bluuebunny/crossref_metadata_2025 huggingface dataset:
pH Homeodynamics and Male Fertility: A Coordinated Regulation of Acid-Based Balance during Sperm Journey to Fertilization
Authors: Dai Pengyuan , Zou Meng , Cai Ziyi , Zeng Xuhui , Zhang Xiaoning , Liang Min
Published: June 2024

pH homeostasis is crucial for spermatogenesis, sperm maturation, sperm physiological function, and fertilization in mammals. HCO3− and H+ are the most significant factors involved in regulating pH homeostasis in the male reproductive system. Multiple pH-regulating transporters and ion channels localize in the testis, epididymis, and spermatozoa, such as HCO3− transporters (solute carrier family 4 and solute carrier family 26 transporters), carbonic anhydrases, and H+-transport channels and enzymes (e.g., Na+-H+ exchangers, monocarboxylate transporters, H+-ATPases, and voltage-gated proton channels). Hormone-mediated signals impose an influence on the production of some HCO3− or H+ transporters, such as NBCe1, SLC4A2, MCT4, etc. Additionally, ion channels including sperm-specific cationic channels for Ca2+ (CatSper) and K+ (SLO3) are directly or indirectly regulated by pH, exerting specific actions on spermatozoa. The slightly alkaline testicular pH is conducive to spermatogenesis, whereas the epididymis’s low HCO3− concentration and acidic lumen are favorable for sperm maturation and storage. Spermatozoa pH increases substantially after being fused with seminal fluid to enhance motility. In the female reproductive tract, sperm are subjected to increasing concentrations of HCO3− in the uterine and fallopian tube, causing a rise in the intracellular pH (pHi) of spermatozoa, leading to hyperpolarization of sperm plasma membranes, capacitation, hyperactivation, acrosome reaction, and ultimately fertilization. The physiological regulation initiated by SLC26A3, SLC26A8, NHA1, sNHE, and CFTR localized in sperm is proven for certain to be involved in male fertility. This review intends to present the key factors and characteristics of pHi regulation in the testes, efferent duct, epididymis, seminal fluid, and female reproductive tract, as well as the associated mechanisms during the sperm journey to fertilization, proposing insights into outstanding subjects and future research trends.
Nest Design and Breeding Success: Replicability of Methodologies and Research Findings in Secondary Hole Nesting Passerines
Authors: Lambrechts Marcel M. , Deeming D. Charles
Published: June 2024

More than 40 hypotheses predict associations between features of nest architecture and at least one measure of reproductive success. However, quantitative studies of reproductive consequences of nest characteristics remain scarce. In addition, most studies were conducted on model species of which nests can be easily monitored with artificial nest-boxes. Here, we review the replicability of research protocols and findings in model species, with many repeat studies focusing mainly on nest-size components, animal-derived nest material, or fresh greenery in model species of secondary hole-nesting birds: Blue Tits (Cyanistes caeruleus), Great Tits (Parus major), Tree Swallows (Tachycineta bicolor), and Starlings (Sturnus spp.). The studies looked for correlations between nest traits and aspects of breeding performance that can be easily quantified in the field, such as clutch size, brood size at hatching or fledging, the percentage of eggs that hatch or fledge, or nestling characteristics assumed to reflect qualities associated with survival probabilities (e.g., morphometry, body condition, blood profiles). We discuss the consequences of poor replicability of research methodologies and provide explanations for why many of these studies reported poor associations between nest design and breeding success at different spatiotemporal scales. We also make suggestions for future research.
Research on Unmanned Aerial Vehicle (UAV) Visual Landing Guidance and Positioning Algorithms
Authors: Liu Xiaoxiong , Xue Wanhan , Xu Xinlong , Zhao Minkun , Qin Bin
Published: June 2024

Considering the weak resistance to interference and generalization ability of traditional UAV visual landing navigation algorithms, this paper proposes a deep-learning-based approach for airport runway line detection and fusion of visual information with IMU for localization. Firstly, a coarse positioning algorithm based on YOLOX is designed for airport runway localization. To meet the requirements of model accuracy and inference speed for the landing guidance system, regression loss functions, probability prediction loss functions, activation functions, and feature extraction networks are designed. Secondly, a deep-learning-based runway line detection algorithm including feature extraction, classification prediction and segmentation networks is designed. To create an effective detection network, we propose efficient loss function and network evaluation methods Finally, a visual/inertial navigation system is established based on constant deformation for visual localization. The relative positioning results are fused and optimized with Kalman filter algorithms. Simulation and flight experiments demonstrate that the proposed algorithm exhibits significant advantages in terms of localization accuracy, real-time performance, and generalization ability, and can provide accurate positioning information during UAV landing processes.
Emerging Research Topics in Drone Healthcare Delivery
Authors: Campbell Hamish , Bosiocic Vanya , Hvala Aliesha , Brady Mark , Campbell Mariana , Skelton Kade , Luiz Osmar
Published: June 2024

