bluuebunny/crossref_metadata_2025 huggingface dataset:
Detection and Localization of Albas Velvet Goats Based on YOLOv4
Authors: Guo Ying , Wang Xihao , Han Mingjuan , Xin Jile , Hou Yun , Gong Zhuo , Wang Liang , Fan Daoerji , Feng Lianjie , Han Ding
Published: October 2023

In order to achieve goat localization to help prevent goats from wandering, we proposed an efficient target localization method based on machine vision. Albas velvet goats from a farm in Ertok Banner, Ordos City, Inner Mongolia Autonomous Region, China, were the main objects of study. First, we proposed detecting the goats using a shallow convolutional neural network, ShallowSE, with the channel attention mechanism SENet, the GeLU activation function and layer normalization. Second, we designed three fully connected coordinate regression network models to predict the spatial coordinates of the goats. Finally, the target detection algorithm and the coordinate regression algorithm were combined to localize the flock. We experimentally confirmed the proposed method using our dataset. The proposed algorithm obtained a good detection accuracy and successful localization rate compared to other popular algorithms. The overall number of parameters in the target detection algorithm model was only 4.5 M. The average detection accuracy reached 95.89% and the detection time was only 8.5 ms. The average localization error of the group localization algorithm was only 0.94 m and the localization time was 0.21 s. In conclusion, the method achieved fast and accurate localization, which helped to rationalize the use of grassland resources and to promote the sustainable development of rangelands.
Interactive Dairy Goat Image Segmentation for Precision Livestock Farming
Authors: Zhang Lianyue , Han Gaoge , Qiao Yongliang , Xu Liu , Chen Ling , Tang Jinglei
Published: October 2023

Semantic segmentation and instance segmentation based on deep learning play a significant role in intelligent dairy goat farming. However, these algorithms require a large amount of pixel-level dairy goat image annotations for model training. At present, users mainly use Labelme for pixel-level annotation of images, which makes it quite inefficient and time-consuming to obtain a high-quality annotation result. To reduce the annotation workload of dairy goat images, we propose a novel interactive segmentation model called UA-MHFF-DeepLabv3+, which employs layer-by-layer multi-head feature fusion (MHFF) and upsampling attention (UA) to improve the segmentation accuracy of the DeepLabv3+ on object boundaries and small objects. Experimental results show that our proposed model achieved state-of-the-art segmentation accuracy on the validation set of DGImgs compared with four previous state-of-the-art interactive segmentation models, and obtained 1.87 and 4.11 on mNoC@85 and mNoC@90, which are significantly lower than the best performance of the previous models of 3 and 5. Furthermore, to promote the implementation of our proposed algorithm, we design and develop a dairy goat image-annotation system named DGAnnotation for pixel-level annotation of dairy goat images. After the test, we found that it just takes 7.12 s to annotate a dairy goat instance with our developed DGAnnotation, which is five times faster than Labelme.
Assessment Heartworm Disease in the Canary Islands (Spain): Risk of Transmission in a Hyperendemic Area by Ecological Niche Modeling and Its Future Projection
Authors: Morchón Rodrigo , Rodríguez-Escolar Iván , Lambraño Ricardo E. Hernández , Agudo José Ángel Sánchez , Montoya-Alonso José Alberto , Serafín-Pérez Irene , Fernández-Serafín Carolina , Carretón Elena
Published: October 2023

Heartworm disease is a vector-borne zoonotic disease caused by Dirofilaria immitis. The Canary Islands (Spain), geolocated close to the coast of Western Sahara, is an archipelago considered hyperendemic where the average prevalence in domestic dogs is high, heterogeneous, and non-uniform. In addition, Culex theileri has been reported as a vector of the disease on two of the most populated islands. Our aim was to develop a more accurate transmission risk model for dirofilariosis for the Canary Islands. For this purpose, we used different variables related to parasite transmission; the potential distribution of suitable habitats for Culex spp. was calculated using the ecological niche model (ENM) and the potential number of generations of D. immitis. The resulting model was validated with the geolocation of D. immitis-infected dogs from all islands. In addition, the impact of possible future climatic conditions was estimated. There is a risk of transmission on all islands, being high in coastal areas, moderate in midland areas, and minimal in higher altitude areas. Most of the dogs infected with D. immitis were geolocated in areas with a high risk of transmission. In 2080, the percentage of territory that will have been gained by Culex spp. is small (5.02%), although it will occur toward the midlands from coastal areas. This new model provides a high predictive power for the study of cardiopulmonary dirofilariosis in the Canary Islands, as a hyperendemic area of the disease, and can be used as a tool for its prevention and control.
Health Status Classification for Cows Using Machine Learning and Data Management on AWS Cloud
Authors: Dineva Kristina , Atanasova Tatiana
Published: October 2023

