bluuebunny/crossref_metadata_2025 huggingface dataset:
MS-AGAN: Road Extraction via Multi-Scale Information Fusion and Asymmetric Generative Adversarial Networks from High-Resolution Remote Sensing Images under Complex Backgrounds
Authors: Lin Shaofu , Yao Xin , Liu Xiliang , Wang Shaohua , Chen Hua-Min , Ding Lei , Zhang Jing , Chen Guihong , Mei Qiang
Published: July 2023

Extracting roads from remote sensing images is of significant importance for automatic road network updating, urban planning, and construction. However, various factors in complex scenes (e.g., high vegetation coverage occlusions) may lead to fragmentation in the extracted road networks and also affect the robustness of road extraction methods. This study proposes a multi-scale road extraction method with asymmetric generative adversarial learning (MS-AGAN). First, we design an asymmetric GAN with a multi-scale feature encoder to better utilize the context information in high-resolution remote sensing images (HRSIs). Atrous spatial pyramid pooling (ASPP) and feature fusion are integrated into the asymmetric encoder–decoder structure to avoid feature redundancy caused by multi-level cascading operations and enhance the generator network’s ability to extract fine-grained road information at the pixel level. Second, to maintain road connectivity, topologic features are considered in the pixel segmentation process. A linear structural similarity loss (LSSIM) is introduced into the loss function of MS-AGAN, which guides MS-AGAN to generate more accurate segmentation results. Finally, to fairly evaluate the performance of deep models under complex backgrounds, the Bayesian error rate (BER) is introduced into the field of road extraction for the first time. Experiments are conducted via Gaofen-2 (GF-2) high-resolution remote sensing images with high vegetation coverage in the Daxing District of Beijing, China, and the public DeepGlobe dataset. The performance of MS-AGAN is compared with a list of advanced models, including RCFSNet, CoANet, UNet, DeepLabV3+, and DiResNet. The final results show that (1) with respect to road extraction performance, the Recall, F1, and IoU values of MS-AGAN on the Daxing dataset are 2.17%, 0.04%, and 2.63% higher than the baselines. On DeepGlobe, the Recall, F1, and IoU of MS-AGAN improve by 1.12%, 0.42%, and 0.25%, respectively. (2) On road connectivity, the Conn index of MS-AGAN from the Daxing dataset is 46.39%, with an improvement of 0.62% over the baselines, and the Conn index of MS-AGAN on DeepGlobe is 70.08%, holding an improvement of 1.73% over CoANet. The quantitative and qualitative analyses both demonstrate the superiority of MS-AGAN in preserving road connectivity. (3) In particular, the BER of MS-AGAN is 20.86% over the Daxing dataset with a 0.22% decrease compared to the best baselines and 11.77% on DeepGlobe with a 0.85% decrease compared to the best baselines. The proposed MS-AGAN provides an efficient, cost-effective, and reliable method for the dynamic updating of road networks via HRSIs.
Machine Learning and VIIRS Satellite Retrievals for Skillful Fuel Moisture Content Monitoring in Wildfire Management
Authors: Schreck John S. , Petzke William , Jiménez Pedro A. , Brummet Thomas , Knievel Jason C. , James Eric , Kosović Branko , Gagne David John
Published: July 2023

Monitoring the fuel moisture content (FMC) of 10 h dead vegetation is crucial for managing and mitigating the impact of wildland fires. The combination of in situ FMC observations, numerical weather prediction (NWP) models, and satellite retrievals has facilitated the development of machine learning (ML) models to estimate 10 h dead FMC retrievals over the contiguous US (CONUS). In this study, ML models were trained using variables from the National Water Model, the High-Resolution Rapid Refresh (HRRR) NWP model, and static surface properties, along with surface reflectances and land surface temperature (LST) retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument on the Suomi-NPP satellite system. Extensive hyper-parameter optimization resulted in skillful FMC models compared to a daily climatography RMSE (+44%) and an hourly climatography RMSE (+24%). Notably, VIIRS retrievals played a significant role as predictors for estimating 10 h dead FMC, demonstrating their importance as a group due to their high band correlation. Conversely, individual predictors within the HRRR group exhibited relatively high importance according to explainability techniques. Removing both HRRR and VIIRS retrievals as model inputs led to a significant decline in performance, particularly with worse RMSE values when excluding VIIRS retrievals. The importance of the VIIRS predictor group reinforces the dynamic relationship between 10 h dead fuel, the atmosphere, and soil moisture. These findings underscore the significance of selecting appropriate data sources when utilizing ML models for FMC prediction. VIIRS retrievals, in combination with selected HRRR variables, emerge as critical components in achieving skillful FMC estimates.
Recent Advances in Smart Epidural Spinal Needles
Authors: Althobaiti Murad , Ali Sajid , Hariri Nasir G. , Hameed Kamran , Alagl Yara , Alzahrani Najwa , Alzahrani Sara , Al-Naib Ibraheem
Published: July 2023

