bluuebunny/crossref_metadata_2025 huggingface dataset:
A Novel Approach for the Synthesis of Responsive Core–Shell Nanogels with a Poly(N-Isopropylacrylamide) Core and a Controlled Polyamine Shell
Authors: Harsányi Anna , Kardos Attila , Xavier Pinchu , Campbell Richard A. , Varga Imre
Published: September 2024

Microgel particles can play a key role, e.g., in drug delivery systems, tissue engineering, advanced (bio)sensors or (bio)catalysis. Amine-functionalized microgels are particularly interesting in many applications since they can provide pH responsiveness, chemical functionalities for, e.g., bioconjugation, unique binding characteristics for pollutants and interactions with cell surfaces. Since the incorporation of amine functionalities in controlled amounts with predefined architectures is still a challenge, here, we present a novel method for the synthesis of responsive core–shell nanogels (dh < 100 nm) with a poly(N-isopropylacrylamide) (pNIPAm) core and a polyamine shell. To achieve this goal, a surface-functionalized pNIPAm nanogel was first prepared in a semi-batch precipitation polymerization reaction. Surface functionalization was achieved by adding acrylic acid to the reaction mixture in the final stage of the precipitation polymerization. Under these conditions, the carboxyl functionalities were confined to the outer shell of the nanogel particles, preserving the core’s temperature-responsive behavior and providing reactive functionalities on the nanogel surface. The polyamine shell was prepared by the chemical coupling of polyethyleneimine to the nanogel’s carboxyl functionalities using a water-soluble carbodiimide (EDC) to facilitate the coupling reaction. The efficiency of the coupling was assessed by varying the EDC concentration and reaction temperature. The molecular weight of PEI was also varied in a wide range (Mw = 0.6 to 750 kDa), and we found that it had a profound effect on how many polyamine repeat units could be immobilized in the nanogel shell. The swelling and the electrophoretic mobility of the prepared core–shell nanogels were also studied as a function of pH and temperature, demonstrating the successful formation of the polyamine shell on the nanogel core and its effect on the nanogel characteristics. This study provides a general framework for the controlled synthesis of core–shell nanogels with tunable surface properties, which can be applied in many potential applications.
Partially Bio-Based and Biodegradable Poly(Propylene Terephthalate-Co-Adipate) Copolymers: Synthesis, Thermal Properties, and Enzymatic Degradation Behavior
Authors: Song Ping , Li Mingjun , Wang Haonan , Cheng Yi , Wei Zhiyong
Published: September 2024

A series of partially bio-based and biodegradable poly(propylene terephthalate-co-adipate) (PPTA) random copolymers with different components were prepared by the melt polycondensation of petro-based adipic acid and terephthalic acid with bio-based 1,3-propanediol. The microstructure, crystallization behavior, thermal properties, and enzymatic degradation properties were further investigated. The thermal decomposition kinetics was deeply analyzed using Friedman’s method, with the thermal degradation activation energy ranging from 297.8 to 302.1 kJ/mol. The crystallinity and wettability of the copolymers decreased with the increase in the content of the third unit, but they were lower than those of the homopolymer. The thermal degradation activation energy E, carbon residue, and reaction level n all showed a decreasing trend. Meanwhile, the initial thermal decomposition temperature (Td) was higher than 350 °C, which can meet the requirements for processing and use. The PPTA copolymer material still showed excellent thermal stability. Adding PA units could regulate the crystallinity, wettability, and degradation rate of PPTA copolymers. The composition of PPTA copolymers in different degradation cycles was characterized by 1H NMR analysis. Further, the copolymers’ surface morphology during the process of enzymatic degradation also was observed by scanning electron microscopy (SEM). The copolymers’ enzymatic degradation accorded with the surface degradation mechanism. The copolymers showed significant degradation behavior within 30 days, and the rate increased with increasing PA content when the PA content exceeded 45.36%.
Recent Progress of Electrospun Nanofiber Dressing in the Promotion of Wound Healing
Authors: Lu Xiaoqi , Zhou Libo , Song Weiye
Published: September 2024

The nanofiber materials of three-dimensional spatial structure synthesized by electrospun have the characteristics of high porosity, high specific surface area, and high similarity to the natural extracellular matrix (ECM) of the human body. These are beneficial for absorbing wound exudate, effectively blocking the invasion of external bacteria, and promoting cell respiration and proliferation, which provides an ideal microenvironment for wound healing. Moreover, electrospun nanofiber dressings can flexibly load drugs according to the condition of the wound, further promoting wound healing. Recently, electrospun nanofiber materials have shown promising application prospects as medical dressings in clinical. Based on current research, this article reviewed the development history of wound dressings and the principles of electrospun technology. Subsequently, based on the types of base material, polymer-based electrospun nanofiber dressing and electrospun nanofiber dressing containing drug-releasing factors were discussed. Furthermore, the application of electrospun nanofiber dressing on skin tissue is highlighted. This review aims to provide a detailed overview of the current research on electrospun nanomaterials for wound healing, addressing challenges and suggesting future research directions to advance the field of electrospun dressings in wound healing.
Development of Replica Molding Processes for Hypervariable Microstructural Components
Authors: Weng Yung-Jin , Gao Yu-Zhe , Chen Yu-Ming
Published: September 2024

