bluuebunny/crossref_metadata_2025 huggingface dataset:
Energy Transfer between AGuIX Nanoparticles and Photofrin under Light or X-ray Excitation for PDT Applications
Authors: Dhaini Batoul , Arnoux Philippe , Daouk Joël , Lux François , Tillement Olivier , Hagège Agnès , Hamieh Tayssir , Shafirstein Gal , Frochot Céline
Published: August 2024

Photodynamic therapy is an accepted therapy cancer treatment. Its advantages encourage researchers to delve deeper. The use of nanoparticles in PDT has several advantages including the passive targeting of cancer cells. The aim of this article is to evaluate the effectiveness of AGuIX nanoparticles (activation and guiding of irradiation by X-ray) in the presence or absence of a photosensitizer, Photofrin, under illumination of 630 nm or under X-ray irradiation. The goal is to improve local tumor control by combining PDT with low-dose-X-ray-activated NPs in the treatment of locally advanced metastatic lung cancer. The study of the energy transfer, which occurs after excitation of Gd/Tb chelated in AGuIX in the presence of Photofrin, was carried out. We could observe the formation of singlet oxygen after the light or X-ray excitation of Gd and Tb that was not observed for AGuIX or Photofrin alone, proving that it is possible to realize energy transfer between both compounds.
Thermoplastic Vulcanizates with an Integration of High Wear-Resistant and Anti-Slip Properties Based on Styrene Ethylene Propylene Styrene Block Copolymer/Styrene Ethylene Butylene Styrene Block Copolymer/Solution-Polymerization Styrene-Butadiene Rubber
Authors: Li Zhicheng , Xiao Jianbin
Published: August 2024

Distinguished from traditional vulcanized rubber, which is not reusable, thermoplastic elastomer (TPV) is a material that possesses both the excellent resilience of traditional vulcanized rubber and the recyclability of thermoplastic, and TPVs have been widely studied in both academia and industry because of their outstanding green properties. In this study, new thermoplastic elastomers based on solution polymerized styrene butadiene rubber (SSBR) and thermoplastic elastomers (SEPSs/SEBSs) were prepared by the first dynamic vulcanization process. The high slip resistance and abrasion resistance of SSBR are utilized to improve the poor slip resistance of SEPSs/SEBSs, which provides a direction for the recycling of shoe sole materials. In this paper, the effects of different ratios of the rubber/plastic phase (R/P) on the mechanical properties, rheological properties, micro-morphology, wear resistance, and anti-slip properties of SSBR/TPE TPVs are investigated. The results show that the SSBR/TPE TPVs have good mechanical properties. The tensile strength, tear strength, hardness, and resilience of the TPVs decrease slightly with an increasing R/P ratio. Still, TPVs have a tensile strength of 18.1 MPa when the ratio of R/P is 40/100, and this reaches the performance of the vulcanized rubber sole materials commonly used in the market. In addition, combined with microscopic morphology analysis (SEM), it was found that, with the increase in the R/P ratio, the size of the rubber particles gradually increased, forming a stronger crosslinking network, but the rheological properties of TPVs gradually decreased; crosslinking network enhancement led to the increase in the size of the rubber particles, and the increase in the size of rubber particles made the material in the abrasion of rubber particles fall easily, thus increasing its abrasion volume. Through dynamic mechanical analysis and anti-slip tests, when the R/P ratio was 40/100, the tan δ of TPVs at 0 °C was 0.35, which represents an ordinary vulcanized rubber sole material in the market. The viscoelasticity of TPVs increased with the increase in the R/P ratio, which improved the anti-slip performance of TPVs. SSBR/TPE TPVs are expected to be used in footwear and automotive fields due to their excellent abrasion resistance and anti-slip performance.
Polysaccharide as a Separation Medium for Gel Electrophoresis
Authors: Arakawa Tsutomu , Nakagawa Masataka , Sakuma Chiaki , Tomioka Yui , Kurosawa Yasunori , Akuta Teruo
Published: August 2024

Gel electrophoresis and size exclusion chromatography (SEC) are vital techniques in biochemical research, employing gel matrix structures made of polysaccharides or synthetic polymers like polyacrylamide for the analysis and separation of macromolecules. Polysaccharides, such as agarose, offer safer alternatives to acrylamide. Polysaccharide gels, notably agarose, facilitate the analysis and purification of proteins and nucleic acids through a molecular sieving mechanism. Gel electrophoresis for proteins is mainly divided into denaturing and native methods. Denaturing electrophoresis with sodium dodecyl sulfate (SDS) simplifies protein migration but disrupts molecular interactions. Conversely, native gel electrophoresis, without SDS, allows proteins to migrate based on the running pH and the isoelectric point of the proteins, while nucleic acids consistently migrate toward the anode. The electrophoresis of proteins with variable charges presents complexes. This review focuses on the use of polysaccharides, particularly agarose, for native gel electrophoresis, highlighting their applications in separating macromolecules. It also discusses the applications and limitations of agarose gels when used as a matrix for electrophoresis. Such information should help in designing electrophoresis experiments using polysaccharides.
Potential Implications of Multi-Drug Exposure with Synthetic Cannabinoids: A Scoping Review of Human Case Studies
Authors: Thomsen Lucy R. , Rosengren Rhonda J. , Glass Michelle
Published: August 2024