The health and welfare of livestock are significant for ensuring the sustainability and profitability of the agricultural industry. Addressing efficient ways to monitor and report the health status of individual cows is critical to prevent outbreaks and maintain herd productivity. The purpose of the study is to develop a machine learning (ML) model to classify the health status of milk cows into three categories. In this research, data are collected from existing non-invasive IoT devices and tools in a dairy farm, monitoring the micro- and macroenvironment of the cow in combination with particular information on age, days in milk, lactation, and more. A workflow of various data-processing methods is systematized and presented to create a complete, efficient, and reusable roadmap for data processing, modeling, and real-world integration. Following the proposed workflow, the data were treated, and five different ML algorithms were trained and tested to select the most descriptive one to monitor the health status of individual cows. The highest result for health status assessment is obtained by random forest classifier (RFC) with an accuracy of 0.959, recall of 0.954, and precision of 0.97. To increase the security, speed, and reliability of the work process, a cloud architecture of services is presented to integrate the trained model as an additional functionality in the Amazon Web Services (AWS) environment. The classification results of the ML model are visualized in a newly created interface in the client application.
Comprehensive Analysis of Virulence Determinants and Genomic Islands of blaNDM-1-Producing Enterobacter hormaechei Clinical Isolates from Greece
Authors: Mavroidi Angeliki , Gartzonika Konstantina , Spanakis Nick , Froukala Elisavet , Kittas Christos , Vrioni Georgia , Tsakris Athanasios
Published: October 2023

Nosocomial outbreaks of multidrug-resistant (MDR) Enterobacter cloacae complex (ECC) are often reported worldwide, mostly associated with a small number of multilocus-sequence types of E. hormaechei and E. cloacae strains. In Europe, the largest clonal outbreak of blaNDM-1-producing ECC has been recently reported, involving an ST182 E. hormaechei strain in a Greek teaching hospital. In the current study, we aimed to further investigate the genetic make-up of two representative outbreak isolates. Comparative genomics of whole genome sequences (WGS) was performed, including whole genome-based taxonomic analysis and in silico prediction of virulence determinants of the bacterial cell surface, plasmids, antibiotic resistance genes and virulence factors present on genomic islands. The enterobacterial common antigen and the colanic antigen of the cell surface were identified in both isolates, being similar to the gene clusters of the E. hormaechei ATCC 49162 and E. cloacae ATCC 13047 type strains, whereas the two strains possessed different gene clusters encoding lipopolysaccharide O-antigens. Other virulence factors of the bacterial cell surface, such as flagella, fimbriae and pili, were also predicted to be encoded by gene clusters similar to those found in Enterobacter spp. and other Enterobacterales. Secretion systems and toxin–antitoxin systems, which also contribute to pathogenicity, were identified. Both isolates harboured resistance genes to multiple antimicrobial classes, including β-lactams, aminoglycosides, quinolones, chloramphenicol, trimethoprim, sulfonamides and fosfomycin; they carried blaTEM-1, blaOXA-1, blaNDM-1, and one of them also carried blaCTXM-14, blaCTXM-15 and blaLAP-2 plasmidic alleles. Our comprehensive analysis of the WGS assemblies revealed that blaNDM-1-producing outbreak isolates possess components of the bacterial cell surface as well as genomic islands, harbouring resistance genes to several antimicrobial classes and various virulence factors. Differences in the plasmids carrying β-lactamase genes between the two strains have also shown diverse modes of acquisition and an ongoing evolution of these mobile elements.
Role of Nanoparticle-Conjugates and Nanotheranostics in Abrogating Oxidative Stress and Ameliorating Neuroinflammation
Authors: Patel Tapan A. , Kevadiya Bhavesh D. , Bajwa Neha , Singh Preet Amol , Zheng Hong , Kirabo Annet , Li Yu-Long , Patel Kaushik P.
Published: October 2023

Oxidative stress is a deteriorating condition that arises due to an imbalance between the reactive oxygen species and the antioxidant system or defense of the body. The key reasons for the development of such conditions are malfunctioning of various cell organelles, such as mitochondria, endoplasmic reticulum, and Golgi complex, as well as physical and mental disturbances. The nervous system has a relatively high utilization of oxygen, thus making it particularly vulnerable to oxidative stress, which eventually leads to neuronal atrophy and death. This advances the development of neuroinflammation and neurodegeneration-associated disorders such as Alzheimer’s disease, Parkinson’s disease, epilepsy, dementia, and other memory disorders. It is imperative to treat such conditions as early as possible before they worsen and progress to irreversible damage. Oxidative damage can be negated by two mechanisms: improving the cellular defense system or providing exogenous antioxidants. Natural antioxidants can normally handle such oxidative stress, but they have limited efficacy. The valuable features of nanoparticles and/or nanomaterials, in combination with antioxidant features, offer innovative nanotheranostic tools as potential therapeutic modalities. Hence, this review aims to represent novel therapeutic approaches like utilizing nanoparticles with antioxidant properties and nanotheranostics as delivery systems for potential therapeutic applications in various neuroinflammation- and neurodegeneration-associated disease conditions.
Multi-Objective Optimization for High-Speed Railway Network Based on “Demand–Supply–Management” Model
Authors: Hu Qizhou , Bian Lishuang , Lin Juanjuan , Tan Minjia
Published: October 2023

