bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of a High Linoleic Acid Diet on Pregnant Women and Their Offspring
Authors: Nayyar Deepti , Said Joanne M. , McCarthy Helen , Hryciw Deanne H. , O’Keefe Lannie , McAinch Andrew J.
Published: September 2024

Nutritional intake during pregnancy can affect gestational length, fetal development, and impact postnatal growth and health in offspring. Perturbations in maternal nutrition with either an excess or deficiency in nutrients during pregnancy may have harmful effects on the offspring’s development and increase the risk of developing chronic diseases later in life. In pregnancy, nutrients transfer from the mother to the fetus via the placenta. Essential fatty acids, linoleic acid (LA) and alpha linoleic acid (ALA), can only be obtained in the diet. In Western countries, the ratio of LA and ALA in the diet has increased dramatically in recent decades. Some animal and human studies have found a correlation between maternal intake of LA and birth weight; however, the association varies. In contrast, some human studies have demonstrated inconclusive findings regarding the correlation between cord blood levels of LA and birth outcomes. In addition, high dietary LA intake in animal studies in pregnancy increased the production of inflammatory markers such as prostaglandins, leukotrienes, cytokines, and tumour necrosis factor-alpha. This review aims to highlight the effect of high dietary LA intake during pregnancy on birth outcomes, obesity, maternal inflammatory markers, and the transfer of fatty acids across the placenta.
Validation of Two Instruments for the Measurement of Dehumanization and Self-Dehumanization in Healthcare Settings
Authors: Roupa Aikaterini , Patelarou Athina , Fradelos Evangelos C. , Fousiani Kyriaki , Miliaraki Marianna , Giakoumidakis Konstantinos , Patelarou Evridiki
Published: September 2024

Understanding and addressing dehumanization in healthcare is crucial due to its profound impact on patient care, ethical implications on patient dignity and autonomy, and its potential to affect the psychological well-being of healthcare professionals. The primary aim of this study was to establish reliable and valid instruments measuring two different types of dehumanization, namely animalistic dehumanization (i.e., stripping one of their uniquely human characteristics) and mechanistic dehumanization (i.e., stripping one of their human nature characteristics) among healthcare professionals. In this cross-sectional validation study among healthcare professionals, we tested measures of both animalistic and mechanistic dehumanization, focusing on the dehumanization of patients (hetero-dehumanization) and oneself (self-dehumanization), respectively. All measures were developed and validated based on a concept analysis, a literature review, and an appraisal of pre-existing scales. The research was conducted among 400 nurses and medical doctors employed in Greek public hospitals. Coefficient validity ratio results showed that 100% of items were acceptable for both measures. The newly established and validated hetero-dehumanization scale encompassed two factors (factor 1: animalistic dehumanization, factor 2: mechanistic dehumanization; Cronbach’s alpha was equal to 0.86 for each measure). The self-dehumanization scale was a mono-factorial measure of mechanistic dehumanization (Cronbach’s alpha = 0.97). Two validated measures of (self- and hetero-) animalistic and mechanistic dehumanization measures were developed for the assessment of dehumanization among health professionals, which will form the basis for future research in this important scientific field.
Social Networks as a Tool for Evidence-Based Health Education: Umbrella Review
Authors: Sufrate-Sorzano Teresa , Corton-Carrasco Olatz , Garrote-Cámara María-Elena , Navas-Echazarreta Noelia , Pozo-Herce Pablo del , Di Nitto Marco , Juárez-Vela Raúl , Santolalla-Arnedo Iván
Published: September 2024

Background: The number of people who access social networking sites continues to increase at an exponential rate. The use of technology is an essential skill for nursing professionals and its development represents a challenge in improving health education, promotion and care. The objective of this systematic review is to analyse the use of social networking sites by healthcare professionals as an intervention tool for evidence-based public health education. Methods: The protocol of this umbrella review was registered in the International Prospective Register of Systematic Reviews (CRD42023407249). Searches were carried out in MEDLINE (PubMed), Web of Science, CINAHL, PsycINFO (EBSCOhost), and Cochrane Library of Systematic Reviews, in February 2023. A narrative synthesis of the results was conducted. Results: 1896 articles were found, of which 15 reviews fulfilled the inclusion criteria. Social networks broadened the profession; they were YouTube, X (formerly Twitter), Facebook, and Instagram. The target population was mainly young professionals, and they came across different topic areas that addressed health education. Conclusions: It is important to have information backed by scientific evidence to make health decisions. Health professionals active on social networking sites have a unique opportunity to educate the public about health by sharing scientific evidence in an accessible and clear way, which helps to combat misinformation.
Content, Behaviour Change Techniques, and Quality of Postpartum Depression Apps to Be Recommended by Midwives: Systematic Search and Evaluation
Authors: Ureña-Lorenzo Amalia , Fernandez-Alvarez Maria del Mar , Cachero-Rodríguez Judit , Martin-Payo Ruben
Published: September 2024

