bluuebunny/crossref_metadata_2025 huggingface dataset:
Amount of Dentifrice and Fluoride Concentration Affect the pH and Inorganic Composition of Dual-Species Biofilms of Streptococcus mutans and Candida albicans
Authors: Sampaio Caio , Delbem Alberto Carlos Botazzo , Hosida Thayse Yumi , Fernandes Ana Vitória Pereira , do Amaral Bruna , de Morais Leonardo Antônio , Monteiro Douglas Roberto , Pessan Juliano Pelim
Published: April 2024

This work assessed the influence of the amount of dentifrice and fluoride (F) concentration in the product on the pH and inorganic components of Streptococcus mutans and Candida albicans dual-species biofilms. The biofilms were treated with suspensions of fluoride dentifrices containing 550 or 1100 ppm of F (550 F or 1100 F, respectively) administered at comparable intensities: (i-1) 550 F/0.08 g or 1100 F/0.04 g; (i-2) 550 F/0.16 g or 1100 F/0.08 g; and (i-3) 550 F/0.32 g or 1100 F/0.16 g. A placebo dentifrice (without NaF, 0.32 g) was used as a negative control. After the last treatment, the biofilm pH was measured and the F, calcium (Ca), and phosphorus (P) concentrations were determined. Data were subjected to an ANOVA/Kruskal–Wallis test, and a Student–Newman–Keuls test. The highest biofilm pH and F concentrations (biomass and fluid) were observed for 1100 F at i-3. Overall, 1100 F resulted in F levels similar to 550 F for i-1 and i-2. In addition, 550 F applied at i-2 and i-3 led to higher F in the biomass/fluid compared to 1100 F applied at i-1 and i-2, respectively. In biomass, the lowest Ca concentrations were observed for 1100 F at i-3. The conclusion drawn is that the treatment intensity holds greater significance as a parameter compared to the concentration of F or the amount of dentifrice when considered individually.
Exploring the Impact of Hepatic Impairment on Pralsetinib Pharmacokinetics
Authors: Cheung Kit Wun Kathy , Tang Yang , Anders Doreen , Barata Teresa , Scalori Astrid , Agarwal Priya , Sane Rucha , Cheeti Sravanthi
Published: April 2024

Pralsetinib is a kinase inhibitor indicated for the treatment of metastatic rearranged during transfection (RET) fusion-positive non-small cell lung cancer. Pralsetinib is primarily eliminated by the liver and hence hepatic impairment (HI) is likely alter its pharmacokinetics (PK). Mild HI has been shown to have minimal impact on the PK of pralsetinib. This hepatic impairment study aimed to determine the pralsetinib PK, safety and tolerability in subjects with moderate and severe HI, as defined by the Child–Pugh and National Cancer Institute Organ Dysfunction Working Group (NCI-ODWG) classification systems, in comparison to subjects with normal hepatic function. Based on the Child–Pugh classification, subjects with moderate and severe HI had similar systemic exposure (area under the plasma concentration time curve from time 0 to infinity [AUC0–∞]) to pralsetinib, with AUC0–∞ geometric mean ratios (GMR) of 1.12 and 0.858, respectively, compared to subjects with normal hepatic function. Results based on the NCI-ODWG classification criteria were comparable; the AUC0–∞ GMR were 1.22 and 0.858, respectively, for subjects with moderate and severe HI per NCI-ODWG versus those with normal hepatic function. These results suggested that moderate and severe hepatic impairment did not have a meaningful impact on the exposure to pralsetinib, thus not warranting a dose adjustment in this population.
Performance Characterisation of the Airvo2TM Nebuliser Adapter in Combination with the Aerogen SoloTM Vibrating Mesh Nebuliser for in Line Aerosol Therapy during High Flow Nasal Oxygen Therapy
Authors: MacLoughlin Ronan , Mac Giolla Eain Marc
Published: April 2024

High flow oxygen (HFO) therapy is a well-established treatment in respiratory disease. Concurrent aerosol delivery can greatly expediate their recovery. The aim of this work was to complete a comprehensive characterisation of one such HFO therapy system, the Airvo2TM, used in combination with the Aerogen SoloTM vibrating mesh nebuliser. Representative adult, infant, and paediatric head models were connected to a breathing simulator via a collection filter placed at the level of the trachea. A tracheostomy interface and nasal cannulas were used to deliver the aerosol. Cannula size and gas flow rate were varied across the full operating range recommended by the manufacturer. The tracheal and emitted doses were quantified via UV-spectrophotometry. The aerosol droplet diameter at the exit of the nares and tracheal interface was measured via cascade impaction. High gas flow rates resulted in low emitted and tracheal doses (%). Nasal cannula size had no significant effect on the tracheal dose (%) available in infant and paediatric models. Higher gas flow rates resulted in smaller aerosol droplets at the exit of the nares and tracheostomy interface. Gas flow rate was found to be the primary parameter affecting aerosol delivery. Thus, gas flow rates should be kept low and where possible, delivered using larger nasal cannulas to maximise aerosol delivery.
Nucleolin-Targeting AS1411 Aptamer-Conjugated Nanospheres for Targeted Treatment of Glioblastoma
Authors: Seo Kyeongjin , Hwang Kihwan , Nam Kyung Mi , Kim Min Ju , Song Yoon-Kyu , Kim Chae-Yong
Published: April 2024

