bluuebunny/crossref_metadata_2025 huggingface dataset:
Reverse Engineering of Radical Polymerizations by Multi-Objective Optimization
Authors: Fiosina Jelena , Sievers Philipp , Kanagaraj Gavaskar , Drache Marco , Beuermann Sabine
Published: March 2024

Reverse engineering is applied to identify optimum polymerization conditions for the synthesis of polymers with pre-defined properties. The proposed approach uses multi-objective optimization (MOO) and provides multiple candidate polymerization procedures to achieve the targeted polymer property. The objectives for optimization include the maximal similarity of molar mass distributions (MMDs) compared to the target MMDs, a minimal reaction time, and maximal monomer conversion. The method is tested for vinyl acetate radical polymerizations and can be adopted to other monomers. The data for the optimization procedure are generated by an in-house-developed kinetic Monte-Carlo (kMC) simulator for a selected recipe search space. The proposed reverse engineering algorithm comprises several steps: kMC simulations for the selected recipe search space to derive initial data, performing MOO for a targeted MMD, and the identification of the Pareto optimal space. The last step uses a weighted sum optimization function to calculate the weighted score of each candidate polymerization condition. To decrease the execution time, clustering of the search space based on MMDs is applied. The performance of the proposed approach is tested for various target MMDs. The suggested MOO-based reverse engineering provides multiple recipe candidates depending on competing objectives.
Reinforcement and Deacidification for a Textile Scroll Painting (AD 1881) Using the CNF and MgO Suspensions
Authors: Wei Hanyu , Zhao Fangnan , Qi Yunpeng , Jia Zhihui , Zhou Yajun , Chao Xiaolian , Shi Meirong , Luo Yujia , Xing Huiping
Published: March 2024

The scroll paintings for ancestor trees have been used to inherit the spirit of ancestor worship as a historical record of family development since the late Ming Dynasty in China. A severely degraded scroll painting of an ancestor tree (made of cotton textiles) needs intervention and conservation treatment to mitigate further deterioration. On the basis of the previously reported characterization results for the painting, in this paper, a suspension that is composed of 0.6% cellulose nanofibril (CNF) and nanosized 0.15% MgO in aqueous solvent (denoted as the CNF-MgO susairpension) was prepared. Conventional characterization methods were used to assess the properties of model samples before and after treatment with the CNF-MgO suspension, as well as before and after degradation under two sets of conditions. The results show that the treated model samples are slightly alkaline, given the deposit of alkaline particles, and demonstrate good mechanical properties before and after degradation due to the increase in fiber-to-fiber bond and mitigation of acid-catalyzed hydrolysis. In spite of the non-transparency of CNF and MgO nanoparticles, they have little impact on the optical properties of textiles, as verified by transmittance data and the determination of color changes. This suspension was then used to reinforce and restore the scroll painting in a practical conservation process. The application of CNF and MgO nanoparticles on textile objects investigated in this study would expand our understanding of the conservation of such objects, especially for those that have already become acidic and degraded.
Spray Coating of Wood with Nanoparticles from Lignin and Polylactic Glycolic Acid Loaded with Thyme Essential Oils
Authors: Zikeli Florian , Jusic Jasmina , Palocci Cleofe , Mugnozza Giuseppe Scarascia , Romagnoli Manuela
Published: March 2024

Ensuring the longevity of wooden constructions depends heavily on the preservation process. However, several traditional preservation methods involving fossil-based compounds have become outdated because they pose a significant risk to the environment and to human health. Therefore, the use of bio-based and bioactive solutions, such as essential oils, has emerged as a more sustainable alternative in protecting wood from biotic attacks. The entrapment of essential oils in polymeric carrier matrices provides protection against oxidation and subsequent degradation or rapid evaporation, which implies the loss of their biocidal effect. In this work, lignin as well as PLGA nanoparticles containing the essential oils from two different thyme species (Thymus capitatus and T. vulgaris) were applied on beech wood samples using spray coating. The prepared coatings were investigated using FTIR imaging, SEM, as well as LSM analysis. Release experiments were conducted to investigate the release behavior of the essential oils from their respective lignin and PLGA carrier materials. The study found that lignin nanoparticles were more effective at trapping and retaining essential oils than PLGA nanoparticles, despite having larger average particle diameters and a more uneven particle size distribution. An analysis of the lignin coatings showed that they formed a uniform layer that covered most of the surface pores. PLGA nanoparticles formed a film-like layer on the cell walls, and after leaching, larger areas of native wood were evident on the wood samples treated with PLGA NPs compared to the ones coated with lignin NPs. The loading capacity and efficiency varied with the type of essential oil, while the release behaviors were similar between the two essential oil types applied in this study.
Effect of Operational Variables on Supercritical Foaming of Caffeic Acid-Loaded Poly(lactic acid)/Poly(butylene adipate-co-terephthalate) Blends for the Development of Sustainable Materials
Authors: Rivera Patricia , Torres Alejandra , Romero Julio , Alarcón Álvaro , Martínez Sara , Arrieta Marina P. , Rodríguez-Mercado Francisco , Galotto María José
Published: March 2024

