bluuebunny/crossref_metadata_2025 huggingface dataset:
Clindamycin-Loaded Polyhydroxyalkanoate Nanoparticles for the Treatment of Methicillin-Resistant Staphylococcus aureus-Infected Wounds
Authors: Ullah Muneeb , Lee Juho , Hasan Nurhasni , Hakim Md. Lukman , Kwak Dongmin , Kim Hyunwoo , Lee Eunhye , Ahn Jeesoo , Mun Bora , Lee Eun Hee , Jung Yunjin , Yoo Jin-Wook
Published: October 2024

Background/Objectives: Owing to the growing resistance of methicillin-resistant Staphylococcus aureus (MRSA) to conventional antibiotics, the development of innovative therapeutic strategies for the treatment of MRSA-infected cutaneous wounds poses a significant challenge. Methods: Here, by using polyhydroxyalkanoates (PHA), emerging biodegradable and biocompatible polymers naturally produced by various microorganisms, we developed clindamycin-loaded PHA nanoparticles (Cly-PHA NPs) as a novel approach for the treatment of MRSA-infected cutaneous wounds. Results: Cly-PHA NPs were characterized in terms of mean particle size (216.2 ± 38.9 nm), polydispersity index (0.093 ± 0.03), zeta potential (11.3 ± 0.5 mV), and drug loading (6.76 ± 0.19%). Owing to the sustained release of clindamycin over 2 days provided by the PHA, Cly-PHA NPs exhibited potent antibacterial effects against MRSA. Furthermore, Cly-PHA NPs significantly facilitated wound healing in a mouse model of MRSA-infected full-thickness wounds by effectively eradicating MRSA from the wound bed. Conclusions: Therefore, our results suggest that Cly-PHA NPs offer a promising approach for combating MRSA infections and accelerating cutaneous wound healing.
Exploring the Origins of Low-Temperature Thermochromism in Polydiacetylenes
Authors: Wilk-Kozubek Magdalena , Potaniec Bartłomiej , Gazińska Patrycja , Cybińska Joanna
Published: October 2024

This review article delves into the intriguing phenomenon of low-temperature thermochromism, whereby materials change color in response to temperature variations, with a particular focus on its applications in temperature-sensitive fields like medical storage. By closely examining thermochromic materials, this article highlights their potential to offer innovative solutions for monitoring and preserving thermolabile products that require strict temperature control. This leads to a special emphasis on polydiacetylenes (PDAs), a class of conjugated polymers with unique low-temperature thermochromic properties, positioning them as promising candidates for reliable temperature indicators. This article then explores the underlying mechanisms for fine-tuning the thermochromic behavior of PDAs, particularly discussing recent advancements in PDA design, such as structural alterations of monomers to achieve low-temperature thermochromism. These modifications, influenced by factors like side-chain length, hydrogen-bonding interactions, and the use of copolymers, are intended to result in irreversible color transitions at specific low temperatures, which is crucial to maintaining the integrity of thermally sensitive products. Finally, this article discusses the potential applications of PDAs as thermochromic sensors in tissue biobanking, where their ability to provide visual indications of temperature fluctuations could significantly enhance the monitoring and management of biological samples.
Ultraviolet Irradiation Surface Treatment to Enhance the Bonding Strength of Polyamide-Based Carbon Fiber-Reinforced Thermoplastic Polymers
Authors: Hwang Mun Young , Yoon Soon Ho , Kim Minkook
Published: October 2024

Adhesive bonding is a suitable joining method to satisfy the increasing industrial demand for carbon fiber-reinforced polymers without the need for a machining process. However, thermoplastic-based carbon fiber-reinforced polymers have small adhesive strength with structural thermoset adhesives. In this study, an ultraviolet irradiation surface treatment was developed to improve the adhesive bonding strength for polyamide-based carbon fiber-reinforced polymer. The type of ultraviolet wavelength, irradiation distance and irradiation time were optimized. Surface treatment with simultaneous UV irradiation of 185 nm and 254 nm wavelength generated unbound N-H stretching that was capable of chemical bonding with epoxy adhesives through a photo-scission reaction of the amide bond of polyamide matrix. Therefore, ultraviolet irradiation treatment improved the wettability and functional groups of the polyamide-based carbon fiber-reinforced polymers for adhesive bonding. As a result, the adhesive strength of the polyamide-based carbon fiber-reinforced polymers was increased by more than 230%.
Critical Review of Lithium Recovery Methods: Advancements, Challenges, and Future Directions
Authors: Jose Subin Antony , Stoll Jonathan Luke , Smith Tyson , Jackson Chase , Dieleman Trent , Leath Easton , Eastwood Nicholas , Menezes Pradeep L.
Published: October 2024

