bluuebunny/crossref_metadata_2025 huggingface dataset:
Impact Behavior and Residual Strength of PEEK/CF-Laminated Composites with Various Stacking Sequences
Authors: Eremin Alexander V. , Burkov Mikhail V. , Bogdanov Alexey A. , Kononova Anastasia A. , Lyubutin Pavel S.
Published: March 2024

Carbon fiber-reinforced composites are popular due to their high strength and light weight; thus, the structures demonstrate high performance and specific strength. However, these composites are susceptible to impact damage. The objective of this research was to study the behavior of carbon fiber-reinforced laminates based on a polyetheretherketone (PEEK) matrix with six stacking sequences under static and impact loading. Four-point bending, short-beam bending, drop weight impact, and compression after impact tests were carried out. The results were complemented with digital shearography to estimate the damaged areas. Finite element modeling served to assess the failure mechanisms, such as fiber and matrix failure, in different layers due to tension of compression. Three behavior pattern of layups under drop-weight impact were found: (i)—energy redistribution due to mostly linear behavior (like a trampoline) and thus lower kinetic energy absorption for damage initiation, (ii)—moderate absorption of energy with initiation and propagation of concentrated damage with depressed redistribution of energy in the material, (iii)—moderate energy absorption with good redistribution due to initiation of small, dispersed damage. The results can be used to predict the mechanical behavior of composites with different stacking sequences in materials for proper structural design.
Carboxymethylated and Sulfated Furcellaran from Furcellaria lumbricalis and Its Immobilization on PLA Scaffolds
Authors: Štěpánková Kateřina , Ozaltin Kadir , Sáha Petr , Vargun Elif , Domincová-Bergerová Eva , Vesel Alenka , Mozetič Miran , Lehocký Marian
Published: March 2024

This study involved the creation of highly porous PLA scaffolds through the porogen/leaching method, utilizing polyethylene glycol as a porogen with a 75% mass ratio. The outcome achieved a highly interconnected porous structure with a thickness of 25 μm. To activate the scaffold’s surface and improve its hydrophilicity, radiofrequency (RF) air plasma treatment was employed. Subsequently, furcellaran subjected to sulfation or carboxymethylation was deposited onto the RF plasma treated surfaces with the intention of improving bioactivity. Surface roughness and water wettability experienced enhancement following the surface modification. The incorporation of sulfate/carboxymethyl group (DS = 0.8; 0.3, respectively) is confirmed by elemental analysis and FT-IR. Successful functionalization of PLA scaffolds was validated by SEM and XPS analysis, showing changes in topography and increases in characteristic elements (N, S, Na) for sulfated (SF) and carboxymethylated (CMF). Cytocompatibility was evaluated by using mouse embryonic fibroblast cells (NIH/3T3).
Phase Behavior of Polydisperse Y-Shaped Polymer Brushes under Good Solvent Conditions
Authors: Fridrich Petr , Posel Zbyšek
Published: March 2024

Y-shaped polymer brushes represent a special class of binary mixed polymer brushes, in which a combination of different homopolymers leads to unique phase behavior. While most theoretical and simulation studies use monodisperse models, experimental systems are always polydisperse. This discrepancy hampers linking theoretical and experimental results. In this theoretical study, we employed dissipative particle dynamics to study the influence of polydispersity on the phase behavior of Y-shaped brushes grafted to flat surfaces under good solvent conditions. Polydispersity was kept within experimentally achievable values and was modeled via Schulz–Zimm distribution. In total, 10 systems were considered, thus covering the phase behavior of monodisperse, partially polydisperse and fully polydisperse systems. Using such generic representation of real polymers, we observed a rippled structure and aggregates in monodisperse systems. In addition, polydisperse brushes formed a stable perforated layer not observed previously in monodisperse studies, and influenced the stability of the remaining phases. Although the perforated layer was experimentally observed under good solvent conditions and in the melt state, further confirmation of its presence in systems under good solvent conditions required mapping real polymers onto mesoscale models that reflected, for example, different polymer rigidity, and excluded volume effects or direct influence of the surface, just to mention a few parameters. Finally, in this work, we show that mesoscale modeling successfully describes polydisperse models, which opens the way for rapid exploring of complex systems such as polydisperse Y-shaped brushes in selective or bad solvents or under non-equilibrium conditions.
Barrier, Mechanical, Thermal, and Rheological Properties of Plasticized Biopolymeric Films Manufactured by Co-Extrusion
Authors: Calambás Pulgarin Heidy Lorena , Caicedo Carolina
Published: March 2024