Expanded polystyrene will account for 5.3% of total global plastic production in 2021 and is widely used for food packaging due to its excellent moisture resistance and thermal insulation. However, some of these packages are often used only once before being discarded, generating large amounts of environmentally harmful plastic waste. A very attractive alternative to the conventional methods used for polymer processing is the use of supercritical carbon dioxide (scCO2) since it has mass-transfer properties adapted to the foam morphology, generating different path lengths for the diffusion of active compounds within its structure and can dissolve a wide range of organic molecules under supercritical conditions. The objective of this research was to evaluate the effect of operational variables on the process of caffeic acid (CA) impregnation and subsequent foaming of polylactic acid (PLA) as well as two PLA/poly(butylene-co-terephthalate-adipate) (PBAT) blends using scCO2. The results showed an increase in the degree of crystallinity of the CA-impregnated samples due to the nucleation effect of the active compound. On the other hand, SEM micrographs of both films and foams showed significant differences due to the presence of PBAT and its low miscibility with PLA. Finally, the results obtained in this work contribute to the knowledge of the important parameters to consider for the implementation of the impregnation and foaming process of PLA and PLA/PBAT blends with potential use in food packaging.
Embracing Sustainability: The World of Bio-Based Polymers in a Mini Review
Authors: Righetti Grazia Isa C. , Faedi Filippo , Famulari Antonino
Published: March 2024

The proliferation of polymer science and technology in recent decades has been remarkable, with synthetic polymers derived predominantly from petroleum-based sources dominating the market. However, concerns about their environmental impacts and the finite nature of fossil resources have sparked interest in sustainable alternatives. Bio-based polymers, derived from renewable sources such as plants and microbes, offer promise in addressing these challenges. This review provides an overview of bio-based polymers, discussing their production methods, properties, and potential applications. Specifically, it explores prominent examples including polylactic acid (PLA), polyhydroxyalkanoates (PHAs), and polyhydroxy polyamides (PHPAs). Despite their current limited market share, the growing awareness of environmental issues and advancements in technology are driving increased demand for bio-based polymers, positioning them as essential components in the transition towards a more sustainable future.
Phenolic Compounds, Fatty Acid Composition, and Antioxidant Activities of Some Flaxseed (Linum usitatissimum L.) Varieties: A Comprehensive Analysis
Authors: Koçak Mehmet Zeki
Published: March 2024

Flaxseed, also known as flax or linseed (Linum usitatissimum L.), is one of the oldest crops. It is used for oil and fiber production. The species displays a broad range of biological activities due to its chemical compounds. It has a widespread geographical distribution, and a large number of its varieties have been evaluated for their biological efficacy and nutritional value. This study investigates fifteen varieties of the species, some of which are examined for the first time. In this regard, a series of chemical composition analyses and antioxidant assays were carried out. Accordingly, total phenolic content ranged between 613.6 (Michael) and 3164.6 (Atalanta) mg GAE/g, whilst total flavonoid content varied from 176.25 (BonnyDoon) to 689.20 (Mcduff) mg QE/g. Regarding the radical scavenging assays, the values obtained were significantly higher than those of the standard antioxidant (ascorbic acid). Furthermore, the extracts exhibited chelating activity for ferrous ions and a cupric reducing capacity that was comparable to that of the standard. The oil content values of the varieties ranged from 0.82 g/100 g (Michael) to 2.14 g/100 g (McGregor). The percentage of α-linolenic acid varied between 39.21% (McGregor) and 54.13% (Nareum).
Experimental Investigation on Oxy-Hydrogen Gas Flame Injecting Coal Powder Gasification and Combustion
Authors: Cui Jie , Zhao Honglei , Xu Youning , Yang Shuo , Pan Honggang , Xiao Wenke , Fu Yudong , Xue Zhijia
Published: March 2024

Hydrogen energy is an important carrier for energy terminals to achieve green and low-carbon transformation. Hydrogen, as a carbon-free fuel, has great research and development value in the field of thermal power generation. This article proposes a solution for the stable combustion of coal powder using Oxy-hydrogen Gas ignition technology. An Oxy-hydrogen Gas flame injection coal powder combustion testing device was constructed to experimentally study the temperature distribution in the combustion chamber under Oxy-hydrogen Gas ignition technology, with primary air coal powder concentrations of 0.27, 0.32, and 0.36 (kg coal powder/kg air), as well as the concentration changes of volatile CO emissions during the ignition of coal powder using both Oxy-hydrogen Gas and CH4 flames. The sensitivity of the NO generation during coal gasification combustion under the Oxy-hydrogen Gas ignition was simulated and analyzed. The results show that at a coal powder concentration of 0.32 (kg coal/kg air) and an Oxy-hydrogen Gas flow rate of 2.1 L/min, the combustion effect of coal powder is the best, and the highest combustion chamber temperature can reach 1156 K; when the concentration of coal powder varies within a range from 0.32 to 0.27, the combustion chamber temperature can be maintained at around 850K, achieving stable combustion conditions for coal powder. The only product generated by the Oxy-hydrogen Gas combustion is high-temperature water vapor, which helps the rapid gasification of coal powder and releases a large amount of volatile CO, which is beneficial for the ignition and stable combustion of coal powder.
Ultra-Short-Term Power Prediction of Large Offshore Wind Farms Based on Spatiotemporal Adaptation of Wind Turbines
Authors: An Yuzheng , Zhang Yongjun , Lin Jianxi , Yi Yang , Fan Wei , Cai Zihan
Published: March 2024