The integration of lithium into technological applications has profoundly influenced human development, particularly in energy storage systems like lithium-ion batteries. With global demand for lithium surging alongside technological advancements, the sustainable extraction and recovery of this critical material have become increasingly vital. This paper explores lithium’s role, its chemical properties, and the environmental and economic considerations associated with its extraction and recovery. We examine various lithium recovery methods, including conventional techniques such as hydrometallurgy, pyrometallurgy, and direct physical recycling, as well as emerging technologies like mechanochemistry, ion pumping, and bioleaching while emphasizing the need for sustainable practices to address environmental challenges. The review also discusses the challenges and future directions of lithium recovery. Advances in technology have streamlined lithium recovery processes and spurred the development of innovative extraction techniques. This paper concludes with an emphasis on further research essential to deepen our understanding of lithium recovery methods, their strengths and limitations, and the need to explore new strategies to meet the growing global demand for this indispensable resource.
A Study on Acid Dissolution Characteristics and the Permeability Enhancement of Deep Coal Rock
Authors: Wang Chen , Luo Weijiang , Dai Xiancai , Wu Jian , Zhou Xing , Huang Kai , Zhang Nan
Published: October 2024

In order to reveal the acidification and dissolution characteristics of deep coal rock, core acidification and dissolution experiments are carried out based on low-field nuclear magnetic resonance technology to study the dissolution characteristics of different acid types when applied to coal rock, and to quantitatively evaluate the dissolution characteristics of acid solutions when applied to different-scale pore throats and the karst corrosion characteristics of primary fractures. This will help to further understand the dissolution rate and pore volume growth rate of coal powder under the action of different acid types. Improving the seepage effect of coal seams is of great significance. The results show that 15% acetic acid has the best effect with regard to karst erosion and permeability. The pore volume growth rate is 442.49%, and the permeability increases by up to 31 times. With large pores, the rapid dissolution stage of mud acid, hydrochloric acid, and mixed acid mainly occurred in the first 36 h, and the rapid dissolution stage of acetic acid and hydrofluoric acid applied to the core mainly occurred at 36–72 h. The dissolution rate of acid solution is strongly correlated with porosity and permeability, and the higher the acetic acid concentration, the larger the permeability increase.
‘Every One of You Is a Leader’: Investigating the Experience of Being a Brown British Muslim Woman in Professional Contexts
Authors: Zaidi Saiyyidah
Published: October 2024

The Hadith paraphrased as ‘every one of you is a leader’ does not discriminate in gender in any way—both men and women are leaders. What does this mean in practice, and how are Muslim women perceived and received in line with this Hadith? Only in very recent years are Muslim women in Britain starting to occupy prominent positions: 13 Muslim women Members of British Parliament were elected in 2024; celebrities such as baker Nadia Hussain are regularly seen on British TV; and others such as Fatima Manji in the media. In this article, I explore how different ‘contextual intersectionalities’ influence and impact a Muslim woman and her leadership role. How does the intersectionality of her outward expression of faith identify her? How does a Muslim woman navigate a space where she is the leader and her skills are sought, yet her faith representation may instigate unconscious/conscious biases? Using an autoethnographic method, I investigate the impact of my identities as a Brown British Muslim woman in three distinct settings. First, in the professional and academic space of British Christian practical theology, where I was the first British Muslim to obtain a doctorate in the subject and was the first Muslim Trustee and Committee Member of the British and Irish Association for Practical Theology (BIAPT) between 2020 and 2024. Second, as a leadership advisor and executive coach to FTSE-listed companies, where I support C-suite leaders to generate sustained change in individuals, teams, and systems. And third, as a tutor and supervisor of proven business leaders to master the skills of coaching in their own right. I discuss how I am met in these spaces and the impact of that on my being. In conclusion, I call for increased understanding and awareness of the emotional tax paid by Muslim women who choose to take leadership roles.
The Value of Sentinel-1 Ocean Wind Fields Component for the Study of Polar Lows
Authors: Khachatrian Eduard , Asemann Patricia
Published: October 2024

Polar lows can pose serious threats to maritime operations and coastal communities in polar regions, especially due to their extreme wind speeds. The accurate and reliable representation of their wind field thus plays a crucial role in forecasting and mitigating the risks associated with this phenomenon. This study aims to evaluate the value of the SAR-based Sentinel-1 Ocean Wind Field product compared to two reanalysis products—regional CARRA and global ERA5—in studying the spatial wind speed distribution of polar lows. A visual comparison of the wind direction and wind speed fields was performed, as well as a brief quantitative analysis of wind speeds. Despite notable differences in spatial resolution, all of the data sources are able to identify the polar lows. However, the SAR-based product remains unmatched in capturing the intricate structure of the wind field. Although CARRA resolves more details than ERA5, it still deviates from the SAR image to a degree that suggests that the difference in spatial resolution is not the only source of disparity between the sources. Both CARRA and ERA5 underestimate the maximum wind speed as compared to the SAR data. Only the SAR data seems capable of providing the information necessary to study the details of the wind field of polar lows.
Land Cover Mapping in East China for Enhancing High-Resolution Weather Simulation Models
Authors: Ma Bingxin , Shao Yang , Yang Hequn , Lu Yiwen , Gao Yanqing , Wang Xinyao , Xie Ying , Wang Xiaofeng
Published: October 2024

