bluuebunny/crossref_metadata_2025 huggingface dataset:
Chemical Characterization and Effect of a Lactobacilli-Postbiotic on Streptococcus mutans Biofilm In Vitro
Authors: Santana Guilherme Bandeira , Quelemes Patrick Veras , da Silva Neta Enedina Rodrigues , de Lima Sidney Gonçalo , Vale Gláuber Campos
Published: April 2024

Postbiotic is the term used to define the soluble factors, metabolic products, or byproducts released by live probiotic bacteria or after its lysis. The objective of this study was to carry out the chemical characterization of the postbiotic of Lacticaseibacillus rhamnosus LR-32 and to evaluate its in vitro effect on the development of the Streptococcus mutans biofilm. After the cultivation of the probiotic strain, the postbiotic was extracted by centrifuging the culture and filtering the supernatant. This postbiotic was characterized by using gas chromatography coupled with mass spectrometry (GC–MS), and then it was used to determine the growth inhibition of S. mutans in its planktonic form; additionally, its effects on the following parameters in 48 h biofilm were evaluated: viable bacteria, dry weight, and gene expression of glucosyltransferases and VicR gene. The control group consisted of the biofilm without any treatment. A paired t-test was performed for statistical analysis, with the p-value set at 5%. Seventeen compounds of various chemical classes were identified in the postbiotic, including sugars, amino acids, vitamins, and acids. The treatment with the postbiotic led to an inhibition of the growth of S. mutans in its planktonic form, as well as a decrease in the number of viable bacteria, reduction in dry weight, and a negative regulation of the gene expression of gtfB, gtfC, gtfD, and vicR in its biofilm state, compared with the nontreated group (p < 0.05). The postbiotic of L. rhamnosus impaired the development of S. mutans biofilm.
Mantle Sources and Geochemical Evolution of the Picture Gorge Basalt, Columbia River Basalt Group
Authors: Cahoon Emily B. , Streck Martin J. , Carlson Richard W. , Bindeman Ilya N.
Published: April 2024

The Columbia River Basalt Group (CRBG) is the youngest continental flood basalt province, proposed to be sourced from the deep-seated plume that currently resides underneath Yellowstone National Park. If so, the earliest erupted basalts from this province, such as those in the Picture Gorge Basalt (PGB), aid in understanding and modeling plume impingement and the subsequent evolution of basaltic volcanism. Using geochemical and isotopic data, this study explores potential mantle sources and magma evolution of the PGB. Long known geochemical signatures of the PGB include overall large ion lithophile element (LILE) enrichment and relative depletion of high field strength elements (HFSE) typical of other CRBG main-phase units. Basaltic samples of the PGB have 87Sr/86Sr ratios on the low end of the range displayed by other CRBG lavas and mantle-like δ18O values. The relatively strong enrichment of LILE and depletion of HFSE coupled with depleted isotopic signatures suggest a metasomatized upper mantle as the most likely magmatic source for the PGB. Previous geochemical modeling of the PGB utilized the composition of two high-MgO primitive dikes exposed in the northern portion of the Monument Dike swarm as parental melt. However, fractionation of these dike compositions cannot generate the compositional variability illustrated by basaltic lavas and dikes of the PGB. This study identifies a second potential parental PGB composition best represented by basaltic flows in the extended spatial distribution of the PGB. This composition also better reflects the lowest stratigraphic flows identified in the previously mapped extent of the PGB. Age data reveal that PGB lavas erupted first and throughout eruptions of main-phase CRBG units (Steens, Imnaha, Grande Ronde Basalt). Combining geochemical signals with these age data indicates cyclical patterns in the amounts of contributing mantle components. Eruption of PGB material occurred in two pulses, demonstrated by a ~0.4 Ma temporal gap in reported ages, 16.62 to 16.23 Ma. Coupling ages with observed geochemical signals, including relative elemental abundances of LILE, indicates increased influence of a more primitive, potentially plume-like source with time.
Impact of Carbon-Based Nanoparticles on Polyvinyl Alcohol Polarizer Features: Photonics Applications
Authors: Kamanina Natalia , Fedorova Larisa , Likhomanova Svetlana , Zubtcova Yulia , Kuzhakov Pavel
Published: April 2024

Among different inorganic and organic polarizer elements, thin-film light polarizers occupy a special place because of their flexibility, ease of integration into any optoelectronic circuit, and good functioning in the visible and near-infrared spectral range and can compete with Glan and Nicolas volumetric prisms. This paper presents the results of a study on how carbon-based nanoparticles influence on the basic properties of a well-known PVA-based polymer matrix, using which it is possible to obtain good transparency for parallel light components. An accent is made on graphene oxide nanoparticles, which are used as PVA sensitizers. It was shown for the first time that the structuring of PVA with graphene oxides allows an increased transmittance of the parallel light component to be obtained, saving the transmittance of the orthogonal one. Moreover, the graphene network can increase the mechanical strength of such thin-film PVA-based polarizers and provoke a change in the wetting angle. These advantages make it possible to use graphene oxide-structured thin-film light polarizers based on a PVA matrix as an independent optoelectronic element. Some comparative results for polarizers based on PVA-C70 structures are shown as well.
Enhancing Multi-Spectral Fingerprint Sensing for Trace Explosive Molecules with All-Silicon Metasurfaces
Authors: Lin Jie , Xue Ying , Wang Weijin , Sun Mingjun , Shi Shengnan , Zhang Shan , Shi Yanpeng
Published: April 2024

