bluuebunny/crossref_metadata_2025 huggingface dataset:
Thermocatalytic Decomposition of Dimethyl Methylphosphonate Based on CeO2 Catalysts with Different Morphologies
Authors: Kong Weimin , Wang Xuwei , Wang Kunpeng , He Qingrong , Zhou Shuyuan , Yang Piaoping , Dong Yanchun
Published: February 2023

The catalytic performances of the catalysts and decomposition mechanisms of dimethyl methylphosphonate (DMMP), a commonly used nerve agent simulant, are well understood based on previous studies. However, the effects of the morphology of the catalyst on DMMP decomposition performance and mechanisms remain unexplored. Thus, in this work, experimental studies were conducted on the thermocatalytic decomposition of DMMP on CeO2 nanomaterials with different morphologies, e.g., irregular nanoparticles, nanorods, and nanocubes. From the performance evaluation, CeO2 nanorods exhibited higher DMMP thermocatalytic decomposition performance as compared to irregular nanoparticles and nanocubes. The primary reaction pathways were the same on all three morphologies of materials, according to in situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) study, whereas side reaction paths showed variable behaviors. According to the catalytic reaction mechanism study, the surface lattice oxygen played a vital role in the thermocatalytic decomposition of DMMP and the accumulation of phosphates, carbonates, and formates were the main factors for deactivation of the catalyst. The behavior of CeO2 catalyst with different morphologies in the thermocatalytic decomposition of DMMP was revealed in this work, and this will be useful for the future design of high-performing catalysts for the efficient degradation of chemical toxicant.
A Study of Breast Cancer Classification Algorithms by Fusing Machine Learning and Deep Learning
Authors: Sun Lifei , Li Sen
Published: February 2023

Although breast cancer, with easy recurrence and high mortality, has become one of the leading causes of cancer death in women, early and accurate diagnosis of breast cancer can effectively increase the likelihood of a cure. Therefore, it is particularly important to improve the accuracy of early diagnosis of breast cancer. However, conventional early diagnosis relies on human experience and has a low accuracy rate. Therefore, many researchers have proposed various machine learning methods to improve the accuracy and efficiency of prediction. Most of the existing studies around breast cancer classification adopt a single algorithm to fit breast cancer data but ignore the applicability of different breast cancer data features to the model. In this paper, we adopt machine algorithms to strip the features of machine learning methods from the rest of the features and attempt to enhance the model effect by designing deep learning model structures to find the hidden patterns in the rest of the features. In addition, due to strict medical data privacy requirements and high collection difficulty and cost, the model designed in this paper will be trained on a small number of samples. As a result, we attempt to find a minimization model for a breast cancer classification algorithm that features both low cost and high efficiency. At the same time, the deep learning model is further designed to complement the original model when it is possible to introduce complex data indicators. Experimental values show that the design model in this paper performs best not only under limited data and limited indicators but also under limited data complex indicators, demonstrating the effectiveness of the approach of mixed comparison and feature selection of multiple classification algorithms. In summary, the fusion model designed and implemented in this paper performs well in the experiments, and the accuracy of the model test reaches 98.3%.
Intelligent Stuck Pipe Type Recognition Using Digital Twins and Knowledge Graph Model
Authors: Li Qian , Wang Junze , Yin Hu
Published: February 2023

During drilling operations, stuck pipe occurs from time to time due to various reasons such as continuous changes of the formation lithology, failure to return the drill cuttings in time, shrinkage or collapse caused by soaking the formation with drilling fluid, and steps in the well wall caused by the drill-down. After the stuck pipe, the identification of the stuck pipe type can only be guessed by manual experience due to the jamming of the drill stem downhole, which lacks a scientific basis. Moreover, there is a lack of studies on the stuck pipe type. Therefore, scientific and accurate identification of the stuck pipe type is of great significance for timely unsticking and resuming drilling. In this paper, based on the friction torque rigid rod model of a3D well track, we obtained the degree of deviation of measured parameters from the normal trend, which can scientifically evaluate the degree of stuck pipe. Based on the SAX morphological symbolic aggregation approximation method, we obtained the changing trend of measured parameters during the stuck pipe, which can accurately describe the change laws of characteristic parameters during the stuck pipe. Based on the statistical characterization laws of different stuck pipe types in Sichuan and Chongqing, we established the knowledge graph of stuck pipe types, which can correlate with the complex knowledge of different stuck pipe types. The stuck pipe type can be identified according to the degree of stuck pipe, the changing trend of the characteristic parameters of stuck pipe, and the knowledge graph of stuck pipe types. The results show that the method can combine digital twins and the knowledge graph to accurately identify the stuck pipe type and provide a basis for taking targeted deconstruction measures.
Low-Cost Broadband Circularly Polarized Array Antenna with Artificial Magnetic Conductor for Indoor Applications
Authors: Hao Han , Wang Shuqi , Gao Huanhuan , Ma Xia , Huang Xiaojun
Published: February 2023