Lumbar puncture is a minimally invasive procedure that utilizes a spinal needle to puncture the lumbar epidural space to take a sample from the cerebrospinal fluid or inject drugs for diagnostic and therapeutic purposes. Physicians rely on their expertise to localize epidural space. Due to its critical procedure, the failure rate can reach up to 28%. Hence, a high level of experience and caution is required to correctly insert the needle without puncturing the dura mater, which is a fibrous layer protecting the spinal cord. Failure of spinal anesthesia is, in some cases, related to faulty needle placement techniques since it is blindly inserted. Therefore, advanced techniques for localization of the epidural space are essential to avoid any possible side effects. As for epidural space localization, various ideas were carried out over recent years to provide accurate identification of the epidural space. Subsequently, several methodologies based on mechanical and optical schemes have been proposed. Several research groups worked from different aspects of the problem, namely, the clinical and engineering sides. Hence, the main goal of this paper is to review this research with the aim of remedying the gap between the clinical side of the problem and the engineering side by examining the main techniques in building sensors for such purposes. This manuscript provides an understanding of the clinical needs of spinal needles from an anatomical point of view. Most importantly, it discusses the mechanical and optical approaches in designing and building sensors to guide spinal needles. Finally, the standards that must be followed in building smart spinal needles for approval procedures are also presented, along with some insight into future directions.
Safe and Robust Map Updating for Long-Term Operations in Dynamic Environments
Authors: Stefanini Elisa , Ciancolini Enrico , Settimi Alessandro , Pallottino Lucia
Published: July 2023

Ensuring safe and continuous autonomous navigation in long-term mobile robot applications is still challenging. To ensure a reliable representation of the current environment without the need for periodic remapping, updating the map is recommended. However, in the case of incorrect robot pose estimation, updating the map can lead to errors that prevent the robot’s localisation and jeopardise map accuracy. In this paper, we propose a safe Lidar-based occupancy grid map-updating algorithm for dynamic environments, taking into account uncertainties in the estimation of the robot’s pose. The proposed approach allows for robust long-term operations, as it can recover the robot’s pose, even when it gets lost, to continue the map update process, providing a coherent map. Moreover, the approach is also robust to temporary changes in the map due to the presence of dynamic obstacles such as humans and other robots. Results highlighting map quality, localisation performance, and pose recovery, both in simulation and experiments, are reported.
Code Injection Attacks in Wireless-Based Internet of Things (IoT): A Comprehensive Review and Practical Implementations
Authors: Noman Haitham Ameen , Abu-Sharkh Osama M. F.
Published: July 2023

The Internet of Things (IoT) has transformed various domains in our lives by enabling seamless communication and data exchange between interconnected devices, necessitating robust networking infrastructure. This paper presents a comprehensive analysis of code injection attacks in IoT, focusing on the wireless domain. Code injection attacks exploit security weaknesses in applications or software and can have severe consequences, such as data breaches, financial losses, and denial of service. This paper discusses vulnerabilities in IoT systems and examines how wireless frames in state-of-the-art wireless technologies, which serve IoT applications, are exposed to such attacks. To demonstrate the severity of these threats, we introduce a comprehensive framework illustrating code injection attacks in the wireless domain. Several code injection attacks are performed on Wireless Fidelity (Wi-Fi) devices operating on an embedded system commonly used in IoT applications. Our proof of concept reveals that the victims’ devices become further exposed to a full range of cyber-attacks following a successful severe code injection attack. We also demonstrate three scenarios where malicious codes had been detected inside the firmware of wireless devices used in IoT applications by performing reverse engineering techniques. Criticality analysis is conducted for the implemented and demonstrated attacks using Intrusion Modes and Criticality Analysis (IMECA). By understanding the vulnerabilities and potential consequences of code injection attacks on IoT networks and devices, researchers and practitioners can develop more secure IoT systems and better protect against these emerging threats.
Utilising Sentinel-1’s Orbital Stability for Efficient Pre-Processing of Radiometric Terrain Corrected Gamma Nought Backscatter
Authors: Navacchi Claudio , Cao Senmao , Bauer-Marschallinger Bernhard , Snoeij Paul , Small David , Wagner Wolfgang
Published: July 2023

