bluuebunny/crossref_metadata_2025 huggingface dataset:
UNDERSTANDING THE IMPACT OF SOCIAL INFLUENCE ON ENGAGEMENT IN IOT SPORTS TECHNOLOGY: A MEDIATION ANALYSIS
Authors: Khalid Samhale  , Soukaina Samhale
Published: December 2023

The Internet of Things (IoT) is a major revolution that is transforming the world of sports and enabling athletes to improve their physical performance. The use of IoT devices for sports is based primarily on the sharing of athletes' achievements online and the interaction between them, which allows them to motivate and encourage each other to improve their physical performance. Our research analyzes the influence of family and friends (INF_SOC) on engagement (ENGAG) in the ongoing and long-term use of these IoT devices and studies the mediating effect of perceived control (CP), perceived provider credibility (CRD), and trust (CONF) in this relationship. The population is the athletes who use IoT to measure their performance. Convenience sampling was deployed to collect the data using a questionnaire. 313 respondents participated in this study. To analyze our theoretical model, we used the method of partial least squares under SmartPls. The results confirmed all the theoretical hypotheses introduced and validated the mediating effects. They allowed us to generate interesting recommendations for IoT providers and users to reinforce engagement in the use of the Internet of Things for sports.
NOMINATION AND REMUNERATION COMMITTEE AND FIRM PERFORMANCE: EVIDENCE FROM JORDAN
Authors: Ahmad Mohammad Aldegis  , Yuvaraj Ganesan  , Osamah Jamal Alorayni  , Abdullah Mohammed Sadaa
Published: December 2023

The study examines how the composition of the nomination and remuneration committee (NRC) affects firm performance in Jordanian publicly traded companies. The study consists of 68 non-financial public companies listed on the Amman Stock Exchange for the years between 2017 and 2020. This study uses four different statistical models to analyse the panel data including fixed effects, pooled OLS, random effects, and robust standard errors. The findings show that committee size, overlap, and expertise have a positive association with Tobin's Q. On the other hand, the committee's independence, meetings, and female membership have an insignificant impact on Tobin's Q. Also, the committee's independence and size have a positive and significant effect on ROA, while the committee's expertise, meetings, overlaps, and female membership have no significant effect on ROA. This study contributes to the Jordanian corporate governance structure, including the composition of the nomination and remuneration committees, and contributes to the insights of regulators and shareholders into the role of NRC features in a developing market, such as Jordan, and their impact on firm performance. This study focuses on setting up an NRC for non-financial firms, it is suggested that for certain firms, but not all public corporations in Jordan and other developing countries. Therefore, exploring whether similar findings would be achieved if financial firms were required to establish such a committee would be a topic of interest for researchers.
SMES’ ENERGY EFFICIENCY IMPROVEMENT: THE ROLES OF KNOWLEDGE, GOVERNMENT INTERVENTION AND PERSONAL NORMS
Authors: Helen SH Lee  , Lean Hooi Hooi
Published: December 2023

Energy efficiency is like the ‘low hanging fruit’ in which improving energy efficiency is one of the easiest ways to lessen energy demand. Firms, large or small, can make a major positive contribution to energy sustainability by improving energy efficiency. This research attempts to examine the effects of SMEs’ perceptions in terms of knowledge, government intervention and personal norms on their support for energy efficiency improvement. A survey was carried out to collect data and empirical testing of the research model was conducted. The estimated results suggest that the factors of knowledge and government intervention have significant effects on SMEs’ support for energy efficiency improvement. In addition, the results show that personal norms is an important mediator variable in influencing SMEs’ decision to improve energy efficiency. The findings of this research provide some fresh insights on energy efficiency improvement of SMEs and several notable implications can be drawn from this research.
Jungle Within Urban Context
Authors: Irvin Roman Tan
Published: December 2023

The scheme emphasizes on quality living in the air through greenery within the site.  The carbon content transfer also allows the residents to enjoy quality of life with a more sustainable lifestyle.  Spaces within and outside the building allows the jungle experience within the site.
A geometric analysis of slip rate variation with depth in listric normal faults
Authors: Zhang Yao
Published: December 2023

Fault slip rates are critical parameters for assessing regional strain accumulation and seismic hazards. Previous investigations on fault slip rates primarily concentrated on shallow depths or along the strike of the fault, neglecting the variation with depth. This study focuses on listric normal faults, commonly observed in tectonic extensional zones, and investigates the variation of slip rates with depth. The relationships between slip rates along different fault segments are derived based on the inclined shear geometric models. The study finds that slip rates on different segments of listric normal faults are generally not equal and depend on the type of bend (concave or convex), the dip angles of the fault segments and axial surfaces. Inferring regional horizontal extension solely from shallow segment displacements or growth strata thickness may lead to inaccurate conclusions. In accordance with the methodology outlined in this paper, slip rates at various depths along the Chengnan Fault for the three time intervals: 24.6–33 Ma, 33–43.5 Ma, and 43.5–65 Ma, are estimated. The results of this study offer valuable insights into the kinematics of listric normal faults, facilitating a better comprehension of the discrepancy between slip rates measured at surface and slip rates measured at depth.
Quantitative characterization and analysis of pore-fractures in tar-rich coal under high-temperature pyrolysis based on micro-CT imaging
Authors: Yang Fu , Jiang Pengfei , Duan Zhonghui , Cheng Zhongyan , Wang Zhendong
Published: December 2023