Background: Postpartum depression is a public health problem that affects a considerable percentage of women. Despite the proliferation of related apps, there are limited data available on the best apps to prevent postpartum depression. We identified which apps available in Spanish could be recommended by midwives based on their content, quality, and behaviour change techniques, as a complementary tool for preventing postpartum depression in women. Methods: A systematic search was performed to identify apps available on iOS App Store and Google Play, which were used to replicate how patients’ access “postpartum depression prevention” apps. Apps’ quality, behaviour change potential, and contents were assessed. Results: A total of 1408 apps were identified, of which 7 were retrieved for assessment (0.5%). The mean objective and subject quality were 3.1 (SD = 1.01) and 2.7 (SD = 1.27), respectively. A total of 24 topics were identified. The mean ABACUS score was 6.6 (SD = 3.64), and the mean number of topics addressed by the apps was 9.9 (SD = 5.90). Conclusion: The results of the present study suggest that a specific free app is not available in Spanish for the prevention of postpartum depression, and only a small percentage of free apps should be recommended based on their quality, BCTs, and contents. The systematic review protocol was not registered.
A Review on Barbituric Acid and Its Derivatives: Synthesis, Reactions, and Bio-Applications
Authors: Kaur Navneet , Kaur Manvinder , Sohal Harvinder Singh , Han Haesook , Bhowmik Pradip K.
Published: September 2024

Barbituric acid is a heterocyclic compound with various pharmacological and biological applications. This review paper provides a comprehensive overview of barbituric acid’s synthesis, reactions, and bio-applications, highlighting its multifaceted role in various fields. Many heterocyclic derivatives were formed based on barbituric acid, for instance, pyrano-fused pyrimidine derivatives, spiro-oxindole derivatives, chrome-based barbituric acid derivatives, and many more via the atom economic method, Michael addition reaction, Knoevenagel condensation reaction, etc. In the context of bio-applications, this review examines the production of a wide range of bioactive drugs like anti-histamine, anti-leprotic, sedative–hypnotic, anti-inflammatory, anti-urease, antiviral, anti-AIDS, antimicrobial, antioxidant, anticonvulsant, anesthetic agent, antitumor, and anticancer drugs using efficient multicomponent reactions. By showcasing the versatility and potential of this compound, it aims to inspire further research and innovation in the field, leading to the development of novel barbituric acid derivatives with enhanced properties and diverse applications, with coverage of the literature relevant up to 2024.
Methanolic Extract of Rhizophora mangle (Rhizophoraceae) Leaves: Phytochemical Characterization and Anthelmintic Evaluation against Schistosoma mansoni
Authors: França Wilza W. M. , Filho Sérgio D. Magalhães , Cavalcante Lucas A. O. , Gomes Mary A. A. S. , Gonçalves Maria T. V. , Diniz Emily G. M. , Nascimento Wheverton R. C. , Neto Reginaldo G. Lima , Albuquerque Mônica C. P. A. , Filho Iranildo J. Cruz , Araújo Hallysson D. A. , Aires André L. , Vieira Jeymesson R. C.
Published: September 2024

Rhizophora mangle is commonly used in traditional medicine to treat infections, reduce inflammation, and promote healing. This study aimed to analyze the phytochemical profile of the methanolic extract of R. mangle leaves (MELRm) and evaluate its in vitro schistosomicidal activity against Schistosoma mansoni as well as its cytotoxicity. Plant material was collected in Itamaracá City, Pernambuco, Brazil. The extract was analyzed using UV/Vis spectrophotometry and high-performance liquid chromatography (HPLC). The motility, mortality, and cell viability of adult worms were assessed in a schistosomicidal assay, while cytotoxicity was evaluated through a colorimetric assay with MTT on RAW 264.7 cells. The primary compounds identified in MELRm were phenolic compounds. In the schistosomicidal assay, all concentrations of MELRs induced changes in the motility of adult worms. At a concentration of 400 μg/mL, MELRs resulted in 56.25% mortality after 72 h of incubation. After 120 h, mortality rates of 75%, 62.5%, and 50% were observed at MELRm concentrations of 400, 200, and 100 μg/mL, respectively. No eggs were detected at any MELRm concentration. MELRs did not show cytotoxicity towards RAW 264.7 cells at the concentrations tested. These results indicate that MELRs demonstrate schistosomicidal activity in vitro, suggesting they are promising candidates for in vivo studies.
Astodrimer Sodium Nasal Spray versus Placebo in Non-Hospitalised Patients with COVID-19: A Randomised, Double-Blinded, Placebo-Controlled Trial
Authors: Winchester Stephen , Castellarnau Alex , Jabbar Kashif , Nadir Meera , Ranasinghe Kapila , Masramon Xavier , Kinghorn George R. , John Isaac , Paull Jeremy R. A.
Published: September 2024