Post-operative chemotherapy is still required for the treatment of glioblastoma (GBM), for which nanocarrier-based drug delivery has been identified as one of the most effective methods. However, the blood-brain barrier (BBB) and non-specific delivery to non-tumor tissues can significantly limit drug accumulation in tumor tissues and cause damage to nearby normal tissues. This study describes a targeted cancer therapy approach that uses AS1411 aptamer-conjugated nanospheres (100–300 nm in size) loaded with doxorubicin (Dox) to selectively identify tumor cells overexpressing nucleolin (NCL) proteins. The study demonstrates that the active target model, which employs aptamer-mediated drug delivery, is more effective than non-specific enhanced permeability and maintenance (EPR)-mediated delivery and passive drug delivery in improving drug penetration and maintenance in tumor cells. Additionally, the study reveals the potential for anti-cancer effects through 3D spheroidal and in vivo GBM xenograft models. The DNA-protein hybrid nanospheres utilized in this study offer numerous benefits, such as efficient synthesis, structural stability, high drug loading, dye labeling, biocompatibility, and biodegradability. When combined with nanospheres, the 1411 aptamer has been shown to be an effective drug delivery carrier allowing for the precise targeting of tumors. This combination has the potential to produce anti-tumor effects in the active targeted therapy of GBM.
Unveiling the Potential of Extracellular Vesicles as Biomarkers and Therapeutic Nanotools for Gastrointestinal Diseases
Authors: Arrè Valentina , Mastrogiacomo Rita , Balestra Francesco , Serino Grazia , Viti Federica , Rizzi Federica , Curri Maria Lucia , Giannelli Gianluigi , Depalo Nicoletta , Scavo Maria Principia
Published: April 2024

Extracellular vesicles (EVs), acting as inherent nanocarriers adept at transporting a range of different biological molecules such as proteins, lipids, and genetic material, exhibit diverse functions within the gastroenteric tract. In states of normal health, they participate in the upkeep of systemic and organ homeostasis. Conversely, in pathological conditions, they significantly contribute to the pathogenesis of gastrointestinal diseases (GIDs). Isolating EVs from patients’ biofluids facilitates the discovery of new biomarkers that have the potential to offer a rapid, cost-effective, and non-invasive method for diagnosing and prognosing specific GIDs. Furthermore, EVs demonstrate considerable therapeutic potential as naturally targeted physiological carriers for the intercellular delivery of therapeutic cargo molecules or as nanoscale tools engineered specifically to regulate physio-pathological conditions or disease progression. Their attributes including safety, high permeability, stability, biocompatibility, low immunogenicity, and homing/tropism capabilities contribute to their promising clinical therapeutic applications. This review will delve into various examples of EVs serving as biomarkers or nanocarriers for therapeutic cargo in the context of GIDs, highlighting their clinical potential for both functional and structural gastrointestinal conditions. The versatile and advantageous properties of EVs position them as promising candidates for innovative therapeutic strategies in advancing personalized medicine approaches tailored to the gastroenteric tract, addressing both functional and structural GIDs.
Advances in the Production of Sustainable Bacterial Nanocellulose from Banana Leaves
Authors: Dáger-López David , Chenché Óscar , Ricaurte-Párraga Rayner , Núñez-Rodríguez Pablo , Bajaña Joaquin Morán , Fiallos-Cárdenas Manuel
Published: April 2024

Interest in bacterial nanocellulose (BNC) has grown due to its purity, mechanical properties, and biological compatibility. To address the need for alternative carbon sources in the industrial production of BNC, this study focuses on banana leaves, discarded during harvesting, as a valuable source. Banana midrib juice, rich in nutrients and reducing sugars, is identified as a potential carbon source. An optimal culture medium was designed using a simplex-centroid mixing design and evaluated in a 10 L bioreactor. Techniques such as Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), X-ray diffraction (XRD), and thermogravimetric analysis (TGA) were used to characterize the structural, thermal, and morphological properties of BNC. Banana midrib juice exhibited specific properties, such as pH (5.64), reducing sugars (15.97 g/L), Trolox (45.07 µM), °Brix (4.00), and antioxidant activity (71% DPPH). The model achieved a 99.97% R-adjusted yield of 6.82 g BNC/L. Physicochemical analyses revealed distinctive attributes associated with BNC. This approach optimizes BNC production and emphasizes the banana midrib as a circular solution for BNC production, promoting sustainability in banana farming and contributing to the sustainable development goals.
Specifics of Porous Polymer and Xenogeneic Matrices and of Bone Tissue Regeneration Related to Their Implantation into an Experimental Rabbit Defect
Authors: Aleynik Diana Ya. , Zhivtscov Oleg P. , Yudin Vladimir V. , Kovylin Roman S. , Komarov Roman N. , Charykova Irina N. , Linkova Daria D. , Rubtsova Yulia P. , Guseva Maria S. , Vasyagina Tatyana I. , Morozov Alexander G. , Chesnokov Sergey A. , Egorikhina Marfa N.
Published: April 2024