This study investigates pore distribution and permeability behavior of tar-rich coal following high-temperature pyrolysis at 500°C using X-ray computed tomography (CT) scanning. Coal samples post-pyrolysis were CT scanned, generating 1755 cross-sectional slices for three-dimensional reconstruction. An axial algorithm extracted pore distribution features, and geometric parameters were computed. An Equivalent Pore Network Model analyzed permeability characteristics. The results show that Post-pyrolysis pore distribution in tar-rich coal exhibited nonuniformity with a significant range in pore size distribution. Pores displayed concentrated spatial patterns. Total porosity was 14.24%, with 12.34% being connected. Most pores in Representative Elementary Volume (REV) regions fell within 10–50 μm in width and 20–60 μm in length, constituting over 40% of the total. Pore surface area peaked between 200–100 μm2, also comprising over 40% of the total. The Pore Network Model showed distinct characteristics in two REV regions: REV-1 demonstrated an early stage of development with poor connectivity, while REV-2 displayed a well-developed network with a bimodal coordination number histogram. The study highlights nonuniform post-pyrolysis pore distribution and significant pore size variations in tar-rich coal. This study is crucial for understanding permeability behavior in tar-rich coal after high-temperature pyrolysis.
Liquefaction test and seismic load simulation of saturated sand subgrade with different salt contents
Authors: Zhang Lei , Qiao Hongxia , Shi HongZhuang , Ma Weizhong , Song Shanglin , Wang Hanghang
Published: December 2023

On the basis of the highway subgrade construction of North Hobson Salt Lake mining area in Qinghai Province, the soil samples of high saline saturated sand adjacent to the salt lake were remolded, and four types of saturated sand containing 0%, 10%, 20%, and 30% salt were configured respectively, so as to explore the liquefaction effect of saline saturated sand under seismic load. After the indoor geotechnical test, the soil samples were reshaped, The liquefaction test of saline saturated sand was carried out by using the GDS dynamic triaxial device to simulate seismic load to investigate the liquefaction mechanism of saturated sand under different salt contents and verify the liquefaction discrimination standard, then based on the data of dynamic elastic modulus, dynamic shear modulus, damping ratio coefficient, and soil density obtained from previous soil tests and dynamic triaxial tests, four subgrade models with different salt contents were constructed using ANSYS simulation software according to actual engineering, and the settlement and deformation of the subgrade were analyzed by inputting EL-concrete seismic waves. The results show that the maximum value of axial stress of saturated sand decreases with the increase of salt content; the load vibration times of saline saturated sand reaching the initial liquefaction state are more than that of common saturated sand; the higher the salt content, the weaker the liquefaction resistance of saturated sand; when the pore pressure ratio coefficient reaches 1, the axial double amplitude strain εd of the soil also reaches 5%, and the soil reaches the initial liquefaction state; the area of shear stress and strain hysteresis curve increases with vibration load, and the elastic modulus decreases gradually; the shear dilation and compaction effects occur in each hysteresis cycle, and the former is greater than the latter, the research results have guiding significance for similar studies in highly saline saturated sand areas.
Dynamic evolution and numerical analysis of rock deformation under impact failure based on corner correlation method
Authors: Zhou Xingui , Ye Zhongbao , Miao Hong , Li Wei , Wang Xueru , Li Chenfeng
Published: December 2023

To fulfill the requirement for deformation measurement within the failure area during rock failure, a newly developed digital image algorithm, known as the corner correlation method, was implemented. A corner correlation measurement system was established by utilizing a Split Hopkinson Pressure Bar (SHPB) and a high-speed camera. The study focused on monitoring the deformation and failure characteristics of sandstone samples under dynamic loading. The results show that the corner correlation method has its unique advantages in rock dynamic mechanics experiments, and can obtain the deformation of the failure region during the rock failure process. Specifically, the initiation, extension, and contraction processes of surface cracks on sandstone were examined. Parameters such as crack width, width propagation rate, and extension shrinkage rate were measured. The entire crack development process was analyzed, including crack width, crack initiation point, crack extension and contraction trajectory, elongation and contraction velocity, width expansion rate, and longitudinal crack penetration, which were obtained at any given time on the surface of the rock sample. Additionally, the attenuation law of stress, characterized by an exponential decay of the stress peak value, was obtained through numerical simulation using a similar model.
Study of a high-precision complex 3D geological modelling method based on a fine KNN and kriging coupling algorithm: a case study for Jiangsu, China
Authors: Liu Xiaozheng , Zhang Peng , Guo Yakun , Ma Guotao , Liu Ming
Published: December 2023