Accurately predicting the active power output of offshore wind power is of great significance for reducing the uncertainty in new power systems. By utilizing the spatiotemporal correlation characteristics among wind turbine unit outputs, this paper embeds the Diffusion Convolutional Neural Network (DCNN) into the Gated Recurrent Unit (GRU) for the feature extraction of spatiotemporal correlations in wind turbine unit outputs. It also combines graph structure learning to propose a sequence-to-sequence model for ultra-short-term power prediction in large offshore wind farms. Firstly, the electrical connection graph within the wind farm is used to preliminarily determine the reference adjacency matrix for the wind turbine units within the farm, injecting prior knowledge of the adjacency matrix into the model. Secondly, a convolutional neural network is utilized to convolve the historical curves of units within the farm along the time dimension, outputting a unit connection probability vector. The Gumbel–softmax reparameterization method is then used to make the probability vector differentiable, thereby generating an optimal adjacency matrix for the prediction task based on the probability vector. At the same time, the difference between the two adjacency matrices is added as a regularization term to the loss function to reduce model overfitting. The simulation of actual cases shows that the proposed model has good predictive performance in ultra-short-term power prediction for large offshore wind farms.
Efficiency of Supercritical CO2 and Ultrasound-Assisted Extraction Techniques for Isolation of Bioactive Molecules from Sea Buckthorn (Elaeagnus rhamnoides (L.) A. Nelson) Berry Oils and Cakes
Authors: Čulina Patricija , Balbino Sandra , Jokić Stela , Dragović-Uzelac Verica , Pedisić Sandra
Published: March 2024

This study compared the efficiency of advanced supercritical CO2 extraction (SC-CO2) and conventional Soxhlet extraction (SE) in the isolation of lipophilic bioactive molecules (BAMs) from the oils of different sea buckthorn (SB) berries (Leikora and Ascola). A SB cake, a valuable by-product of oil extraction, was analyzed for phenolic compounds and antioxidant activity (AA). For this purpose, ultrasound-assisted extraction (UAE) with 70% (v/v) ethanol was optimized, and the following optimal UAE conditions were determined: ultrasonic power of 60%, temperature of 50 °C, and extraction time of 20 min. The individual BAMs in the oils and cake extracts were analyzed chromatographically, while the AAs were determined using the ORAC method. The oils extracted with SC-CO2 had a higher content of lipophilic BAMs and a higher AA value than the oils extracted with SE. Palmitic and palmitoleic acids, β-sitosterol, and α-tocopherol dominated the Leikora oil, while a higher content of oleic, linoleic, and α-linolenic acids and carotenoids was found in the Ascola oil. The highest phenolic content was found in the Ascola cake, with the flavonols isorhamnetin-3-hexoside, isorhamnetin-3-rutinoside, and quercetin-3-glucoside being the most abundant. Oil and cake extracts of the Ascola variety also had a higher AA.
Influence of Partition Wall Length on Inlet Flow Regime of a Pumping Station Arranged in Parallel with a Sluice Gate
Authors: Shi Wei , Lv Fusheng , Yu Xianlei , Wang Xichen , Ni Chun , Lu Weigang , Xu Lei
Published: March 2024

When a sluice gate is arranged in parallel with pumping station units, biased flow occurs in the forebay when the units are operating. The transverse flow velocity in front of the channel inlet is relatively high, and, in severe cases, it may lead to the formation of suction vortices, impacting the stable operation of pump units. Taking the Liushan Pumping Station project of the Eastern Route of the South-to-North Water Diversion Project as a case study, this paper investigates the effect of the partition wall length on the inlet flow regime of pumping station units arranged in parallel with the sluice gate to reduce the transverse flow velocity in front of the channel inlet. Using numerical simulations, the inlet flow regimes for different partition wall lengths were compared. Moreover, the flow field distributions in the forebay under different operating conditions were analyzed alongside the transverse flow velocity in front of the channel inlet and the uniformity of flow velocity distribution in the section behind the channel inlet. Hydraulic model tests were then conducted to validate the simulation results. The results indicate that in the original design, there existed a vortex zone in the forebay in front of the inlet of the channel where the transverse flow was relatively high. However, the introduction of partition walls significantly reduced the transverse flow velocity in front of the inlet of the channel in the forebay. The optimal effect was achieved when the length of the partition wall was twice the width of the inlet channel. Furthermore, the uniformity of velocity distribution at the inlet of the channel increased by an average of 7.4%, leading to a substantial improvement in the inlet flow regime of the pumping station. The addition of partition walls in the forebay effectively resolved the issues related to the flow regime in the forebay, providing valuable references for similar engineering studies in the future.
Ternary Precursor Centrifuge Rolling Bearing Fault Diagnosis Based on Adaptive Sample Length Adjustment of 1DCNN-SeNet
Authors: Xu Feng , Sui Zhen , Ye Jiangang , Xu Jianliang
Published: March 2024

