 mm might be the most suitable heel height to potentially avoid or alleviate musculoskeletal problems during the third trimester.
Molecular Diversity from Longipinenes of Santolina viscosa Lag. through Acid Catalysis: Biocidal Activity
Authors: Torres-García Irene , Quílez del Moral José F. , Barrero Alejandro F. , González-Coloma Azucena , Andrés María Fe , López-Pérez José L. , Álvarez-Corral Miriam , Rodríguez-García Ignacio , Muñoz-Dorado Manuel
Published: July 2024

The search for new compounds with biocidal potential was carried out, focusing on the longipinenes 1–7 from the plant species Santolina viscosa Lag. Compounds 1, 2, and 5 showed remarkable molecular diversity when treated in acidic reaction conditions. Protonic, Lewis, and heterogeneous compounds were used in the treatment. Three main models of reaction have been observed: isomerization of the double bond (8–10); rearrangements to longibornane-based skeleton (11–15) and ring-opening to himachalane-based skeleton (16–18). Secolongibornane aldehydes 23 and 24 were obtained after epoxide opening under the same reaction conditions. The elucidation of the structures of the new compounds was carried out using spectroscopic data and was supported by computational theoretical calculations of 13C NMR spectra. Additionally, high-resolution mass spectrometry and single-crystal X-ray diffraction analysis were employed for certain compounds. Natural longipinenes 4–7, methyl esters 1–3 of corresponding natural carboxylic acids and the isomerized and derivatives compounds 8–19 exhibit moderate to high insecticidal activity against R. padi and M. persicae insects. Longipinene 5 shows potent inhibition against the root growth of the plants L. perenne and L. sativa, as well as compound 2 on the leaves of L. perenne. Furthermore, significant ixocidal and nematicidal activity was found for this latter compound.
PPARs in Clinical Experimental Medicine after 35 Years of Worldwide Scientific Investigations and Medical Experiments
Authors: Skoczyńska Anna , Ołdakowska Monika , Dobosz Agnieszka , Adamiec Rajmund , Gritskevich Sofya , Jonkisz Anna , Lebioda Arleta , Adamiec-Mroczek Joanna , Małodobra-Mazur Małgorzata , Dobosz Tadeusz
Published: July 2024

This year marks the 35th anniversary of Professor Walter Wahli’s discovery of the PPARs (Peroxisome Proliferator-Activated Receptors) family of nuclear hormone receptors. To mark the occasion, the editors of the scientific periodical Biomolecules decided to publish a special issue in his honor. This paper summarizes what is known about PPARs and shows how trends have changed and how research on PPARs has evolved. The article also highlights the importance of PPARs and what role they play in various diseases and ailments. The paper is in a mixed form; essentially it is a review article, but it has been enriched with the results of our experiments. The selection of works was subjective, as there are more than 200,000 publications in the PubMed database alone. First, all papers done on an animal model were discarded at the outset. What remained was still far too large to describe directly. Therefore, only papers that were outstanding, groundbreaking, or simply interesting were described and briefly commented on.
A Machine Learning-Based Web Tool for the Severity Prediction of COVID-19
Authors: Christodoulou Avgi , Katsarou Martha-Spyridoula , Emmanouil Christina , Gavrielatos Marios , Georgiou Dimitrios , Tsolakou Annia , Papasavva Maria , Economou Vasiliki , Nanou Vasiliki , Nikolopoulos Ioannis , Daganou Maria , Argyraki Aikaterini , Stefanidis Evaggelos , Metaxas Gerasimos , Panagiotou Emmanouil , Michalopoulos Ioannis , Drakoulis Nikolaos
Published: July 2024

Predictive tools provide a unique opportunity to explain the observed differences in outcome between patients of the COVID-19 pandemic. The aim of this study was to associate individual demographic and clinical characteristics with disease severity in COVID-19 patients and to highlight the importance of machine learning (ML) in disease prognosis. The study enrolled 344 unvaccinated patients with confirmed SARS-CoV-2 infection. Data collected by integrating questionnaires and medical records were imported into various classification machine learning algorithms, and the algorithm and the hyperparameters with the greatest predictive ability were selected for use in a disease outcome prediction web tool. Of 111 independent features, age, sex, hypertension, obesity, and cancer comorbidity were found to be associated with severe COVID-19. Our prognostic tool can contribute to a successful therapeutic approach via personalized treatment. Although at the present time vaccination is not considered mandatory, this algorithm could encourage vulnerable groups to be vaccinated.
Centralizing the Knowledge and Interpretation of Pain in Chemotherapy-Induced Peripheral Neuropathy: A Paradigm Shift towards Brain-Centric Approaches
Authors: Cunha Mário , Tavares Isaura , Costa-Pereira José Tiago
Published: July 2024