In this paper, we report a sequential-phase fed broadband circularly polarized array antenna loaded with an artificial magnetic conductor-reflecting surface. Our proposed antenna is a two-part group. The upper structure is a sequential-phase fed circular wide-slot antenna. The lower part is an artificial magnetic conductor structured reflective surface. The overall antenna size is 106 × 106 × 14.9 mm3, both adopting 1.6 mm thick FR4 material. The thickness of the air layer sandwiched between the antenna and the artificial magnetic conductor reflective surface is 14 mm. The antenna consists of four circular wide-slot antenna units with a sequential rotation technique. To broaden the axial ratio bandwidth, three L-shaped branches and four metal plates are attached to the circular wide-slot antenna unit and around the artificial magnetic conductor-reflecting surface, respectively. To verify the accuracy of the simulation, we fabricated the sample and then tested it in an anechoic chamber. The measured results demonstrate that the proposed broadband circularly polarized array antenna realizes an impedance bandwidth of 77.67% (2.74–6.22 GHz) and an axial ratio bandwidth of 65.16% (3.00–5.90 GHz) with a peak gain of 11.1 dBi. The design can be used in space-constrained environments, such as indoor and dense building areas.
Review of the Main Cable Shape Control of the Suspension Bridge
Authors: Huang Pingming , Li Chongjin
Published: February 2023

The main cable shape control confronts new challenges when a suspension bridge’s span exceeds two kilometers. As a suspension bridge’s primary load-bearing component, it is difficult to alter the alignment once the erection is completed. Hence, the accurate calculation and control of the main cable shape has significant scientific research value from various aspects. This paper systematically reviews the research progress of the suspension bridge’s main cable shape control technology. The current research progress is introduced from three aspects of main cable shape-finding, construction control technology, and control analysis, and both the current shortcomings and future research directions are summarized. This review paper is expected to be a solid reference for investigators and experts in this crucial field of structural engineering.
New Trends in the Control of Robots and Mechatronic Systems
Authors: Bruzzone Luca
Published: February 2023

In recent years, research into the control of robotic and mechatronic systems has led to a wide variety of advanced paradigms and techniques, which have been extensively analysed and discussed in the scientific literature [...]
A Machine Learning Based Full Duplex System Supporting Multiple Sign Languages for the Deaf and Mute
Authors: Saleem Muhammad Imran , Siddiqui Atif , Noor Shaheena , Luque-Nieto Miguel-Angel , Nava-Baro Enrique
Published: February 2023

This manuscript presents a full duplex communication system for the Deaf and Mute (D-M) based on Machine Learning (ML). These individuals, who generally communicate through sign language, are an integral part of our society, and their contribution is vital. They face communication difficulties mainly because others, who generally do not know sign language, are unable to communicate with them. The work presents a solution to this problem through a system enabling the non-deaf and mute (ND-M) to communicate with the D-M individuals without the need to learn sign language. The system is low-cost, reliable, easy to use, and based on a commercial-off-the-shelf (COTS) Leap Motion Device (LMD). The hand gesture data of D-M individuals is acquired using an LMD device and processed using a Convolutional Neural Network (CNN) algorithm. A supervised ML algorithm completes the processing and converts the hand gesture data into speech. A new dataset for the ML-based algorithm is created and presented in this manuscript. This dataset includes three sign language datasets, i.e., American Sign Language (ASL), Pakistani Sign Language (PSL), and Spanish Sign Language (SSL). The proposed system automatically detects the sign language and converts it into an audio message for the ND-M. Similarities between the three sign languages are also explored, and further research can be carried out in order to help create more datasets, which can be a combination of multiple sign languages. The ND-M can communicate by recording their speech, which is then converted into text and hand gesture images. The system can be upgraded in the future to support more sign language datasets. The system also provides a training mode that can help D-M individuals improve their hand gestures and also understand how accurately the system is detecting these gestures. The proposed system has been validated through a series of experiments resulting in hand gesture detection accuracy exceeding 95%.
Person Identification and Gender Classification Based on Vision Transformers for Periocular Images
Authors: Suravarapu Vasu Krishna , Patil Hemprasad Yashwant
Published: February 2023

Many biometrics advancements have been widely used for security applications. This field’s evolution began with fingerprints and continued with periocular imaging, which has gained popularity due to the pandemic scenario. CNN (convolutional neural networks) has revolutionized the computer vision domain by demonstrating various state-of-the-art results (performance metrics) with the help of deep-learning-based architectures. The latest transformation has happened with the invention of transformers, which are used in NLP (natural language processing) and are presently being adapted for computer vision. In this work, we have implemented five different ViT- (vision transformer) based architectures for person identification and gender classification. The experiment was performed on the ViT architectures and their modified counterparts. In general, the samples selected for train:val:test splits are random, and the trained model may get affected by overfitting. To overcome this, we have performed 5-fold cross-validation-based analysis. The experiment’s performance matrix indicates that the proposed method achieved better results for gender classification as well as person identification. We also experimented with train-val-test partitions for benchmarking with existing architectures and observed significant improvements. We utilized the publicly available UBIPr dataset for performing this experimentation.
Inter-Seasonal Estimation of Grass Water Content Indicators Using Multisource Remotely Sensed Data Metrics and the Cloud-Computing Google Earth Engine Platform
Authors: Masenyama Anita , Mutanga Onisimo , Dube Timothy , Sibanda Mbulisi , Odebiri Omosalewa , Mabhaudhi Tafadzwanashe
Published: February 2023