To address the issues of uneven sample lengths in the centrifuge machine bearings of the ternary precursor, inaccurate fault feature extraction, and insensitivity of important feature channels in rolling bearings, a rolling bearing fault diagnosis method based on adaptive sample length adjustment of one-dimensional convolutional neural network (1DCNN) and squeeze-and-excitation network (SeNet) is proposed. Firstly, by controlling the cumulative variance contribution rate in the principal component analysis algorithm, adaptive adjustment of sample length is achieved, reducing data with uneven sample lengths to the same dimensionality for various classes. Then, the 1DCNN extracts local features from bearing signals through one-dimensional convolution-pooling operations, while the SeNet network introduces a channel attention mechanism which can adaptively adjust the importance between different channels. Finally, the 1DCNN-SeNet model is compared with four classic models through experimental analysis on the CWRU bearing dataset. The experimental results indicate that the proposed method exhibits high diagnostic accuracy in rolling bearings, demonstrating good adaptability and generalization capabilities.
Special Issue on “Current Trends in Food and Food Byproducts Processing”
Authors: Dziki Dariusz
Published: March 2024

The growing interest in healthy lifestyles has contributed to an increased demand for nutrient-rich foods, such as plant-based proteins, vitamins, and minerals [...]
Evaluation of the Bioenergy Potential of Blends (Green Coconut Shells and Fish Scales) as a Feedstock in Thermochemical Processes for Clean Energy Production
Authors: Monroe Ayrton Pablo Raiol , Silva Arthur Vinicius Sousa , Melo Mariana Silva , da Silva Julie Brenda Santos , Peña Garcia Ramón Raudel , Rios Maria Alexsandra de Sousa , Bizzo Waldir Antônio , Cruz Glauber
Published: March 2024

Brazil is among the world’s largest producers of green coconut, which contributes to inappropriate disposal and socioenvironmental impacts. Concomitantly, some of its coastal cities produce a great diversity of fish and large amounts of solid waste. This paper reports on the use of samples of fish scales (100FS) and green coconut shells (100GCS) and their mixtures in 75%FS:25%GCS (B25), 50%FS:50%GCS (B50), and 25%FS:75%GCS (B75) proportions and quantification of their Higher Heating Values (HHV) and Lower Heating Values (LHV), and Ultimate (UA) and Proximate Analyses (PA). Their thermal behavior was investigated by thermogravimetry (TG/DTG) and differential scanning calorimetry (DSC), whereas scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), X-ray diffraction (XRD), and Fourier transformed infrared (FTIR) were employed for analyses of their physicochemical and morphostructural properties. When compared to in natura samples, SEM images of the blends detected a structural disorder and a highly fibrous structure with an elongated chain and surface roughness. HHV were superior in samples with 100GCS (16.64 MJ kg−1), B75 (15.80 MJ kg−1), and B50 (14.98 MJ kg−1), and lower in B25 (14.16 MJ kg−1) and 100FS (13.03 MJ kg−1), with acceptable values for different biomasses. TG/DTG and DSC curves showed similarities among the samples, with the detection of their main thermoconversion stages. According to the data, the samples can be applied as renewable energy sources to mitigate socioecological illnesses and social vulnerabilities resulting from the archaic and inadequate management of solid waste.
Sustainable Electricity Production Using Avocado Waste
Authors: Rojas-Flores Segundo , Vives-Garnique Juan , Díaz Félix , De La Cruz-Noriega Magaly
Published: March 2024

Agroindustry waste has exponentially increased in recent years, generating economic losses and environmental problems. In addition, new ways to generate sustainable alternative electrical energy are currently being sought to satisfy energy demand. This investigation proposes using avocado waste as fuel for electricity generation in single-chamber MFCs. The avocado waste initially operated with an ambient temperature (22.4 ± 0.01 °C), DO of 2.54 ± 0.01 mg/L, TDS of 1358 ± 1 mg/L and COD of 1487.25 ± 0.01 mg/L. This research managed to generate its maximum voltage (0.861 ± 0.241 V) and current (3.781 ± 0.667 mA) on the fourteenth day, operating at an optimal pH of 7.386 ± 0.147, all with 126.032 ± 8.888 mS/cm of electrical conductivity in the substrate. An internal resistance of 67.683 ± 2.456 Ω was found on day 14 with a PD of 365.16 ± 9.88 mW/cm2 for a CD of 5.744 A/cm2. Micrographs show the formation of porous biofilms on both the anodic and cathodic electrodes. This study gives preliminary results of using avocado waste as fuel, which can provide outstanding solutions to agro-industrial companies dedicated to selling this fruit.
Blue Circular Economy—Reuse and Valorization of Bivalve Shells: The Case of Algarve, Portugal
Authors: Magalhães Fernanda Caroline , Bellei Poliana , Flores-Colen Inês , da Costa Eduarda Marques
Published: March 2024