Spectroscopy is a powerful tool to identify the specific fingerprints of analytes in a label-free way. However, conventional sensing methods face unavoidable barriers in analyzing trace-amount target molecules due to the difficulties of enhancing the broadband molecular absorption. Here, we propose a sensing scheme to achieve strong fingerprint absorption based on the angular-scanning strategy on an all-silicon metasurface. By integrating the mid-infrared and terahertz sensing units into a single metasurface, the sensor can efficiently identify 2,4-DNT with high sensitivity. The results reveal that the fingerprint peak in the enhanced fingerprint spectrum is formed by the linked envelope. It exhibits a significant enhancement factor exceeding 64-fold in the terahertz region and more than 55-fold in the mid-infrared region. Particularly, the corresponding identification limit of 2,4-DNT is 1.32 µg cm−2, respectively. Our study will provide a novel research idea in identifying trace-amount explosives and advance practical applications of absorption spectroscopy enhancement identification in civil and military security industries.
Study on Spectrum Shifting and Pulse Splitting of Mode-Locked Fiber Lasers Based on NPR Technology
Authors: Hao Zhenhua , Hu Yu , Zhou Siyu , Liu Jinhui , Li Xiaohui , Wang Yishan , Gao Cunxiao
Published: April 2024

We conducted a systematic investigation into the spectral and pulse characteristics of C and L-band Nonlinear Polarization Rotation (NPR) mode-locked fiber lasers effectively employing nonlinear polarization rotation technology. In our experimental setup, we achieved a stable mode-locked state at 1560.076 nm, exhibiting a 3 dB spectral bandwidth of 9.1 nm. As the pump power increased, we observed spectral shifts accompanied by shifts in the first Kelly sideband and the generation of new Kelly sidebands. In this paper, the phenomenon of spectral deviation is elucidated through the interplay of self-phase modulation, group velocity drift, and polarization-dependent isolator (PD-ISO) filter effect, with an analysis of the formation and deviation of Kelly sidebands. Notably, spectral shift persisted even when the pump power exceeded 200 mW. However, continuous pump power escalation led to soliton splitting, resulting in the formation of new soliton beams. Based on the simultaneous generation of spectral shift and pulse splitting, our study contributes to an enhanced understanding of soliton dynamics in ultrafast fiber lasers and lays a foundation for the application of high-repetition-frequency harmonic mode-locked lasers with tunable wavelengths.
Efficacy of Laurus nobilis L. for Tight Junction Protein Imbalance in Leaky Gut Syndrome
Authors: Shin Yelim , Kim Jiyeon , Song Youngcheon , Kim Sangbum , Kong Hyunseok
Published: April 2024

Laurus nobilis L. (LNL) belongs to the evergreen Lauraceae family. It is native to the Mediterranean and widely distributed in the southern United States, Europe, and the Middle East. LNL is rich in active ingredients of the sesquiterpene lactone series and has been reported to have antioxidant, anti-inflammatory, and anticancer effects. And parthenolide, known as a sesquiterpene lactone-based compound, inhibits the activation of lipopolysaccharide-binding protein (LBP), which is a major trigger for leaky gut syndrome. However, the effectiveness of LNL in improving the state of increased intestinal permeability has not yet been reported. Therefore, we demonstrated the efficacy of LNL, which is known to be rich in parthenolide, in improving intestinal permeability induced by IL-13. We investigated the improvement in permeability and analyzed major tight junction proteins (TJs), permeability-related mechanisms, weight and disease activity indices, and corresponding cytokine mechanisms. LNL maintained TJs homeostasis and clinical improvement by reducing increased claudin-2 through the inhibition of IL-13/STAT6 activation in TJ-damaged conditions. These results are expected to be effective in preventing leaky gut syndrome through the TJ balance and to further improve intestinal-related diseases, such as inflammatory bowel disease.
Propranolol Hydrochloride Psychiatric Effectiveness and Oxidative Stress: An Update
Authors: Serreau Raphaël , Amirouche Ammar , Benyamina Amine , Berteina-Raboin Sabine
Published: April 2024

In this review, in addition to the potential cardiovascular applications of β-blockers and, more specifically, propranolol, we wanted to list the more recent applications in psychiatry as well as current knowledge on the impact of oxidative stress on propranolol hydrochloride and the oxidative stress that could be limited by the latter. In fact, a number of studies show that this molecule is modified by oxidative stress but is also able to limit it. Mention is also made to studies on the increasingly important problem of eliminating drug waste and its impact on the environment, particularly the marine environment. Given the increase in the consumption of medicines, more rigorous waste management is needed to avoid impacting biodiversity.
In Silico Design of Potential Small-Molecule Antibiotic Adjuvants against Salmonella typhimurium Ortho Acetyl Sulphydrylase Synthase to Address Antimicrobial Resistance
Authors: Elebiju Oluwadunni F. , Oduselu Gbolahan O. , Ogunnupebi Temitope A. , Ajani Olayinka O. , Adebiyi Ezekiel
Published: April 2024