A high-precision, complex, three-dimensional (3D) geological model can directly express the attributes of stratum thickness, geological structure, lithology and spatial form, which can provide a reliable basis for the development and utilization of underground space and planning decisions. However, it is difficult to perform accurate modelling due to the lack of basic data. As such, this paper proposes coupling a machine learning algorithm (K-nearest neighbour (KNN)) with the kriging algorithm to construct the topological relationship between the Delaunay triangle and the Thiessen polygon in order to perform the simulation and prediction of virtual drilling. Based on KNN, support vector machine (SVM) and neural network algorithms as well as the virtual borehole encryption data, data standardization processing and analysis are carried out. Through model verification, algorithm optimization is realized, and the optimal modelling method is explored. The results show that the fine KNN algorithm improved by Bayesian optimization can effectively improve the modelling accuracy through 0.1-m encryption, standardization processing and 5-fold cross-validation. Stratum modelling combined with the fine KNN and kriging algorithms can obtain a more accurate modelling without adding virtual boreholes. The improved levels of upper and lower hybrid modelling with an appropriate number of profile boreholes can also effectively optimize model accuracy. Both modelling accuracy and efficiency can be significantly improved by using Delaunay triangles and Thiessen polygons with virtual boreholes. Stratum modelling can effectively express the geological pinch-out in areas with adequate degrees of stratification, and hybrid modelling performs well in irregular geological bodies such as karsts and lenses.
Experimental study on gas and coal dust explosive overpressure and flame dynamic characteristics in an engineering-level test roadway
Authors: Niu Yihui , Si Rongjun , Li Ziran , Wang Lei , Huang Zichao , Jia Quansheng
Published: December 2023

The continuous development of coal science and technology has made gas and coal dust explosion disasters an important factor that restricts efficient and intelligent coal mining, which seriously threatens the safe production process of coal mines. To explore the gas and coal dust explosive overpressure and flame propagation characteristics in an actual roadway, the dynamic characteristics of gas and coal dust mixed explosion propagation and evolution laws of explosion flames were investigated using an integrated explosion test system and a high-speed image acquisition system in an engineering-level test roadway with a length of about 700 m and a cross-sectional area of 7.2 m2. Experimental results showed that the peak overpressure measured at each measuring point during the propagation process of explosion shock wave in the roadway did not rise or fall monotonously but fluctuated. The power of explosion shock wave was significantly strengthened by adding coal dust, while the flame propagation speed sharply increased in a certain zone, which generally showed a first increasing and then declining trend. In addition, the flame was blue white after the gas in the roadway was ignited, developed in an irregular shape, and ignited the surrounding combustible gas soon, which further ignited the coal dust under the combined action of pressure wave and flame front. In this case, the flame was deep yellow on the whole. The gas and coal dust explosion flame propagated along the longitudinal section above the roadway, and the flame propagated at an accelerated speed on the transverse section due to the disturbance of obstacles. The study results will provide an important theoretical basis for the R&D of technical active explosion suppression equipment in coal mines and the improvement in their installation technologies.
Smart grid power load type forecasting: research on optimization methods of deep learning models
Authors: Sun Huadong , Ren Yonghao , Wang Shanshan , Zhao Bing , Yin Rui
Published: December 2023

Introduction: In the field of power systems, power load type prediction is a crucial task. Different types of loads, such as domestic, industrial, commercial, etc., have different energy consumption patterns. Therefore, accurate prediction of load types can help the power system better plan power supply strategies to improve energy utilization and stability. However, this task faces multiple challenges, including the complex topology of the power system, the diversity of time series data, and the correlation between data. With the rapid development of deep learning methods, researchers are beginning to leverage these powerful techniques to address this challenge. This study aims to explore how to optimize deep learning models to improve the accuracy of load type prediction and provide support for efficient energy management and optimization of smart grids.

Methods: In this study, we propose a deep learning method that combines graph convolutional networks (GCN) and sequence-to-sequence (Seq2Seq) models and introduces an attention mechanism. The methodology involves multiple steps: first, we use the GCN encoder to process the topological structure information of the power system and encode node features into a graph data representation. Next, the Seq2Seq decoder takes the historical time series data as the input sequence and generates a prediction sequence of the load type. We then introduced an attention mechanism, which allows the model to dynamically adjust its attention to input data and better capture the relationship between time series data and graph data.

Results: We conducted extensive experimental validation on four different datasets, including the National Grid Electricity Load Dataset, the Canadian Electricity Load Dataset, the United States Electricity Load Dataset, and the International Electricity Load Dataset. Experimental results show that our method achieves significant improvements in load type prediction tasks. It exhibits higher accuracy and robustness compared to traditional methods and single deep learning models. Our approach demonstrates advantages in improving load type prediction accuracy, providing strong support for the future development of the power system.