Chemotherapy-induced peripheral neuropathy (CIPN) is a side effect of cancer treatment, often linked with pain complaints. Patients report mechanical and thermal hypersensitivity that may emerge during chemotherapy treatment and may persist after cancer remission. Whereas the latter situation disturbs the quality of life, life itself may be endangered by the appearance of CIPN during cancer treatment. The causes of CIPN have almost entirely been ascribed to the neurotoxicity of chemotherapeutic drugs in the peripheral nervous system. However, the central consequences of peripheral neuropathy are starting to be unraveled, namely in the supraspinal pain modulatory system. Based on our interests and experience in the field, we undertook a review of the brain-centered alterations that may underpin pain in CIPN. The changes in the descending pain modulation in CIPN models along with the functional and connectivity abnormalities in the brain of CIPN patients are analyzed. A translational analysis of preclinical findings about descending pain regulation during CIPN is reviewed considering the main neurochemical systems (serotoninergic and noradrenergic) targeted in CIPN management in patients, namely by antidepressants. In conclusion, this review highlights the importance of studying supraspinal areas involved in descending pain modulation to understand the pathophysiology of CIPN, which will probably allow a more personalized and effective CIPN treatment in the future.
Changes in Spatiotemporal Dynamics of Default Network Oscillations between 19 and 29 Years of Age
Authors: Fehr Thorsten , Mehrens Sophia , Haag Marie-Christine , Amelung Anneke , Gloy Kilian
Published: July 2024

The exploration of functional resting-state brain developmental parameters and measures can help to improve scientific, psychological, and medical applications. The present work focussed on both traditional approaches, such as topographical power analyses at the signal space level, and advanced approaches, such as the exploration of age-related dynamics of source space data. The results confirmed the expectation that the third life decade would show a kind of stability in oscillatory signal and source-space-related parameters. However, from a source dynamics perspective, different frequency ranges appear to develop quite differently, as reflected in age-related sequential network communication profiles. Among other discoveries, the left anterior cingulate source location could be shown to reduce bi-directional network communication in the lower alpha band, whereas it differentiated its uni- and bidirectional communication dynamics to sub-cortical and posterior brain locations. Higher alpha oscillations enhanced communication dynamics between the thalamus and particularly frontal areas. In conclusion, resting-state data appear to be, at least in part, functionally reorganized in the default mode network, while quantitative measures, such as topographical power and regional source activity, did not correlate with age in the third life decade. In line with other authors, we suggest the further development of a multi-perspective approach in biosignal analyses.
The Food Addiction Clinical Treatment (FACT) Manual: A Harm Reduction Treatment Approach
Authors: O’Hea Erin L. , Edwards-Hampton Shenelle A. , Beall Brown Dana L. , Sonneville Kendrin R. , Ziedonis Douglas M. , Gearhardt Ashley N.
Published: July 2024

While the construct of food addiction has been controversial, there is growing evidence that certain foods can activate biobehavioral and neurological mechanisms consistent with addiction to other substances. Despite increased evidence and acceptance of certain foods as addictive substances amongst the scientific community, there is a paucity of interventions available that are uniquely suited for the treatment of this condition. Further, many of the addiction and disordered eating treatment models currently utilized for food addiction are seemingly at odds, with the former often recommending complete abstinence from trigger foods and the latter promoting intake of all foods in moderation. The Food Addiction Clinical Treatment (FACT) manual was created as an alternative using an empirically supported harm-reduction model specifically targeted to treat the addiction and disordered eating features of food addiction. The purpose of the current article is to expose readers to the key tenets of the FACT manual, demonstrate the feasibility of this intervention with a sample of participants with severe food addiction, and discuss future directions for the treatment of food addiction. Positive outcomes from this intervention provide preliminary evidence for the efficacy of FACT for the treatment of food addiction with minimal negative adverse effects. Future research using randomized control trials and longer follow-up is needed to validate the FACT manual as an empirically supported treatment for food addiction.
Family Climate as a Mediator of the Relationship between Stress and Life Satisfaction: A Study with Young University Students
Authors: Morales Almeida Paula , Nunes Cristina
Published: July 2024