The inhibition of O-acetyl sulphydrylase synthase isoforms has been reported to represent a promising approach for the development of antibiotic adjuvants. This occurs via the organism developing an unpaired oxidative stress response, causing a reduction in antibiotic resistance in vegetative and swarm cell populations. This consequently increases the effectiveness of conventional antibiotics at lower doses. This study aimed to predict potential inhibitors of Salmonella typhimurium ortho acetyl sulphydrylase synthase (StOASS), which has lower binding energy than the cocrystalized ligand pyridoxal 5 phosphate (PLP), using a computer-aided drug design approach including pharmacophore modeling, virtual screening, and in silico ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) evaluation. The screening and molecular docking of 4254 compounds obtained from the PubChem database were carried out using AutoDock vina, while a post-screening analysis was carried out using Discovery Studio. The best three hits were compounds with the PubChem IDs 118614633, 135715279, and 155773276, possessing binding affinities of −9.1, −8.9, and −8.8 kcal/mol, respectively. The in silico ADMET prediction showed that the pharmacokinetic properties of the best hits were relatively good. The optimization of the best three hits via scaffold hopping gave rise to 187 compounds, and they were docked against StOASS; this revealed that lead compound 1 had the lowest binding energy (−9.3 kcal/mol) and performed better than its parent compound 155773276. Lead compound 1, with the best binding affinity, has a hydroxyl group in its structure and a change in the core heterocycle of its parent compound to benzimidazole, and pyrimidine introduces a synergistic effect and consequently increases the binding energy. The stability of the best hit and optimized compound at the StOASS active site was determined using RMSD, RMSF, radius of gyration, and SASA plots generated from a molecular dynamics simulation. The MD simulation results were also used to monitor how the introduction of new functional groups of optimized compounds contributes to the stability of ligands at the target active site. The improved binding affinity of these compounds compared to PLP and their toxicity profile, which is predicted to be mild, highlights them as good inhibitors of StOASS, and hence, possible antimicrobial adjuvants.
Evaluating the Safety of Herbal Medicine on Renal Function: A Comprehensive Analysis from Six Randomized Controlled Trials Conducted with Four Formulations from Traditional Korean Medicine
Authors: Joung Jin-Yong , Son Chang-Gue
Published: April 2024

The growing popularity of herbal medicine raises concerns about potential nephrotoxicity risks, while limited evidence hinders a comprehensive impact assessment. This study aims to investigate the overall risk features of herbal medicine on kidney injury. We conducted a retrospective analysis on renal function changes, including blood urea nitrogen (BUN), serum creatinine, and estimated glomerular filtration rate (eGFR), through data from six randomized controlled trials (RCTs) in South Korea. A total of 407 participants (142 males, 265 females) received either one of four different herbal medicines (240 participants) or a placebo (167 participants). When comparing changes in eGFR regarding the mean, 90th-percentile value, and 20% reduction after treatment, there was no significant difference between the herbal-treated and placebo groups. This study provided a helpful reference for examining the safety issues of herbal remedies, especially regarding kidney function.
Art after the Untreatable: Psychoanalysis, Sexual Violence, and the Ethics of Looking in Michaela Coel’s I May Destroy You
Authors: Wright Melissa A.
Published: April 2024

This essay brings psychoanalytic theory on trauma together with film and television criticism on rape narrative in an analysis of Michael Coel’s 2020 series I May Destroy You. Beyond the limited carceral framework of the police procedural, which dislocates the act of violence from the survivor’s history and context, Coel’s polyvalent, looping narrative metabolizes rape television’s forms and genres in order to stage and restage both trauma and genre again and anew. Contesting common conceptions of vulnerability and susceptibility that prefigure a violent breach of autonomy, Coel’s series and her interviews about it invite an ethics of looking that embraces a curiosity in the unknowable and untreatable kernel of subjective experience and defies and resists a policing of the survivor’s thoughts and emotions. By emphasizing and exploring what psychoanalysis calls the “afterwardness” of trauma, Coel foregrounds her main character’s subjectivity prior to her victimization, widens the sphere of consequence beyond the victim and criminal justice system to the survivor’s larger community, and entreats that community to preserve a space for her to look and look again at everything, without judgment.
A Physics-Based Tweedie Exponential Dispersion Process Model for Metal Fatigue Crack Propagation and Prognostics
Authors: Yang Lin , Wang Zirong , Chen Zhen , Pan Ershun
Published: April 2024