This paper provides a study of two bone substitutes: a hybrid porous polymer and an osteoplastic matrix based on a bovine-derived xenograft. Both materials are porous, but their pore characteristics are different. The osteoplastic matrix has pores of 300–600 µm and the hybrid polymer has smaller pores, generally of 6–20 µm, but with some pores up to 100 µm across. SEM data confirmed the porometry results and demonstrated the different structures of the materials. Therefore, both materials were characterized by an interconnected porous structure and provided conditions for the adhesion and vital activity of human ASCs in vitro. In an experimental model of rabbit shin bone defect, it was shown that, during the 6-month observation period, neither of the materials caused negative reactions in the experimental animals. By the end of the observation period, restoration of the defects in animals in both groups was completed, and elements of both materials were preserved in the defect areas. Data from morphological examinations and CT data demonstrated that the rate of rabbit bone tissue regeneration with the hybrid polymer was comparable to that with the osteoplastic matrix. Therefore, the hybrid polymer has good potential for use in further research and improvement in biomedical applications.
Theoretical Analysis of Thermophysical Properties of 3D Carbon/Epoxy Braided Composites with Varying Temperature
Authors: Jiang Li-Li , Li Zhen-Guo , Wang Dong-Ye , Zhai Jun-Jun , Kong Xiang-Xia
Published: April 2024

A three-dimensional helix geometry unit cell is established to simulate the complex spatial configuration of 3D braided composites. Initially, different types of yarn factors, such as yarn path, cross-sectional shape, properties, and braid direction, are explained. Then, the multiphase finite element method is used to develop a new theoretical calculation procedure based on the unit cell for predicting the impacts of environmental temperature on the thermophysical properties of 3D four-direction carbon/epoxy braided composites. The changing rule and distribution characteristics of the thermophysical properties for 3D four-direction carbon/epoxy braided composites are obtained at temperatures ranging from room temperature to 200 °C. The influences of environmental temperature on the coefficients of thermal expansion (CTE) and the coefficients of thermal conduction (CTC) are evaluated, by which some important conclusions are drawn. A comparison is conducted between theoretical and experimental results, revealing that variations in temperature exert a notable influence on the thermophysical characteristics of 3D four-directional carbon/epoxy braided composites. The theoretical calculation procedure is an effective tool for the mechanical property analysis of composite materials with complex geometries.
Optimization of Environment-Friendly and Sustainable Polylactic Acid (PLA)-Constructed Triply Periodic Minimal Surface (TPMS)-Based Gyroid Structures
Authors: Razi Syed Saarim , Pervaiz Salman , Susantyoko Rahmat Agung , Alyammahi Mozah
Published: April 2024

The demand for robust yet lightweight materials has exponentially increased in several engineering applications. Additive manufacturing and 3D printing technology have the ability to meet this demand at a fraction of the cost compared with traditional manufacturing techniques. By using the fused deposition modeling (FDM) or fused filament fabrication (FFF) technique, objects can be 3D-printed with complex designs and patterns using cost-effective, biodegradable, and sustainable thermoplastic polymer filaments such as polylactic acid (PLA). This study aims to provide results to guide users in selecting the optimal printing and testing parameters for additively manufactured/3D-printed components. This study was designed using the Taguchi method and grey relational analysis. Compressive test results on nine similarly patterned samples suggest that cuboid gyroid-structured samples perform the best under compression and retain more mechanical strength than the other tested triply periodic minimal surface (TPMS) structures. A printing speed of 40 mm/s, relative density of 60%, and cell size of 3.17 mm were the best choice of input parameters within the tested ranges to provide the optimal performance of a sample that experiences greater force or energy to compress until failure. The ninth experiment on the above-mentioned conditions improved the yield strength by 16.9%, the compression modulus by 34.8%, and energy absorption by 29.5% when compared with the second-best performance, which was obtained in the third experiment.
Tolerance for the Tolerant “Other”—Moses Mendelssohn’s Claim for Tolerance in the “Vorrede/Preface” (1782)
Authors: Matviyets Anne Sarah
Published: April 2024

In this paper I discuss Moses Mendelssohn’s argumentation on religious tolerance in his “Vorrede” (“preface”) that he added to his translation of Rabbi Manasseh Ben Israel’s letter “Vindiciae judaeorum” in 1782. Instead of solely deducing Mendelssohn’s idea of religious tolerance, I examine Mendelssohn’s argumentation strategies. For this purpose, I firstly determine the political and social conditions in which Mendelssohn wrote the “Vorrede”. Secondly, I examine the normative reasons or resources that Mendelssohn argues for tolerance with. In my observation, he is legitimizing religious tolerance on the normative resources of philosophical reasons (natural law/universal reason) and pragmatic reasons (utility). Further, I will analyse Mendelssohn’s concept of a tolerant Judaism in the “Vorrede”.
Impact of Wildfires on Land Surface Cold Season Climate in the Northern High-Latitudes: A Study on Changes in Vegetation, Snow Dynamics, Albedo, and Radiative Forcing
Authors: Linares Melissa , Ni-Meister Wenge
Published: April 2024

