bluuebunny/crossref_metadata_2025 huggingface dataset:
Investigation of Hole-Transfer Dynamics through Simple EL De-Convolution in Non-Fullerene Organic Solar Cells
Authors: Lee Dongchan , Kim Do Hui , Oh Chang-Mok , Park Sujung , Krishna Narra Vamsi , Wibowo Febrian Tri Adhi , Hwang In-Wook , Jang Sung-Yeon , Cho Shinuk
Published: October 2023

In conventional fullerene-based organic photovoltaics (OPVs), in which the excited electrons from the donor are transferred to the acceptor, the electron charge transfer state (eECT) that electrons pass through has a great influence on the device’s performance. In a bulk-heterojunction (BHJ) system based on a low bandgap non-fullerene acceptor (NFA), however, a hole charge transfer state (hECT) from the acceptor to the donor has a greater influence on the device’s performance. The accurate determination of hECT is essential for achieving further enhancement in the performance of non-fullerene organic solar cells. However, the discovery of a method to determine the exact hECT remains an open challenge. Here, we suggest a simple method to determine the exact hECT level via deconvolution of the EL spectrum of the BHJ blend (ELB). To generalize, we have applied our ELB deconvolution method to nine different BHJ systems consisting of the combination of three donor polymers (PM6, PBDTTPD-HT, PTB7-Th) and three NFAs (Y6, IDIC, IEICO-4F). Under the conditions that (i) absorption of the donor and acceptor are separated sufficiently, and (ii) the onset part of the external quantum efficiency (EQE) is formed solely by the contribution of the acceptor only, ELB can be deconvoluted into the contribution of the singlet recombination of the acceptor and the radiative recombination via hECT. Through the deconvolution of ELB, we have clearly decided which part of the broad ELB spectrum should be used to apply the Marcus theory. Accurate determination of hECT is expected to be of great help in fine-tuning the energy level of donor polymers and NFAs by understanding the charge transfer mechanism clearly.
Mask R-CNN–Based Landslide Hazard Identification for 22.6 Extreme Rainfall Induced Landslides in the Beijiang River Basin, China
Authors: Wu Zhibo , Li Hao , Yuan Shaoxiong , Gong Qinghua , Wang Jun , Zhang Bing
Published: October 2023

Landslides triggered by extreme precipitation events pose a significant threat to human life and property in mountainous regions. Therefore, accurate identification of landslide locations is crucial for effective prevention and mitigation strategies. During the prolonged heavy rainfall events in Guangdong Province between 21 May and 21 June 2022, shallow and clustered landslides occurred in the mountainous regions of the Beijiang River Basin. This research used high-resolution satellite imagery and integrated the Mask R-CNN algorithm model with spectral, textural, morphological and physical characteristics of landslides in remote sensing imagery, in addition to landslide-influencing factors and other constraints, to interpret the landslides induced by the event through remote sensing techniques. The detection results show that the proposed methodology achieved a high level of accuracy in landslide identification, with a precision rate of 81.91%, a recall rate of 84.07% and an overall accuracy of 87.28%. A total of 3782 shallow landslides were detected, showing a distinct clustered distribution pattern. The performance of Mask R-CNN, Faster-CNN, U-Net and YOLOv3 models in landslide identification was further compared, and the effects of setting the rotation angle and constraints on the identification results of the Mask R-CNN algorithm model were investigated. The results show that each model improves the evaluation indices, but the Mask R-CNN model has the best detection performance; the rotation angle can effectively improve the generalization ability and robustness of the model, and the landslide-inducing factor data and texture feature sample data are the best for landslide identification. The research results provide valuable references and technical support for deepening our understanding of the distribution patterns of rainfall-triggered shallow and cluster landslides in the Beijiang River Basin.
An Improved U-Net Network for Sandy Road Extraction from Remote Sensing Imagery
Authors: Nie Yunfeng , An Kang , Chen Xingfeng , Zhao Limin , Liu Wantao , Wang Xing , Yu Yihao , Luo Wenyi , Li Kewei , Zhang Zhaozhong
Published: October 2023

