bluuebunny/crossref_metadata_2025 huggingface dataset:
Pharmacovigilance Practices by Healthcare Providers in Oncology: A Cross-Sectional Study
Authors: Alkofide Hadeel , Almalag Haya M. , Alromaih Mashael , Alotaibi Lama , Altuwaijri Njoud , Al Aloola Noha , Alsabhan Jawza F. , Bawazeer Ghada A. , Al Juffali Lobna , Alfaraj Rihaf , Alkhudair Nora , Aljadeed Raniah , Aljadeed Rana , Alnaim Lamya S.
Published: May 2024

Investigating pharmacovigilance (PV) practices among oncology healthcare providers (HCPs) is crucial for patient safety in oncology settings. This study aimed to assess the awareness, attitudes, and practices towards PV and identify barriers to effective adverse drug reaction (ADR) reporting for HCPs working in oncology-related settings. Employing a cross-sectional survey design, we collected data from 65 HCPs, focusing on their experiences with ADR reporting, education on ADR management, and familiarity with PV protocols. The results showed that about half of the responders were pharmacists. Around 58.9% of the respondents reported ADRs internally, and 76.9% had received some form of ADR-related education. However, only 38.5% were aware of formal ADR review procedures. Methotrexate and paclitaxel emerged as the drugs most frequently associated with ADRs. The complexity of cancer treatments was among the common reasons for the low reporting of ADRs by the study participants. The findings highlight the need for enhanced PV education and standardized reporting mechanisms to improve oncology care. We conclude that reinforcing PV training and streamlining ADR-reporting processes are critical to optimizing patient outcomes and safety in oncology, advocating for targeted educational interventions and the development of unified PV guidelines.
Association of Polyp Regression after Loading Phase with 12-Month Outcomes of Eyes with Polypoidal Choroidal Vasculopathy
Authors: Kimura Misa , Sakurada Yoichi , Fukuda Yoshiko , Matsubara Mio , Kotoda Yumi , Kasai Yuka , Sugiyama Atsushi , Kikushima Wataru , Tsuru Daphne Viel , Kashiwagi Kenji
Published: May 2024

Purpose: We compared 12-month outcomes of eyes with polypoidal choroidal vasculopathy (PCV) with or without complete regression of polyps observed one month after three monthly intravitreal administrations (loading phase) of aflibercept (2.0 mg/0.05 mL) or brolucizumab (6.0 mg/0.05 mL). Methods: All patients underwent indocyanine green angiography at both baseline and 3 months after initial injection and were followed up monthly with an as-needed regimen for up to 12 months. A total of 62 patients with PCV were included: 30 eyes were treated with brolucizumab, and 32 were treated with aflibercept. Eyes with complete regression of polyps (regression group) had significantly smaller maximum polyp diameter and were more frequently treated with brolucizumab than those without complete regression (non-regression) group. Results: Best corrected visual acuity was comparable between the two groups at 12 months. Although the 12-month retreatment-free proportion was comparable between the two groups (33.0% versus 27.0%, p = 0.59), a retreatment-free period was significantly longer in the regression group than in the non-regression group (8.3 ± 3.3 versus 6.5 ± 3.6 months, p = 0.022), and the number of additional injections was significantly fewer in the regression group than in the non-regression group (1.2 ± 1.2 versus 3.0 ± 2.6, p = 0.007). Conclusions: Complete regression of polyps observed after the initial phase possibly prolongs the retreatment-free period and reduces the number of additional injections irrespective of aflibercept or brolucizumab.
Herbal Cannabis and Depression: A Review of Findings Published over the Last Three Years
Authors: Haller Jozsef
Published: May 2024

Public perception contrasts scientific findings on the depression-related effects of cannabis. However, earlier studies were performed when cannabis was predominantly illegal, its production was mostly uncontrolled, and the idea of medical cannabis was incipient only. We hypothesized that recent changes in attitudes and legislations may have favorably affected research. In addition, publication bias against cannabis may have also decreased. To investigate this hypothesis, we conducted a review of research studies published over the last three years. We found 156 relevant research articles. In most cross-sectional studies, depression was higher in those who consumed cannabis than in those who did not. An increase in cannabis consumption was typically followed by an increase in depression, whereas withdrawal from cannabis ameliorated depression in most cases. Although medical cannabis reduced depression in most studies, none of these were placebo-controlled. In clinical studies published in the same period, the placebo also ameliorated depression and, in addition, the average effect size of the placebo was larger than the average effect size of medical cannabis. We also investigated the plausibility of the antidepressant effects of cannabis by reviewing molecular and pharmacological studies. Taken together, the reviewed findings do not support the antidepressant effects of herbal cannabis.
Harnessing the Power of Polyphenols: A New Frontier in Disease Prevention and Therapy
Authors: El Oirdi Mohamed
Published: May 2024