This study was designed to develop a 30 m resolution land cover dataset to improve the performance of regional weather forecasting models in East China. A 10-class land cover mapping scheme was established, reflecting East China’s diverse landscape characteristics and incorporating a new category for plastic greenhouses. Plastic greenhouses are key to understanding surface heterogeneity in agricultural regions, as they can significantly impact local climate conditions, such as heat flux and evapotranspiration, yet they are often not represented in conventional land cover classifications. This is mainly due to the lack of high-resolution datasets capable of detecting these small yet impactful features. For the six-province study area, we selected and processed Landsat 8 imagery from 2015–2018, filtering for cloud cover. Complementary datasets, such as digital elevation models (DEM) and nighttime lighting data, were integrated to enrich the inputs for the Random Forest classification. A comprehensive training dataset was compiled to support Random Forest training and classification accuracy. We developed an automated workflow to manage the data processing, including satellite image selection, preprocessing, classification, and image mosaicking, thereby ensuring the system’s practicality and facilitating future updates. We included three Weather Research and Forecasting (WRF) model experiments in this study to highlight the impact of our land cover maps on daytime and nighttime temperature predictions. The resulting regional land cover dataset achieved an overall accuracy of 83.2% and a Kappa coefficient of 0.81. These accuracy statistics are higher than existing national and global datasets. The model results suggest that the newly developed land cover, combined with a mosaic option in the Unified Noah scheme in WRF, provided the best overall performance for both daytime and nighttime temperature predictions. In addition to supporting the WRF model, our land cover map products, with a planned 3–5-year update schedule, could serve as a valuable data source for ecological assessments in the East China region, informing environmental policy and promoting sustainability.
Utilizing IoMT-Based Smart Gloves for Continuous Vital Sign Monitoring to Safeguard Athlete Health and Optimize Training Protocols
Authors: Ucar Mustafa Hikmet Bilgehan , Adjevi Arsene , Aktaş Faruk , Solak Serdar
Published: October 2024

This paper presents the development of a vital sign monitoring system designed specifically for professional athletes, with a focus on runners. The system aims to enhance athletic performance and mitigate health risks associated with intense training regimens. It comprises a wearable glove that monitors key physiological parameters such as heart rate, blood oxygen saturation (SpO2), body temperature, and gyroscope data used to calculate linear speed, among other relevant metrics. Additionally, environmental variables, including ambient temperature, are tracked. To ensure accuracy, the system incorporates an onboard filtering algorithm to minimize false positives, allowing for timely intervention during instances of physiological abnormalities. The study demonstrates the system’s potential to optimize performance and protect athlete well-being by facilitating real-time adjustments to training intensity and duration. The experimental results show that the system adheres to the classical “220-age” formula for calculating maximum heart rate, responds promptly to predefined thresholds, and outperforms a moving average filter in noise reduction, with the Gaussian filter delivering superior performance.
Exploring the Role of Artificial Intelligence in Internet of Things Systems: A Systematic Mapping Study
Authors: Khadam Umair , Davidsson Paul , Spalazzese Romina
Published: October 2024

The use of Artificial Intelligence (AI) in Internet of Things (IoT) systems has gained significant attention due to its potential to improve efficiency, functionality and decision-making. To further advance research and practical implementation, it is crucial to better understand the specific roles of AI in IoT systems and identify the key application domains. In this article we aim to identify the different roles of AI in IoT systems and the application domains where AI is used most significantly. We have conducted a systematic mapping study using multiple databases, i.e., Scopus, ACM Digital Library, IEEE Xplore and Wiley Online. Eighty-one relevant survey articles were selected after applying the selection criteria and then analyzed to extract the key information. As a result, six general tasks of AI in IoT systems were identified: pattern recognition, decision support, decision-making and acting, prediction, data management and human interaction. Moreover, 15 subtasks were identified, as well as 13 application domains, where healthcare was the most frequent. We conclude that there are several important tasks that AI can perform in IoT systems, improving efficiency, security and functionality across many important application domains.
Effects of Smart Glasses on the Visual Acuity and Eye Strain of Employees in Logistics and Picking: A Six-Month Observational Study
Authors: Herold Robert , Gevorgyan Hayarpi , Damerau Lukas S. , Hartmann Ulrich , Friemert Daniel , Ellegast Rolf , Schiefer Christoph , Karamanidis Kiros , Harth Volker , Terschüren Claudia
Published: October 2024

