bluuebunny/crossref_metadata_2025 huggingface dataset:
MSLUnet: A Medical Image Segmentation Network Incorporating Multi-Scale Semantics and Large Kernel Convolution
Authors: Zhu Shijuan , Cheng Lingfei
Published: August 2024

In recent years, various deep-learning methodologies have been developed for processing medical images, with Unet and its derivatives proving particularly effective in medical image segmentation. Our primary objective is to enhance the accuracy of these networks while also reducing the number of parameters and computational demands to facilitate deployment on mobile medical devices. To this end, we introduce a novel medical image segmentation network, MSLUnet, which aims to minimize parameter count and computational load without compromising segmentation effectiveness. The network features a U-shaped architecture. In the encoder module, we utilize multiple small convolutional kernels for successive convolutions rather than large ones, allowing for capturing multi-scale feature information at granular levels through varied receptive field scales. In the decoder module, an inverse bottleneck structure with depth-separable convolution employing large kernels is incorporated. This design effectively extracts spatial dimensional information and ensures a comprehensive integration of both shallow and deep features. Additionally, a lightweight three-branch attention mechanism within the skip connections enhances information transfer by capturing global contextual data across spatial and channel dimensions. Experimental evaluations conducted on several publicly available medical image datasets indicate that MSLUnet is more competitive than existing models in terms of efficiency and effectiveness.
Enkephalins and Pain Modulation: Mechanisms of Action and Therapeutic Perspectives
Authors: García-Domínguez Mario
Published: August 2024

Enkephalins, a subclass of endogenous opioid peptides, play a pivotal role in pain modulation. Enkephalins primarily exert their effects through opioid receptors located widely throughout both the central and peripheral nervous systems. This review will explore the mechanisms by which enkephalins produce analgesia, emotional regulation, neuroprotection, and other physiological effects. Furthermore, this review will analyze the involvement of enkephalins in the modulation of different pathologies characterized by severe pain. Understanding the complex role of enkephalins in pain processing provides valuable insight into potential therapeutic strategies for managing pain disorders.
Optimizing the Extraction Process of Bioactive Compounds for Sustainable Utilization of Vitis vinifera and Citrus sinensis Byproducts
Authors: Aranda-Ledesma Nestor Everardo , Aguilar-Quintero Israel , Robledo-Jiménez Claudia Lizeth , Aguilar-Zárate Pedro , Rojas Romeo , Martínez-Ávila Guillermo Cristian Guadalupe
Published: August 2024

The objective of this work was to optimize the ultrasound-assisted extraction process of bioactive compounds from byproducts of V. vinifera and C. sinensis using the Taguchi methodology. Moreover, the flavonoid content and the subsequent evaluation of the antioxidant potential through three different assays (ABTS∙+ radical inhibition, ferric-reducing power (FRAP), and OH• radical inhibition) were determined. Furthermore, the potential of these metabolites to inhibit the α-amylase enzyme and their protective effect to inhibit the hemolysis due to oxidative processes was assessed. In addition, functional group analysis was performed using Fourier transform infrared spectroscopy. The Taguchi L9 statistical model enabled the increase in bioactive compound yields by evaluating factors such as particle size, temperature, time, and solvent concentration. The samples were found to contain flavonoid-type compounds, which translated into their ability to inhibit free radicals (ABTS∙+ and OH•) and act as reducing agents (FRAP). They exhibited inhibitory effects on the α-amylase enzyme involved in the assimilation of starch and its derivatives, along with providing over 50% protection to erythrocytes in the presence of free radicals generated by AAPH. Furthermore, FTIR analysis facilitated the identification of characteristic functional groups of phenolic compounds (O–H, C–H, C=C, C–C, C=O). These findings suggest that the analyzed byproducts can effectively serve as sources of bioactive compounds with potential applications in the formulation of functional foods and medicines. However, it is necessary to conduct compound identification and toxicity analysis to ensure the safety of these bioactive compounds.
Examining an Evolving Biologically Inspired Design Professional Learning Environment through Conjecture Mapping and Design-Based Research
Authors: Rehmat Abeera , Towner Alexandra , Alemdar Meltem , Helms Michael , Rosen Jeffrey , Moore Roxanne , Weissburg Marc
Published: August 2024

Biologically inspired design (BID) in engineering is a convergent, systematic approach that uses analogies from biological organisms to develop solutions for human engineering and design problems. Based on outcomes from prior studies of integrating BID in higher education, incorporating BID into pre-college education is a logical evolution. For effective BID instruction of these convergent concepts in pre-college education, teachers need to be well-equipped with biological, engineering, and pedagogical knowledge, both in general and those unique to the convergent, still evolving discipline. In this paper, we investigate the Professional Learning (professional learning) environment designed to foster engineering teachers’ understanding of BID integration in engineering and to determine to what extent the evolving professional learning environment fostered engineering teachers’ conceptual knowledge of BID across the three-year project. This design study applies conjecture mapping with design-based research (DBR) to examine a professional learning environment that changed over three summers and its impact on teachers’ conceptual understanding of BID integration in engineering. The analysis indicates that a combination of experiential and informal learning experiences along with engagement in a formal design challenge promoted teacher enthusiasm and a conceptual understanding of BID across the three years. Professional learning fostered teachers’ understanding of BID integration in engineering and enabled them to integrate BID into their engineering teaching practice.
Machine Learning Assists in the Design and Application of Microneedles
Authors: He Wenqing , Kong Suixiu , Lin Rumin , Xie Yuanting , Zheng Shanshan , Yin Ziyu , Huang Xin , Su Lei , Zhang Xueji
Published: August 2024