Synthetic cannabinoids are a rapidly evolving, diverse class of new psychoactive substances. Synthetic cannabinoid use results in a higher likelihood of adverse events and hospitalization when compared to cannabis use. The mechanisms behind synthetic cannabinoid toxicity remain elusive. Furthermore, poly-substance use may be a significant contributing factor in many cases. This scoping review aimed to identify the key characteristics of synthetic cannabinoid co-exposure cases and discuss the potential implications of poly-substance use in humans. There were 278 human cases involving 64 different synthetic cannabinoids extracted from the databases. Cases involved a total of 840 individual co-exposures, with an average of four substances involved in each case. The most common co-exposures were alcohol (11.4%), opioids (11.2%), and cannabis (11.1%). When analyzed by case outcome, co-exposure to either antipsychotics/antidepressants, alcohol, or tobacco were significantly associated with mortality as an outcome (p < 0.05). Drug-use history (63.4%), mental illness (23.7%), and hypertensive and atherosclerotic cardiovascular disease (20.1%) were prevalent patient histories in the case cohort. There are several potential pharmacodynamic and pharmacokinetic interactions between co-exposure drugs and synthetic cannabinoids that could worsen clinical presentation and toxicity in synthetic cannabinoid users. Individuals with substance-use disorders or psychiatric illness would be especially vulnerable to these multi-drug interactions. Further research into these complex exposures is needed for the successful prevention and treatment of synthetic cannabinoid-related harms.
Crop Water Status Analysis from Complex Agricultural Data Using UMAP-Based Local Biplot
Authors: Triana-Martinez Jenniffer Carolina , Álvarez-Meza Andrés Marino , Gil-González Julian , De Swaef Tom , Fernandez-Gallego Jose A.
Published: August 2024

To optimize growth and management, precision agriculture relies on a deep understanding of agricultural dynamics, particularly crop water status analysis. Leveraging unmanned aerial vehicles, we can efficiently acquire high-resolution spatiotemporal samples by utilizing remote sensors. However, non-linear relationships among data features, localized within specific subgroups, frequently emerge in agricultural data. Interpreting these complex patterns requires sophisticated analysis due to the presence of noise, high variability, and non-stationarity behavior in the collected samples. Here, we introduce Local Biplot, a methodological framework tailored for discerning meaningful data patterns in non-stationary contexts for precision agriculture. Local Biplot relies on the well-known uniform manifold approximation and projection method, such as UMAP, and local affine transformations to codify non-stationary and non-linear data patterns while maintaining interpretability. This lets us find important clusters for transformation and projection within a single global axis pair. Hence, our framework encompasses variable and observational contributions within individual clusters. At the same time, we provide a relevance analysis strategy to help explain why those clusters exist, facilitating the understanding of data dynamics while favoring interpretability. We demonstrated our method’s capabilities through experiments on both synthetic and real-world datasets, covering scenarios involving grass and rice crops. Moreover, we use random forest and linear regression models to predict water status variables from our Local Biplot-based feature ranking and clusters. Our findings revealed enhanced clustering and prediction capability while emphasizing the importance of input features in precision agriculture. As a result, Local Biplot is a useful tool to visualize, analyze, and compare the intricate underlying patterns and internal structures of complex agricultural datasets.
Performance Analysis of an Aerial Remote Sensing Platform Based on Real-Time Satellite Communication and Its Application in Natural Disaster Emergency Response
Authors: He Xiangli , Xu Chong , Tang Shengquan , Huang Yuandong , Qi Wenwen , Xiao Zikang
Published: August 2024

The frequency of natural disasters has increased recently, posing a huge threat to human society. Rapid, accurate, authentic, and comprehensive acquisition and transmission of disaster information are crucial in emergency response. In this paper, we propose a design scheme for an aerial remote sensing platform based on real-time satellite communication. This platform mainly includes a civilian heavy-duty unmanned aerial vehicle, ground observation system with the self-developed orthographic image stabilization device, wireless communication system with an airborne mobile communication device using Ku band, ground satellite information receiving station, and data processing and application analysis system. The image stabilization capability of the ground observation system and the communication capability of the wireless communication system were verified through ground and flight tests respectively. The results showed that the stability accuracy of the platform was better than the theoretical threshold, the system transmission rate was not less than 2 M bandwidth, the data packet loss rate was low, and the time delay was not more than 2 s. The images captured in the experiment were clear, with a resolution of less than 1cm and an overlap rate of more than 70%. These all results meet the emergency observation requirement, which indicates that the aerial remote sensing platform based on real-time satellite communication has great potential for application in natural disaster emergency response.
The Effectiveness of eHMI Displays on Pedestrian–Autonomous Vehicle Interaction in Mixed-Traffic Environments
Authors: Alhawiti Ali , Kwigizile Valerian , Oh Jun-Seok , Asher Zachary D. , Hakimi Obaidullah , Aljohani Saad , Ayantayo Sherif
Published: August 2024