Background/Objectives: Dendrimer-based astodrimer sodium nasal spray was assessed for its ability to reduce SARS-CoV-2 load in outpatients with COVID-19, which remains a severe illness for vulnerable groups. Methods: This was a randomised, double-blind, placebo-controlled clinical investigation evaluating the efficacy of astodrimer nasal spray in reducing SARS-CoV-2 viral burden in the nasopharynx of outpatients with COVID-19. Non-hospitalised adults with SARS-CoV-2 infection were randomised 1:1 to astodrimer or placebo four times daily from Day 1 to Day 7. Nasopharyngeal swabs for SARS-CoV-2 load determination were self-obtained daily from Day 1 to Day 8. The primary endpoint was an area under the curve of SARS-CoV-2 RNA copies/mL through Day 8 (vAUCd1–8). The primary analysis population was the modified intent-to-treat population (mITT: all randomised participants exposed to the study treatment who had at least one post-baseline viral load determination). Safety analyses included all randomised participants exposed to the study treatment. Study registration: ISRCTN70449927; Results: 231 participants were recruited between 9 January and 20 September 2023. The safety population comprised 109 and 113 participants randomised to astodrimer and placebo, respectively, with 96 and 101 participants in the mITT. Astodrimer sodium nasal spray reduced the SARS-CoV-2 burden (vAUCd1–8) vs. placebo in non-hospitalised COVID-19 patients aged 16 years and over (−1.2 log10 copies/mL × Day). The reduction in SARS-CoV-2 load was statistically significant in those aged 45 years and older (−3.7, p = 0.017) and the effect increased in older age groups, including in those aged 65 years and older (−7.3, p = 0.005). Astodrimer sodium nasal spray increased the rate of viral clearance and helped alleviate some COVID-19 symptoms, especially loss of sense of smell. Overall, 31 participants (14%) had ≥1 adverse event (AE). Four AEs were deemed possibly related to treatment. Most AEs were of mild severity and occurred at similar rates in both treatment arms. Conclusions: Astodrimer nasal spray reduces viral burden and accelerates viral clearance, especially in older populations, and is well tolerated.
Direct and Abscopal Antitumor Responses Elicited by AlPcNE-Mediated Photodynamic Therapy in a Murine Melanoma Model
Authors: Morais José Athayde Vasconcelos , Barros Pedro H. A. , Brigido Marcelo de Macedo , Marina Clara Luna , Bocca Anamelia , Mariano André de Lima e Silva , Souza Paulo E. N. de , Paiva Karen L. R. , Simões Marina Mesquita , Bao Sonia Nair , Camargo Luana C. , Longo João P. Figueiró , Morais Amanda Alencar Cabral , Azevedo Ricardo B. de , Fonseca Marcio J. P. , Muehlmann Luis A.
Published: September 2024

Melanoma, the most aggressive form of skin cancer, presents a major clinical challenge due to its tendency to metastasize and recalcitrance to traditional therapies. Despite advances in surgery, chemotherapy, and radiotherapy, the outlook for advanced melanoma remains bleak, reinforcing the urgent need for more effective treatments. Photodynamic therapy (PDT) has emerged as a promising alternative, leading to targeted tumor destruction with minimal harm to surrounding tissues. In this study, the direct and abscopal antitumor effects of PDT in a bilateral murine melanoma model were evaluated. Although only one of the two tumors was treated, effects were observed in both. Our findings revealed significant changes in systemic inflammation and alterations in CD4+ and CD8+ T cell populations in treated groups, as evidenced by blood analyses and flow cytometry. High-throughput RNA sequencing (RNA-Seq) further unveiled shifts in gene expression profiles in both treated and untreated tumors. This research sheds light on the novel antitumor and abscopal effects of nanoemulsion of aluminum chloride phthalocyanine (AlPcNE)-mediated PDT in melanoma, highlighting the potential of different PDT protocols to modulate immune responses and to achieve more effective and targeted cancer treatments.
Exploitation of Waste Algal Biomass in Northern Italy: A Cost–Benefit Analysis
Authors: Baldi Andrea , Pronti Andrea , Mazzanti Massimiliano , Pasti Luisa
Published: September 2024

Aquaculture and waste valorization have the potential to show solid achievements toward food security and improvements in the circularity of resources, which are crucial aspects of achieving a sustainable lifestyle in agreeance with Agenda 2030 goals. This study aims to optimize and simplify the decision-making processes for the valorization of marine wastes (natural and from aquaculture) as secondary raw materials to produce high-value-added market goods. However, significant concentrations of pollutants may be present within wastes, compromising overall quality, and social dynamics can hinder their usage further. Goro’s lagoon was chosen as a case study, where the relations between the ecosystem services, a thriving bivalve economy, and social dynamics are deeply rooted and intertwined. Therefore, in the manuscript cost–benefit and foresight analyses are conducted to determine the best usage for algal biomass considering pollution, social acceptance, and profitability. These analyses are virtually conducted on bio-refineries that could be operating in the case study’s area: briefly, for a thirty-year running bio-plant, the CBA indicates the two best alternatives with an income of 5 billion euros (NPV, with a 5% discount rate) for a biofuel-only production facility, and a half for a multiproduct one, leading to the conclusion that the first is the best alternative. The foresight, instead, suggests a more cautious approach by considering external factors such as the environment and local inhabitants. Hence, the main innovation of this work consists of the decision-maker’s holistic enlightenment toward the complexities and the hidden threats bound to this kind of closed-loop efficiency-boosting process, which eventually leads to optimized decision-making processes.
Unraveling the Effect of Strain Rate and Temperature on the Heterogeneous Mechanical Behavior of Polymer Nanocomposites via Atomistic Simulations and Continuum Models
Authors: Youssef Ali A. , Reda Hilal , Harmandaris Vagelis
Published: September 2024