The extraction of sandy roads from remote sensing images is important for field ecological patrols and path planning. Extraction studies on sandy roads face limitations because of various factors (e.g., sandy roads may have poor continuity, may be obscured by external objects, and/or have multi-scale and banding characteristics), in addition to the absence of publicly available datasets. Accordingly, in this study, we propose using the remote sensing imagery of a sandy road (RSISR) dataset and design a sandy road extraction model (Parallel Attention Mechanism-Unet, or PAM-Unet) based on Gaofen-2 (GF-2) satellite images. Firstly, the model uses a residual stacking module, which can solve the problem of poor road feature consistency and improve the extraction of fine features. Secondly, we propose a parallel attention module (PAM), which can reduce the occlusion effect of foreign objects on roads during the extraction process and improve feature map reduction. Finally, with this model, the SASPP (Strip Atrous Spatial Pyramid Pooling) structure, which enhances the model’s ability to perceive contextual information and capture banding features, is introduced at the end of the encoder. For this study, we conducted experiments on road extraction using the RSISR dataset and the DeepGlobe dataset. The final results show the following: (a) On the RSISR dataset, PAM-Unet achieves an IoU value of 0.762, and its F1 and IoU values are improved by 2.7% and 4.1%, respectively, compared to U-Net. In addition, compared to the models Unet++ and DeepLabv3+, PAM-Unet improves IoU metrics by 3.6% and 5.3%, respectively. (b) On the DeepGlobe dataset, the IoU value of PAM-Unet is 0.658; compared with the original U-Net, the F1 and IoU values are improved by 2.5% and 3.1%, respectively. The experimental results show that PAM-Unet has a positive impact by way of improving the continuity of sandy road extraction and reducing the occlusion of irrelevant features, and it is an accurate, reliable, and effective road extraction method.
Analysis of Electromagnetic Wave and Multipath Suppression from Overhead Perspective
Authors: Luo Haolan , Zhang Wenqiang , Ren Zhaoting , Tang Chuantian , Ou Yu , Cui Guolong , Guo Shisheng
Published: October 2023

The multipath problem in indoor target detection has always been a long-standing research hotspot. Although there are many solutions to the multipath problem in a horizontal line of sight, the multipath problem of single-station radar from an overhead perspective still needs to be solved. At present, there is a lack of detailed analysis on the multipath propagation law of electromagnetic waves from an overhead perspective. This paper first analyzes the multipath propagation law of overhead perspective and reveals a combination multipath propagation phenomenon that is easily overlooked, which is formed by walls, ground, and targets. In addition, during the analysis process, the influence of coherent sources generated by multipath on angle estimation was fully considered, and verified through simulation and measured data. Then, based on the result of propagation analysis, this paper proposes a multipath ghost target suppression method. This method first establishes a multipath ghost target location dictionary based on building information, and then matches the tracking results with the dictionary to suppress successfully matched multipath ghost targets. Finally, several experiments are carried out to verify the effectiveness of this method.
Secrecy Performance Analysis of Backscatter Communications with Side Information
Authors: Kaveh Masoud , Rostami Ghadi Farshad , Jäntti Riku , Yan Zheng
Published: October 2023

Backscatter communication (BC) systems are a promising technology for internet of things (IoT) applications that allow devices to transmit information by modulating ambient radio signals without the need for a dedicated power source. However, the security of BC systems is a critical concern due to the vulnerability of the wireless channel. This paper investigates the impact of side information (SI) on the secrecy performance of BC systems. SI mainly refers to the additional knowledge that is available to the communicating parties beyond transmitted data, which can be used to enhance reliability, efficiency, security, and quality of service in various communication systems. In particular, in this paper, by considering a non-causally known SI at the transmitter, we derive compact analytical expressions of average secrecy capacity (ASC) and secrecy outage probability (SOP) for the proposed system model to analyze how SI affects the secrecy performance of BC systems. Moreover, a Monte Carlo simulation validates the accuracy of our analytical results and reveals that considering such knowledge at the transmitter has constructive effects on the system performance and ensures reliable communication with higher rates than the conventional BC systems without SI, namely, lower SOP and higher ASC are achievable.
A Nanocomposite Paste Electrode Sensor for Simultaneous Detection of Uric Acid and Bisphenol A Using Zinc Hydroxide Nitrate-Sodium Dodecylsulfate Bispyribac
Authors: Yulkifli Yulkifli , Yandes Widya Putri , Isa Illyas Md , Hashim Norhayati , Ulianas Alizar , Sharif Sharifah Norain Mohd , Saidin Mohamad Idris , Ahmad Mohamad Syahrizal , Yazid Siti Nur Akmar Mohd , Suyanta Suyanta , Nuryadi Ratno , Abd Azis Nurashikin
Published: October 2023

The fabrication of a zinc hydroxide nitrate-sodium dodecylsulfate bispyribac modified with multi-walled carbon nanotube (ZHN-SDS-BP/MWCNT) paste electrode for uric acid and bisphenol A detection was presented in this study. Electrochemical impedance spectroscopy, chronocoulometry, square-wave voltammetry, and cyclic voltammetry were all used to examine the electrocatalytic activities of modified paste electrodes. The modified electrode’s sensitivity and selectivity have been considered in terms of the composition of the modifier in percentages, the types of supporting electrolytes used, the pH of the electrolyte, and square-wave voltammetry parameters like frequency, pulse size, and step increment. Square-wave voltammetry is performed by applying a small amplitude square-wave voltage to a scanning potential from −0.3 V to +1.0 V, demonstrating a quick response time and high sensitivity. The ZHN-SDS-BP/MWCNT sensor demonstrated a linear range for uric acid and bisphenol A from 5.0 µM to 0.7 mM, with a limit of detection of 0.4 µM and 0.8 µM, respectively, with good reproducibility, repeatability, and stability as well. The modified paste electrode was successfully used in the determination of uric acid and bisphenol A in samples of human urine and lake water.
Self-Attention-Based Deep Convolution LSTM Framework for Sensor-Based Badminton Activity Recognition
Authors: Deng Jingyang , Zhang Shuyi , Ma Jinwen
Published: October 2023