Radiometric Terrain Corrected (RTC) gamma nought backscatter, which was introduced around a decade ago, has evolved into the standard for analysis-ready Synthetic Aperture Radar (SAR) data. While working with RTC backscatter data is particularly advantageous over undulated terrain, it requires substantial computing resources given that the terrain flattening is more computationally demanding than simple orthorectification. The extra computation may become problematic when working with large SAR datasets such as the one provided by the Sentinel-1 mission. In this study, we examine existing Sentinel-1 RTC pre-processing workflows and assess ways to reduce processing and storage overheads by considering the satellite’s high orbital stability. By propagating Sentinel-1’s orbital deviations through the complete pre-processing chain, we show that the local contributing area and the shadow mask can be assumed to be static for each relative orbit. Providing them as a combined external static layer to the pre-processing workflow, and streamlining the transformations between ground and orbit geometry, reduces the overall processing times by half. We conducted our experiments with our in-house developed toolbox named wizsard, which allowed us to analyse various aspects of RTC, specifically run time performance, oversampling, and radiometric quality. Compared to the Sentinel Application Platform (SNAP) this implementation allowed speeding up processing by factors of 10–50. The findings of this study are not just relevant for Sentinel-1 but for all SAR missions with high spatio-temporal coverage and orbital stability.
Data-Driven Insights through Industrial Retrofitting: An Anonymized Dataset with Machine Learning Use Cases
Authors: Atzeni Daniele , Ramjattan Reshawn , Figliè Roberto , Baldi Giacomo , Mazzei Daniele
Published: July 2023

Small and medium-sized enterprises (SMEs) often encounter practical challenges and limitations when extracting valuable insights from the data of retrofitted or brownfield equipment. The existing literature fails to reflect the full reality and potential of data-driven analysis in current SME environments. In this paper, we provide an anonymized dataset obtained from two medium-sized companies leveraging a non-invasive and scalable data-collection procedure. The dataset comprises mainly power consumption machine data collected over a period of 7 months and 1 year from two medium-sized companies. Using this dataset, we demonstrate how machine learning (ML) techniques can enable SMEs to extract useful information even in the short term, even from a small variety of data types. We develop several ML models to address various tasks, such as power consumption forecasting, item classification, next machine state prediction, and item production count forecasting. By providing this anonymized dataset and showcasing its application through various ML use cases, our paper aims to provide practical insights for SMEs seeking to leverage ML techniques with their limited data resources. The findings contribute to a better understanding of how ML can be effectively utilized in extracting actionable insights from limited datasets, offering valuable implications for SMEs in practical settings.
Multiple-Input-Multiple-Output Filtered Multitone Time Reversal Acoustic Communications Using Direct Adaptation-Based Turbo Equalization
Authors: Sun Lin , Li Haisen
Published: July 2023

This paper proposes using direct adaptation (DA)-based turbo equalization in multiple-input-multiple-output (MIMO) filtered multitone (FMT) time reversal (TR) acoustic communications to jointly suppress noise, residual co-channel interference (CCI) and intersymbol interference (ISI) after the TR process. Soft information-based adaptive decision feedback equalization (ADFE) adjusted according to the recursive expected least squares (RELS) algorithm, including interference cancellation and decoding, is used to construct the DA-based turbo equalization. In the proposed method, soft information is exchanged between soft symbols with soft decisions of decoding iteratively, and interference suppression is proceeded successively and iteratively until the performance is stable. The principle of the proposed method is analyzed, and based on the acoustic channel responses measured in a real experiment, the performance is assessed in relation to that of anther two methods. Compared with the MIMO-FMT TR underwater acoustic communication using interference suppression without error control coding (ECC), the proposed method performs better, benefitting from the ECC included in turbo equalization. Additionally, compared with the MIMO-FMT TR underwater acoustic communication using interference suppression based on hard decision equalization and decoding, the proposed method exhibits superior performance by exploiting soft information.
An Improved Distributed Sampling PPO Algorithm Based on Beta Policy for Continuous Global Path Planning Scheme
Authors: Xiao Qianhao , Jiang Li , Wang Manman , Zhang Xin
Published: July 2023

Traditional path planning is mainly utilized for path planning in discrete action space, which results in incomplete ship navigation power propulsion strategies during the path search process. Moreover, reinforcement learning experiences low success rates due to its unbalanced sample collection and unreasonable design of reward function. In this paper, an environment framework is designed, which is constructed using the Box2D physics engine and employs a reward function, with the distance between the agent and arrival point as the main, and the potential field superimposed by boundary control, obstacles, and arrival point as the supplement. We also employ the state-of-the-art PPO (Proximal Policy Optimization) algorithm as a baseline for global path planning to address the issue of incomplete ship navigation power propulsion strategy. Additionally, a Beta policy-based distributed sample collection PPO algorithm is proposed to overcome the problem of unbalanced sample collection in path planning by dividing sub-regions to achieve distributed sample collection. The experimental results show the following: (1) The distributed sample collection training policy exhibits stronger robustness in the PPO algorithm; (2) The introduced Beta policy for action sampling results in a higher path planning success rate and reward accumulation than the Gaussian policy at the same training time; (3) When planning a path of the same length, the proposed Beta policy-based distributed sample collection PPO algorithm generates a smoother path than traditional path planning algorithms, such as A*, IDA*, and Dijkstra.
Making a Brand Loved Rather Than Sustainable? Cosmopolitanism and Brand Love as Competing Communication Claims
Authors: Bey Christoph , Moosmayer Dirk C.
Published: July 2023