There are a wide variety of phytochemicals collectively known as polyphenols. Their structural diversity results in a broad range of characteristics and biological effects. Polyphenols can be found in a variety of foods and drinks, including fruits, cereals, tea, and coffee. Studies both in vitro and in vivo, as well as clinical trials, have shown that they possess potent antioxidant activities, numerous therapeutic effects, and health advantages. Dietary polyphenols have demonstrated the potential to prevent many health problems, including obesity, atherosclerosis, high blood sugar, diabetes, hypertension, cancer, and neurological diseases. In this paper, the protective effects of polyphenols and the mechanisms behind them are investigated in detail, citing the most recent available literature. This review aims to provide a comprehensive overview of the current knowledge on the role of polyphenols in preventing and managing chronic diseases. The cited publications are derived from in vitro, in vivo, and human-based studies and clinical trials. A more complete understanding of these naturally occurring metabolites will pave the way for the development of novel polyphenol-rich diet and drug development programs. This, in turn, provides further evidence of their health benefits.
Population Pharmacokinetics in Oncology and Its Clinical Applications
Authors: Widmer Nicolas , Guidi Monia , Buclin Thierry
Published: May 2024

Most traditional cytotoxic drugs are characterized by steep dose–response relationships and narrow therapeutic windows [...]
High-Performance Fiber Ring Laser Based on Polarization Space Parity-Time Symmetry Breaking
Authors: Zhang Fengling , Wu Zhengmao , Tong Xin , Xia Guangqiong
Published: May 2024

This work proposes and experimentally demonstrates a high-performance polarization space parity-time (PT) symmetric fiber ring laser to achieve a low-noise, narrow-linewidth, and highly stable single-longitudinal-mode output. The gain/loss and coupling coefficients are regulated by adjusting a polarization controller (PC) and the pumping current of an erbium-doped fiber amplifier (EDFA) within the ring cavity. The results show that the single longitudinal mode oscillation of the laser can be implemented by PT symmetry breaking. The frequency noise spectral density and the linewidth characteristics of the laser are evaluated by the short-delay self-heterodyne method. The results reveal that excellent low-frequency noise (181 Hz2/Hz at a 10 kHz offset frequency) and narrow fundamental linewidth (68 Hz) can be achieved. Additionally, the laser exhibits outstanding stability with only 0.64 pm wavelength drift over 30 min. By tuning an optical tunable filter (OTF), the wavelength tunable range of the laser can cover the entire C-band. Furthermore, the impacts of different fiber length on the frequency noise spectral density and the filter bandwidth on stability are analyzed, offering guidance for component selection in such laser systems.
Interference Mitigation in VLC Systems using a Variable Focus Liquid Lens
Authors: Bera Krishnendu , Karmakar Nemai
Published: May 2024

The field of view (FOV) is an important parameter of a visible light communication (VLC) receiver. A variable FOV can be useful to mitigate interference from neighboring cells in a multi-cell VLC network. The existing works on dynamic FOV VLC receivers have used a mechanical iris to control the receiver’s FOV, making the VLC receiver bulky, slow, and power-consuming. In this article, an electronically controlled variable focus liquid lens is used to vary the FOV of the receiver dynamically. A low-cost microcontroller-based feedback control system controls the effective FOV of the receiver to reject signals from unwanted transmitters, thus maximizing the signal-to-interference plus noise ratio (SINR) of the received signal. Experimental results show that the proposed technique effectively improves SINR performance in a multi-cell VLC network. To the best of the authors’ knowledge, this is the first reported work to utilize an electronically controlled compact liquid lens for designing a dynamic FOV VLC receiver.
Gradient Probabilistic Algorithm for Compact Lithium Niobate Integrated Photonic Devices
Authors: Sheng Lizhe , Zhang Haiting , Zhang Jingjing , Tong Yanqun , Song Xiaoxian , Dai Zijie , Yu Yu , Wang Yanan , Gao Zhongkun , Guan Shuaichen , Guo Kai , Yao Jianquan
Published: May 2024

Compact photonic devices are highly desired in photonic integrated circuits. In this work, we use an efficient inverse design method to design a 50/50 beam splitter in lithium niobate integrated platforms. We employ the Gradient Probability Algorithm (GPA), which is built upon traditional gradient algorithms. The GPA utilizes the adjoint method for the comprehensive calculation of the electric field across the entire design area in a single iteration, thereby deriving the gradient of the design area. This enhancement significantly accelerates the algorithm’s execution speed. The simulation results show that an ultracompact beam splitter with a footprint of 13μm × 4.5μm can be achieved in lithium niobate integrated platforms, where the insertion loss falls below 0.5 dB within the 1500 nm to 1700 nm range, thus reaching its lowest point of 0.15 dB at 1550 nm.
Dual-Wavelength Differential Detection of Fiber Bragg Grating Sensors: Towards a Sensor Ecosystem
Authors: Ouellette François
Published: May 2024

We discuss how the dual-wavelength differential detection (DWDD) of fiber Bragg grating sensors can be used to build standardized high-resolution, high-accuracy, large-measurement-range, multichannel instruments and associated sensors. We analyze the system resolution and experimentally show that the high signal-to-noise ratio can allow the design of sensors with a ratio of range to resolution superior to 14 bits, and temperature measurement ranges of more than 180 °C. We propose a scheme for real-time signal correction to cancel the drift of the instrument using two internal reference sensors, and a calibration method using centralized golden sensors that allows traceability to international standards for all instruments and sensors, allowing the creation of a global sensor/instrument ecosystem.
Seed-Coat Pigmentation Plays a Crucial Role in Partner Selection and N2 Fixation in Legume-Root–Microbe Associations in African Soils
Authors: Jaiswal Sanjay K. , Dakora Felix D.
Published: May 2024