The application of drones to assist with healthcare delivery has grown rapidly over the last decade. This industry is supported by a growing research field, and we have undertaken a systematic review of the published literature. Web-based searches returned 290 relevant manuscripts published between 2010 and 2024. We applied Topic Modelling to this corpus of literature, which examines word association and connectedness within the research papers. The modelling identified two emerging research themes with little connection between them: those who used drones to deliver time-critical medical items and those who used drones to deliver non-time-critical medical items. The former was in response to medical emergencies, while the latter was for enhancing resilience in the healthcare supply chain. The topics within these research themes exhibited notable differences. The delivery of time-critical medical items theme comprised the topics of ‘Emergency Response’, ‘Defibrillator and Organ Delivery’, and ‘Search and Rescue’, whilst non-time-critical delivery researched the topics of ‘Supply Chain Optimisation’ and ‘Cost-Effectiveness’, ‘Overcoming Remoteness’, and ‘Pandemic Response’. Research on ‘Engineering and Design Considerations’ and ‘Ethical and Social Considerations’ cut across both research themes. We undertook further analysis to assess research topic alignment and identify knowledge gaps. We found that efforts are needed to establish a more standardised terminology for better alignment across the two emerging research themes. Future studies should focus on evaluating the impact of drone delivery on patient health using systematic methods. Additionally, exploring the economic viability of drone-based health services and addressing regulatory barriers are crucial for efficient and effective drone deployment in healthcare delivery systems.
Using Google Earth Engine to Assess the Current State of Thermokarst Terrain on Arga Island (the Lena Delta)
Authors: Kartoziia Andrei
Published: June 2024

The mapping of thermokarst landscapes and the assessment of their conditions are becoming increasingly important in light of a rising global temperature. Land cover maps provide a basis for quantifying changes in landscapes and identifying areas that are vulnerable to permafrost degradation. The study is devoted to assessing the current state of thermokarst terrain on Arga Island. We applied a random forests algorithm using the capabilities of the Google Earth Engine cloud platform for the supervised classification of the composite image. The analyzed composite consists of a Sentinel-2 image and a set of calculated indices. The study found that thermokarst-affected terrains occupy 35% of the total area, and stable terrains cover 29% at the time of image acquisition. The classifier has also mapped water bodies, slopes, and blowouts. The accuracy assessment revealed that the overall accuracy for all the different land cover classes was 98.34%. A set of other accuracy metrics also demonstrated a high level of performance. This study presents significant findings for assessing landscape changes in a region with unique environmental features. It also provides a potential basis for future interdisciplinary research and for predicting future thermokarst landscape changes in the Lena Delta area.
Construction and Validation of an Educational Technology to Promote the Health of Postmenopausal Women with Dry Eye Syndrome
Authors: de Almeida Junior Álvaro , Bezerra Italla , Alves Sabrina , Tristan-Cheever Elisa , Salati Thiago , Abreu Luiz
Published: June 2024

The climacteric heralds a transition from reproductive to non-reproductive life in women, often accompanied by various clinical manifestations such as dry eye, impacting their quality of life. This study focuses on systematically developing and suitability an educational digital booklet to promote eye health practices and prevent problems among postmenopausal women. The methodological approach encompassed semi-structured interviews with women diagnosed with Dry Eye Syndrome (DES), then constructing the material integrating content, script, illustrations, and layout informed by the interview findings. Subsequent validation involved assessment by 24 health experts for content, appearance, and evaluation by a target audience of 22 women. The booklet attained high suitability scores, with a Global Content Validity Index (CVI) of 0.96, indicating superior suitability as judged by experts. Additionally, it achieved a Global CVI of 0.98 for validation by the target audience. In conclusion, the educational booklet emerges as a suitable and reliable resource for promoting eye heath among DES and non-syndrome women, offering significant potential for broader application in relevant populations.
Comparison Analysis of Multimodal Fusion for Dangerous Action Recognition in Railway Construction Sites
Authors: Amel Otmane , Siebert Xavier , Mahmoudi Sidi Ahmed
Published: June 2024

The growing demand for advanced tools to ensure safety in railway construction projects highlights the need for systems that can smoothly integrate and analyze multiple data modalities, such as multimodal learning algorithms. The latter, inspired by the human brain’s ability to integrate many sensory inputs, has emerged as a promising field in artificial intelligence. In light of this, there has been a rise in research on multimodal fusion approaches, which have the potential to outperform standard unimodal solutions. However, the integration of multiple data sources presents significant challenges to be addressed. This work attempts to apply multimodal learning to detect dangerous actions using RGB-D inputs. The key contributions include the evaluation of various fusion strategies and modality encoders, as well as identifying the most effective methods for capturing complex cross-modal interactions. The superior performance of the MultConcat multimodal fusion method was demonstrated, achieving an accuracy of 89.3%. Results also underscore the critical need for robust modality encoders and advanced fusion techniques to outperform unimodal solutions.
Ethereum Smart Contract Vulnerability Detection and Machine Learning-Driven Solutions: A Systematic Literature Review
Authors: Kiani Rasoul , Sheng Victor S.
Published: June 2024