Polymer nanocomposites are characterized by heterogeneous mechanical behavior and performance, which is mainly controlled by the interaction between the nanofiller and the polymer matrix. Optimizing their material performance in engineering applications requires understanding how both the temperature and strain rate of the applied deformation affect mechanical properties. This work investigates the effect of strain rate and temperature on the mechanical properties of poly(ethylene oxide)/silica (PEO/SiO2) nanocomposites, revealing their behavior in both the melt and glassy states, via atomistic molecular dynamics simulations and continuum models. In the glassy state, the results indicate that Young’s modulus increases by up to 99.7% as the strain rate rises from 1.0 × 10−7 fs−1 to 1.0 × 10−4 fs−1, while Poisson’s ratio decreases by up to 39.8% over the same range. These effects become even more pronounced in the melt state. Conversely, higher temperatures lead to an opposing trend. A local, per-atom analysis of stress and strain fields reveals broader variability in the local strain of the PEO/SiO2 nanocomposites as temperature increases and/or the deformation rate decreases. Both interphase and matrix regions lose rigidity at higher temperatures and lower strain rates, blurring their distinctiveness. The results of the atomistic simulations concerning the elastic modulus and Poisson’s ratio are in good agreement with the predictions of the Richeton–Ji model. Additionally, these findings can be leveraged to design advanced polymer composites with tailored mechanical properties and could optimize structural components by enhancing their performance under diverse engineering conditions.
Hidden Variables in Quantum Mechanics from the Perspective of Boltzmannian Statistical Mechanics
Authors: Lazarovici Dustin
Published: September 2024

This paper examines no-hidden-variables theorems in quantum mechanics from the point of view of statistical mechanics. It presents a general analysis of the measurement process in the Boltzmannian framework that leads to a characterization of (in)compatible measurements and reproduces several features of quantum probabilities often described as “non-classical”. The analysis is applied to versions of the Kochen–Specker and Bell theorems to shed more light on their implications. It is shown how, once the measurement device and the active role of the measurement process are taken into account, contextuality appears as a natural feature of random variables. This corroborates Bell’s criticism that no-go results of the Kochen–Specker type are based on gratuitous assumptions. In contrast, Bell-type theorems are much more profound, but should be understood as nonlocality theorems rather than no-hidden-variables theorems. Finally, the paper addresses misunderstandings and misleading terminology that have confused the debate about hidden variables in quantum mechanics.
Religion and Medicine Interplay in Eastern Orthodoxy: A Healthcare Practice-Oriented Scoping Review
Authors: Papazoglou Andreas S. , Moysidis Dimitrios V. , Loudovikou Anna , Tsagkaris Christos , Cudjoe Thomas , Mazin Rafael , Linos Dimitrios , Galiatsatos Panagis
Published: September 2024

A growing body of evidence has shown the influence that religion and spirituality have on individuals duelling with illness, especially at advanced stages of disease. It is often vital for such individuals to have their spiritual and religious beliefs respected and, potentially, integrated in a compassionate caregiving setting for healthcare systems. However, given the diversity in religions and spiritual practices, healthcare professionals ought to approach such cultural significance with cultural humility, i.e., approaching differences in beliefs without relying on stereotypes or assumptions. This process requires sufficient training and communication skills among healthcare providers, as well as a successful medico-pastoral partnership. The aim of this review is to provide insight into one specific religion, Eastern Orthodoxy, and discuss current evidence of the religion’s influence on healthcare and medicine. Within this review, we aim to provide a summary on religion and spirituality specific to the Eastern Orthodox identity and religious practices, along with providing clinical guidance on approaching people who identify with this faith in certain healthcare settings in a manner compatible with the principles of cultural humility. The review will discuss important key elements of religious belief and practice of Εastern Orthodoxy that may influence health and healthcare decisions by patients.
The Eden Complex: Transgression and Transformation in the Bible, Freud and Jung
Authors: Drob Sanford
Published: September 2024

Freud chose the myth of Oedipus as the foundation for his understanding of human development, obedience to the law, and his theory of civilization, and he wrote that he saw no psychological value in analyzing the expulsion of Adam and Eve from the Garden of Eden. Drawing on biblical interpretation, the Kabbalah, and the work of C. G. Jung, it is argued that Adam and Eve’s transgression serves as an archetype for an “Eden Complex” that provides a broad and useful paradigm for understanding the dynamics of individual development, parent–child conflict, morals and values, and both psychotherapeutic and societal change.
Risk of Obstetric Anal Sphincter Injuries after Labor Induction
Authors: Salinas Melissa A. , Potarazu Savita , Rahman Sara , Lee Do H. , Deaton Lydia , Whitley Julia , Hill Devin , Chea-Howard Kharastin L. , Bryson Ciara , Carter-Brooks Charelle M.
Published: September 2024