Legume–rhizobia symbiosis is the most important plant–microbe interaction in sustainable agriculture due to its ability to provide much needed N in cropping systems. This interaction is mediated by the mutual recognition of signaling molecules from the two partners, namely legumes and rhizobia. In legumes, these molecules are in the form of flavonoids and anthocyanins, which are responsible for the pigmentation of plant organs, such as seeds, flowers, fruits, and even leaves. Seed-coat pigmentation in legumes is a dominant factor influencing gene expression relating to N2 fixation and may be responsible for the different N2-fixing abilities observed among legume genotypes under field conditions in African soils. Common bean, cowpea, Kersting’s groundnut, and Bambara groundnut landraces with black seed-coat color are reported to release higher concentrations of nod-gene-inducing flavonoids and anthocyanins compared with the Red and Cream landraces. Black seed-coat pigmentation is considered a biomarker for enhanced nodulation and N2 fixation in legumes. Cowpea, Bambara groundnut, and Kersting’s bean with differing seed-coat colors are known to attract different soil rhizobia based on PCR-RFLP analysis of bacterial DNA. Even when seeds of the same legume with diverse seed-coat colors were planted together in one hole, the nodulating bradyrhizobia clustered differently in the PCR-RFLP dendrogram. Kersting’s groundnut, Bambara groundnut, and cowpea with differing seed-coat colors were selectively nodulated by different bradyrhizobial species. The 16S rRNA amplicon sequencing also found significant selective influences of seed-coat pigmentation on microbial community structure in the rhizosphere of five Kersting’s groundnut landraces. Seed-coat color therefore plays a dominant role in the selection of the bacterial partner in the legume–rhizobia symbiosis.
Saffron, a Potential Bridge between Nutrition and Disease Therapeutics: Global Health Challenges and Therapeutic Opportunities
Authors: Mir Rakeeb Ahmad , Tyagi Anshika , Hussain Sofi Javed , Almalki Mohammed A. , Zeyad Mohammad Tarique , Deshmukh Rupesh , Ali Sajad
Published: May 2024

Plants are an important source of essential bioactive compounds that not only have a beneficial role in human health and nutrition but also act as drivers for shaping gut microbiome. However, the mechanism of their functional attributes is not fully understood despite their significance. One such important plant is Crocus sativus, also known as saffron, which possesses huge medicinal, nutritional, and industrial applications like food and cosmetics. The importance of this plant is grossly attributed to its incredible bioactive constituents such as crocins, crocetin, safranal, picrocrocin, and glycosides. These bioactive compounds possess a wide range of therapeutic activities against multiple human ailments. Since a huge number of studies have revealed negative unwanted side effects of modern-day drugs, the scientific communities at the global level are investigating a large number of medicinal plants to explore natural products as the best alternatives. Taken into consideration, the available research findings indicate that saffron has a huge scope to be further explored to establish alternative natural-product-based drugs for health benefits. In this review, we are providing an update on the role of bioactive compounds of saffron as therapeutic agents (human disorders and antimicrobial activity) and its nutritional values. We also highlighted the role of omics and metabolic engineering tools for increasing the content of key saffron bioactive molecules for its mass production. Finally, pre-clinical and clinical studies seem to be necessary to establish its therapeutic potential against human diseases.
Physical Seed Dormancy in Legumes: Molecular Advances and Perspectives
Authors: Wen Zhaozhu , Lu Xuran , Wen Jiangqi , Wang Zengyu , Chai Maofeng
Published: May 2024

Physical dormancy of seeds is a form of dormancy due to the presence of an impermeable seed coat layer, and it represents a feature for plants to adapt to environmental changes over an extended period of phylogenetic evolution. However, in agricultural practice, physical dormancy is problematic. because it prevents timely and uniform seed germination. Therefore, physical dormancy is an important agronomical trait to target in breeding and domestication, especially for many leguminous crops. Compared to the well-characterized physiological dormancy, research progress on physical dormancy at the molecular level has been limited until recent years, due to the lack of suitable research materials. This review focuses on the structure of seed coat, factors affecting physical dormancy, genes controlling physical dormancy, and plants suitable for studying physical dormancy at the molecular level. Our goal is to provide a plethora of information for further molecular research on physical dormancy.
In Vivo Study of Moringa oleifera Seed Extracts as Potential Sources of Neuroprotection against Rotenone-Induced Neurotoxicity
Authors: Raza Chand , Mohsin Sehrish , Faheem Mehwish , Hanif Uzma , Alkhathlan Hamad Z. , Shaik Mohammed Rafi , Riaz Hasib Aamir , Anjum Rabia , Jurrat Husna , Khan Merajuddin
Published: May 2024