External human–machine interfaces (eHMIs) serve as communication bridges between autonomous vehicles (AVs) and road users, ensuring that vehicles convey information clearly to those around them. While their potential has been explored in one-to-one contexts, the effectiveness of eHMIs in complex, real-world scenarios with multiple pedestrians remains relatively unexplored. Addressing this gap, our study provides an in-depth evaluation of how various eHMI displays affect pedestrian behavior. The research aimed to identify eHMI configurations that most effectively convey an AV’s information, thereby enhancing pedestrian safety. Incorporating a mixed-methods approach, our study combined controlled outdoor experiments, involving 31 participants initially and 14 in a follow-up session, supplemented by an intercept survey involving 171 additional individuals. The participants were exposed to various eHMI displays in crossing scenarios to measure their impact on pedestrian perception and crossing behavior. Our findings reveal that the integration of a flashing green LED, robotic sign, and countdown timer constitutes the most effective eHMI display. This configuration notably increased pedestrians’ willingness to cross and decreased their response times, indicating a strong preference and enhanced concept understanding. These findings lay the groundwork for future developments in AV technology and traffic safety, potentially guiding policymakers and manufacturers in creating safer urban environments.
Fast, Zero-Reference Low-Light Image Enhancement with Camera Response Model
Authors: Wang Xiaofeng , Huang Liang , Li Mingxuan , Han Chengshan , Liu Xin , Nie Ting
Published: August 2024

Low-light images are prevalent in intelligent monitoring and many other applications, with low brightness hindering further processing. Although low-light image enhancement can reduce the influence of such problems, current methods often involve a complex network structure or many iterations, which are not conducive to their efficiency. This paper proposes a Zero-Reference Camera Response Network using a camera response model to achieve efficient enhancement for arbitrary low-light images. A double-layer parameter-generating network with a streamlined structure is established to extract the exposure ratio K from the radiation map, which is obtained by inverting the input through a camera response function. Then, K is used as the parameter of a brightness transformation function for one transformation on the low-light image to realize enhancement. In addition, a contrast-preserving brightness loss and an edge-preserving smoothness loss are designed without the requirement for references from the dataset. Both can further retain some key information in the inputs to improve precision. The enhancement is simplified and can reach more than twice the speed of similar methods. Extensive experiments on several LLIE datasets and the DARK FACE face detection dataset fully demonstrate our method’s advantages, both subjectively and objectively.
Research on Division of Labor Decision and System Stability of Swarm Robots Based on Mutual Information
Authors: Feng Zhongyuan , Sun Yi
Published: August 2024

In rational decision-making processes, the information interaction among individual robots is a critical factor influencing system stability. We establish a game-theoretic model based on mutual information to address division of labor decision-making and stability issues arising from differential information interaction among swarm robots. Firstly, a mutual information model is employed to measure the information interaction among robots and analyze its influence on the behavior of individual robots. Secondly, employing the Cournot model and the Stackelberg model, we model the diverse decision-making behaviors of swarm robots influenced by discrepancies in mutual information. The intricate decision dynamics exhibited by the system under the disparity mutual information values during the game process, along with the stability of Nash equilibrium points, are analyzed. Finally, dynamic complexity simulations of the game models are simulated under the disparity mutual information values: (1) When ν1 of the game model varies within a certain range, the Nash equilibrium point loses stability and enters a chaotic state. (2) As I(X;Y) increases, the decision-making pattern of robots transitions gradually from the Cournot game to the Stackelberg game. Concurrently, the sensitivity of swarm robotics systems to changes in decision parameter decreases, reducing the likelihood of the system entering a chaotic state.
A Non-Source Optical Fiber Sensor for Multi-Point Methane Detection
Authors: Ma Li , Liu Xu , Si Ganshang
Published: August 2024

Fast, accurate, real-time measurement of gas concentration is an important task for preventing coal mining disasters. In order to develop an accurate monitoring method for methane gas concentration at different locations in a mine environment, a non-source optical fiber sensor for multi-point methane detection has been developed in this paper. A 16-channel fiber splitter and a multi-channel time-sharing acquisition module are employed within the sensor, enabling simultaneous detection of methane gas at 16 points by a host. Furthermore, the methane sensors are connected to the monitoring host via an all-optical method, achieving non-source and long-range detection of methane. To assess the performance of the methane gas sensor, experiments were conducted to evaluate its detection range, response time, and stability. The results indicated that the average detection error was approximately 1.84% across the full range, and the response time did not exceed 10 s. The minimum detection limit of the sensor, as determined by the 1σ criteria, was obtained as 58.42 ppm. Additionally, the concentrations of methane gas measured at varying distances (1 km, 2 km, 5 km) were found to be essentially consistent over an extended period. These results suggest that the development of this non-source optical fiber sensor holds significant potential for providing a method for mine environment, multi-point online methane gas detection.
One-Pot Preparation of Ratiometric Fluorescent Molecularly Imprinted Polymer Nanosensor for Sensitive and Selective Detection of 2,4-Dichlorophenoxyacetic Acid
Authors: Cui Yuhong , Li Xintai , Wang Xianhong , Liu Yingchun , Hu Xiuli , Chen Shengli , Qu Xiongwei
Published: August 2024