This paper develops a multi-objective optimization model for national high-speed railway network planning. Three objectives are proposed from the macro, meso, and micro perspectives, and these three objectives are considered simultaneously. Using real data, case studies are conducted to optimize China’s “four east–west and four north–south railway lines” network, which includes Beijing, Shanghai, Wuhan, Guangzhou, and Zhengzhou as main hubs. The results show that, on the one hand, the optimization model reduces the overlap between long-distance passengers and short-distance passengers on the high-speed railway line, facilitates the travel of passengers, and improves the line service capability; on the other hand, optimization of the network shortens the travel time of the passengers, reduces the cost of the railway, and improves the operation efficiency of the high-speed railway line network. The results show that the total travel time of all high-speed railway passengers in the optimization model is reduced by 18.4%, while the benefit rate of the operator increased by 21.99%.
Lithium-Ion Battery State-of-Health Prediction for New-Energy Electric Vehicles Based on Random Forest Improved Model
Authors: Liang Zijun , Wang Ruihan , Zhan Xuejuan , Li Yuqi , Xiao Yun
Published: October 2023

The lithium-ion battery (LIB) has become the primary power source for new-energy electric vehicles, and accurately predicting the state-of-health (SOH) of LIBs is of crucial significance for ensuring the stable operation of electric vehicles and the sustainable development of green transportation. We collected multiple sets of charge–discharge cycle experimental data for LiFePO4 LIB and employed several traditional machine learning models to predict the SOH of LIBs. It was found that the RF model yielded relatively superior predictive results, confirming the feasibility of applying the RF model to SOH prediction for the electric vehicle LIB. Building upon this foundation, further research was conducted on the RF improved model for LIB SOH prediction. The PSO algorithm was employed to adaptively optimize five major parameters of the RF model: max_depth, n_estimators, max_features, min_samples_split, and min_samples_leaf. This adaptation addresses the issue of prediction errors that stem from human experience to optimize parameters in the RF model. The results indicate that the RF improved model proposed in this paper can further improve the prediction accuracy of LIB SOH. Its model evaluation index also outperform others, demonstrating the effectiveness of this approach in the management of LIB SOH for new-energy electric vehicles. This contributes significantly to urban environmental protection and the development of green transportation.
Special Issue on the Progress and Application of Electromagnetic Materials
Authors: Fu Xiaojian
Published: October 2023

Electromagnetic materials refer to materials that can manipulate electromagnetic waves, which can control the amplitude, phase, polarization, spectrum, and other characteristics of electromagnetic waves [...]
Predicting the Rectal Temperature of Dairy Cows Using Infrared Thermography and Multimodal Machine Learning
Authors: Brezov Danail , Hristov Hristo , Dimov Dimo , Alexiev Kiril
Published: October 2023

The paper proposes an approach for estimating the rectal temperature of dairy cows based on the non-invasive real-time monitoring of their respiration rates and the temperature-humidity index (THI) of the environment, combined with the analysis of infrared images. We use multimodal machine learning for the joint processing (fusion) of these different types of data. The implementation is performed using a new open source AutoML Python module named AutoGluon. After training and optimizing three different regression models (a neural network and two powerful boosting algorithms), it reduces the variance of the result using level 2 stacking. The evaluation metrics we work with are the mean absolute error, MAE, and the coefficient of determination, R2. For a sample of 295 studied animals, a weighted ensemble provides quite decent results: R2=0.73 and MAE ≈0.1 °C. For another sample of 118 cows, we additionally use the pulse rate as a predictor and we achieve R2=0.65, MAE ≈0.2 °C. The maximal error is almost 1 °C due to outliers, but the median absolute error in both cases is significantly lower: MedAE <0.1 °C, with the standard deviations respectively being 0.118° and 0.137°. These encouraging results give us confidence that tabular and visual data fusion in ML models has great potential for the advancement of non-invasive real-time monitoring and early diagnostics methods.
Analysis of Combat in Sport JU-JITSU during the World Championships in Fighting Formula
Authors: Ambroży Tadeusz , Rydzik Łukasz , Wąsacz Wojciech , Małodobry Zbigniew , Cynarski Wojciech J. , Ambroży Dorota , Kędra Andrzej
Published: October 2023