The usage of smart glasses in goods logistics and order picking has mainly been studied through cross-sectional experimental studies. Our longitudinal field study investigated the effects of smart glasses on the eyesight of 43 employees at two German companies. We combined ophthalmological examinations and questionnaire surveys at two points in time, six months apart. The vision of the employees was examined before and after each work shift. Mixed effects logistic regression was conducted to determine the associations between smart glasses use and effects on visual acuity. In the baseline examination, differences in eyesight before and after shifts were small and not statistically significant. However, some individuals experienced deteriorations, especially in visual acuity at near distances (n = 7 for the right eye, n = 6 for the left). Participants over 40 years of age had 16.1 times higher odds of deterioration compared to those under 40 years (95% CI: 2.7–95.9, p = 0.002). The most commonly reported eye strains were eye fatigue (n = 32), rubbing (n = 25), and burning (n = 24). If smart glasses are to be implemented in logistics companies, it is recommended to offer employees eye tests with an industrial physician in advance.
Lamb Wave Probabilistic Damage Identification Based on the Exchanging-Element Time-Reversal Method
Authors: Shu Zeyu , He Jian , Hu Muping , Wu Zonghui , Sun Xiaodan
Published: October 2024

The commonly used baseline-free Lamb wave damage identification methods often require a large amount of sensor data to eliminate the dependence on baseline signals. To improve the efficiency of damage localization, this paper proposes a new Lamb wave damage location method, namely the probabilistic exchanging-element time-reversal method (PEX-TRM), which is based on the exchanging-element time-reversal method (EX-TRM) and the probabilistic damage identification method. In this method, the influence of the damage wave packet migration on the correlation coefficient between the reconstructed signals of each sensing path and the initial excitation signal is analyzed, and the structure is divided into multiple regional units corresponding to the damage to locate damage. In addition, the influence of the number of sensing paths on the location accuracy is also analyzed. A method of damage probability imaging based on structural symmetry is proposed to enhance location accuracy in the case of sparse sensing paths. The experimental and simulation results verify that the method can achieve damage location with fewer excitation times. Moreover, this method can avoid the problem that the damage wave packet is difficult to extract, improve the efficiency of damage location, and promote the engineering application of the Lamb wave damage location method.
Event Stream Denoising Method Based on Spatio-Temporal Density and Time Sequence Analysis
Authors: Jiang Haiyan , Wang Xiaoshuang , Tang Wei , Song Qinghui , Song Qingjun , Hao Wenchao
Published: October 2024

An event camera is a neuromimetic sensor inspired by the human retinal imaging principle, which has the advantages of high dynamic range, high temporal resolution, and low power consumption. Due to the interference of hardware and software and other factors, the event stream output from the event camera usually contains a large amount of noise, and traditional denoising algorithms cannot be applied to the event stream. To better deal with different kinds of noise and enhance the robustness of the denoising algorithm, based on the spatio-temporal distribution characteristics of effective events and noise, an event stream noise reduction and visualization algorithm is proposed. The event stream enters fine filtering after filtering the BA noise based on spatio-temporal density. The fine filtering performs time sequence analysis on the event pixels and the neighboring pixels to filter out hot noise. The proposed visualization algorithm adaptively overlaps the events of the previous frame according to the event density difference to obtain clear and coherent event frames. We conducted denoising and visualization experiments on real scenes and public datasets, respectively, and the experiments show that our algorithm is effective in filtering noise and obtaining clear and coherent event frames under different event stream densities and noise backgrounds.
The Possibility of Improving the Range and Quality of Air-to-Ground WebRTC-Based IoT Communication in Emergency Situations by Employing an External Transceiver
Authors: Wajda Krzysztof , Chodorek Agnieszka , Chodorek Robert Ryszard
Published: October 2024

The proliferation of new services, either interpersonal or machine-oriented, has generated new demands concerning the flexibility and efficiency of transmission. The ubiquity of multimedia communication in the current internet is seamlessly and successfully supported by the WebRTC concept. This paper reports on the study of the usage of a solution employing a proxy transmission unit for air-to-ground delivery of video streaming multiplexed with sensor data in the UAV-IoT system when using the WebRTC protocol stack. The comparative experiments were carried out for two cases employing the 802.11ac network with WebRTC: the first scenario (S1) without an external transceiver and the second scenario (S2) with an external transceiver working as a proxy of the ground receiver. The presented results compare the transmission conditions without (scenario S1) and with (scenario S2) the external transceiver in terms of the RSSI, the available data rate, and total throughput of transmission of multimedia data (video stream from the UAV camera and bursty data coming from employed sensors. The usefulness of the external transceiver used in a wide range of transmission conditions is clearly proven.
Multi-Agent DRL for Air-to-Ground Communication Planning in UAV-Enabled IoT Networks
Authors: Qureshi Khalid Ibrahim , Lu Bingxian , Lu Cheng , Lodhi Muhammad Ali , Wang Lei
Published: October 2024