Most structural faults in metal parts can be attributed to fatigue crack propagation. The analysis and prognostics of fatigue crack propagation play essential roles in the health management of mechanical systems. Due to the impacts of different uncertainty factors, the crack propagation process exhibits significant randomness, which causes difficulties in fatigue life prediction. To improve prognostic accuracy, a physics-based Tweedie exponential dispersion process (TEDP) model is proposed via integrating Paris Law and the stochastic process. This TEDP model can capture both the crack growth mechanism and uncertainty. Compared with other existing models, the TEDP taking Wiener process, Gamma process, and inverse process as special cases is more general and flexible in modeling complex degradation paths. The probability density function of the model is derived based on saddle-joint approximation. The unknown parameters are calculated via maximum likelihood estimation. Then, the analytic expressions of the distributions of lifetime and product reliability are presented. Significant findings include that the proposed TEDP model substantially enhances predictive accuracy in lifetime estimations of mechanical systems under varying operational conditions, as demonstrated in a practical case study on fatigue crack data. This model not only provides highly accurate lifetime predictions, but also offers deep insights into the reliability assessments of mechanically stressed components.
Wanwuyiti and Finding God in All Things: A Comparative Study between Neo-Confucian Self-Cultivation and Ignatian Spirituality
Authors: Fu Amy Yu
Published: April 2024

It seems that the early Jesuits misinterpreted the key Neo-Confucian terms taiji/li from an Aristotelian perspective in the seventeenth century, thereby leading to a dialogical failure in their initial encounter with Neo-Confucian tradition. What necessitates interreligious dialogue today is a pluralistic stance that deems all religious quests worthy in their own context. Therefore, this paper renews the dialogue between two spiritual traditions, long overdue, by reading two representative texts, side by side, from each tradition on self-cultivation: Reflections on Things at Hand (twelfth century) and The Spiritual Exercises (sixteenth century). The comparison showcases that the notion of “wanwuyiti”, a concomitant of the Confucian ren, is tantamount to a religious imperative for human ethical engagements, and the Ignatian axiom “Finding God in All Things” energizes a spiritual self-transformation to forge an intimate bond with God and the world. While Neo-Confucian cultivation focuses on the removal of desires, seeking to maintain “equilibrium” and “centrality”, the Ignatian exercises foreground commitment to “discernment” and “indifference”. The Neo-Confucians address human and worldly affairs in a procedural manner, with ever-broadening horizons, to establish an orderly society. In contrast, the Ignatian self is directed toward an orderly life to serve, love, and bring ever more to God’s Divine Majesty.
Intellectual Humility and the Argument from Evil: A Reply to Zain Ali
Authors: Bishop John , Perszyk Ken
Published: April 2024

This is a response to Zain Ali’s critique in this journal of our presentation of a ‘right relationship’ normatively relativised ‘logical’ Argument from Evil. Our argument aims to show that the existence of horrendous evils (as defined by Marilyn Adams) is incompatible with the existence of the personal omniGod (a person or personal being who is all-powerful, all-knowing, and perfectly good), given certain reasonable judgments about what a personal God’s perfect goodness would imply about the way God relates to those caught up in horrendous evils. We reply to Ali’s main criticism that our assumptions about divine goodness are unjustified, and show a lack of intellectual humility. We defend the claim that, if God is a person, then God’s goodness is moral goodness according to our best human theory of what that implies. We accept that God’s situation as creator and sustainer of all that exists may justify ‘divine exceptionalism’: God’s personal moral goodness may be consistent with ways of relating to others that would fall far short of perfection in human-to-human relationships. But in that case, we argue, intellectual humility may be better served by accepting that God is so exceptional that God should not be understood as a person at all, which is the prevailing Muslim view, as Ali himself acknowledges.
Elimination of Irregular Boundaries and Seams for UAV Image Stitching with a Diffusion Model
Authors: Chen Jun , Luo Yongxi , Wang Jie , Tang Honghua , Tang Yixian , Li Jianhui
Published: April 2024

Unmanned aerial vehicle (UAV) image stitching refers to the process of combining multiple UAV images into a single large-format, wide-field image, and the stitched image often contains large irregular boundaries and multiple stitching seams. Usually, irregular boundaries are addressed using grid-constrained methods, while seams are optimized through the design of energy functions and penalty terms applied to the pixels at the seams. The above-mentioned two solutions can only address one of the two issues individually and are often limited to pairwise stitching of images. To the best of our knowledge, there is no unified approach that can handle both seams and irregular boundaries in the context of multi-image stitching for UAV images. Considering that addressing irregular boundaries involves completing missing information for regularization and that mitigating seams involves generating images near the stitching seams, both of these challenges can be viewed as instances of a mask-based image completion problem. This paper proposes a UAV image stitching method based on a diffusion model. This method uniformly designs masks for irregular boundaries and stitching seams, and the unconditional score function of the diffusion model is then utilized to reverse the process. Additional manifold gradient constraints are applied to restore masked images, eliminating both irregular boundaries and stitching seams and resulting in higher perceptual quality. The restoration maintains high consistency in texture and semantics. This method not only simultaneously addresses irregular boundaries and stitching seams but also is unaffected by factors such as the number of stitched images, the shape of irregular boundaries, and the distribution of stitching seams, demonstrating its robustness.
Short-Term Foredune Dynamics in Response to Invasive Vegetation Control Actions
Authors: Bastos Ana Pestana , Taborda Rui , Andrade César , Ponte Lira Cristina , Nobre Silva Ana
Published: April 2024

