bluuebunny/crossref_metadata_2025 huggingface dataset:
Conceptual Framework for Modelling of an Electric Tractor and Its Performance Analysis Using a Permanent Magnet Synchronous Motor
Authors: Gade Chandrasekhar Reddy , Wahab Razia Sultana
Published: October 2023

The purpose of this article is to model and validate the propulsion system of an electric tractor. Initially, the mathematical model of the ET and its load characteristics are developed based on the velocity profiles used in the agriculture ploughing operations. A PMSM is selected from the torque and power requirement of the ET. In order to replicate the ET load on the PMSM, the load is simulated in accordance with the specifications of the PMSM drive system. This article proposes a novel load-tracking control of the propulsion unit with the arrangement of a PMSM and dynamometer. The speed and torque of the propulsion motor are controlled separately using a decoupled control algorithm. The performance of the propulsion unit is verified under different load profiles of the ET. The load-tracking algorithm is used to improve the dynamic response of the propulsion motor. The propulsion system containing the mathematical model of the ET, PMSM and dynamometer is developed in MATLAB/SIMULINK and validated in a real-time environment using a hardware-in-the-loop (HIL) simulator (OPAL-RT OP5700). The result proves that the proposed load-tracking algorithm provided a better dynamic performance of the propulsion unit.
Analysis of Regional Division of Labor in Value Chain Patterns and Driving Factors in the Yangtze River Delta Region Using the Electronic Information Manufacturing Industry as an Example
Authors: Kang Jiangjiang , Yang Chuankai , Ning Yuemin
Published: October 2023

The electronic information manufacturing industry is characterized by a very significant intra-product specialization and can display the characteristics of a regional division of labor. Looking at the existing literature, most studies have mainly examined the position of different countries in the spatial division of labor from the perspective of global value chains, with fewer empirical analyses at the city level or regional scale. Furthermore, deepening the regional division of labor in value chains is an effective way to promote regional industrial synergy and high-quality economic development. Based on the number of listed enterprises and the total number of parent–subsidiary investment connections in the electronic information manufacturing industry, this study reveals the characteristics of the deeper regional division of labor among cities by analyzing the Value Chain Division Index (VCDI). Subsequently, we used the fractional response regression model to analyze influencing factors. We found that, firstly, the core cities are dominated by the production of high-value parts, while the peripheral cities are mainly dominated by the production of middle- and low-value parts. Specifically, northern Anhui, northern Jiangsu, and southwestern Zhejiang are obviously in a disadvantaged position regarding the regional division of labor in the value chain. In the production of middle- and high-value parts, there are close investment connections between the core cities, and only a few peripheral cities maintain a certain degree of connection with the core cities. Therefore, there is a need to further strengthen industrial investment connections between the core and peripheral cities. Secondly, the regional division of labor in the value chain in the Yangtze River Delta region shows the following characteristic: a “one super, many strong” pattern. That is to say, the VCDI value of Shanghai is the highest, and the VCDI value of Suzhou, Ningbo, and Wuxi is also relatively high, while the VCDI value of peripheral cities is relatively low. Furthermore, we found that there is a relatively obvious regional division of labor among cities, but the core cities have strong homogeneity in the high-value areas. Therefore, it is necessary to further strengthen the dislocation of competition between core cities. Thirdly, the model results show that rising land prices and construction in the development zones at the provincial and national levels both have significant contributing effects on the enhancement of the regional division of labor in the value chain, while the innovation inputs, innovation outputs, and their interaction terms show a negative effect. There is a need to further enhance the efficiency of innovation transformation and improve the quality of innovation transformation in order to promote upgrading in the value chain.
Comparative Analysis of Pigeonpea Stalk Biochar Characteristics and Energy Use under Different Biochar Production Methods
Authors: Kumar Nallagatla Vinod , Sawargaonkar Gajanan L. , Rani C. Sudha , Singh Ajay , Prakash T. Ram , Triveni S. , Kamdi Prasad J. , Pasumarthi Rajesh , Karthik Rayapati , Venkatesh Bathula
Published: October 2023