Microneedles (MNs), characterized by their micron-sized sharp tips, can painlessly penetrate the skin and have shown significant potential in disease treatment and biosensing. With the development of artificial intelligence (AI), the design and application of MNs have experienced substantial innovation aided by machine learning (ML). This review begins with a brief introduction to the concept of ML and its current stage of development. Subsequently, the design principles and fabrication methods of MNs are explored, demonstrating the critical role of ML in optimizing their design and preparation. Integration between ML and the applications of MNs in therapy and sensing were further discussed. Finally, we outline the challenges and prospects of machine learning-assisted MN technology, aiming to advance its practical application and development in the field of smart diagnosis and treatment.
Effects of Different Fiber Dosages of PVA and Glass Fibers on the Interfacial Properties of Lightweight Concrete with Engineered Cementitious Composite
Authors: Al-Baghdadi Haider M. , Kadhum Mohammed M.
Published: August 2024

The bond strength at the interface zone between two concrete sections plays a critical role in enhancing long-term durability, ensuring that both materials perform homogenously. Ensuring compatibility at the interfaces between repair and concrete materials is one of the most challenging aspects of constructing composite systems. Despite various studies, a comprehensive understanding of the engineered cementitious composite (ECC) bonding mechanism at the repair interface is still limited. The objective of this research is to identify the interfacial properties between lightweight concrete (LWC) and engineered cementitious composite (ECC) with varying fiber dosages of polyvinyl alcohol (PVA) and glass fibers under different surface roughness conditions. The study tested LWC-ECC specimens in direct shear using slant shear and bi-surface shear tests, recording the maximum shear stress at failure. Two grades of LWC—normal-strength lightweight concrete (NSLW) and high-strength lightweight concrete (HSLW)—were used as substrates, while the ECC overlays contained varying fiber dosages: 2% PVA, 1.5% PVA with 0.5% glass, 1.0% PVA with 1.0% glass, and 0.5% PVA with 1.5% glass. The surface conditions considered included grooved and as-cast substrates. The results indicated that the highest bond strength was achieved by specimens with 1.5% PVA and 0.5% glass fiber, with a maximum shear strength of 24.05 MPa for grooved HSLW substrates. Interface roughness had minimal impact on shear strength for NSLW substrates but significantly affected HSLW substrates, with bond strengths varying from 13.81 MPa to 24.05 MPa for grooved surfaces. This study demonstrates the critical role of fiber dosage and surface roughness in enhancing the bond performance of composite materials.
Correlation between Biomarkers and Severity of Clinical Categories in COVID-19 Patients: A Hospital-Based Study in Arunachal Pradesh, India
Authors: Nobin Hage , Paley Tamar , Anu Rubu , Yami Hibu , Tago Nani , Saikia Naba Jyoti , Nyodu Rajni
Published: August 2024

Background: The COVID-19 pandemic has brought about widespread effects on communities on a global scale, with impacts varying among different demographics. This study focuses on a unique cohort of COVID-19-positive patients from Arunachal Pradesh, a region in northeast India with a substantial indigenous population. This study aims to delve into the features and consequences of COVID-19 in this indigenous population within this distinct demographic region, with a special focus on assessing the effects on tribal communities. Out of a total of 1627 COVID-19 positive cases, 1392 belonged to various tribes of Arunachal Pradesh, categorized as the indigenous population of the region. Our research primarily focuses on examining the biochemical and inflammatory indicators that forecast the clinical results of COVID-19 patients, specifically within both indigenous and non-indigenous groups. Methods: Biochemical markers, including hematological parameters, liver and kidney function biomarkers, D-DIMER, and inflammatory markers, were assessed along with immune-inflammatory ratios: neutrophil-to-lymphocyte ratio (NLR) and lymphocyte-to-monocyte ratio (LMR). Differences in biomarker levels and ratios across disease severities were analyzed, and significant data were observed using descriptive statistics. Results: Elevated levels of Ferritin (CRP, IL-6, D-DIMER, Creatinine, Urea, AST/SGOT, and ALT/SGPT) were associated with increasing disease severity in COVID-19 cases, reflecting increased inflammation, multi-organ dysfunction, and coagulopathy in the severe COVID-19 category amongst the indigenous population. The data showed an aligned report with the non-indigenous population of India when compared with various other studies (using a Spearman rank correlation test). Similarly, an increase in the neutrophil-to-lymphocyte ratio (NLR) and a declining shift in the lymphocyte-to-monocyte ratio (LMR) indicated deregulated immune responses and systemic inflammation in the severe category of COVID-19. Conclusion: This population-based study from northeast India offers important perspectives into the pathophysiology of COVID-19 and its link with disease severity among indigenous and non-indigenous populations.
Welcome to Cryo: A New Open-Access Journal
Authors: Li Yanzhong
Published: August 2024