In this paper, we present a novel method to enhance the sum-rate effectiveness in full-duplex unmanned aerial vehicle (UAV)-assisted communication networks. Existing approaches often couple uplink and downlink associations, resulting in suboptimal performance, particularly in dynamic environments where user demands and network conditions are unpredictable. To overcome these limitations, we propose a decoupling of uplink and downlink associations for ground-based users (GBUs), significantly improving network efficiency. We formulate a comprehensive optimization problem that integrates UAV trajectory design and user association, aiming to maximize the overall sum-rate efficiency of the network. Due to the problem’s non-convexity, we reformulate it as a Partially Observable Markov Decision Process (POMDP), enabling UAVs to make real-time decisions based on local observations without requiring complete global information. Our framework employs multi-agent deep reinforcement learning (MADRL), specifically the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, which balances centralized training with distributed execution. This allows UAVs to efficiently learn optimal user associations and trajectory controls while dynamically adapting to local conditions. The proposed solution is particularly suited for critical applications such as disaster response and search and rescue missions, highlighting the practical significance of utilizing UAVs for rapid network deployment in emergencies. By addressing the limitations of existing centralized and distributed solutions, our hybrid model combines the benefits of centralized training with the adaptability of distributed inference, ensuring optimal UAV operations in real-time scenarios.
Overview of Vivaldi Antenna Selection for Through-Wall Radar Applications
Authors: Amador Mariana , Rouco André , Albuquerque Daniel , Pinho Pedro
Published: October 2024

This paper analyzes broadband antennas, with a special focus on Vivaldi antennas, for their suitability for through-wall radar applications. It assesses various antenna designs, emphasizing high gain, wide impedance bandwidth, and effective wall penetration capabilities. Vivaldi antennas are superior due to their broad bandwidth, high gain, and directional radiation patterns. This study further explores structural optimizations, feeding techniques, and performance enhancement strategies to refine Vivaldi antenna designs for through-wall radar systems. Through a comparative analysis and technical evaluation, this paper highlights Vivaldi antennas’ potential for improving through-wall radar systems’ imaging and sensing capabilities. This presents a pathway for future ultra-wideband advancements.
Semantic Segmentation of Satellite Images for Landslide Detection Using Foreground-Aware and Multi-Scale Convolutional Attention Mechanism
Authors: Yu Chih-Chang , Chen Yuan-Di , Cheng Hsu-Yung , Jiang Chi-Lun
Published: October 2024

Advancements in satellite and aerial imagery technology have made it easier to obtain high-resolution remote sensing images, leading to widespread research and applications in various fields. Remote sensing image semantic segmentation is a crucial task that provides semantic and localization information for target objects. In addition to the large-scale variation issues common in most semantic segmentation datasets, aerial images present unique challenges, including high background complexity and imbalanced foreground–background ratios. However, general semantic segmentation methods primarily address scale variations in natural scenes and often neglect the specific challenges in remote sensing images, such as inadequate foreground modeling. In this paper, we present a foreground-aware remote sensing semantic segmentation model. The model introduces a multi-scale convolutional attention mechanism and utilizes a feature pyramid network architecture to extract multi-scale features, addressing the multi-scale problem. Additionally, we introduce a Foreground–Scene Relation Module to mitigate false alarms. The model enhances the foreground features by modeling the relationship between the foreground and the scene. In the loss function, a Soft Focal Loss is employed to focus on foreground samples during training, alleviating the foreground–background imbalance issue. Experimental results indicate that our proposed method outperforms current state-of-the-art general semantic segmentation methods and transformer-based methods on the LS dataset benchmark.
Comparative Analysis of Acid Leaching for the Efficient Recovery of Lanthanum and Cerium from Phosphate
Authors: Abdulvaliyev Rinat , Ultarakova Almagul , Mukangaliyeva Arailym , Lokhova Nina , Kassymzhanov Kaisar
Published: October 2024

The extraction of rare earth elements (REEs) from mineral resources is of significant global importance due to their critical role in modern technologies. This study focuses on the leaching behavior of artificial cerium and lanthanum phosphates using nitric, sulfuric, and hydrochloric acids under varying conditions of acid concentration and temperature. Our experiments demonstrated that the maximum extraction efficiency of lanthanum and cerium was achieved with nitric acid solutions at concentrations of 12.5 mol/dm3, with 85.0% and 79.1% extraction efficiency, respectively. The leaching mechanism involved the protonation of phosphate anions, which disrupted the phosphate matrix, facilitating the dissolution of lanthanum and cerium into solution. Sulfuric acid, although less effective at room temperature, proved to be the most thermodynamically favorable leaching agent at higher temperatures due to the formation of stable sulfate complexes. Additionally, hydrochloric acid displayed high selectivity for cerium extraction, although its applicability is limited by complexation and environmental concerns. This study offers new insights into optimizing REE recovery from phosphates, demonstrating the advantages of sulfuric acid for industrial-scale leaching processes due to its economic and thermodynamic benefits. The novelty of this work lies in its systematic comparison of the three acids’ effects on REE extraction, providing a comprehensive framework for selecting optimal leaching agents based on specific operational conditions.
Dehydration by Pervaporation of an Organic Solution for the Direct Synthesis of Diethyl Carbonate
Authors: Aziaba Kouessan , Annerl Marco , Greilinger Gerhard , Teufner-Kabas Magdalena , Kabas Florian , Jordan Christian , Harasek Michael
Published: October 2024