The current study investigates the development of a replica molding process for hypervariable microstructures. Initially, the mold deformation theory for these hypervariable microstructures was derived. Based on this theory, a metal material with magnetic properties was selected as the structural material to create a negative Poisson’s ratio (NPR) geometric structure. The experimental results, obtained by fabricating the NPR geometric mold layer with a metal material with adjustable magnetic properties and controlling microstructure deformation indirectly, validate the deformation theory and its predictions. These results demonstrate that the developed molding process, integrated with the magnetic NPR regulation system, exhibits excellent stability and replication capability. In this study, at the zero height (z = 0) position on the interface between the NPR geometric structure layer and the Polydimethylsiloxane (PDMS), the variation becomes more pronounced with increasing distance from the center of the microstructure. Furthermore, the tendency of the function curve varies accordingly. The primary cause is the lack of constraints on the free ends of both sides and the excessive constraints on the intermediate parts. Under the conditions in this study, the maximum ratio of its influence on the radial diameter thickness was 2.1%. This innovative process facilitates the rapid imprinting of microstructural components and offers the advantage of efficient molding.
What Are the Boundaries? Discerning “Pietas” from “Superstitio” in a Frontier Diocese: The Pastoral Action of the Bishops of Como between the Sixteenth and Seventeenth Centuries
Authors: Portone Paolo , Giorgetta Valerio
Published: September 2024

Between the 16th and 17th centuries, the conservative characteristic of rural environments and mountain communities represented one of the main worries of the Larian Church, which, despite the work of reform of religious customs undertaken by the order of preachers in the late Middle Ages (not unrelated to the genesis of the accusation of diabolic witchcraft), it found itself confronted with the shortcomings (from the interference of the laity in religious life to suspicious devotions via the mixture of the sacred and magical animistic legacies) originating from decades of neglect in the management of valley parishes and the laxity of the secular clergy. This concern had to be reconciled, from the first decades of the sixteenth century onward, with the need to counter the Protestant presence. The “singular” way in which diocesan ordinaries sought in the aftermath of the Tridentine Council to re-establish orthopraxy in the only diocese in the peninsula subject to secular authorities of the Reformed faith, and in which an Italophone Protestant community was permanently present for several decades, represents an important case study for understanding the anomaly of the local bishop’s courts (and the inquisition) transformed during this time from bitter enemies of the secta strigiarum into “witch lawyers”, and for illuminating the deeper reasons for the limits of the fight against superstitions in the entire peninsula.
Greenhouse Gas Emissions Associated with Tree Pruning Residues of Urban Areas of Northeast Brazil
Authors: Araujo Yuri Rommel Vieira , Souza Bartolomeu Israel , Carvalho Monica
Published: September 2024

There are environmental concerns (especially regarding climate change) associated with the negative effects of some pruning waste management practices. Converting urban tree waste into valuable products can help mitigate climate change, but it is important to quantify the repercussions of tree waste scenarios in an urban context. The objective of this study was to quantify the greenhouse gas (GHG) emissions for six scenarios of urban pruning waste in urban areas. To this end, the life cycle assessment methodology was applied to real data obtained from five municipalities of the Paraíba state in 2012–2021 (northeast Brazil). The six scenarios were: sanitary landfill (current practice), sanitary landfill with methane capture, municipal incineration, reuse of wood, heat generation and electricity generation. Considering the 10-year period, the sanitary landfill emitted 1048 kt CO2e, and when methane was captured at the landfill, emissions decreased to 1033 kt CO2e. The lowest emissions were associated with electricity generation, with 854 kt CO2e. The municipality of João Pessoa presented the highest emissions, followed by Cabedelo, Santa Rita, Bayeux, and Conde. Transportation was responsible for the highest share of GHG emissions. Disposal of urban pruning waste at the sanitary landfill presented the highest emissions, and it has been demonstrated herein that pruning waste can be used for the production of bioenergy, with significant potential to mitigate GHG emissions at local levels.
Estimating Three-Dimensional Resistivity Distribution with Magnetotelluric Data and a Deep Learning Algorithm
Authors: Liu Xiaojun , Craven James A. , Tschirhart Victoria , Grasby Stephen E.
Published: September 2024

In this study, we describe a deep learning (DL)-based workflow for the three-dimensional (3D) geophysical inversion of magnetotelluric (MT) data. We derived a mathematical connection between a 3D resistivity model and the surface-observed electric/magnetic field response by using a fully connected neural network framework (U-Net). Limited by computer hardware functionality, the resistivity models were generated by using a random walk technique to enlarge the generalization coverage of the neural network model, and 15,000 paired datasets were utilized to train and validate it. Grid search was used to select the optimal configuration parameters. With the optimal model framework from the parameter tuning phase, the metrics showed stable convergence during model training/validation. In the test period, the trained model was applied to predict the resistivity distribution by using both the simulated synthetic and the real MT data from the Mount Meager area, British Columbia. The reliability of the model prediction was verified with noised input data from the synthetic model. The calculated results can be used to reconstruct the position and shape trends of bodies with anomalous resistivity, which verifies the stability and performance of the DL-based 3D inversion algorithm and showcases its potential practical applications.
Spatiotemporal Prediction of Conflict Fatality Risk Using Convolutional Neural Networks and Satellite Imagery
Authors: Goodman Seth , BenYishay Ariel , Runfola Daniel
Published: September 2024