In recent years, emerging trends like smart contracts (SCs) and blockchain have promised to bolster data security. However, SCs deployed on Ethereum are vulnerable to malicious attacks. Adopting machine learning methods is proving to be a satisfactory alternative to conventional vulnerability detection techniques. Nevertheless, most current machine learning techniques depend on sufficient expert knowledge and solely focus on addressing well-known vulnerabilities. This paper puts forward a systematic literature review (SLR) of existing machine learning-based frameworks to address the problem of vulnerability detection. This SLR follows the PRISMA statement, involving a detailed review of 55 papers. In this context, we classify recently published algorithms under three different machine learning perspectives. We explore state-of-the-art machine learning-driven solutions that deal with the class imbalance issue and unknown vulnerabilities. We believe that algorithmic-level approaches have the potential to provide a clear edge over data-level methods in addressing the class imbalance issue. By emphasizing the importance of the positive class and correcting the bias towards the negative class, these approaches offer a unique advantage. This unique feature can improve the efficiency of machine learning-based solutions in identifying various vulnerabilities in SCs. We argue that the detection of unknown vulnerabilities suffers from the absence of a unique definition. Moreover, current frameworks for detecting unknown vulnerabilities are structured to tackle vulnerabilities that exist objectively.
Navigating through the Storm—The Challenges of the Energy Transition in the European Union
Authors: Panait Mirela , Iacob Ștefan , Voica Cătălin , Iacovoiu Viorela , Iov Daniela , Mincă Carmen , Teodorescu Cristian
Published: June 2024

As civilization and technology have developed, there have been multiple energy transitions that have pushed various resources like coal, wood, oil, and gas to the forefront of the energy mix. Due to a variety of geopolitical initiatives, there have been progressively more restrictions on the energy sector in recent years. One of the main concerns of researchers is the widespread use of renewable energy and the replacement of fossil resources. The utilization of non-renewable energy sources results in a detrimental increase in greenhouse gas emissions. One of the most crucial strategies for lowering energy consumption and enhancing the energy system’s sustainability is to increase energy efficiency. Numerous studies note that energy transition has become necessary in the modern day. Using the analysis of the main components as an extraction method, hierarchical cluster analysis, this study focuses on the situation of the states of the European Union in their race for energy transition, taking into account the main challenges generated by geopolitical tensions and the energy poverty spectrum for the population. The conclusions of the study call for a reconfiguration of the energy mix based on renewable energy. Increasing the share of renewable energy is a goal pursued by EU countries, but energy policies must be considered in a broader context that includes the social aspects of the energy transition. So, just transition is a new concept that reshapes the actions of public authorities on the path to a low-carbon economy. Just transition is a necessity that puts the consumer at the center of attention so that the negative externalities generated by the move away from fossil fuels are minimized in terms of social impact.
Characterization of the Solution Properties of Sodium Dodecylsulphate Containing Alkaline–Surfactant–Polymer Flooding Media
Authors: Bús Csaba , Kutus Bence , Ágoston Áron , Janovák László , Sipos Pál
Published: June 2024

Alkaline–surfactant–polymer (ASP) flooding by means of which alkali additives, surfactant and polymer are inserted as the same slug is one of the most favourable worldwide focuses of Chemical Enhanced Oil Recovery (cEOR) research and field trials, due to the individual synergy of the three chemical components. To develop efficient oil recovery chemicals, it is essential to fully understand the mechanism behind ASP flooding. Nonetheless, there are hardly any studies reporting a systematic characterization of the ASP process. Thus, the present paper focuses on modelling this process in a laboratory by the use of an anionic surfactant—sodium dodecyl sulphate (SDS) in alkaline–polymer media—which is composed of a commercial water-soluble polymer (Flopaam AN125SH®, SNF Floerger, Andrézieux-Bouthéon, France) and alkali compounds (NaOH and Na2CO3). The samples were characterized using rheometry, dynamic light scattering (DLS), infrared spectroscopy (IR) and measurement of inferfacial tension (IFT) between the samples and rapeseed oil. In accordance with the experimental results, surprisingly lower IFT values were recorded between the alkaline–polymer solutions and rapeseed oil than the samples which contained SDS. Increasing polymer and sodium chloride concentration caused a decrease (from 0.591 mN/m to 0.0486 mN/m) in IFT between the surfactant containing samples and rapeseed oil. The IR measurements confirmed that the surfactant was not detected in the oil phase in the absence of NaOH and Na2CO3. The effects of SDS on the viscosity of the mixtures were also investigated, as viscosity is a considerably important parameter in processes using polymers.
Exploring the Role of the Gut Microbiota in Colorectal Cancer Development
Authors: Ionescu Vlad Alexandru , Gheorghe Gina , Georgescu Teodor Florin , Buica Vlad , Catanescu Mihai-Stefan , Cercel Iris-Andreea , Budeanu Beatrice , Budan Mihail , Bacalbasa Nicolae , Diaconu Camelia
Published: June 2024

Colorectal cancer is currently a public health concern due to its high incidence, morbidity, and mortality rates. Researchers have identified the intestinal microbiome as a crucial factor in the development of this disease. Currently, specialized literature data support the role of the microbiota in both the development of colorectal cancer and resistance to oncological therapies. Therefore, studying the composition of the gut microbiome can aid in creating risk assessment tools to identify specific populations that would benefit from tailored screening approaches. Also, manipulation of the intestinal microbiome can be useful in improving the response to chemotherapy or immunotherapy. Identifying the pathogenic mechanisms responsible for this causal link can aid in the discovery of novel treatment targets. This article will provide the latest information regarding the influence of the intestinal microbiota on the development and progression of colorectal cancer.
The Relationship between the Construction of Transportation Infrastructure and the Development of New Urbanization
Authors: Shen Jia , Ren Xiaohong , Wu Honglin , Feng Zhitao
Published: June 2024