Sensor-based human activity recognition aims to classify human activities or behaviors according to the data from wearable or embedded sensors, leading to a new direction in the field of Artificial Intelligence. When the activities become high-level and sophisticated, such as in the multiple technical skills of playing badminton, it is usually a challenging task due to the difficulty of feature extraction from the sensor data. As a kind of end-to-end approach, deep neural networks have the capacity of automatic feature learning and extracting. However, most current studies on sensor-based badminton activity recognition adopt CNN-based architectures, which lack the ability of capturing temporal information and global signal comprehension. To overcome these shortcomings, we propose a deep learning framework which combines the convolutional layers, LSTM structure, and self-attention mechanism together. Specifically, this framework can automatically extract the local features of the sensor signals in time domain, take the LSTM structure for processing the badminton activity data, and focus attention on the information that is essential to the badminton activity recognition task. It is demonstrated by the experimental results on an actual badminton single sensor dataset that our proposed framework has obtained a badminton activity recognition (37 classes) accuracy of 97.83%, which outperforms the existing methods, and also has the advantages of lower training time and faster convergence.
Analysis of Spatiotemporal Variation and Influencing Factors of PM2.5 in China Based on Multisource Data
Authors: Kan Xi , Liu Xu , Zhou Zhou , Zhang Yonghong , Zhu Linglong , Sian Kenny Thiam Choy Lim Kam , Liu Qi
Published: October 2023

The urbanization process over the past decades has resulted in increasing attention being paid to atmospheric pollution by researchers, especially changes in PM2.5 concentration. This study attempted to explore the spatiotemporal changes in PM2.5 concentration in China from 2000 to 2021, as well as their interaction patterns and intensities with temperature, precipitation, vegetation coverage, and land use types. This was carried out by analyzing monthly average PM2.5 concentration data and various meteorological and geographical factors. Suggestions have also been made to reduce PM2.5 concentration and improve air quality. The results show that in the past 22 years, the overall concentration of PM2.5 in China has shown a downward trend, with an average annual rate of 1.42 μg/m3 from 2013 to 2021, accompanied by a clear spatial pattern and significant seasonal changes. The high pollution areas are mainly concentrated in the Tarim Basin, Sichuan Basin, North China Plain, and the Middle and Lower Yangtze Valley Plain, where the PM2.5 concentration in autumn and winter is significantly higher than that in spring and summer. In addition, based on the national spatial scale, PM2.5 concentration is negatively correlated with precipitation and vegetation coverage, while it is significantly positively correlated with arable land and impervious surfaces. Strengthening the control of farmland pollution, accelerating urban greening construction, further expanding the scale of forests and grasslands, and enriching vegetation types will help reduce PM2.5 concentration and improve air quality.
Awareness and Use of Sustainable Land Management Practices in Smallholder Farming Systems
Authors: Bwalya Bridget , Mutandwa Edward , Chiluba Brian Chanda
Published: October 2023

Sustainable land management (SLM) practices are often touted as a vehicle for simultaneously increasing agricultural productivity and food security in rural areas. In Eastern Zambia, numerous initiatives such as the Zambia Integrated Forest Landscape Project (ZIFLP) have been implemented. Yet, empirical data suggest relatively low levels of SLM uptake in the smallholder farming sector. Therefore, the broad objective of this study was to estimate the relationship between smallholder farmer awareness of SLM technologies and land allocated to SLM at the farm level. We hypothesized the following: H1: Increased farmer awareness of SLM practices leads to more land allocated to SLM activities in Zambia’s Eastern Province; and H2: Adoption of specific SLM practices influences the extent of land allocated to SLM. Using an intra-household cross-sectional survey, data were collected from 761 randomly selected households from 11 chiefdoms of the Eastern Province. The Heckman selection procedure was used to analyze the study’s overarching hypothesis. Findings showed that farmers were generally conversant with SLM as a construct (>90%), with choices being influenced by gender. Conservation agriculture in the form of crop rotations, use of manure, mixed cropping, tree planting, and minimum tillage methods were the most commonly known SLM technologies among farmers. Findings also indicated that awareness is an important antecedent in the use of SLM practices (χ2 = 76.6, p = 0.00), with greater access to extension being positively associated with farmer awareness (p < 0.05). The land allotted to SLM hinged on crop diversity, ownership of different types of livestock, and access to agricultural extension. These findings suggest that long-term commitments to training farmers in SLM is critical. This will be achieved when there is coherence in the information on SLM being given to farmers by all the actors working in the region.
Optimization of CI Engine Performance and Emissions Using Alcohol–Biodiesel Blends: A Regression Analysis Approach
Authors: Dey Suman , Singh Akhilendra Pratap , Gajghate Sameer Sheshrao , Pal Sagnik , Saha Bidyut Baran , Deb Madhujit , Das Pankaj Kumar
Published: October 2023