The Circular Economy emerges as an alternative to reinvent the linear production model (take–make–waste), focusing on reintegrating waste into the production cycle, and aiming to minimize both environmental disposal and the unrestrained extraction of raw materials. In this context, the concept of Blue Economy arises, an approach centered on preserving and valorizing marine and coastal resources. This article aims to develop a model for the circuit of bivalve shells, emphasizing the transformation of the residues into new products and identifying how these processes affect sociocultural, economic, and environmental dimensions. The methodology involved the surveying of local stakeholders directly involved in bivalve production and consumption to identify the relationship of these stakeholders with the production, marketing, and disposal of bivalves. It is concluded that biowaste has potential, and there is interest among local stakeholders in reusing it, but a lack of knowledge and connection among stakeholders ultimately leads to the devaluation of the product. The circuit of bivalves is necessary to identify value, propose correct collection, and stimulate interest in their reuse, both by other industries and by the aquaculture industry itself. Exploring the potential for reusing bivalves and mitigating their waste, as well as preventing improper disposal, could drive the development of the Blue Circular Economy in coastal regions.
Artificial Life, Divinity, and Mythology in Star Trek
Authors: Norgard Amy L.
Published: March 2024

The Star Trek franchise’s depiction of artificial intelligence (AI) and affiliated technologies—namely, supercomputers, androids, and holograms—evokes common themes and motifs from the myths of the ancient Mediterranean. This article analyzes the mythological underpinnings of Star Trek’s historical treatment and approach to AI, from The Original Series to The Next Generation and up through the newest additions to the canon, Short Treks, and Picard. AI in Star Trek, like Data, the Doctor, and Zora, expresses qualities associated with divinity: superhuman strength, intelligence, and agelessness. These very qualities distinguish them from humans and bar them from considerations of personhood. Like the Greek gods of myth, AI can present as immortal, which fundamentally distinguishes it from mortal humans, as seen in the tensions between gods and humans in Homer’s Odyssey and the Homeric Hymns. The ancient tension between mortal and immortal is manifested in the combative relationship between organic creator and artificial creation, a common sci-fi trope, that can lead to a cycle of fear and hostility evocative of the divine generational warfare in Hesiod’s Theogony. The artificial–organic tension resonates with the contemporary audience’s conflicted experiences with evolving technologies and problematizes the show’s presentation of the evolution of humanity into a posthuman existence. Just as mythology is used to consider humanity relative to the divine, narratives about AI are fertile ground to analyze what it means to be human and establish parameters for what is decidedly not human.
Walking in “Masses and Elites”: Investigation into Donald MacGillivray’s Missionary Strategies in China (1888–1930)
Authors: Song Yanhua , Zhao Wei , Ma Doucheng , Tan Shulin
Published: March 2024

From his arrival in China in 1888 to his departure in 1930, Canadian missionary, Donald MacGillivray (季理斐), was in China for more than 40 years. According to changes in the Chinese missionary situation, the key target of his mission was frequently adjusted. From his initial work in the early days in North Honan, to his work with officials and intellectuals in Shanghai in the late Qing Dynasty, then to students, women, and children in the Republic of China, Donald MacGillivray continued to preach to both the masses and the elites. His approach was flexible, ranging from oral preaching to academic publications. Relying on his interpersonal network, MacGillivray paid close attention to the social changes occurring in modern China. An evaluation of his activities in China can not only reveal the impact of individual missionaries in the process of Western learning and the transformation of Chinese knowledge in modern times, but also provide insight into the integration of Christianity into the indigenization process of China.
The Difference of Indifference: Marcel Duchamp and the Possibilities of Dialogical Personalism
Authors: Garrett Stephen M.
Published: March 2024

Joseph Kosuth, one of Concept Art’s influential practitioners, credited Marcel Duchamp in an important 1969 essay, “Art After Philosophy”, with instigating the shift from the visual to the conceptual by means of indifference and dematerialization. Duchamp’s approach to art was not limited, however, to the realm of artistic intention but also included the (re)contextualization provoked by his readymades. This (re)contextualization elucidated the embodied, dialogical encounters of the artist-artwork-audience, what I identify as an “aesthetics of difference”. This designation sets forth a framework of meaning that draws upon a burgeoning subset of early-twentieth-century personalist philosophy called dialogical personalism in order to offer a more suitable plausibility structure than the usual explanations of Duchamp and his approach to art, which typically revolve around nihilism, absurdity, and a solipsistic understanding of freedom. In doing so, Duchamp’s artistic approach retains not only a more viable ontology for continuing to question the nature of art, but also has important epistemological and ethical implications.
Mass of the Ages 18–39: The Sudden Revival of the Tridentine Latin Mass and Lessons for a More Robust Post-Conciliar Theological Aesthetics in Liturgy
Authors: Thomas Sean C.
Published: March 2024

The Tridentine Latin Mass (TLM) is rapidly growing in popularity. The movement that has formed around it has grown so attached to it as to threaten the unity of the Catholic Church. I attended TLMs in multiple distinct settings, studied the worshippers’ ordinary theology, and proceeded hermeneutically using the Circle Method. The most useful insight to emerge from this is that the theological aesthetics of the post-Conciliar Mass could be more deeply symbolic and synergistic with Conciliar intellectual theology. The TLM’s aesthetics offer worshippers assurances of certainty, but these assurances are empty. Therefore, parishes should facilitate the self-expression of the faithful, both to foster engagement with mystery and to inspire liturgical aesthetics. From these expressions, contextually meaningful symbols will emerge, which, through communal discernment guided by the Holy Spirit, may prove worthy to the task of enhancing liturgical aesthetics.
Senior Mental Health Scenarios in Thai Buddhist Contexts: A Qualitative Study
Authors: Langgapin Saowalak , Boonchieng Waraporn , Chautrakarn Sineenart , Maneeton Narong
Published: March 2024