Sustainability labels on products improve consumers’ product quality perceptions, suggests existing research. To understand this link in detail, the interaction of attitudes towards sustainability and attitudes towards the brand are explored. Theoretically, a branding view is contrasted with moral consumer cosmopolitanism: the view that people consider sustainability issues a planetary challenge that must be met by all of humanity. The theory is tested using the case of a global hazelnut spread brand and investigating the interaction between attitudes toward the brand (branding view) and attitude toward firm-NGO collaboration (cosmopolitanism) on consumers’ intention to purchase. Stepwise moderated regression analysis is applied to a sample of 109 French responses from an online consumer survey. Analyses show that for those who rated brand attitude in the highest category (brand lovers), the effect of NGO attitude on purchase intention was negative. By theoretically integrating branding and a cosmopolitan lens, the study contributes to the nascent debate around the potential negative effects of eco-labels. The negative moderation implies that sustainability researchers take a more nuanced approach to different levels of brand attitude and that brand managers consider withholding their sustainability performance from their brand lovers.
A Hybridization of Spatial Modeling and Deep Learning for People’s Visual Perception of Urban Landscapes
Authors: Farahani Mahsa , Razavi-Termeh Seyed Vahid , Sadeghi-Niaraki Abolghasem , Choi Soo-Mi
Published: July 2023

The visual qualities of the urban environment influence people’s perception and reaction to their surroundings; hence the visual quality of the urban environment affects people’s mental states and can have detrimental societal effects. Therefore, people’s perception and understanding of the urban environment are necessary. This study used a deep learning-based approach to address the relationship between effective spatial criteria and people’s visual perception, as well as spatial modeling and preparing a potential map of people’s visual perception in urban environments. Dependent data on people’s visual perception of Tehran, Iran, was gathered through a questionnaire that contained information about 663 people, 517 pleasant places, and 146 unpleasant places. The independent data consisted of distances to industrial areas, public transport stations, recreational attractions, primary streets, secondary streets, local passages, billboards, restaurants, shopping malls, dilapidated areas, cemeteries, religious places, traffic volume, population density, night light, air quality index (AQI), and normalized difference vegetation index (NDVI). The convolutional neural network (CNN) algorithm created the potential map. The potential visual perception map was evaluated using the receiver operating characteristic (ROC) curve and area under the curve (AUC), with the estimates of AUC of 0.877 and 0.823 for pleasant and unpleasant visuals, respectively. The maps obtained using the CNN algorithm showed that northern, northwest, central, eastern, and some southern areas of the city are potent in pleasant sight, and southeast, some central, and southern regions had unpleasant sight potential. The OneR method results demonstrated that distance to local passages, population density, and traffic volume is most important for pleasant and unpleasant sights.
Numerical Investigation of a Reactivity-Controlled Compression Ignition Engine Fueled with N-Heptane and Iso-Octane
Authors: Halis Serdar , Solmaz Hamit , Polat Seyfi , Yücesu H.
Published: July 2023

In this numerical study, the effects of the premixed ratio, intake manifold pressure and intake air temperature on a four-cylinder, four-stroke, direct injection, low-compression-ratio gasoline engine, operated in reactivity-controlled compression ignition (RCCI) combustion mode at a constant engine speed of 1000 rpm, were investigated using Converge CFD software. The results of numerical analyses showed that the maximum in-cylinder pressure and heat release rate (HRR) increased and the combustion phase advanced depending on the rise in both intake manifold pressure and intake air temperature. The CA50 shifted by 18.5 °CA with an increment in the intake air temperature from 60 °C to 100 °C. It was observed that the combustion duration dropped from 44 °CA to 38 °CA upon boosting the intake manifold pressure from 103 kPa to 140 kPa. Moreover, a delay in the combustion phase occurred at a constant intake air temperature with an increasing premixed ratio. The maximum value of in-cylinder pressure was recorded as 36.15 bar (at 11 °CA aTDC) with the use of PRF20. Additionally, as the content of iso-octane in the fuel mixture was increased, combustion delay occurred, and the maximum value of in-cylinder temperature obtained was 11 °CA aTDC using PRF20 fuel at the earliest point. While HC and CO emissions reached the highest values at a 60 °C intake air temperature, NOx and soot emission values were detected at quite low levels at this temperature. The values of all these emissions increased with rising intake manifold pressure and reached their highest values at 140 kPa. In addition, while the highest HC and CO emission values were observed with the use of PRF60 fuel, the results revealed that the control of the combustion phase in the RCCI strategy is notably affected by the premixed ratio, intake manifold pressure and intake air temperature.
Farmers’ Variety Naming and Crop Varietal Diversity of Two Cereal and Three Legume Species in the Moroccan High Atlas, Using DATAR
Authors: Bernis-Fonteneau Agnès , Aakairi Meryem , Saadani-Hassani Omar , Castangia Giandaniele , Ait Babahmad Rachid , Colangelo Paolo , D’Ambrosio Ugo , Jarvis Devra I.
Published: July 2023