Discussion: The results of our study highlight the potential of deep learning techniques, specifically the combination of GCN and Seq2Seq models with attention mechanisms, in addressing the challenges of load type prediction in power systems. By improving prediction accuracy and robustness, our approach can contribute to more efficient energy management and the optimization of smart grids.
Does activation of oxytocinergic reward circuits postpone the decline of the aging brain?
Authors: Buemann Benjamin
Published: December 2023

Oxytocin supports reproduction by promoting sexual- and nursing behavior. Moreover, it stimulates reproductive organs by different avenues. Oxytocin is released to the blood from terminals of oxytocinergic neurons which project from the hypothalamus to the pituitary gland. Concomitantly, the dendrites of these neurons discharge oxytocin into neighboring areas of the hypothalamus. At this location it affects other neuroendocrine systems by autocrine and paracrine mechanisms. Moreover, sensory processing, affective functions, and reward circuits are influenced by oxytocinergic neurons that reach different sites in the brain. In addition to its facilitating impact on various aspects of reproduction, oxytocin is revealed to possess significant anti-inflammatory, restoring, and tranquilizing properties. This has been demonstrated both in many in-vivo and in-vitro studies. The oxytocin system may therefore have the capacity to alleviate detrimental physiological- and mental stress reactions. Thus, high levels of endogenous oxytocin may counteract inadequate inflammation and malfunctioning of neurons and supportive cells in the brain. A persistent low-grade inflammation increasing with age—referred to as inflammaging—may lead to a cognitive decline but may also predispose to neurodegenerative diseases such as Alzheimer’s and Parkinson. Interestingly, animal studies indicate that age-related destructive processes in the body can be postponed by techniques that preserve immune- and stem cell functions in the hypothalamus. It is argued in this article that sexual activity—by its stimulating impact on the oxytocinergic activity in many regions of the brain—has the capacity to delay the onset of age-related cerebral decay. This may also postpone frailty and age-associated diseases in the body. Finally, oxytocin possesses neuroplastic properties that may be applied to expand sexual reward. The release of oxytocin may therefore be further potentiated by learning processes that involves oxytocin itself. It may therefore be profitable to raise the consciousness about the potential health benefits of sexual activity particularly among the seniors.
Considering the food environment can help to promote the consumption of aquatic foods for healthy diets
Authors: Kennedy Gina , Ahern Molly B. , Iannotti Lora L. , Vie Sydney , Sherburne Lisa , Thilsted Shakuntala H.
Published: December 2023

Aquatic foods ensure food and nutrition security for billions of consumers around the world. As part of food systems, aquatic foods provide nutritious, affordable, convenient options for healthy diets, and can also foster sustainable food production. Within the food system framework, the food environment is the space that connects food procurement to consumption. The food environment influences consumer decisions on which foods to acquire. To date there has been relatively little focus on creating an enabling food environment that supports consumers in decisions to obtain aquatic foods. To fill this gap, we conducted a narrative review of literature from 2000–2020 to document the availability, affordability, convenience, promotion, quality and sustainability of aquatic foods within diverse food environments. Our review highlighted several opportunities that can support development and promotion of convenient, high quality aquatic foods. We also noted several research gaps. For example, some consumers, especially those in high income countries, respond well to labels related to sustainability and also to messaging to consume diverse types of fish, especially lower tropic species like anchovy. However, less is documented on how promotion influences consumers from LMIC. The paper also notes a gap in assessment of the price and affordability of aquatic foods. Most price and affordability assessments do not provide details on which aquatic foods were considered in the costing assessment. In addition, wild or home-harvested aquatic foods are often not accounted for in price and affordability assessments. Using case studies, we demonstrate how considering the food environment in research and implementation strategies can add value to program design. For example, processing tuna frames and underutilized small fish species into powder is one innovation that reduces food waste and also creates a convenient, quality product. These results provide the foundation for deepening our understanding of how key elements of the food environment influence consumers’ decision-making and how these elements can be considered in future research, programming and policy efforts.
Turbulent Flow Heat Transfer and Thermal Stress Improvement of Gas Turbine Blade Trailing Edge Cooling with Diamond-Type TPMS Structure
Authors: Yeranee Kirttayoth , Rao Yu , Xu Chao , Zhang Yueliang , Su Xiyuan
Published: December 2023

Additive manufacturing allows the fabrication of relatively complex cooling structures, such as triply periodic minimal surface (TPMS), which offers high heat transfer per unit volume. This study shows the turbulent flow heat transfer and thermal stress of the Diamond-TPMS topology in the gas turbine blade trailing edge channel. The thermal-fluid-solid analysis of the Diamond-TPMS structure, made of directionally solidified GTD111, at the nearly realistic gas turbine condition is executed, and the results are compared with the conventional pin fin array at the Reynolds number of 30,000. Compared to the baseline pin fin structure, the Diamond-TPMS model distributes flow characteristics more uniformly throughout the channel. The overall heat transfer enhancement, friction factor ratio, and thermal performance are increased by 145.3%, 200.9%, and 32.5%, respectively. The temperature, displacement, and thermal stress in the Diamond-TPMS model are also distributed more evenly. The average temperature on the external surface in the Diamond-TPMS model is lower than the baseline pin fin array by 19.9%. The Diamond-TPMS network in the wedge-shaped cooling channel helps reduce the volume displacement due to the material thermal expansion by 29.3%. Moreover, the volume-averaged von Mises stress in the Diamond-TPMS structure is decreased by 28.8%.
Testing the Impact of Robotic Lawn Mowers on European Hedgehogs (Erinaceus europaeus) and Designing a Safety Test
Authors: Rasmussen Sophie Lund , Schrøder Bettina Thuland , Berger Anne , Sollmann Rahel , Macdonald David W. , Pertoldi Cino , Alstrup Aage Kristian Olsen
Published: December 2023