Transport infrastructure plays a crucial role in facilitating the high-quality development of new urbanization. Based on the provincial panel data of 31 provinces in China from 2013 to 2020, this study empirically analyzed the impact and mechanism of transportation infrastructure on the high-quality development of new urbanization from multiple perspectives. The results showed that transportation infrastructure can significantly promote the development of new urbanization, and the promoting effect was significantly positive in the eastern and western regions, while it was positive but not significant in the central region. Transportation infrastructure can promote the development of new urbanization by promoting industrial agglomeration. When the population density is lower than the corresponding threshold value, the transport infrastructure can significantly promote the development of new urbanization; when the population density is higher than the corresponding threshold value, the transport infrastructure will significantly hinder the development of new urbanization. Transport infrastructure has a significant positive spatial spillover effect on the development of new urbanization, and the positive spatial spillover effect has been significant in the eastern, central and western regions.
Highly Efficient Separation of Ethanol Amines and Cyanides via Ionic Magnetic Mesoporous Nanomaterials
Authors: Zhao Yuxin , Yang Fangchao , Wu Jina , Qu Gang , Yang Yuntao , Yang Yang , Li Xiaosen
Published: June 2024

Simple and efficient sample pretreatment methods are important for analysis and detection of chemical warfare agents (CWAs) in environmental and biological samples. Despite many commercial materials or reagents that have been already applied in sample preparation, such as SPE columns, few materials with specificity have been utilized for purification or enrichment. In this study, ionic magnetic mesoporous nanomaterials such as poly(4-VB)@M-MSNs (magnetic mesoporous silicon nanoparticles modified by 4-vinyl benzene sulfonic acid) and Co2+@M-MSNs (magnetic mesoporous silicon nanoparticles modified by cobalt ions) with high absorptivity for ethanol amines (EAs, nitrogen mustard degradation products) and cyanide were successfully synthesized. The special nanomaterials were obtained by modification of magnetic mesoporous particles prepared based on co-precipitation using -SO3H and Co2+. The materials were fully characterized in terms of their composition and structure. The results indicated that poly(4-VB)@M-MSNs or Co2+@M-MSNs had an unambiguous core-shell structure with a BET of 341.7 m2·g−1 and a saturation magnetization intensity of 60.66 emu·g−1 which indicated the good thermal stability. Poly(4-VB)@M-MSNs showed selective adsorption for EAs while the Co2+@M-MSNs were for cyanide, respectively. The adsorption capacity quickly reached the adsorption equilibrium within the 90 s. The saturated adsorption amounts were MDEA = 35.83 mg·g−1, EDEA = 35.00 mg·g−1, TEA = 17.90 mg·g−1 and CN−= 31.48 mg·g−1, respectively. Meanwhile, the adsorption capacities could be maintained at 50–70% after three adsorption–desorption cycles. The adsorption isotherms were confirmed as the Langmuir equation and the Freundlich equation, respectively, and the adsorption mechanism was determined by DFT calculation. The adsorbents were applied for enrichment of targets in actual samples, which showed great potential for the verification of chemical weapons and the destruction of toxic chemicals.
Impact of Serotonin Deficiency on Circadian Dopaminergic Rhythms
Authors: Maddaloni Giacomo , Barsotti Noemi , Migliarini Sara , Giordano Martina , Nazzi Serena , Picchi Marta , Errico Francesco , Usiello Alessandro , Pasqualetti Massimo
Published: June 2024

Physiology and behavior are structured temporally to anticipate daily cycles of light and dark, ensuring fitness and survival. Neuromodulatory systems in the brain—including those involving serotonin and dopamine—exhibit daily oscillations in neural activity and help shape circadian rhythms. Disrupted neuromodulation can cause circadian abnormalities that are thought to underlie several neuropsychiatric disorders, including bipolar mania and schizophrenia, for which a mechanistic understanding is still lacking. Here, we show that genetically depleting serotonin in Tph2 knockout mice promotes manic-like behaviors and disrupts daily oscillations of the dopamine biosynthetic enzyme tyrosine hydroxylase (TH) in midbrain dopaminergic nuclei. Specifically, while TH mRNA and protein levels in the Substantia Nigra (SN) and Ventral Tegmental Area (VTA) of wild-type mice doubled between the light and dark phase, TH levels were high throughout the day in Tph2 knockout mice, suggesting a hyperdopaminergic state. Analysis of TH expression in striatal terminal fields also showed blunted rhythms. Additionally, we found low abundance and blunted rhythmicity of the neuropeptide cholecystokinin (Cck) in the VTA of knockout mice, a neuropeptide whose downregulation has been implicated in manic-like states in both rodents and humans. Altogether, our results point to a previously unappreciated serotonergic control of circadian dopamine signaling and propose serotonergic dysfunction as an upstream mechanism underlying dopaminergic deregulation and ultimately maladaptive behaviors.
Flexible Permeable-Pavement System Sustainability: A Methodology for Stormwater Management Based on PM Granulometry
Authors: Ranieri Vittorio , Coropulis Stefano , Fedele Veronica , Intini Paolo , Sansalone John Joseph
Published: June 2024