Cryogenics is an important branch of physics, representing a foundational field in modern science [...]
Frequency-Modulated Continuous-Wave Radar Perspectives on Unmanned Aerial Vehicle Detection and Classification: A Primer for Researchers with Comprehensive Machine Learning Review and Emphasis on Full-Wave Electromagnetic Computer-Aided Design Tools
Authors: Sayed Ahmed N. , Ramahi Omar M. , Shaker George
Published: August 2024

Unmanned Aerial Vehicles (UAVs) represent a rapidly increasing technology with profound implications for various domains, including surveillance, security, and commercial applications. Among the number of detection and classification methodologies, radar technology stands as a cornerstone due to its versatility and reliability. This paper presents a comprehensive primer written specifically for researchers starting on investigations into UAV detection and classification, with a distinct emphasis on the integration of full-wave electromagnetic computer-aided design (EM CAD) tools. Commencing with an elucidation of radar’s pivotal role within the UAV detection paradigm, this primer systematically navigates through fundamental Frequency-Modulated Continuous-Wave (FMCW) radar principles, elucidating their intricate interplay with UAV characteristics and signatures. Methodologies pertaining to signal processing, detection, and tracking are examined, with particular emphasis placed on the pivotal role of full-wave EM CAD tools in system design and optimization. Through an exposition of relevant case studies and applications, this paper underscores successful implementations of radar-based UAV detection and classification systems while elucidating encountered challenges and insights obtained. Anticipating future trajectories, the paper contemplates emerging trends and potential research directions, accentuating the indispensable nature of full-wave EM CAD tools in propelling radar techniques forward. In essence, this primer serves as an indispensable roadmap, empowering researchers to navigate the complex terrain of radar-based UAV detection and classification, thereby fostering advancements in aerial surveillance and security systems.
The Relationship between Big Five Personality Traits and Depression in the German-Speaking D-A-CH Region Including an Investigation of Potential Moderators and Mediators
Authors: Strohmaier Susanne , Pillai Manuel , Weitzer Jakob , Han Emilie , Zenk Lukas , Birmann Brenda M. , Bertau Martin , Caniglia Guido , Laubichler Manfred D. , Steiner Gerald , Schernhammer Eva S.
Published: August 2024

Considerable evidence links the “Big Five” personality traits (neuroticism, extroversion, conscientiousness, agreeableness, and openness) with depression. However, potential mediating and moderating factors are less well understood. We utilized data from a cross-sectional survey of 3065 German-speaking adults from the D-A-CH region to estimate multivariable-adjusted odds ratios and 95% confidence intervalsbetween personality traits and lifetime prevalence of depression (overall and stratified by sex and age). We further explored proportions mediated by psychosocial factors optimism, empathy, perspective-taking, work–life balance, and interpersonal trust. High levels of neuroticism were associated with more than two-fold higher odds of depression, whereas higher levels of conscientiousness were associated with approximately 30% lower odds of depression. The association with neuroticism persisted in all investigated subgroups; apparently, stronger associations for females and participants aged ≥60 years did not correspond to statistically significant interactions. Overall and across all strata, the association of neuroticism with depression appeared to be mediated in part by the considered psychosocial factors; optimism explained the largest proportion of the association. Our results provide empirical evidence for the dynamic predisposition model. Further investigations of these relationships are warranted in longitudinal data with more precise outcome assessments.
A Prospective Study of the Family Quality of Life, Illness Perceptions, and Coping in Mothers of Children Newly Diagnosed with Autism Spectrum Disorder and Communication Difficulties
Authors: Papadopoulos Angelos , Tsapara Angeliki , Gryparis Alexandros , Tafiadis Dionysios , Trimmis Nikolaos , Plotas Panagiotis , Skapinakis Petros , Tzoufi Meropi , Siafaka Vassiliki
Published: August 2024

(1) Background: This study assesses the impact of mothers’ illness perceptions about autism spectrum disorder and their coping strategies on the family’s quality of life during the initial period following diagnosis and one year afterward. (2) Method: The sample consisted of 53 mothers of children newly diagnosed with autism spectrum disorder and having communication difficulties who completed the following: the Beach Center Family Quality of Life Scale, the Brief Illness Perception Questionnaire, and the Brief-COPE. (3) Results: The findings revealed a moderate family quality of life in the initial assessment and a lack of a statistically significant change one year later. Notably, statistically significant changes were observed in coping strategies, as in the second assessment, and the score in denial and self-blame decreased. Pearson and Eta analyses indicated several correlations between socio-demographic characteristics, illness perceptions, coping strategies, and family quality of life. Multiple regression analysis showed that positive reframing was positively associated with total family quality of life in the initial period following diagnosis and one year afterward, while self-blame was associated with poorer quality of life in the time after diagnosis. Furthermore, the belief about the controllability of the disorder was correlated with better family quality of life one year after the diagnosis. (4) Conclusions: Illness perceptions and coping can be considered as predictors of family quality of life outcomes one year after the diagnosis of autism spectrum disorder. The focus of interventions, apart from controlling the disorder’s symptoms, should aim to strengthen specific strategies and weaken others.
Social Media Use and Consumption of Prescription-Free Medications for Anxiety, Sleep, and Pain among Norwegian University Students
Authors: Saqib Wajiha , Gazerani Parisa
Published: August 2024