The thermal, rheological, mechanical, and barrier properties of flat biopolymeric films processed by extrusion with different proportions of plasticizer and surfactant were evaluated. In the first stage, pellets were developed through twin-screw extrusion using a temperature profile in the ascending step process. These samples were analyzed using rotational rheology analysis to understand the viscoelastic transitions through the behavior of the storage and loss modulus, as well as the incidence of complex viscosity concerning concentration. The interaction among the components was analyzed under infrared spectroscopy after the two processing stages, revealing the miscibility of the mixture due to the action of the surfactant. The degradation temperatures increased by more than 20 °C, generating thermal stability, and the temperatures related to polymer transitions were determined. In the second stage, co-extrusion was carried out using pellets from the blend with a melt flow index (MFI) suitable for this process. The samples TPS50-PLA50-T5 and TPS75-PLA25-T5-A10 presented MFI values of 2.27 and 1.72 g/10 min, respectively. These samples were co-extruded for the production of films, impacting the physical properties. The resistance to traction, Young’s modulus, and elongation showed limited effectiveness of plasticizer and surfactant, with high resistance and elongation values (4.276 MPa and 2.63%) in the TPS50-PLA50-T5 film. Additionally, morphological analysis showed the detailed action of the plasticizer on the regular shapes of threads as a product of deformation during material processing. The barrel properties exhibited limited biopolymer–plastic–tensile miscibility, resulting in different water vapor permeability for the TPS75-PLA25-T5-A10 film on each side (a difference of two orders of magnitude). The contact angle corroborated the effect, with values in each case ranging from 103.7° to 30.3°. In conclusion, we assert that biopolymeric films, when modified with plasticizers and surfactants, can be tailored for various applications within the packaging sector while maintaining control over each film.
Using Neural Networks as a Data-Driven Model to Predict the Behavior of External Gear Pumps
Authors: Peric Benjamin , Engler Michael , Schuler Marc , Gutsche Katja , Woias Peter
Published: March 2024

This study presents a method for predicting the volume flow output of external gear pumps using neural networks. Based on operational measurements across the entire energy chain, the neural network learns to map the internal leakage of the pumps in use and consequently to predict the output volume flow over the entire operating range of the underlying dosing process. As a consequence, the previously used volumetric flow sensors become obsolete within the application itself. The model approach optimizes the higher-level dosing system in order to meet the constantly growing demands of industrial applications. We first describe the mode of operation of the pumps in use and focus on the internal leakage of external gear pumps, as these primarily determine the losses of the system. The structure of the test bench and the data processing for the neural network are discussed, as well as the architecture of the neural network. An error flow rate of approximately 1% can be achieved with the presented approach considering the entire operating range of the pumps, which until now could only be realized with multiple computationally intensive CFD simulations. The results are put into perspective by a hyperparameter study of possible neural architectures. The biggest obstacle considering the industrial scaling of this solution is the data generation process itself for various operating points. To date, an individual dataset is required for each pump because the neural architectures used are difficult to transfer, due to the tolerances of the manufactured pumps.
Should I Buy the Current Narrative about Predatory Journals? Facts and Insights from the Brazilian Scenario
Authors: Cena Cicero , Gonçalves Daniel A. , Câmara Giuseppe A.
Published: March 2024

The burgeoning landscape of scientific communication, marked by an explosive surge in published articles, journals, and specialized publishers, prompts a critical examination of prevailing assumptions. This article advocates a dispassionate and meticulous analysis to avoid policy decisions grounded in anecdotal evidence or superficial arguments. The discourse surrounding so-called predatory journals has been a focal point within the academic community, with concerns ranging from alleged lack of peer review rigor to exorbitant publication fees. While the consensus often leans towards avoiding such journals, this article challenges the prevailing narrative. It calls for a more nuanced understanding of what constitutes predatory practices and underscores the importance of skeptical inquiry within our daily academic activities. The authors aim to dispel misconceptions and foster a more informed dialogue by scrutinizing APCs, impact factors, and retractions. Furthermore, the authors delve into the evolving landscape of scientific publishing, addressing the generational shifts and emerging trends that challenge traditional notions of prestige and impact. In conclusion, this article serves as a call to action for the scientific community to engage in a comprehensive and nuanced debate on the complex issues surrounding scientific publishing.
Coastal Acid-Sulfate Soils of Kalimantan, Indonesia, for Food Security: Characteristics, Management, and Future Directions
Authors: Sulaeman Yiyi , Maftuáh Eni , Noor Muhammad , Hairani Anna , Nurzakiah Siti , Mukhlis Mukhlis , Anwar Khairil , Fahmi Arifin , Saleh Muhammad , Khairullah Izhar , Rumanti Indrastuti Apri , Alwi Muhammad , Noor Aidi , Ningsih Rina Dirgahayu
Published: March 2024