Local agrobiodiversity in remote areas such as the Moroccan High Atlas is poorly studied, despite being of great importance for the sustainability and resilience of mountainous populations. This includes important species such as wheat (Triticum spp.), barley (Hordeum vulgare), fava beans (Vicia faba), peas (Pisum sativum), and alfalfa (Medicago sativa). This study aimed to better understand varietal naming by farmers and the traits they use for assessing the current diversity of the five species, in 22 locations, distributed across three hubs of the High Atlas. The data were provided by 282 Amazigh informants during focus-group discussions, household surveys, and market surveys, with the support of the Diversity Assessment Tool for Agrobiodiversity and Resilience (DATAR). The use of local terminology for variety names and systematically collected morphological, ecological, and use descriptors appears to be a valuable way to assess local intraspecific diversity, and further comparisons with genomic results are recommended. Furthermore, the results also indicate low diversity at the household level, which contrasts with the greater diversity at the community level. Larger areas are still planted with landraces compared to areas planted with modern varieties, although the levels of richness (number) of both landraces and modern varieties are equivalent overall. Many factors influence this diversity: the biophysical characteristics of the sites, the socio-economic and management practices of farmers, and the availability of varietal diversity and of modern varieties or landraces. Although selection processes have reduced the local diversity available for economically important crops, we found that farmers still rely greatly on landraces, which present traits and variability that allow them to adapt to local conditions.
Wavelet Packet-Fuzzy Optimization Control Strategy of Hybrid Energy Storage Considering Charge–Discharge Time Sequence
Authors: Zhao Xinyu , Zhang Yunxiao , Cui Xueying , Wan Le , Qiu Jinlong , Shang Erfa , Zhang Yongchang , Zhao Haisen
Published: July 2023

A hybrid energy storage system (HESS) can effectively suppress the high and low-frequency power fluctuations generated by wind farms under the intermittency and randomness of wind. However, for the existing power distribution strategies of HESS, power-type and energy-type energy storage have the problem of inconsistent charge–discharge states in the same time sequence, which makes it difficult to achieve optimal operation in terms of charge–discharge coordination and energy flow. To solve this problem, this study firstly adopts adaptive wavelet packet decomposition (WPD) to decompose the original wind power to acquire grid-connected power and HESS initial distribution power, to ensure that the supercapacitor and battery undertake the corresponding high and low-frequency power fluctuations, respectively; Then, for the inconsistent charge–discharge states, a charge–discharge time sequence optimization strategy based on the consistency index is proposed to correct the initial power distribution of HESS for the first time; Finally, aiming at the stage of charge (SOC) over-limit problem, the fuzzy optimization method is adopted to correct the HESS output power for the second time, which can reduce the unnecessary charge–discharge energy effectively. With typical daily output data of a 100 MW wind farm, the proposed control strategy is verified. The results show that it can make different energy storage technologies synchronously suppress wind power fluctuation in the same time sequence; compared with not considering charge–discharge time sequence optimization, the charge–discharge conversion times of the battery obtained by the proposed method are reduced from 71 to 14 times, and the charge–discharge conversion times of supercapacitor are reduced from 390 to 61 times; The cumulative reduction of unnecessary charge–discharge energy by HESS is 12.12 MWh. Besides, the SOC curves of HESS are controlled at a normal level, thus improving the economy and service life of HESS.
A Review of Veterinary Drug Residue Detection: Recent Advancements, Challenges, and Future Directions
Authors: Wu Haoting , Zhao Junfang , Wan Jianqing
Published: July 2023

Veterinary drug residues of common food (milk, meat) have posed serious threats to the environment and human health, making the quality and safety of agricultural, livestock, and aquatic products increasingly prominent. With the widespread use of veterinary drugs and the requirements for food safety, it has become urgent to detect veterinary drug residues in animal-derived foods. So far, few studies have systematically reviewed the progresses, challenges, and future directions in veterinary drug residue detection. A thorough review on the current advancements, challenges, and potential future directions of veterinary drug residue detection will be extremely beneficial and timely. This study reviewed recent developments of detection technology of veterinary drug residues. The current issues and challenges in veterinary drug residue detection were examined and highlighted. Finally, future proposals on directions and prospects for veterinary drug residue detection were suggested. High-throughput and high-sensitivity veterinary drug detection technology, sample pretreatment technology for rapid processing, and the fusion of multiple detection methods were recommended as the main directions for the future development of veterinary drug residue detection. It was suggested to develop the analysis and detection technologies of veterinary drug residue towards high automation, high sensitivity, and high throughput in the future. This review provides new ideas and strategies for the rapid development of animal husbandry industry and protecting consumers’ physical health and food safety.
Evaluation of the Implementation of Sustainable Stormwater Management Practices for Landed Residential Areas: A Case Study in Malaysia
Authors: Abdul Khadir Fatin Khalida Binti , Yee Ng Cheng , Takaijudin Husna Binti , Zawawi Noor Amila Wan Abdullah , Alaloul Wesam Salah , Musarat Muhammad Ali
Published: July 2023