A relationship has been recognized between social media use and health issues. However, no studies have explored the potential link between social media use and consumption of over-the-counter (OTC) medications. We examined social media use, self-reported anxiety, depression, sleep problems, pain, and OTC medications use among Norwegian university students. The goal was to gain insights that would guide preventive health strategies for this target group. A quantitative, cross-sectional study was conducted with an online questionnaire distributed to university student Facebook groups in Norway. A total of 132 completed surveys were analyzed. Among the respondents, 28% experienced anxiety, 35% depression, 64% sleep problems, 71% headaches, and 78% musculoskeletal pain. Moreover, 56% reported using OTC analgesics or sleep aids, mostly purchased from community pharmacies. No statistically significant correlation was found between social media use and headache, musculoskeletal pain, sleep disturbances, or consumption of OTC medications among university students in Norway. The findings, however, demonstrated a positive trend, highlighting the need for further research with larger, more diverse samples, and potentially employing a qualitative or longitudinal design. We propose increased awareness of the potential negative effects of social media among university students, the inclusion of social media and health topics in study curricula, and the more proactive engagement of community pharmacists with young clients concerning the consumption of OTC medications.
Biomass-Fueled Organic Rankine Cycles: State of the Art and Future Trends
Authors: Heidarnejad Parisa , Genceli Hadi , Hashemian Nasim , Asker Mustafa , Al-Rawi Mohammad
Published: August 2024

Biomass-fueled organic Rankine cycles (ORCs) are widely utilized technologies for power production because of their simplicity, low cost, and relatively high efficiencies. Furthermore, raw material availability and topographical independency make these systems preferable to other renewable-fueled power generation systems. A deep and comprehensive understanding of biomass-fueled organic Rankine cycles will provide researchers with a solid foundation to prioritize their investigations and assist future developments in this field. In this regard, feedstocks and their properties, biomass conversion mechanisms, and biomass-fueled power generation systems are discussed in this study. Power generation technologies based on coal and waste as feedstock have been widely investigated in the literature due to higher energy content and technological maturity. Additionally, depending on the type of biomass available, the scale of the power plant, and economic and environmental considerations, the most common technologies utilized for biomass conversion are combustion, gasification, and anaerobic digestion. Finally, the authors investigate various aspects of biomass-fueled organic Rankine cycles, including working fluids, analysis methods, and environmental issues. Since maximizing product yield is key in biomass-based power generation systems, technical assessment of these systems has been a primary focus of many studies. Further research is required on integrated environmental and socio-economic approaches, along with Machine Learning algorithms. Future advancements focusing on integration of feedstock with other renewable energy sources, efficient working fluids like nanofluids, and high-tech heat exchangers will drive the development of biomass-fueled ORC systems.
Sensorless Control for a Permanent Magnet Synchronous Motor Based on a Sliding Mode Observer
Authors: Liang Jinfa , Wu Jun , Wang Yong , Zhong Zhihong , Bai Xinxin
Published: August 2024

This paper proposes a sensorless control strategy for permanent magnet synchronous motors (PMSMs) based on a sliding mode observer (SMO), and high-speed PMSM sensorless velocity control is realized. To solve the serious chattering and phase lag problems of conventional SMOs, the continuous function is used as the control function, and the low-pass filter is improved into a back electromotive force (EMF) observer with an adaptive structure. In addition, the phase-locked loop is combined to perform the SMO-based sensorless control. The simulations and experiments prove the effectiveness of the proposed strategy.
Cultivar Differences in the Response of the Fruiting Characteristics of Camellia oleifera Abel to the Concentration of Potassium Dihydrogen Phosphate during Foliar Spraying
Authors: Liu Huiyun , Wang Jiawei , Zeng Huijie , Ren Zhihua , Cheng Li , Zhang Yunyu , Cheng Qinhua , Shi Xueyun , Zhou Zengliang , Hu Dongnan
Published: August 2024