Permeable-pavement design methodologies can improve the hydrologic and therefore the environmental benefits of rural and urban roadway systems. By contrast, conventional impervious pavements perturb the hydrologic cycle, altering the relationship between the rainfall loading and runoff response. Impervious pavements create a hydraulically conductive interface for the transport of traffic-generated chemicals and particulate matter (PM), deleteriously impacting their proximate environments. Permeable-pavement systems are countermeasures to mitigate hydrologic, chemical, and PM impacts. However, permeable pavements are not always equally implementable due to costs, PM loadings, and design constraints. A potential solution to facilitate environmental benefits while meeting the traffic load capacity is the combination of two filtration systems placed at the pavement shoulders and/or pedestrian sidewalks: a bituminous-pavement open-graded friction course (BPFC) and an aggregate-filled infiltration trench. This solution is presented in this manuscript together with the methodological framework and the first results of the investigations into designing and validating such a combined system. The research was conducted at the laboratories of the Polytechnic University of Bari and the University of Florida, while an operational and full-scale physical model was constructed in Bari, Italy. The first results presented characterize the PM deposition on public roads based on granulometry (particle size distributions (PSDs) and particle number densities (PNDs)). Samples (n = 16) were collected and analyzed at eight different sites with different land uses, traffic, and pavements from different cities (Bari and Taranto, Italy). The PM analysis showed similar distributions (PSDs and PNDs), except for two samples. The gravimetric-based PSDs of the PM had granulometric distributions in the sand-size range. In contrast, the PNDs, modeled by a Power Law Model (PLM) (R2 ≥ 0.92), illustrated an exponentially increasing number of particles in the fine silt and clay-size range, representing less than 10% of the PSD mass. Moreover, the results indicate that PM sourced from permeable-pavement systems has differing impacts on the pavement service life.
Calcinosis Prevalence in Autoimmune Connective Tissue Diseases—A Retrospective Study
Authors: Róbert Lili , Németh Krisztián , Marschalkó Márta , Holló Péter , Hidvégi Bernadett
Published: June 2024

Background/Objectives: Calcinosis cutis is the deposition of insoluble calcium salts, which may cause inflammation, ulceration, pain, and restricted joint mobility. It rarely develops in damaged tissues (dystrophic subtype), most frequently in autoimmune connective tissue diseases (CTDs), but there is very limited data on the prevalence. Also, therapy remains an unsolved issue. In this study, we aimed to collect data on the prevalence of calcinosis in CTD patients to highlight that it is a considerable problem. Methods: A retrospective study was conducted in our department to assess the epidemiology of dystrophic calcinosis in CTDs between January 2003 and January 2024. Results: A total of 839 CTD patients were identified, of whom 56 had calcinosis (6.67%). The mean age of the calcinosis patients at diagnosis of underlying CTD was 41.16 ± 19.47 years. The mean time interval from the onset of calcinosis was 5.96 ± 8.62 years. Systemic sclerosis was the most common CTD complicated by calcinosis (n = 22). Conclusions: Our results are comparable to those reported previously in the literature. Although calcinosis is rare in the overall population, it is a present and unsolved problem in CTD patients. Therefore, further studies are needed on the factors involved in the development and progression of calcinosis as well as its treatment.
Mechanical Properties and Thermal Conductivity of Y-Si and Gd-Si Silicides: First-Principles Calculations
Authors: Peng Kexue , Huang Panxin , Han Guifang , Liu Huan , Zhang Weibin , Wang Weili , Zhang Jingde
Published: June 2024

The traditional Si bonding layer in environmental barrier coatings has a low melting point (1414 °C), which is a significant challenge in meeting the requirements of the next generation higher thrust-to-weight ratio aero-engines. To seek new bonding layer materials with higher melting points, the mechanical properties of Y-Si and Gd-Si silicides were calculated by the first-principles method. Subsequently, empirical formulae were employed to compute the sound velocities, Debye temperatures, and the minimum coefficients of thermal conductivity for the YSi, Y5Si4, Y5Si3, GdSi, and Gd5Si4. The results showed that Y5Si4 has the best plasticity and ductility among all these materials. In addition, Gd5Si4 has the minimum Debye temperature (267 K) and thermal conductivity (0.43 W m−1 K−1) compared with others. The theoretical calculation results indicate that some silicides in the Y-Si and Gd-Si systems possess potential application value in high-temperature bonding layers for thermal and/or environmental barrier coating.
Comparing Degradation Mechanisms, Quality, and Energy Usage for Pellet- and Filament-Based Material Extrusion for Short Carbon Fiber-Reinforced Composites with Recycled Polymer Matrices
Authors: Baddour Marah , Fiorillo Chiara , Trossaert Lynn , Verberckmoes Annabelle , Ghekiere Arthur , D’hooge Dagmar R. , Cardon Ludwig , Edeleva Mariya
Published: June 2024