This study delves into the global mental health challenges confronting the elderly within Thailand’s Buddhist context. It explores seniors’ perspectives on mental health distress, factors, and interventions, alongside monks’ viewpoints on traditional Buddhist approaches and their role in addressing these challenges. Our thematic analysis of qualitative research engaged 36 participants, comprising health volunteer monks and seniors from Northern Thailand, to identify primary themes and sub-themes. The perspectives on senior mental health scenarios highlight seniors’ experiences of stress, anxiety, sadness, and loneliness, influenced by factors like age, health, family, finances, and social isolation. Interventions encompass health care, religious practices, and community support. Monks advocate for integrating Buddhism into daily life, encouraging active participation, and addressing senior mental health issues, emphasizing their pivotal role, the embodiment of monastic ideals, and the challenges hindering their involvement. The research highlights the significance of empowering monastic involvement, acknowledging monks as representatives of monastic principles, even in the face of obstacles limiting their participation. This study uncovers a trend in Thai Buddhist communities where physical health and religious aspects take precedence over the mental well-being of seniors. It advocates for a comprehensive approach that integrates religious and mental health strategies, highlighting Buddhism’s impact on seniors’ mental wellness. The implications span spirituality, religious studies, mental health, and elderly care policy, emphasizing the crucial role of Buddhist practices and monks in enhancing the mental well-being of the elderly.
Why Can’t Angels See Our Future? Aquinas’s View of the Relation between Continuous and Discrete Time
Authors: Feingold Francis
Published: March 2024

Aquinas’s account of angelic cognition presents an intriguing puzzle. Aquinas denies, for both theological and philosophical reasons, that angelic natural knowledge extends to future contingents. Yet Aquinas also insists, strictly for philosophical reasons, that angelic life is measured by a “discrete time” radically distinct from our physical universe’s continuous time. How can two times share the same “now,” such that what is future for us would also be future for the angels? Aquinas does little to address this problem, but I propose two possible solutions. Both solutions argue that angelic instants, like the instants of aeviternity and eternity, can be simultaneous with physical time periods, but only if the corresponding act aims at physical things. This means the angel cannot “skip” physical time, and so determinately aligns angelic instants with physical times. The solutions diverge over whether the extensionless angelic instant is simultaneous with the physical time period all at once, like God’s eternity, or successively, thanks to a temporally extended simultaneity-relation. The first solution prevents angelic natural foreknowledge of what follows an angelic intervention, since intervention requires a new angelic instant. The second solution prevents angelic natural foreknowledge tout court by tying “all-at-once” simultaneity to causal knowledge, which is God’s unique privilege.
Scientific Wonder, Artificial Intelligence, and Awe of the Divine
Authors: Konigsburg Joyce Ann
Published: March 2024

Science employs wonder and its associated emotions to explore unknown mysteries in the pursuit of knowledge about the natural world. Discovering scientific truth may inspire awe—a transcendent, indescribable experience of enhanced awareness and astonishment at the extensive interconnectedness of reality. The emotion of awe expands human consciousness and also mediates possible spiritual encounters with the Divine. Prompted by wonder and curiosity, scientific studies of the human mind and cognition yield insights that contribute to artificial intelligence research, especially the potential development of conscious artificial general intelligence. Yet, emerging artificial intelligence technologies raise religious and sociological questions about consciousness, personhood, and whether conscious artificial general intelligence is capable of expressing wonder and experiencing awe of the Divine.
Iterative Signal Detection and Channel Estimation with Superimposed Training Sequences for Underwater Acoustic Information Transmission in Time-Varying Environments
Authors: Li Lin , Han Xiao , Ge Wei
Published: March 2024

Underwater signal processing is primarily based on sound waves because of the unique properties of water. However, the slow speed and limited bandwidth of sound introduce numerous challenges, including pronounced time-varying characteristics and significant multipath effects. This paper explores a channel estimation method utilizing superimposed training sequences. Compared with conventional schemes, this method offers higher spectral efficiency and better adaptability to time-varying channels owing to its temporal traversal. To ensure success in this scheme, it is crucial to obtain time-varying channel estimation and data detection at low SNRs given that superimposed training sequences consume power resources. To achieve this goal, we initially employ coarse channel estimation utilizing superimposed training sequences. Subsequently, we employ approximate message passing algorithms based on the estimated channels for data detection, followed by iterative channel estimation and equalization based on estimated symbols. We devise an approximate message passing channel estimation method grounded on a Gaussian mixture model and refine its hyperparameters through the expectation maximization algorithm. Then, we refine the channel information based on time correlation by employing an autoregressive hidden Markov model. Lastly, we perform numerical simulations of communication systems by utilizing a time-varying channel toolbox to simulate time-varying channels, and we validate the feasibility of the proposed communication system using experimental field data.
Gaussian Process Regression Hybrid Models for the Top-of-Atmosphere Retrieval of Vegetation Traits Applied to PRISMA and EnMAP Imagery
Authors: Pascual-Venteo Ana B. , Garcia Jose L. , Berger Katja , Estévez José , Vicent Jorge , Pérez-Suay Adrián , Van Wittenberghe Shari , Verrelst Jochem
Published: March 2024