One of the main reasons for the low yield of Camellia oleifera Abel is the large number of flowers and fruits that fall off before ripening. The aim of this study was to investigate the effect of foliar spraying of potassium dihydrogen phosphate (KH2PO4) on the fruiting characteristics of C. oleifera, and to provide technical support for its flower and fruit preservation and yield increasing. Three C. oleifera cultivars, ‘Changlin 18’, ‘Changlin 166’, and ‘Changlin 40’, were used as experimental materials to conduct foliar spraying experiments with different water concentrations of KH2PO4. The effects of KH2PO4 on the fruit retention rate, fruit properties, and seed oil quality of each cultivar were measured and analyzed. The application of the optimal concentration of KH2PO4 significantly enhanced various fruit quality metrics of three C. oleifera cultivars. Specifically, the total fruit retention rate was increased by 30.02~87.53%, the transverse diameter by 7.36~21.21%, and the longitudinal diameter by 18.56%, and the fruit weight of ‘Changlin 40’ could increase by 83.63%. It also increased dry seed yield by 27.87~80.81%, dry kernel rate by 10.29~30.12%, dry seed oil content by 28.00~29.77%, total unsaturated fatty acids (TUFAs) by 0.63~5.3%, monounsaturated fatty acids (MUFAs) by 0.30~5.37%, and squalene by 0.09~0.14% during the maturing stage. However, camellia cultivars had the different responses to KH2PO4 concentrations. To promote the fruiting of C. oleifera, improve the economic traits of fruits, and enhance the quality of tea oil, the recommended concentrations of KH2PO4 solution are 4.50 g·L−1, 1.50 g·L−1, and 1.50 g·L−1, for mist spraying on the trees of ‘Changlin 18’, ‘Changlin 166’, and ‘Changlin 40’, respectively. For mixed cultivars of C. oleifera which planted randomly, the recommended concentration range of KH2PO4 solution for spraying is 1.50 to 4.50 g·L−1.
The Secreted Aminopeptidase of Pseudomonas aeruginosa (PaAP)
Authors: Kessler Efrat
Published: August 2024

Pseudomonas aeruginosa is an opportunistic pathogen that causes severe infections in compromised hosts. P. aeruginosa infections are difficult to treat because of the inherent ability of the bacteria to develop antibiotic resistance, secrete a variety of virulence factors, and form biofilms. The secreted aminopeptidase (PaAP) is an emerging virulence factor, key in providing essential low molecular weight nutrients and a cardinal modulator of biofilm development. PaAP is therefore a new potential target for therapy of P. aeruginosa infections. The present review summarizes the current knowledge of PaAP, with special emphasis on its biochemical and enzymatic properties, activation mechanism, biological roles, regulation, and structure. Recently developed specific inhibitors and their potential as adjuncts in the treatment of P. aeruginosa infections are also described.
The Impact of Math-Gender Stereotypes on Students’ Academic Performance: Evidence from China
Authors: Luo Yilei , Chen Xinqi
Published: August 2024

This study investigates the impact of math-gender stereotypes on students’ academic performance using data from the China Education Panel Survey (CEPS), which surveyed nationally representative middle schools in China. Our sample comprises over 2000 seventh-grade students, with an average age of 13 and a standard deviation of 0.711. Among these students, 52.4% are male, and 47.6% are female. Employing a fixed effects model and instrumental variable, our findings are as follows. First, over half of the male students believe that boys are better at math than girls, and they also perceive that their parents and society hold the same belief. In contrast, fewer than half of the female students hold this belief or perception. Intriguingly, among these students, female math performance surpasses that of males. Second, stereotypes hinder female math performance, especially among low-achieving ones, while benefiting high-achieving male students. Finally, perceptions of societal stereotypes have the greatest effect on math performance, followed by self-stereotypes and perceptions of parental stereotypes. Understanding the implications of these findings highlights the importance of addressing math-gender stereotypes to promote equal participation and success for both genders in STEM fields.
A Sensitivity Analysis of Ship–Bridge Spacing under the Coupling Effect of Turbulence and Ship Motion
Authors: Ye Yasi , Liu Xiaoping , Ye Yukang , Li Anbin , Zhang Jiaqiang , Ren Qijiang
Published: August 2024

The hydrodynamics of the flow around piers affects the motion of ships navigating near these structures, while the motion of the ships, in turn, affects the distribution of the flow field near the piers. This study investigates the forces exerted on a ship in various ship–pier transverse distances using commercial computational fluid dynamics (CFD) software, Fluent 13.0, based on the RNG k-ε model, complemented by experiments with a physical model. The interaction between the ship’s motion and the flow field near the piers was considered. The results indicate that during the encounter between the ship and the pier, the boundary of the approaching ship affects the flow field near the pier, thereby affecting the generation and detachment of vortices behind the pier. The yaw moment of the ship demonstrates a marked “positive peak–negative peak–positive peak” pattern. Moreover, as the ship–pier transverse distance increases, the impact of the pier on the ship’s motion decreases, and it becomes negligible when the distance reaches 0.9 times the diameter of the pier (D), suggesting that the pier has a minimal impact on ship navigation if the ship–pier transverse distance exceeds this threshold.
Rapid Screening of Methane-Reducing Compounds for Deployment via Water with a Commercial Livestock Supplement Using In Vitro and FTIR-ATR Analyses
Authors: Batley Ryan J. , Romanzini Elieder Prates , Johnson Joel B. , de Souza William Luiz , Naiker Mani , Trotter Mark G. , Quigley Simon P. , de Souza Congio Guilhermo Francklin , Costa Diogo Fleury Azevedo
Published: August 2024