Background: The family environment has a significant influence on the personality development of young people and their assessment of life satisfaction. Perceived stress is related to life satisfaction, but this relationship is also influenced by the family context. The present study analysed the impact of perceived stress on life satisfaction and the mediating role of family climate in university students. Methods: A sample of 920 university students was recruited for this study. The instruments used included the Perceived Stress Scale, the Satisfaction with Life Scale, and the Family Social Climate Scale, and socioeducational information was collected. Results: The results indicate that perceived stress had a significant and positive effect on both conflictive and violent family climates, while life satisfaction was negatively impacted by these negative family climates. Additionally, supportive and cohesive family climates, along with difficulty expressing feelings and opinions, were found to have significant positive effects on life satisfaction. Conclusions: These findings highlight the importance of the family system in shaping individual conditions and how it can regulate the relationship between stress and life satisfaction.
Adaptability Analysis of Integrated Project Delivery Method in Large- and Medium-Sized Engineering Projects: A FAHP-Based Modeling Solution
Authors: He Huiyu , Gan Xiwei , Liu Lin , Zhang Xing
Published: July 2024

With the emerging large- and medium-sized engineering projects, prominent project delivery methods make sense in terms of cost, risk, management, and schedule. Among these, the Integrated Project Delivery (IPD) method stands out due to its adaptability for growing scale and complexity projects. This study compares the IPD method with other methods, emphasizing its benefits in large- and medium-sized projects and introducing the Fuzzy Analytic Hierarchy Process (FAHP) model to analyze IPD’s adaptability quantitatively. By conducting a matrix calculation of eighteen second-level indicators, this study derived weight values for four first-level indicators: Cost control, Risk control, Management control, and Schedule control. These first-level indicators were then used to formulate the total evaluation index calculation. Based on this foundation, we verified the calculations using a case study in Fujian. Implementing the IPD method led to a lower cost than the Owner’s Representative method and a one-year schedule acceleration. The FAHP model introduced in this study offers a novel and objective approach for adaptability analysis of the IPD method in large- and medium-sized engineering projects, coupling decision theory into project management.
Study of the Strain Law and Model of an Open-Air Steel Column under Daily Temperature Changes in Winter
Authors: Lian Xugang , Chen Chunyang , Zhan Xinbin , Chen Yucheng , Zhang Yu , Chen Yang , Liu Cheng , Cai Yongxin , Yu Qiang
Published: July 2024

Steel structures with light weight, high rigidity, and easy assembly have become the first choice for large-span complex building materials. At the same time, transparent materials are widely used for the sake of practicality and aesthetics. However, steel structures will be deformed due to changes in temperature, which will affect the accuracy of closure. The components are restricted from free deformation as a result of multiple statically indeterminate structures. A safety hazard will occur if the residual temperature stress is not released. At present, the strain law of open-air steel structures caused by temperature change is still unclear, and the corresponding temperature–strain model has not been established. This paper is based on the third-phase reconstruction and expansion project of Taiyuan Wusu Airport in Xiaodian District, Taiyuan City, Shanxi Province (37°45′ N, 112°38′ E, average altitude of 774 m), winter long time series temperature measured data, deduced daily temperature change laws, and the established relationship model between air temperature and steel surface temperature. Based on the measured data of long-term stress and strain in winter, the strain law of open-air steel columns under temperature change is discussed. According to the results, the air temperature can be utilized to determine the strain of the open-air steel column during winter. The determination coefficient of the temperature–stress model can reach 0.868, and the radial bending stress caused by the change in daily temperature cannot be ignored, accounting for 15.7% of the radial stress at the same time, which can provide a reference for stress calculations of similar structures.
Investigating Large-Scale Tuned Liquid Dampers through Real-Time Hybrid Simulations
Authors: Ashasi Sorkhabi Ali , Qiu Barry , Mercan Oya
Published: July 2024

As buildings become taller and slenderer, managing their vibrational response and mitigating it pose significant challenges in design. Tuned liquid dampers (TLDs) are liquid (usually water)-filled tanks that can mitigate structural vibrations by leveraging the sloshing motion of the contained fluid. However, the dynamic behavior of TLDs and their interaction with structures is complex. While most research on TLDs has focused on mitigating wind-induced vibrations, less attention has been paid to their seismic control of structural responses. Moreover, existing literature on the experimental research involving TLDs mostly pertains to small-scale models. This study aims to experimentally explore the effectiveness of large-scale TLDs in mitigating vibrations in both linear and nonlinear structures under seismic loads. A real-time hybrid simulation is employed as the experimental method, where only the TLD is physically constructed and tested, while the rest of the system is simulated numerically in a coupled manner, allowing for obtaining the dynamic response of the structure equipped with the TLD in real time. This approach offers the flexibility to significantly scale up the TLD size for physical testing while exploring various TLD-structure scenarios by numerically adjusting the structural properties within the simulation.
Strategic Approaches to Design Teams for Construction Quality Management and Green Building Performance
Authors: Elforgani Mohamed S. Abd. , Alabsi Akram A. N. , Alwarafi Abbas
Published: July 2024