The development of fluorescent molecular imprinting sensors for direct, rapid, and sensitive detection of small organic molecules in aqueous systems has always presented a significant challenge in the field of detection. In this study, we successfully prepared a hydrophilic colloidal molecular imprinted polymer (MIP) with 2,4-dichlorophenoxyacetic acid (2,4-D) using a one-pot approach that incorporated polyglycerol methacrylate (PGMMA-TTC), a hydrophilic macromolecular chain transfer agent, to mediate reversible addition-fragmentation chain transfer precipitation polymerization (RAFTPP). To simplify the polymerization process while achieving ratiometric fluorescence detection, red fluorescent CdTe quantum dots (QDs) and green fluorescent nitrobenzodiazole (NBD) were introduced as fluorophores (with NBD serving as an enhancer to the template and QDs being inert). This strategy effectively eliminated background noise and significantly improved detection accuracy. Uniform-sized MIP microspheres with high surface hydrophilicity and incorporated ratiometric fluorescent labels were successfully synthesized. In aqueous systems, the hydrophilic ratio fluorescent MIP exhibited a linear response range from 0 to 25 μM for the template molecule 2,4-D with a detection limit of 0.13 μM. These results demonstrate that the ratiometric fluorescent MIP possesses excellent recognition characteristics and selectivity towards 2,4-D, thus, making it suitable for selective detection of trace amounts of pesticide 2,4-D in aqueous systems.
Fluid–Solid Interaction Analysis for Developing In-Situ Strain and Flow Sensors for Prosthetic Valve Monitoring
Authors: Puleo Silvia , Pasta Salvatore , Scardulla Francesco , D’Acquisto Leonardo
Published: August 2024

Transcatheter aortic valve implantation (TAVI) was initially developed for adult patients, but there is a growing interest to expand this procedure to younger individuals with longer life expectancies. However, the gradual degradation of biological valve leaflets in transcatheter heart valves (THV) presents significant challenges for this extension. This study aimed to establish a multiphysics computational framework to analyze structural and flow measurements of TAVI and evaluate the integration of optical fiber and photoplethysmography (PPG) sensors for monitoring valve function. A two-way fluid–solid interaction (FSI) analysis was performed on an idealized aortic vessel before and after the virtual deployment of the SAPIEN 3 Ultra (S3) THV. Subsequently, an analytical analysis was conducted to estimate the PPG signal using computational flow predictions and to analyze the effect of different pressure gradients and distances between PPG sensors. Circumferential strain estimates from the embedded optical fiber in the FSI model were highest in the sinus of Valsalva; however, the optimal fiber positioning was found to be distal to the sino-tubular junction to minimize bending effects. The findings also demonstrated that positioning PPG sensors both upstream and downstream of the bioprosthesis can be used to effectively assess the pressure gradient across the valve. We concluded that computational modeling allows sensor design to quantify vessel wall strain and pressure gradients across valve leaflets, with the ultimate goal of developing low-cost monitoring systems for detecting valve deterioration.
Coordinated Multi-Scenario Optimization Strategy for Park Photovoltaic Storage Based on Master–Slave Game
Authors: Wang Jiang , Lan Jinchen , Wang Lianhui , Lin Yan , Hao Meimei , Zhang Yan , Xiang Yang , Qin Liang
Published: August 2024

Optimizing the operation of photovoltaic (PV) storage systems is crucial for meeting the load demands of parks while minimizing curtailment and enhancing economic efficiency. This paper proposes a multi-scenario collaborative optimization strategy for PV storage systems based on a master–slave game model. Three types of energy storage system (ESS) application scenarios are designed to comprehensively stabilize PV fluctuations, compensate for load transfers, and participate in the frequency regulation (FR) market, thereby optimizing the overall operational strategy of PV storage systems in parks. The upper-level objective is to maximize the park operators’ profit, while the lower-level objective is to minimize the user’s power supply costs. Case studies demonstrate that this strategy can significantly increase the economic benefits for park operators by 25.8%, reduce user electricity expenditures by 5.27%, and lower curtailment through a load response mechanism, thereby promoting the development and construction of PV storage parks.
Unsupervised Domain Adaptation for Inter-Session Re-Calibration of Ultrasound-Based HMIs
Authors: Lykourinas Antonios , Rottenberg Xavier , Catthoor Francky , Skodras Athanassios
Published: August 2024