The addition of methane-reducing compounds (MRCs) to livestock drinking water presents an alternative method for enteric methane mitigation in extensive systems where these compounds cannot be fed through the diet. This work evaluated several such compounds with the potential to be deployed in this manner. Methane-reducing compounds were selected based on the existing literature and likelihood of dissolution when combined with a commercially available water-based nutrient supplement (uPRO) (uPRO ORANGE®, DIT AgTech, QLD, Australia). This, in turn, would demonstrate the capacity for MRCs to be administered through animal drinking water when such supplements are in use. This technique requires the analysis of MRC solubility and stability in solution, which was completed via Fourier transform infrared-attenuated total reflectance spectroscopy. The uPRO supplement is comprised of urea, urea phosphate, and ammonium sulfate, providing nitrogen, phosphorus, and sulfur—limiting nutrients for ruminants grazing extensive systems during drier periods of the year. Accordingly, medium-quality Rhodes grass hay was used in fermentation runs to simulate a basal diet during the dry season. Methane-reducing compounds were assessed in accordance with each variable measured (gas/methane production, dry matter digestibility, stability under different environmental conditions) along with existing research in the field to determine the most suitable compound for co-administration. Whilst most compounds examined in this study appeared to retain their structure in solution with uPRO, fermentation results varied in terms of successful methane mitigation. The additive Agolin Ruminant L emerged as the most promising compound for further in vivo investigation.
A Preliminary Assessment of Underground Space Resources for Hydrogen Storage in Salt Caverns in Lambton County, Southern Ontario, Canada
Authors: Li Ling , Yin Shunde , Wang Zhizhang
Published: August 2024

Underground hydrogen storage (UHS) is considered to solve the intermittency problem of renewable energy. A geological assessment indicated that the B unit of the Salina Group in Southern Ontario, Canada, is the most promising for UHS because it is the thickest and most regionally extensive salt rock deposit. However, the comprehensive geological knowledge of potential sites and overall salt volume for UHS remains undiscovered. This paper collected 1112 wells’ logging data to assess the geologic potential for UHS in Lambton County. The geological characteristic analysis of the B unit was conducted using high-frequency stratigraphic sequences and logging interpretation. The internal lithologies and thicknesses of the B unit were interpreted from 426 available wells. The storage capacity of the salt caverns was calculated from simplified cylinder models. The results indicate that the B unit can be subdivided into three high-frequency sequences, denoted as the SQ1, SQ2, and SQ3 subunits. SQ1 corresponds to salt–limestone, SQ2 corresponds to bedded salt rocks, and SQ3 corresponds to massive salt rocks. Well sections and thickness maps indicate that the study area can be divided into two sub-areas along the Wilikesport, Oil Spring, and Watford line. To the northwest, unit B was thicker and deeper in terms of paleo-water depth, and to the southeast, less of the B unit was deposited on the paleo-highs. The main thicknesses in SQ1, SQ2, and SQ3 range from 20 to 30 m, 25 to 35 m, and 30 to 40 m, respectively. In conclusion, the best subunit for UHS is SQ3, with a secondary target being SQ2. The main factor impacting cavern storage capacity for the SQ2 subunit is high mud content, while for SQ3, it is the meters-thick anhydrite developed towards the base of the unit. The available underground storage volume of the salt caverns in the B unit is 9.10 × 106 m3. At the standard state, the working gas volume is 557.80 × 106 m3. The favorable area for UHS is the western part surrounded by Wallaceburg, Oil Spring, and Watford. The thickness distribution of the B unit is the combined result of paleo-topography, sea-level changes, and tectonic movement in Lambton. The geological storage capacity of the salt caverns exhibits significant potential.
Utilization of Matrix Effect for Enhancing Resolution in Cation Exchange Chromatography
Authors: Páll Boglárka , Kormány Róbert , Horváth Krisztián
Published: August 2024

In ion chromatography studies, the matrix effect of other inorganic ions present in the sample is a well-known phenomenon. In this work, the behavior of inorganic and organic ions was studied in a system overloaded with ammonium ions. The ammonium ions came from a solution of ammonium hydroxide in various concentrations (0.25–1.25%). In this system, which was significantly overloaded with ammonium ions, the behavior of three ions were tested (lithium, tris, and sodium cations). The measurements were performed at different eluent concentrations (6–17 mM), chromatographic column temperatures (25–40 °C), and injected volumes (15–40 µL). The retention times of sodium and lithium ions increased with increasing amounts of injected ammonium, while tris remained essentially unchanged, indicating that the resolution of these ions can be influenced by varying the concentration of the matrix. The results suggested that the observed effect was due to a combination of the pH change caused by the injected matrix, the dissociation of tris ions, the dissociation of the carbocylic ion-exchange groups of stationary phase, the change in buffer capacity, and the amount of ammonium ion introduced. It has been shown that in a well-designed experiment, the addition of ammonium hydroxide to the sample at concentrations greater than 1% can improve the efficiency of organic and inorganic cation separation. It was found that 8 mM methanesulfonic acid eluent, 30 °C, 1% ammonium hydroxide matrix concentration, and 25 µL injection were optimal for the baseline separation of tris and sodium ions on the high-capacity Dionex CS16 column. These ions could not be separated on this column without the presence of the ammonium matrix.
Effect of Process Parameters on the Appearance of Defects of Flake-Pigmented Metallic Polymer
Authors: Choi Seungkwon , Kim Naksoo
Published: August 2024