Coastal acid-sulfate soils are crucial for producing crops and thus, for food security. However, over time, these soil resources experience degradation, leading to higher agro-input, lower yields, and environmental hazards that finally threaten food security. The optimal use of this fragile resource is only attained by implementing vigorous integrated water–soil–crop management technologies amid the climate change impact. This study aimed to review the distribution, properties, use, and management of acid-sulfate soils in Kalimantan, Indonesia. Acid-sulfate soils cover about 3.5 Mha of the coastal area in Kalimantan and have high acidity, high-risk iron and aluminum toxicity, and low fertility, requiring precise water management, amelioration and fertilizer application, crop variety selection, and rice cultivation technologies. Lime, biochar, organic fertilizer, compost, ash, and fly ash are ameliorants that raise pH, reduce iron and aluminum toxicity, and improve crop yield. Rice cultivation has developed from traditional to modern but needs re-designing to fit local conditions. Depending on the soil nutrient status, rice cultivation requires 80–200 kg ha−1 of urea, 50–150 kg ha−1 of SP36, 50–150 kg ha−1 of KCl, and 125–400 kg ha−1 of NPK compound fertilizer, but is affected by CH4 and CO2 emissions. Good water management impacts the effective implementation of amelioration and fertilizer application technologies. The remaining challenges and future directions for water management, amelioration, fertilizer application, crop varieties, cultivation techniques, land use optimization, climate change adaptation and mitigation, technology adoption and implementation, and resource conservation are outlined. Acid-sulfate soils remain a resource capital that supports food security regionally and nationally in Indonesia.
BayesNet: Enhancing UAV-Based Remote Sensing Scene Understanding with Quantifiable Uncertainties
Authors: Sagar A. S. M. Sharifuzzaman , Tanveer Jawad , Chen Yu , Dang L. Minh , Haider Amir , Song Hyoung-Kyu , Moon Hyeonjoon
Published: March 2024

Remote sensing stands as a fundamental technique in contemporary environmental monitoring, facilitating extensive data collection and offering invaluable insights into the dynamic nature of the Earth’s surface. The advent of deep learning, particularly convolutional neural networks (CNNs), has further revolutionized this domain by enhancing scene understanding. However, despite the advancements, traditional CNN methodologies face challenges such as overfitting in imbalanced datasets and a lack of precise uncertainty quantification, crucial for extracting meaningful insights and enhancing the precision of remote sensing techniques. Addressing these critical issues, this study introduces BayesNet, a Bayesian neural network (BNN)-driven CNN model designed to normalize and estimate uncertainties, particularly aleatoric and epistemic, in remote sensing datasets. BayesNet integrates a novel channel–spatial attention module to refine feature extraction processes in remote sensing imagery, thereby ensuring a robust analysis of complex scenes. BayesNet was trained on four widely recognized unmanned aerial vehicle (UAV)-based remote sensing datasets, UCM21, RSSCN7, AID, and NWPU, and demonstrated good performance, achieving accuracies of 99.99%, 97.30%, 97.57%, and 95.44%, respectively. Notably, it has showcased superior performance over existing models in the AID, NWPU, and UCM21 datasets, with enhancements of 0.03%, 0.54%, and 0.23%, respectively. This improvement is significant in the context of complex scene classification of remote sensing images, where even slight improvements mark substantial progress against complex and highly optimized benchmarks. Moreover, a self-prepared remote sensing testing dataset is also introduced to test BayesNet against unseen data, and it achieved an accuracy of 96.39%, which showcases the effectiveness of the BayesNet in scene classification tasks.
Machine Learning-Based Wetland Vulnerability Assessment in the Sindh Province Ramsar Site Using Remote Sensing Data
Authors: Aslam Rana Waqar , Shu Hong , Naz Iram , Quddoos Abdul , Yaseen Andaleeb , Gulshad Khansa , Alarifi Saad S.
Published: March 2024

Wetlands provide vital ecological and socioeconomic services but face escalating pressures worldwide. This study undertakes an integrated spatiotemporal assessment of the multifaceted vulnerabilities shaping Khinjhir Lake, an ecologically significant wetland ecosystem in Pakistan, using advanced geospatial and machine learning techniques. Multi-temporal optical remote sensing data from 2000 to 2020 was analyzed through spectral water indices, land cover classification, change detection and risk mapping to examine moisture variability, land cover modifications, area changes and proximity-based threats over two decades. The random forest algorithm attained the highest accuracy (89.5%) for land cover classification based on rigorous k-fold cross-validation, with a training accuracy of 91.2% and a testing accuracy of 87.3%. This demonstrates the model’s effectiveness and robustness for wetland vulnerability modeling in the study area, showing 11% shrinkage in open water bodies since 2000. Inventory risk zoning revealed 30% of present-day wetland areas under moderate to high vulnerability. The cellular automata–Markov (CA–Markov) model predicted continued long-term declines driven by swelling anthropogenic pressures like the 29 million population growth surrounding Khinjhir Lake. The research demonstrates the effectiveness of integrating satellite data analytics, machine learning algorithms and spatial modeling to generate actionable insights into wetland vulnerability to guide conservation planning. The findings provide a robust baseline to inform policies aimed at ensuring the health and sustainable management and conservation of Khinjhir Lake wetlands in the face of escalating human and climatic pressures that threaten the ecological health and functioning of these vital ecosystems.
Two-Layer Inkjet-Printed Microwave Split-Ring Resonators for Detecting Analyte Binding to the Gold Surface
Authors: Paul Matthias , Kühnel Harald , Oberpertinger Rudolf , Mehofer Christoph , Pollhammer Doris , Wellenzohn Markus
Published: March 2024