Previous research has established that some models of robotic lawn mowers are potentially harmful to hedgehogs. As the market for robotic lawn mowers is expanding rapidly and the populations of European hedgehogs (Erinaceus europaeus) are in decline, it is important to investigate this risk further to understand the potential threat which some robotic lawn mowers may pose to hedgehogs. We tested 19 models of robotic lawn mowers in collision with hedgehog cadavers to measure their effect on hedgehogs. Our results showed that some models of robotic lawn mowers may injure hedgehogs, whereas others are not harmful to them. Apart from one single incidence, all robotic lawn mowers had to physically touch the hedgehog carcasses to detect them. Larger hedgehog cadavers were less likely to be “injured”, with height being the most influential measure of size. The firmness of the tested hedgehog cadavers (frozen or thawed) did not influence the outcome of the collision tests. Neither the position of the hedgehog cadavers nor the selected technical features of the lawn mowers affected the probability of injury. Based on the results, we designed a standardised safety test to measure the effect of a specific model of robotic lawn mower on hedgehogs.
Determining the Presence and Size of Shoulder Lesions in Sows Using Computer Vision
Authors: Bery Shubham , Brown-Brandl Tami M. , Jones Bradley T. , Rohrer Gary A. , Sharma Sudhendu Raj
Published: December 2023

Shoulder sores predominantly arise in breeding sows and often result in untimely culling. Reported prevalence rates vary significantly, spanning between 5% and 50% depending upon the type of crate flooring inside a farm, the animal’s body condition, or an existing injury that causes lameness. These lesions represent not only a welfare concern but also have an economic impact due to the labor needed for treatment and medication. The objective of this study was to evaluate the use of computer vision techniques in detecting and determining the size of shoulder lesions. A Microsoft Kinect V2 camera captured the top-down depth and RGB images of sows in farrowing crates. The RGB images were collected at a resolution of 1920 × 1080. To ensure the best view of the lesions, images were selected with sows lying on their right and left sides with all legs extended. A total of 824 RGB images from 70 sows with lesions at various stages of development were identified and annotated. Three deep learning-based object detection models, YOLOv5, YOLOv8, and Faster-RCNN, pre-trained with the COCO and ImageNet datasets, were implemented to localize the lesion area. YOLOv5 was the best predictor as it was able to detect lesions with an mAP@0.5 of 0.92. To estimate the lesion area, lesion pixel segmentation was carried out on the localized region using traditional image processing techniques like Otsu’s binarization and adaptive thresholding alongside DL-based segmentation models based on U-Net architecture. In conclusion, this study demonstrates the potential of computer vision techniques in effectively detecting and assessing the size of shoulder lesions in breeding sows, providing a promising avenue for improving sow welfare and reducing economic losses.
Precipitation Extremes over India in a Coupled Land–Atmosphere Regional Climate Model: Influence of the Land Surface Model and Domain Extent
Authors: Mishra Alok Kumar , Dinesh Anand Singh , Kumari Amita , Pandey Lokesh Kumar
Published: December 2023

The frequency and intensity of extreme precipitation events are on the rise worldwide. Despite extensive efforts, regional climate models still show significant biases for extreme precipitation events, often due to factors like improper physics, the choice of land surface model, and spatial domain. Thus, this study uses a Coupled Land–Atmosphere Regional Climate Model version 4.7 (RegCM4.7) to explore how the choice of land surface models (LSMs) and domain extent affects the simulation of extreme precipitation over India. In this regard, a total of four sensitivity experiments have been carried out using two LSMs (CLM4.5 and BATS) over each of the two domains (one over the bigger South Asia CORDEX domain and another for the smaller domain over the Indian region). The main objective is to provide a holistic idea for obtaining an optimum model domain and LSMs for precipitation extremes over India. The model performance is demonstrated for extreme precipitation and associated processes. The result shows the systematic discrepancy in simulating extreme precipitation with a strong inter-simulation spread, indicating the strong sensitivity of extreme precipitation on the LSMs as well as the model domain. The BATS configuration shows a significant overestimation of consecutive wet days and very low precipitation, partially associated with a deficiency in convection. By contrast, the considerable underestimation of intense precipitation can be attributed to the presence of frequent, light drizzle, which hinders the accumulation of moisture in the atmosphere to a sufficient degree to prevent extreme rainfall. Despite significant improvement, the best-configured model (CLM with Indian domain) still indicates substantial bias for extreme precipitation. This deficiency in the model could potentially be mitigated by enhancing both horizontal and vertical resolutions. Nevertheless, further research is needed to explore other physics parameterizations and dynamic mechanisms to address this issue.
Particle Number Size Distribution in Three Different Microenvironments of London
Authors: Kalaiarasan Gopinath , Kumar Prashant , Tomson Mamatha , Zavala-Reyes Juan C. , Porter Alexandra E. , Young Gloria , Sephton Mark A. , Abubakar-Waziri Hisham , Pain Christopher C. , Adcock Ian M. , Mumby Sharon , Dilliway Claire , Fang Fangxing , Arcucci Rossella , Chung Kian Fan
Published: December 2023