This research paper investigates the optimum engine operating parameters, namely engine load, palm biodiesel, and ethanol percentage, by using a regression analysis approach. The study was conducted on a single-cylinder, four-stroke diesel engine at varying engine loads and constant speed. A general full factorial design was established using Minitab software (Version 17) for three different input factors with their varying levels. The test results based on the regression model are used to optimize the engine load and percentages of palm biodiesel and ethanol in diesel–biodiesel–ethanol ternary blends. The analysis of variance (ANOVA) revealed a significant effect on performance and emission parameters for all three factors at a 95% confidence level. From the regression study, optimum brake thermal efficiency (BTE), nitrogen oxide (NOx), carbon monoxide (CO), and unburnt hydrocarbon (UHC) emissions were found to be 12.57%, 436.2 ppm, 0.03 vol.%, and 79.2 ppm, respectively, at 43.43% engine load, 11.06% palm biodiesel, and 5% ethanol share. The findings of this study can be used to optimize engine performance and emission characteristics. The regression analysis approach presented in this study can be used as a tool for future research on optimizing engine performance and emission parameters.
Ergoecology Factors Influencing Healthy and Sustainable Workplace in Healthcare Organisation
Authors: Thomas Albi , Ma Suresh , Rehman Ateekh Ur , Muthuswamy Shanthi
Published: October 2023

Ergoecology in sustainable healthcare refers to incorporating the aspects of ergonomics, which emphasises tailoring the healthy workplace to the abilities and constraints of healthcare employees, and ecological considerations, which consider the overall environmental impact of healthcare activities. The purpose of this study is to identify ergoecology factors influencing healthy and sustainable workplaces in the context of healthcare organisations and to develop a conceptual model. Literature review and opinions of healthcare experts have opted to identify the factors influencing ergoecology in healthcare organisations. The identified factors were face-validated by the healthcare experts. Scheduled interviews and closed-ended questionnaires were employed to collect data for this research. This research employed total interpretive structural modelling (TISM) and matrix multiplication applied to classification (MICMAC) analysis. TISM methodology is utilised to better comprehend how the components interact with one another, and why. MICMAC analysis is employed to prioritise factors and categorise these factors into four categories: driving factors, autonomous factors, dependent factors, and linkage factors. It is observed that analysis of surroundings and the adoption of ecological and ergonomic measures have strong driving power, but weak dependence. These factors are key factors and are termed as independent factors of ergoecology in healthcare for a healthy and sustainable workplace. The structural model and methodological approach are employed in a healthcare organisation; this approach can be extended to different industries in the future. This study helps to understand the concept of ergoecology and the influencing factors for ergoecology in the context of healthcare organisations. Ergoecology concept not only benefits the environment and the health of healthcare employees but also boosts productivity within the healthcare industry. In this study, the total interpretive structural modelling and matrix multiplication applied to classification analysis for healthcare is proposed as an innovative approach to address the ergo-ecology concept in the context of healthcare for a healthy and sustainable workplace to improve productivity within the healthcare industry.
Traffic Status Prediction Based on Multidimensional Feature Matching and 2nd-Order Hidden Markov Model (HMM)
Authors: Li Fei , Liu Kai , Chen Jialiang
Published: October 2023

Spatiotemporal data from urban road traffic are pivotal for intelligent transportation systems and urban planning. Nonetheless, missing data in traffic datasets is a common challenge due to equipment failures, communication issues, and monitoring limitations, especially the missing not at random (MNAR) problem. This research introduces an approach to address MNAR-type missing data in traffic status prediction, utilizing a multidimensional feature sequence and a second-order hidden Markov model (2nd-order HMM). First, this approach involves extracting spatiotemporal features for the preset data sections and spatial features for the sections to be predicted based on the traffic spatiotemporal characteristics. Second, using the extracted features, distinctive road traffic features are generated for each section. Furthermore, at specific intervals within the defined time period, nearest distance feature matching is introduced to ascertain the traffic attributes of the road section under prediction. Finally, relying on the matched status results, a 2nd-order HMM is employed to forecast the traffic status for subsequent moments within the defined time period. Experiments were carried out using datasets from Shenzhen City and compared against the hidden Markov models and contrast measure (HMM-C) method to affirm the efficacy of the proposed approach.
Towards Sustainable Water Resources Management Considering Climate Change in the Case of Saudi Arabia
Authors: Alodah Abdullah
Published: October 2023

Saudi Arabia is one of the most water-scarce nations in the world, with a huge demand-supply gap, and the situation is expected to worsen due to climate change. Conventional surface water resources are limited, while nonrenewable groundwater sources are depleted. To build a more resilient and sustainable water sector, the production of non-conventional water resources, specifically desalinated seawater and treated domestic wastewater, has steadily increased in recent years. As the country lacks perennial water resources, such as rivers or water bodies, it relies mainly on nonrenewable groundwater and desalinated water to meet its daily requirements. Although the government is attempting to regulate the agricultural sector, water consumption in agriculture remains relatively high. It presents an environmental challenge due to its heavy reliance on non-renewable groundwater resources. The anticipated increase in temperature and highly uncertain changes in the rainfall patterns in Saudi Arabia could lead to greater uncertainty when attempting to develop effective water resource management plans. In this work, we review the status of the present and future of water resources and the challenges local authorities face in managing water resources amidst a changing climate in Saudi Arabia. This study employed a narrative research methodology, utilizing various databases, including Scopus, Web of Science, and Science Direct, to extract relevant articles within the subject area. This study proposes a number of recommendations and conclusions aimed at improving decision-makers’ ability to adapt to and mitigate the anticipated adverse impacts of climate change to manage scarce water resources sustainably.
Pandemic-Induced Shifts in Climate Change Perception and Energy Consumption Behaviors: A Cross-Country Analysis of Belgium, Italy, Romania, and Sweden
Authors: Iancu Ioana Ancuta , Hendrick Patrick , Micu Dan Doru , Cote Adrian
Published: October 2023