Efforts to control the spread of invasive alien species (IAS) in coastal dunes are essential to protect biodiversity and maintain the integrity of the ecosystem. However, the timing and extent of these control measures often do not consider their potential impact on dune morphodynamics. This study investigated how IAS control measures can affect sand dune mobility. Research involved monitoring short-term morphological and vegetation changes using close-range remote sensing along with historical wind data. Findings revealed that changes in vegetation cover significantly impacted dune system evolution, notably increasing sand mobility when IAS vegetation was removed. Considering the seasonal wind regime, dominated by offshore winds in the summer, removing vegetation during this period can promote seaward sediment transport, potentially resulting in sediment loss to the beach. These findings highlight the importance of understanding sediment mobility patterns when planning vegetation control actions within dune restoration strategies.
Revealing Subtle Active Tectonic Deformation: Integrating Lidar, Photogrammetry, Field Mapping, and Geophysical Surveys to Assess the Late Quaternary Activity of the Sava Fault (Southern Alps, Slovenia)
Authors: Jamšek Rupnik Petra , Atanackov Jure , Horn Barbara , Mušič Branko , Zajc Marjana , Grützner Christoph , Ustaszewski Kamil , Tsukamoto Sumiko , Novak Matevž , Milanič Blaž , Markelj Anže , Ivančič Kristina , Novak Ana , Jež Jernej , Žebre Manja , Bavec Miloš , Vrabec Marko
Published: April 2024

We applied an interdisciplinary approach to analyze the late Quaternary activity of the Sava Fault in the Slovenian Southern Alps. The Sava Fault is an active strike-slip fault, and part of the Periadriatic Fault System that accommodated the convergence of Adria and Europe. It is one of the longest faults in the Southern Alps. Using high-resolution digital elevation models from lidar and photogrammetric surveys, we were able to overcome the challenges of assessing fault activity in a region with intense surface processes, dense vegetation, and relatively low fault slip rates. By integrating remote sensing analysis, geomorphological mapping, structural geological investigations, and near-surface geophysics (electrical resistivity tomography and ground penetrating radar), we were able to find subtle geomorphological indicators, detect near-surface deformation, and show distributed surface deformation and a complex fault pattern. Using optically stimulated luminescence dating, we tentatively estimated a slip rate of 1.8 ± 0.4 mm/a for the last 27 ka, which exceeds previous estimates and suggests temporal variability in fault behavior. Our study highlights the importance of modern high-resolution remote sensing techniques and interdisciplinary approaches in detecting tectonic deformation in relatively low-strain rate environments with intense surface processes. We show that slip rates can vary significantly depending on the studied time window. This is a critical piece of information since slip rates are a key input parameter for seismic hazard studies.
Transferability of Machine Learning Models for Crop Classification in Remote Sensing Imagery Using a New Test Methodology: A Study on Phenological, Temporal, and Spatial Influences
Authors: Hoppe Hauke , Dietrich Peter , Marzahn Philip , Weiß Thomas , Nitzsche Christian , Freiherr von Lukas Uwe , Wengerek Thomas , Borg Erik
Published: April 2024

Machine learning models are used to identify crops in satellite data, which achieve high classification accuracy but do not necessarily have a high degree of transferability to new regions. This paper investigates the use of machine learning models for crop classification using Sentinel-2 imagery. It proposes a new testing methodology that systematically analyzes the quality of the spatial transfer of trained models. In this study, the classification results of Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Stochastic Gradient Descent (SGD), Multilayer Perceptron (MLP), Support Vector Machines (SVM), and a Majority Voting of all models and their spatial transferability are assessed. The proposed testing methodology comprises 18 test scenarios to investigate phenological, temporal, spatial, and quantitative (quantitative regarding available training data) influences. Results show that the model accuracies tend to decrease with increasing time due to the differences in phenological phases in different regions, with a combined F1-score of 82% (XGBoost) when trained on a single day, 72% (XGBoost) when trained on the half-season, and 61% when trained over the entire growing season (Majority Voting).
Transfer Learning-Based Hyperspectral Image Classification Using Residual Dense Connection Networks
Authors: Zhou Hao , Wang Xianwang , Xia Kunming , Ma Yi , Yuan Guowu
Published: April 2024

The extraction of effective classification features from high-dimensional hyperspectral images, impeded by the scarcity of labeled samples and uneven sample distribution, represents a formidable challenge within hyperspectral image classification. Traditional few-shot learning methods confront the dual dilemma of limited annotated samples and the necessity for deeper, more effective features from complex hyperspectral data, often resulting in suboptimal outcomes. The prohibitive cost of sample annotation further exacerbates the challenge, making it difficult to rely on a scant number of annotated samples for effective feature extraction. Prevailing high-accuracy algorithms require abundant annotated samples and falter in deriving deep, discriminative features from limited data, compromising classification performance for complex substances. This paper advocates for an integration of advanced spectral–spatial feature extraction with meta-transfer learning to address the classification of hyperspectral signals amidst insufficient labeled samples. Initially trained on a source domain dataset with ample labels, the model undergoes transference to a target domain with minimal samples, utilizing dense connection blocks and tree-dimensional convolutional residual connections to enhance feature extraction and maximize spatial and spectral information retrieval. This approach, validated on three diverse hyperspectral datasets—IP, UP, and Salinas—significantly surpasses existing classification algorithms and small-sample techniques in accuracy, demonstrating its applicability to high-dimensional signal classification under label constraints.
Human Activity Recognition in a Free-Living Environment Using an Ear-Worn Motion Sensor
Authors: Boborzi Lukas , Decker Julian , Rezaei Razieh , Schniepp Roman , Wuehr Max
Published: April 2024