Human–Machine Interfaces (HMIs) have gained popularity as they allow for an effortless and natural interaction between the user and the machine by processing information gathered from a single or multiple sensing modalities and transcribing user intentions to the desired actions. Their operability depends on frequent periodic re-calibration using newly acquired data due to their adaptation needs in dynamic environments, where test–time data continuously change in unforeseen ways, a cause that significantly contributes to their abandonment and remains unexplored by the Ultrasound-based (US-based) HMI community. In this work, we conduct a thorough investigation of Unsupervised Domain Adaptation (UDA) algorithms for the re-calibration of US-based HMIs during within-day sessions, which utilize unlabeled data for re-calibration. Our experimentation led us to the proposal of a CNN-based architecture for simultaneous wrist rotation angle and finger gesture prediction that achieves comparable performance with the state-of-the-art while featuring 87.92% less trainable parameters. According to our findings, DANN (a Domain-Adversarial training algorithm), with proper initialization, offers an average 24.99% classification accuracy performance enhancement when compared to no re-calibration setting. However, our results suggest that in cases where the experimental setup and the UDA configuration may differ, observed enhancements would be rather small or even unnoticeable.
Large Language Models for Wearable Sensor-Based Human Activity Recognition, Health Monitoring, and Behavioral Modeling: A Survey of Early Trends, Datasets, and Challenges
Authors: Ferrara Emilio
Published: August 2024

The proliferation of wearable technology enables the generation of vast amounts of sensor data, offering significant opportunities for advancements in health monitoring, activity recognition, and personalized medicine. However, the complexity and volume of these data present substantial challenges in data modeling and analysis, which have been addressed with approaches spanning time series modeling to deep learning techniques. The latest frontier in this domain is the adoption of large language models (LLMs), such as GPT-4 and Llama, for data analysis, modeling, understanding, and human behavior monitoring through the lens of wearable sensor data. This survey explores the current trends and challenges in applying LLMs for sensor-based human activity recognition and behavior modeling. We discuss the nature of wearable sensor data, the capabilities and limitations of LLMs in modeling them, and their integration with traditional machine learning techniques. We also identify key challenges, including data quality, computational requirements, interpretability, and privacy concerns. By examining case studies and successful applications, we highlight the potential of LLMs in enhancing the analysis and interpretation of wearable sensor data. Finally, we propose future directions for research, emphasizing the need for improved preprocessing techniques, more efficient and scalable models, and interdisciplinary collaboration. This survey aims to provide a comprehensive overview of the intersection between wearable sensor data and LLMs, offering insights into the current state and future prospects of this emerging field.
Online Adaptive Kalman Filtering for Real-Time Anomaly Detection in Wireless Sensor Networks
Authors: Ahmad Rami , Alkhammash Eman H.
Published: August 2024

Wireless sensor networks (WSNs) are essential for a wide range of applications, including environmental monitoring and smart city developments, thanks to their ability to collect and transmit diverse physical and environmental data. The nature of WSNs, coupled with the variability and noise sensitivity of cost-effective sensors, presents significant challenges in achieving accurate data analysis and anomaly detection. To address these issues, this paper presents a new framework, called Online Adaptive Kalman Filtering (OAKF), specifically designed for real-time anomaly detection within WSNs. This framework stands out by dynamically adjusting the filtering parameters and anomaly detection threshold in response to live data, ensuring accurate and reliable anomaly identification amidst sensor noise and environmental changes. By highlighting computational efficiency and scalability, the OAKF framework is optimized for use in resource-constrained sensor nodes. Validation on different WSN dataset sizes confirmed its effectiveness, showing 95.4% accuracy in reducing false positives and negatives as well as achieving a processing time of 0.008 s per sample.
High-Sensitivity Refractive Index Sensor with Dual-Channel Based on Surface Plasmon Resonance Photonic Crystal Fiber
Authors: Wang Fengmin , Wei Yong , Han Yanhong
Published: August 2024

In order to achieve a high-precision synchronous detection of two different refractive index (RI) analytes, a D-type surface plasmon resonance (SPR) photonic crystal fiber (PCF) RI sensor based on two channels is designed in this paper. The sensor uses a D-shaped planar region of the PCF and a large circular air hole below the core as the sensing channels. Surface plasmon resonance is induced by applying a coating of gold film on the surface. The full-vector finite-element method (FEM) is used to optimize the structural parameters of the optical fiber, and the sensing characteristics are studied, including wavelength sensitivity, RI resolution, full width at half maximum (FWHM), figure of merit (FOM), and signal-to-noise ratio (SNR). The results show that the channel 1 (Ch 1) can achieve RI detection of 1.36–1.39 in the wavelength range of 1500–2600 nm, and the channel 2 (Ch 2) can achieve RI detection of 1.46–1.57 in the wavelength range of 2100–3000 nm. The two sensing channels can detect independently or simultaneously measure two analytes with different RIs. The maximum wavelength sensitivity of the sensor can reach 30,000 nm/RIU in Channel 1 and 9900 nm/RIU in Channel 2. The RI resolutions of the two channels are 3.54 × 10−6 RIU and 10.88 × 10−6 RIU, respectively. Therefore, the sensor realizes dual-channel high- and low-RI synchronous detection in the ultra-long wavelength band from near-infrared to mid-infrared and achieves an ultra-wide RI detection range and ultra-high wavelength sensitivity. The sensor has a wide application prospect in the fields of chemical detection, biomedical sensing, and water environment monitoring.
Application of Guar Gum Treatment of Basalt Residual-Soil Shallow Slope in Early Ecological Restoration
Authors: Shen Xianfei , Wang Lina , Pan Xuemin , Yang Bijin , Han Jiayuan , Zhang Lianxing
Published: August 2024