The disposal of crop residues from agricultural fields is often seen as a burden due to the difficulties involved. However, this study aims to turn pigeonpea stalks into biochar, which can serve as a fuel substitute and soil amendment to sequester carbon. Different pyrolysis methods were employed to investigate the variations in yield, physicochemical characteristics, and higher heating value (HHV) of biochar produced from pigeonpea stalks. The biochar produced using a muffle furnace exhibited higher fixed carbon and ash content. These characteristics make it beneficial for restoring degraded agricultural soils by enhancing carbon sequestration. In addition, the muffle furnace biochar demonstrated a total potential carbon ranging from 262.8 to 264.3 g of carbon per kilogram of biochar, along with a CO2 reduction potential ranging from 77.17 to 79.68 CO2 eq per kg. Both the European Biochar Certificate and the International Biochar Initiative confirmed the agronomic abilities of the biochar and its compliance with the highest quality standards for soil carbon sequestration, with 0.11 H/C and 0.7 O/C ratios. Furthermore, biochar produced by muffle furnace from pigeonpea stalks exhibited superior fixed carbon recovery efficiency (181.66 to 184.62%), densification (5.86 to 6.83%), energy density (1.77 to 2.06%), energy retention efficiency (54.80 to 56.64%), fuel ratio (18.95 to 22.38%), and HHV (30.66 to 32.56 MJ kg−1). Additionally, it had lower H/C and O/C ratios, suggesting its potential as an alternative solid fuel. The results of the characterization of biochar with scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR) revealed that the biochar samples prepared with both the methods had carbonyl (C=O), C=C, and aromatic C-H functional groups; however, the biochar prepared in the muffle furnace had more porosity. In summary, this study highlights the potential of using pigeonpea stalks to produce biochar, which can be utilized as a renewable fuel substitute and soil amendment to sequester carbon. The biochar derived from the muffle furnace exhibited desirable physicochemical characteristics, high carbon content, and excellent energy properties, making it a promising option for various applications.
Sustainable Supply Chain Practices: An Empirical Investigation from the Manufacturing Industry
Authors: Abuzawida Shaker Salem , Alzubi Ahmad Bassam , Iyiola Kolawole
Published: October 2023

Recently, firms have adopted sustainable supply chain practices (SSCPs) to reduce the social and environmental impacts in their supply chain network. The primary objective is to reduce adversarial negative impacts on the environment, particularly those stemming from consumption of energy and water. Embracing sustainable business practices is increasingly essential for organizations and in addressing pressing global challenges. Based on 439 valid responses obtained through cross-sectional research from Turkish manufacturing firms, and combining practice-based view and organizational information processing theory, this study examines the effect of SSCP on economic performance (EP). The mediating roles of social performance (SP) and environmental performance (ENP) were examined. The moderating role of Industry 4.0 was further explored with regard to the aforementioned links. The results revealed that SSCP has a positive effect on EP. SSCP has positive effects on SP and ENP. SP and ENP have positive effects on EP. The relationship between SSCP and EP is partially mediated by both SP and ENP. Further, it was discovered that in firms with a high level of Industry 4.0 technologies usage, the impact of SSCP on ENP was stronger, and in firms with high level of Industry 4.0, the impact of SSCP on EP was also stronger. The findings offer valuable insights for research, as well as for managers, supply chain practitioners, and policymakers, in developing an integrated strategic and effective approach that promotes sustainable operations by using improved understanding of Industry 4.0, which also yields better economic outcomes.
Unlocking Tourism’s Potential: Pricing Strategies for the Post-COVID Renaissance
Authors: Al-Moustafa Sara S. , Hassan Thowayeb H. , Saleh Mahmoud I. , Helal Mohamed Y. , Salem Amany E. , Ghazy Khaled
Published: October 2023

In response to the challenges faced by tourism managers in devising effective pricing strategies to overcome the impacts of the COVID-19 pandemic, this study aims to make a valuable contribution to the existing literature on tourism management and marketing. Specifically, we investigate the pricing strategies that tourists are most likely to prefer in the aftermath of the pandemic. To achieve this, we conducted semi-structured interviews with a sample size of experienced tourists in Egypt. Our findings reveal that tourists exhibit a strong inclination towards hedonic and bundling pricing strategies. This suggests that tourism managers should prioritize the incorporation of these strategies into their post-pandemic business plans. However, it is crucial for managers to carefully consider tourists’ behavioral responses to dynamic and pay-what-you-want pricing strategies, as these are influenced by various constraints that necessitate careful attention. Furthermore, our study highlights potential negative consequences associated with the implementation of the dual-pricing strategy following the pandemic. This strategy has the potential to compromise tourists’ intentions to utilize certain services, thereby leading to unfavorable behavioral outcomes for tourism destinations. The implications of this study are of practical significance for tourism managers seeking to design more effective pricing strategies to capitalize on the opportunities presented by the post-pandemic recovery period. By understanding tourists’ preferences and considering the potential drawbacks associated with certain pricing strategies, managers can make informed decisions that promote the sustainable and successful revival of the tourism industry. Moreover, our study employed the checkpoints method, which allowed us to identify respondents’ priorities for the pricing strategies during the crisis, further enhancing the validity and reliability of our findings.
Urban Resilience of Important Node Cities in Population Migration under the Influence of COVID-19 Based on Mamdani Fuzzy Inference System
Authors: Wang Huilong , Wang Meimei , Yang Rong , Yang Huijuan
Published: October 2023