This research explores the impact of the COVID-19 pandemic on consumer behavior and preferences related to household energy consumption through actions to fight climate change in Belgium, Romania, Italy, and Sweden. Using data from two Eurobarometer surveys conducted in 2019 and 2021, the study examines shifts in climate change perception, actions to combat climate change, and the influence of socio-economic and demographic variables on these actions. Depending on the country, the findings reveal significant pandemic-induced changes in public perceptions of climate change and personal actions to combat it. Age, gender, and education level were found to influence climate change actions. Financial constraints also significantly influenced the adoption of energy-efficient behaviors. Our research enriches existing knowledge by exploring the influence of the COVID-19 pandemic on climate change perceptions and actions across diverse European countries, shedding light on the interplay between global crises and sustainability. The research methodology, including chi-square tests, logistic regression, and effect size measurements, provides a robust framework for understanding how economic factors and consumer behaviors are contributing to the development of effective energy policies.
Research on the Supervision and Implementation System of Territorial Space Planning from the Perspective of Resilience
Authors: Chen Siyun , Zhu Chuhai , Ouyang Xiao , Han Yong
Published: October 2023

Enhancing the resilience of territorial space is a necessary means to cope with future risks and development uncertainties. Resilience is the goal and connotation of implementing monitoring of territorial space planning, and planning supervision is the guarantee for the formation of resilient territorial space. Although domestic and foreign planning implementation monitoring has reflected resilience thinking in the background and objectives, it has achieved good results in ecological and social resilience of territorial space, there are still shortcomings in institutional systems and methodological techniques. At present, China’s territorial space planning is still in the exploratory stage, and the supervision and implementation system has not been perfected, which has not played the expected role in enhancing the resilience of territorial space. Therefore, with a focus on the development of China’s economy and society, it is necessary to build an efficient monitoring system for the implementation of territorial space based on China’s national conditions, and improve it in terms of ideology,, theoretical, policy, regulatory, methods and techniques, in order to promote the modernization of China’s territorial space governance and the construction of an ecological civilization system, and also provide reference for the spatial planning of other countries.
Classification of Speed Change and Unstable Flow Segments Using Geohash-Encoded Vehicle Big Data
Authors: Chong Kyu Soo
Published: October 2023

Precise and detailed speed information is indispensable for ensuring safe and efficient transportation. This is particularly true within unstable flow (UF) segments, which are especially prone to accidents due to the significant speed variations between vehicles and across lanes, and in the context of evolving transportation systems, where autonomous and non-autonomous vehicles are increasingly mixing. To address the limitations of existing methods in providing such data, this study aims to improve the detail, accuracy, and granularity of road information for micro-segments by leveraging individual vehicle big data. The proposed approach utilizes the geohash algorithm for spatial segmentation and introduces a novel criterion for identifying UF segments based on the relationship between space mean speed (SMS) and time mean speed (TMS). The presented strategy was validated through a comprehensive analysis of DTG (Digital Tachograph) data from freight vehicles on Expressway No. 50 in the Gyeonggi region in the Republic of Korea. As a result, a total of 301 segments were identified, including 178 eastbound and 123 westbound segments. UF segments corresponded to partitions falling beyond the reference standard deviation range. Compared with VDS (Vehicle Detection System) and conzone speeds, the proposed method provided more precise and continuous speed information, surpassing those obtained from conventional link-based approaches.
Exergy Analysis of a Sugarcane Crop: A Planting-to-Harvest Approach
Authors: Righetto Felipe Godoy , Mady Carlos Eduardo Keutenedjian
Published: October 2023

The objective of this study was to conduct an exergy analysis of sun–plant interactions in sugarcane using mathematical models, aiming to estimate plant production and exergy flows and describe their photosynthetic efficiency during sugarcane cultivation. Sugarcane productivity was determined based on the Brazilian BRCANE model. The efficiency of this crop was evaluated through a simple control volume, where the exergy of solar radiation serves as the sole energy input, and the exergy of the culms and straw represents the useful exergy. The findings revealed an average second-law efficiency of 5% for sugarcane photosynthesis production from solar radiation, with an estimated harvest of approximately 16.29 kWh/m2 of useful extended exergy after a year, and an estimated water consumption of 111.2 m3/ton of harvested stalks. Moreover, this study highlights that exergy efficiency varies significantly in response to seasonal changes. The method developed here can be utilized in future studies to estimate mass and energetic flows and exergy analyses.
Research on the Performance Parameters of a Compression-Ignition Engine Fueled by Blends of Diesel Fuel, Rapeseed Methyl Ester and Hydrotreated Vegetable Oil
Authors: Žaglinskis Justas , Rimkus Alfredas
Published: October 2023