This paper found that environmentally friendly guar gum biopolymers are helpful for stopping the erosion of basalt residual-soil shallow slopes, while also improving the problems of poor stability, difficult growth of early vegetation, and weak initial resistance to the rainfall scouring of these slopes under extreme climatic conditions. Then, to illustrate the effects of the guar gum treatment, laboratory tests have been conducted, including a soil strength test, water retention and water absorption tests, a disintegration test, and a simulated rainfall erosion test, and the pattern of its effect on vegetation growth has been explored. The results indicate that as the content of guar gum increases, both the cohesion and angle of internal friction exhibit a trend of first increasing and then decreasing; the angle of internal friction varies within a range of 21° to 26°. The evaporation rate, water absorption rate, and disintegration rate of this guar gum-treated soil were significantly reduced, while the cracking of the surface layer was significantly improved. The disintegration rate of the soil is only about 2%, as the guar gum content is greater than 1%. Moreover, there is no sign indicating that vegetation germination was affected by the guar gum, meaning that it maintains a favorable environment for vegetation to grow. Guar gum-cured slopes were significantly protected under heavy rainfall washout conditions, with a 94.85% reduction in total soil loss from the slope surface compared to untreated slopes. Since the pores of soil are filled with guar gum hydrogel, the erosion resistance of soil is greatly enhanced. The results of this study will provide a scientific basis for engineering the protection of shallow slopes of basalt residual soils.
Advancing Sustainable Construction: Discrete Modular Systems & Robotic Assembly
Authors: Liu Yuxi , Belousov Boris , Schneider Tim , Harsono Kevin , Cheng Tsung-Wei , Shih Shen-Guan , Tessmann Oliver , Peters Jan
Published: August 2024

This research explores the SL-Block system within an architecture framework by embracing building modularity, combinatorial design, topological interlocking, machine learning, and tactile sensor-based robotic assembly. The SL-Block, composed of S and L-shaped tetracubes, possesses a unique self-interlocking feature that allows for reversible joining and the creation of various 2D or 3D structures. In architecture modularity, the high degree of reconfigurability and adaptability of the SL-Block system introduces a new element of interest. Unlike modularization strategies that emphasize large-scale volumetric modules or standardized building components, using small-scale generic building blocks provides greater flexibility in maximizing design variations and reusability. Furthermore, the serial repetition and limited connectivity of building elements reduce the efforts required for bespoke manufacturing and automated assembly. In this article, we present our digital design and robotic assembly strategies for developing dry-jointed modular construction with SL-Blocks. Drawing on combinatorics and graph theory, we propose computational design methods that can automatically generate hierarchical SL-Block assemblies from given shapes. To address the physical complexities of contact-rich assembly tasks, we develop robotics using two distinct methods: pre-programmed assembly and sensor-based reinforcement learning. Through a series of demonstrators, we showcase the ability of SL-Blocks not only to reconfigure conventional building tectonics but also to create new building configurations.
Examining Energy Poverty among Vulnerable Women-Led Households in Urban Housing before and after COVID-19 Lockdown: A Case Study from a Neighbourhood in Madrid, Spain
Authors: Cuerdo-Vilches Teresa , Navas-Martín Miguel Ángel
Published: August 2024

People with lower incomes often live in homes with poor thermal properties, making it difficult for them to maintain a comfortable indoor temperature. This vulnerability is closely related to the quality and maintenance of housing, which negatively affects indoor environmental comfort, especially in terms of energy usage, having an impact on health and well-being. Studying energy poverty from a qualitative perspective allows us to delve deeper into the experience of these people. A qualitative study was carried out through a case study of women in a situation of household vulnerability and energy poverty. Semi-structured interviews were conducted with five women-led households and two key informants before and after the COVID-19 confinement, in one of the most vulnerable neighbourhoods of Madrid. Unveiling the complexity of this topic, three categories were identified: household composition and economic resources, perception and proposals for household improvements, and household health and well-being. Furthermore, the results suggest that reliance on inefficient solutions such as electric radiators or butane-cylinder heaters for space heating in winter (more affordable in the short term, but unsustainable in the long one) shows evidence of energy precariousness, which, together with other poor housing conditions and users’ behaviours, impact clearly on health, generating or worsening chronic diseases. Research in vulnerable populations requires interventions beyond visibility, supported by key informants. Social workers and educators are essential to improving the living conditions of the most vulnerable people; however, they need social policies and adequate intervention plans and strategies to support and make their efforts effective.
Sustainability Measures of the Apparel Industry: A Longitudinal Analysis of Apparel Corporations’ Sustainability Efforts
Authors: Cheramie Lance , Balasubramanian Mahendran
Published: August 2024