Short carbon fiber (sCF)-based polymer composite parts enable one to increase in the material property range for additive manufacturing (AM) applications. However, room for technical and material improvement is still possible, bearing in mind that the commonly used fused filament fabrication (FFF) technique is prone to an extra filament-making step. Here, we compare FFF with direct pellet additive manufacturing (DPAM) for sCF-based composites, taking into account degradation reactions, print quality, and energy usage. On top of that, the matrix is based on industrial waste polymers (recycled polycarbonate blended with acrylonitrile butadiene styrene polymer and recycled propylene), additives are explored, and the printing settings are optimized, benefiting from molecular, rheological, thermal, morphological, and material property analyses. Despite this, DPAM resulted in a rougher surface finish compared to FFF and can be seen as a faster printing technique that reduces energy consumption and molecular degradation. The findings help formulate guidelines for the successful DPAM and FFF of sCF-based composite materials in view of better market appreciation.
New Perspectives on the Impact of Human Activities on Natural Resources in Oasis Areas: A Case Study of Oasis in Wuwei, China
Authors: Yang Xuedi , Hu Hailin , Li Ya , Zhang Suhan , Li Danni , Qiao Fuwei , Chen Xingpeng
Published: June 2024

An accurate analysis of the relationship between humans and the land in oasis areas is essential for the formulation of policies for differentiated oasis urban development and resource management measures. Therefore, researchers have conducted numerous studies based on the linear relationship between “people and land” in oasis areas. To address this gap, this paper takes the Wuwei oasis as an example and re-examines the impact of human activities on the sustainability of natural capital from a new research perspective (nonlinear perspective). The study selected four variables, namely planted forests, scientific R&D investment, urbanization, and population density, as the main human activities affecting the Wuwei oasis area. The NARDL model, the nonlinear Granger causality test, is employed to investigate the interactions between the variables and the oasis natural capital in both the short and long term. The results indicate the following: (1) During the study period, the consumption of natural capital in the Wuwei oasis has been increasing annually and has long exceeded the local ecological carrying capacity. (2) In the short term, planting forests is the most beneficial human activity. The most beneficial human activity is the planting of trees, while in the long term, scientific R&D investment has the most positive effect on enhancing the oasis’ resource reserves. (3) Urbanization, population density, and planted forest land all have a direct effect on the sustainable development of natural resources in the oasis. The findings of the study indicate that the application of scientific and technological innovation to promote the sustainable development of resources and the environment is a more reliable approach for oasis cities with a high degree of industrialization.
Malnutrition and the Post-Acute Sequelae of Severe Acute Respiratory Syndrome Coronavirus 2 Infection: A Multi-Institutional Population-Based Propensity Score-Matched Analysis
Authors: Lee Cheng-Ya , Liang Yung-Chun , Hsu Wan-Hsuan , Tsai Ya-Wen , Liu Ting-Hui , Huang Po-Yu , Chuang Min-Hsiang , Hung Kuo-Chuan , Lee Mei-Chuan , Yu Tsung , Lai Chih-Cheng , Weng Tzu-Chieh , Wu Jheng-Yan
Published: June 2024

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to a global health crisis, exacerbating issues like malnutrition due to increased metabolic demands and reduced intake during illness. Malnutrition, a significant risk factor, is linked to worse outcomes in patients with COVID-19, such as increased mortality and extended hospital stays. This retrospective cohort study investigated the relationship between malnutrition and clinical outcomes within 90–180 days using data obtained from the TriNetX database. Patients aged >18 years diagnosed with COVID-19 between 1 January 2022, and 31 March 2024 were enrolled in the study. The propensity score-matching (PSM) method was used to match patients with malnutrition (malnutrition group) and those without malnutrition (control group). The primary composite outcome was the cumulative hazard ratio (HR) for post-COVID-19 condition, all-cause hospitalization, and all-cause mortality between 90 days and 180 days after COVID-19 diagnosis. The secondary outcomes were the individual components of the primary outcomes. Two cohorts, each consisting of 15,004 patients with balanced baseline characteristics, were identified using PSM. During the 90–180-day follow-up period, the malnutrition group exhibited a higher incidence of all-cause hospitalization, mortality, or post-COVID-19 condition (HR = 2.315, 95% confidence interval: 2.170–2.471, p < 0.0001). Compared with patients with COVID-19 without malnutrition, those with malnutrition may be associated with a higher risk of adverse clinical outcomes.
Multi-Objective Optimization of Adhesive Joint Strength and Elastic Modulus of Adhesive Epoxy with Active Learning
Authors: Kraisornkachit Paripat , Naito Masanobu , Kang Chao , Sato Chiaki
Published: June 2024

Studying multiple properties of a material concurrently is essential for obtaining a comprehensive understanding of its behavior and performance. However, this approach presents certain challenges. For instance, simultaneous examination of various properties often necessitates extensive experimental resources, thereby increasing the overall cost and time required for research. Furthermore, the pursuit of desirable properties for one application may conflict with those needed for another, leading to trade-off scenarios. In this study, we focused on investigating adhesive joint strength and elastic modulus, both crucial properties directly impacting adhesive behavior. To determine elastic modulus, we employed a non-destructive indentation method for converting hardness measurements. Additionally, we introduced a specimen apparatus preparation method to ensure the fabrication of smooth surfaces and homogeneous polymeric specimens, free from voids and bubbles. Our experiments utilized a commercially available bisphenol A-based epoxy resin in combination with a Poly(propylene glycol) curing agent. We generated an initial dataset comprising experimental results from 32 conditions, which served as input for training a machine learning model. Subsequently, we used this model to predict outcomes for a total of 256 conditions. To address the high deviation in prediction results, we implemented active learning approaches, achieving a 50% reduction in deviation while maintaining model accuracy. Through our analysis, we observed a trade-off boundary (Pareto frontier line) between adhesive joint strength and elastic modulus. Leveraging Bayesian optimization, we successfully identified experimental conditions that surpassed this boundary, yielding an adhesive joint strength of 25.2 MPa and an elastic modulus of 182.5 MPa.
Modulating Electron Density of Boron–Oxygen Groups in Borate via Metal Electronegativity for Propane Oxidative Dehydrogenation
Authors: Li Panpan , Yao Yongbin , Chai Shanshan , Li Zhijian , Xue Fan , Wang Xi
Published: June 2024