This study investigates the influence of the main process parameters of injection molding(mold temperature, melt temperature, and injection rate) on the appearance of defects of flake-pigmented metallic polymer parts. To understand the influence of process parameters, an appearance defects index (ADI) is proposed to quantify the appearance defects. In this process, we propose a criterion for judging the appearance of defects based on the results of fiber orientation and tensor distribution analyses of the skin layer, which is then verified analytically by simulating experiments from the literature. Using the Taguchi experimental method, we designed an L25 orthogonal array to systematically evaluate the influence of process parameters. For each experimental condition, the signal-to-noise ratio (S/N ratio) was calculated to determine the optimal level of each factor and its influence on the appearance of defects. According to the results, mold temperature has the greatest influence on the appearance of defects, with an influence of 48.7%, followed by injection rate with an influence of 40.8%, and melt temperature with an influence of 10.5%. The optimal process parameters were found to be a mold temperature of 40 °C, a melt temperature of 250 °C, and an injection rate of 10 cm3/s, which resulted in a 12.6% improvement in the Appearance defects index (ADI) compared to the standard injection molding condition of ABS materials. This study confirmed that it is possible to improve the appearance of defects by adjusting the process parameters of injection molding.
Quantum Correlation Enhanced Optical Imaging
Authors: Vernekar Siddhant , Xavier Jolly
Published: August 2024

Quantum correlations, especially time correlations, are crucial in ghost imaging for significantly reducing the background noise on the one hand while increasing the imaging resolution. Moreover, the time correlations serve as a critical reference, distinguishing between signal and noise, which in turn enable clear visualization of biological samples. Quantum imaging also addresses the challenge involved in imaging delicate biological structures with minimal photon exposure and sample damage. Here, we explore the recent progress in quantum correlation-based imaging, notably its impact on secure imaging and remote sensing protocols as well as on biological imaging. We also exploit the quantum characteristics of heralded single-photon sources (HSPS) combined with decoy state methods for secure imaging. This method uses Quantum Key Distribution (QKD) principles to reduce measurement uncertainties and protect data integrity. It is highly effective in low-photon number regimes for producing high-quality, noise-reduced images. The versatility of decoy state methods with WCSs (WCS) is also discussed, highlighting their suitability for scenarios requiring higher photon numbers. We emphasize the dual advantages of these techniques: improving image quality through noise reduction and enhancing data security with quantum encryption, suggesting significant potential for quantum imaging in various applications, from delicate biological imaging to secure quantum imaging and communication.
Patient Satisfaction Experience and Outcomes after CT-Guided Bone Marrow Biopsy Versus In-Office Bone Marrow Biopsy
Authors: Srivastava Udayan , Pezeshk Parham , Chhabra Avneesh
Published: August 2024

Aim: To evaluate patient satisfaction outcomes with respect to pain, discomfort, and quality of life with hematology/oncology referrals undergoing CT-guided bone marrow biopsy and compare these scores with those of patients undergoing in-office biopsy. Methods: A retrospective chart review was performed over 2 years with all patients who underwent CT-guided bone marrow biopsy at our university set-up. Age, gender, BMI, radiation dose (CTDI/DLP), number of in-office biopsies, number of CT-guided biopsies, type/amount of moderate sedation used, technical and pathologic success rates, and complication rates were recorded. All patients who underwent both in-office and CT-guided biopsy were contacted by telephone to answer a brief survey regarding pain, discomfort, quality of life, and future preference with respect to each biopsy. Results: A total of 32 patients underwent CT-guided bone marrow biopsy. Moderate sedation was utilized for all CT patients, and 19 patients underwent both in-office and CT-guided biopsies. Upon surveying the 19 patients who underwent both kinds of biopsies, on a scale of 1–10 (10 = highest discomfort and highest pain), the patients on an average reported 7.8 for in-office vs. 2.1 for CT for the discomfort level (p < 0.001) and 7.9 vs. 1.7 for the pain (p < 0.001). The patients reported an average quality-of-life score of 82 (out of a scale of 100) after CT procedures and 53 for in-office (p < 0.001). All patients reported that they would prefer CT-guided procedures with sedation versus in-office procedures in the future. Conclusion: CT-guided bone marrow biopsy is the preferred and more comfortable procedure, especially in low-pain-tolerant patients, although it involves more cost, conscious sedation, and radiation exposure.
Real-Time Wildfire Monitoring Using Low-Altitude Remote Sensing Imagery
Authors: Tong Hongwei , Yuan Jianye , Zhang Jingjing , Wang Haofei , Li Teng
Published: August 2024