Parkinson’s disease (PD) is a leading neurodegenerative disorder affecting 1–3 percent of the elderly population. Oxidative stress is the primary factor for the neurodegeneration of Substantia Nigra (SN). The current study aims to assess the seed extracts of Moringa oleifera (MO) on rotenone-mediated motor function impairments in a PD mouse model. For this purpose, two different seed extracts of MO were prepared, including aqueous MO (AqMO) and ethanolic MO (EthMO). Male Swiss albino mice were grouped into five groups. Mice received 2.5 mg/kg rotenone for 21 consecutive days, and control mice received the vehicle. Extract-treated mice received 200 mg/kg AqMO and EthMO separately, orally and daily for 28 days. Sinemet-treated mice received 20 mg/kg, oral dose, as a positive group. The motor function performance was evaluated using standard neurobehavioral tests. The antioxidant potentials of MO seed extracts were estimated by lipid peroxidation (LPO), reduced glutathione (GSH), glutathione-s-transferase (GST) and catalase (CAT) activities in mice brain homogenates. The PD mice brain SN sections were investigated for neurodegeneration. MO seed extract-treated mice showed a significant reduction in motor dysfunction compared to rotenone-treated mice as assessed through the open field, beam walk, pole climb-down, tail suspension, stride length and stepping tests. Increased antioxidant capacities of the PD mice brains of MO extract-administered groups were observed compared to the control. A histological study showed reduced signs of neurodegeneration, vacuolation around multipolar cells and cytoplasmic shrinkage in MO extract-treated mice SN brain sections. Collectively, MO seed extracts protected the animals from locomotor deficits induced by rotenone, possibly through antioxidant means, and seem to have potential applications in neurodegenerative diseases.
Multifunctional Adjuvants Affect Sulfonylureas with Synthetic Auxin Mixture in Weed and Maize Grain Yield
Authors: Idziak Robert , Waligóra Hubert , Majchrzak Leszek , Szulc Piotr
Published: May 2024

A field study in the years 2017–2019 was carried out to evaluate the impact of novel adjuvant formulations on the efficacy of sulfonylurea and synthetic auxin herbicides. Treatments included nicosulfuron + rimsulfuron + dicamba (N+R+D) at full and reduced rates with three multicomponent (TEST-1, TEST-2, TEST-3) as well as standard (MSO, S) adjuvants. In this greenhouse study, Echinochloa crus-galli seeds were planted and treated with N+R+D at 2–3 leaf stages. The water with the desired pH (4, 7, and 9) for the preparation of the spray liquid was prepared by incorporating citric acid or K3PO4 to either lower or raise the pH of the water. Adjuvant TEST-1 added to the spray liquid at pH 4 increased the effectiveness to 68%, TEST-2 to 81%, and TEST-3 to 80%, compared to 73% and 66% with the MSO and S. The efficacy of N+R+D at pH 7 with TEST-1 increased to 83%, TEST-2 to 82%, and TEST-3 to 77%, but with MSO, it increased to 81%, and 71% with S. Adjuvants TEST-1, TEST-2, and TEST-3 in the liquid at pH 9 increased efficacy to 76 and 80%, compared to 79 and 63% with MSO or S adjuvants. N+R+D applied with TEST-1, TEST-2, and TEST-3 provided greater weed control than herbicides with surfactant (S) and similar or even better than with standard methylated seed oil (MSO) adjuvants. Maize grain yield after herbicide-with-tested-adjuvant application was higher than from an untreated check, and comparable to yield from herbicide-with-MSO treatment, but higher than from S treatment.
Fabrication of Waterborne Silicone-Modified Polyurethane Nanofibers for Nonfluorine Elastic Waterproof and Breathable Membranes
Authors: Li Fang , Weng Kai , Tanaka Toshihisa , He Jianxin , Zheng Haimin , Noda Daisuke , Irifune Shinji , Sato Hiromasa
Published: May 2024

Waterproof and breathable membranes have a huge market demand in areas, such as textiles and medical protection. However, existing fluorinated nanofibrous membranes, while possessing good waterproof and breathable properties, pose health and environmental hazards. Consequently, fabricating fluorine-free, eco-friendly waterborne membranes by integrating outstanding waterproofing, breathability, and robust mechanical performance remains a significant challenge. Herein, we successfully prepared waterborne silicone-modified polyurethane nanofibrous membranes with excellent elasticity, waterproofing, and breathability properties through waterborne electrospinning, using a small quantity of poly(ethylene oxide) as a template polymer and in situ doping of the poly(carbodiimide) crosslinking agent, followed by a simple hot-pressing treatment. The silicone imparted the nanofibrous membrane with high hydrophobicity, and the crosslinking agent enabled its stable porous structure. The hot-pressing treatment (120 °C) further reduced the pore size and improved the water resistance. This environmentally friendly nanofibrous membrane showed a high elongation at break of 428%, an ultra-high elasticity of 67.5% (160 cycles under 400% tensile strain), an air transmission of 13.2 mm s−1, a water vapor transmission rate of 5476 g m−2 d−1, a hydrostatic pressure of 51.5 kPa, and a static water contact angle of 137.9°. The successful fabrication of these environmentally friendly, highly elastic membranes provides an important reference for applications in healthcare, protective textiles, and water purification.
Physicochemical and Mechanical Properties of Non-Isocyanate Polyhydroxyurethanes (NIPHUs) from Epoxidized Soybean Oil: Candidates for Wound Dressing Applications
Authors: Morales-González Maria , Valero Manuel F. , Díaz Luis E.
Published: May 2024