Background: The observation and specialized analysis of confrontations in combat sports are fundamental for making corrections in training programs as well as for modifying individual technical–tactical profiles of athletes in such activities. These actions comprehensively assess the course of sports activities and ultimately inspire and guide the type of training in academies and sports clubs. The aim of the study was a general and detailed analysis of sport fighting in JU-JITSU during a top-tier tournament, in the fighting formula for the entire competition and for each weight category. Methods: The research material consisted of multimedia recordings of sports fights taken during the World JU-JITSU Championships in the fighting formula, Wroclaw 2016. A total of 229 tournament fights were analyzed in seven weight categories. For the purpose of evaluating the structure of the fight, a retrospective analysis of the recorded empirical material was conducted, and technical–tactical preparation (TTII) indicators were calculated, both in a global tournament context and for individual weight categories. Results: Of the 229 matches, more than half were decided by the advantage of technical points (58.52%) within the regulatory fight time, while in 74 clashes, victory was declared by Full Ippon (32.31%) before the designated fight time. Activity and attack effectiveness, as well as the number of technical points, were highest in the first part of the fight. Significant variations were observed in terms of activity, attack effectiveness, and point gains for all parts of the clash, and for effectiveness in the weight categories compared to the second part. The most frequently occurring penalty was the minor shido penalty, while the offense was the lack of fighting in the second part of the duel. The total fight time was 256 s, of which 144 s were effective fighting, and 112 s were breaks. Conclusions: The analysis of sports fight observations revealed that the majority of fights ended with a technical point advantage win (58.52%), with notable activity and attack effectiveness in part I. Middleweight fighters were most active early on, while heavyweight categories dominated later phases. Attack efficiency varied across weight categories. Penalties were predominantly minor (shido), and the total fight time included 144 s of effective fighting and 112 s of breaks.
Novel Microfluidic Septum to Optimize Energy Recovery in Single-Chamber Microbial Fuel Cells
Authors: Spisni Giacomo , Massaglia Giulia , Bertana Valentina , Vasile Nicolò , Pirri Fabrizio C. , Bianco Stefano , Quaglio Marzia
Published: October 2023

This study proposes a redesign of asymmetric single-chamber microbial fuel cells (a-SCMFCs) with the goal of optimizing energy production. In the present work, the new approach is based on the introduction of a novel intermediate microfluidic septum (IMS) inside the electrolyte chamber. This IMS was designed as a relatively simple and inexpensive method to optimize both electrolyte flow and species transfer inside the devices. a-SCMFCs, featuring the IMS, are compared to control cells (IMS-less), when operated with sodium acetate as the carbon energy source. Performances of cells are evaluated in terms both of maximum output potential achieved, and energy recovery (Erec) as the ratio between the energy yield and the inner electrolyte volume. The a-SCMFCs with the novel IMS are demonstrated to enhance the energy recovery compared to control cells exhibiting Erec values of (37 ± 1) J/m3, which is one order of magnitude higher than that achieved by control cells (3.0 ± 0.3) J/m3. Concerning the maximum output potential, IMS cells achieve (2.8 ± 0.2) mV, compared to control cells at (0.68 ± 0.07) mV. Furthermore, by varying the sodium acetate concentration, the Erec and maximum potential output values change accordingly. By monitoring the activity of a-SCMFCs for over one year, the beneficial impact of the IMS on both the initial inoculation phase and the long-term stability of electrical performance are observed. These improvements support the effectiveness of IMS to allow the development of efficient biofilms, likely due to the reduction in oxygen cross-over towards the anode. Electrochemical characterizations confirm that the presence of the IMS impacts the diffusion processes inside the electrolytic chamber, supporting the hypothesis of a beneficial effect on oxygen cross-over.
Machine Learning in Gamification and Gamification in Machine Learning: A Systematic Literature Mapping
Authors: Swacha Jakub , Gracel Michał
Published: October 2023

Albeit in different ways, both machine learning and gamification have transfigured the user experience of information systems. Although both are hot research topics, so far, little attention has been paid to how these two technologies converge with each other. This relation is not obvious as while it is feasible to enhance gamification with machine learning, it is also feasible to support machine learning with gamification; moreover, there are applications in which machine learning and gamification are combined yet not directly connected. In this study, we aim to shed light on the use of both machine learning in gamification and gamification in machine learning, as well as the related topics of using gamification in machine learning education and machine learning in gamification research. By performing a systematic literature mapping, we not only identify prior works addressing these respective themes, but also analyze how their popularity evolved in time, investigate the areas of application reported by prior works, used machine learning techniques and software tools, as well as the character of research contribution and the character of evaluation results for works that presented them.
Digital Twin-Driven Framework for TBM Performance Prediction, Visualization, and Monitoring through Machine Learning
Authors: Latif Kamran , Sharafat Abubakar , Seo Jongwon
Published: October 2023

The rapid development in underground infrastructure is encouraging faster and more modern ways, such as TBM tunneling, to meet the needs of the world. However, tunneling activities generate complex and heterogeneous data, which makes it difficult to visualize the performance of a project. Advancements in information technology, such as digital twins and machine learning, provide platforms for digital demonstration, visualization, and system performance monitoring of such data. Therefore, this study proposes a digital twin-driven framework for TBM performance prediction through machine learning, visualization, and monitoring. This novel approach integrates machine learning and real-time performance data to predict, visualize, and monitor the status of the tunnel construction progress. A digital twin virtual model of TBM was constructed based on TBM design parameters, the input parameter, boring energy, RPM, torque, thrust force, speed, gripper pressure, total revolution, and Q-value provided to SVR and ANN models to predict the TBM AR and PR, and TBM daily progress was visualized continuously. The predictive performance indices R2 (0.97) and RMSE (0.011) were estimated for AR prediction, showing the accuracy of the proposed model. To demonstrate the proposed framework, this study shows the its effectiveness. By implementing this framework, stakeholders can minimize the risk associated with the cost and schedule of a tunneling project by simultaneously visualizing and monitoring the performance of TBMs through digital twin and machine learning algorithms.
Artificial Intelligence-Driven Eye Disease Classification Model
Authors: Wahab Sait Abdul Rahaman
Published: October 2023