This work focuses on demonstrating the working principle of inkjet-printed Au nanoparticle (NP) two-layer Gigahertz (2.6 GHz) microwave split-ring resonators (SRRs) as a novel platform for the detection of analytes on flexible substrates. In contrast to the standard fabrication of split-ring resonator biosensors using printed circuit board technology, which results in a seven-layer system, the resonators in this work were fabricated using a two-layer system. A ground plane is embedded in the SRR measurement setup. In this method, a microwave electromagnetic wave is coupled into the Au SRR via an inkjet-printed Cu-NP stripline that is photonically sintered. This coupling mechanism facilitates the detection of analytes by inducing resonance shifts in the SRR. In this study, the functionality of the printed sensors was demonstrated using two different Au functionalization processes, firstly, with HS-PEG7500-COOH, and, secondly, with protein G with an N-terminal cysteine residue. The sensing capabilities of the printed structures are shown by the attachment of biomolecules to the SRR and the measurement of the resulting resonance shift. The experiments show a clear shift of the resonance frequency in the range of 20–30 MHz for both approaches. These results demonstrate the functionality of the simplified printed two-layer microwave split-ring resonator for use as a biosensor.
Assessment of Performance Measurement Systems’ Ability to Mitigate or Eliminate Typical Barriers Compromising Organisational Sustainability
Authors: Cunha Flávio , Dinis-Carvalho José , Sousa Rui M.
Published: March 2024

This paper aims to identify the main performance measurement systems (PMSs) documented in the literature and assess their ability to overcome/mitigate a set of 19 specific barriers (identified in a previous paper) to their effectiveness. It also aims to understand what makes each PMS capable of or not capable of dealing with these barriers (i.e., what traits it has) and to explore their connection to some sustainable development goals (SDG). The PRISMA methodology was used to identify the relevant publications, which were then subjected to a detailed content analysis with statistical treatment, followed by the assessment of the potential of each PMS to deal with the barriers. The results made it possible to identify the PMSs most referred to in the literature (ordered list), quantitatively classify the PMSs according to their ability to overcome/mitigate barriers, and identify the barriers most and least addressed by the PMSs. While no single PMS offers a comprehensive solution, certain common traits contribute significantly to overcoming prevalent barriers. The complex interplay between barriers means that some traits can effectively address multiple barriers either directly or indirectly. Regarding implications, these findings provide important inputs (e.g., key recommendations) for developing or improving PMS frameworks that are able to comprehensively address the barriers, thus contributing to organisational effectiveness and, consequently, to the achievement of the SDGs. This constitutes the innovative contribution of this paper. As for limitations, this work is based on the analysis of 28 PMSs resulting from the systematic literature review in two databases (Scopus and Web of Science).
Framework for a Holistic Assessment of the Quality of Agri-Food Governance in Bulgaria
Authors: Bachev Hrabrin , Ivanov Bozhidar
Published: March 2024

A holistic Good Agents Means Processes Order Sustainability (GAMPOS) framework for an adequate understanding of agri-food governance and assessing its quality is suggested, incorporating the interdisciplinary new institutional economics methodology. Agri-food governance is defined as a complex system with five components: (1) agri-food and related agents, (2) means (rules, forms, and mechanisms) that govern agents’ behavior, activities, and relationships, (3) processes and activities related to making managerial decisions, (4) specific social order resulting from the governing process, and (5) outcomes of the functioning of the system in terms of the realization of sustainable development goals. To assess the quality of agri-food governance, a multidimensional hierarchical system with good governance 11 principles, 21 criteria, and 36 indicators and reference values is presented. A comprehensive assessment of agrarian governance in Bulgaria, based on statistical and expert data, showed that its overall quality is at a moderate EU level. In terms of sustainability, the quality of governance is at a good level, while for process, means, and order, it is at a satisfactory level. The quality of agrarian governance is highest in terms of equity and solidarity and the good functioning public sector. The quality of agrarian governance is lowest in terms of stakeholder involvement and the good working private sector. In the future, in the latter two areas, combined actions of public, private, and collective agents are needed to improve the country’s agri-food governance.
The Impact of Industrial Robots on the Sustainable Development of Zombie Firms in China
Authors: Zhou Rongyun , Zhang Qian
Published: March 2024

Utilizing panel data from listed Chinese manufacturing companies spanning the period from 2011 to 2018, this paper investigates the impact of industrial-robot applications on the resurgence of zombie firms. The fixed-effects-estimation results reveal that a 1% increase in industrial-robot utilization corresponds to a 0.49% rise in the revival rate of financially distressed entities. The paper underscores the instrumental role of industrial robots in fostering the sustainable development of zombie firms, emphasizing improvements in capital returns, labor productivity, and overall factor productivity. Heterogeneity analysis further highlights the varied influences of geographical location, capital intensity, and pollution emissions on this relationship. This paper contributes to a comprehensive understanding of the crucial role played by industrial automation in revitalizing struggling enterprises within the Chinese manufacturing landscape.
The Challenge Posed by Emerging Environmental Contaminants: An Assessment of the Effectiveness of Phenoxyethanol Biological Removal from Groundwater through Mesocosm Experiments
Authors: Ducci Laura , Rizzo Pietro , Bucci Antonio , Pinardi Riccardo , Monaco Pamela , Celico Fulvio
Published: March 2024