Only 0.1% of polyurethanes available on the market are from renewable sources. With increasing concern about climate change, the substitution of monomers derived from petrochemical sources and the application of eco-friendly synthesis processes is crucial for the development of biomaterials. Therefore, polyhydroxyurethanes have been utilized, as their synthesis route allows for the carbonation of vegetable oils with carbon dioxide and the substitution of isocyanates known for their high toxicity, carcinogenicity, and petrochemical origin. In this study, polyhydroxyurethanes were obtained from carbonated soybean oil in combination with two diamines, one that is aliphatic (1,4-butadiamine (putrescine)) and another that is cycloaliphatic (1,3-cyclohexanobis(methylamine)). Four polyhydroxyurethanes were obtained, showing stability in hydrolytic and oxidative media, thermal stability above 200 °C, tensile strength between 0.9 and 1.1 MPa, an elongation at break between 81 and 222%, a water absorption rate up 102%, and contact angles between 63.70 and 101.39. New formulations of bio-based NIPHUs can be developed with the inclusion of a cycloaliphatic diamine (CHM) for the improvement of mechanical properties, which represents a more sustainable process for obtaining NIPHUs with the physicochemical, mechanical, and thermal properties required for the preparation of wound dressings.
Recognition of Longitudinal Cracks on Slab Surfaces Based on Particle Swarm Optimization and eXtreme Gradient Boosting Model
Authors: Liu Yu , Jiang Lai , Shi Jing , Liu Jiabin , Li Guohui , Wang Zhaofeng , Zhang Zhi
Published: May 2024

Longitudinal cracks are a common defect on the surface of continuous casting slabs, and cause increases in additional processing costs or long-time interruptions. The accurate identification of surface longitudinal cracks is helpful to ensure the casting process is adjusted in time, which significantly improves the quality of slabs. In this research, the typical temperature characteristics of thermocouples at the position of longitudinal cracks and their adjacent locations were extracted. The principal component analysis (PCA) method was used to reduce the dimensions of these characteristics to remove the redundant information. The particle swarm optimization (PSO) method was introduced to optimize the parameter. On this basis, a recognition model of surface longitudinal cracks was established, based on a particle swarm optimization–eXtreme gradient boosting (XGBOOST) model. Finally, this model was trained and tested using longitudinal crack and non-longitudinal crack samples and compared with the decision tree, the gradient boosting decision tree (GBDT) and XGBOOST models. The test results showed that PSO-XGBOOST had the best identification performance in all evaluation indexes. The accuracy, F1 score and alarm rate results were 95.8%, 92.3% and 100%, respectively, and the false alarm rate was as low as 5.5%. The research results provide a theoretical basis and a reliable model for surface longitudinal crack identification.
Study of Methane Solubility Calculation Based on Modified Henry’s Law and BP Neural Network
Authors: Zhao Ying , Yu Jiahao , Shi Hailei , Guo Junyao , Liu Daqian , Lin Ju , Song Shangfei , Wu Haihao , Gong Jing
Published: May 2024

Methane (CH4), a non-polar molecule characterized by a tetrahedral structure, stands as the simplest organic compound. Predominantly constituting conventional natural gas, shale gas, and combustible ice, it plays a pivotal role as a carbon-based resource and a key raw material in the petrochemical industry. In natural formations, CH4 and H2O coexist in a synergistic system. This interplay necessitates a thorough examination of the phase equilibrium in the CH4-H2O system and CH4’s solubility under extreme conditions of temperature and pressure, which is crucial for understanding the genesis and development of gas reservoirs. This study synthesizes a comprehensive solubility database by aggregating extensive solubility data of CH4 in both pure and saline water. Utilizing this database, the study updates and refines the key parameters of Henry’s law. The updated Henry’s law has a prediction error of 22.86% at less than 40 MPa, which is an improvement in prediction accuracy compared to before the update. However, the modified Henry’s law suffers from poor calculation accuracy under certain pressure conditions. To further improve the accuracy of solubility prediction, this work also trains a BP (Back Propagation) neural network model based on the database. In addition, MSE (Mean-Square Error) is used as the model evaluation index, and pressure, temperature, compression coefficient, salinity, and fugacity are preferred as input variables, which finally reduces the mean relative error of the model to 16.32%, and the calculation results are more accurate than the modified Henry’s law. In conclusion, this study provides a novel and more accurate method for predicting CH4 solubility by comparing modified Henry’s law to neural network modeling.
Application of Lactiplantibacillus plantarum LP95 as a Functional Starter Culture in Fermented Tofu Production
Authors: Letizia Francesco , Fusco Giovanna Marta , Fratianni Alessandra , Gaeta Ilenia , Carillo Petronia , Messia Maria Cristina , Iorizzo Massimo
Published: May 2024

Several studies have shown that lactic acid bacteria (LAB) fermentation plays an important role in the development and application of soy-based products and could increase their nutritional values and content of bioactive substances. Lactiplantibacillus plantarum LP95 has shown in previous studies to be a promising candidate as a probiotic and microbial culture in fermented soymilk production. In this study, the suitability of Lp. plantarum LP95 as a functional starter culture in tofu production was verified, with a focus on evaluating the isoflavone and amino acid content in the final product after 21 days of storage at 4 °C. Lp. plantarum LP95 was found able to ferment monosaccharides and disaccharides naturally present in soymilk (D-glucose, D-fructose, D-galactose and D-sucrose) after 24 h while leaving the content of galacto-oligosaccharides (stachyose and raffinose) unaffected. The rich amino acid profile of tofu has undergone some quantitative but not qualitative variations compared to the soy milk used, highlighting the high nutritional value of the product obtained. The enzymatic activity of Lp. plantarum LP95 allowed the release of isoflavone aglycones (daidzein, glycitein and genistein) that were not further metabolized during the fermentation phase of soymilk and during storage at 4 °C. In addition, Lp. plantarum LP95 showed a good viability after 21 days of tofu storage at 4 °C. The results obtained highlighted the suitability of this LAB strain to be used as a microbial culture capable of playing a pro-technological role in the production of fermented tofu, which has good nutritional and functional properties.
Sacred Journeys: Exploring Emotional Experiences and Place Attachment in Religious Tourism at Monasteries in Serbia
Authors: Obradović Sanja
Published: May 2024