COVID-19 has resulted in a great inconvenience and has had a severe impact on the economy and residents’ daily life in China and even the world. Urban resilience, as the key representation of social and economic stability, can directly reflect the development and stability of cities. In addition, the Mamdani fuzzy inference system (MFIS), as one of the commonly used fuzzy inference systems, has been successfully applied in various application problems involving imprecise or vague information since it was proposed. In this paper, we mainly consider the urban resilience of 50 important node cities for population migration (50INCPM) in China in 2020 under the influence of COVID-19. We apply MFIS for approximating the urban resilience index (URI) based on multiple inputs, which includes the population density resilience index (PRI), gross domestic product per capita resilience index (GRI), in-degree centrality resilience index (IRI), out-degree centrality resilience index (ORI), confirmed cases number (CCN), recovery rate (RR) and mortality rate (MR). Meanwhile, based on the big data of population migration and COVID-19 data in China from 15 January to 15 March in 2020, we calculate the URI of 50INCPM in China in 2020 under the influence of COVID-19. Moreover, we show the spatial difference of URI and its changes in different stages. The results show that (1) the URI of 50INCPM decreases from the eastern coastal area to the western inland, and the cities with URI more than 0.5 are gathered in the eastern coastal area of China. As COVID-19 is controlled, the URI is gradually rising, and the growth rate of URI in southeast coastal cities exceeds that of inland cities. (2) The second-tier and third-tier cities have stronger resilience in the case of large-scale emergencies. (3) There exists a positive correlation in URI and RR. The expectation of the research finding gives a basis for judging the economic and social situation under the impact of COVID-19, which can help local governments accurately judge city resilience, and provide a reference for the decision on resuming production and work, so it is of positive significance for national economic resilience and social stability. Finally, on the basis of universal vaccine coverage, we hold that the GOC should promote the cities’ resilience in China, especially in the first-tier city in inland China (Beijing, Shanghai, Guangzhou and Shenzhen). On the other hand, on the premise of implementing epidemic prevention and control measures, local governments should stimulate the resilience of each city in terms of population and economy.
A Novel Adaptive Neural Network-Based Thermoelectric Parameter Prediction Method for Enhancing Solid Oxide Fuel Cell System Efficiency
Authors: Wu Yaping , Wu Xiaolong , Xu Yuanwu , Cheng Yongjun , Li Xi
Published: October 2023

Efficiency prediction plays a crucial role in the ongoing development of electrochemical energy technology. Our industries heavily depend on a reliable energy supply for power and electricity, and solid oxide fuel cell (SOFC) systems stand out as renewable devices with immense potential. SOFCs, as one of the various types of fuel cells, are renowned for their capability of combined heat and power generation. They can achieve an efficiency of up to 90% in operation. Furthermore, due to the fact that water is the byproduct of their electricity generation process, they are extremely environmentally friendly, contributing significantly to humanity’s sustainable development. With the advancement of renewable energy technologies and the increasing emphasis on sustainable development requirements, predicting and optimizing the efficiency of SOFC systems is gaining importance. This study leverages data collected from an SOFC system and applies an improved neural network structure, specifically the dendritic network (DN) architecture, to forecast thermoelectric efficiency. The key advantage of this method lies in the adaptive neural network algorithm based on the dendritic network structure without manually setting hidden nodes. Moreover, the predicted model of thermoelectric efficiency is validated using 682 and 1099 h of operational data from the SOFC system, and the results are compared against a conventional machine learning method. After comparison, it is found that when the novel method with adaptive characteristics proposed was used for SOFC system efficiency prediction, the MAE and RMSE values were both lower than 0.014; the result is significantly better than from other traditional methods. Additionally, this study demonstrated its effectiveness in predicting the thermoelectric efficiency of SOFC systems through secondary experiments. This study offers guidance on enhancing SOFC systems thermoelectric efficiency. Therefore, this study provides a foundation for the future industrialization of fuel cell systems.
Ensemble Learning Based Sustainable Approach to Carbonate Reservoirs Permeability Prediction
Authors: Musleh Dhiaa A. , Olatunji Sunday O. , Almajed Abdulmalek A. , Alghamdi Ayman S. , Alamoudi Bassam K. , Almousa Fahad S. , Aleid Rayan A. , Alamoudi Saeed K. , Jan Farmanullah , Al-Mofeez Khansa A. , Rahman Atta
Published: October 2023

Permeability is a crucial property that can be used to indicate whether a material can hold fluids or not. Predicting the permeability of carbonate reservoirs is always a challenging and expensive task while using traditional techniques. Traditional methods often demand a significant amount of time, resources, and manpower, which are sometimes beyond the limitations of under developing countries. However, predicting permeability with precision is crucial to characterize hydrocarbon deposits and explore oil and gas successfully. To contribute to this regard, the current study offers some permeability prediction models centered around ensemble machine learning techniques, e.g., the gradient boost (GB), random forest (RF), and a few others. In this regard, the prediction accuracy of these schemes has significantly been enhanced using feature selection and ensemble techniques. Importantly, the authors utilized actual industrial datasets in this study while evaluating the proposed models. These datasets were gathered from five different oil wells (OWL) in the Middle Eastern region when a petroleum exploration campaign was conducted. After carrying out exhaustive simulations on these datasets using ensemble learning schemes, with proper tuning of the hyperparameters, the resultant models achieved very promising results. Among the numerous tested models, the GB- and RF-based algorithms offered relatively better performance in terms of root means square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) while predicting permeability of the carbonate reservoirs. The study can potentially be helpful for the oil and gas industry in terms of permeability prediction in carbonate reservoirs.
Enhancing Income as a Starting Point for Designing Sustainable Health Product-Service Systems for Chronically Ill Elderly in Rural Areas
Authors: Wang Xingyu , Yang Dongfang , Zha Li , Liao Binzhou
Published: October 2023