The occurrence of emerging pollutants (EPs) such as pharmaceuticals and personal care products (PPCPs) has raised serious concerns about the possible adverse effects on ecosystem integrity and human health. Wastewater treatment facilities appear to be the main sources of PPCPs released in aquatic environments. This research examines the effectiveness of groundwater microbial community activities to remove phenoxyethanol (Phy-Et), currently exploited as a preservative in many cosmetic formulations at a maximum concentration of 1% but which has shown, at higher levels of exposure, adverse systemic effects on animals. Mesocosm experiments were carried out for 28 days using two different concentrations of the substance (5.2 mg/L and 27.4 mg/L). The main results obtained through chemical and microbiological investigations revealed a significant Phy-Et reduction (≈100% when added at a concentration of 5.2 mg/L and ≈84% when added at a concentration of 27.4 mg/L), demonstrating that some autochthonous microorganisms in the analyzed samples played a “key role” in removing this compound, despite its proven antimicrobial activity. Nevertheless, the decrease in the “natural attenuation” efficacy (≈16%) when using higher concentrations of the chemical suggests the existence of a “dose-dependent effect” of Phy-Et on the process of biodegradation. Biomolecular investigations carried out through next-generation sequencing (NGS) revealed (i) the presence of a significant fraction of hidden microbial diversity to unravel, (ii) variations of the composition and species abundance of the groundwater microbial communities induced by Phy-Et, and (iii) a biodiversity reduction trend correlated to the increase of Phy-Et concentrations. Overall, the preliminary information obtained from the experiments carried out at the laboratory scale appears encouraging, although it reflects only partially the complexity of the phenomena that occur in natural environments and influences their “auto-purification capability”. Accordingly, this research paves the way for more in-depth investigations to develop appropriate tools and protocols to evaluate the occurrence and fate of Phy-Et in nature and assess the impact of its release and the effects of long-term exposure (even at low concentrations) on ecosystems and health.
Government Support Mechanisms for Sustainable Agriculture: A Systematic Literature Review and Future Research Agenda
Authors: Barbosa Marcelo Werneck
Published: March 2024

Government support for enterprises can be provided in different forms, such as subsidies, tax incentives, or direct public investment. Government support can also be given to develop environmentally and socially responsible initiatives. In the agriculture sector, government support involves providing early-warning information from meteorological monitoring, policies to encourage companies and citizens to engage in sustainable behavior, and the organization of disaster relief activities. A comprehensive view of the government support mechanisms employed in the agriculture industry and their possible effects on the adoption of sustainable initiatives is yet to be provided in the literature. Through a systematic literature review of articles published from 1992 to 2023 in the Web of Science and Scopus databases, this study found that subsidies, programs, regulations, and financial assistance are the main mechanisms used by governments to support sustainable initiatives in agriculture. Moreover, our review also found that the reduction in environmental impacts, the stimulus to organic production, industry growth, and rural development are the most common goals of using government support for sustainable agriculture. We also geographically categorize research in this field by showing where research has been produced and where it should be improved. We call for more research focused on support from governments in South America. We also observed that environmental protection, the supply of organic foods, and urban agriculture are frequently impacted when there is not enough government support. At last, we propose some research questions to guide future studies. We expect that policymakers will use the outcomes of this study to guide the design and implementation of new government support mechanisms aimed at improving the sustainable performance of agriculture firms. The outcomes of this study will also help managers make investment decisions.
A Study on the Impact of Digital Transformation on Green Resilience in China
Authors: Wang Shaohua , Song Yanfei , Zhang Wei
Published: March 2024

Enhancing green resilience is an important element in realizing environmental protection and green development, and with the continuous development of digital technology, digital transformation has become a new driving force for enhancing green resilience. Based on the panel data of 31 provinces in China from 2013 to 2021, this study examines the impact of digital transformation on green resilience. The results show that digital transformation can significantly enhance green resilience, and this conclusion is still valid after considering a series of robustness tests and endogenous problems; the heterogeneity analysis shows that in the eastern and central regions, the enhancement of green resilience by digital transformation is still significant, while in the western region, digital transformation has curbed green resilience but is not significant; both resource-based and non-resource-based provinces’ digital transformation has enhanced green resilience, and the enhancement is more significant in resource-based provinces; in different levels of green resilience, digital transformation has a stronger impact on provinces with high levels of green resilience, showing the “Matthew effect”; the mediation effect results show that digital transformation can enhance green resilience by attracting government investment, fostering industrial integration and increasing public environmental concern; the threshold results show that digital transformation contributes to green resilience across the sample. However, with the development of digital platforms, the growth rate of digital transformation on the promotion of green resilience will first increase and then gradually decrease and eventually rebound, showing an “N-shaped” relationship.
Sustainability Indicators Model Applied to Waste Management in Brazil Using the DPSIR Framework
Authors: Santos Eduardo , Fonseca Fernando , Santiago Aníbal , Rodrigues Daniel
Published: March 2024