This research compares the air pollution (CO, CO2, HC, NOx, smoke), energy (brake-specific fuel consumption, thermal efficiency) and noise indicators of a compression ignition engine fueled by first-generation biodiesel (rapeseed methyl ester (RME)) and second-generation biodiesel (hydrogenated vegetable oils (HVO)), or conventional (fossil) diesel fuel blends. The concentration of first- and second-generation biodiesel in two-component blends with diesel fuel was up to 15% and 30% (RME15, RME30, HVO15, and HVO30); for comparison, the three-component blend of diesel fuel, HVO and RME (RME15–HVO15) was considered. The fuels’ physical and chemical properties were tested in a specialized laboratory, and the engine load conditions were ensured by the engine brake stand. Referring to ship power plants with constant-speed engines, detailed research was carried out in one speed mode (n = 2000 rpm). Studies have shown that two-component fuel blends with HVO are superior to conventional diesel fuel and two-component blends with RME in almost all cases. The HVO in fuel blends reduced fuel consumption up to 1.8%, while the thermal efficiency was close to that of fossil diesel fuel. In addition, a reduction in pollutants was observed: CO by ~12.5–25.0%; HC by ~5.0–12.0%; NOx by ~6.5%; smokiness by ~11–18% (two-component blend) and up to ~29% (three-component blend). The CO2 and noise characteristics were close to those of fossil diesel fuel; however, the trend of reduced smoke emission was clearly seen. A fundamental obstacle to the wide use of HVO can be seen, however, which is the price, which is 25–90% (depending on the EU country) higher than the price of conventional (fossil) diesel fuel.
A Review of the Various Control Algorithms for Trajectory Control of Unmanned Underwater Vehicles
Authors: Bashir Adeel , Khan Sikandar , Iqbal Naveed , Bashmal Salem , Ullah Sami , Fayyaz Fayyaz , Usman Muhammad
Published: October 2023

Unmanned underwater vehicles (UUVs) have become increasingly popular in recent years due to their use in various applications. The motivations for using UUVs include the exploration of difficult and dangerous underwater environments, military tasks in mine detection, intelligence gathering and surveillance, the inspection of offshore oil and gas infrastructure in the oil and gas industry, scientific research for studying marine life, and the search and rescue of missing persons or submerged airplanes or boats in underwater environments. UUVs offer many advantages in achieving the desired applications with increased safety, efficiency, and cost-effectiveness. However, there are also several challenges associated with their communication, navigation, power requirements, maintenance, and payload limitations. These types of vehicles are also prone to various disturbances caused by currents of the ocean, propulsion systems, and unmolded uncertainties. Practically, it is a challenging task to design a controller that will ensure optimal performance under these conditions. Therefore, the control system design is of prime importance in the overall development of UUVs. Also, the UUV controller receives input from different sensors, and the data from these sensors are used by the controller to perform different tasks. The control systems of UUVs should take into account all uncertainties and make them stable so that all sensors can perform optimally. This paper presents a complete review of different control system design algorithms for UUVs. The basic logic designs of several control system algorithms are also presented. A comparison is made based on reliability, robustness, precession, and the ability of the controller to handle the nonlinearity that is faced by UUVs during their missions. Simulation and experimental results are thoroughly studied to gain insight into each algorithm. The advantages and disadvantages of each algorithm are also presented, which will facilitate the selection of a suitable algorithm for the control system design of UUVs.
Manufacturing of Clay Bricks Using Hybrid Waste Marble Powder and Sugarcane Bagasse Ash: A Sustainable Building Unit
Authors: Abbas Safeer , Baig Arslan , Hameed Rashid , Kazmi Syed Minhaj Saleem , Munir Muhammad Junaid , Shaukat Sbahat
Published: October 2023

In masonry construction, the most commonly used building unit all over the world is the burnt clay brick. Adding waste materials in certain percentages to these bricks helps in eliminating the environmental burden occurring in the form of excessive waste accumulation on open land sites, leading to sustainable and economical construction. This research program aimed to examine the feasibility of using waste marble powder (WMP) and sugarcane bagasse ash (SBA) in the manufacturing of clay bricks. WMP was collected from local marble cutting workshops, whereas SBA was prepared by burning the waste sugarcane obtained from various sugar mills in the local area. Brick specimens incorporating 5%, 10%, 15%, and 20% of hybrid WMP and SBA were prepared at a local brick kiln. Burnt clay bricks were transported to the laboratory, and their mechanical and durability properties were evaluated. A reduction in weight per unit area of brick specimens incorporating waste materials was observed, allowing them to be easily handled and transported. Decreased compressive strength was due to the addition of waste materials in comparison with conventional clay bricks. However, waste percentages up to 15% satisfied the criteria for the minimum compressive strength as per the Building Code of Pakistan (BCP). All tested samples showed flexural strength greater than 0.65 MPa. Tested bricks incorporating 10% and 20% of waste materials had water absorption values of 18% and 21%, respectively, which are higher than that of conventional clay bricks. Moreover, bricks incorporating waste materials exhibited a higher initial rate of absorption than conventional clay brick; therefore, such bricks need to be wet well before use in masonry construction. Brick specimens showed less than 1% weight loss, and bricks exhibited no signs of distress and cracking after 50 freeze-thaw cycles. A decrease in compressive strength was observed due to sulphate exposure. However, specimens with 10% waste materials still satisfied the minimum compressive strength requirement of BCP. Based on this study, it can be concluded that bricks with up to 10% hybrid waste materials (WMP and SBA) will assist in the environmental issues of these wastes, leading to more sustainable and economical masonry construction.
Consumer Purchasing Power Prediction of Interest E-Commerce Based on Cost-Sensitive Support Vector Machine
Authors: Ye Rendao , Yang Mengyao , Sun Peng
Published: October 2023