With rising global temperatures, wildfires frequently occur worldwide during the summer season. The timely detection of these fires, based on unmanned aerial vehicle (UAV) images, can significantly reduce the damage they cause. Existing Convolutional Neural Network (CNN)-based fire detection methods usually use multiple convolutional layers to enhance the receptive fields, but this compromises real-time performance. This paper proposes a novel real-time semantic segmentation network called FireFormer, combining the strengths of CNNs and Transformers to detect fires. An agile ResNet18 as the encoding component tailored to fulfill the efficient fire segmentation is adopted here, and a Forest Fire Transformer Block (FFTB) rooted in the Transformer architecture is proposed as the decoding mechanism. Additionally, to accurately detect and segment small fire spots, we have developed a novel Feature Refinement Network (FRN) to enhance fire segmentation accuracy. The experimental results demonstrate that our proposed FireFormer achieves state-of-the-art performance on the publicly available forest fire dataset FLAME—specifically, with an impressive 73.13% IoU and 84.48% F1 Score.
A New Approach to Prevent Injuries Related to Manual Handling of Carts: Correcting Resistive Forces between Floors and Wheels to Evaluate the Maximal Load Capacity
Authors: Gille Stephane , Clerc-Urmès Isabelle
Published: August 2024

Test methods that use pushing forces to evaluate the maximal load capacities of carts in design standards require a flat, smooth and horizontal steel plate and thus do not take into account the real conditions of work. Resistive forces of a single wheel of a cart in a uniform rectilinear motion were measured using a unique test bench with five loads. Forty-four wheels were tested (varying diameters, treads and bearings) with one steel plate and four resilient floor coverings. Based on a linear mixed model, all the following results were significant (p < 0.05). Resistive forces were increased linearly with the load and depended on the characteristics of both the wheel and floor. These forces decreased as the diameter increased. They were important for wheels with sleeve bearings but decreased for cone ball bearings and precision ball bearings. Resistive forces depended on the material of the tread and were higher for solid rubber treads. In contrast, the hardness of the tread had little effect. Resistive forces strongly depended on the hardness of the base foam of resilient floor coverings: the softer the base foam, the higher the resistive forces. Test methods in design standards should be reviewed, using corrective forces based on these present results, to prevent musculoskeletal disorders.
Network Security Challenges and Countermeasures for Software-Defined Smart Grids: A Survey
Authors: Agnew Dennis , Boamah Sharon , Bretas Arturo , McNair Janise
Published: August 2024

The rise of grid modernization has been prompted by the escalating demand for power, the deteriorating state of infrastructure, and the growing concern regarding the reliability of electric utilities. The smart grid encompasses recent advancements in electronics, technology, telecommunications, and computer capabilities. Smart grid telecommunication frameworks provide bidirectional communication to facilitate grid operations. Software-defined networking (SDN) is a proposed approach for monitoring and regulating telecommunication networks, which allows for enhanced visibility, control, and security in smart grid systems. Nevertheless, the integration of telecommunications infrastructure exposes smart grid networks to potential cyberattacks. Unauthorized individuals may exploit unauthorized access to intercept communications, introduce fabricated data into system measurements, overwhelm communication channels with false data packets, or attack centralized controllers to disable network control. An ongoing, thorough examination of cyber attacks and protection strategies for smart grid networks is essential due to the ever-changing nature of these threats. Previous surveys on smart grid security lack modern methodologies and, to the best of our knowledge, most, if not all, focus on only one sort of attack or protection. This survey examines the most recent security techniques, simultaneous multi-pronged cyber attacks, and defense utilities in order to address the challenges of future SDN smart grid research. The objective is to identify future research requirements, describe the existing security challenges, and highlight emerging threats and their potential impact on the deployment of software-defined smart grid (SD-SG).
Ontology-Based Deep Learning Model for Object Detection and Image Classification in Smart City Concepts
Authors: Adegun Adekanmi Adeyinka , Fonou-Dombeu Jean Vincent , Viriri Serestina , Odindi John
Published: August 2024

Object detection in remotely sensed (RS) satellite imagery has gained significance in smart city concepts, which include urban planning, disaster management, and environmental monitoring. Deep learning techniques have shown promising outcomes in object detection and scene classification from RS satellite images, surpassing traditional methods that are reliant on hand-crafted features. However, these techniques lack the ability to provide in-depth comprehension of RS images and enhanced interpretation for analyzing intricate urban objects with functional structures and environmental contexts. To address this limitation, this study proposes a framework that integrates a deep learning-based object detection algorithm with ontology models for effective knowledge representation and analysis. The framework can automatically and accurately detect objects and classify scenes in remotely sensed satellite images and also perform semantic description and analysis of the classified scenes. The framework combines a knowledge-guided ontology reasoning module into a YOLOv8 objects detection model. This study demonstrates that the proposed framework can detect objects in varying environmental contexts captured using a remote sensing satellite device and incorporate efficient knowledge representation and inferences with a less-complex ontology model.