Municipal solid waste is a chronic problem in many developing countries like Brazil. Addressing the challenges of waste management requires robust frameworks for assessing the policies’ impacts. In this context, sustainability indicators may help decision-makers in adopting more efficient waste policies. The main goal of this study is to propose a framework for identifying and classifying indicators related to waste management within Brazilian public policies. This evaluation was based on extensive legislative surveys aimed at identifying goals, targets, and obligations outlined in these policies. A comprehensive Driving Force–Pressure–State–Impact–Response (DPSIR) framework was employed to identify and classify the usefulness of these indicators in the context of waste management. A total of 151 indicators were identified, alongside 283 waste goals, targets, and obligations, which were primarily related to municipal waste and were mostly sourced from plans, laws, presidential decrees, and collegiate resolutions. Within the framework, 66 indicators, mostly related to waste collection and recycling, were deemed useful, comprising 34 classified as Responses, 14 as Pressures, six as Driving Forces, four as States, two as Impacts, and six as Pressure/Responses. The challenges associated with the DPSIR framework and strategies to enhance the efficiency of public waste policies in Brazil are also discussed.
Toward a Sustainable World: Affective Factors Explain How Emotional Salary Influences Different Performance Indicators
Authors: Junça Silva Ana , Burgette Ana Rita , Fontes da Costa João
Published: March 2024

The concept of emotional salary refers to the non-monetary rewards granted to workers, focusing on improving interpersonal relationships, strengthening productivity, and enhancing the competitiveness of organizations. The topic of emotional salary is still recent and lacks empirical studies demonstrating its beneficial effects for both workers (e.g., job satisfaction) and organizations (e.g., performance). Therefore, to expand knowledge about the benefits of emotional salary, the present study used the self-determination theory to hypothesize that motivation and satisfaction would serve as affective mechanisms linking emotional salary to workers’ performance. Through a non-experimental correlational study, an online questionnaire was administered to 215 workers from various organizations. The results showed that emotional salary influenced performance (task, contextual, and adaptive) by increasing motivation and job satisfaction. The results also indicated evidence of a serial mediation path between emotional salary, motivation, satisfaction, and then performance. From a management perspective, considering emotional salary as an organizational resource capable of motivating and satisfying workers is a starting point for acknowledging the practical and theoretical importance of this concept, as well as a strategy to contribute to organizational sustainability.
Correlations of Spatial Form Characteristics on Wind–Thermal Environment in Hill-Neighboring Blocks
Authors: Zhao Liang , Zhang Yijie , Li Yiting , Feng Zichao , Wang Yuetao
Published: March 2024

The spatial forms of hill-neighboring blocks and the wind–thermal environment are crucial components of urban planning and urban ecology. Understanding their correlation is significant for creating a healthy community and enhancing the sustainable level of buildings and their systems. Therefore, Jinan, a multi-mountainous city in China, is taken as the research area, and the ideal model of hill-neighboring blocks is built based on categories of all these block types by aerial imagery and a semantic segmentation algorithm. Then, we use the CFD simulation software PHOENICS and the evaluation system weighted by random forest to simulate and assess the wind–thermal environment. Ultimately, the correlations and specific mathematical equations between the spatial form indicators and wind–thermal environment are obtained by a parametric method. The results demonstrate that the interface density on the near-hill side and hill surface roughness are the most related indicators to the overall wind–thermal environment. The variation in block spatial form has the most distinct influence on the proportion of breeze area and PMV. The relationship determined herein can provide strategic recommendations for decision makers for optimizing the outdoor air flow of blocks and enhancing the thermal comfort of pedestrians, which helps to create a healthy and comfortable outdoor environment in multi-mountainous cities.
Mitigation and Adaptation Strategies for Different Urban Fabrics to Face Increasingly Hot Summer Days Due to Climate Change
Authors: Lassandro Paola , Zaccaro Sara Antonella , Di Turi Silvia
Published: March 2024

As global warming and heat waves are becoming more frequent and severe, cities, with their different morphological districts, must be at the forefront of environmental challenges. Notably, many Mediterranean towns maintain the original medieval urban fabric and the regular one. The research focuses on the development of a methodology with the application of high-resolution 3D modelling software ENVI-met V5.1 to analyze the microclimatic effects of mitigation and adaptation strategies derived from the study of medieval and regular urban fabric. The aim is to address contemporary challenges such as heat waves and urban heat island (UHI) effects in modern cities. By studying outdoor energy behavior in a southern Italian city (Bari), the research proposes scenarios for urban settlements in the face of climate change. This approach provides recommendations for creating more climate-resilient urban environments both in the historic and modern city. The use of trees with large crowns and tall shrubs and the inclusion of fountain jets are strategies to achieve sky view factor and air temperatures in the modern city similar to those in the historical fabric. Increasing albedo values and the use of green roofs prove to be further strategies for improving outdoor climatic conditions.
Hierarchical Object Part Learning Using Deep Lp Smooth Symmetric Non-Negative Matrix Factorization
Authors: Li Shunli , Song Chunli , Lu Linzhang , Chen Zhen
Published: March 2024