Buildings exert a profound influence on the environment, with the design phase recognized as the pivotal determinant of a building’s overall performance. Green building design, in particular, introduces heightened complexity, where the attributes of the design team play a pivotal role in shaping performance outcomes. Consequently, the characteristics of the design team emerge as crucial factors in the enhancement of both green building design performance and client attributes. This study aims to empirically examine a model formulated to gauge the extent to which Effective Design Team Attributes contribute to the enhancement of performance in designing green buildings and influencing client attributes. To achieve this objective, a comprehensive questionnaire survey was administered to professionals within the architecture and engineering domains actively engaged in the design and consulting sectors of the building industry. The collected data underwent meticulous scrutiny for authenticity and dependability using the WINSTEPS 5.2.5 software before undergoing subsequent analysis. Statistical analyses were conducted using SPSS version 19, with Principal Components Analysis (PCA) and the Structural Equation Modeling (SEM) approach implemented through Amos version 18 to derive the most robust model. The findings underscore the pivotal role of an adeptly managed design team in significantly improving both the performance of green building designs and the qualities of clients. Rasch’s analysis confirmed the validity of our 5-point Likert scale for Design Green Building Performance (DGBP), Effective Design Team Attributes (EDTA), and Client Qualities (CQ). All items demonstrated excellent reliability, separation, and discrimination, ensuring robust data quality. Dimensionality tests revealed the appropriateness of response categories, indicating satisfactory scale performance. The Effective Design Team Model, validated through Principal Components Analysis (PCA), exhibited a satisfactory fit, supported by significant chi-square statistics, high goodness-of-fit indices, and acceptable root mean square residual values. Client attributes displayed a strong association with effective design team management, validating key model elements. The intricacies inherent in the design process can be mitigated by adopting the green design charrette approach. Consequently, the establishment of an effective design team, coupled with green design leadership, active participation, and clarity in roles and responsibilities, emerges as a potent strategy for elevating the performance level of green building designs.
Casein Kinase 1α—A Target for Prostate Cancer Therapy?
Authors: Lishman-Walker Emma , Coffey Kelly
Published: July 2024

The androgen receptor (AR) is a key driver of prostate cancer (PCa) and, as such, current mainstay treatments target this molecule. However, resistance commonly arises to these therapies and, therefore, additional targets must be evaluated to improve patient outcomes. Consequently, alternative approaches for indirectly targeting the AR are sought. AR crosstalk with other signalling pathways, including several protein kinase signalling cascades, has been identified as a potential route to combat therapy resistance. The casein kinase 1 (CK1) family of protein kinases phosphorylate a multitude of substrates, allowing them to regulate a diverse range of pathways from the cell cycle to DNA damage repair. As well as its role in several signalling pathways that are de-regulated in PCa, mutational data suggest its potential to promote prostate carcinogenesis. CK1α is one isoform predicted to regulate AR activity via phosphorylation and has been implicated in the progression of several other cancer types. In this review, we explore how the normal biological function of CK1 is de-regulated in cancer, the impact on signalling pathways and how this contributes towards prostate tumourigenesis, with a particular focus on the CK1α isoform as a novel therapeutic target for PCa.
Decoding the Intricate Landscape of Pancreatic Cancer: Insights into Tumor Biology, Microenvironment, and Therapeutic Interventions
Authors: Argentiero Antonella , Andriano Alessandro , Caradonna Ingrid Catalina , de Martino Giulia , Desantis Vanessa
Published: July 2024