As both satellite imagery and image-based machine learning methods continue to improve and become more accessible, they are being utilized in an increasing number of sectors and applications. Recent applications using convolutional neural networks (CNNs) and satellite imagery include estimating socioeconomic and development indicators such as poverty, road quality, and conflict. This article builds on existing work leveraging satellite imagery and machine learning for estimation or prediction, to explore the potential to extend these methods temporally. Using Landsat 8 imagery and data from the Armed Conflict Location & Event Data Project (ACLED) we produce subnational predictions of the risk of conflict fatalities in Nigeria during 2015, 2017, and 2019 using distinct models trained on both yearly and six-month windows of data from the preceding year. We find that predictions at conflict sites leveraging imagery from the preceding year for training can predict conflict fatalities in the following year with an area under the receiver operating characteristic curve (AUC) of over 75% on average. While models consistently outperform a baseline comparison, and performance in individual periods can be strong (AUC > 80%), changes based on ground conditions such as the geographic scope of conflict can degrade performance in subsequent periods. In addition, we find that training models using an entire year of data slightly outperform models using only six months of data. Overall, the findings suggest CNN-based methods are moderately effective at detecting features in Landsat satellite imagery associated with the risk of fatalities from conflict events across time periods.
Charging Scheduling Method for Wireless Rechargeable Sensor Networks Based on Energy Consumption Rate Prediction for Nodes
Authors: Huang Songjiang , Sha Chao , Zhu Xinyi , Wang Jingwen , Wang Ruchuan
Published: September 2024

With the development of the IoT, Wireless Rechargeable Sensor Networks (WRSNs) derive more and more application scenarios with diverse performance requirements. In scenarios where the energy consumption rate of sensor nodes changes dynamically, most existing charging scheduling methods are not applicable. The incorrect estimation of node energy requirement may lead to the death of critical nodes, resulting in missing events. To address this issue, we consider both the spatial imbalance and temporal dynamics of the energy consumption of the nodes, and minimize the Event Missing Rate (EMR) as the goal. Firstly, an Energy Consumption Balanced Tree (ECBT) construction method is proposed to prolong the lifetime of each node. Then, we transform the goal into Maximizing the value of the Evaluation function of each node’s Energy Consumption Rate prediction (MEECR). Afterwards, the setting of the evaluation function is explored and the MEECR is further transformed into a variant of the knapsack problem, namely “the alternating backpack problem”, and solved by dynamic programming. After predicting the energy consumption rate of the nodes, a charging scheduling scheme that meets the Dual Constraints of Nodes’ energy requirements and MC’s capability (DCNM) is developed. Simulations demonstrate the advantages of the proposed method. Compared to the baselines, the EMR was reduced by an average of 35.2% and 26.9%.
High-Resolution and Robust One-Bit Direct-of-Arrival Estimation via Reweighted Atomic Norm Estimation
Authors: Li Rui , Yang Jianchao , Dai Zheng , Lu Xingyu , Tan Ke , Su Weimin
Published: September 2024

In recent years, one-bit quantization has attracted widespread attention in the field of direction-of-arrival (DOA) estimation as a low-cost and low-power solution. Many researchers have proposed various estimation algorithms for one-bit DOA estimation, among which atomic norm minimization algorithms exhibit particularly attractive performance as gridless estimation algorithms. However, current one-bit DOA algorithms with atomic norm minimization typically rely on approximating the trace function, which is not the optimal approximation and introduces errors, along with resolution limitations. To date, there have been few studies on how to enhance resolution under the framework of one-bit DOA estimation. This paper aims to improve the resolution constraints of one-bit DOA estimation. The log-det heuristic is applied to approximate and solve the atomic norm minimization problem. In particular, a reweighted binary atomic norm minimization with noise assumption constraints is proposed to achieve high-resolution and robust one-bit DOA estimation. Finally, the alternating direction method of multipliers algorithm is employed to solve the established optimization problem. Simulations are conducted to demonstrate that the proposed algorithm can effectively enhance the resolution.
Numerical Investigation on the Influence of Turbine Rotor Parameters on the Eddy Current Sensor for the Dynamic Blade Tip Clearance Measurement
Authors: Zhao Lingqiang , Liu Fulin , Lyu Yaguo , Liu Zhenxia , Zhao Ziyu
Published: September 2024