Insufficient income, inadequate healthcare resources, and insufficient chronic disease management awareness pose a fundamental obstacle to the sustained health maintenance of elderly individuals with chronic diseases in rural areas. In response to these challenges, this study adopted a research-through-design approach and introduced system design thinking by incorporating new stakeholders, experiential insights, and services. Initially, during the background research stage, a semi-structured interview was conducted to gain an understanding of the pains, needs, and potential opportunities of the target users. Subsequently, in the design model construction stage, desk research was carried out to refine and integrate the common components identified in various design models. In a focus group, these components were then incorporated into the development of a Sustainable Health Product-Service System Design Model tailored for rural elderly with chronic diseases after refining the pains, needs, and potential opportunities defined in the first stage. This design model encompassed elements including a background layer, a stakeholder layer, a funding layer, a product layer, a service layer, and an information layer. In the system design and design model validation stage, a design project aimed at health maintenance was launched within Liyang Town, with the design thinking method following a process of empathizing, defining, ideating, prototyping, and testing. Throughout the project, the established model guided the design process and facilitated the conceptualization and implementation of a Product-Service system. In conclusion, an investigation encompassing 15 elderly individuals was undertaken to analyze their income balance, health condition, health awareness, and their satisfaction with the health system. This analysis spanned the period both before and after their involvement in the project. The outcomes of this examination revealed notable enhancements across all three dimensions evaluated, highlighting the positive impact of the project on the elderly participants. Furthermore, the project’s social benefits were also comprehensively established.
Evaluating the Sustainability of Fashion Brands Using a Neutrosophical ORESTE Approach
Authors: Karadayi-Usta Saliha , Tirkolaee Erfan Babaee
Published: October 2023

The fashion industry represents a significant source of consumerism within the global economy and requires substantial funding, eco-friendly practices, and ethical attitudes towards human capital, which are the triple bottom line of sustainability. Conscious customers are the key to reshaping the fashion industry by shopping and supporting fashion brands that adopt sustainable practices. Thus, the purpose of this study is to extract the factors affecting the decision-making process of conscious customers in selecting fashion brands adopting ethical and sustainable implementations, to present a case study covering alternative fashion brands, and to evaluate them in terms of sustainability measures. A bibliometric analysis is conducted within the scope of this research to address the most suitable and original decision-making technique; hence, a novel neutrosophic set-based ORESTE approach is proposed to rank the alternative brands. Next, after reaching the case study findings, a sensitivity analysis is conducted to illustrate the parameter changes’ impact on the result in order to validate the findings’ outcomes. Thus, this paper contributes to the literature by proposing a novel approach as an alternative to Besson ranking, extracting sustainable and ethical fashion practices of brands, and identifying the related reports and websites as references to measure sustainability in the fashion industry. The practitioners and managers can comprehend the factors to be taken into consideration by assessing the sustainability status of the particular fashion enterprises and can examine their positions in the global market within their competitors.
Effect of Acid and Thermo-Mechanical Attacks on Compressive Strength of Geopolymer Mortar with Different Eco-Friendly Materials
Authors: Teshnizi Ebrahim Sharifi , Karimiazar Jafar , Baldovino Jair Arrieta
Published: October 2023

This research examined how changing the ratios of certain substances affected the strength and durability of a specific type of building material when exposed to acid and heat. This study used various combinations of zeolite, metakaolin, slag, and Portland cement as primary materials. It also used different amounts of potassium hydroxide (KOH) to make the geopolymer mortar. The concentrations of KOH used were 8 M, 12 M, 14 M, and 16 M. The cement-based material had the highest water absorption. A total of 240 tests were conducted, including 20 samples for each mix design tested at curing times of 7, 14, 21, 28, and 90 days. The results showed that the samples made with slag base material and 8 M mixing design had the highest average compressive strength at 28 and 90 days in the acidic environment test, and the zeolite and metakaolin base material samples had the highest corrosion and weight loss, possibly due to their high specific surface and aluminosilicate origin. The samples made with slag-based material had better resistance and the highest average compressive strength in the 300 °C and 500 °C thermo-mechanical tests. The lowest average compressive strength in the thermal and mechanical stress test was related to the samples made with a metakaolin base material. The tests performed on the samples made with slag base material had better compressive strength than the three other base materials in the acid and heat tests. The zeolite-based mortar lost the most weight under 30% acidic sulfuric water. The findings suggest that changes in the molar ratios of alkaline activators can significantly affect the durability properties and strength of geopolymer mortar, and the slag-based material with an 8 M mixing design had the best performance; also, SEM analysis verified this mechanism.
Material Tradeoff of Rotor Architecture for Lightweight Low-Loss Cost-Effective Sustainable Electric Drivetrains
Authors: Selema Ahmed
Published: October 2023