Pancreatic ductal adenocarcinoma (PDAC) presents significant oncological challenges due to its aggressive nature and poor prognosis. The tumor microenvironment (TME) plays a critical role in progression and treatment resistance. Non-neoplastic cells, such as cancer-associated fibroblasts (CAFs) and tumor-associated macrophages (TAMs), contribute to tumor growth, angiogenesis, and immune evasion. Although immune cells infiltrate TME, tumor cells evade immune responses by secreting chemokines and expressing immune checkpoint inhibitors (ICIs). Vascular components, like endothelial cells and pericytes, stimulate angiogenesis to support tumor growth, while adipocytes secrete factors that promote cell growth, invasion, and treatment resistance. Additionally, perineural invasion, a characteristic feature of PDAC, contributes to local recurrence and poor prognosis. Moreover, key signaling pathways including Kirsten rat sarcoma viral oncogene (KRAS), transforming growth factor beta (TGF-β), Notch, hypoxia-inducible factor (HIF), and Wnt/β-catenin drive tumor progression and resistance. Targeting the TME is crucial for developing effective therapies, including strategies like inhibiting CAFs, modulating immune response, disrupting angiogenesis, and blocking neural cell interactions. A recent multi-omic approach has identified signature genes associated with anoikis resistance, which could serve as prognostic biomarkers and targets for personalized therapy.
Identification of Distinct, Quantitative Pattern Classes from Emergent Tissue-Scale hiPSC Bioelectric Properties
Authors: Norfleet Dennis Andre , Melendez Anja J. , Alting Caroline , Kannan Siya , Nikitina Arina A. , Caldeira Botelho Raquel , Yang Bo , Kemp Melissa L.
Published: July 2024

Bioelectric signals possess the ability to robustly control and manipulate patterning during embryogenesis and tissue-level regeneration. Endogenous local and global electric fields function as a spatial ‘pre-pattern’, controlling cell fates and tissue-scale anatomical boundaries; however, the mechanisms facilitating these robust multiscale outcomes are poorly characterized. Computational modeling addresses the need to predict in vitro patterning behavior and further elucidate the roles of cellular bioelectric signaling components in patterning outcomes. Here, we modified a previously designed image pattern recognition algorithm to distinguish unique spatial features of simulated non-excitable bioelectric patterns under distinct cell culture conditions. This algorithm was applied to comparisons between simulated patterns and experimental microscopy images of membrane potential (Vmem) across cultured human iPSC colonies. Furthermore, we extended the prediction to a novel co-culture condition in which cell sub-populations possessing different ionic fluxes were simulated; the defining spatial features were recapitulated in vitro with genetically modified colonies. These results collectively inform strategies for modeling multiscale spatial characteristics that emerge in multicellular systems, characterizing the molecular contributions to heterogeneity of membrane potential in non-excitable cells, and enabling downstream engineered bioelectrical tissue design.
Supramolecular Sensing Platforms: Techniques for In Vitro Biosensing
Authors: Lahiri Hiya , Basu Kingshuk
Published: July 2024

Supramolecular chemistry is a relatively new field of study that utilizes conventional chemical knowledge to produce new edges of smart materials. One such material use of supramolecular chemistry is the development of sensing platforms. Biologically relevant molecules need frequent assessment both qualitatively and quantitatively to explore several biological processes. In this review, we have discussed supramolecular sensing techniques with key examples of sensing several kinds of bio-analytes and tried to cast light on how molecular design can help in making smart materials. Moreover, how these smart materials have been finally used as sensing platforms has been discussed as well. Several useful spectroscopic, microscopic, visible, and electronic outcomes of sensor materials have been discussed, with a special emphasis on device-based applications. This kind of comprehensive discussion is necessary to widen the scope of sensing technology.
Selective Determination of 4,4′-Oxydianiline (4,4′-ODA) in Plastic Packaging Using Molecularly Imprinted Polymer Sensor Integrated with Pyrolyzed Copper/Carbon Composite
Authors: Zhou Xuejun , Ye Pengcheng , Huang Zhiding , Yang Chun , Ren Jiefang , Wang Jin , Tang Shali
Published: July 2024