Indicators of grass water content (GWC) have a significant impact on eco-hydrological processes such as evapotranspiration and rainfall interception. Several site-specific factors such as seasonal precipitation, temperature, and topographic variations cause soil and ground moisture content variations, which have significant impacts on GWC. Estimating GWC using multisource data may provide robust and accurate predictions, making it a useful tool for plant water quantification and management at various landscape scales. In this study, Sentinel-2 MSI bands, spectral derivatives combined with topographic and climatic variables, were used to estimate leaf area index (LAI), canopy storage capacity (CSC), canopy water content (CWC) and equivalent water thickness (EWT) as indicators of GWC within the communal grasslands in Vulindlela across wet and dry seasons based on single-year data. The results illustrate that the use of combined spectral and topo-climatic variables, coupled with random forest (RF) in the Google Earth Engine (GEE), improved the prediction accuracies of GWC variables across wet and dry seasons. LAI was optimally estimated in the wet season with an RMSE of 0.03 m−2 and R2 of 0.83, comparable to the dry season results, which exhibited an RMSE of 0.04 m−2 and R2 of 0.90. Similarly, CSC was estimated with high accuracy in the wet season (RMSE = 0.01 mm and R2 = 0.86) when compared to the RMSE of 0.03 mm and R2 of 0.93 obtained in the dry season. Meanwhile, for CWC, the wet season results show an RMSE of 19.42 g/m−2 and R2 of 0.76, which were lower than the accuracy of RMSE = 1.35 g/m−2 and R2 = 0.87 obtained in the dry season. Finally, EWT was best estimated in the dry season, yielding a model accuracy of RMSE = 2.01 g/m−2 and R2 = 0.91 as compared to the wet season (RMSE = 10.75 g/m−2 and R2 = 0.65). CSC was best optimally predicted amongst all GWC variables in both seasons. The optimal variables for estimating these GWC variables included the red-edge, near-infrared region (NIR) and short-wave infrared region (SWIR) bands and spectral derivatives, as well as environmental variables such as rainfall and temperature across both seasons. The use of multisource data improved the prediction accuracies for GWC indicators across both seasons. Such information is crucial for rangeland managers in understanding GWC variations across different seasons as well as different ecological gradients.
Robot Time-Optimal Trajectory Planning Based on Quintic Polynomial Interpolation and Improved Harris Hawks Algorithm
Authors: Xu Jing , Ren Chaofan , Chang Xiaonan
Published: February 2023

Time-optimal trajectory planning is one of the most important ways to improve work efficiency and reduce cost and plays an important role in practical application scenarios of robots. Therefore, it is necessary to optimize the running time of the trajectory. In this paper, a robot time-optimal trajectory planning method based on quintic polynomial interpolation and an improved Harris hawks algorithm is proposed. Interpolation with a quintic polynomial has a smooth angular velocity and no acceleration jumps. It has widespread application in the realm of robot trajectory planning. However, the interpolation time is usually obtained by testing experience, and there is no unified criterion to determine it, so it is difficult to obtain the optimal trajectory running time. Because the Harris hawks algorithm adopts a multi-population search strategy, compared with other swarm intelligent optimization algorithms such as the particle swarm optimization algorithm and the fruit fly optimization algorithm, it can avoid problems such as single population diversity, low mutation probability, and easily falling into the local optimum. Therefore, the Harris hawks algorithm is introduced to overcome this problem. However, because some key parameters in HHO are simply set to constant or linear attenuation, efficient optimization cannot be achieved. Therefore, the nonlinear energy decrement strategy is introduced in the basic Harris hawks algorithm to improve the convergence speed and accuracy. The results show that the optimal time of the proposed algorithm is reduced by 1.1062 s, 0.5705 s, and 0.3133 s, respectively, and improved by 33.39%, 19.66%, and 12.24% compared with those based on particle swarm optimization, fruit fly algorithm, and Harris hawks algorithms, respectively. In multiple groups of repeated experiments, compared with particle swarm optimization, the fruit fly algorithm, and the Harris hawks algorithm, the computational efficiency was reduced by 4.7019 s, 1.2016 s, and 0.2875 s, respectively, and increased by 52.40%, 21.96%, and 6.30%. Under the optimal time, the maximum angular displacement, angular velocity, and angular acceleration of each joint trajectory meet the constraint conditions, and their average values are only 75.51%, 38.41%, and 28.73% of the maximum constraint. Finally, the robot end-effector trajectory passes through the pose points steadily and continuously under the cartesian space optimal time.
Identification of Internal Short-Circuit Faults in Lithium-Ion Batteries Based on a Multi-Machine Learning Fusion
Authors: Zhu Guangying , Sun Tao , Xu Yuwen , Zheng Yuejiu , Zhou Long
Published: February 2023