The continuous monitoring of the terrestrial Earth system by a growing number of optical satellite missions provides valuable insights into vegetation and cropland characteristics. Satellite missions typically provide different levels of data, such as level 1 top-of-atmosphere (TOA) radiance and level 2 bottom-of-atmosphere (BOA) reflectance products. Exploiting TOA radiance data directly offers the advantage of bypassing the complex atmospheric correction step, where errors can propagate and compromise the subsequent retrieval process. Therefore, the objective of our study was to develop models capable of retrieving vegetation traits directly from TOA radiance data from imaging spectroscopy satellite missions. To achieve this, we constructed hybrid models based on radiative transfer model (RTM) simulated data, thereby employing the vegetation SCOPE RTM coupled with the atmosphere LibRadtran RTM in conjunction with Gaussian process regression (GPR). The retrieval evaluation focused on vegetation canopy traits, including the leaf area index (LAI), canopy chlorophyll content (CCC), canopy water content (CWC), the fraction of absorbed photosynthetically active radiation (FAPAR), and the fraction of vegetation cover (FVC). Employing band settings from the upcoming Copernicus Hyperspectral Imaging Mission (CHIME), two types of hybrid GPR models were assessed: (1) one trained at level 1 (L1) using TOA radiance data and (2) one trained at level 2 (L2) using BOA reflectance data. Both the TOA- and BOA-based GPR models were validated against in situ data with corresponding hyperspectral data obtained from field campaigns. The TOA-based hybrid GPR models revealed a range of performance from moderate to optimal results, thus reaching R2 = 0.92 (LAI), R2 = 0.72 (CCC) and 0.68 (CWC), R2 = 0.94 (FAPAR), and R2 = 0.95 (FVC). To demonstrate the models’ applicability, the TOA- and BOA-based GPR models were subsequently applied to imagery from the scientific precursor missions PRISMA and EnMAP. The resulting trait maps showed sufficient consistency between the TOA- and BOA-based models, with relative errors between 4% and 16% (R2 between 0.68 and 0.97). Altogether, these findings illuminate the path for the development and enhancement of machine learning hybrid models for the estimation of vegetation traits directly tailored at the TOA level.
The Retrieval of Ground NDVI (Normalized Difference Vegetation Index) Data Consistent with Remote-Sensing Observations
Authors: Zhao Qi , Qu Yonghua
Published: March 2024

The Normalized Difference Vegetation Index (NDVI) is widely used for monitoring vegetation status, as accurate and reliable NDVI time series are crucial for understanding the relationship between environmental conditions, vegetation health, and productivity. Ground digital cameras have been recognized as important potential data sources for validating remote-sensing NDVI products. However, differences in the spectral characteristics and imaging methods between sensors onboard satellites and ground digital cameras hinder direct consistency analyses, thereby limiting the quantitative application of camera-based observations. To address this limitation and meet the needs of vegetation monitoring research and remote-sensing NDVI validation, this study implements a novel NDVI camera. The proposed camera incorporates narrowband dual-pass filters designed to precisely separate red and near-infrared (NIR) spectral bands, which are aligned with the configuration of sensors onboard satellites. Through software-controlled imaging parameters, the camera captures the real radiance of vegetation reflection, ensuring the acquisition of accurate NDVI values while preserving the evolving trends of the vegetation status. The performance of this NDVI camera was evaluated using a hyperspectral spectrometer in the Hulunbuir Grassland over a period of 93 days. The results demonstrate distinct seasonal characteristics in the camera-derived NDVI time series using the Green Chromatic Coordinate (GCC) index. Moreover, in comparison to the GCC index, the camera’s NDVI values exhibit greater consistency with those obtained from the hyperspectral spectrometer, with a mean deviation of 0.04, and a relative root mean square error of 9.68%. This indicates that the narrowband NDVI, compared to traditional color indices like the GCC index, has a stronger ability to accurately capture vegetation changes. Cross-validation using the NDVI results from the camera and the PlanetScope satellite further confirms the potential of the camera-derived NDVI data for consistency analyses with remote sensing-based NDVI products, thus highlighting the potential of camera observations for quantitative applications The research findings emphasize that the novel NDVI camera, based on a narrowband spectral design, not only enables the acquisition of real vegetation index (VI) values but also facilitates the direct validation of vegetation remote-sensing NDVI products.
A Spectral–Spatial Context-Boosted Network for Semantic Segmentation of Remote Sensing Images
Authors: Li Xin , Yong Xi , Li Tao , Tong Yao , Gao Hongmin , Wang Xinyuan , Xu Zhennan , Fang Yiwei , You Qian , Lyu Xin
Published: March 2024