Human activity recognition (HAR) technology enables continuous behavior monitoring, which is particularly valuable in healthcare. This study investigates the viability of using an ear-worn motion sensor for classifying daily activities, including lying, sitting/standing, walking, ascending stairs, descending stairs, and running. Fifty healthy participants (between 20 and 47 years old) engaged in these activities while under monitoring. Various machine learning algorithms, ranging from interpretable shallow models to state-of-the-art deep learning approaches designed for HAR (i.e., DeepConvLSTM and ConvTransformer), were employed for classification. The results demonstrate the ear sensor’s efficacy, with deep learning models achieving a 98% accuracy rate of classification. The obtained classification models are agnostic regarding which ear the sensor is worn and robust against moderate variations in sensor orientation (e.g., due to differences in auricle anatomy), meaning no initial calibration of the sensor orientation is required. The study underscores the ear’s efficacy as a suitable site for monitoring human daily activity and suggests its potential for combining HAR with in-ear vital sign monitoring. This approach offers a practical method for comprehensive health monitoring by integrating sensors in a single anatomical location. This integration facilitates individualized health assessments, with potential applications in tele-monitoring, personalized health insights, and optimizing athletic training regimes.
Compressed Sensing for Biomedical Photoacoustic Imaging: A Review
Authors: Wang Yuanmao , Chen Yang , Zhao Yongjian , Liu Siyu
Published: April 2024

Photoacoustic imaging (PAI) is a rapidly developing emerging non-invasive biomedical imaging technique that combines the strong contrast from optical absorption imaging and the high resolution from acoustic imaging. Abnormal biological tissues (such as tumors and inflammation) generate different levels of thermal expansion after absorbing optical energy, producing distinct acoustic signals from normal tissues. This technique can detect small tissue lesions in biological tissues and has demonstrated significant potential for applications in tumor research, melanoma detection, and cardiovascular disease diagnosis. During the process of collecting photoacoustic signals in a PAI system, various factors can influence the signals, such as absorption, scattering, and attenuation in biological tissues. A single ultrasound transducer cannot provide sufficient information to reconstruct high-precision photoacoustic images. To obtain more accurate and clear image reconstruction results, PAI systems typically use a large number of ultrasound transducers to collect multi-channel signals from different angles and positions, thereby acquiring more information about the photoacoustic signals. Therefore, to reconstruct high-quality photoacoustic images, PAI systems require a significant number of measurement signals, which can result in substantial hardware and time costs. Compressed sensing is an algorithm that breaks through the Nyquist sampling theorem and can reconstruct the original signal with a small number of measurement signals. PAI based on compressed sensing has made breakthroughs over the past decade, enabling the reconstruction of low artifacts and high-quality images with a small number of photoacoustic measurement signals, improving time efficiency, and reducing hardware costs. This article provides a detailed introduction to PAI based on compressed sensing, such as the physical transmission model-based compressed sensing method, two-stage reconstruction-based compressed sensing method, and single-pixel camera-based compressed sensing method. Challenges and future perspectives of compressed sensing-based PAI are also discussed.
DEMA: A Deep Learning-Enabled Model for Non-Invasive Human Vital Signs Monitoring Based on Optical Fiber Sensing
Authors: Zhang Qichang , Wang Qing , Lyu Weimin , Yu Changyuan
Published: April 2024

Optical fiber sensors are extensively employed for their unique merits, such as small size, being lightweight, and having strong robustness to electronic interference. The above-mentioned sensors apply to more applications, especially the detection and monitoring of vital signs in medical or clinical. However, it is inconvenient for daily long-term human vital sign monitoring with conventional monitoring methods under the uncomfortable feelings generated since the skin and devices come into direct contact. This study introduces a non-invasive surveillance system that employs an optical fiber sensor and advanced deep-learning methodologies for precise vital sign readings. This system integrates a monitor based on the MZI (Mach–Zehnder interferometer) with LSTM networks, surpassing conventional approaches and providing potential uses in medical diagnostics. This could be potentially utilized in non-invasive health surveillance, evaluation, and intelligent health care.
Enhanced Tracer Particle Detection in Dynamic Bulk Systems Based on Polarimetric Radar Signature Correlation
Authors: Hattenhorst Birk , Karsch Nicholas , Musch Thomas
Published: April 2024