Religious tourism holds a significant place in travel experiences, particularly at monasteries, where visitors often encounter profound emotional experiences. This study aims to investigate the emotional experiences and place attachment of religious tourism at monasteries in Serbia, utilizing quantitative methods and an online survey approach. Through an online survey administered to visitors of Serbian monasteries, this study seeks to quantify and analyze the emotional experiences and two-dimensional place attachments reported by participants during their sacred journeys. The survey instrument includes measures to understand place attachment through place dependence and place identity, satisfaction, emotional experience, and destination loyalty under one framework. Additionally, demographic variables will be examined to identify potential differences in emotional experiences and place attachment among different visitor groups. The results indicate that place attachment is influenced by emotional experience and satisfaction, which further influences destination loyalty. The findings of this study will contribute to a deeper understanding of the destination loyalty and place attachment aspects of religious tourism at Serbian monasteries, providing valuable insights for tourism management and destination marketing efforts.
Multiple-Object Grasping Using a Multiple-Suction-Cup Vacuum Gripper in Cluttered Scenes
Authors: Jiang Ping , Oaki Junji , Ishihara Yoshiyuki , Ooga Junichiro
Published: May 2024

Multiple-suction-cup grasping can improve the efficiency of bin picking in cluttered scenes. In this paper, we propose a grasp planner for a vacuum gripper to use multiple suction cups to simultaneously grasp multiple objects or an object with a large surface. To take on the challenge of determining where to grasp and which cups to activate when grasping, we used 3D convolution to convolve the affordable areas inferred by a neural network with the gripper kernel in order to find graspable positions of sampled gripper orientations. The kernel used for 3D convolution in this work was encoded, including cup ID information, which helps to directly determine which cups to activate by decoding the convolution results. Furthermore, a sorting algorithm is proposed to determine the optimal grasp among the candidates. Our planner exhibited good generality and successfully found multiple-cup grasps in previous affordance map datasets. Our planner also exhibited improved picking efficiency using multiple suction cups in physical robot-picking experiments. Compared with single-object (single-cup) grasping, multiple-cup grasping contributed to 1.45×, 1.65×, and 1.16× increases in efficiency for picking boxes, fruits, and daily necessities, respectively.
A Registration Method of Overlap Aware Point Clouds Based on Transformer-to-Transformer Regression
Authors: Zhao Yafei , Chen Lineng , Zhou Quanchen , Zuo Jiabao , Wang Huan , Ren Mingwu
Published: May 2024

Transformer has recently become widely adopted in point cloud registration. Nevertheless, Transformer is unsuitable for handling dense point clouds due to resource constraints and the sheer volume of data. We propose a method for directly regressing the rigid relative transformation of dense point cloud pairs. Specifically, we divide the dense point clouds into blocks according to the down-sampled superpoints. During training, we randomly select point cloud blocks with varying overlap ratios, and during testing, we introduce the overlap-aware Rotation-Invariant Geometric Transformer Cross-Encoder (RIG-Transformer), which predicts superpoints situated within the common area of the point cloud pairs. The dense points corresponding to the superpoints are inputted into the Transformer Cross-Encoder to estimate their correspondences. Through the fusion of our RIG-Transformer and Transformer Cross-Encoder, we propose Transformer-to-Transformer Regression (TTReg), which leverages dense point clouds from overlapping regions for both training and testing phases, calculating the relative transformation of the dense points by using the predicted correspondences without random sample consensus (RANSAC). We have evaluated our method on challenging benchmark datasets, including 3DMatch, 3DLoMatch, ModelNet, and ModelLoNet, demonstrating up to a 7.2% improvement in registration recall. The improvements are attributed to our RIG-Transformer module and regression mechanism, which makes the features of superpoints more discriminative.
Hyperspectral Data for Early Identification and Classification of Potassium Deficiency in Soybean Plants (Glycine max (L.) Merrill)
Authors: Furlanetto Renato Herrig , Crusiol Luís Guilherme Teixeira , Nanni Marcos Rafael , de Oliveira Junior Adilson , Sibaldelli Rubson Natal Ribeiro
Published: May 2024

Identifying potassium (K+) deficiency in plants has traditionally been a difficult and expensive process. Traditional methods involve inspecting leaves for symptoms and conducting a laboratory analysis. These methods are not only time-consuming but also use toxic reagents. Additionally, the analysis is performed during the reproductive stage of growth, which does not allow enough time for corrective fertilization. Moreover, soybean growers do not have other tools to analyze the nutrition status during the earlier stages of development. Thus, this study proposes a quick approach for monitoring K+ in soybean crops using hyperspectral data through principal component analysis (PCA) and linear discriminant analysis (LDA) with a wavelength selection algorithm. The experiment was carried out at the Brazilian National Soybean Research Center in the 2017–2018, 2018–2019, and 2019–2020 soybean crop seasons, at the stages of development V4–V5, R1–R2, R3–R4, and R5.1–R5.3. Three treatments were evaluated that varied in K+ availability: severe potassium deficiency (SPD), moderate potassium deficiency (MPD), and an adequate supply of potassium (ASP). Spectral data were collected using an ASD Fieldspec 3 Jr. hyperspectral sensor. The results showed a variation in the leaf spectral signature based on the K+ availability, with SPD having higher reflectance in the visible region due to a lower concentration of pigments. PCA explained 100% of the variance across all stages and seasons, making it possible to distinguish SPD at an early development stage. LDA showed over 70% and 59% classification accuracies for discriminating a K+ deficiency in the simulation and validation stages. This study demonstrates the potential of the method as a rapid nondestructive and accurate tool for identifying K+ deficiency in soybean leaves.
Hydraulic Fracturing Shear/Tensile/Compressive Crack Investigation Using Microseismic Data
Authors: Li Han , Chang Xu , Hao Jinlai
Published: May 2024