Pervaporation has been a central subject in the research community within the scope of the further development of energy- and cost-efficient alternatives to conventional liquid–liquid separation technologies. The potential eligibility of four commercial membranes (ZEBREX ZX0, PERVAPTM 4155-80, PERVAPTM 4100, PERVAPTM 4101) for use in an integrated dehydration application of a diethyl carbonate/water/ethanol mixture by pervaporation was assessed experimentally. The impact of feed concentration, operating temperature, pressure, and sweep gas flow rate on membrane separation performance, including permeation flux, permeate quality, selectivity, and permeance, was thoroughly investigated. Applying the ZX0 membrane delivered the best qualities of all tested membranes of the permeate stream, with a water concentration of mostly >98%. In comparing the water flux, the ZX0 membrane remained reasonably competitive with the polymer membranes. Furthermore, the sweep gas volume flow rate and the operating temperature were identified as influencing the flux significantly but not the product composition. At the same time, the feed concentration of water also influenced the water purity within the permeate. The experiments were monitored with a partial least squares model, allowing a quick assessment of obtained samples while delivering accurate results.
Measuring the Bioeconomy Economically: Exploring the Connections between Concepts, Methods, Data, Indicators and Their Limitations
Authors: Leavy Sebastián , Allegretti Gabriela , Presotto Elen , Montoya Marco Antonio , Talamini Edson
Published: October 2024

Despite its relevance, measuring the contributions of the bioeconomy to national economies remains an arduous task that faces limitations. Part of the difficulty is associated with the lack of a clear and widely accepted concept of the bioeconomy and moves on to the connections between methods, data and indicators. The present study aims to define the concepts of bioeconomy and to explore the connections between concepts, methods, data, and indicators when measuring the bioeconomy economically and the limitations involved in this process. The bioeconomy concepts were defined based on a literature review and a content analysis of 84 documents selected through snowballing procedures to find articles measuring “how big is the bioeconomy?” The content of the 84 documents was uploaded to the Quantitative Data Analysis (QDA Miner) software and coded according to the bioeconomy concept, the methods or models used, the data sources accessed, the indicators calculated, and the limitations reported by the authors. The results of the occurrence and co-occurrence of the codes were extracted and analyzed statistically, indicating the following: the measurement of the bioeconomy (i) needs to recognize and pursue the proposed concept of a holistic bioeconomy; (ii) rarely considered aspects of a holistic bioeconomy (3.5%); (iii) is primarily based on the concept of biomass-based bioeconomy (BmBB) (94%); (iv) the association with the concept of biosphere (BsBB) appeared in 26% of the studies; (v) the biotech-based bioeconomy (BtBB) was the least frequent (1.2%); (vi) there is a diversity of methods and models, but the most common are those traditionally used to measure macroeconomic activities, especially input-output models; (vii) depending on the prevailing methods, the data comes from various official statistical databases, such as national accounts and economic activity classification systems; (viii) the most frequently used indicators are value added, employment, and Greenhouse Gases (GHG) emissions; (ix) there are various limitations related to the concept, methods and models, data, indicators, and others, like incomplete, missing, or lack of data, aggregated data, outdated data or databases, uncertainty of the estimated values, the subjectivity in the bio-shares weighting procedures, and other limitations inherent to methods and models. We conclude that current efforts only partially measure the contributions of the bioeconomy, and efforts should be encouraged toward a full assessment, starting by recognizing that the measurement of a holistic bioeconomy should be pursued.
The Mediating Role of Strategic Adaptability on the Relationship between Human Resource Management Strategies and Innovation
Authors: Megdad Zaid , Çaglar Dilber
Published: October 2024

Dynamic environmental changes continue to impact organizations’ performance and goals, prompting them to adapt and develop strategies that foster innovation continually. Thus, HRMS enables talented, retaining skilled, and innovative employees who contribute with creative ideas and creative problem-solving problems to enhance innovation practices in organizations. Therefore, the findings of previous studies are insufficient and considered as empirical evidence to investigate the research constructs relationship. This study aims to examine the gap in strategic adaptability via HRM strategies and innovation in Jordanian banks. The study employs data analysis and hypotheses testing, descriptive analysis approach, and (SEM) structural equation modeling through SPSS-24 and PLS-SEM-4 software. The research population includes 16 Jordanian banks, and a stratified sampling method conducted on 468 respondents resulted in 455 completed ones, the respondents are middle level managers and department heads. The findings reveal a positive significant impact of HRMS and innovation (INN), a significant positive impact of HRMS and strategic adaptability (SA), and a significant positive effect between strategic adaptability (SA) and innovation (INN). In addition, the findings indicate a partial indirect relationship effect between strategic adaptability (SA) via HRMS and innovation (INN). The conclusion shows that the bank’s performance is highly improved by strategic adaptability, which allows the bank to quickly respond to local and global environmental changes, challenges, crises, and market trends, and provides valuable theoretical and practical insights regarding the role of strategic adaptability (SA) relationship between HRM strategies (HRMS) and innovation (INN). These findings are relevant to the global banking sector due to the similar operating conditions and environments. Moreover, a better understanding of these relationships by practitioners and researchers for future studies in different environments, and sectors.
Development Perspectives on Wellness and Spa Tourism in the Context of Tourism Business Sustainability
Authors: Arsić Martina , Vujko Aleksandra , Knežević Miroslav
Published: October 2024