Eye diseases can result in various challenges and visual impairments. These diseases can affect an individual’s quality of life and general health and well-being. The symptoms of eye diseases vary widely depending on the nature and severity of the disease. Early diagnosis can protect individuals from visual impairment. Artificial intelligence (AI)-based eye disease classification (EDC) assists physicians in providing effective patient services. However, the complexities of the fundus image affect the classifier’s performance. There is a demand for a practical EDC for identifying eye diseases in the earlier stages. Thus, the author intends to build an EDC model using the deep learning (DL) technique. Denoising autoencoders are used to remove the noises and artifacts from the fundus images. The single-shot detection (SSD) approach generates the key features. The whale optimization algorithm (WOA) with Levy Flight and Wavelet search strategy is followed for selecting the features. In addition, the Adam optimizer (AO) is applied to fine-tune the ShuffleNet V2 model to classify the fundus images. Two benchmark datasets, ocular disease intelligent recognition (ODIR) and EDC datasets, are utilized for performance evaluation. The proposed EDC model achieved accuracy and Kappa values of 99.1 and 96.4, and 99.4 and 96.5, in the ODIR and EDC datasets, respectively. It outperformed the recent EDC models. The findings highlight the significance of the proposed EDC model in classifying eye diseases using complex fundus images. Healthcare centers can implement the proposed model to improve their standards and serve a more significant number of patients. In the future, the proposed model can be extended to identify a comprehensive range of eye diseases.
Anthropometric Values in Spanish Elite Soccer: Differences between Divisions and Playing Positions
Authors: Porta Mireia , Sebastiá-Rico Jaime , Martínez-Sanz José Miguel , Contreras Carlos , Vaquero-Cristóbal Raquel , López-Cáceres Pablo A.
Published: October 2023

Body composition is an important factor in a soccer player’s performance, and anthropometry is one of the most widely used methods of measurement. The physical demands of Spanish soccer have evolved over time, so the ideal body composition requirements must be adapted to the present day. The aim of this study was to describe the anthropometric and body composition profiles of professional soccer players in the second, second B, and third divisions during the 2019–2020 season in order to compare the anthropometric parameters of players among positions and competitive divisions. A cross-sectional study was conducted to determine differences in kinanthropometric and derived variables in a sample of Spanish soccer players. A total of 615 soccer players from the second (116 players), second B (310 players), and third (189 players) divisions participated in this study. After comparing the groups according to the playing position and category, it was observed that at higher levels of play, soccer players show lower values of fat mass (FM) and higher values of muscle mass (MM) and bone mass (BM); at lower levels of play, soccer players show more anthropometric differences between playing positions. Lastly, the somatotype of the elite soccer player is balanced mesomorphic, with higher levels of mesomorphy at higher levels of play. In summary, this is the first study to evaluate the body composition of the different Spanish soccer divisions.
Assessment of Internal and External Factors Causing Slow-Moving Landslides
Authors: Park Jae-Hyeon , Lee Sang-Hyeon , Kim Hyun
Published: October 2023

There have been limited studies on slow-moving landslides in South Korea despite their frequent occurrence. Moreover, a national slow-moving landslide hazard information system (SMLHIS) is needed. Herein, we conducted an overlap analysis of 15 slow-moving landslide areas with clear occurrence timings with national landslide hazard maps (LHMs) using the geographic information system data. Additionally, internal and external factors causing slow-moving landslides were analyzed. The results of the overlap analysis showed that slow-moving landslide areas occurred in low-hazard and excluded non-hazard areas on the LHM. The study of internal factors revealed that slow-moving landslides occurred mainly in the Gyeongsang supergroup, which has sedimentary rock type and sandy loam. The analysis of external factors, e.g., rainfall, showed that slow-moving landslides occurred during intensive rainfall, with continuous and 15-day antecedent rainfall exceeding 100 and 200 mm, respectively. The longer the continuous rainfall duration before a slow-moving landslide; the greater the rainfall on the day of the landslide; the greater the maximum hourly rainfall; the greater the 3-, 5-, and 7-day antecedent rainfalls; and the greater the rainfall intensity during the landslide, the greater the size of the slow-moving landslide. This study provides information for developing a national SMLHIS, presenting novel perspectives for slow-moving landslide research.
On a Remarkable New Genus and Species of Alpheid Shrimps (Malacostraca: Decapoda: Caridea) from the Tropical Western Atlantic
Authors: Anker Arthur
Published: October 2023

Synalpheopsis gen. nov. is established for a remarkable new alpheid species, Synalpheopsis laureae sp. nov., presently known only from the male holotype collected at 111–162 m east of La Désirade, Guadeloupe, Lesser Antilles. Synalpheopsis gen. nov. peculiarly combines features of two genera, Alpheopsis Coutière, 1897 and Synalpheus Spence Bate, 1888; however, it is presumably closer to the former genus. The new genus is characterised by the moderately developed orbital hoods, well-developed rostrum and orbital teeth, sixth pleonite without articulated flap, tip of the third maxilliped with crown of spiniform setae, chelipeds with two strong teeth on distolateral margin and lacking snapping mechanism on finger cutting edges, and gill formula without mastigobranchs and setobranchs.
Climate Change Impacts on the Density Altitude of Chinese Airports in Summer
Authors: Kang Xianbiao , Meng Zijing , Feng Wan , Liu Yunfeng
Published: October 2023