In Malaysia, the Stormwater Management Manual for Malaysia (Manual Saliran Mesra Alam or MSMA) was introduced to manage stormwater and solve water-related problems. However, massive development caused the conventional stormwater system to be unable to cater to the excessive runoff and led to flooding, also affecting residential areas. This shows that there is an urgent requirement for a sustainable stormwater management practice (SSMP) in residential areas. This study is conducted to evaluate stormwater practitioners’ opinions on the proposed SSMPs, including green roofs, rain gardens/bioretention systems, and porous pavements, based on Strength, Weakness, Opportunity, and Threat (SWOT) factors through surveys and correlation analysis. The questionnaire was distributed to 14 branches of the Department of Irrigation and Drainage (DID), 14 branches of the City Council, and 28 selected private engineering companies. In total, 118 respondents were targeted to obtain their perspectives on the SWOT factors for each selected SSMP according to the Likert scale. The survey showed that the respondents agreed with most of the SWOT factors on the selected SSMPs. The results of the distributed questionnaire were used as the data for the correlation analysis. The analysis indicated that green roofs, rain gardens/bioretention systems, and porous pavements have a strong positive relationship, with a p-value of less than 0.05 for the Strength and Opportunity factors, and a weak positive relationship, with a p-value of more than 0.05 for the Weakness and Threat factors. This shows that the proposed SSMPs are suitable to implement in residential areas. Observations were conducted to obtain the residents’ opinions on the performance of stormwater management in their residential areas and to evaluate the suitability of the proposed SSMPs to be implemented in the observed areas. Based on the observations, it can be concluded that only rain gardens/bioretention systems and porous pavements are suitable when compared to green roofs. An interview session was conducted with practitioners in stormwater management to gain their opinions on the studies and the proposed SSMPs. The interviewees agreed with the issues and that the SSMPs should be implemented in landed residential areas.
Sustainable Digital Transformation in Healthcare: Advancing a Digital Vascular Health Innovation Solution
Authors: Akinola Segun , Telukdarie Arnesh
Published: July 2023

This systematic review focuses on digital transformation in vascular healthcare to identify key focus areas for innovation, benefits, challenges, best practices, sustainable approaches, and the impact on patient outcomes. This study includes mobile health technologies and artificial intelligence (AI), examining aspects such as data analytics and interoperability with patient-centered care. The finding reveals a growing utilization of telehealth, AI, and mobile health technologies in vascular (circulatory) healthcare. The use of telehealth services facilitates remote patient monitoring with an enhancement in patient access to care. AI supports predictive models and decision support, while mobile health technologies promote patient engagement and self-management. This review emphasizes that prioritizing digital transformation in vascular healthcare brings various advantages, including improved efficiency and enhanced patient outcomes with a reduction in cost. However, challenges such as limitations, interoperability issues, and data security concerns must be addressed. The best practice comprises stakeholders’ engagement and comprehensive training. The impact of digital transformation on patient outcomes in circulatory healthcare is positive, mainly in the management of life-threatening conditions. This review offers valuable insights for prioritizing the digital transformation and enhancement of patient outcomes in vascular healthcare.
Assessing the Role and Efficiency of Thermal Insulation by the “BIO-GREEN PANEL” in Enhancing Sustainability in a Built Environment
Authors: Almusaed Amjad , Almssad Asaad , Alasadi Asaad , Yitmen Ibrahim , Al-Samaraee Sammera
Published: July 2023

The pressing concern of climate change and the imperative to mitigate CO2 emissions have significantly influenced the selection of outdoor plant species. Consequently, evaluating CO2’s environmental effects on plants has become integral to the decision-making process. Notably, reducing greenhouse gas (GHG) emissions from buildings is significant in tackling the consequences of climate change and addressing energy deficiencies. This article presents a novel approach by introducing plant panels as an integral component in future building designs, epitomizing the next generation of sustainable structures and offering a new and sustainable building solution. The integration of environmentally friendly building materials enhances buildings’ indoor environments. Consequently, it becomes crucial to analyze manufacturing processes in order to reduce energy consumption, minimize waste generation, and incorporate green technologies. In this context, experimentation was conducted on six distinct plant species, revealing that the energy-saving potential of different plant types on buildings varies significantly. This finding contributes to the economy’s improvement and fosters enhanced health-related and environmental responsibility. The proposed plant panels harmonize various building components and embody a strategic approach to promote health and well-being through bio-innovation. Furthermore, this innovative solution seeks to provide a sustainable alternative by addressing the challenges of unsustainable practices, outdated standards, limited implementation of new technologies, and excessive administrative barriers in the construction industry. The obtained outcomes will provide stakeholders within the building sector with pertinent data concerning performance and durability. Furthermore, these results will enable producers to acquire essential information, facilitating product improvement.
Spatial Impact of Industrial Structure Upgrading and Corporate Social Responsibility on Carbon Emissions: Evidence from China
Authors: Deng Jiangsheng , Zhang Rongguang , Qiu Qiwen
Published: July 2023