Anthropogenic climate change is increasing the occurrence of wildfires, especially in northern high latitudes, leading to a shift in land surface climate. This study aims to determine the predominant climatic effects of fires in boreal forests to assess their impact on vegetation composition, surface albedo, and snow dynamics. The influence of fire-induced changes on Earth’s radiative forcing is investigated, while considering variations in burn severity and postfire vegetation structure. Six burn sites are explored in central Alaska’s boreal region, alongside six control sites, by utilizing Moderate Resolution Imaging Spectroradiometer (MODIS)-derived albedo, Leaf Area Index (LAI), snowmelt timing data, AmeriFlux radiation, National Land Cover Database (NLCD) land cover, and Monitoring Trends in Burn Severity (MTBS) data. Key findings reveal significant postfire shifts in land cover at each site, mainly from high- to low-stature vegetation. A continuous increase in postfire surface albedo and negative surface shortwave forcing was noted even after 12 years postfire, particularly during the spring and at high-severity burn areas. Results indicate that the cooling effect from increased albedo during the snow season may surpass the warming effects of earlier snowmelt. The overall climate impact of fires depends on burn severity and vegetation composition.
Modelling Inductive Sensors for Arc Fault Detection in Aviation
Authors: Barroso-de-María Gabriel , Robles Guillermo , Martínez-Tarifa Juan Manuel , Cuadrado Alexander
Published: April 2024

Modern aircraft are being equipped with high-voltage and direct current (HVDC) architectures to address the increase in electrical power. Unfortunately, the rise of voltage in low pressure environments brings about a problem with unexpected ionisation phenomena such as arcing. Series arcs in HVDC cannot be detected with conventional means, and finding methods to avoid the potentially catastrophic hazards of these events becomes critical to assure further development of more electric and all electric aviation. Inductive sensors are one of the most promising detectors in terms of sensitivity, cost, weight and adaptability to the circuit wiring in aircraft electric systems. In particular, the solutions based on the detection of the high-frequency (HF) pulses created by the arc have been found to be good candidates in practical applications. This paper proposes a method for designing series arc fault inductive sensors able to capture the aforementioned HF pulses. The methodology relies on modelling the parameters of the sensor based on the physics that intervenes in the HF pulses interaction with the sensor itself. To this end, a comparative analysis with different topologies is carried out. For every approach, the key parameters influencing the HF pulses detection are studied theoretically, modelled with a finite elements method and tested in the laboratory in terms of frequency response. The final validation tests were conducted using the prototypes in real cases of detection of DC series arcs.
COVID-19 Hierarchical Classification Using a Deep Learning Multi-Modal
Authors: Althenayan Albatoul S. , AlSalamah Shada A. , Aly Sherin , Nouh Thamer , Mahboub Bassam , Salameh Laila , Alkubeyyer Metab , Mirza Abdulrahman
Published: April 2024

Coronavirus disease 2019 (COVID-19), originating in China, has rapidly spread worldwide. Physicians must examine infected patients and make timely decisions to isolate them. However, completing these processes is difficult due to limited time and availability of expert radiologists, as well as limitations of the reverse-transcription polymerase chain reaction (RT-PCR) method. Deep learning, a sophisticated machine learning technique, leverages radiological imaging modalities for disease diagnosis and image classification tasks. Previous research on COVID-19 classification has encountered several limitations, including binary classification methods, single-feature modalities, small public datasets, and reliance on CT diagnostic processes. Additionally, studies have often utilized a flat structure, disregarding the hierarchical structure of pneumonia classification. This study aims to overcome these limitations by identifying pneumonia caused by COVID-19, distinguishing it from other types of pneumonia and healthy lungs using chest X-ray (CXR) images and related tabular medical data, and demonstrate the value of incorporating tabular medical data in achieving more accurate diagnoses. Resnet-based and VGG-based pre-trained convolutional neural network (CNN) models were employed to extract features, which were then combined using early fusion for the classification of eight distinct classes. We leveraged the hierarchal structure of pneumonia classification within our approach to achieve improved classification outcomes. Since an imbalanced dataset is common in this field, a variety of versions of generative adversarial networks (GANs) were used to generate synthetic data. The proposed approach tested in our private datasets of 4523 patients achieved a macro-avg F1-score of 95.9% and an F1-score of 87.5% for COVID-19 identification using a Resnet-based structure. In conclusion, in this study, we were able to create an accurate deep learning multi-modal to diagnose COVID-19 and differentiate it from other kinds of pneumonia and normal lungs, which will enhance the radiological diagnostic process.
FPGA-Microprocessor Based Sensor for Faults Detection in Induction Motors Using Time-Frequency and Machine Learning Methods
Authors: Osornio-Rios Roque Alfredo , Cueva-Perez Isaias , Alvarado-Hernandez Alvaro Ivan , Dunai Larisa , Zamudio-Ramirez Israel , Antonino-Daviu Jose Alfonso
Published: April 2024