This contribution focuses on the detection of tracer particles within non-homogeneous bulk media, aiming to enhance insights into particulate systems. Polarimetric radar measurements are employed, utilizing cross-polarizing channels in order to mitigate interference from bulk media reflections. To distinguish the tracer particle in the measurements, a resonant cross-polarizing structure is constructed, facilitating the isolation of frequency signatures from the surrounding bulk clutter. In addition to characterizing the bulk and tracer components, this study provides a detailed presentation and discussion of the measurement setup, along with the employed signal processing methods. The effectiveness of the proposed methods is demonstrated through comprehensive measurements, where a tracer particle is systematically positioned at various locations. The results affirm the feasibility and efficacy of the approach, highlighting its applicability for enhanced dynamic monitoring in particulate systems within industrial processes.
Hydrolases Control Soil Carbon Sequestration in Alpine Grasslands in the Tibetan Plateau
Authors: Zhang Yuanye , Wang Xia , Sun Yuxin , Wu Jinhong , Deng Tao , Yuan Menghan , Duan Wenhui , Zhao Yunfei
Published: April 2024

Microbial-sourced carbon is an important component of soil organic carbon (SOC) and influences SOC’s size and turnover. Soil extracellular enzymes can participate in the degradation of plants in the soil to produce substances needed by microorganisms, which in turn affects microbial sources of carbon. Most of the current studies focus on the effects of soil extracellular enzymes on SOC pools, while there is a lack of clarity regarding the effects on microbial sources of carbon during SOC pool formation. In this paper, three typical grassland types (alpine meadow, alpine grassland, and desert grassland, respectively) on the Tibetan Plateau were selected as research objects to investigate the effects of grassland type and soil depth on microbial-sourced carbon (amino sugars) and soil extracellular enzymes (hydrolytic enzymes: β-glucosidase and cellulase; oxidative enzymes: peroxidase and polyphenol oxidase) in the soil profiles. Our study shows that the content of amino sugars in the three grassland types followed the order: alpine meadow > alpine grassland > desert grassland; the content of hydrolytic enzyme followed the order of alpine meadow > alpine grassland > desert grassland; the content of oxidative enzyme followed the order of desert grassland > alpine grassland > alpine meadow; amino sugars content showed a positive correlation with hydrolytic enzymes and a negative correlation with oxidative enzymes; and the hydrolytic enzyme was the main factor promoting the accumulation of amino sugars. The environmental conditions of alpine meadows and alpine grasslands are more favorable for the formation of microbial-derived carbon and have greater sequestration potential, while desert grasslands are not favorable for the formation of microbial-derived carbon. The results of this study provide a reference basis for exploring the model of organic carbon sequestration in the Tibetan Plateau.
Multi-Model Comprehensive Inversion of Surface Soil Moisture from Landsat Images Based on Machine Learning Algorithms
Authors: Lv Weitao , Hu Xiasong , Li Xilai , Zhao Jimei , Liu Changyi , Li Shuaifei , Li Guorong , Zhu Haili
Published: April 2024

Soil moisture plays an important role in maintaining ecosystem stability and sustainable development, especially for the upper reaches of the Yellow River region. Therefore, accurately and conveniently monitoring soil moisture has become the focus of scholars. This study combines three machine learning algorithms: random forest (RF), support vector machine (SVM), and back propagation neural network (BPNN)—with the traditional monitoring of soil moisture using remote sensing indices to construct a more accurate soil moisture inversion model. To enhance the accuracy of the soil moisture inversion model, 27 environmental variables were screened and grouped, including vegetation index, salinity index, and surface temperature, to determine the optimal combination of variables. The results show that screening the optimal independent variables in the Xijitan landslide distribution area lowered the root mean square error (RMSE) of the RF model by 16.95%. Of the constructed models, the combined model shows the best applicability, with the highest R2 of 0.916 and the lowest RMSE of 0.877% with the test dataset; the further research shows that the BPNN model achieved higher overall accuracy than the other two individual models, with the test set R2 being 0.809 and the RMSE 0.875%. The results of this study can provide a theoretical reference for the effective use of Landsat satellite data to monitor the spatial and temporal distribution of and change in soil water content on the two sides of the upper Yellow River basin under vegetation cover.
Progress and Prospects of Non-Grain Production of Cultivated Land in China
Authors: Su Haizhen , Liu Fenggui , Zhang Haifeng , Ma Xiaofan , Sun Ailing
Published: April 2024

Cultivated land is essential for grain production. As a major agricultural country, China’s non-grain use of cultivated land not only affects national food security and sustainable agricultural development but also impacts the quality of cultivated land and farmers’ livelihoods. This study used bibliometrics to visualize and analyze 413 articles from the China Knowledge Network (CNKI) and Web of Science (WOS) databases concerning non-grain production of cultivated land (NGPCL). The results reveal the following: (1) The number of annual publications in this area has increased from 2009 to 2023 with the focus of NGPCL research shifting from describing the phenomenon to analyzing its driving mechanisms and then to exploring spatial patterns and governance. (2) The spatial distribution of research institutions is marked by the convergence of multiple entities with the central and eastern regions of China being pivotal research areas and transnational collaborative research becoming increasingly visible. (3) High-frequency keywords include NGPCL, food security, and farmland transfer, focusing on the historical context, effects, driving mechanisms and preventative strategies related to NGPCL. There is a notable evolutionary relationship between farmland transfer and NGPCL themes. (4) Future research should broaden the geographical scope and assess NGPCL trends from global, regional, and multiscale perspectives. Efforts should be made to enhance data accuracy and conduct spatial and temporal simulations, trend analyses, and risk assessments. Furthermore, policy design should consider the structure of human–land relationships to support sustainable agricultural development. This study provides an important reference for deepening and broadening the field of NGPCL.
Study on River Protection and Improvement Based on a Comprehensive Statistical Model in a Coastal Plain River Network
Authors: Wang Junmin , Fu Lei , Lu Cheng , Wang Shiwu , Zhu Yongshu , Xu Zeqi , Gui Zihan
Published: April 2024