Internal short-circuit (ISC) faults are a common cause of thermal runaway in lithium-ion batteries (LIBs), which greatly endangers the safety of LIBs. Different LIBs have common features related to ISC faults. Due to the insufficient volume of acquired ISC fault data, conventional machine learning models could not effectively identify ISC faults. To compensate for the above deficiencies, this paper proposes a multi-machine learning fusion method to predict ISC faults and to perform faults warning classification under multiple operating conditions using the input of voltage normalization. Firstly, learning data acquisition is captured by experiments and simulation. Secondly, the simulation data are inputted into the ResNet-convolutional neural network (CNN) for pretraining, followed by the transfer learning method to freeze parts of the model layers in the CNN, and part of the experimental data are also inputted into the CNN model for parameter fine-tuning to build a multi-machine learning model. Finally, the degree of ISC faults within the laboratory battery is predicted based on the multi-machine learning model. The results show that the CNN model had a 99.9% prediction accuracy on the simulated dataset, and the multi-machine learning fusion model after transfer learning had a 96.67% prediction accuracy on the laboratory battery dataset, which can accurately identify different levels of ISC faults in batteries and realize the graded warning of ISC faults.
The Back Muscle Surface Electromyography-Based Fatigue Index: A Digital Biomarker of Human Neuromuscular Aging?
Authors: Ebenbichler Gerold , Habenicht Richard , Blohm Peter , Bonato Paolo , Kollmitzer Josef , Mair Patrick , Kienbacher Thomas
Published: February 2023

As part of our quest for digital biomarkers of neuromuscular aging, and encouraged by recent findings in healthy volunteers, this study investigated if the instantaneous median frequency (IMDF) derived from back muscle surface electromyographic (SEMG) data monitored during cyclic back extensions could reliably differentiate between younger and older individuals with cLBP. A total of 243 persons with cLBP participated in three experimental sessions: at baseline, one to two days after the first session, and then again approximately six weeks later. During each session, the study participants performed a series of three isometric maximal voluntary contractions (MVC) of back extensors using a dynamometer. These were followed by an isometric back extension at 80% MVC, and—after a break—25 slow cyclic back extensions at 50% MVC. SEMG data were recorded bilaterally at L5 (multifidus), L2 (longissimus dorsi), and L1 (iliocostalis lumborum). Linear mixed-effects models found the IMDF-SEMG time-course changes more rapidly in younger than in older individuals, and more prominently in male participants. The absolute and relative reliabilities of the SEMG time–frequency representations were well compared between older and younger participants. The results indicated an overall good relative reliability, but variable absolute reliability levels. IMDF-SEMG estimates derived from cyclic back extensions proved to be successful in reliably detecting differences in back muscle function in younger vs. older persons with cLBP. These findings encourage further research, with a focus on assessing whether an IMDF-SEMG-based index could be utilized as a tool to achieve the preclinical detection of back muscle aging, and possibly predict the development of back muscle sarcopenia.
An Integrated In Vivo/In Vitro Protein Production Platform for Site-Specific Antibody Drug Conjugates
Authors: Hanson Jeffrey , Groff Dan , Carlos Abi , Usman Hans , Fong Kevin , Yu Abigail , Armstrong Stephanie , Dwyer Allison , Masikat Mary Rose , Yuan Dawei , Tran Cuong , Heibeck Tyler , Zawada James , Chen Rishard , Hallam Trevor , Yin Gang
Published: February 2023

The XpressCF+® cell-free protein synthesis system is a robust platform for the production of non-natural amino acids containing antibodies, which enable the site-specific conjugation of homogeneous antibody drug conjugates (ADCs) via click chemistry. Here, we present a robust and scalable means of achieving a 50–100% increase in IgG titers by combining the high productivity of cell-based protein synthesis with the unique ability of XpressCF+® reactions to produce correctly folded and assembled IgGs containing multiple non-natural amino acids at defined positions. This hybrid technology involves the pre-expression of an IgG light-chain (LC) protein in a conventional recombinant E. coli expression system, engineered to have an oxidizing cytoplasm. The prefabricated LC subunit is then added as a reagent to the cell-free protein synthesis reaction. Prefabricated LC increases IgG titers primarily by reducing the protein synthesis burden per IgG since the cell free translation machinery is only responsible for synthesizing the HC protein. Titer increases were demonstrated in four IgG products in scales ranging from 100-µL microplate reactions to 0.25-L stirred tank bioreactors. Similar titer increases with prefabricated LC were also demonstrated for a bispecific antibody in the scFvFc-FabFc format, demonstrating the generality of this approach. Prefabricated LC also increases robustness in cell-free reactions since it eliminates the need to fine-tune the HC-to-LC plasmid ratio, a critical parameter influencing IgG assembly and quality when the two IgG subunits are co-expressed in a single reaction. ADCs produced using prefabricated LC were shown to be identical to IgGs produced in cell-free alone by comparing product quality, in vitro cell killing, and FcRn receptor binding assays. This approach represents a significant step towards improving IgG titers and the robustness of cell-free protein synthesis reactions by integrating in vivo and in vitro protein production platforms.
PCL/Graphene Scaffolds for the Osteogenesis Process
Authors: Anitasari Silvia , Wu Ching-Zong , Shen Yung-Kang
Published: February 2023