This study focuses on the synthesis, fabrication, and characterization of a molecularly imprinted polymer (MIP) sensor tailored for the selective determination of 4,4′-oxydianiline (4,4′-ODA) in plastic products. Notably, by integrating the sensor matrix with pyrolyzed copper/carbon material derived from Cu-BTC MOF, a remarkable enhancement in electrochemical performance is achieved. The Cu-BTC material is grown successfully on the surface of carbon nanotubes (CNTs) and subjected to calcination at 800 °C, yielding a CNT/Cu/C composite. This composite exhibits an increased surface area and enhanced electron transfer capability, resulting in an improved current response. To augment the selective detection capability of the modified electrodes for 4,4′-ODA, molecularly imprinted polymers (MIPs) were incorporated onto the composite surface. The modified electrode (CNT-2/Cu/C/MIP/GCE) was synthesized using acrylamide (AM) and methacrylic acid (MAA) as dual-functional monomers with 4,4′-ODA as a template molecule via precipitation polymerization. The differential pulse voltammetric (DPV) current response to 4,4′-ODA showed a favorable linear relationship within the concentration range of (0.15–10 μM,10–100 μM), with a detection limit of 0.05 μM. Moreover, the CNT-2/Cu/C/MIP/GCE sensor demonstrates exceptional sensitivity, specificity, consistency, and durability. Furthermore, this approach has proven effective in detecting 4,4′-ODA in spiked nylon spatula samples, with recovery rates ranging from 86.3% to 103.5%.
Enhancement of Scan Strategy for Improving Surface Characteristics of the SLMed Inconel 718 Alloy Component
Authors: Tang Sida , Han Jitai
Published: July 2024

The technique of selective laser melting has garnered significant interest due to its capacity to fabricate metal parts of any shape using a single printing process. In this study, a scan strategy was proposed for printing the inner structure part based on the varying performance of the Inconel 718 alloy part produced using different scan techniques. The test findings indicated that the surface quality of the printed material along the scan line exhibited significantly superior performance in comparison to that produced in a vertical direction to the scan line. Regarding the scan strategy, it is evident that the zigzag shape scan strategy exhibited superior performance in terms of the microstructure of the printed part. This is attributed to the more consistent cooling rate on each scan track. On the other hand, the square-framed scan strategy demonstrated a relatively optimal condition for the bending deformation of the printed part compared to other scan strategies. This is due to the more appropriate behavior of the molten pool during the outer edge printing process. After considering all of these criteria, a novel scan approach was created that included various scan strategies. The test findings indicated that the component produced using this novel scanning technique exhibited the combined benefits of both the square-shaped and zigzag-shaped scanning strategies.
Advancements in ZnO-Based Photocatalysts for Water Treatment: A Comprehensive Review
Authors: Abou Zeid Souad , Leprince-Wang Yamin
Published: July 2024

Water contamination remains a pressing global concern, necessitating the development of effective and sustainable water treatment solutions. Zinc oxide (ZnO) has garnered significant attention for its potential applications in photocatalysis due to its unique properties and versatile nature. This review synthesizes recent research findings on the advancement in ZnO-based photocatalysts for water treatment, encompassing synthesis methods, structure modifications for photocatalytic efficiency enhancement, toxicity assessments, and applications in diverse water treatment processes. By critically analyzing the strategies to enhance the photocatalytic performance of ZnO and its role in addressing water pollution challenges, this review provides valuable insights into the evolving landscape of ZnO-based photocatalysts for achieving efficient and environmentally friendly water treatment systems. This review emphasizes the transformative potential of ZnO-based photocatalysts in revolutionizing water treatment methodologies and underscores the importance of continued research and innovation in harnessing ZnO’s capabilities for sustainable water purification.
Effect of Nb and B on the Precipitation Behaviors in Al-Ti-Nb Balanced-Ratio Ni-Based Superalloy: A Phase-Field Study
Authors: Ta Na , Zhou Hongguang , Zhang Cong , Zhang Ruijie , Zhang Lijun
Published: July 2024

In this paper, quantitative two-dimensional (2-D) phase-field simulations were performed to gain insight into the effects of B and Nb for Al-Ti-Nb balanced-ratio GH4742 alloys. The microstructure evolution during the precipitation process was simulated using the MICRESS (MICRostructure Evolution Simulation Software) package developed in the formalism of the multi-phase field model. The coupling to CALPHAD (CALculation of PHAse Diagram) thermodynamic databases was realized via the TQ interface. The morphological evolution, concentration distribution, and thermodynamic properties were extensively analyzed. It is indicated that a higher Nb content contributes to a faster precipitation rate and higher amounts and the smaller precipitate size of the γ′ phase, contributing to better mechanical properties. The segregation of the W element in γ′ precipitate due to its sluggish diffusion effect has also been observed. Higher temperatures and lower B contents accelerate the dissolution of boride and reduce the precipitation of borides. With the increased addition of B, the formation of borides may have a pinning effect on the grain boundary to hinder the kinetic process. In addition, borides are prone to precipitate around the interface rather than in the bulk phase. Once the M3B2 borides nucleate, they grow in the consumption of γ′ phases.