Semantic segmentation of remote sensing images (RSIs) is pivotal for numerous applications in urban planning, agricultural monitoring, and environmental conservation. However, traditional approaches have primarily emphasized learning within the spatial domain, which frequently leads to less than optimal discrimination of features. Considering the inherent spectral qualities of RSIs, it is essential to bolster these representations by incorporating the spectral context in conjunction with spatial information to improve discriminative capacity. In this paper, we introduce the spectral–spatial context-boosted network (SSCBNet), an innovative network designed to enhance the accuracy semantic segmentation in RSIs. SSCBNet integrates synergetic attention (SYA) layers and cross-fusion modules (CFMs) to harness both spectral and spatial information, addressing the intrinsic complexities of urban and natural landscapes within RSIs. Extensive experiments on the ISPRS Potsdam and LoveDA datasets reveal that SSCBNet surpasses existing state-of-the-art models, achieving remarkable results in F1-scores, overall accuracy (OA), and mean intersection over union (mIoU). Ablation studies confirm the significant contribution of SYA layers and CFMs to the model’s performance, emphasizing the effectiveness of these components in capturing detailed contextual cues.
Monitoring Dynamically Changing Migratory Flocks Using an Algebraic Graph Theory-Based Clustering Algorithm
Authors: Jiang Qi , Wang Rui , Zhang Wenyuan , Jiao Longxiang , Li Weidong , Wu Chunfeng , Hu Cheng
Published: March 2024

Migration flocks have different forms, including single individuals, formations, and irregular clusters. The shape of a flock can change swiftly over time. The real-time clustering of multiple groups with different characteristics is crucial for the monitoring of dynamically changing migratory flocks. Traditional clustering algorithms need to set various prior parameters, including the number of groups, the number of nearest neighbors, or the minimum number of individuals. However, flocks may display complex group behaviors (splitting, combination, etc.), which complicate the choice and adjustment of the parameters. This paper uses a real-time clustering-based method that utilizes concepts from the algebraic graph theory. The connected graph is used to describe the spatial relationship between the targets. The similarity matrix is calculated, and the problem of group clustering is equivalent to the extraction of the partitioned matrices within. This method needs only one prior parameter (the similarity distance) and is adaptive to the group’s splitting and combination. Two modifications are proposed to reduce the computation burden. First, the similarity distance can be broadened to reduce the exponent of the similarity matrix. Second, the omni-directional measurements are divided into multiple sectors to reduce the dimension of the similarity matrix. Finally, the effectiveness of the proposed method is verified using the experimental results using real radar data.
An Object-Based Approach to Extract Aquaculture Ponds with 10-Meter Resolution Sentinel-2 Images: A Case Study of Wenchang City in Hainan Province
Authors: Hu Yingwen , Zhang Li , Chen Bowei , Zuo Jian
Published: March 2024

Coastal aquaculture has made an important contribution to global food security and the economic development of coastal zones in recent decades. However, it has also damaged these coastal zones’ ecosystems. Moreover, coastal aquaculture is poised to play a key role in the achievement of Sustainable Development Goals (SDGs). Consequently, extracting aquaculture has become crucial and valuable. However, due to the limitations of remote sensing image spatial resolution and traditional extraction methods, most research studies focus on aquaculture areas containing dikes rather than individually separable aquaculture ponds (ISAPs). This is not an accurate estimation of these aquaculture areas’ true size. In our study, we propose a rapid and effective object-based method of extracting ISAPs. We chose multi-scale segmentation to generate semantically meaningful image objects for various types of land cover, and then built a decision tree classifier according to the unique features of ISAPs. The results show that our method can remove small rivers and other easily confused features, which has thus far been difficult to accomplish with conventional methods. We obtained an overall precision value of 85.61% with a recall of 84.04%; compared to the support vector machine’s (SVM) overall precision value of 78.85% and recall rate of 61.21%, our method demonstrates greater accuracy and efficiency. We used this method to test the transferability of the algorithm to nearby areas, and the obtained accuracy exceeded 80%. The method proposed in this study could provide a readily available solution for the simple and efficient extracting of ISAPs and shows high spatiotemporal transferability.
Satellite-Derived Bathymetry in Support of Maritime Archaeological Research—VENμS Imagery of Caesarea Maritima, Israel, as a Case Study
Authors: Diaz Gerardo , Lehahn Yoav , Nantet Emmanuel
Published: March 2024

Deriving bathymetry by means of multispectral satellite imagery proves to be a replicable method, offering high-resolution coverage over large areas while keeping costs low. Maritime archaeologists often require bathymetric mapping at a high resolution and with a large spatial coverage. In this paper, we demonstrate the implementation of SDB in maritime archaeology using high-resolution (5 m/pixel) data from Vegetation and Environment monitoring on a New Micro-Satellite (VENμS) imagery. We focus on the area of the Roman harbour of Sebastos, located at Caesarea Maritima along the Israeli coast of the Eastern Mediterranean. For extracting SDB, we take an empirical approach, which is based on the integration of satellite imagery and sonar depth measurements, resulting in a blue-green band ratio algorithm that provides reliable results up to a water depth of 17 m. Comparison with in situ depth measurements yielded an RMSE of 0.688 m. The SDB mapping is complemented by satellite-based identification of above- and below-water rocks. The presented approach can readily be replicated in other regions using various types of multispectral satellite imagery, particularly when only coarse bathymetric sonar data are available, thus substantially contributing to our ability to perform maritime archaeological research.