This study examines the projected impact of climate change on the density altitude (DA) at Chinese airports during the summer by the end of the 21st century. Findings indicate that climate change is expected to significantly increase the DA at all Chinese airports, with an estimated rise between 300 and 800 feet. The analysis suggests that temperature increases will universally contribute to a rise in DA. Pressure changes, however, are more variable. Most airports are predicted to see an increase in pressure, which could offset some temperature effects on the DA. Airports in Eastern China are expected to see a decrease in pressure, amplifying the effects on DA and creating operational challenges.
Using the Ordered Weighted Average Operator to Gauge Variation in Agriculture Commodities in India
Authors: Wankhade Sandeep , Sahni Manoj , Mellado-Cid Cristhian , Leon-Castro Ernesto
Published: October 2023

Agricultural product prices are subject to various uncertainties, including unpredictable weather conditions, pest infestations, and market fluctuations, which can significantly impact agricultural yields and productivity. Accurately assessing and understanding price is crucial for farmers, policymakers, and stakeholders in the agricultural sector to make informed decisions and implement appropriate risk management strategies. This study used the ordered weighted average (OWA) operator and its extensions as mathematical aggregation techniques incorporating ordered weights to capture and evaluate the factors influencing price variation. By generating different vectors related to different inputs to the traditional formulation, it is possible to aggregate information to calculate and provide a new view of the outcomes. The results of this research can help enhance risk management practices in agriculture and support decision-making processes to mitigate the adverse effects of price.
An Interval Type 2 Fuzzy Decision-Making Framework for Exploring Critical Issues for the Sustenance of the Tea Industry
Authors: Kumar Manoj , Biswas Sanjib , Kar Samarjit , Božanić Darko , Puška Adis
Published: October 2023

The purpose of the present study is to propose an interval-valued type 2 fuzzy set (IT2FS)-based analytic hierarchy process (AHP) framework to unfold the critical challenging factors influencing the sustenance and growth of the Indian tea industry. The current work follows an expert opinion-based group decision-making approach. The challenging factors have been identified through a literature review and finalized after a pilot study based on the opinions of professionals, consumers, and experts. Finally, the critical challenging factors and sub-factors have been figured out through analysis of the responses of the experts. To offset the subjective bias, an IT2FS-based granular analysis has been carried out. The findings reveal that market diversification and productivity are the central issues. Additionally, it is important to give attention to improving the quality of the products, increasing the use of modern technology and organic farming, and developing a variety of products. The result shows a considerable level of consistency in the group decision-making (CR < 0.1) for all pairwise comparisons. The present work shall be of use to formulate appropriate strategies and policy decisions. It shows a robust application of IT2FS-AHP for complex decision-making in real life.
Comprehensive Transcriptomic Profiling of m6A Modification in Age-Related Hearing Loss
Authors: Feng Menglong , Zhou Xiaoqing , Hu Yaqin , Zhang Juhong , Yang Ting , Chen Zhiji , Yuan Wei
Published: October 2023

Age-related hearing loss (ARHL), also known as presbycusis, is one of the most common neurodegenerative disorders in elderly individuals and has a prevalence of approximately 70–80% among individuals aged 65 and older. As ARHL is an intricate and multifactorial disease, the exact pathogenesis of ARHL is not fully understood. There is evidence that transcriptional dysregulation mediated by epigenetic modifications is widespread in ARHL. However, the potential role of N6-methyladenosine (m6A) modification, as a crucial component of epigenetics, in ARHL progression remains unclear. In this study, we confirmed that the downregulation of m6A modification in cochlear tissues is related to ARHL and found that the expression of the m6A methylation regulators Wilms tumour suppressor-1-associated protein (WTAP), methyltransferase-like 3 (METTL3), ALKB homologous protein 5 (ALKBH5) and fat mass and obesity-associated protein (FTO) is decreased significantly at the mRNA and protein levels in ARHL mice. Then, we used methylated RNA immunoprecipitation sequencing (MeRIP-Seq) and RNA sequencing (RNA-Seq) to identify the differentially m6A-methylated genes in the cochlear tissues of ARHL mice. A total of 3438 genes with differential m6A methylation were identified, of which 1332 genes were m6A-hypermethylated and 2106 genes were m6A-hypomethylated in the ARHL group compared to the control group according to MeRIP-seq. Further joint analysis of RNA-Seq and MeRIP-Seq data showed that 262 genes had significant differences in both mRNA expression and m6A methylation. GO and KEGG analyses indicated that 262 unique genes were enriched mainly in the PI3K-AKT signalling pathway. In conclusion, the results of this study reveal differential m6A methylation patterns in the cochlear tissues of ARHL mice, providing a theoretical basis for further study of the pathogenesis of ARHL and potential therapeutic strategies.
Association of Cytokine Gene Polymorphisms and Their Impact on Active and Latent Tuberculosis in Brazil’s Amazon Region
Authors: da Silva Graça Amoras Ednelza , de Morais Thais Gouvea , do Nascimento Ferreira Rafaella , Gomes Samara Tatielle Monteiro , de Sousa Francisca Dayse Martins , de Paula Souza Iury , Ishak Ricardo , Vallinoto Antonio Carlos Rosário , Queiroz Maria Alice Freitas
Published: October 2023