Eddy current sensors are increasingly being used to measure the dynamic blade tip clearance in turbines due to their robust anti-interference capabilities and non-contact measurement advantages. However, the current research primarily focuses on enhancing the performance of eddy current sensors themselves, with few studies investigating the influence of turbine rotor parameters on the measurements taken by these sensors for dynamic blade tip clearance. Hence, this paper addresses this gap by using COMSOL Multiphysics 6.2 software to establish a finite model with circuit interfaces. Additionally, the model’s validity was verified through experiments. This model is used to simulate the voltage output of the sensor and the measurement of dynamic blade tip clearance under various rotor parameters. The results indicate that the length and number of blades, as well as the hub radius, significantly affect the sensor voltage output in comparison to rotation speed. Furthermore, we show that traditional static calibration methods are inadequate for measuring dynamic blade tip clearance using eddy current sensors. Instead, it is demonstrated that incorporating rotor parameters into the calibration of eddy current sensors can enhance the accuracy of dynamic blade tip clearance measurements.
On the Development of an Acoustic Image Dataset for Unexploded Ordnance Classification Using Front-Looking Sonar and Transfer Learning Methods
Authors: Ściegienka Piotr , Blachnik Marcin
Published: September 2024

This research aimed to develop a dataset of acoustic images recorded by a forward-looking sonar mounted on an underwater vehicle, enabling the classification of unexploded ordnances (UXOs) and objects other than unexploded ordnance (nonUXOs). The dataset was obtained using digital twin simulations performed in the Gazebo environment utilizing plugins developed within the DAVE project. It consists of 69,444 sample images of 512 × 399 resolution organized in two classes annotated as UXO and nonUXO. The obtained dataset was then evaluated by state-of-the-art image classification methods using off-the-shelf models and transfer learning techniques. The research included VGG16, ResNet34, ResNet50, ViT, RegNet, and Swin Transformer. Its goal was to define a base rate for the development of other specialized machine learning models. Neural network experiments comprised two stages—pre-training of only the final layers and pre-training of the entire network. The experiments revealed that to obtain high accuracy, it is required to pre-train the entire network, under which condition, all the models achieved comparable performance, reaching 98% balanced accuracy. Surprisingly, the highest accuracy was obtained by the VGG model.
Multiple-Point Metamaterial-Inspired Microwave Sensors for Early-Stage Brain Tumor Diagnosis
Authors: Wongkasem Nantakan , Cabrera Gabriel
Published: September 2024

Simple, instantaneous, contactless, multiple-point metamaterial-inspired microwave sensors, composed of multi-band, low-profile metamaterial-inspired antennas, were developed to detect and identify meningioma tumors, the most common primary brain tumors. Based on a typical meningioma tumor size of 5–20 mm, a higher operating frequency, where the wavelength is similar or smaller than the tumor target, is crucial. The sensors, designed for the microwave Ku band range (12–18 GHz), where the electromagnetic property values of tumors are available, were implemented in this study. A seven-layered head phantom, including the meningioma tumors, was defined using actual electromagnetic parametric values in the frequency range of interest to mimic the actual human head. The reflection coefficients can be recorded and analyzed instantaneously, reducing high electromagnetic radiation consumption. It has been shown that a single-band detection point is not adequate to classify the nonlinear tumor and head model parameters. On the other hand, dual-band and tri-band metamaterial-inspired antennas, with additional detecting points, create a continuous function solution for the nonlinear problem by adding extra observation points using multiple-band excitation. The point mapping values can be used to enhance the tumor detection capability. Two-point mapping showed a consistent trend between the S11 value order and the tumor size, while three-point mapping can also be used to demonstrate the correlation between the S11 value order and the tumor size. This proposed multi-detection point technique can be applied to a sensor for other nonlinear property targets. Moreover, a set of antennas with different polarizations, orientations, and arrangements in a network could help to obtain the highest sensitivity and accuracy of the whole system.
The Role of Deep Learning and Gait Analysis in Parkinson’s Disease: A Systematic Review
Authors: Franco Alessandra , Russo Michela , Amboni Marianna , Ponsiglione Alfonso Maria , Di Filippo Federico , Romano Maria , Amato Francesco , Ricciardi Carlo
Published: September 2024

Parkinson’s disease (PD) is the second most common movement disorder in the world. It is characterized by motor and non-motor symptoms that have a profound impact on the independence and quality of life of people affected by the disease, which increases caregivers’ burdens. The use of the quantitative gait data of people with PD and deep learning (DL) approaches based on gait are emerging as increasingly promising methods to support and aid clinical decision making, with the aim of providing a quantitative and objective diagnosis, as well as an additional tool for disease monitoring. This will allow for the early detection of the disease, assessment of progression, and implementation of therapeutic interventions. In this paper, the authors provide a systematic review of emerging DL techniques recently proposed for the analysis of PD by using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The Scopus, PubMed, and Web of Science databases were searched across an interval of six years (between 2018, when the first article was published, and 2023). A total of 25 articles were included in this review, which reports studies on the movement analysis of PD patients using both wearable and non-wearable sensors. Additionally, these studies employed DL networks for classification, diagnosis, and monitoring purposes. The authors demonstrate that there is a wide employment in the field of PD of convolutional neural networks for analyzing signals from wearable sensors and pose estimation networks for motion analysis from videos. In addition, the authors discuss current difficulties and highlight future solutions for PD monitoring and disease progression.
Rapid and Resilient LoRa Leap: A Novel Multi-Hop Architecture for Decentralised Earthquake Early Warning Systems
Authors: Ranasinghe Vinuja , Udara Nuwan , Mathotaarachchi Movindi , Thenuwara Tharindu , Dias Dileeka , Prasanna Raj , Edirisinghe Sampath , Gayan Samiru , Holden Caroline , Punchihewa Amal , Stephens Max , Drummond Paul
Published: September 2024