The art of the successful design of high-speed electrical machines comes with many challenges in the mass, size, reliability, and energy efficiency. Material engineering of electrical machines has been identified as a key solution for higher power dense electric drivetrains. One of the main challenges at high speed is the eddy-current losses in the active electromagnetic parts, especially magnetic materials and permanent magnets (PMs). This study is devoted to the selection of PM rotor materials using multidisciplinary design optimization for a high-speed electric drivetrain. Beside AC loss minimization, more disciplines are considered, such as the minimization of weight, and cost. Different laminations are investigated with different magnetic properties as well as cost. Additionally, different PMs are optimized considering low-cost ferrite and high-coercivity permanent magnets (HCPMs). Moreover, the optimal materials are identified which have the best balance between loss, weight, cost, ripples. Finally, different rotor designs are prototyped, assembled, and tested using the same stator configuration. Also, the best rotor design is selected, and the electromagnetic performance is measured and compared with conventional designs. The optimal design results in 8% extra torque with at least 20% weight reduction.
Mohr–Coulomb and Modified Hoek–Brown Strength Criteria of Layered Sandstone Considering the Unloading Effect and Anisotropy
Authors: Song Zhixiang , Zhang Junwen , Wu Shaokang
Published: October 2023

The Mohr–Coulomb (M-C) and Hoek–Brown (H-B) strength criteria are widely used in various engineering fields, such as mining engineering, tunnel engineering and so on. To investigate the M-C and H-B strength criteria considering the unloading effect and anisotropy, series of triaxial loading (unloading) tests on layered sandstone were conducted. The results revealed that the peak strength was significantly affected by the unloading effect. Moreover, the cohesion and internal friction angle had a significant nonlinear relationship with the bedding angle. Additionally, the M-C and modified H-B strength criteria were established considering the unloading effect and anisotropy. Then, according to the strength criteria established, the peak strength could be estimated theoretically. Furthermore, compared to the M-C strength criteria, the modified H-B strength criteria were more appropriate for accurately estimating the triaxial compressive strength of layered sandstones. The conclusions obtained could provide certain references for the stability control of deep excavation engineering.
Determinants of Management Accounting Application Use in Vietnamese Telecommunications Companies: The Moderating Role of Organisational Culture
Authors: Tran Phuong Thi Kim , Ting Irene Wei Kiong , Nguyen Thien Huu , Tran Hanh Thi My
Published: October 2023

This study aims to investigate the effects of the determinants of management accounting application use in the telecommunications industry whilst considering the moderating role of organisational culture. The determinants examined in this study were company size, business strategy, accountants’ qualifications, market competition and managers’ awareness. Survey questionnaires were distributed to telecommunications companies in Vietnam, which resulted in a dataset of 146 respondents from 164 Vietnamese telecommunications companies and a response rate of 89%. The target respondents were directors, vice directors, managers and chief accountants. The questionnaires were distributed to each company, and the collected data were processed using Microsoft Excel and analysed for conformity using SPSS and Smart-PLS. Path analysis, specifically, structural equation modelling with partial least squares structural equation modelling, was conducted. The findings indicated that all the determinants except company size positively impacted management accounting application use. However, in the presence of organisational culture, company size did positively influence management accounting application use. Moreover, organisational culture enhanced the influence of managers’ awareness on management accounting application use in the Vietnamese telecommunications companies. This research can deepen our understanding of the determinants of management accounting application use and moderating role of organisational culture in the context of the telecommunications industry. The findings can provide empirical evidence to managers of Vietnamese telecommunications companies seeking to enhance the performance of their organisation.
What Affects the Depth of the Human–Garden Relationship in Freely Accessible Urban Sensory Gardens with Therapeutic Features in Various Users?
Authors: Krzeptowska-Moszkowicz Izabela , Moszkowicz Łukasz , Porada Karolina
Published: October 2023