Some genetic variations in cytokine genes can alter their expression and influence the evolution of Mycobacterium tuberculosis (Mtb) infection. This study aimed to investigate the association of polymorphisms in cytokine genes and variability in plasma levels of cytokines with the development of tuberculosis (TB) and latent tuberculosis infection (LTBI). Blood samples from 245 patients with TB, 80 with LTBI, and healthy controls (n = 100) were included. Genotyping of the IFNG +874A/T, IL6 -174G/C, IL4 -590C/T, and IL10 -1082A/G polymorphisms was performed by real-time PCR, and cytokine levels were determined by flow cytometry. Higher frequencies of genotypes AA (IFNG +874A/T), GG (IL6 -174G/C), TT (IL4 -590C/T), and GG (IL10 -1082A/G) were associated with an increased risk of TB compared to that of LTBI (p = 0.0027; p = 0.0557; p = 0.0286; p = 0.0361, respectively) and the control (p = <0.0001, p = 0.0021; p = 0.01655; p = 0.0132, respectively). In combination, the A allele for IFNG +874A/T and the T allele for IL4 -590C/T were associated with a higher chance of TB (p = 0.0080; OR = 2.753 and p < 0.0001; OR = 3.273, respectively). The TB group had lower levels of IFN-γ and higher concentrations of IL-6, IL-4, and IL-10. Cytokine levels were different between the genotypes based on the polymorphisms investigated (p < 0.05). The genotype and wild-type allele for IFNG +874A/T and the genotype and polymorphic allele for IL4 -590C/T appear to be more relevant in the context of Mtb infection, which has been associated with the development of TB among individuals infected by the bacillus and with susceptibility to active infection but not with susceptibility to latent infection.
Caring for Dementia Caregivers: Psychosocial Factors Related to Engagement in Self-Care Activities
Authors: Lin Xinyao , Moxley Jerad H. , Czaja Sara J.
Published: October 2023

Caregivers often prioritize the needs of the care recipient and neglect their own health needs. It is imperative to understand the factors related to their self-care practices and engagement in self-care activities. The present study examined the extent to which dementia caregivers engaged in self-care activities, how this varied depending on caregiver characteristics, and whether self-care engagement mediated the relationship between social support and caregiver outcomes. The study utilized baseline data from a diverse sample of dementia caregivers (N = 243) who participated in a randomized trial evaluating a psychosocial technology-based caregiver intervention. Results showed that the dementia caregivers engaged in low levels of self-care activities and that their engagement varied based on the caregivers’ background characteristics (age, gender, race/ethnicity, relationship to the care recipient, and employment status). Less caregiver involvement (e.g., less ADL/IADL help provided and more caregiver preparedness) and more social support predicted higher self-care activity engagement. Self-care activity engagement served as a mediator, such that more social support predicted more self-care activities, which, in turn, were associated with more positive perceptions of caregiving and less caregiver burden and depression. The findings suggest a need for interventions that promote self-care engagement among dementia caregivers and underscore the importance of social support and caregiver preparedness to caregivers’ well-being.
How Does Secure-Base Leadership Affect Employees’ Taking-Charge Behavior: The Role of Psychological Availability and Independent Self-Construal
Authors: Li Huanhuan , Zhang Yanbo
Published: October 2023

Under the VUCA background, enterprises need to actively change to meet the requirements of internal and external environmental changes. This article surveyed 250 employees. Using statistical software SPSS 27.0 and Process 4.1, we address the outcome of secure-base leadership on employees’ taking-charge behavior by considering the influencing mechanisms and boundary conditions. The results indicate that secure-base leadership positively shapes employees’ taking-charge behavior, and psychological availability plays a mediating role in the relationship between secure-base leadership and such employee behavior. Independent self-construal positively moderates the impact of psychological availability on employees’ taking-charge behavior and positively moderates the indirect impact of secure-base leadership on such behavior through psychological availability. Our findings could enrich the empirical research on employees’ taking-charge behavior by secure-base leadership, thereby promoting the sustainable development of organizations.
Blast Mitigation of Reinforced Concrete Structures Incorporating Shear Walls in Modern Building Designs
Authors: Raikar Rohan G. , Kangda Muhammed Zain , Wadki Venkatesh , Noroozinejad Farsangi Ehsan
Published: October 2023