We introduce a novel LoRa-based multi-hop communication architecture as an alternative to the public internet for earthquake early warning (EEW). We examine its effectiveness in generating a meaningful warning window for the New Zealand-based decentralised EEW sensor network implemented by the CRISiSLab operating with the adapted Propagation of Local Undamped Motion (PLUM)-based earthquake detection and node-level data processing. LoRa, popular for low-power, long-range applications, has the disadvantage of long transmission time for time-critical tasks like EEW. Our network overcomes this limitation by broadcasting EEWs via multiple short hops with a low spreading factor (SF). The network includes end nodes that generate warnings and relay nodes that broadcast them. Benchmarking with simulations against CRISiSLab’s EEW system performance with internet connectivity shows that an SF of 8 can disseminate warnings across all the sensors in a 30 km urban area within 2.4 s. This approach is also resilient, with the availability of multiple routes for a message to travel. Our LoRa-based system achieves a 1–6 s warning window, slightly behind the 1.5–6.75 s of the internet-based performance of CRISiSLab’s system. Nevertheless, our novel network is effective for timely mental preparation, simple protective actions, and automation. Experiments with Lilygo LoRa32 prototype devices are presented as a practical demonstration.
Structural Plastic Damage Warning and Real-Time Sensing System Based on Cointegration Theory
Authors: Gao Qiang , Huo Junzhou , Wang Youfu , Wang Xiaotian , Wang Chongru
Published: September 2024

Structural damage can affect the long-term operation of equipment. Real-time damage warning for structures can effectively avoid accidents caused by structural damage. In this paper, a real-time warning method of structural plastic damage based on the cointegration theory is proposed. This method calculates the cointegration relationship between the strain signals at different measuring points, and the stability of the strain signal relationships is also evaluated. The problem of inaccurate detection caused by the error of strain measurement and environmental influence can be eliminated by the comprehensive judgment of strain between asymmetrical measuring points. A real-time damage sensing system is developed in this paper. In order to improve the real-time and practicability of the system, this paper proposes and determines the residual warning coefficient by analyzing the proportion of the strain residuals exceeding the residual threshold. The research on this sensing system has certain value for the engineering application of damage monitoring methods.
Physical Security Auditing for Utilities: A Guide to Resilient Substation
Authors: Mahato Nawaraj Kumar , Yang Jiaxuan , Yang Junfeng , Gong Gangjun , Hao Jianhong
Published: September 2024

Electric power substations, as critical components of modern power grids, are increasingly becoming targets for intentional physical attacks, including vandalism, theft, and sabotage. These threats, coupled with the potential for cyber-attacks and the weaponization of technologies, necessitate robust security measures and comprehensive auditing practices. Despite utilities providers’ focus on understanding grid vulnerability and implementing physical security upgrades, there is a recognized gap in evaluating the effectiveness and long-term usability of these measures. This paper addresses the need for regular security audits to identify vulnerabilities and ensure the overall resilience of substations against evolving threats. The rationale behind this study is to propose a conventional auditing method that includes an auditing framework, checklists, inspections, and post-inspection suggestions. Through the systematic identification and addressing of vulnerabilities via security auditing, the framework aims to significantly enhance the resilience of substations against physical threats. This paper provides a comprehensive guideline for the physical security auditing procedure, which is essential for the reliable operation of the power grid.
Evaluation of Native Festuca Taxa for Sustainable Application in Urban Environments: Their Characteristics, Ornamental Value, and Germination in Different Growing Media
Authors: Szabó-Szöllösi Tünde , Baracsi Éva , Csontos Péter , Papp László , Kisvarga Szilvia , Orlóci László , Házi Judit , Kende Zoltán , Saláta Dénes , Fuchs Márta , Keleti Judit , Tarnawa Ákos , Rusvai Katalin , Penksza Károly
Published: September 2024