Climate change is a paramount challenge of our era, with profound implications for various sectors, including the apparel industry, known for its extensive environmental footprint throughout the entire clothing lifecycle. This study assesses the industry’s carbon emission trends using six years of data (2017–2022) focusing on Scope 1 and Scope 2. Employing a one-way repeated measures ANOVA, this study evaluates the impact of sustainability strategies on emissions. The results indicate a notable decrease in Scope 2 and overall carbon emissions, while Scope 1 emissions remain relatively unchanged despite a downward trend. These findings underscore the success of current sustainability measures in reducing emissions, especially in Scope 2, and highlight the need for ongoing and intensified efforts in Scope 1 areas. This research offers valuable insights into the apparel industry’s environmental impact, emphasizing the critical role of continued strategic actions in addressing climate change.
Developing a Strategic Methodology for Circular Economy Roadmapping: A Theoretical Framework
Authors: Abu-Bakar Halidu , Charnley Fiona
Published: August 2024

In response to the pressing need for effective implementation of the Circular Economy (CE), this research introduces a comprehensive theoretical framework for CE roadmapping. This study addresses the prevalent issue of fragmented and inconsistent CE strategies that often hinder the successful translation of long-term goals into achievable KPIs. Through a carefully designed methodology, this framework incorporates strategic management, systems thinking, sustainability science, and other disciplines, providing a structured approach to navigating the complexities of long-term planning for CE. The foundation for this framework is laid through an extensive review of the literature, which identifies theoretical gaps and guides its development. Practical insights are gained through an examination of various CE roadmaps, including sectoral, municipal, national, and regional strategies, thereby enhancing this framework’s applicability and robustness. This research highlights substantial variations in current CE roadmapping methods, emphasizing the need for a standardized approach. While acknowledging limitations due to the diverse contexts of the case studies, this study offers significant implications for policymakers, business leaders, and sustainability practitioners by presenting a versatile tool for strategic CE planning. Its interdisciplinary construction mirrors the multifaceted nature of CE, rendering it exceptionally adaptable across various disciplines and levels, reflecting the diverse reality of CE applications.
A Protocol of Prevention and Protection Measures on New Occupational Risk Factors in Green Jobs in Italian Workplaces
Authors: Vitale Ermanno , Salvago Pietro , Campanella Andrea Filippo , Cirrincione Luigi
Published: August 2024

The Green Transition aims to protect the health of our planet through changes at the economic, political, and social levels while also having a significant impact on the world of work through the creation of sustainable occupations, referred to as ‘Green Jobs’. The aim of our research was to identify the new emerging occupational risks associated with Green Jobs and to propose a protocol to promote the adoption of more appropriate prevention and protection measures. Starting with a few keywords chosen by the authors, we conducted a narrative review of the scientific articles published in the literature. The results obtained show that the new occupational risks emerging in the activities involved in the green transition are mainly related to the introduction of new materials or new technologies and to modes of production processes. These risks may pose a danger to those exposed to them on a daily basis, causing damage to health. Our protocol proposal calls for a more careful and adequate risk assessment for Green Jobs, providing specific training on these issues for new professionals in the sector and introducing specific prevention and protection measures for the different occupational risks analysed in the workplace. By adopting these recommendations in the workplace and conducting in-depth research on these issues, it will be possible to contribute to the improvement of prevention and protection for these new emerging occupational risks, achieving a positive economic impact and better safety conditions for workers.
Simulation of Groundwater Dissolved Organic Carbon in Yufu River Basin during Artificial Recharge: Improving through the SWAT-MODFLOW-RT3D Reaction Module
Authors: Hong Xiaotao , Chen Xuequn , Xia Kezheng , Zhang Wenqing , Wang Zezheng , Liu Dan , Li Shuxin , Zhang Wenjing
Published: August 2024

To keep groundwater levels stable, Jinan’s government has implemented several water management measures. However, considerable volumes of dissolved organic carbon (DOC) can enter groundwater via water exchange, impacting groundwater stability. In this study, a SWAT-MODFLOW-RT3D model designed specifically for the Yufu River Basin is developed, and part of the code of the RT3D module is modified to simulate changes in DOC concentrations in groundwater under different artificial recharge scenarios. The ultimate objective is to offer valuable insights into the effective management of water resources in the designated study region. The modified SWAT-MODFLOW-RT3D model simulates the variations of DOC concentration in groundwater under three artificial recharge scenarios, which are (a) recharged by Yellow River water; (b) recharged by Yangtze River water; and (c) recharged by Yangtze River and Yellow River water. The study shows that the main source of groundwater DOC in the basin is exogenous water. The distribution of DOC concentration in groundwater in the basin shows obvious spatial variations due to the influence of infiltration of surface water. The area near the upstream riverbank is the earliest to be affected. With the prolongation of the artificial recharge period, the DOC concentration in groundwater gradually rises from upstream to downstream, and from both sides of the riverbank to the surrounding area. By 2030, the maximum level of DOC in the basin will exceed 6.20 mg/l. The Yellow River water recharge scenario provides more groundwater recharge and less DOC input than the other two scenarios. The findings of this study indicate that particularly when recharge water supplies are enhanced with organic carbon, DOC concentrations in groundwater may alter dramatically during artificial recharge. This coupled modeling analysis is critical for assessing the impact of recharge water on groundwater quality to guide subsequent recharge programs.
The Impacts of Dams on Streamflow in Tributaries to the Lower Mekong Basin
Authors: Khoeun Romduol , Sor Ratha , Chann Kimsan , Phy Sophea Rom , Oeurng Chantha , Sok Ty
Published: August 2024