Material science advancements have resulted in the development of high-strength concrete and steel reinforcement, allowing more efficient and stable buildings against natural and manmade disasters. Increasing security concerns and the potential threat from terrorist activities have led to the safety and resilience of structures against blast loads in modern construction. The present study investigates the performance of reinforced concrete shear walls in mitigating blast-induced vibrations. The study examines four different reinforced concrete buildings based on their shapes, namely square, rectangular, C-shaped, and L-shaped, to understand the blast behaviours with and without shear walls. The study presents a methodology to protect the regular and irregular buildings equipped with shear walls against blast loads at varying standoff distances of 100 m, 200 m, 300 m, and 400 m, respectively. The study also compares the efficiency of passive control dampers and shear walls in enhancing the buildings’ performance against blast vibrations. The best placement of the shear walls is also evaluated for all the selected buildings. The study also considers the effect of shear wall thickness in mitigating blast-induced vibrations in multi-storey buildings. The study also discusses the design guidelines and reinforcement detailing of shear walls to protect buildings against detrimental blast effects.
Evaluation of Two Chilean Native Macroalgae: “Pelillo” (Gracilaria chilensis) and “Lamilla” (Ulva sp.) for Thermal Insulation Application
Authors: Rojas Herrera Carlos Javier , Rodríguez Neira Karin , Cárdenas-Ramírez Juan Pablo
Published: October 2023

Energy consumption in the residential sector and air pollution are relevant topics for the global population. One of the causes, especially in cold climate cities, is that buildings maintain a high energy consumption for heating and cooling, primarily using low-efficiency biomass combustion for heating, which releases a significant amount of particulate matter into the environment. In this context, thermal insulation materials play a crucial role in reducing the energy demand of buildings, requiring advancements in the sustainable development of such materials within the context of climate change. This study carried out an evaluation of two algae species found along the Chilean coasts, with the aim of characterizing them and creating a prototype of a sustainable material. Their physicochemical properties were analyzed, and the results demonstrate that the algae exhibit excellent thermal insulation properties, with an average thermal conductivity of 0.036 [W/mK]. This result is comparable to expanded polystyrene (EPS), a widely used material in the Chilean and global markets, which has an average thermal conductivity value of 0.038 [W/mK]. Additionally, the algae show a good thermal stability, and their morphology contributes to the development of a bulk material, as they possess a porous structure with air chambers between the fibers.
Evaluation of an Existing Validated Emirati House versus a New Parametric Design Based on the Local Social Environment through the Application of Advanced Tools
Authors: Bande Lindita , Asmelash Yosan , Ahmad Anwar , Cyiza Aybin , Berengueres Jose
Published: October 2023

Al Ain is the second-largest city in the Abu Dhabi Emirate, and the population of Al Ain has been growing rapidly for the last 50 years. The residential units in Al Ain are arranged using different concepts in relation to household social and economic behaviors. While Al Ain city has mostly low-rise and mid-rise residential buildings, the local population tends to live in traditional low-rise villas. The governmental statistics show a high ratio of energy consumption in the form of electricity for cooling loads, and it is estimated to increase with the rapid growth of the population. In this context, it is important to investigate different strategies to control the energy consumption of residential buildings. The purpose of this study was to assess the energy usage and demand of an existing villa in Al Ain and see how a newer design approach can help to reduce the annual energy consumption of households. The newer design option is based on a parametric (application of a parametric façade) approach whilst taking sustainable design approaches. The newer design options are compared to the existing villa and a traditional extension villa attached to the existing villa in terms of annual electricity consumption. The process of design and energy modeling of all cases used the Estidama baseline standards for technical and construction specifications. The process started with selecting an existing six-bedroom villa in Al Ain. Moreover, the selected villa had a planned extension to be constructed in the future. Then, an annual energy model of the existing villa was created in Rhinoceros 7.0 with the Grasshopper 3D plug-in. The energy results were validated against the real energy bills of the villa. Once the energy model was validated, the newer options of the design were modeled, and the projected energy consumption was compared with the base case results to see how energy-efficient the newer model would be. The research shows that it is possible to save up to 60% of electricity annually by carefully selecting a sustainable design in the early stages.
Simulation Analysis of Supply Chain Resilience of Prefabricated Building Projects Based on System Dynamics
Authors: Liu Wei , Liu Zixuan
Published: October 2023

In light of the intricate dynamics and uncertain risk parameters inherent in the supply chains of prefabricated building projects, bolstering the resilience of these supply chains can substantially mitigate disruption risks and facilitate superior operational outcomes for involved enterprises. This study identifies key metrics emblematic of supply chain resilience within prefabricated building projects, spanning five critical resilience dimensions: predictive prowess, absorptive potential, adaptability, inherent resilience, and growth capability. Employing the Analytic Hierarchy Process (AHP) and system dynamics (SD), we formulate a resilience simulation model specific to these supply chains. Utilizing the Nanchang Yinwang Village Comprehensive Housing Community Project as a case study, we forecast the trajectory of supply chain resilience over a five-year span and simulate the resilience variations in response to diverse variable magnitudes. Our findings reveal a consistent upward resilience trend over the five-year period. Moreover, the resilience stature of the prefabricated building project supply chain exhibits variability under distinct variable shifts. Of all the subsystems, the most reactive secondary factors encompass risk cognizance, logistics support level, collaboration intensity, supply chain reconfiguration aptitude, and managerial strategic decision-making prowess. Notably, amplifying the absorptive potential of resilience yields the most profound enhancement in overall resilience.