The traditional e-commerce business chain is being reconstructed around the content of short videos and live streams, and the interest e-commerce is thriving as a new trend in the e-commerce industry. Diversified content promotes the rapid development of interest e-commerce. For consumers, their preferences for different content reflect their consumption level to a certain extent. The purpose of this study is to accurately predict the purchasing power level with the consumer content preference, and provide new ideas for interest e-commerce business. In this paper, the new swarm intelligence algorithm is used to find the optimal misclassification cost, and three cost-sensitive models are established. On this basis, the content preference of interest e-commerce consumers is used to predict the level of purchasing power. The results show that the content preference of interest e-commerce consumers, such as “fashion”, “photography” and “interpretation”, have a significant effect on the prediction of purchasing power at the 95% confidence level. The accuracies of the optimized cost-sensitive support vector machine in predicting consumer purchasing power are all above 0.9, and the highest is 0.9792. This study effectively alleviates the problem that the classification results tend to be biased towards negative samples, especially when the imbalanced rate of the sample is high. It not only provides researchers with an efficient parameter optimization method, but also reflects the relationship between consumer content preference and purchasing power, providing data support for interest e-commerce operations.
Impact of Digital Transformation toward Sustainable Development
Authors: Alojail Mohammed , Khan Surbhi Bhatia
Published: October 2023

The rapid advancements in digital technologies have prompted organizations to embrace digital transformations (DTs) in order to enhance efficiency, gain a competitive advantage, and achieve long-term sustainability objectives. However, the successful adoption of innovative digital technologies necessitates the careful consideration of various factors, such as stakeholder engagement, resource allocation, risk mitigation, and the availability of resources and implementation support. This study examines the sustainable adoption of innovative digital technologies (DTs) within digital transformations. The data for this study were collected from 760 stakeholders through a questionnaire survey and analyzed using SPSS software (Version 27). This study’s results underscore the significance of considering the efficiency of the transformation process and the long-term sustainability outcomes for organizations. The findings of the analysis clarify that integrating sustainability principles and DT has a positive impact on the effectiveness of the transformation, as indicated by environmental, social, and economic performance indicators. This study’s novelty lies in its focus on incorporating sustainability principles into the digital transformation process. The results of this study demonstrate that organizations’ long-term sustainability outcomes are enhanced when their digital transformation goals align with the Sustainable Development Goals (SDGs). The purpose of this study emphasizes the importance of arranging digital transformations with sustainable objectives to ensure the overall success and longevity of transformation efforts.
Designing Mobility Policies for Vulnerable Users Employing the Living Lab Approach: Cases of a Demand-Responsive Transit Service in Ljubljana and Maribor
Authors: Mariano Pietro , Trolese Marco , Kastelec David , Bitenc Mateja , Jurgec Deja
Published: October 2023

In recent years, transport policy has strongly been oriented to develop more inclusive cities and to design mobility services aligned to specific users’ needs. In an attempt to guarantee an adequate level of accessibility, especially to vulnerable users, approaches based on the dialogue between public administration, operators, citizens and researchers (so-called “quadruple helix”) have been proposed. The two case studies presented in this article refer to the development of a minibus demand-responsive transport system, devoted mainly to people with reduced mobility, in the cities of Ljubljana and Maribor (Slovenia) designed using a Living Lab participative approach. In fact, urban mobility stakeholders were engaged in this process during the design and monitoring phases of the pilot projects. Their involvement resulted in producing positive outcomes: citizens actively participated in the projects, and the designed service was perceived to be useful and effective. Both projects were successful, as statistics demonstrate, and are expected to be confirmed and strengthened over the next few years.
Vermicompost: An Eco-Friendly and Cost-Effective Alternative for Sustainable Agriculture
Authors: Katiyar Rajesh Babu , Sundaramurthy Suresh , Sharma Anil Kumar , Arisutha Suresh , Pratap-Singh Anubhav , Mishra Satyam , Ayub Rashid , Jeon Byong-Hun , Khan Moonis Ali
Published: October 2023