Nowadays, deep representations have gained significant attention due to their outstanding performance in a wide range of tasks. However, the interpretability of deep representations in specific applications poses a significant challenge. For instances where the generated quantity matrices exhibit symmetry, this paper introduces a variant of deep matrix factorization (deep MF) called deep Lp smooth symmetric non-negative matrix factorization (DSSNMF), which aims to improve the extraction of clustering structures inherent in complex hierarchical and graphical representations in high-dimensional datasets by improving the sparsity of the factor matrices. We successfully applied DSSNMF to synthetic datasets as well as datasets related to post-traumatic stress disorder (PTSD) to extract several hierarchical communities. Specifically, we identified non-disjoint communities within the partial correlation networks of PTSD psychiatric symptoms, resulting in highly meaningful clinical interpretations. Numerical experiments demonstrate the promising applications of DSSNMF in fields like network analysis and medicine.
A Proposal for a Tokenized Intelligent System: A Prediction for an AI-Based Scheduling, Secured Using Blockchain
Authors: Younis Osama , Jambi Kamal , Eassa Fathy , Elrefaei Lamiaa
Published: March 2024

Intelligent systems are being proposed every day as advances in cloud systems are increasing. Mostly, the services offered by these cloud systems are dependent only on their providers, without the inclusion of services from other providers, specialized third parties, or individuals. This ‘vendor lock-in’ issue and the limitations related to offering tailored services could be resolved by allowing multiple providers or individuals to collaborate through intelligent task scheduling. To address such real-world systems’ limitations in provisioning and executing heterogeneous services, we employed Blockchain and Deep Reinforcement Learning here; the first is used for the token-based secured communication between parties, and the latter is to predict the appropriate task scheduling; hence, we guarantee the quality of not only the immediate decision but also the long-term. The empirical results show a high reward achieved, meaning that it accurately selected the candidates and adaptably assigned the tasks based on job nature and executors’ individual computing capabilities, with 95 s less than the baseline in job completion time to maintain the Quality of Service. The successful collaboration between parties in this tokenized system while securing transactions through Blockchain and predicting the right scheduling of tasks makes it a promising intelligent system for advanced use cases.
Identification and Mechanistic Analysis of Toxic Degradation Products in the Advanced Oxidation Pathways of Fluoroquinolone Antibiotics
Authors: Sun Shuhai , Wang Zhonghe , Pu Qikun , Li Xinao , Cui Yuhan , Yang Hao , Li Yu
Published: March 2024

The degradation of fluoroquinolones (FQs) via advanced oxidation processes (AOPs) is a promising avenue, yet the complete mineralization of certain FQ molecules remains elusive, raising concerns about the formation of toxic by-products. This study delineates five primary AOP degradation pathways for 16 commercially available FQ molecules, inferred from existing literature. Density functional theory (DFT) was employed to calculate the bond dissociation energies within these pathways to elucidate the correlation between bond strength and molecular architecture. Subsequently, Comparative Molecular Similarity Index Analysis (CoMSIA) models were constructed for various degradation reactions, including piperazine ring cleavage, defluorination, hydroxylation, and piperazine ring hydroxylation. Three-dimensional contour maps generated from these models provide a deeper understanding of the interplay between FQ molecular structure and bond dissociation energy. Furthermore, toxicity predictions for 16 FQ molecules and their advanced oxidation intermediates, conducted using VEGA 1.2.3 software, indicate that degradation products from pathways P2 and P5 pose a heightened health risk relative to their parent compounds. Furthermore, the application of the Multwfn program to compute the Fukui function for FQ molecules discerns the disparity in degradation propensities, highlighting that N atoms with higher f0 values can augment the likelihood of piperazine ring cleavage. HOMO-LUMO distribution diagrams further confirm that methoxy substitution at the 1-position leads to a dilution of HOMOs on the piperazine ring and an increased energy gap for free radical reactions, diminishing the reactivity with hydroxyl radicals. This study elucidates the pivotal role of structural characteristics in FQ antibiotics for their degradation efficiency within AOPs and unveils the underlying mechanisms of bond dissociation energy disparities. The toxicity parameter predictions for FQ molecules and their intermediates offer unique perspectives and theoretical underpinnings for mitigating the use of high-risk FQs and for devising targeted degradation strategies to circumvent the generation of toxic intermediates in AOPs through molecular structure optimization.
Modulation of Equid Herpesvirus-1 Replication Dynamics In Vitro Using CRISPR/Cas9-Assisted Genome Editing
Authors: Hassanien Rabab T. , Thieulent Côme J. , Carossino Mariano , Li Ganwu , Balasuriya Udeni B. R.
Published: March 2024