We estimated the particle number distributions (PNDs), particle number concentrations (PNCs), physicochemical characteristics, meteorological effects, and respiratory deposition doses (RDD) in the human respiratory tract for three different particle modes: nucleation (N6–30), accumulation (N30–300), and coarse (N300–10,000) modes. This study was conducted in three different microenvironments (MEs) in London (indoor, IN; traffic intersection, TI; park, PK) measuring particles in the range of 6 nm–10,000 nm using an electrical low-pressure impactor (ELPI+). Mean PNCs were 1.68 ± 1.03 × 104 #cm−3, 7.00 ± 18.96 × 104 #cm−3, and 0.76 ± 0.95 × 104 #cm−3 at IN, TI, and PK, respectively. The PNDs were high for nucleation-mode particles at the TI site, especially during peak traffic hours. Wind speeds ranging from 0 to 6 ms−1 exhibit higher PNCs for nucleation- and accumulation-mode particles at TI and PK sites. Physicochemical characterisation shows trace metals, including Fe, O, and inorganic elements, that were embedded in a matrix of organic material in some samples. Alveolar RDD was higher for the nucleation and accumulation modes than the coarse-mode particles. The chemical signatures from the physicochemical characterisation indicate the varied sources at different MEs. These findings enhance our understanding of the different particle profiles at each ME and should help devise ways of reducing personal exposure at each ME.
Computational Study of Aerodynamic Effects of the Dihedral and Angle of Attack of Biomimetic Grids Installed on a Mini UAV
Authors: Bardera Rafael , Rodríguez-Sevillano Ángel Antonio , Barroso Barderas Estela , Matias Garcia Juan Carlos
Published: December 2023

In this paper, a numerical analysis of a biomimetic unmanned aerial vehicle (UAV) is presented. Its wings feature three grids at the tip similar to the primary feathers of birds in order to modify the lift distribution over the wing and help in reducing the induced drag. Numerical analysis using computational fluid dynamics (CFD) is presented to analyze the aerodynamic effects of the changes in dihedral and angle of attack (with respect of the rest of the wing) of these small grids at the tip. The aerodynamic performances (lift, drag, and efficiency) and rolling capabilities are obtained under different flight conditions. The effects of changing the dihedral are small. However, the change in the grid angle of attack increases aerodynamic efficiency by up to 2.5 times when the UAV is under cruise flight conditions. Changes to the angle of attack of the grids also provide increased capabilities for rolling. Finally, boundary values of the pressure coefficient and non-dimensional velocity contours are presented on the surfaces of the UAV, in order to relate the aerodynamic results to the aerodynamic patterns observed over the wing.
AI-Powered Segmentation of Invasive Carcinoma Regions in Breast Cancer Immunohistochemical Whole-Slide Images
Authors: Liu Yiqing , Zhen Tiantian , Fu Yuqiu , Wang Yizhi , He Yonghong , Han Anjia , Shi Huijuan
Published: December 2023

Aims: The automation of quantitative evaluation for breast immunohistochemistry (IHC) plays a crucial role in reducing the workload of pathologists and enhancing the objectivity of diagnoses. However, current methods face challenges in achieving fully automated immunohistochemistry quantification due to the complexity of segmenting the tumor area into distinct ductal carcinoma in situ (DCIS) and invasive carcinoma (IC) regions. Moreover, the quantitative analysis of immunohistochemistry requires a specific focus on invasive carcinoma regions. Methods and Results: In this study, we propose an innovative approach to automatically identify invasive carcinoma regions in breast cancer immunohistochemistry whole-slide images (WSIs). Our method leverages a neural network that combines multi-scale morphological features with boundary features, enabling precise segmentation of invasive carcinoma regions without the need for additional H&E and P63 staining slides. In addition, we introduced an advanced semi-supervised learning algorithm, allowing efficient training of the model using unlabeled data. To evaluate the effectiveness of our approach, we constructed a dataset consisting of 618 IHC-stained WSIs from 170 cases, including four types of staining (ER, PR, HER2, and Ki-67). Notably, the model demonstrated an impressive intersection over union (IoU) score exceeding 80% on the test set. Furthermore, to ascertain the practical utility of our model in IHC quantitative evaluation, we constructed a fully automated Ki-67 scoring system based on the model’s predictions. Comparative experiments convincingly demonstrated that our system exhibited high consistency with the scores given by experienced pathologists. Conclusions: Our developed model excels in accurately distinguishing between DCIS and invasive carcinoma regions in breast cancer immunohistochemistry WSIs. This method paves the way for a clinically available, fully automated immunohistochemistry quantitative scoring system.
Reconstruction of the Anterior Skull Base Using the Nasoseptal Flap: A Review
Authors: Werner Michael T. , Yeoh Desmond , Fastenberg Judd H. , Chaskes Mark B. , Pollack Aron Z. , Boockvar John A. , Langer David J. , D’Amico Randy S. , Ellis Jason A. , Miles Brett A. , Tong Charles C. L.
Published: December 2023