Identifying the spatial attributes of economic, social and environmental development is a prerequisite for China to raise the quality of development. Based on the parallel data of 30 Chinese provinces from 2010 to 2019, this study uses a spatial econometric model to explore the spatial impacts of corporate social responsibility (CSR) and industrial structure upgrading on carbon emissions. The regulating effect of CSR during industrial structure adjustment for carbon emission reduction was also analyzed. It was found that regional carbon emissions were reduced due to CSR and industrial structure adjustment, and the former was beneficial during industrial structure adjustment for carbon emission reduction. The carbon emissions in neighboring areas have also been suppressed to some extent as a result of industrial structure adjustment. However, CSR can encourage some “free riding” behaviors, due to the economic externalities, which emit more carbon into the surroundings. Additionally, carbon emissions show different responses to CSR in various regions. Therefore, strategies must enhance overall social responsibility and formulate different policies in various regions to promote CSR as an influential factor in curbing carbon emissions. Moreover, spatial governance should consider the comparative advantages of different regions, form complementary advantages and fully enhance the cooperation between CSR and industrial structure upgrading on carbon emission reduction.
Community-Level Household Waste Disposal Behavior Simulation and Visualization under Multiple Incentive Policies—An Agent-Based Modelling Approach
Authors: Ma Hancong , Li Mei , Tong Xin , Dong Ping
Published: July 2023

The classification and recycling of household waste becomes a major issue in today’s urban environmental protection and domestic waste disposal. Although various policies promoting household waste classification have been introduced, the recovery rate failed to reach the expected result. Existing studies on incentive policies for household waste recycling tried to integrate subjective and objective factors in human behavior decisions. To explore how effective interventions can promote household waste classification in communities, this article developed an Agent-Based Model (ABM) based on Theory of Planned Behavior (TPB) to simulate the participation of households under eight different policy scenarios. The result shows that: monetary incentive is most effective in inducing participation, while social norms have different impacts on household decision under different policy intervention. Under policy stimulus, the participation rate of garbage sorting increased from 18% to 76%. This model has been applied into an online community-based participatory virtual simulation 3D system, which aims to help university students better understand how policies affect household recycling behaviors, which end up affecting the environment.
Linking Subsidies for Agriculture and Food to Dietary Styles: Estimates for Switzerland
Authors: Schläpfer Felix , Lobsiger Michael
Published: July 2023

Agriculture contributes substantially to global greenhouse gas emissions and other environmental impacts. Current agricultural policies tolerate these impacts and sometimes even aggravate them through subsidies for unsustainable production systems and products. Against this background, we explore the resulting transfers to agriculture and food associated with different dietary styles. Using data from Switzerland, we consider both direct transfers through government subsidies and indirect transfers through toleration of environmental impacts and costs. Gross transfers were quantified based on existing studies that (i) define eight dietary styles in terms of the quantity of the different product groups, (ii) allocate subsidies for agriculture to product groups, (iii) allocate environmental impacts to product groups using life cycle assessment (LCA) methods, and (iv) estimate external costs of agriculture using politically endorsed expenditures for damage avoidance. To quantify net transfers associated with the dietary styles, we subtracted individual tax contributions from the subsidies received and average external costs individually suffered from external costs caused. We found that the gross transfers from government to agriculture range from about CHF 500 to about CHF 2000 annually per taxpayer/consumer depending on dietary styles. Net transfers per taxpayer/consumer range from about CHF −1000 to about CHF 1000 depending on dietary style and income. The estimates highlight that current policies strongly privilege and thereby also endorse environmentally problematic dietary styles over more environmentally friendly ones, contradicting official strategies and targets in the domains of environment and public health.
Diversity, Distribution and Vegetation Assessment of Woody Plant Species in the Cold Desert Environment, North-Western Himalaya, India
Authors: Rawat Yashwant S. , Negi Vikram S. , Moussa Ihab Mohamed , Zaman Wajid , Elansary Hosam O.
Published: July 2023