This research is part of a Hungarian Research OTKA project that examines the vegetation of sandy grasslands along the Danube. During this study, Festuca wagneri and Festuca tomanii were identified as potentially suitable grass species for urban planting and turf establishment based on preliminary research. Our aim was to determine the germination success of seeds from aesthetically selected individuals and to identify the growing media on which they germinate most effectively. From the collected Festuca individuals, we analyzed 30 specimens of each taxon under garden conditions and selected the individuals for germination. The Festuca tomanii individuals were uniform, so we selected only 5 individuals. The Festuca wagneri individuals were categorized into three groups: leaves and inflorescence densely upright, inflorescence shoots spread out, and low ’dwarf’ form (compact and dense but short in stature). It was assumed that Festuca species seeds would germinate better in sandy soils. To test our hypothesis, seeds from ten Festuca wagneri and five Festuca tomanii individuals, selected based on aesthetic criteria, were sown in six different substrates: a sand–peat mixture, sand, coconut fiber, peat, coconut fiber–sand mixture, and native sandy soil (Calcaric Arenosol). Contrary to our expectations, the growth and germination rates of seeds sown in peat and coconut fiber substrates were higher than those in native sandy soil. These results suggest that Festuca seeds germinate better on substrates resembling dead plant debris with a peat-like structure or on the surface of live mosses rather than on bare sand. Among the examined individuals, the seeds from the spreading Festuca wagneri group exhibited the highest germination rate, making this group particularly suitable for urban environments. Additionally, one of the upright Festuca wagneri individuals showed the highest leaf average length and should also be considered for urban planting. In contrast, despite their uniform appearance, the Festuca tomanii individuals did not demonstrate similar germination trends. In fact, the seeds from two clumps did not germinate at all, indicating that further research is necessary.
Management of Mining Brownfields for Support of Regional Tourism
Authors: Pavolová Henrieta , Bakalár Tomáš , Molokáč Mário
Published: September 2024

Turbulent political and economic changes in 1989 caused the gradual decline of the mining industry in the Slovak Republic. Abandoned territorial localities were created, affected by mining activity, without any use with devastated mining objects, or even a certain form of environmental burden. These territorial locations used for mining in the past, unused currently, in varying degrees of devastation, are referred to as mining brownfields. This issue is topical, as there is constant urbanization of new territories. Mining brownfields often represent a certain form of territorial reserve to support tourism development. The present study deals with identifying the tourist potential of the Fedö shaft mining brownfield in the Červenica—Dubník area (Slovakia), which is included in the list of national cultural monuments. The study points out the need for reclamation from the point of view of supporting the development of a tourist destination based on the accessible mining brownfield—the Jozef tunnel—making it possible to use the interaction links of both mining brownfields in the investigated area of tourism. Based on the results of the conducted SWOT analysis, the study presents the quantification of the tourism development support potential of the analyzed mining brownfield. It also includes selected environmental, social, and economic aspects of the reclamation, and the definition of an effective strategy for usage of the examined mining brownfield as a tool to support tourism development. The article concludes a model of effective management of the use of mining brownfields in the field of tourism in Slovakia, which is constructed as open and modifiable in its interaction with the specification of diverse conditions of tourist destinations with integrated mining brownfields, which include old mine works.
Preliminary Findings on the Bioaccumulation and Marine Trophic Transfer of the Antifouling Biocide DCOIT in Soluble and Nanostructured Forms
Authors: Campos Bruno Galvão de , Abessa Denis Moledo de Souza , Martins Roberto
Published: September 2024

DCOIT (4,5-Dichloro-2-octylisothiazol-3(2H)-one) is a widely used antifouling biocide that emerged after the ban on tributyltin. It has been immobilized in nanostructured silica (SiNC–DCOIT) to reduce its hazard in maritime coatings. This study aimed to compare the bioaccumulation, trophic transfer, and biomagnification of DCOIT in its soluble and nanostructured forms on mussels Mytilus galloprovincialis, using three different uptake routes: aqueous exposure (i.e., contaminated seawater), dietary exposure (i.e., microalgae Tetraselmis chuii as a contaminated food), and both contaminated food and seawater. DCOIT was determined on water and tissues after 1, 3, and 24 h of uptake and after 72 h of depuration. Briefly, mussels were able to rapidly uptake and metabolize DCOIT and SiNC–DCOIT. Both compounds were non-bioaccumulative, as their bioconcentration and bioaccumulation factor values were lower than 2000. However, the predator–prey biomagnification factors indicated that both forms could be transferred across the trophic web. Therefore, while our findings provide further insight into the environmental risk assessment of DCOIT and SiNC–DCOIT, they do not rule out the possibility of long-term DCOIT bioaccumulation, particularly in areas with constant DCOIT influx. Further studies are thus needed using larger experimental designs and under continuous exposure scenarios to increase the sustainability of the innovative nanomaterial.
Systematic LCA-AHP Approach to Compare Hydrometallurgical Routes for Copper Recovery from Printed Circuit Boards: Environmental Analysis
Authors: Dias Josinaldo , de Holanda José Nilson França , Pinho Sílvia Cardinal , de Miranda Júnior Gilson Mendonça , da Silva Angelus Giuseppe Pereira
Published: September 2024