Induction motors (IM) play a fundamental role in the industrial sector because they are robust, efficient, and low-cost machines. Changes in the environment, installation errors, or modifications to working conditions can generate faults in induction motors. The trend on IM fault detection is focused on the design techniques and sensors capable of evaluating multiple faults with various signals using non-invasive analysis. The methodology is based on processing electric current signals by applying the short-time Fourier transform (STFT). Additionally, the computation of the mean and standard deviation of infrared thermograms is proposed as main indicators. The proposed system combines both parameters by means of Support Vector Machine and k-nearest-neighbor classifiers. The development of the diagnostic system was done with digital hardware implementations using a Xilinx PYNQ Z2 card that integrates an FPGA with a microprocessor, thus taking advantage of the acquisition and processing of digital signals and images in hardware. The proposed method has proved to be effective for the classification of healthy (HLT), misalignment (MAMT), unbalance (UNB), damaged bearing (BDF), and broken rotor bar (BRB) faults with an accuracy close to 99%.
Towards a Distributed Digital Twin Framework for Predictive Maintenance in Industrial Internet of Things (IIoT)
Authors: Abdullahi Ibrahim , Longo Stefano , Samie Mohammad
Published: April 2024

This study uses a wind turbine case study as a subdomain of Industrial Internet of Things (IIoT) to showcase an architecture for implementing a distributed digital twin in which all important aspects of a predictive maintenance solution in a DT use a fog computing paradigm, and the typical predictive maintenance DT is improved to offer better asset utilization and management through real-time condition monitoring, predictive analytics, and health management of selected components of wind turbines in a wind farm. Digital twin (DT) is a technology that sits at the intersection of Internet of Things, Cloud Computing, and Software Engineering to provide a suitable tool for replicating physical objects in the digital space. This can facilitate the implementation of asset management in manufacturing systems through predictive maintenance solutions leveraged by machine learning (ML). With DTs, a solution architecture can easily use data and software to implement asset management solutions such as condition monitoring and predictive maintenance using acquired sensor data from physical objects and computing capabilities in the digital space. While DT offers a good solution, it is an emerging technology that could be improved with better standards, architectural framework, and implementation methodologies. Researchers in both academia and industry have showcased DT implementations with different levels of success. However, DTs remain limited in standards and architectures that offer efficient predictive maintenance solutions with real-time sensor data and intelligent DT capabilities. An appropriate feedback mechanism is also needed to improve asset management operations.
Matchmaking in Off-Grid Energy System Planning: A Novel Approach for Integrating Residential Electricity Demands and Productive Use of Electricity
Authors: Schöne Nikolas , Britton Tim Ronan , Delatte Edouard , Saincy Nicolas , Heinz Boris
Published: April 2024

Off-grid electrification planning increasingly recognizes the importance of productive use of electricity (PUE) to promote community value creation and (financial) project sustainability. To ensure a sustainable and efficient integration in the community and energy system, PUE assets must be carefully evaluated to match both the community needs and the residential electricity demand patterns. We propose a novel methodology interlinking qualitative interviews, statistical analysis and energy system modeling to optimize decision making for PUE integration in off-grid energy systems in rural Madagascar by aligning relevant PUE effectively with anticipated residential electricity demand patterns based on socio-economic determinants of the community. We find that a possible contribution of the PUE to reducing the electricity costs depends significantly on three factors: (1) The residential electricity consumption patterns, which are influenced by the socio-economic composition of the community; (2) The degree of flexibility of (i) PUE assets and (ii) operational preferences of the PUE user; and (3) The capacity of community members to finance and operate PUE assets. Our study demonstrates that significant cost reductions for PUE-integrated off-grid energy systems can be achieved by applying our proposed methodology. When matching PUE and residential consumption patterns, the integration of PUE assets in residential community energy systems can reduce the financial risk for operators, provided the PUE enterprise operates reliably and sustainably. We highlight that the consideration of local value chains and co-creation approaches are essential to ensure the energy system is addressing the community’s needs, creates value for the community, enhances the project’s financial sustainability and is achieving the overall objectives of decentralized energy system planning.
Corporate Sustainability Reporting and Stakeholders’ Interests: Evidence from China
Authors: Xu Lu , Xie Li , Mei Shengjun , Hao Jianli , Zhang Yuqian , Song Yu
Published: April 2024

This paper examines whether the adoption of the Global Reporting Initiative (GRI) by listed firms could enhance the alignment between corporate sustainability reporting and stakeholders’ interests in China. Drawing on content analysis of the environmental, social, and governance (ESG) reports of 48 selected listed firms and a questionnaire survey of 409 respondents, this study shows that most of the sampled firms with GRI adoption have more sustainability activities identified in the content analysis than their peers that do not follow the GRI guidelines in the same industries; both groups of firms have a similar pattern of disclosure frequency in light of the six dimensions developed in this study; and there is a disconnect between the stakeholders’ needs and the sustainability reporting practice of the sampled listed firms. The findings reflect that the current corporate social responsibility reporting practice could be interpreted as a strategic response to the government’s policy priorities, rather than a direct attempt to address stakeholders’ concerns.
A Review on Harnessing Renewable Energy Synergies for Achieving Urban Net-Zero Energy Buildings: Technologies, Performance Evaluation, Policies, Challenges, and Future Direction
Authors: Noh Yoorae , Jafarinejad Shahryar , Anand Prashant
Published: April 2024