(1) Background: equid alphaherpesvirus-1 (EHV-1) is a highly contagious viral pathogen prevalent in most horse populations worldwide. Genome-editing technologies such as CRISPR/Cas9 have become powerful tools for precise RNA-guided genome modifications; (2) Methods: we designed single guide RNAs (sgRNA) to target three essential (ORF30, ORF31, and ORF7) and one non-essential (ORF74) EHV-1 genes and determine their effect on viral replication dynamics in vitro; (3) Results: we demonstrated that sgRNAs targeting essential lytic genes reduced EHV-1 replication, whereas those targeting ORF74 had a negligible effect. The sgRNAs targeting ORF30 showed the strongest effect on the suppression of EHV-1 replication, with a reduction in viral genomic copy numbers and infectious progeny virus output. Next-generation sequencing identified variants with deletions in the specific cleavage site of selective sgRNAs. Moreover, we evaluated the combination between different sgRNAs and found that the dual combination of sgRNAs targeting ORF30 and ORF7 significantly suppressed viral replication to lower levels compared to the use of a single sgRNA, suggesting a synergic effect; (4) Conclusion: data demonstrate that sgRNA-guided CRISPR/Cas9 can be used to inhibit EHV-1 replication in vitro, indicating that this programmable technique can be used to develop a novel, safe, and efficacious therapeutic and prophylactic approach against EHV-1.
Continuous Heterogeneous Fenton for Swine Wastewater Treatment: Converting an Industry Waste into a Wastewater Treatment Material
Authors: Lincho João , Gomes João , Martins Rui C. , Domingues Eva
Published: March 2024

Swine wastewater (SW) was treated using industrial wastes as raw materials in a pre-treatment process (coagulation or adsorption), followed by a continuous heterogeneous Fenton reaction. Before the treatment conducted as a continuous operation, two different batch optimization strategies were evaluated, in which the effects of H2O2 concentration and pH were studied. The results show that using excessive H2O2 results in the same behavior, regardless of whether the pH is 3 or 7.5, while at low H2O2 concentrations, the acidic pH improves the chemical oxygen demand (COD) removal due to a higher solubility of iron. The partial addition of H2O2 after 60 min of the reaction proved to be unbeneficial. Considering other perspectives, a continuous Fenton process using iron filings (IF) as the iron source ([H2O2] = 50 mg/L) was applied after the SW pre-treatment, consisting of adsorption with red mud (RM) or coagulation with poly-diallyldimethylammonium chloride (PDADMAC). The RM adsorption presented higher COD removal and lower toxicity than the PDADMAC coagulation, revealing to be a suitable material for this purpose, but for both pre-treatments, the application of a subsequent continuous Fenton process revealed to be essential to achieve the COD discharge limits imposed by the Portuguese law. In addition, high amounts of dissolved iron were present in the samples (55–58 mg/L) after the Fenton process. However, after the overall treatment, the samples showed no harmful characteristics for Lepidium sativum, being classified as “non-toxic”, contrary to the initial wastewater.
Effects of a Fishing Ban on the Ecosystem Stability and Water Quality of a Plateau Lake: A Case Study of Caohai Lake, China
Authors: Yang Tangwu , Li Dianpeng , Xu Qing , Zhu Yijia , Zhu Zhengjie , Leng Xin , Zhao Dehua , An Shuqing
Published: March 2024

Long-term fishing bans have spurred extensive debate regarding their impacts on ecosystem structures, functions, and water qualities. However, data on the effects of specific changes induced by fishing bans on ecosystem structures, functions, and water qualities in lakes are still lacking. Therefore, the present study addresses this knowledge gap by employing an Ecopath model to assess alterations in an ecosystem’s structure and function before (2011) and after (2021) the implementation of the fishing ban in Caohai Lake and its association with changes in water quality. (1) We observed a substantial reduction in the area covered by submerged aquatic vegetation after the ban, amounting to a 65% decrease in coverage compared with that before the ban, and a 60% reduction in the total ecosystem’s biomass. (2) Following the ban, the number of fish species increased from 7 to 14, and this was accompanied by a rise in the fish biomass from 14.16 t·km−2 to 25.81 t·km−2; a 4.5-fold increase in the total system consumption was observed, signifying accelerated energy and material flows within the ecosystem. (3) The fishing ban exhibited no significant impact on the total nitrogen concentration; however, it significantly reduced the water’s transparency and increased the total phosphorus, ammonia nitrogen, chemical oxygen demand, and chlorophyll contents (p < 0.05). This shift in nutrient dynamics fostered a transformation from a macrophyte-dominant lake to an alga-dominant lake. The fish abundance and diversity increase in closed-type macrophytic lakes, thereby accelerating energy and material flows within food webs. These findings present novel insights into the effective policy management of fishing bans within the Yangtze River Basin, thus enhancing our understanding of sustainable lake ecosystem management.