To ensure that metal recovery processes in electronic waste are truly sustainable from an industrial perspective, studies on the performance of such methodologies are necessary to verify the economic, environmental, social, and technological viabilities. The importance of conducting multicriteria and comparative investigations into the actual performances of methods used in the recovery of these materials is emphasized, considering trade-offs such as high efficiency in metal extraction balanced against intense consumption of energy and chemical reagents. The analytical hierarchy process, multicriteria decision support tool, and the life cycle assessment tool are proposed to be used in combination in this work to assess and contrast the environmental effects of two hydrometallurgical paths for the recuperation of copper in electronic circuit boards (PCBs). The results indicate that the sulfuric acid method had a copper solubilization efficiency of 90.05%, whereas the route employing the combination of ammonium sulfate and ammonia had an estimated copper solubilization efficacy of 49%. It was feasible to calculate the life cycle effects of the hydrometallurgical procedures connected to the copper recovery activities on the PCBs with regard to the LCA. Compared to the acidic leaching pathway, alkaline leaching was responsible for about 71% of the environmental damage discovered in the study, according to the AHP tool.
Ghost Gear in the Gulf of Gabès (Tunisia): An Urgent Need for a Conservation Code of Conduct
Authors: Boussellaa Wiem , Bradai Mohamed , Mallat Hamed , Enajjar Samira , Saidi Bechir , Jribi Imed
Published: September 2024

The Gulf of Gabès, located off the southern coast of Tunisia, is a region of significant ecological and economic importance, yet it faces a growing threat from abandoned, lost, or otherwise discarded fishing gear, commonly known as ghost gear. This paper addresses the urgent need for a comprehensive conservation code of conduct to mitigate the impacts of ghost gear on marine ecosystems and local communities. Drawing on data and insights from the Life MedTurtles and MedBycatch projects, as well as consultations with local stakeholders, we propose a set of principles and guidelines tailored to the specific socio-economic and political context of Tunisia. Our findings indicate that ghost gear not only endangers marine biodiversity but also affects the livelihoods of local fishers and the sustainability of the region’s fishing industry. The proposed code of conduct emphasizes the roles of government, local communities, and non-governmental organizations in implementing effective management strategies. We also explore the alignment of the proposed measures with existing international laws and policies, ensuring no conflicts arise while reinforcing global conservation efforts. This paper concludes by highlighting the feasibility of the proposed code within the Tunisian context, identifying potential challenges and opportunities for its implementation. Our recommendations aim to foster a collaborative approach to managing ghost gear, contributing to the long-term sustainability of the Gulf of Gabès and serving as a model for similar regions worldwide.
Applying the Circular Economy Framework to Blockchain Agricultural Production
Authors: Santos Rosangela de Araújo , Teles Eduardo Oliveira , Freires Francisco Gaudêncio Mendonça
Published: September 2024

Agriculture is one of the most economically important practices in the world; it is essential for food security and socioeconomic development in several countries. However, the use of pesticides, which are essential for fighting pests and weeds and guaranteeing agricultural yields, has brought about an environmental issue pertaining to the management of empty pesticide packaging. The improper disposal of pesticide packaging can put both environmental and human health at risk. Therefore, the implementation of reverse logistics systems will be essential if we are to ensure that packages are collected, recycled, and disposed of safely. Blockchain technology is an innovative solution that offers transparent and immutable recording of data, thereby facilitating traceability. In this study, we present the application of a circular economy-based framework to agricultural production via blockchain (and involving all actors within production and consumption) to enable the more responsible disposal of empty pesticide packaging for eventual reuse, recovery, or recycling. Bibliographical research was carried out through Scopus and Web of Science from 2018 to 2023; we principally aimed to provide an overview of this pertinent area of research using the following keywords: “blockchain”, “circular economy”, “pesticide packaging”, and “reverse logistics”. Bibliometrics using graphs and tables made it possible to refine the information collected from the databases. We analyzed how blockchain can be integrated alongside reverse logistics, highlighting how it can promote the principles of the circular economy through various methods of sustainable and responsible agriculture.
Influence of Forest Management on the Sustainability of Community Areas in Northern Inland Portugal: A Simulated Case Study Assessment
Authors: Sandim André , Araújo Dalila , Fonseca Teresa , Silva Maria Emília
Published: September 2024

The northern inland region of Portugal has experienced significant population decline due to the exodus of younger generations and an aging population. This has led to the abandonment of traditional activities in these territories, contributing to territorial abandonment, degradation of local economic conditions, increased social vulnerability, and a heightened risk of rural fires. The presence of communal lands, known as “baldios”, is an important facilitator for implementing actions that revitalize local villages, making them more attractive to the community. Forests, which are abundant in the baldios of northern inland Portugal, have the potential to generate environmental, social, and economic value through carbon sequestration, job creation, population stabilization, and wealth generation in the villages. However, the viability of this asset as a driver for sustainable development depends on the forest management model implemented. This case study aims to demonstrate that different forest management models have varied impacts on sustainability indicators, particularly economic and environmental sustainability. Based on naturally regenerated forests in the Carvalhelhos baldio in the Tâmega Valley region, data were collected to simulate in software four management scenarios, varying the number (0 to 4) and age of thinnings until the final cut. The simulation allowed for the calculation of the following economic indicators: Internal Rate of Return (IRR), Net Present Value (NPV), and Net Profitability Index (NPI), as well as environmental indicators related to carbon capture and accumulation, such as Gross Carbon Accumulation, Net Carbon Accumulation, Carbon accumulated in dead and suppressed trees, and carbon balance per management model. The simulations indicate that, for the studied area, Scenario 2, which involves only one thinning, yielded the highest total wood volume (cubic meters per hectare) over the cycle, making it the most suitable for biomass production. Meanwhile, Scenario 4, with three thinnings, showed the best results for individual volume (cubic meters per tree), making it more suitable for producing higher-value logs. Scenario 5 presented the best economic results and carbon capture. In all simulations, Scenario 1 showed the worst performance in the analyzed indicators. It was found that the indicators varied among the studied crop plans, highlighting that the adoption of a silvicultural regime depends on the forest characteristics, objectives, exploitation conditions, and local population sensitivity to regional priorities.
Estimating the Canopy Nitrogen Content in Maize by Using the Transform-Based Dynamic Spectral Indices and Random Forest
Authors: Yang Shuting , Li Jianbei , Li Ji , Zhang Xuejian , Ma Cong , Liu Zhengyu , Ren Mengyan
Published: September 2024