A human being comes into contact with the environment through the senses. That is why in the space of cities, where various intense stimuli negatively affect the living of people, there is important greenery that has a positive impact. Significant types of gardens within urban green areas are sensory gardens. In our article, we intended to answer the question of what specifically affects the formation of deeper human–garden relations in urban, publicly accessible gardens designed to have a sensory impact. Our research was conducted mainly in Poland. We used a method of assessing the behavior of garden visitors, using a five-point scale. We found that the existence of specific interiors in gardens that have been designed in such a way as to stimulate two to three selected senses, which we call the leading senses, can create an environment that allows for deeper relationships with the garden. We also concluded that when designing a public sensory garden, adaptation to specific user groups is one of the most important guidelines. A deeper contact with the sensory garden for people visiting a city, e.g., tourists, may occur especially when there is a positive surprise or when an additional need of this group is met in the garden. Sensory gardens, although they are a relatively new type of urban greenery, can become a permanent element of cities if they are carefully designed and meet the expectations of their recipients.
Measurement of CO2 Emissions Efficiency and Analysis of Influencing Factors of the Logistics Industry in Nine Coastal Provinces of China
Authors: Wang Hanxin , Liu Weiqian , Liang Yi
Published: October 2023

The surge in CO2 emissions affects global climate change and the development of society. The logistics industry, being a swiftly advancing industry, demonstrates an escalating trend in CO2 emissions. Therefore, this paper selects the more developed coastal provinces (districts) in China’s logistics industry and takes 2011–2020 as the research period. Using the Super-SBM model and the Malmquist index model, the article analyzes the changes in the carbon emission efficiency of the logistics industry from the static and dynamic perspectives and then explores the factors affecting it using the panel model and the mediating effect model. Findings from research indicate that: (1) The CO2 emission efficiency of the logistics industry is generally moderate when viewed from a static perspective. (2) Taking a dynamic viewpoint, there is a slight declining trend in the overall CO2 emission efficiency. (3) As environmental regulations become more stringent, the CO2 emission efficiency follows the “U”-shaped pattern, initially declining and then rising. Environmental regulations can influence CO2 emission efficiency by affecting technological innovation. Additionally, energy efficiency plays a positive role in promoting CO2 emission efficiency. Recommendations: Implement differentiated environmental regulations tailored to local conditions. Emphasize technological innovations. Enhance the energy efficiency.
Improving the Resilience of the Road Network in Algeria: A Comparative Analysis of Flexible, Geosynthetically Reinforced, and Rigid Pavements
Authors: Bouacha Nadjet , Bouguerra Abdesselam , Bouafia Abdelhak
Published: October 2023

Despite subsequent strengthening measures, pavements constructed in Algeria suffer rapid deterioration soon after completion, with continued decline in some cases. Various factors contribute to this problem, including cost and availability of materials, build quality, and environmental conditions. The entire Algerian road network is based on flexible and semi-rigid pavements. This study explores pavement structures using numerical predictive models to advance our understanding of road construction and maintenance. Using Abaqus 2022 finite element analysis software, we consider three scenarios: a simple pavement, a pavement reinforced with geosynthetics, and a rigid pavement. We compare the results concerning the vertical stresses, interface stresses, and vertical displacements at the level of the critical pavement sections. The aim of this research was to achieve behavior in flexible pavement similar to that of rigid pavement through the study of the reinforcement effect of flexible pavements with geogrids. The layers were treated as elastic, and a finite element analysis was conducted for various geogrid stiffness values (geogrid/soil stiffness ratios ranging from 1 to 50). The model enabled us to select the geogrid with the appropriate stiffness for each soil stiffness. The main conclusions are presented and discussed in depth, with the results confirming that rigid pavements offer greater strength and better durability. We then provide convincing evidence that the performance characteristics of rigid pavements can be obtained for flexible pavements by integrating a geogrid reinforcement system. This system is based on the choice of the stiffness ratio of the supporting soil and the rigidity of the geogrid. This option allows the state of the supporting soil to be accepted as it is and for intervention to target in the composite soil–geogrid rigidity.
Two Residual Attention Convolution Models to Recover Underexposed and Overexposed Images
Authors: Rinanto Noorman , Su Shun-Feng
Published: October 2023

Inconsistent lighting phenomena in digital images, such as underexposure and overexposure, pose challenges in computer vision. Many studies have developed to address these issues. However, most of these techniques cannot remedy both exposure problems simultaneously. Meanwhile, existing methods that claim to be capable of handling these cases have not yielded optimal results, especially for images with blur and noise distortions. Therefore, this study proposes a system to improve underexposed and overexposed photos, consisting of two different residual attention convolution networks with the CIELab color space as the input. The first model working on the L-channel (luminance) is responsible for recovering degraded image illumination by using residual memory block networks with self-attention layers. The next model based on dense residual attention networks aims to restore degraded image colors using ab-channels (chromatic). A properly exposed image is produced by fusing the output of these models and converting them to RGB color space. Experiments on degraded synthetic images from two public datasets and one real-life exposure dataset demonstrate that the proposed system outperforms the state-of-the-art algorithms in optimal illumination and color correction outcomes for underexposed and overexposed images.
Multi-Scale CNN: An Explainable AI-Integrated Unique Deep Learning Framework for Lung-Affected Disease Classification
Authors: Sarkar Ovi , Islam Md. Robiul , Syfullah Md. Khalid , Islam Md. Tohidul , Ahamed Md. Faysal , Ahsan Mominul , Haider Julfikar
Published: October 2023