This study aims to characterize the osteoconductivity, optimal bioresorbable, biodegradability, biocompatibility, and mechanical properties of Poly-ε-caprolactone (PCL)/graphene (G) scaffolds at concentrations of 0.5, 1, 1.5, 2, 2.5, and 3 wt%, which are used to support bone regeneration through solvent casting and particulate leaching. The water contact angle measurement revealed a transition from a hydrophobic to a hydrophilic surface after incorporating various G concentrations. The scaffolds with 0.5 wt% G had smaller pores compared to those produced using 3 wt% G. Furthermore, numerous pores were connected, particularly those with larger diameters in the 2 and 3 wt% G samples. The proportion of water absorption varied between 50% and 350% for 4 months, with large percentages of scaffolds containing high G concentrations. Raman spectroscopy and X-ray diffraction, which were used to confirm the presence of nanofiller by increasing the ratios of ID/IG, I2D/IG, and band 2θ = 26.48°. The mechanical properties were improved by the addition of G, with a Young’s modulus of 3 wt% G, four times that of PCL. Measuring cell biocompatibility, adhesion, proliferation, and differentiation with osteoblast-like (MG-63) cells revealed that PCL/G scaffolds with higher concentrations were more biocompatible than PCL as well as those with lower concentrations.
The Potential Role of Microorganisms on Enteric Nervous System Development and Disease
Authors: Chanpong Atchariya , Borrelli Osvaldo , Thapar Nikhil
Published: February 2023

The enteric nervous system (ENS), the inherent nervous system of the gastrointestinal (GI) tract is a vast nervous system that controls key GI functions, including motility. It functions at a critical interface between the gut luminal contents, including the diverse population of microorganisms deemed the microbiota, as well as the autonomic and central nervous systems. Critical development of this axis of interaction, a key determinant of human health and disease, appears to occur most significantly during early life and childhood, from the pre-natal through to the post-natal period. These factors that enable the ENS to function as a master regulator also make it vulnerable to damage and, in turn, a number of GI motility disorders. Increasing attention is now being paid to the potential of disruption of the microbiota and pathogenic microorganisms in the potential aetiopathogeneis of GI motility disorders in children. This article explores the evidence regarding the relationship between the development and integrity of the ENS and the potential for such factors, notably dysbiosis and pathogenic bacteria, viruses and parasites, to impact upon them in early life.
Peptide-Based siRNA Nanocomplexes Targeting Hepatic Stellate Cells
Authors: Lin Chien-Yu , Mamani Umar-Farouk , Guo Yuhan , Liu Yanli , Cheng Kun
Published: February 2023

Liver fibrosis is the excessive accumulation of extracellular matrix (ECM) in the liver due to chronic injuries and inflammation. These injuries activate and transform quiescent hepatic stellate cells (HSCs) into proliferative myofibroblast-like cells, which are the key contributors to the secretin of ECM in the fibrotic liver. The insulin-like growth factor 2 receptor (IGF2R) is a multifunctional receptor that is overexpressed on activated HSCs and is a specific molecular marker of activated HSCs in the fibrotic liver. We recently discovered an IGF2R-specific peptide that significantly increases the binding affinity and uptake of a protein-based siRNA nanocomplex to activated HSCs. However, there is a potential concern about the immunogenicity of protein-based siRNA delivery systems. In this study, we used the IGF2R-specific peptide to modify a small peptide-based siRNA nanocomplex for HSC-specific drug delivery. We incorporated a short spacer and glutamate residues into the IGF2R peptides. The siRNA nanocomplex modified with the IGF2R-3GK6E peptide demonstrated higher HSC specificity compared to an unmodified nanocomplex. This peptide-based nanocomplex provides a promising platform to effectively deliver Pcbp2 siRNA to activated HSCs for the treatment of liver fibrosis.
Endostatin and Cancer Therapy: A Novel Potential Alternative to Anti-VEGF Monoclonal Antibodies
Authors: Méndez-Valdés Gabriel , Gómez-Hevia Francisca , Lillo-Moya José , González-Fernández Tommy , Abelli Joaquin , Cereceda-Cornejo Antonia , Bragato Maria Chiara , Saso Luciano , Rodrigo Ramón
Published: February 2023

Angiogenesis is a physiological process that consists of the formation of new blood vessels from preexisting ones. Angiogenesis helps in growth, development, and wound healing through the formation of granulation tissue. However, this physiological process has also been linked to tumor growth and metastasis formation. Indeed, angiogenesis has to be considered as a fundamental step to the evolution of benign tumors into malignant neoplasms. The main mediator of angiogenesis is vascular endothelial growth factor (VEGF), which is overexpressed in certain cancers. Thus, there are anti-VEGF monoclonal antibodies, such as bevacizumab, used as anti-cancer therapies. However, bevacizumab has shown adverse events, such as hypertension and proteinuria, which in the most severe cases can lead to cessation of therapy, thus contributing to worsening patients’ prognosis. On the other hand, endostatin is an endogenous protein that strongly inhibits VEGF expression and angiogenesis and shows a better safety profile. Moreover, endostatin has already given promising results on small scale clinical studies. Hence, in this review, we present data supporting the use of endostatin as a replacement for anti-VEGF monoclonal antibodies.
Functional Correlates of Microglial and Astrocytic Activity in Symptomatic Sporadic Alzheimer’s Disease: A CSF/18F-FDG-PET Study
Authors: Bonomi Chiara Giuseppina , Chiaravalloti Agostino , Camedda Riccardo , Ricci Francesco , Mercuri Nicola Biagio , Schillaci Orazio , Koch Giacomo , Martorana Alessandro , Motta Caterina
Published: February 2023