The main objective of this research was to present Serbia as a destination for wellness and spa tourism, bearing in mind the fact that there are about 1000 springs in this area with a potential for tourism. Then again, since there are only about 40 active spas in Serbia, the aim of this paper is to show the positive aspects of the development of this form of tourism. For this, the example of good practice of the Bad Gastein Spa (Austria) was used, considering the similarities in the quality and medicinal properties of the water, which was analyzed and compared back in the 19th century. We wanted to use this example of good practice to show all the advantages of the development of wellness and spa tourism. A total of 462 respondents participated, of which 227 were residents of Sokobanja (Serbia), while 235 were from the Bad Gastein Spa (Austria). The authors surveyed the local population by staying in these spas, using a random sample system, and then compared the respondents’ answers. The results showed the positive impacts of the development of wellness and spa tourism in Austria, but also the willingness of the local population of Serbia to develop a modern spa concept related to wellness and spa tourism. It can be concluded that Sokobanja should position itself on the tourist market by creating its own identity through the development of wellness and spa tourism. Wellness and spa tourism in Serbia must be recognized as a priority sustainable form of tourism by decision makers at the highest level. The authors believe that the positive experiences of the development of wellness and spa tourism in Sokobanja will have a domino effect for the activation, transformation and development of all other potential spas in Serbia.
Optimal Site Selection for Solar PV Systems in the Colombian Caribbean: Evaluating Weighting Methods in a TOPSIS Framework
Authors: Robles-Algarín Carlos , Castrillo-Fernández Luis , Restrepo-Leal Diego
Published: October 2024

This research paper proposes a framework utilizing multicriteria tools for optimal site selection of photovoltaic solar farms. A comparative analysis was conducted using three quantitative methods—CRITIC (criteria importance through intercriteria correlation), PCA (principal component analysis), and entropy—to obtain the weights for the selection process. The evaluation considered environmental, demographic, financial, meteorological, and performance system criteria. TOPSIS (technique for order preference by similarity to ideal solution) was employed to rank the alternatives based on their proximity to the ideal positive solution and distance from the ideal negative solution. The capital cities of the seven departments in the Colombian Caribbean region were selected for the assessment, characterized by high annual solar radiation, to evaluate the suitability of the proposed decision-making framework. The results demonstrated that Barranquilla consistently ranked in the top two across all methods, indicating its strong performance. Cartagena, for instance, fluctuated between first and third place, showing some stability but still influenced by the method used. In contrast, Sincelejo consistently ranked among the lowest positions. A sensitivity analysis with equal weight distribution confirmed the top-performing cities, though it also highlighted that the weight assignment method impacted the final rankings. Choosing the appropriate method for weight calculation depended on factors such as the diversity and interdependence of criteria, the availability of reliable data, and the desired sensitivity of the results. For instance, CRITIC captured inter-criteria correlation, while PCA focused on reducing dimensionality, and entropy emphasized the variability of information.
Understanding the Water Quality Changes of the Typical Plain River Network Area Using Comprehensive Assessment Methods
Authors: Hu Haizhen , Wang Jia , Zhou Gang , Tong Sichen , Wang Weifu , Hu Tingting
Published: October 2024

Water quality assessment is an important method for understanding the spatial-temporal variation characteristics of water quality. Therefore, the present study has been performed to evaluate the water quality for a typical plain river network area in Changzhou City, Jiangsu, China, where the river system is characterized by reciprocal flow and diverse pollution sources. The water quality samples from 2017 to 2021 were comprehensively assessed using comprehensive methods that combine the single-factor pollution index (SFPI) method with multivariate statistical analysis. Initially, statistical analyses were conducted to evaluate water quality exceedances and correlations and the SFPI method was applied to classify water quality categories. Furthermore, principal component analysis (PCA) and cluster analysis (CA) were employed to reduce the dimensionality of water quality indicators and group monitoring sections with similar characteristics. The results indicate that the overall water quality in Changzhou City is lightly polluted with a trend of improvement. The primary pollutants identified are total phosphorus (TP) and ammonia nitrogen (NH3-N). This study highlights that organic pollution, self-purification capacity, and eutrophication of river water bodies are the most significant factors affecting water quality. The sampling sites were classified into three groups (good, moderate, and poor). The water quality assessment results of this study provide a theoretical reference for water environment management and ecological protection in plain river network areas.
A Novel Two-Stage Hybrid Model Optimization with FS-FCRBM-GWDO for Accurate and Stable STLF
Authors: Uwimana Eustache , Zhou Yatong
Published: October 2024