Background: Obstetric Anal Sphincter Injuries (OASI) are associated with significant morbidity. Data regarding induction of labor (IOL) and risk of OASI is conflicting. The objective of this study is to evaluate if IOL increases the odds of OASI when compared to spontaneous labor. Methods: This is a retrospective case–control study in women with term, singleton pregnancies, who had a vaginal delivery at a single, academic center in an urban setting from October 2015 to December 2021. Individuals with the primary outcome, OASI, were identified and matched with controls (no OASI) by delivery date. Results: 303 OASI individuals were identified and matched with 1106 controls. Women with OASI were more likely to be White or Asian, have commercial insurance, and have a previous cesarean delivery (CD). IOL increased the likelihood of OASI by 49% compared to spontaneous labor (OR 1.49, 95% CI [1.138, 1.949]). IOL was no longer significant when adjusting for confounding variables and known risk factors. Conclusion: IOL was not associated with OASI when accounting for known antepartum and intrapartum risk factors. Further investigation of modifiable and non-modifiable risks during labor is imperative to decrease the risk of OASI and associated pelvic floor disorders.
Measurement Accuracy and Attitude Compensation of Rayleigh Lidar on an Airborne Floating Platform
Authors: Wu Tong , Zhong Kai , Zhang Xianzhong , Li Fangjie , Li Xinqi , Zhang Xiaojian , Yan Zhaoai , Xu Degang , Yao Jianquan
Published: September 2024

Rayleigh lidar equipped on airborne floating platforms has received increasing attention in recent years due to the demand for exploring the middle atmosphere. However, the inevitable attitude fluctuation of the platform affects the measurement accuracy of the photon profile, which greatly affects temperature retrieval. Here, an extensive theoretical analysis model of geometrical transformations between the actual altitude and detection distance under attitude fluctuations was constructed by taking pitch, roll, and observation angles into consideration. Based on this model and measured attitude angles, the influence of platform fluctuation on lidar measurement was analyzed by calculating the deviations between temperature retrieval results and the NRLMSISE-00 model at different observation angles, which demonstrated that the altitude displacement from the variation of pitch angle is a crucial factor in causing temperature retrieval error, especially at large observation angles. Then, an attitude compensation method was designed to eliminate the impact of fluctuations, incorporating the merits of good robustness. Under the observation angle of 45° and average pitch angle of around 4°, the maximum temperature deviation after attitude compensation was reduced from 21.29 K to 0.366 K, a reduction of around two orders of magnitude, indicating that the method can significantly improve the measurement accuracy of Rayleigh lidar.
GNSS Time Series Analysis with Machine Learning Algorithms: A Case Study for Anatolia
Authors: Özbey Volkan , Ergintav Semih , Tarı Ergin
Published: September 2024

This study addresses the potential of machine learning (ML) algorithms in geophysical and geodetic research, particularly for enhancing GNSS time series analysis. We employed XGBoost and Long Short-Term Memory (LSTM) networks to analyze GNSS time series data from the tectonically active Anatolian region. The primary objective was to detect discontinuities associated with seismic events. Using over 13 years of daily data from 15 GNSS stations, our analysis was conducted in two main steps. First, we characterized the signals by identifying linear trends and seasonal variations, achieving R2 values of 0.84 for the XGBoost v.2.1.0 model and 0.81 for the LSTM model. Next, we focused on the residual signals, which are primarily related to tectonic movements. We applied various threshold values and tested different hyperparameters to identify the best-fitting models. We designed a confusion matrix to evaluate and classify the performance of our models. Both XGBoost and LSTM demonstrated robust performance, with XGBoost showing higher true positive rates, indicating its superior ability to detect precise discontinuities. Conversely, LSTM exhibited a lower false positive rate, highlighting its precision in minimizing false alarms. Our findings indicate that the best fitting models for both methods are capable of detecting seismic events (Mw ≥ 4.0) with approximately 85% precision.
A Space Object Optical Scattering Characteristics Analysis Model Based on Augmented Implicit Neural Representation
Authors: Zhu Qinyu , Xu Can , Zhao Shuailong , Tao Xuefeng , Zhang Yasheng , Tao Haicheng , Wang Xia , Fang Yuqiang
Published: September 2024

The raw data from ground-based telescopic optical observations serve as a key foundation for the analysis and identification of optical scattering properties of space objects, providing an essential guarantee for object identification and state prediction efforts. In this paper, a spatial object optical characterization model based on Augmented Implicit Neural Representations (AINRs) is proposed. This model utilizes a neural implicit function to delineate the relationship between the geometric observation model and the apparent magnitude arising from sunlight reflected off the object’s surface. Combining the dual advantages of data-driven and physical-driven, a novel pre-training procedure method based on transfer learning is designed. Taking omnidirectional angle simulation data as the basic training dataset and further introducing it with real observational data from ground stations, the Multi-Layer Perceptron (MLP) parameters of the model undergo constant refinement. Pre-fitting experiments on the newly developed S−net, R−net, and F−net models are conducted with a quantitative analysis of errors and a comparative assessment of evaluation indexes. The experiment demonstrates that the proposed F−net model consistently maintains a prediction error for satellite surface magnitude values within 0.2 mV, outperforming the other two models. Additionally, preliminary accomplishment of component-level recognition has been achieved, offering a potent analytical tool for on-orbit services.
IoT-Based Wireless System for Gait Kinetics Monitoring in Multi-Device Therapeutic Interventions
Authors: Rathke Christian Lang , Pimentel Victor Costa de Andrade , Alsina Pablo Javier , do Espírito Santo Caroline Cunha , Dantas André Felipe Oliveira de Azevedo
Published: September 2024