Glial and microglial cells contribute to brain glucose consumption and could actively participate in shaping patterns of brain hypometabolism. Here, we aimed to investigate the association between 18F-fluorodeoxyglucose (18F-FDG) uptake and markers of microglial and astrocytic activity in a cohort of patients with Alzheimer’s Disease (AD). We dosed cerebrospinal fluid (CSF) levels of soluble Triggering Receptor Expressed on Myeloid cells (sTREM2), Glial Fibrillary Acidic Protein (GFAP), a marker of reactive astrogliosis, and β-S100, a calcium-binding protein associated with a neurotoxic astrocytic profile. No associations were found between sTREM-2 and 18F-FDG uptake. Instead, 18F-FDG uptake was associated negatively with CSF β-S100 in the left supramarginal gyrus, inferior parietal lobe and middle temporal gyrus (Brodmann Areas (BA) 21 and 40). Increased β-S100 levels could negatively regulate neuronal activity in the temporo-parietal cortex to prevent damage associated with AD hyperactivity, or rather they could reflect neurotoxic astrocytic activation contributing to AD progression in key strategic areas. We also identified a trend of positive association of 18F-FDG uptake with CSF GFAP in the right fronto-medial and precentral gyri (BA 6, 9 and 11), which has been reported in early AD and could either be persisting as an epiphenomenon tied to disease progression or be specifically aimed at preserving functions in the frontal cortex. Overall, CSF markers of astrogliosis seem to correlate with cortical glucose uptake in symptomatic sporadic AD, highlighting the role of astrocytes in shaping regional hypometabolism and possibly clinical presentation.
Rational Design of High-Performance Hemithioindigo-Based Photoswitchable AIE Photosensitizer and Enabling Reversible Control Singlet Oxygen Generation
Authors: Wang Junjie , Wei Jianshuang , Leng Yuehong , Dai Yanfeng , Xie Changqiang , Zhang Zhihong , Zhu Mingqiang , Peng Xingzhou
Published: February 2023

A photosensitizer furnishing with reversible control singlet oxygen generation (1O2) is highly desirable for precise photodynamic therapy (PDT), lessening non-specific harm to healthy tissues. Here, a novel photoswitchable aggregation-induced emission (AIE) photosensitizer based on a triarylamine (TPA)-modified hemithioindigo (HTI), 6Br-HTI-TPA-OMe, was rationally designed. The triarylamine AIE photosensitizing moiety and HTI switch unit were covalently linked in one molecule, permitting reversible regulation of 1O2 production. The photophysical evaluations revealed that 6Br-HTI-TPA-OMe possessed excellent AIE properties and Z/E photoswitch performance in different solvents. Additionally, the amphiphilic phospholipid-fabricated nanoparticles (NPs) also exhibited photochromic behavior in water. The Z-NPs initiated the generation of 1O2 upon 520 nm light-emitting diode (LED) irradiation, but after switching to E-NPs, the generation of 1O2 was inhibited by the competitive energy transfer, suggesting that reversible Z/E isomerization could photocontrol 1O2 generation. The in vitro anti-tumor experiment verified that the 6Br-HTI-TPA-OMe can act as a photoswitchable AIE photosensitizer. This is the first report on the photoswitchable AIE photosensitizer of HTI-based molecules, to the best of our knowledge.
Different Strategies for the Microfluidic Purification of Antibiotics from Food: A Comparative Study
Authors: Lunelli Lorenzo , Germanis Martina , Vanzetti Lia , Potrich Cristina
Published: February 2023

The presence of residual antibiotics in food is increasingly emerging as a worrying risk for human health both for the possible direct toxicity and for the development of antibiotic-resistant bacteria. In the context of food safety, new methods based on microfluidics could offer better performance, providing improved rapidity, portability and sustainability, being more cost effective and easy to use. Here, a microfluidic method based on the use of magnetic microbeads specifically functionalized and inserted in polymeric microchambers is proposed. The microbeads are functionalized either with aptamers, antibodies or small functional groups able to interact with specific antibiotics. The setup of these different strategies as well as the performance of the different functionalizations are carefully evaluated and compared. The most promising results are obtained employing the functionalization with aptamers, which are able not only to capture and release almost all tetracycline present in the initial sample but also to deliver an enriched and simplified solution of antibiotic. These solutions of purified antibiotics are particularly suitable for further analyses, for example, with innovative methods, such as label-free detection. On the contrary, the on-chip process based on antibodies could capture only partially the antibiotics, as well as the protocol based on beads functionalized with small groups specific for sulfonamides. Therefore, the on-chip purification with aptamers combined with new portable detection systems opens new possibilities for the development of sensors in the field of food safety.
Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques
Authors: Beeram Reshma , Vepa Kameswara Rao , Soma Venugopal Rao
Published: February 2023