The robust electronegativity of the [BO3]3− structure enables the extraction of electrons from adjacent metals, offering a strategy for modulating oxygen activation in propane oxidative dehydrogenation. Metals (Ni 1.91, Al 1.5, and Ca 1.0) with varying electronegativities were employed to engineer borate catalysts. Metals in borate lacked intrinsic catalytic activity for propane conversion; instead, they modulated [BO3]3− group reactivity through adjustments in electron density. Moderate metal electronegativity favored propane oxidative dehydrogenation to propylene, whereas excessively low electronegativity led to propane overoxidation to carbon dioxide. Aluminum, with moderate electronegativity, demonstrated optimal performance. Catalyst AlBOx-1000 achieved a propane conversion of 47.5%, with the highest propylene yield of 30.89% at 550 °C, and a total olefin yield of 51.51% with a 58.92% propane conversion at 575 °C. Furthermore, the stable borate structure prevents boron element loss in harsh conditions and holds promise for industrial-scale catalysis.
Recent Advances in the Use of Controlled Nanocatalysts in Methane Conversion Reactions
Authors: e Silva Felipe Anchieta , Rodrigues Thenner Silva
Published: June 2024

This study investigates the utilization of controlled nanocatalysts in methane conversion reactions, addressing the pressing need for the efficient utilization of methane as a feedstock for valuable chemicals and clean energy. The methods employed include a comprehensive review of recent advancements in nanocatalyst synthesis, characterization, and application, as well as the critical analysis of underlying mechanisms and controversies in methane activation and transformation. The main findings reveal significant progress in the design and synthesis of controlled nanocatalysts, enabling enhanced activity, selectivity, and stability in methane conversion reactions. Moreover, the study highlights the importance of resolving controversies surrounding metal–support interactions for rational catalyst design. Overall, the study underscores the pivotal role of nanotechnology in shaping the future of methane utilization and sustainable energy production, providing valuable insights for guiding future research directions and technological developments in this field.
Neosporosis in Naturally Infected Sheep Herds, a Prospective Cohort Study over Three Years
Authors: Tirosh-Levy Sharon , Asher Omri , Peri Markovich Michal , Yasur Landau Daniel , Blinder Elena , Mazuz Monica L.
Published: June 2024

Background: Neospora caninum is a protozoan parasite and a main cause of abortions in cattle worldwide. However, its role in abortions and decreased fertility in sheep is not completely understood, especially due to the complex, multifactorial etiology of abortions. This study aimed to perform a longitudinal field study to investigate the epidemiology of neosporosis and its effect on fertility in endemic sheep herds. Methods: Serological (IFAT) and clinical (outcome of pregnancy) data from 153 ewe-lambs was collected in four intensive management farms in Israel during three consecutive pregnancies. Results: The seroprevalence in ewe-lambs at different farms varied between 24% and 93%. The overall seroprevalence increased from 50% in ewe-lambs to 96.6% at the end of the third pregnancy. Horizontal infection was observed in all farms, with seroconversion in 59% of seronegative sheep. Abortion rates were lower (p = 0.004) in seropositive ewes in the first pregnancy and not significantly higher in seropositive sheep in consecutive pregnancies. Seropositivity or seroconversion were not associated with abortions or repeated abortions; however, many aborting ewes were removed from the flock. Conclusions: No direct short- or long-term association was found between Neopsora infection and abortions. The variations between flocks and pregnancies suggest a more complex etiology.
Ecology of Elodea canadensis Michx. and Elodea nuttallii (Planch.) H. St. John—Insights from National Water Monitoring in Croatia
Authors: Bučar Marija , Rimac Anja , Šegota Vedran , Vuković Nina , Alegro Antun
Published: June 2024