This study presents an IoT-based gait analysis system employing insole pressure sensors to assess gait kinetics. The system integrates piezoresistive sensors within a left foot insole, with data acquisition managed using an ESP32 board that communicates via Wi-Fi through an MQTT IoT framework. In this initial protocol study, we conducted a comparative analysis using the Zeno system, supported by PKMAS as the gold standard, to explore the correlation and agreement of data obtained from the insole system. Four volunteers (two males and two females, aged 24–28, without gait disorders) participated by walking along a 10 m Zeno system path, equipped with pressure sensors, while wearing the insole system. Vertical ground reaction force (vGRF) data were collected over four gait cycles. The preliminary results indicated a strong positive correlation (r = 0.87) between the insole and the reference system measurements. A Bland–Altman analysis further demonstrated a mean difference of approximately (0.011) between the two systems, suggesting a minimal yet significant bias. These findings suggest that piezoresistive sensors may offer a promising and cost-effective solution for gait disorder assessment and monitoring. However, operational factors such as high temperatures and sensor placement within the footwear can introduce noise or unwanted signal activation. The communication framework proved functional and reliable during this protocol, with plans for future expansion to multi-device applications. It is important to note that additional validation studies with larger sample sizes are required to confirm the system’s reliability and robustness for clinical and research applications.
A Vision–Language Model-Based Traffic Sign Detection Method for High-Resolution Drone Images: A Case Study in Guyuan, China
Authors: Yao Jianqun , Li Jinming , Li Yuxuan , Zhang Mingzhu , Zuo Chen , Dong Shi , Dai Zhe
Published: September 2024

As a fundamental element of the transportation system, traffic signs are widely used to guide traffic behaviors. In recent years, drones have emerged as an important tool for monitoring the conditions of traffic signs. However, the existing image processing technique is heavily reliant on image annotations. It is time consuming to build a high-quality dataset with diverse training images and human annotations. In this paper, we introduce the utilization of Vision–language Models (VLMs) in the traffic sign detection task. Without the need for discrete image labels, the rapid deployment is fulfilled by the multi-modal learning and large-scale pretrained networks. First, we compile a keyword dictionary to explain traffic signs. The Chinese national standard is used to suggest the shape and color information. Our program conducts Bootstrapping Language-image Pretraining v2 (BLIPv2) to translate representative images into text descriptions. Second, a Contrastive Language-image Pretraining (CLIP) framework is applied to characterize not only drone images but also text descriptions. Our method utilizes the pretrained encoder network to create visual features and word embeddings. Third, the category of each traffic sign is predicted according to the similarity between drone images and keywords. Cosine distance and softmax function are performed to calculate the class probability distribution. To evaluate the performance, we apply the proposed method in a practical application. The drone images captured from Guyuan, China, are employed to record the conditions of traffic signs. Further experiments include two widely used public datasets. The calculation results indicate that our vision–language model-based method has an acceptable prediction accuracy and low training cost.
A Study on a Radio Source Location Estimation System Using High Altitude Platform Stations (HAPS)
Authors: Furuse Yuta , Tran Gia Khanh
Published: September 2024

Currently, there is a system in Japan to detect illegal radio transmitting sources, known as the DEURAS system. Even though crackdowns on illegal radio stations are conducted on a regular basis every year, the number of illegal emission cases still tends to increase, as ordinary citizens are now able to handle advanced wireless communication technologies, e.g., via software-defined radio. However, the current surveillance system may not be able to accurately detect the source in areas where large buildings are densely packed, such as urban areas, due to the effects of reflected waves. Therefore, in this study, we proposed a system for estimating the location of the source of transmission using a high-flying unmanned aerial vehicle called HAPS. The simulation results using numerical analysis software show that the proposed system can estimate the location of the source over a wider area and with higher accuracy than conventional monitoring systems.
An Integrated Approach: A Hybrid Machine Learning Model for the Classification of Unscheduled Stoppages in a Mining Crushing Line Employing Principal Component Analysis and Artificial Neural Networks
Authors: Viveros Pablo , Moya Cristian , Mena Rodrigo , Kristjanpoller Fredy , Godoy David R.
Published: September 2024