As urbanization continues to accelerate globally, energy demand in cities is reaching unprecedented levels, contributing to greenhouse gas emissions. In response, the concept of net-zero energy building (NZEB) is becoming a sustainable solution for urban energy needs. NZEB aims to achieve a net-zero energy footprint by balancing the energy it consumes with the energy it produces, primarily from renewable energy (RE) sources. This comprehensive literature review-based study explores the role of RE synergies in the context of urban NZEBs, including discussions on definition and development of NZEBs, RE-synergies for achieving NZEBs, sustainable trends and clusters of NZEBs, climate change impacts on NZEBs, their performance evaluation, policy and regulatory frameworks, and challenges and possible solutions related to NZEBs. It has been identified that while customizing NZEB definitions to align with regional energy supply and demand is important, the same is highly dependent on building architectural and micro-climate features. The assessment of climate change effects and NZEB practices should involve evaluating building energy equilibrium, occupant comfort, and interactions with the energy grid. There are still some technical, policy, and socio-economic challenges that need more attention to provide comprehensive solutions for further enhancing the sustainable development/performance of NZEBs and achieving their goal.
Identifying the Contributing Sources of Uncertainties in Urban Flood Vulnerability in South Korea Considering Multiple GCMs, SSPs, Weight Determination Methods, and MCDM Techniques
Authors: Ziarh Ghaith Falah , Kim Jin Hyuck , Chae Seung Taek , Kang Hae-Yeol , Hong Changyu , Song Jae Yeol , Chung Eun-Sung
Published: April 2024

This study quantified uncertainties involved in assessing the future flood vulnerability in 33 urban areas with population exceeding designated thresholds in South Korea. The driver-pressure-state-impact-response (DPSIR) framework was utilized as the study procedure, integrating social, economic, and environmental factors. In addition, a total of 220 cases of combinations were examined, encompassing twenty general circulation models combined with shared socioeconomic pathway scenarios, five weight determination methods, and three multi-criteria decision-making (MCDM) techniques, as sources of inherent uncertainties in the process. The rankings of urban flood vulnerability (UFV) for the selected cities were comprehensively assessed considering all combinations, followed by an analysis of variance test to investigate contributing sources of uncertainties. As a result, Incheon and Busan were found to be vulnerable to flooding, while Yeongcheon and Andong appeared to be safe cities. Some cities exhibited wide ranges in their rankings, such as Daegu, Yangpyeon, and Jeongeup. The identified contributing sources were weighting (58%), MCDM (27%), and the combination of weighting and MCDM methods together (15%). This study revealed that weight determination methods and MCDM techniques are the primary sources of uncertainties in the assessment of future UFV instead of multiple GCMs and SSPs. This finding underscores the importance for decision-makers and stakeholders to carefully consider these uncertainties for sustainable flood risk management and prevention.
Vision-Based Reinforcement Learning Approach to Optimize Bucket Elevator Process for Solid Waste Utilization
Authors: Chavan Akshay , Rosenhövel Tobias , Elbel Alexander , Schmidt Benedikt , Noe Alfons , Aufderheide Dominik
Published: April 2024

An energy-intensive industry such as cement manufacturing requires a constant supply of high amounts of traditional fossil fuels, such as coal or gas, for the calcination process. A way to overcome this fuel need is the usage of solid waste or Alternative Fuel Resources (AFRs), such as wood or paper. An advantage of using such waste is that their combustion byproduct, “ash”, can be used as a raw material alternative in the cement manufacturing process. However, for structural reasons, only bucket elevator technology is feasible to convey the fuel vertically for feeding the calciner in most cement plants. During the fuel feeding process, the inhomogeneous characteristics of AFRs lead to discharge parabolas of these materials varying over the infeed sample. Hence, a need arises to observe these trajectories and estimate a method for their optimal discharge. Thus, the purpose of this study is to develop an intelligent high-performance bucket elevator system. As such, a vision-based reinforcement learning algorithm is proposed in this study to monitor and control the speed of the elevator depending on the material properties observed at the inlet. These inlet materials properties include the type of material used in the simulation, and the particle size distribution within the infeed sample. A relationship is established between the inlet material properties and the speed of the bucket elevator. The best possible scenario is then deduced using a reward function. Here, the reward function is formulated via the deep learning image segmentation algorithm, a novel approach. After observing the test simulation conducted with a random-parameters setup, it was noted that the optimum speed for a given infeed sample was predicted correctly. As such, it can be concluded that the goal of developing an intelligent bucket elevator system was achieved.
Does the Water Rights Trading Policy Improve Water-Use Efficiency? An Environmental Policy Evaluation from China
Authors: He Naiming , Shi Ying , Ding Rijia
Published: April 2024

As a crucial basic natural resource, water resources are the cornerstone for sustainable national economic development. This paper takes the 2014 pilot water rights trading policy (WRT) as an entry point and uses a difference-in-differences (DID) model to test the policy effect of WRT on water-use efficiency (WUE) based on panel data for 30 Chinese provinces from 2005 to 2021. The study shows that WRT can significantly improve the regional WUE, and these results remain valid after a series of robustness tests, such as the parallel trend test, placebo test, and PSM-DID. Mechanistic analysis revealed that WRT can produce the Porter effect, which affects the WUE through technological innovation. The results of the heterogeneity analysis based on the synthetic control method (SCM) showed that WRT effectively improved WUE in Jiangxi, Henan, Ningxia, Hubei, and Guangdong, but did not achieve the expected effect in Inner Mongolia or Gansu. This paper provides solid empirical support for assessing the effectiveness of WRT and accelerating the process of establishing a unified national WRT market in China by 2025.
Climate Change Impact on the Distribution of Forest Species in the Brazilian Amazon
Authors: de Morais Ingrid Lana Lima , de Lima Alexandra Amaro , Santos Ivinne Nara Lobato dos , Meneses Carlos , da Silva Rogério Freire , Lopes Ricardo , Ramos Santiago Linorio Ferreyra , de Aguiar Ananda Virginia , Wrege Marcos Silveira , Lopes Maria Teresa Gomes
Published: April 2024