In unconventional oil and gas development, the hydraulic fracturing (HF) technique is adopted to inject high-pressure fluid into the reservoir and change its pore-fracture connection structure to enhance production. HF causes the rocks to crack and generates microseismic events (with moment magnitudes of Mw≤3). Studying the microseismic focal mechanisms (shear/tensile/compressive HF cracks) is helpful for characterizing fracture geometry, monitoring the in situ stress state, and evaluating the HF effects to optimize the reservoir reconstruction for increasing production. Due to fluid injection activity, there may be non-double-couple (non-DC) mechanisms associated with HF cracks, and the commonly used double-couple (DC) source model may not be suitable. For the moment tensor (MT) source model, which is commonly used to describe the non-DC mechanism, inversion is challenging in single-well monitoring. The shear-tensile general dislocation (GD) model includes a non-DC mechanism, and its inversion is more constrained than the full MT model by specifying the focal mechanism as shear-tensile (or compressive) faulting. This paper reports a focal mechanism inversion case study of HF shear/tensile/compressive cracks in a tight oil reservoir in the Ordos Basin, China. We perform inversions based on the DC, GD, and MT source models, respectively. The results indicate that, for the downhole monitoring geometry in this study, most of the DC inversions fail to obtain proper synthetic and observed waveform fitting results, and the MT inversion results of different microseismic events exhibit worse consistencies than the GD results. According to the GD results, almost all the HF cracks can be explained as strike-slip faulting and most cracks correspond to non-negligible tensile/compressive mechanisms. Our study suggests that the GD source model is preferred in downhole microseismic monitoring to obtain reliable shear/tensile/compressive HF cracks, and the inverted non-zero slope angle reduces the uncertainty in fracturing geometry characterization, which will help improve microseismic studies and HF evaluations for enhanced resource recovery.
Vertical Accuracy Assessment and Improvement of Five High-Resolution Open-Source Digital Elevation Models Using ICESat-2 Data and Random Forest: Case Study on Chongqing, China
Authors: Xu Weifeng , Li Jun , Peng Dailiang , Yin Hongyue , Jiang Jinge , Xia Hongxuan , Wen Di
Published: May 2024

Digital elevation models (DEMs) are widely used in digital terrain analysis, global change research, digital Earth applications, and studies concerning natural disasters. In this investigation, a thorough examination and comparison of five open-source DEMs (ALOS PALSAR, SRTM1 DEM, SRTM3 DEM, NASADEM, and ASTER GDEM V3) was carried out, with a focus on the Chongqing region as a specific case study. By utilizing ICESat-2 ATL08 data for validation and employing a random forest model to refine terrain variables such as slope, aspect, land cover, and landform type, a study was undertaken to assess the precision of DEM data. Research indicates that spatial resolution significantly impacts the accuracy of DEMs. ALOS PALSAR demonstrated satisfactory performance, reducing the corrected root mean square error (RMSE) from 13.29 m to 9.15 m. The implementation of the random forest model resulted in a significant improvement in the accuracy of the 30 m resolution NASADEM product. This improvement was supported by a decrease in the RMSE from 38.24 m to 9.77 m, demonstrating a significant 74.45% enhancement in accuracy. Consequently, the ALOS PALSAR and NASADEM datasets are considered the preferred data sources for mountainous urban areas. Furthermore, the study established a clear relationship between the precision of DEMs and slope, demonstrating a consistent decline in precision as slope steepness increases. The influence of aspect on accuracy was considered to be relatively minor, while vegetated areas and medium-to-high-relief mountainous terrains were identified as the main challenges in attaining accuracy in the DEMs. This study offers valuable insights into selecting DEM datasets for complex terrains in mountainous urban areas, highlighting the critical importance of choosing the appropriate DEM data for scientific research.
Quantitative Analysis of the Vertical Interactions between Dust, Zonal Wind, and Migrating Diurnal Tide on Mars and the Role of Gravity Waves
Authors: Zhang Jie , Sheng Zheng , He Mingyuan
Published: May 2024

In the atmospheric system of Mars, vertical interactions are crucial, yet quantitative studies addressing this issue remain scarce. Based on simulations using the Mars PCM-LMDZ, we present the first frequency-domain quantitative analysis of the vertical interactions among Martian atmospheric dust, zonal circulation, and the migrating diurnal tide (DW1), employing Partial Directed Coherence (PDC) techniques to quantify the strength of associations between different variables. Our findings reveal a chain of influence where sub-seasonal-scale dust signals in the troposphere, through the Doppler effect of middle atmospheric zonal winds, transmit modulated energy to the DW1 in the upper mesosphere, thereby facilitating interlayer atmospheric interactions. The radiative heating from dust activities enhances the residual mean meridional circulation, which, under the influence of the Coriolis force, further accelerates the westerlies. Although gravity wave activity also contributes to the acceleration of the westerlies, its forcing generally remains below 5 m/s, which is relatively weak compared to the impact of intense dust activities in the warm scenario experiments (approximately 20 m/s). Overall, this study quantifies the interactions among atmospheric layers by means of PDC technology and analytically demonstrates how dust energy is transferred to mesospheric tides by shaping the zonal winds in between.
Unveiling Glacier Mass Balance: Albedo Aggregation Insights for Austrian and Norwegian Glaciers
Authors: Ye Fan , Cheng Qing , Hao Weifeng , Hu Anxun , Liang Dong
Published: May 2024