This article implements a hybrid Machine Learning (ML) model to classify stoppage events in a copper-crushing equipment, more specifically, a conveyor belt. The model combines Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) with Principal Component Analysis (PCA) to identify the type of stoppage event when they occur in an industrial sector that is significant for the Chilean economy. This research addresses the critical need to optimise maintenance management in the mining industry, highlighting the technological relevance and motivation for using advanced ML techniques. This study focusses on combining and implementing three ML models trained with historical data composed of information from various sensors, real and virtual, as well from maintenance reports that report operational conditions and equipment failure characteristics. The main objective of this study is to improve the efficiency when identifying the nature of a stoppage serving as a basis for the subsequent development of a reliable failure prediction system. The results indicate that this approach significantly increases information reliability, addressing the persistent challenges in data management within the maintenance area. With a classification accuracy of 96.2% and a recall of 96.3%, the model validates and automates the classification of stoppage events, significantly reducing dependency on interdepartmental interactions. This advancement eliminates the need for reliance on external databases, which have previously been prone to errors, missing critical data, or containing outdated information. By implementing this methodology, a robust and reliable foundation is established for developing a failure prediction model, fostering both efficiency and reliability in the maintenance process. The application of ML in this context produces demonstrably positive outcomes in the classification of stoppage events, underscoring its significant impact on industry operations.
Hybrid Metaheuristic Secondary Distributed Control Technique for DC Microgrids
Authors: Lasabi Olanrewaju , Swanson Andrew , Jarvis Leigh , Khan Mohamed , Aluko Anuoluwapo
Published: September 2024

Islanded DC microgrids are poised to become a crucial component in the advancement of smart energy systems. They achieve this by effectively and seamlessly integrating multiple renewable energy resources to meet specific load requirements through droop control, which ensures fair distribution of load current across the distributed energy resources (DERs). Employing droop control usually results in a DC bus voltage drop. This article introduces a secondary distributed control approach aimed at concurrently achieving current distribution among the DERs and regulating the voltage of the DC bus. The proposed secondary control approach eradicates voltage fluctuations and guarantees equitable current allocation by integrating voltage and current errors within the designed control loop. A novel hybrid particle swarm optimization–grey wolf optimization (HPSO-GWO) has been proposed, which assists in selecting the parameters of the distributed control technique, enabling the achievement of the proposed control objectives. Eigenvalue observation analysis has been utilized through the DC microgrid state-space model designed to assess the influence of the optimized distributed secondary control on the microgrid stability. A real-time testing system was constructed within MATLAB/Simulink® and deployed on Speedgoat™ real-time equipment to validate the operations of the proposed technique for practical applications. The results indicated that the proposed secondary control effectively enhances voltage recovery and ensures proper current distribution following various disturbances, thereby maintaining a continuous power supply. The outcomes also demonstrated the capabilities of the control approach in accomplishing the control objectives within the DC microgrid, characterized by minimal oscillations, overshoots/undershoots, and rapid time responses.
Understanding How Consumers’ Perceived Sustainability Influences Their Continuance Intention to Use Sharing Economy Services
Authors: Huang Shiu-Li , Leau Yu-Ren
Published: September 2024

The sharing economy is beneficial for sustainable development. It effectively utilizes underused resources and reduces unnecessary production, consumption, and waste through resource sharing. This study investigates the factors that can increase consumers’ perceived sustainability of a sharing economy service and examines the impact of perceived sustainability on their intentions to continue using the service. Furthermore, the study considers the moderating effect of perceived green transparency. Internet surveys are conducted to collect responses from users of a transportation service (Uber) and an on-demand logistics service (Uber Eats). This study provides suggestions for service providers in the sharing economy to develop sustainability strategies.
Effects of Nutrient Accumulation and Microbial Community Changes on Tomato Fusarium Wilt Disease in Greenhouse Soil
Authors: Yang Lu , Han Wei , Tan Boyuan , Wu Yue , Li Song , Yi Yanli
Published: September 2024

Fusarium wilt caused by Fusarium oxysporum f. sp. lycopersici has severely threatened sustainable greenhouse tomato production. However, the effects of nutrient enrichment due to excessive fertilization on Fusarium wilt remain unclear. This study aimed to investigate the relationships among soil nutrient enrichment, microbial community structure, and the occurrence of Fusarium wilt under greenhouse conditions. This study used chemical analysis and microbiological techniques to analyze rhizosphere soil samples from greenhouse tomato production areas with varying degrees of Fusarium wilt. The results showed that, as compared with the Health group, the rhizosphere soil of Disease group has a significant nutrient enrichment, which significantly influences bacterial diversity and structure. Particularly when soil NO3−–N content exceeds 170.43 mg kg−1, there was a significant reduction in the relative abundance of key biocontrol bacteria such as Bacillus and Lysinibacillus. This reduction indirectly contributes to an increase in Fusarium oxysporum abundance, subsequently elevating the likelihood of pathogen infection. Furthermore, the Disease group also exhibited a simplified co-occurrence network with a 22.37% reduction in competitive interactions between bacteria and fungi. These changes might collectively increase the risk of tomato Fusarium wilt infection. Meanwhile, the relative abundance of bacteria carrying antibiotic resistance genes significantly increased in the Disease group, which also reduced soil resistance. Together, the results presented here not only uncover the effect of long-term excessive fertilization on the occurrence of Fusarium wilt but also advance our understanding of the interactions among soil nutrient management and microbial communities in the tomato rhizosphere, which provides a scientific basis for formulating strategies to prevent soil-borne diseases in greenhouse tomatoes.
An Empirical Investigation into the Effects of the Digital Economy on Regional Integration: Evidence from Urban Agglomeration in China
Authors: Ru Lifei , Wang Peilin , Lu Yixian
Published: September 2024