Studies using ecological niche models highlight the vulnerability of forest species to climate change. This work aimed to analyze the distribution of timber species Aspidosperma desmanthum, Cariniana micranta, Clarisia racemosa, Couratari oblongifolia, and Vouchysia guianensis, which are targets of deforestation, to predict the impacts of climate change and identify areas for their conservation in the Amazon. For this purpose, 37 environmental variables were used, including climatic and edaphic factors. The models were fitted using five algorithms, and their performance was evaluated by the metrics Area Under the Curve (AUC), True Skill Statistic, and Sorensen Index. The deforestation analysis was conducted using data accumulated over a period of 14 years. The study indicated that under the most pessimistic predictions, considering continued high emissions of greenhouse gases (GHGs) from the use of fossil fuels, SSP5–8.5, potential habitat loss for the studied species was more significant. Analyses of the species show that the Western Amazon has a greater climatic suitability area for the conservation of its genetic resources. Further study of the accumulated deforestation over 14 years showed a reduction in area for all species. Therefore, in situ conservation policies and deforestation reduction are recommended for the perpetuation of the analyzed forest species.
Exploring Ecological, Morphological, and Environmental Controls on Coastal Foredune Evolution at Annual Scales Using a Process-Based Model
Authors: Heminway Selwyn S. , Cohn Nicholas , Davis Elizabeth H. , White Andrew , Hein Christopher J. , Zinnert Julie C.
Published: April 2024

Coastal communities commonly rely upon foredunes as the first line of defense against sea-level rise and storms, thus requiring management guidance to optimize their protective services. Here, we use the AeoLiS model to simulate wind-driven accretion and wave-driven erosion patterns on foredunes with different morphologies and ecological properties under modern-day conditions. Additional sets of model runs mimic potential future climate changes to inform how both morphological and ecological properties may have differing contributions to net dune changes under evolving environmental forcing. This exploratory study, applied to represent the morphological, environmental, and ecological conditions of the northern Outer Banks, North Carolina, USA, finds that dunes experiencing minimal wave collision have similar net volumetric growth rates regardless of beach morphology, though the location and density of vegetation influence sediment deposition patterns across the dune profile. The model indicates that high-density, uniform planting strategies trap sediment close to the dune toe, whereas low-density plantings may allow for accretion across a broader extent of the dune face. The initial beach and dune shape generally plays a larger role in annual-scale dune evolution than vegetation cover. For steeper beach slopes and/or low dune toe elevations, the model generally predicts wave-driven dune erosion at the annual scale.
Advancing Spatial Drought Forecasts by Integrating an Improved Outlier Robust Extreme Learning Machine with Gridded Data: A Case Study of the Lower Mainland Basin, British Columbia, Canada
Authors: Salimi Amirhossein , Noori Amir , Ebtehaj Isa , Ghobrial Tadros , Bonakdari Hossein
Published: April 2024

Droughts have extensive consequences, affecting the natural environment, water quality, public health, and exacerbating economic losses. Precise drought forecasting is essential for promoting sustainable development and mitigating risks, especially given the frequent drought occurrences in recent decades. This study introduces the Improved Outlier Robust Extreme Learning Machine (IORELM) for forecasting drought using the Multivariate Standardized Drought Index (MSDI). For this purpose, four observation stations across British Columbia, Canada, were selected. Precipitation and soil moisture data with one up to six lags are utilized as inputs, resulting in 12 variables for the model. An exhaustive analysis of all potential input combinations is conducted using IORELM to identify the best one. The study outcomes emphasize the importance of incorporating precipitation and soil moisture data for accurate drought prediction. IORELM shows promising results in drought classification, and the best input combination was found for each station based on its results. While high Area Under Curve (AUC) values across stations, a Precision/Recall trade-off indicates variable prediction tendencies. Moreover, the F1-score is moderate, meaning the balance between Precision, Recall, and Classification Accuracy (CA) is notably high at specific stations. The results show that stations near the ocean, like Pitt Meadows, have higher predictability up to 10% in AUC and CA compared to inland stations, such as Langley, which exhibit lower values. These highlight geographic influence on model performance.
A Review of the Use of Hydrogen in Compression Ignition Engines with Dual-Fuel Technology and Techniques for Reducing NOx Emissions
Authors: Rueda-Vázquez Juan Manuel , Serrano Javier , Pinzi Sara , Jiménez-Espadafor Francisco José , Dorado M. P.
Published: April 2024