The nasoseptal flap is a workhorse reconstructive option for anterior skull base defects during endonasal surgery. This paper highlights the versatility of the nasoseptal flap. After providing a brief historical perspective, this review will focus on the relevant primary literature published in the last ten years. We will touch upon new applications of the flap, how the flap has been modified to expand its reach and robustness, and some of the current limitations. We will conclude by discussing what the future holds for improving upon the design and use of the nasoseptal flap in anterior skull base reconstruction.
Validation of a Machine Learning Model to Predict Immunotherapy Response in Head and Neck Squamous Cell Carcinoma
Authors: Lee Andrew Sangho , Valero Cristina , Yoo Seong-keun , Vos Joris L. , Chowell Diego , Morris Luc G. T.
Published: December 2023

Head and neck squamous-cell carcinoma (HNSCC) is a disease with a generally poor prognosis; half of treated patients eventually develop recurrent and/or metastatic (R/M) disease. Patients with R/M HNSCC generally have incurable disease with a median survival of 10 to 15 months. Although immune-checkpoint blockade (ICB) has improved outcomes in patients with R/M HNSCC, identifying patients who are likely to benefit from ICB remains a challenge. Biomarkers in current clinical use include tumor mutational burden and immunohistochemistry for programmed death-ligand 1, both of which have only modest predictive power. Machine learning (ML) has the potential to aid in clinical decision-making as an approach to estimate a tumor’s likelihood of response or a patient’s likelihood of experiencing clinical benefit from therapies such as ICB. Previously, we described a random forest ML model that had value in predicting ICB response using 11 or 16 clinical, laboratory, and genomic features in a pan-cancer development cohort. However, its applicability to certain cancer types, such as HNSCC, has been unknown, due to a lack of cancer-type-specific validation. Here, we present the first validation of a random forest ML tool to predict the likelihood of ICB response in patients with R/M HNSCC. The tool had adequate predictive power for tumor response (area under the receiver operating characteristic curve = 0.65) and was able to stratify patients by overall (HR = 0.53 [95% CI 0.29–0.99], p = 0.045) and progression-free (HR = 0.49 [95% CI 0.27–0.87], p = 0.016) survival. The overall accuracy was 0.72. Our study validates an ML predictor in HNSCC, demonstrating promising performance in a novel cohort of patients. Further studies are needed to validate the generalizability of this algorithm in larger patient samples from additional multi-institutional contexts.
Deciphering Acute Myeloid Leukemia Associated Transcription Factors in Human Primary CD34+ Hematopoietic Stem/Progenitor Cells
Authors: Kreissig Sophie , Windisch Roland , Wichmann Christian
Published: December 2023

Hemato-oncological diseases account for nearly 10% of all malignancies and can be classified into leukemia, lymphoma, myeloproliferative diseases, and myelodysplastic syndromes. The causes and prognosis of these disease entities are highly variable. Most entities are not permanently controllable and ultimately lead to the patient’s death. At the molecular level, recurrent mutations including chromosomal translocations initiate the transformation from normal stem-/progenitor cells into malignant blasts finally floating the patient’s bone marrow and blood system. In acute myeloid leukemia (AML), the so-called master transcription factors such as RUNX1, KMT2A, and HOX are frequently disrupted by chromosomal translocations, resulting in neomorphic oncogenic fusion genes. Triggering ex vivo expansion of primary human CD34+ stem/progenitor cells represents a distinct characteristic of such chimeric AML transcription factors. Regarding oncogenic mechanisms of AML, most studies focus on murine models. However, due to biological differences between mice and humans, findings are only partly transferable. This review focuses on the genetic manipulation of human CD34+ primary hematopoietic stem/progenitor cells derived from healthy donors to model acute myeloid leukemia cell growth. Analysis of defined single- or multi-hit human cellular AML models will elucidate molecular mechanisms of the development, maintenance, and potential molecular intervention strategies to counteract malignant human AML blast cell growth.
Physical Activity Is Associated with Physical Fitness and Executive Function among School Children in the Jiangxi Region of China
Authors: Wu Renle , Kong Sunga , Kang Suh-Jung
Published: December 2023