When considering the contradictions between river management and protection in a typical plain river network, it is always confirmed that the river area has usually been encroached upon due to the development of human society. Based on the analysis of multiple attributes of the river network, a statistical model has been proposed in this study in order to determine the river network protection indices such as river area ratio, storage capacity and flux. In this study, a numerical method is proposed to improve the structure and connectivity of the river network by calculating the occupation and supplement balance. According to the principle of water area dynamic balance, the river network structure and its connectivity are improved through water area adjustment in a typical coastal city. As the simulation results show, the water surface ratio equals 8.17%, the storage capacity equals 112.6 million m3 and the water flux equals to 656.06 m3/s in the selected study area. The flood drainage capacity is introduced as the priority function, other functions are also improved due to river management and protection. The harmonious and sustainable coexistence between human society and the river network is then promoted. This comprehensive statistical model proved to be a good tool for the coastal area to enhance the comprehensive attributes of the coastal plain river network and the sustainable development of the local area in the future.
Searching for Dairy Farm Resilience with the R4D Project: Innovation Needs to Be Sustainable
Authors: Kołoszycz Ewa , Wilczyński Artur , Menghi Alberto , Soffiantini Chiara Serena , Klopčič Marija
Published: April 2024

Farms face new challenges due to both climate change and the influence of economic, social, and institutional factors. The study examines the key aspects of the resilience of dairy farms that, if in place, can ensure their sustainable functioning. The purpose of the article is to diagnose and classify the main needs for strengthening the resilience of dairy farms. In implementing the research, an interdisciplinary approach developed within the framework of the Resilience for Dairy (R4D) project was used. The results of the research reveal differences in the classification of key needs to strengthen resilience by stakeholder groups. Farmers reported a need for solutions that increase their income and production flexibility. The other groups of respondents focused on reducing antibiotic use and improving resource efficiency and environmental protection. The analysis of key needs classifications among respondent groups, segmented by age, and the comparison of needs between respondents from Western European and Central and Eastern European countries showed minor differences, indicating a similarity in the perceived importance of needs. The study also identified needs perceived by respondents as essential to increasing dairy farm resilience. These primarily include the following: the work–life balance of farmers, effective communication with the community, and implementation of technical innovations related to dairy farming.
Influence of Regional Temperature Anomalies on Strawberry Yield: A Study Using Multivariate Copula Analysis
Authors: Unnikrishnan Poornima , Ponnambalam Kumaraswamy , Karray Fakhri
Published: April 2024

A thorough understanding of the impact of climatic factors on agricultural production is crucial for improving crop models and enhancing predictability of crop prices and yields. Fluctuations in crop yield and price can have significant implications for the market sector and farming community. Given the projected increase in frequency and intensity of extreme events, reliable modelling of cropping patterns becomes essential. Temperature anomalies are expected to play a prominent role in future extreme events, emphasizing the need to comprehend their influence on crop yield. Forecasting extreme yield, which encompasses both the highest and lowest levels of agricultural production within a given time period, along with peak crop prices representing the highest market values, poses greater challenges in forecasting compared to other values. Probability-based predictions, accounting for uncertainty and variability, offer a more accurate approach for extreme value estimation and risk assessment. In this study, we employ a multivariate analysis based on vine copula to explore the interdependencies between temperature anomalies and daily strawberry yield in Santa Maria, California. By considering the maximum and minimum daily yields each month, we observe an increased probability of yield loss with rising temperature anomalies. While we do not explicitly consider the specific impacts of temperature anomalies under individual Representative Concentration Pathway (RCP) scenarios, our analysis is conducted within the broader context of the current global warming scenario. This allows us to capture the overall anticipated effects of regional temperature anomalies on agriculture. The findings of this study have potential impacts and consequences for understanding the vulnerability of agricultural systems and improving crop model predictions. By enhancing our understanding of the relationships between temperature anomalies and crop yield, we can inform decision-making processes related to the impact of climate change on agriculture. This research contributes to the ongoing efforts in improving agricultural sustainability and resilience in the face of changing climatic conditions.
The Complicated Relationship between Innovation and Sustainability: Opportunities, Threats, Challenges, and Trends
Authors: Nenni Maria Elena , Di Pasquale Valentina , Boyer James
Published: April 2024

Since the announcement of the 17 Sustainable Development Goals (SDGs) by the UN in 2015, innovation has been recognized as a crucial tool for achieving these goals by 2030 [...]