The accurate, rapid, and stable prediction of electrical energy consumption is essential for decision-making, energy management, efficient planning, and reliable power system operation. Errors in forecasting can lead to electricity shortages, wasted resources, power supply interruptions, and even grid failures. Accurate forecasting enables timely decisions for secure energy management. However, predicting future consumption is challenging due to the variable behavior of customers, requiring flexible models that capture random and complex patterns. Forecasting methods, both traditional and modern, often face challenges in achieving the desired level of accuracy. To address these shortcomings, this research presents a novel hybrid approach that combines a robust forecaster with an advanced optimization technique. Specifically, the FS-FCRBM-GWDO model has been developed to enhance the performance of short-term load forecasting (STLF), aiming to improve prediction accuracy and reliability. While some models excel in accuracy and others in convergence rate, both aspects are crucial. The main objective was to create a forecasting model that provides reliable, consistent, and precise predictions for effective energy management. This led to the development of a novel two-stage hybrid model. The first stage predicts electrical energy usage through four modules using deep learning, support vector machines, and optimization algorithms. The second stage optimizes energy management based on predicted consumption, focusing on reducing costs, managing demand surges, and balancing electricity expenses with customer inconvenience. This approach benefits both consumers and utility companies by lowering bills and enhancing power system stability. The simulation results validate the proposed model’s efficacy and efficiency compared to existing benchmark models.
Super Learner Algorithm for Carotid Artery Disease Diagnosis: A Machine Learning Approach Leveraging Craniocervical CT Angiography
Authors: Özdemir Halil İbrahim , Atman Kazım Gökhan , Şirin Hüseyin , Çalık Abdullah Engin , Senturk Ibrahim , Bilge Metin , Oran İsmail , Bilge Duygu , Çınar Celal
Published: October 2024

This study introduces a machine learning (ML) approach to diagnosing carotid artery diseases, including stenosis, aneurysm, and dissection, by leveraging craniocervical computed tomography angiography (CTA) data. A meticulously curated, balanced dataset of 122 patient cases was used, ensuring reproducibility and data quality, and this is publicly accessible at (insert dataset location). The proposed method integrates a super learner model which combines adaptive boosting, gradient boosting, and random forests algorithms, achieving an accuracy of 90%. To enhance model robustness and generalization, techniques such as k-fold cross-validation, bootstrapping, data augmentation, and the synthetic minority oversampling technique (SMOTE) were applied, expanding the dataset to 1000 instances and significantly improving performance for minority classes like aneurysm and dissection. The results highlight the pivotal role of blood vessel structural analysis in diagnosing carotid artery diseases and demonstrate the superior performance of the super learner model in comparison with state-of-the-art (SOTA) methods in terms of both accuracy and robustness. This manuscript outlines the methodology, compares the results with state-of-the-art approaches, and provides insights for future research directions in applying machine learning to medical diagnostics.
Biodegradation of the Antiretroviral Tenofovir Disoproxil by a Cyanobacteria/Bacterial Culture
Authors: Silva Sandra Regina , Souza-Silva Gabriel , Moreira Carolina Paula de Souza , Vasconcelos Olívia Maria de Sousa Ribeiro , Silveira Micheline Rosa , Barbosa Francisco Antonio Rodrigues , Magalhães Sergia Maria Starling , Mol Marcos Paulo Gomes
Published: October 2024

Tenofovir disoproxil fumarate (TDF) is an antiretroviral drug extensively used by people living with HIV. The TDF molecule is hydrolysed in vivo and liberates tenofovir, the active part of the molecule. Tenofovir is a very stable drug and the discharge of its residues into the environment can potentially lead to risk for aquatic species. This study evaluated the TDF biodegradation and removal by cultures of Microcystis novacekii with the bacteria Pseudomonas pseudoalcaligenes. Concentrations of TDF of 12.5, 25.0, and 50.0 mg/L were used in this study. The process occurred in two stages. In the first 72 h, TDF was de-esterified, forming the tenofovir monoester intermediate by abiotic and enzymatic processes associated in an extracellular medium. In a second step, the monoester was removed from the culture medium by intracellular processes. The tenofovir or other by-products of TDF were not observed in the test conditions. At the end of the experiment, 88.7 to 94.1% of TDF and its monoester derivative were removed from the culture medium over 16 days. This process showed higher efficiency of TDF removal at the concentration of 25 mg/L. Tenofovir isoproxil monoester has partial antiviral activity and has shown to be persistent, maintaining a residual concentration after 16 days in the culture medium, therefore indicating the need to continue research on methods for total removal of this product from the aquatic environment.
The Editorial on the Special Issue “Research on Mycotoxins in Food and Feed: From Detection and Unravelling of Toxicity to Control”
Authors: Abdallah Mohamed F. , Yang Shupeng , Varga Elisabeth
Published: October 2024

In this Special Issue, several interesting research and review articles were published with the aim of filling in some of the existing knowledge gaps in the field of mycotoxins [...]