Based on the urban panel data of Beijing-Tianjin-Hebei from 2009 to 2021, this article constructs an indicator system for the development level of the digital economy and regional integration, evaluates the impact of the digital economy on the integration development levels of different types of cities. The study found that (1) The digital economy significantly promotes the integration level of Beijing, Tianjin, and Hebei. The study divides Beijing-Tianjin-Hebei into two categories: large cities and small and medium-sized cities. Large cities and small and medium-sized cities have different effects on the relationship between the digital economy and integration level. (2) From the analysis of different dimensions of the digital economy, digital network infrastructure improvements, industrial digitization, and digital society can all promote regional integration. However, different digital economy contents have different promotional effects on different types of cities. (3) From the results of the mechanism analysis, it can be seen that the optimization of the allocation of human and capital elements, the increase in patent innovation, and the reduction of transaction costs will help enhance the driving force of the digital economy for regional integration. Among them, innovative development significantly impacts large cities’ integration levels. The promotion effect is more significant, and small and medium-sized cities are mainly affected by reduced transaction costs and the optimized allocation of capital factors. This study further expands the impact mechanism of the digital economy on the level of regional integration development. It provides a more in-depth analysis of the use of the digital economy to achieve coordinated regional development in regions with excessive economic and technological differences through heterogeneity research.
Experimental Investigation on the Effect of Gold Tailings as a Partial Replacement for Sand in Concrete
Authors: Ikotun Jacob O. , Adeyeye Rhoda A. , Otieno Mike
Published: September 2024

This study explores the use of secondary gold tailings (SGTs) in concrete production to solve sand sustainability issues. This approach addresses waste issues and presents a sustainable material alternative to conventional sand, investigating different SGT proportions (ranging from 0% to 100%) to replace fine aggregate in structural concrete. This study examined the fresh, mechanical, and durability properties of concrete containing SGTs. Incorporating SGTs reduced the concrete’s workability, but up to a 75% replacement level resulted in a high fresh concrete density compared with the reference concrete. The results indicated that up to 25% replacement level increased the compressive strength and up to 50% replacement level improved the splitting tensile strength compared with reference concrete. However, all concretes containing SGTs exhibited satisfactory strengths. The statistical analysis confirmed the significant influence of SGTs on concrete strength. In addition, the durability results of the concrete demonstrated good resistance to oxygen, water, and chloride penetration, indicating good concrete quality. SGTs are recommended as a substitute for crusher sand to reduce production costs, conserve natural resources, and promote a sustainable and greener environment.
The Sustainable Innovation of AI: Text Mining the Core Capabilities of Researchers in the Digital Age of Industry 4.0
Authors: Ji Yajun , Zhang Shengtai , Han Fang , Cui Ran , Jiang Tao
Published: September 2024

Sustainable innovation in the field of artificial intelligence (AI) is essential for the development of Industry 4.0. Recognizing the innovation abilities of researchers is fundamental to achieving sustainable innovation within organizations. This study proposes a method for identifying the core innovative competency field of researchers through text mining, which involves the extraction of core competency tags, topic clustering, and calculating the relevance between researchers and topics. Using AI as a case study, the research identifies the core innovative competency field of researchers, uncovers opportunities for sustainable innovation, and highlights key innovators. This approach offers deeper insights for AI R&D activities, providing effective support for promoting sustainable innovation. Compared to traditional expertise identification methods, this approach provides a more in-depth and detailed portrayal of researchers’ expertise, particularly highlighting potential innovation domains with finer granularity. It is less influenced by subjective factors and can be conveniently applied to identify the core innovative competency field of researchers in any other research field, making it especially suitable for interdisciplinary areas. By offering a precise and comprehensive understanding of researchers’ capability fields, this method enhances the strategic planning and execution of innovative projects, ensuring that organizations can effectively leverage the expertise of their researchers to drive forward sustainable innovation.
How Does a Major Corporate Customer’s ESG Performance Drive the Supplier’s Green Innovation?
Authors: Sun Weizheng , Kou Meixin , Zhang Xiaoyue , Cui Yin , Chen Shuning
Published: September 2024

In the current climate of intensifying global demands for sustainability, the influence of major corporate customers in driving environmental initiatives across supply chains has emerged as a pivotal focus of academic research and practical application. This study investigates how these influential customers can drive green innovation along their supply chains, which remains under-explored in the previous literature. The study focuses on critical indicators such as the green patent application to measure green innovation outcomes. Leveraging a panel dataset of paired Chinese listed firms from 2009 to 2022, we examine the impact of customers’ environmental, social, and governance (ESG) performance on suppliers’ green innovation activities. Our model is integrated with fixed effects for both industry and year. Our analysis reveals that customers with firm ESG commitments significantly enhance their suppliers’ green innovation outcomes, particularly when they possess greater bargaining power and advanced green innovation capabilities. Additionally, we identify pressure and collaboration as critical mechanisms through which these effects are realized. These findings extend the discourse on sustainable supply chain management by highlighting the crucial role of customer-driven ESG initiatives in fostering upstream innovation, offering valuable insights for scholars and practitioners.