Surface-enhanced Raman spectroscopy/scattering (SERS) has evolved into a popular tool for applications in biology and medicine owing to its ease-of-use, non-destructive, and label-free approach. Advances in plasmonics and instrumentation have enabled the realization of SERS’s full potential for the trace detection of biomolecules, disease diagnostics, and monitoring. We provide a brief review on the recent developments in the SERS technique for biosensing applications, with a particular focus on machine learning techniques used for the same. Initially, the article discusses the need for plasmonic sensors in biology and the advantage of SERS over existing techniques. In the later sections, the applications are organized as SERS-based biosensing for disease diagnosis focusing on cancer identification and respiratory diseases, including the recent SARS-CoV-2 detection. We then discuss progress in sensing microorganisms, such as bacteria, with a particular focus on plasmonic sensors for detecting biohazardous materials in view of homeland security. At the end of the article, we focus on machine learning techniques for the (a) identification, (b) classification, and (c) quantification in SERS for biology applications. The review covers the work from 2010 onwards, and the language is simplified to suit the needs of the interdisciplinary audience.
MIPs–SERS Sensor Based on Ag NPs Film for Selective Detection of Enrofloxacin in Food
Authors: Neng Jing , Wang Yazhi , Zhang Yilong , Chen Peng , Yang Kai
Published: February 2023

The quinolone antibiotics represented by enrofloxacin (ENRO) are harmful to the ecological environment and human health due to illegal excessive use, resulting in increasing food residues and ENRO levels in the environment. To this end, we developed a MIPs–SERS method using surface-enhanced Raman spectroscopy (SERS) and molecularly imprinted polymers (MIPs) to detect ENRO in food matrices. Firstly, a layer of silver nanoparticles (Ag NPs) with the best SERS effect was synthesized on the surface of copper rods as the enhancing material by in situ reductions, and then MIPs targeting ENRO were prepared by the native polymerization reaction, and the MIPs containing template molecules wrapped on the surface of silver nanoparticle films (Ag NPs–MIPs) were obtained. Our results showed that the Ag NPs–MIPs could specifically identify ENRO from the complex environment. The minimum detection limit for ENRO was 0.25 ng/mL, and the characteristic peak intensity of ENRO was linearly correlated to the concentration with a linear range of 0.001~0.1 μg/mL. The experimental results showed that in comparison to other detection methods, the rapid detection of ENRO in food matrices using Ag NPs–MIPs as the substrate is reliable and offers a cost-effective, time-saving, highly selective, and sensitive method for detecting ENRO residues in real food samples.
Experimental Evidence of the Coupling between the Energy Charge–Discharge and Natural Ventilation in a Full-Scale Passive Building
Authors: Chen Austin Miguel , Bouzouidja Ryad , Bruneau Denis , Sempey Alain , Vogt Wu Tingting , Mora Laurent
Published: February 2023

An experimental study was carried out on a full-scale passive building equipped with global and local instrumentation with the aim of characterizing the energy charge and discharge processes and their coupling to the outdoor thermal stresses, especially natural ventilation in the summertime. The characterization of both charge and discharge processes is necessary to understand the thermal behavior of a building. From the measurement campaigns, the coupling between the charge–discharge and natural ventilation was qualitatively highlighted by the dynamics of the convective heat flux at each instrumented surface of the envelope. From the analysis of the experimental data, during the charge process, the floor and ceiling play an essential role in the attenuation of the rising of the indoor air temperature, while the south-facing glazed facade contributes the most to the heating of the indoor air. During the discharge process, the floor surface also plays an important role in the discharge of the envelope elements since the floor is the colder surface of the envelope. A parametric analysis of the coupling was also performed and showed that it is necessary to consider the convective effects on the slab surface, the ceiling, and the glazed facade individually due to their behavior.
Ionization Air Purifying Lattice Prototype
Authors: Atancuri Manuel Armando , Cobos-Torres Juan-Carlos
Published: February 2023

Environmental pollution caused by human activities, the harmful mixture of gases (CO, CO2, NO2, SO2, PM), resulting from industrial production processes and vehicular traffic is a global environmental health problem in urban and rural areas; it affects vulnerable groups, children and older adults, affecting respiratory, cardiovascular and cancerous diseases. In Ecuador, in the city of Cuenca, among the main sources of contamination is the vehicle fleet in high-traffic areas and avenues; in addition to the industrial zone, that coexists with the city within the urban area in the northwest zone, which generates contamination to properties located around these strips. This paper presents the development of a purifying element based on the “evolutionary development” model. It begins by analyzing and studying concepts of lattices, ionization, and air purification, among others; subsequently, existing air purification systems are investigated in detail and a prototype of an ionizing lattice is generated, whose function is to filter polluted air when installed as an aesthetic part in buildings. Finally, the purification performance of gases and particles typical of the electrostatic precipitator (ESP) was tested by means of air quality stations. By means of a type of correlational applied research, the similarity between external and internal gases and particulate matter was verified, obtaining, as a result, the effective reduction of the same; decreasing by 4.15% of CO2, 30% of CO and 57% of particulate matter. With this analysis, it is concluded that the prototype of the ionization air purifying lattice can be efficient with some of the gases against particulate pollution in domestic interiors, positively influencing the health of the inhabitants of a building.
Experimental Performance and Cost-Effectiveness of a Combined Heating System under Saharan Climate
Authors: Bekkouche Sidi Mohammed El Amine , Djeffal Rachid , Cherier Mohamed Kamal , Hamdani Maamar , Younsi Zohir , Al-Saadi Saleh , Zaiani Mohamed
Published: February 2023