Assessing the regional mass balance of European glaciers presents significant challenges due to limited measurements. While various albedo methods have been explored for individual glaciers, a comprehensive analysis of aggregated albedo methods is lacking. Addressing this gap, in our study, we examined five MODIS aggregated albedos (raw average, minimum average, average minimum, interpolated average, and cumulative) versus the annual mass balance for 12 Austrian and Norwegian glaciers from 2001 to 2020 to establish connections between them. We find that the raw average albedo is strongly correlated with the annual mass balance of Austrian glaciers (r = 0.91), while the interpolated average albedo is significantly correlated with the annual mass balance of Norwegian glaciers (r = 0.90). Moreover, we observe that high-elevation glaciers experience fewer cloud cover days, allowing the raw average albedo to reliably estimate the annual mass balance, whereas low-elevation glaciers are often obscured by clouds, potentially masking the true minimum albedo. Additionally, traditional indicators, such as the equilibrium-line altitude and accumulation area ratio, exhibit significant correlations with the annual mass balance of Norwegian and Austrian glaciers (r = 0.90 and 0.87, respectively), yet albedo demonstrates higher robustness. These findings provide a reference for selecting appropriate aggregation methods to reconstruct glacier mass balance from albedo observations.
PD-LL-Transformer: An Hourly PM2.5 Forecasting Method over the Yangtze River Delta Urban Agglomeration, China
Authors: Zou Rongkun , Huang Heyun , Lu Xiaoman , Zeng Fanmei , Ren Chu , Wang Weiqing , Zhou Liguo , Dai Xiaoyan
Published: May 2024

As the urgency of PM2.5 prediction becomes increasingly ingrained in public awareness, deep-learning methods have been widely used in forecasting concentration trends of PM2.5 and other atmospheric pollutants. Traditional time-series forecasting models, like long short-term memory (LSTM) and temporal convolutional network (TCN), were found to be efficient in atmospheric pollutant estimation, but either the model accuracy was not high enough or the models encountered certain challenges due to their own structure or some specific application scenarios. This study proposed a high-accuracy, hourly PM2.5 forecasting model, poly-dimensional local-LSTM Transformer, namely PD-LL-Transformer, by deep-learning methods, based on air pollutant data and meteorological data, and aerosol optical depth (AOD) data retrieved from the Himawari-8 satellite. This research was based on the Yangtze River Delta Urban Agglomeration (YRDUA), China for 2020–2022. The PD-LL-Transformer had three parts: a poly-dimensional embedding layer, which integrated the advantages of allocating and embedding multi-variate features in a more refined manner and combined the superiority of different temporal processing methods; a local-LSTM block, which combined the advantages of LSTM and TCN; and a Transformer encoder block. Over the test set (the whole year of 2022), the model’s R2 was 0.8929, mean absolute error (MAE) was 4.4523 µg/m3, and root mean squared error (RMSE) was 7.2683 µg/m3, showing great accuracy for PM2.5 prediction. The model surpassed other existing models upon the same tasks and similar datasets, with the help of which a PM2.5 forecasting tool with better performance and applicability could be established.
Enhanced Land-Cover Classification through a Multi-Stage Classification Strategy Integrating LiDAR and SIF Data
Authors: Wang Ailing , Shi Shuo , Man Weihui , Qu Fangfang
Published: May 2024

Light detection and ranging (LiDAR) offers high-precision, 3D information, and the ability to rapidly acquire data, giving it a significant advantage in timely resource monitoring. Currently, LiDAR is widely utilized in land-cover classification tasks. However, the complexity and uneven distribution of land-cover types in rural and township settings pose additional challenges for fine-scale classification. Although the geometric features of LiDAR can provide valuable insights and have been extensively explored, distinguishing between objects with similar 3D characteristics has considerable room for improvement, particularly in complex scenarios where the introduction of additional attribute information is necessary. To address these challenges, this work proposes the integration of solar-induced chlorophyll fluorescence (SIF) features to assist and optimize LiDAR data for land-cover classification, leveraging the sensitivity of SIF to vegetation physiological characteristics. Moreover, a multi-stage classification strategy is introduced to enhance the utilization of SIF information. The implementation of this approach achieves a maximum classification accuracy of 92.45%, yielding satisfactory results with low computational costs. This outcome validates the feasibility of applying SIF information in land-cover classification. Furthermore, the results obtained through the multi-stage classification strategy demonstrate improvements ranging from 6.65% to 9.12% compared with land-cover classification relying solely on LiDAR, effectively highlighting the optimization role of SIF in enhancing LiDAR-based land-cover classification, particularly in complex rural and township environments. Our approach offers a robust framework for precise and efficient land-cover classification by leveraging the combined strengths of LiDAR and SIF.