The Lower Mekong Basin has had extensive hydropower dam development, which changes its hydrologic conditions and threatens the exceptional aquatic biodiversity. This study quantifies the degree of hydrologic change between pre-impact (1965–1968) and post-impact (2018–2021) peak hydropower development in two major tributaries of the Lower Mekong Basin—the Sekong River, with the fewest dams, and the Sesan River, with the most dams. Both rivers have historically supported migratory fishes. We used daily pre- and post-impact data and the Indicators of Hydrologic Alteration framework to evaluate streamflow changes from dam development. We found significant changes in low- and high-magnitude flows in the pre- and post-impact periods of dam development. For the Sekong River, minimum flow had large fluctuations, with increases of 290% to 412% compared to the pre-impact period, while the Sesan River’s minimum flow ranged from 120% to 160% more than pre-impact. Dry season flows increased by 200 ± 63% on average in the Sekong River, which was caused by releases from upstream dams. Meanwhile, the Sesan River’s dry season flows increased by 100 ± 55% on average. This study indicates that seasonal flow changes and extreme flow events occurred more frequently in the two basins following dam construction, which may threaten the ecosystem’s function.
Optimization of Green Multimodal Transport Schemes Considering Order Consolidation under Uncertainty Conditions
Authors: Zhu Pei , Lv Xiaolong , Shao Quan , Kuang Caijin , Chen Weiwang
Published: August 2024

As society becomes increasingly concerned with sustainable development, the demand for high-efficiency, low-cost, and green technology makes air–land multimodal transportation one of the effective means of fast freight transportation. In the actual transportation business, some orders will have overlapping transportation routes, and transporting each order separately will result in resource waste, high costs, and carbon emissions. This paper proposes a multimodal transportation scheme optimization model considering order consolidation to improve transport efficiency and reduce costs and carbon emissions. An improved genetic algorithm incorporating the ride-sharing scheduling method is designed to solve the model. The results show that order consolidation will reduce multimodal transport costs and carbon emissions but increase transportation time slightly, and the advantages in cost and carbon emission reduction will vary with origin–destination scenarios, which are ranked in order of single-origin single-destination, single-origin multi-destinations, multi-origin single-destination, and multi-origin multi-destination. For the fourth scenario, the cost and carbon emissions decrease by 16.6% and 26.69%, respectively, and the time increases by 5.56% compared with no consolidation. For the sensibility of customer demands, it is found that order consolidation has the advantage for price-sensitive, time- and price-sensitive, and time- and carbon emission-sensitive customers; however, it is specifically beneficial for time-sensitive customers only in single-origin single-destination scenarios.
Government Environmental Expenditure, Budget Management, and Regional Carbon Emissions: Provincial Panel Data from China
Authors: Tang Ziru , Zhang Zenglian , Deng Wenyueyang
Published: August 2024

To explore the impact of government fiscal intervention on regional carbon emissions, this paper employs a two-way fixed-effects model to analyze data from 30 provinces in China, spanning the period from 2008 to 2017. This study investigates the effects of local government environmental expenditure and government budget on the per capita volume, intensity, and performance of regional carbon emissions. The results show that government environmental expenditure is beneficial to reducing regional carbon emissions and improving regional carbon emission performance. Second, the smaller the deviation between the government budget and final accounts, the more conducive it is to reducing carbon emissions. Third, we found that government environmental expenditure has the strongest inhibitory effect on regional carbon emissions in the eastern region, followed by the central region, and the weakest in the western region. Finally, government financial transparency positively moderates the inhibitory effect of government budget management on regional carbon emissions, that is, when fiscal transparency is high, the amplification effect of budget deviation on regional carbon emissions is weakened.
Complete Solutions in the Dilatation Theory of Elasticity with a Representation for Axisymmetry
Authors: De Cicco Simona
Published: August 2024

In this paper, we present certain complete solutions in the dilatation theory of elasticity. This model can be derived as a special case of Eringen’s linear theory of microstretched elastic solids when microrotations are absent. It is also a version of the theory of materials with voids. The dilatation theory can be considered the simplest theoretical model of microstructured materials and is suitable for investigating various phenomena that occur in engineering, geomechanics, and biomechanics. We establish three complete solutions to the displacement equations of equilibrium that are the counterpart of the Green–Lamé (GL), Boussinesq–Papkovich–Neuber (BPN), and Cauchy–Kowalevski–Somigliana (CKS) solutions of classical elasticity. The links between these BPN and CKS solutions are established. Then, we present a representation of the BPN solution in the case of axisymmetry. The results presented here are useful for solving axisymmetric problems in semi-infinite and infinite domains.