The solar water heater can be integrated into future residential buildings as the main energy source, which will subsequently reduce the energy cost of water heating. An original configuration for an efficient Domestic Hot Water (DHW) storage tank is developed and experimentally evaluated under Saharan climate. This novel DHW configuration includes a hybrid (solar and electric) energy system with a flat plate solar collector coupled with an electric heater. Additionally, a phase change material (PCM) mixture that is composed of paraffin wax and animal fat with a melting temperature between 35.58 °C and 62.58 °C and latent heat between 180 and 210 kJ/kg is integrated into this novel tank configuration. The experimental results indicated that hot water production by using latent heat storage could be economically attractive. By evaluating the cost of water heating expressed in Algerian dinar per liter (DZD/L), it was found that one liter of hot water may cost around 0.1362 DZD/L (i.e., 0.00096 USD/L) compared to 0.4431 DZD/L for the conventional water heater, an average energy cost savings of 69.26%. On a yearly basis, the average energy cost savings may reach up to 80.25% if optimal tilt for the solar collector is adopted on a monthly basis. The flat plate collector may be vulnerable to convective heat transfer; therefore, other solar collectors, such as vacuum tube collectors, may provide enhanced energy performance.
Wind Resistance Performance of Large-Scale Glass Curtain Walls Supported by a High-Rise Building
Authors: Chen Bo , Jiang Linfei , Zhang Lu , Yue Weiliang , Yang Handi , Yu Hongliang
Published: February 2023

A large-scale glass curtain wall (LGCW) attached to a high-rise building is analyzed using the finite element method to investigate the wind resistance performance of the LGCW with and without the high-rise building. The results show that without the high-rise building, the peak wind-induced response occurs in the center of each glass panel of the LGCW, and it gradually decreases away from the center towards the edges of each glass panel. When the high-rise building is included in the finite element model, the additional wind-induced response on the LGCW caused by the deformation of the high-rise building is large at the upper and lower glass panel edges, and gradually decreases toward the panel center. The high-rise building produces great effects on the displacements of the LGCW but weak effects on the stresses, where the peak displacement of the whole LGCW is increased by 40.5%. The influences of key structural parameters, including the lateral stiffness of the high-rise building and the connection stiffness between the large glass curtain wall and the high-rise building, on the wind resistance performance of the LGCW are further investigated. The results demonstrate that the smaller the lateral stiffness of the high-rise building is, the greater the additional responses caused by the deformation of the high-rise building on the LGCW are, and the greater the total load responses of the LGCW are. The smaller the connection stiffness between the LGCW and the high-rise building is, the greater the responses of the independent LGCW are, while the additional responses induced by the deformation of the high-rise building on the LGCW are not significant.
Consumer Behavior and Sustainability in the Construction Industry—Relevance of Sustainability-Related Criteria in Purchasing Decision
Authors: Weniger Alexandra , Del Rosario Pamela , Backes Jana Gerta , Traverso Marzia
Published: February 2023

The construction industry significantly contributes to global greenhouse gas emissions and consumption of raw materials, and is thus under intense pressure to become more sustainable. Despite the ever-increasing availability of sustainable solutions, they do not seem to be widely adopted. As key decision makers in the early planning stage of a construction project, end consumers strongly impact sustainable development. However, their knowledge of sustainability-related criteria and their influence on purchasing decisions within the German construction industry is not yet sufficiently well established. Therefore, this study aims to determine the effects of sustainability-related aspects on the purchasing behavior of private individuals, as end consumers in the German construction industry. A quantitative online survey involving 306 participants was utilized to determine their understanding of the term “sustainability” and how they identify and measure the sustainability of a building product. Our results indicate that consumers have a fundamental interest in sustainable building products; however, they lack a holistic view of the concept of sustainability. With the focus on environmental aspects, social and economic aspects tend to be neglected. Since private individuals rarely make purchasing decisions in the building product sector, it is the task of the entire construction industry to establish a system in which consumers can quickly and easily obtain understandable information about sustainable products. Consumers need support from experts to understand and drive sustainable development in the construction industry.
Experimental Model for Study of Thickness Effect on Flexural Fatigue Life of Macro-Synthetic-Fiber-Reinforced Concretes
Authors: Daneshfar Mohammad , Hassani Abolfazl , Aliha Mohammad Reza Mohammad , Sadowski Tomasz , Karimi Arastoo
Published: February 2023

As one of the most widely used building materials, concrete has a dominantly brittle or quasi-brittle behavior. Adding fibers to concrete affects its ductility behavior as well as some mechanical properties. Finding the relationship between the addition of fibers and the change in thickness of laboratory test samples made of concrete can help in designing the optimal thickness of real concrete layers (especially concrete pavements) to withstand dynamic loads. The purpose of this research is to provide an experimental model for investigating the effect of concrete specimen size, or the thickness effect of concrete sample, on the fatigue life of concrete. Accordingly, several concrete beams with three thicknesses (80, 100 and 150 mm), constant width, and two lengths (120 mm and 450 mm) were manufactured with fiber percentages of 0 and 4% by fraction volume. The employed fiber was twisted macro synthetic fiber. After curing for 28 days, the samples were subjected to fatigue loading at three stress levels until the onset of failure and cracking stage. Here, the experimental model of the relationship between the number of loading cycles, the stress level and the thickness of the sample is presented. The results show that increasing the specimen thickness and fiber content can enhance the fatigue life of concrete up to 68%.