The use of compression ignition engines (CIEs) is associated with increased greenhouse gas emissions. It is therefore necessary to research sustainable solutions and reduce the negative environmental impact of these engines. A widely studied alternative is the use of H2 in dual-fuel mode. This review has been developed to include the most recent studies on the subject to collect and compare their main conclusions on performance and emissions. Moreover, this study includes most relevant emission control strategies that have not been extensively analyzed in other reviews on the subject. The main conclusion drawn from the literature is the negative effect of the addition of H2 on NOx. This is due to the increase in temperature during combustion, which increases NOx formation, as the thermal mechanism predominates. Therefore, to reduce these emissions, three strategies have been studied, namely exhaust gas recirculation (EGR), water injection (WI), and compression ratio (CR) reduction. The effect of these techniques on NOx reduction, together with their effect on other analyzed performance parameters, have been deeply analyzed. The studies reviewed in this work indicate that hydrogen is an alternative fuel for CIEs when used in conjunction with techniques that have proven to be effective in reducing NOx.
Sustainable Space Transformation Design Strategies for Post-Landfill Closure
Authors: Li Jingwen , Liu Kun
Published: April 2024

This study explores strategies for promoting sustainable development in urban public spaces by focusing on transforming and reusing closed landfill sites. Using landscape regeneration theory and the genius loci principle from architectural phenomenology, this research investigates spatial redesign techniques to facilitate the sustainable utilization of these sites. Through the analysis of three representative case studies and the specific application of these strategies to the Zhangjiawan Landfill site in Xining City, Qinghai Province, northwest China, this study elucidates practical approaches for spatial function transformation, constructing community networks, and heritage preservation and cultural continuity. From an urban macro-planning perspective, the post-closure design of the Zhangjiawan Municipal Landfill emphasizes organic linkages with adjacent city parks, fostering community integration and enhancing recreational opportunities for residents. The transformed area facilitates social interaction and cultivates a harmonious community atmosphere by repurposing the site to incorporate community farms, cultural centers, and outdoor sports facilities. Moreover, integrating leisure spaces, cultural exhibitions, and ecological restoration initiatives contributes to ecosystem rehabilitation while providing residents with leisure, social engagement, and cultural enrichment spaces. This research demonstrates how effective spatial transformation can promote environmental education, heritage preservation, and urban functionality in redeveloping closed landfill sites. The theoretical insights and practical design strategies presented contribute to advancing sustainable practices in urban planning and public space utilization.
Critical Watershed Prioritization through Multi-Criteria Decision-Making Techniques and Geographical Information System Integration for Watershed Management
Authors: Halder Sudipto , RoyChowdhury Abhishek , Kar Sayanti , Ray Debdas , Bhandari Gupinath
Published: April 2024

The Precambrian hard rock topography of the Manbhum-Singhbhum plateau, which is well known for its semi-arid climates prone to drought, is often seen in Purulia district in West Bengal, India. Despite the district’s middling groundwater capacity, 17 out of 20 blocks have exorbitant fluoride pollution in the groundwater that negatively impacts the health of local residents. Approximately 13% of the whole area suffers from severe erosion. It is evident that the river Kangsabati and its tributaries are not well fed by rainwater and thereby there is always a dearth of ground water. The aim of this study was to identify and prioritize integral watersheds in the Purulia area using Multi-Criteria Decision Making (MCDM) and Geographic Information Systems (GIS). The evaluation was carried out in the Bandu sub-watershed, which contains five micro watersheds: 2A2B5m, 2A2B5k, 2A2B5h, 2A2B5b, and 2A2B5j. The analysis considered five major factors: lithological properties, land use and land cover, soil erosion, groundwater recharge, and hydrogeomorphology. The weights of these criteria were determined by the Analytical Hierarchy Process (AHP) model, which was then prioritized using the Techniques for Order of Preference by Similarity to the Ideal Solution (TOPSIS) technique. This study emphasized an integrated approach to assess watershed hazards and to establish rational conservation goals. The Central Ground Water Board (CGWB) of India report was referred during data analysis. As a result of this study, the 2A2B5k watershed emerged as the most critical due to its susceptibility across the analyzed parameters. This thorough plan demonstrated the usefulness of identifying watershed threads and prioritizing conservation efforts.
Organiblò: Engaging People in “Circular” Organizations and Enabling Social Sustainability
Authors: Beretta Edoardo , Burkhalter Christian , Camenisch Pietro , Carcano-Monti Cristina , Citraro Mauro , Manini-Mondia Michela , Traversa Fabrizio
Published: April 2024

The present analysis related to social sustainability aims at evaluating and understanding how a “circular” or “round” organization such as the so-called Organiblò (i.e., a fusion of the terms “organigram” and the Italian word for “porthole”) functions. More precisely, the present article wants to raise awareness among companies that a profound cultural change seems necessary to push the search for sustainable objectives further. In this specific regard, independent interviews with the CEOs of 11 medium-sized enterprises and 46 young middle managers were conducted. Based on their responses, our analysis highlights the advantages of a “circular” organization, which range from better corporate sustainability to greater freedom of staff and cross-functional activities as well as the valorization of individuals and enhanced flexibility and collaborative spirit. However, time is needed to effect such a profound cultural change. The main difficulties consist in the approach to decision-making processes, because top management is often not yet prone to strongly encourage transparency, a culture of feedback and inclusiveness in the workforce. Consequently, a new, additional manager (i.e., a “wheeler manager”) might disseminate a new managing culture and involve employees in contributing to the company’s sustainability.