Elodea canadensis Michx. (common waterweed) and Elodea nuttallii (Planch.) H. St. John (Nuttall’s waterweed), two invasive aquatic plants from North America, have coexisted in European water bodies since the early 20th century. New localities for both species in Croatia continued to be discovered during a study that ran from 2016 to 2023 as a part of the annual implementation of Water Framework Directive monitoring that covered the entire territory of Croatia (786 sampling points in total). Based on these data, the distribution and ecology of both species were analysed. Elodea canadensis was found at 30 sampling points, mostly in rivers, and E. nuttallii at 15 sampling points, mostly in artificial canals. Nearly three-quarters (72.5%) of all elodea sampling points were in the Pannonian Ecoregion. Elodea canadensis was discovered for the first time in the Continental—Dinaric and Mediterranean—Dinaric Subecoregions. To study the ecology of the species, for each sampling point, vegetation relevés were performed and monthly measurements of physico-chemical parameters were collected. The most common accompanying species for both elodeas are presented, and the difference in species assemblages between the sites with E. canadensis and E. nuttallii was confirmed with the ANOSIM test. Furthermore, Indicator Species Analysis revealed eight species characteristic of E. canadensis sites and eleven species characteristic of E. nuttallii sites. Fitting multivariate models (CCA and NPMR) to species abundance revealed the ecological reaction of E. canadensis and E. nuttallii to environmental descriptors. The most strongly contributing environmental descriptors that influence the distribution of both Elodea species are biochemical oxygen demand, electrical conductivity and total phosphorus. In Croatia, the replacement of E. canadensis with E. nuttallii was observed in several water bodies with high nutrient loads.
Neuroprosthetics of the Hand: Current Hot Research Topics, Research Trends and Challenges, and Recent Innovations
Authors: Otte Andreas
Published: June 2024

If you want to buy a new car today, you can expect a flood of digital features and assistance systems that initially make the analog human heart beat faster [...]
Biocompatible Electrical and Optical Interfaces for Implantable Sensors and Devices
Authors: Wan Yuxin , Wang Caiyi , Zhang Bingao , Liu Yixuan , Yang Hailong , Liu Fengyu , Xu Jingjing , Xu Shengyong
Published: June 2024

Implantable bioelectronics hold tremendous potential in the field of healthcare, yet the performance of these systems heavily relies on the interfaces between artificial machines and living tissues. In this paper, we discuss the recent developments of tethered interfaces, as well as those of non-tethered interfaces. Among them, systems that study neural activity receive significant attention due to their innovative developments and high relevance in contemporary research, but other functional types of interface systems are also explored to provide a comprehensive overview of the field. We also analyze the key considerations, including perforation site selection, fixing strategies, long-term retention, and wireless communication, highlighting the challenges and opportunities with stable, effective, and biocompatible interfaces. Furthermore, we propose a primitive model of biocompatible electrical and optical interfaces for implantable systems, which simultaneously possesses biocompatibility, stability, and convenience. Finally, we point out the future directions of interfacing strategies.
Study on an Automatic Classification Method for Determining the Malignancy Grade of Glioma Pathological Sections Based on Hyperspectral Multi-Scale Spatial–Spectral Fusion Features
Authors: Chen Jiaqi , Yang Jin , Wang Jinyu , Zhao Zitong , Wang Mingjia , Sun Ci , Song Nan , Feng Shulong
Published: June 2024

This study describes a novel method for grading pathological sections of gliomas. Our own integrated hyperspectral imaging system was employed to characterize 270 bands of cancerous tissue samples from microarray slides of gliomas. These samples were then classified according to the guidelines developed by the World Health Organization, which define the subtypes and grades of diffuse gliomas. We explored a hyperspectral feature extraction model called SMLMER-ResNet using microscopic hyperspectral images of brain gliomas of different malignancy grades. The model combines the channel attention mechanism and multi-scale image features to automatically learn the pathological organization of gliomas and obtain hierarchical feature representations, effectively removing the interference of redundant information. It also completes multi-modal, multi-scale spatial–spectral feature extraction to improve the automatic classification of glioma subtypes. The proposed classification method demonstrated high average classification accuracy (>97.3%) and a Kappa coefficient (0.954), indicating its effectiveness in improving the automatic classification of hyperspectral gliomas. The method is readily applicable in a wide range of clinical settings, offering valuable assistance in alleviating the workload of clinical pathologists. Furthermore, the study contributes to the development of more personalized and refined treatment plans, as well as subsequent follow-up and treatment adjustment, by providing physicians with insights into the underlying pathological organization of gliomas.
Quantitative Detection of Vertical Track Irregularities under Non-Stationary Conditions with Variable Vehicle Speed
Authors: Wang Qiushi , Zhao Hui , Gong Dao , Zhou Jinsong , Xiao Zhongmin
Published: June 2024

Track irregularities directly affect the quality and safety of railway vehicle operations. Quantitative detection and real-time monitoring of track irregularities are of great importance. However, due to the frequent variable vehicle speed, vehicle operation is a typical non-stationary process. The traditional signal analysis methods are unsuitable for non-stationary processes, making the quantitative detection of the wavelength and amplitude of track irregularities difficult. To solve the above problems, this paper proposes a quantitative detection method of track irregularities under non-stationary conditions with variable vehicle speed by order tracking analysis for the first time. Firstly, a simplified wheel–rail dynamic model is established to derive the quantitative relationship between the axle-box vertical vibration and the track vertical irregularities. Secondly, the Simpson double integration method is proposed to calculate the axle-box vertical displacement based on the axle-box vertical acceleration, and the process error is optimized. Thirdly, based on the order tracking analysis theory, the angular domain resampling is performed on the axle-box vertical displacement time-domain signal in combination with the wheel rotation speed signals, and the quantitative detection of the track irregularities is achieved. Finally, the proposed method is validated based on simulation and field test analysis cases. We provide theoretical support and method reference for the quantitative detection method of track irregularities.