Lung-related diseases continue to be a leading cause of global mortality. Timely and precise diagnosis is crucial to save lives, but the availability of testing equipment remains a challenge, often coupled with issues of reliability. Recent research has highlighted the potential of Chest X-ray (CXR) images in identifying various lung diseases, including COVID-19, fibrosis, pneumonia, and more. In this comprehensive study, four publicly accessible datasets have been combined to create a robust dataset comprising 6650 CXR images, categorized into seven distinct disease groups. To effectively distinguish between normal and six different lung-related diseases (namely, bacterial pneumonia, COVID-19, fibrosis, lung opacity, tuberculosis, and viral pneumonia), a Deep Learning (DL) architecture called a Multi-Scale Convolutional Neural Network (MS-CNN) is introduced. The model is adapted to classify multiple numbers of lung disease classes, which is considered to be a persistent challenge in the field. While prior studies have demonstrated high accuracy in binary and limited-class scenarios, the proposed framework maintains this accuracy across a diverse range of lung conditions. The innovative model harnesses the power of combining predictions from multiple feature maps at different resolution scales, significantly enhancing disease classification accuracy. The approach aims to shorten testing duration compared to the state-of-the-art models, offering a potential solution toward expediting medical interventions for patients with lung-related diseases and integrating explainable AI (XAI) for enhancing prediction capability. The results demonstrated an impressive accuracy of 96.05%, with average values for precision, recall, F1-score, and AUC at 0.97, 0.95, 0.95, and 0.94, respectively, for the seven-class classification. The model exhibited exceptional performance across multi-class classifications, achieving accuracy rates of 100%, 99.65%, 99.21%, 98.67%, and 97.47% for two, three, four, five, and six-class scenarios, respectively. The novel approach not only surpasses many pre-existing state-of-the-art (SOTA) methodologies but also sets a new standard for the diagnosis of lung-affected diseases using multi-class CXR data. Furthermore, the integration of XAI techniques such as SHAP and Grad-CAM enhanced the transparency and interpretability of the model’s predictions. The findings hold immense promise for accelerating and improving the accuracy and confidence of diagnostic decisions in the field of lung disease identification.
Stochastic Interventional Vaccine Efficacy and Principal Surrogate Analyses of Antibody Markers as Correlates of Protection against Symptomatic COVID-19 in the COVE mRNA-1273 Trial
Authors: Huang Ying , Hejazi Nima S. , Blette Bryan , Carpp Lindsay N. , Benkeser David , Montefiori David C. , McDermott Adrian B. , Fong Youyi , Janes Holly E. , Deng Weiping , Zhou Honghong , Houchens Christopher R. , Martins Karen , Jayashankar Lakshmi , Flach Britta , Lin Bob C. , O’Connell Sarah , McDanal Charlene , Eaton Amanda , Sarzotti-Kelsoe Marcella , Lu Yiwen , Yu Chenchen , Kenny Avi , Carone Marco , Huynh Chuong , Miller Jacqueline , El Sahly Hana M. , Baden Lindsey R. , Jackson Lisa A. , Campbell Thomas B. , Clark Jesse , Andrasik Michele P. , Kublin James G. , Corey Lawrence , Neuzil Kathleen M. , Pajon Rolando , Follmann Dean , Donis Ruben O. , Koup Richard A. , Gilbert Peter B. ,   on behalf of the Immune Assays,   Moderna, Inc.,   Coronavirus Vaccine Prevention Network (CoVPN)/Coronavirus Efficacy (COVE),   United States Government (USG)/CoVPN Biostatistics Teams
Published: October 2023