Previous studies have suggested that a positive relationship exists between physical activity (PA), physical fitness, and cognitive function in children and adolescents. However, research remains limited on the relationship among these three variables in Chinese individuals not living in big cities or specific regions. Therefore, this study investigated the association of PA with physical fitness and cognitive function (specifically, executive function) among 1100 children aged 9–12 years living in the Jiangxi region of China. Participants’ PA levels were measured using the PA questionnaire for older children. Physical fitness was assessed based on cardiorespiratory endurance, muscle strength, muscular endurance, flexibility, and body mass index (BMI). Executive function was assessed using the Behavior Rating in Inventory of Executive Function questionnaire. Data were analyzed using one-way analyses of variance, Scheffé tests, one-way analyses of covariance, and Pearson’s correlation coefficients. The results showed that PA is significantly associated (p < 0.01) with cardiorespiratory endurance (r = 0.460), muscular endurance (r = 0.270), muscle strength (r = 0.190), BMI (r = −0.114), and executive function (r = −0.140). Moreover, all components of physical fitness and executive function differed significantly based on PA level, with and without age and sex as covariates (p < 0.05). Overall, our results highlight the importance of higher PA levels during childhood to improve physical fitness and cognitive function. Including the goal of improving health-related fitness factors, such as cardiorespiratory endurance and muscle strength, is particularly important in PA programs for children.
Guided Growth Technique for Epiphysiodesis and Hemiepiphysiodesis: Safety and Performance Evaluation
Authors: Di Gennaro Giovanni Luigi , Trisolino Giovanni , Stallone Stefano , Ramella Marco , Rocca Gino , Gallone Giovanni
Published: December 2023

Background: Guided-growth modulation is a first-line treatment widely adopted to correct lower-limb angular deformities and limb-length discrepancies (LLD) in the paediatric population. Methods: We conducted a retrospective study to evaluate the safety and performance of a new construct (8-Plate Plus or Guided-Growth Plate System Plus, Orthofix S.r.l) used to correct angular deformities and LLD in non-skeletally mature children. The primary endpoint was safety (from plate implantation to removal). The secondary endpoint was performance; patients treated for LLD achieved complete correction if a pre- and post-surgery difference of <0 was observed; angular deformities performance was measured in terms of IMD, ICD, mMPTA, and mLDFA. Results: We performed 69 procedures in 41 patients. A total of 10 patients had an LLD, and 31 had an angular deformity. We observed nine minor complications in the hemiepiphysiodesis group. One patient experienced rebound. All 10 LLD patient treatments were successful. A total of 30/31 patients with an angular deformity had a successful treatment; the remaining patient had a partial correction. Conclusions: Guided-growth by temporary epiphysiodesis or hemiepiphysiodesis was safe and effective for angular deformities and limb-length discrepancies. Further prospective and/or randomized controlled trial studies assessing more significant cohorts of patients and a comparison group could add evidence to our findings.
Research Progress of Reinforced Modification of Fast-Growing Wood
Authors: Zhang Zhaohong , Guo Qiang , Huang Xuanxuan , Zhang Qian , Fan Jinlong , Huang Jintian
Published: December 2023

The public’s requirements for a high-quality residential environment and the general improvement in ecological safety awareness have made renewable resource wood and products more favored by furniture, construction and other industries. However, on the one hand, the supply of natural forests is extremely limited, and on the other hand, the materials of artificial forests have defects such as low surface strength and poor dimensional stability due to a loose fiber structure, which restricts their promotion as an alternative to high-quality wood. In this paper, based on the mechanism of wood reinforcement, several reinforcement techniques, such as impregnation, compaction and surface modification, which are widely used in industry, are briefly introduced. On this basis, the possibility of impregnation as a pretreatment method, surface modification and densification to enhance wood was prospected. It is expected to provide reference for improving the added value of plantation wood and alleviating the contradiction between wood supply and demand.
Theoretical Design of Near-Infrared Tunable Surface Plasmon Resonance Biosensors Based on Gate-Controlled Graphene Plasmons
Authors: Xiao Yi , Cui Danting , Zhong Yongchun , Li Zhibin , Zhang Jun , Yu Jianhui
Published: December 2023

A tunable near-infrared surface plasmon resonance (SPR) biosensor based on gate-controlled graphene plasmons is numerically investigated by using the finite element method (FEM) and the transfer matrix method (TMM). The novel properties of chemical potential sensing make the proposed sensor promising in the application of ultra-sensitive and highly specific biosensing technology. The sensitivity of chemical potential sensing in wavelength interrogation mode can be calculated to be 1.5, 1.89, 2.29, 3.21, 3.73 and 4.68 nm/meV, respectively, at the resonance wavelengths of 1100, 1200, 1310, 1550, 1700 and 1900 nm. The figure of merit (FOM) achieves 129.3, 101.1, 84.5, 67.7, 69.5 and 59.7 eV−1, respectively, at these resonance wavelengths. The sensitivity of chemical potential sensing in gate voltage interrogation mode also can be calculated to be 156.9822, 143.6147, 131.0779, 111.0351, 101.3415 and 90.6038 mV/meV, respectively, at the incident wavelengths of 1100, 1200, 1310, 1550, 1700 and 1900 nm. The FOM achieves 135.6, 103.0, 88.9, 62.2, 66.6 and 61.5 eV−1, respectively, at these incident wavelengths. Theoretical estimates suggest that the limit of detection (LOD) of the sensor’s DNA sensing can reach the level of femtomolar or even attomolar, comparable to and even lower than that of 2D nanomaterial-enhanced metal SPR sensors with AuNPs as a sensitivity enhancement strategy. The feasibility of preparation and operation of this new concept SPR biosensor is also analyzed and discussed.