Vermicomposting emerges as an eco-friendly solution to manage a blend of agricultural residues and digested biogas slurry (DBS). This research probes the influence of two specific earthworm species, Eisenia fetida and Eugilius euganiae, on the composting dynamics of agro-residues and DBS. Moreover, it gauges their consequential impact on the growth of chili and brinjal plants. The research was conducted at the Sharda Vihar Campus in Bhopal. Several process variables, such as pH, salinity, moisture levels, temperature, carbon-to-nitrogen (C/N) ratio, nitrogen (N), phosphorus (P), potassium (K), presence of pathogens, and monoculture trends, were assessed for their influence on vermicompost yield and its effect on chili and brinjal growth. Intriguingly, reactors employing E. fetida exhibited a vermicast recovery rate of 89.7%, whereas those utilizing E. eugeniae achieved 68.2% recovery, especially with an earthworm density of 125 individuals per liter. Notably, the derived NPK values from various composted and vermicomposted materials ranged from 1.5 to 1.7% for N, 0.98 to 1.19% for P, and 1.1 to 1.49% for K. This suggests its viability as both a fertilizer and soil enhancer. The E. fetida vermicompost-enriched soil notably boosted the yield of chili and brinjal. Overall, these insights highlight vermicomposting’s dual utility in waste management and augmenting bioresources.
A New Hydro-Mechanical Coupling Numerical Model for Predicting Water Inflow in Karst Tunnels Considering Deformable Fracture
Authors: Li Guodong , Li Changlong , Liao Jianxing , Wang Hong
Published: October 2023

The accurate prediction of groundwater inflow in tunnels in karst regions has been a difficult problem to overcome for a long time. This study proposes an equivalent fracture model that takes into account unsaturated seepage and fracture deformation to predict tunnel water inflow, which is constructed based on the TOUGH-FLAC3D framework. The proposed model with complete failure mechanisms of fracture, including shear failure and tensile failure, was applied to predict the water inflow of the Jianxing Tunnel in Guizhou Province to verify its effectiveness. The results indicate that the proposed numerical model was found to be comparable to on-site observations in predicting inflow rate. The inflow rate in a fractured network reaches a steady state faster than that in a non-fractured network. There is a significant difference of 100 times between the highest transient rate and the stable rate between the fracture network and the non-fractured model. The excavation-induced stress redistribution resulted in slip fracture occurring within a distance of approximately 8.2 m from the tunnel wall, which can increase the fracture width and in turn increases the amount of water flowing into the tunnel by about 50%. In addition, this paper also analyzes the impact of the factors of fracture density, incline angle, stress anisotropy, and initial fracture width on the inflow rate during tunnel construction. The study emphasizes the significance of considering deformable fractures and provides valuable insights for improving numerical tools for inflow prediction during tunnel construction.
Designing Socially and Organizationally Sustainable Industry 4.0 Systems: Requirements for Modeling Approaches
Authors: Kannengiesser Udo
Published: October 2023

Industry 4.0 (I4.0) systems are often designed without sufficiently considering the needs of stakeholders and the organizational processes to be supported, leading to solutions that are socially and organizationally unsustainable. In this study, the notions of social and organizational sustainability were viewed from a micro-level perspective, referring to the ability of technology to sustain the concerns of people and work organization within the socio-technical system, as opposed to a macro-level perspective related to concerns outside the system. Through a literature review, this study shows that social and organizational sustainability is covered by principles originally proposed in agile software engineering. A set of core requirements for model-based design approaches were then derived from the agile principles, based on insights from design research and model theory. The requirements include (1) the coverage of function and behavior, (2) simplicity, (3) executability and (4) modularity. They were then used to evaluate an existing modeling approach—subject-oriented process modeling (S-BPM)—to demonstrate their applicability and usefulness.
BiLSTM for Predicting Post-Construction Subsoil Settlement under Embankment: Advancing Sustainable Infrastructure
Authors: Wang Liyang , Li Taifeng , Wang Pengcheng , Liu Zhenyu , Zhang Qianli
Published: October 2023

The load and settlement histories of stage-constructed embankments provide critical insights into long-term surface behavior under embankment loading. However, these data often remain underutilized in predicting post-construction settlement in the absence of geotechnical subsoil characterization. To address this limitation, the current study integrates bidirectional long short-term memory (BiLSTM) into a three-phase framework: data preparation, model construction, and performance evaluation. In the data preparation phase, the feature vector comprises basal pressure, pressure increments, time intervals, and prior settlement values to facilitate a rolling forecast. To manage unevenly spaced data, an Akima spline standardizes the desired time intervals. The model’s efficacy is validated using observational data from two distinct construction case studies, each featuring diverse soil conditions. BiLSTM proves effective in identifying key attributes from load and settlement data during the staged construction process. Compared to traditional curve-fitting methods, the BiLSTM model exhibits superior performance, robustness, and adaptability to varying soil conditions. Additionally, the model demonstrates low sensitivity to the range of post-construction data, allowing for a data collection period reduction—from six months to three—without compromising prediction accuracy (relative error = 0.92%). These advantages not only optimize resource allocation but also contribute to broader sustainability objectives.