The species richness, distribution and community structure in cold desert regions across the world are poorly understood because of their inaccessibility and remoteness. Similarly, the structure and composition of forest resources, including other management units (i.e., agroforestry and forestry systems), have hardly been studied in the cold desert of the Lahaul valley. However, such information is a prerequisite to understanding the trends and changes in the vegetation distribution under global climate change scenarios, especially considering the sensitivity of plant species in high-altitude areas of the Himalayan region. High anthropogenic activity has exerted tremendous pressure on available forest resources, including other management units in the cold desert of the Lahaul valley. Standard ecological methods were used to obtain an ecological (i.e., status, structure, composition and vegetation patterns) understanding of the region for biodiversity conservation and environmental sustainability. The present study was aimed at understanding the trend, structure and composition of plant species in the cold desert region of the western Himalaya. A total of 64 species (27 trees and 37 shrubs) of vascular plants were recorded in the present study. Tree diversity demonstrated greater variation along the gradients and slope aspects. Salix fragilis trees, with a 102 tree ha−1 density and a few trees of Populus nigra, were found to be sparsely distributed under the agroforestry system on the south-facing slopes in Khoksar. In Jahlma, Salix fragilis grew in an agroforestry system with a density of 365 tree ha−1. However, in Hinsa, Juniperus polycarpos was a dominant tree species in the agroforestry system, with a density of 378 tree ha−1. On the north-facing slopes in Kuthar, a higher number of trees and bushes were present due to natural regeneration maintained by farmers along the edges of terraced agricultural fields. The south-facing slopes showed a relatively lower species richness and diversity as compared to north-facing slopes at similar locations due to relatively less favourable growth conditions under sun-exposed, extremely xeric soil conditions. The highest level of species turnover was found between the altitudes of 2400 m and 3000 m. Betula utilis showed the highest adaptability at higher altitudes (>3500 m). The vegetation analysis results and information generated in the present study are useful for gaining an ecological understanding of the cold desert ecosystem in the Lahaul valley. Sustainable forest resource management, including other management units (e.g., agroforestry and forestry systems), is crucial for improving the vegetation pattern, structure and function of the cold desert ecosystem, thereby contributing to climate change mitigation, adaptation, biodiversity and ecosystem service conservation.
Building Resilience in Cultural Landscapes: Exploring the Role of Transdisciplinary and Participatory Planning in the Recovery of the Shushtar Historical Hydraulic System
Authors: Shirvani Dastgerdi Ahmadreza , Kheyroddin Reza
Published: July 2023

Cultural landscapes are living landscapes shaped by human activity and imbued with cultural and historical significance. However, cultural landscapes are threatened by climate change because of the direct and indirect impacts it has on all types of cultural heritage resources. This research aims to investigate the efficacy of transdisciplinary and participatory planning approaches in augmenting the resilience of cultural landscapes in the face of climate change risks. This article presents a novel contribution by examining the recovery process of the Shushtar Historical Hydraulic System (SHHS) through the lens of international principles aimed at mitigating natural hazards and fostering resilience. The methodology adopts a comprehensive approach that combines a systematic literature review with a detailed analysis of the recovery process of the SHHS during the 2019 flooding in Khuzestan Province, Iran. The research findings highlight the significance of employing a transdisciplinary and participatory planning approach to enhance climate resilience within cultural landscapes. The active involvement of the local community throughout the process of resilience strategy development and implementation emerges as a pivotal factor. Furthermore, the success of resilience strategies in cultural landscapes relies on key elements such as accurate identification and communication, capacity building, fostering trust, and ensuring sufficient funding.
Enhancing Forest Canopy Height Retrieval: Insights from Integrated GEDI and Landsat Data Analysis
Authors: Zhu Weidong , Yang Fei , Qiu Zhenge , He Naiying , Zhu Xiaolong , Li Yaqin , Xu Yuelin , Lu Zhigang
Published: July 2023

Canopy height is a crucial indicator for assessing the structure and function of the forest ecosystems. It plays a significant role in carbon sequestration, sink enhancement, and promoting green development. This study aimed to evaluate the accuracy of GEDI L2A version 2 data in estimating ground elevation and canopy height by comparing it with airborne laser scanning (ALS) data. Among the six algorithms provided by the GEDI L2A data, algorithm a2 demonstrated higher accuracy than the others in detecting ground elevation and canopy height. Additionally, a relatively strong correlation (R-squared = 0.35) was observed between rh95 for GEDI L2A and RH90 for ALS. To enhance the accuracy of canopy height estimation, this study proposed three backpropagation (BP) neural network inversion models based on GEDI, Landsat 8 OLI, and Landsat 9 OLI-2 data. Multiple sets of relative heights and vegetation indices were extracted from the GEDI and Landsat datasets. The random forest (RF) algorithm was employed to select feature variables with a cumulative importance score of 90% for training the BP neural network inversion models. Validation against RH90 of ALS revealed that the GEDI model outperformed the OLI or OLI-2 data models in terms of accuracy. Moreover, the quality improvement of OLI-2 data relative to OLI data contributed to enhanced inversion accuracy. Overall, the models based on a single dataset exhibited relatively low accuracy. Hence, this study proposed the GEDI and OLI and GEDI and OLI-2 models, which combine the two types of data. The results demonstrated that the combined model integrating GEDI and OLI-2 data exhibited the highest performance. Compared to the weakest OLI data model, the inversion accuracy R-squared improved from 0.38 to 0.74, and the MAE, RMSE, and rRMSE decreased by 1.21 m, 1.81 m, and 8.09%, respectively. These findings offer valuable insights for the remote sensing monitoring of forest sustainability.