The COVE trial randomized participants to receive two doses of mRNA-1273 vaccine or placebo on Days 1 and 29 (D1, D29). Anti-SARS-CoV-2 Spike IgG binding antibodies (bAbs), anti-receptor binding domain IgG bAbs, 50% inhibitory dilution neutralizing antibody (nAb) titers, and 80% inhibitory dilution nAb titers were measured at D29 and D57. We assessed these markers as correlates of protection (CoPs) against COVID-19 using stochastic interventional vaccine efficacy (SVE) analysis and principal surrogate (PS) analysis, frameworks not used in our previous COVE immune correlates analyses. By SVE analysis, hypothetical shifts of the D57 Spike IgG distribution from a geometric mean concentration (GMC) of 2737 binding antibody units (BAU)/mL (estimated vaccine efficacy (VE): 92.9% (95% CI: 91.7%, 93.9%)) to 274 BAU/mL or to 27,368 BAU/mL resulted in an overall estimated VE of 84.2% (79.0%, 88.1%) and 97.6% (97.4%, 97.7%), respectively. By binary marker PS analysis of Low and High subgroups (cut-point: 2094 BAU/mL), the ignorance interval (IGI) and estimated uncertainty interval (EUI) for VE were [85%, 90%] and (78%, 93%) for Low compared to [95%, 96%] and (92%, 97%) for High. By continuous marker PS analysis, the IGI and 95% EUI for VE at the 2.5th percentile (519.4 BAU/mL) vs. at the 97.5th percentile (9262.9 BAU/mL) of D57 Spike IgG concentration were [92.6%, 93.4%] and (89.2%, 95.7%) vs. [94.3%, 94.6%] and (89.7%, 97.0%). Results were similar for other D29 and D57 markers. Thus, the SVE and PS analyses additionally support all four markers at both time points as CoPs.
The International Virus Bioinformatics Meeting 2023
Authors: Hufsky Franziska , Abecasis Ana B. , Babaian Artem , Beck Sebastian , Brierley Liam , Dellicour Simon , Eggeling Christian , Elena Santiago F. , Gieraths Udo , Ha Anh D. , Harvey Will , Jones Terry C. , Lamkiewicz Kevin , Lovate Gabriel L. , Lücking Dominik , Machyna Martin , Nishimura Luca , Nocke Maximilian K. , Renard Bernard Y. , Sakaguchi Shoichi , Sakellaridi Lygeri , Spangenberg Jannes , Tarradas-Alemany Maria , Triebel Sandra , Vakulenko Yulia , Wijesekara Rajitha Yasas , González-Candelas Fernando , Krautwurst Sarah , Pérez-Cataluña Alba , Randazzo Walter , Sánchez Gloria , Marz Manja
Published: October 2023

The 2023 International Virus Bioinformatics Meeting was held in Valencia, Spain, from 24–26 May 2023, attracting approximately 180 participants worldwide. The primary objective of the conference was to establish a dynamic scientific environment conducive to discussion, collaboration, and the generation of novel research ideas. As the first in-person event following the SARS-CoV-2 pandemic, the meeting facilitated highly interactive exchanges among attendees. It served as a pivotal gathering for gaining insights into the current status of virus bioinformatics research and engaging with leading researchers and emerging scientists. The event comprised eight invited talks, 19 contributed talks, and 74 poster presentations across eleven sessions spanning three days. Topics covered included machine learning, bacteriophages, virus discovery, virus classification, virus visualization, viral infection, viromics, molecular epidemiology, phylodynamic analysis, RNA viruses, viral sequence analysis, viral surveillance, and metagenomics. This report provides rewritten abstracts of the presentations, a summary of the key research findings, and highlights shared during the meeting.
Existing Evidence for Influenza B Virus Adaptations to Drive Replication in Humans as the Primary Host
Authors: Pekarek Matthew J. , Weaver Eric A.
Published: October 2023

Influenza B virus (IBV) is one of the two major types of influenza viruses that circulate each year. Unlike influenza A viruses, IBV does not harbor pandemic potential due to its lack of historical circulation in non-human hosts. Many studies and reviews have highlighted important factors for host determination of influenza A viruses. However, much less is known about the factors driving IBV replication in humans. We hypothesize that similar factors influence the host restriction of IBV. Here, we compile and review the current understanding of host factors crucial for the various stages of the IBV viral replication cycle. While we discovered the research in this area of IBV is limited, we review known host factors that may indicate possible host restriction of IBV to humans. These factors include the IBV hemagglutinin (HA) protein, host nuclear factors, and viral immune evasion proteins. Our review frames the current understanding of IBV adaptations to replication in humans. However, this review is limited by the amount of research previously completed on IBV host determinants and would benefit from additional future research in this area.
A Novel Rotavirus Reverse Genetics Platform Supports Flexible Insertion of Exogenous Genes and Enables Rapid Development of a High-Throughput Neutralization Assay
Authors: Wei Jiajie , Radcliffe Scott , Pirrone Amanda , Lu Meiqing , Li Yuan , Cassaday Jason , Newhard William , Heidecker Gwendolyn , Rose II William , He Xi , Freed Daniel , Citron Michael , Espeseth Amy , Wang Dai
Published: October 2023

Despite the success of rotavirus vaccines, rotaviruses remain one of the leading causes of diarrheal diseases, resulting in significant childhood morbidity and mortality, especially in low- and middle-income countries. The reverse genetics system enables the manipulation of the rotavirus genome and opens the possibility of using rotavirus as an expression vector for heterologous proteins, such as vaccine antigens and therapeutic payloads. Here, we demonstrate that three positions in rotavirus genome—the C terminus of NSP1, NSP3 and NSP5—can tolerate the insertion of reporter genes. By using rotavirus expressing GFP, we develop a high-throughput neutralization assay and reveal the pre-existing immunity against rotavirus in humans and other animal species. Our work shows the plasticity of the rotavirus genome and establishes a high-throughput assay for interrogating humoral immune responses, benefiting the design of next-generation rotavirus vaccines and the development of rotavirus-based expression platforms.