The monitoring of maize health status is crucial for achieving sustainable agricultural development. Canopy nitrogen content (CNC) is essential for the synthesis of proteins and chlorophyll in maize leaves and, thus, significantly influences maize growth and yield. In this study, we developed a CNC spectral estimation model based on transform-based dynamic spectral indices (TDSI) and the random forest (RF) algorithm, enabling the rapid monitoring of CNC in maize canopy leaves. A total of 60 maize canopy leaf samples and the corresponding field canopy spectra were collected. Subsequently, the canopy spectra data were transformed using centralization transformation (CT), first derivative (D1), second derivative (D2), detrend transformation (DT), and min-max normalization (MMN) methods. Three types of band combination methods (band difference, band ratio, and normalized difference) were used to construct the TDSIs. Finally, the optimal TDSI was selected and used as the independent variable, and the measured CNC was used as the dependent variable to build a CNC spectral estimation model based on the RF algorithm. Results indicated that (1) TDSIs can more accurately characterize the CNC in maize, with a correlation coefficient approximately 102% higher than those of raw spectral bands. (2) The optimal TDSIs included TDSI1247,1249CT-RI, TDSI625,641CT-NDI, TDSI540,703D1-RI, TDSI514,540D1-RI, TDSI514,530D1-DI, TDSI540,697D1-NDI, TDSI970,1357D2-DI, TDSI523,1031D2-NDI, TDSI617,620DT-RI, and TDSI2109,2127MMN-NDI. (3) The CNC spectral estimation model based on the optimal TDSIs, and the RF algorithm achieved accuracy indices with R2 and RPIQ of 0.92 and 4.99, respectively, representing a maximum improvement of approximately 67.27% over the traditional CNC spectral estimation model (based on the R2 value). This study provides an approach for the rapid and accurate estimation of CNC in maize, contributing to the sustainable development of agriculture.
BESS Reserve Optimisation in Energy Communities
Authors: Rozas-Rodriguez Wolfram , Pastor-Vargas Rafael , Peacock Andrew D. , Kane David , Carpio-Ibañez José
Published: September 2024

This paper investigates optimising battery energy storage systems (BESSs) to enhance the business models of Local Energy Markets (LEMs). LEMs are decentralised energy ecosystems facilitating peer-to-peer energy trading among consumers, producers, and prosumers. By incentivising local energy exchange and balancing supply and demand, LEMs contribute to grid resilience and sustainability. This study proposes a novel approach to BESS optimisation, utilising advanced artificial intelligence techniques, such as multilayer perceptron neural networks and extreme gradient boosting regressors. These models accurately forecast energy consumption and optimise BESS reserve allocation within the LEM framework. The findings demonstrate the potential of these AI-driven strategies to improve the BESS reserve capacity setting. This optimal setting will target meeting Energy Community site owners’ needs and avoiding fines from the distribution system operator for not meeting contract conditions.
The Era of Digital Transition in the Prism of the Existential Threat of Job Loss: Corporate Social Responsibility
Authors: Shestakova Irina
Published: September 2024

This article explores the question of the rate of digital progress in the context of the labour market. Specific features of the current situation are discussed: the temporality of socio-technological transformations, which is becoming less and less compatible with the harmonious development of man and society; the pace at which machines acquire intelligence; the total devaluation of mental labour; the unresolved issue of the role of man in the world of intelligent machines; and the criticality of the problem of the labour market due to its global nature, social significance and the rate of socio-technological changes. It is emphasised that these circumstances, already in the short term, threaten the sustainable development of global society, whose reactions to the transformation of technological and socio-economic infrastructure are significantly lagging behind. It is concluded that there is an urgent need to strengthen social responsibility, determined by the new ethics of relations between humans and machines with AI, supplemented by the primacy of the dignity of the social role of humans. The authors point out the urgent need to revise ideas about work as the main purpose of a person and about realisation in the profession as the main factor that determines the self-esteem of an individual and his social status.