Output Patterns Research Articles

VISUALISING TRENDS AND PATTERNS OF THREE DECADES OF RESEARCH ON MILLET PRODUCTION IN INDIA: A SCIENTOMETRICS ANALYSIS

This study analyses three decades of Millet Production research in India (1989-2023) with a Scientometrics approach, visualizing trends and patterns in scholarly output. India is the worlds largest producer of millet, with significant regional variations in millet cultivation. Major contributing states include Rajasthan, Karnataka, Maharashtra, and Madhya Pradesh. Scientometrics analysis, carried out on the Web of Science database using VOSviewer software, produced 600 publications with 12,028 citations. Development of millet research was found to be consistent with specific growth beginning after 2012, peaking during the International Year of Millets, 2023. Visualization of collaborative networks indicated the core institutions to be ICRISAT and ICAR. Prominent authors including Kumar A and Kumar S, and journals such as the Indian Journal of Agricultural Sciences are also featured in the Table. Keyword analysis showed that the research focus was on pearl millet, which primarily connects to agricultureand nourishment. Co-authorship and co-citation analyses underscore a robust collaborative environment in terms of research spanning several disciplines, concerning agronomy, biotechnology, and sustainable agriculture. This research focuses on the millet production research arena concerning food security, sustainable agricultural practices, and environmental challenges.

Contact area and tissue growth dynamics shape synthetic juxtacrine signaling patterns

Cell-cell communication through direct contact, or juxtacrine signaling, is important in development, disease, and many areas of physiology. Synthetic forms of juxtacrine signaling can be precisely controlled and operate orthogonally to native processes, making them a powerful reductionist tool with which to address fundamental questions in cell-cell communication in vivo. Here, we investigate how cell-cell contact length and tissue growth dynamics affect juxtacrine signal responses through implementing a custom synthetic gene circuit in Drosophila wing imaginal discs alongside mathematical modeling to determine synthetic Notch (synNotch) activation patterns. We find that the area of contact between cells largely determines the extent of synNotch activation, leading to the prediction that the shape of the interface between signal-sending and signal-receiving cells will impact the magnitude of the synNotch response. Notably, synNotch outputs form a graded spatial profile that extends several cell diameters from the signal source, providing evidence that the response to juxtacrine signals can persist in cells as they proliferate away from source cells, or that cells remain able to communicate directly over several cell diameters. Our model suggests that the former mechanism may be sufficient, since it predicts graded outputs without diffusion or long-range cell-cell communication. Overall, we identify that cell-cell contact area together with output synthesis and decay rates likely govern the pattern of synNotch outputs in both space and time during tissue growth, insights that may have broader implications for juxtacrine signaling in general.

Increasing Adult Hippocampal Neurogenesis affects Neuronal Output Pattern from the Ventral Subiculum to the Hypothalamus

Adult Hippocampal Neurogenesis, or AHN, has been discovered to alleviate anxiety and depressive behavior, thus leading to the presumption that the mechanisms underlying it might be somewhat linked to the regulatory circuitry of anxiety and depression. The subiculum is a region of the Hippocampus connecting the CA1 neurons and the Entorhinal cortex. It is the main output region of the Hippocampus. A great portion of the subiculum neurons form neuronal projections to the lateral hypothalamus, which is known to play a crucial role in the regulation of anxiety via hormone secretion in both rodents and humans. Thus, we hypothesize that Adult Hippocampal Neurogenesis regulates anxiety via altering the neural output from the subiculum to the lateral hypothalamus. In this research proposal we aim to prove that increasing AHN causes a direct impact on neuronal activity in the main output region of the Hippocampus--the subiculum region, under the effect of anxiety. This study uses the Fos-TRAP2 model to induce tdTomato expression in neurons with high recent activity while the mice was under the effect of corticosterone-induced anxiety. We compare the amount of TRAPed neurons in the subiculum region of the brain during anxiety, in mice with and without weeks of voluntary exercise (running), which leads to an increased neurogenesis. This provides insight into the effects of AHN on the subiculum-hypothalamus pathway, thus the mechanisms underlying its effect of anxiety alleviation.

Impact of Adult Hippocampal Neurogenesis on Neuronal Output Patterns from the Subiculum to the Lateral Hippocampus During Anxiety

Adult hippocampal neurogenesis (AHN) has an effect on mood regulations. Promoting AHN could recover stressed-induced symptoms such as depression and anxiety. In this study, we used voluntary physical exercise, fosTRAP2, functional magnetic resonance imaging (FMRI) and positron emission tomography (PET) technology to evaluate the effect of AHN on mice during unpredictable chronic mild stress (UCMS). Additionally, we inject fluoxetine to increase AHN.

Estimation of missing streamflow data using various artificial intelligence methods in peninsular Malaysia

ABSTRACT Missing streamflow data is a common issue in Peninsular Malaysia, as the technologies used in hydrological studies often fail to collect data accurately. Additionally, conventional methods are still widely used in the region, which are less accurate compared to artificial intelligence (AI) methods in estimating missing streamflow data. Therefore, this study aims to estimate the missing streamflow data from 11 stations in Peninsular Malaysia by using different AI methods and determine the most appropriate method. Four homogeneity tests were applied to check the quality of data, and the results of the tests indicated that the streamflow data in most stations were homogenous. Two AI methods were applied in this study, which were artificial neural network and artificial neuro-fuzzy inference systems (ANFIS). The proposed AI methods were compared with five different conventional methods. All streamflow missing data, constituting 30% of data from each year were estimated on a daily time scale, and evaluated using root mean square error, mean absolute error and correlation coefficient values. The results indicated that ANFIS was the best due to its learning abilities and the fuzzy inference systems, which enable it to handle complicated input–output patterns and provide highly accurate estimation results.

Land Degradation in Southern Africa: Restoration Strategies, Grazing Management, and Livelihoods

Land degradation in communal rangelands poses significant challenges to environmental sustainability, agricultural productivity, and livelihoods in southern Africa. This study presents a bibliometric analysis of research trends, key contributors, thematic evolution, and collaborative networks in the field of land degradation in communal rangelands from 1997 to 2024. Utilizing data obtained from the Scopus database, we examined 66 publications to identify patterns in publication output, leading journals, influential articles, and prominent authors and institutions. The analysis demonstrates an overall increase in research output, with a notable surge in publications during the past decade, indicating a growing academic and policy interest in this field. Major themes identified include sustainable land management, restoration strategies, and the impacts of grazing management on ecosystem health. Networks showcasing co-authorship and keyword co-occurrence reveal robust collaborative connections among researchers and a concentrated focus on specific dominant themes. Consequently, these findings propose opportunities for expanding interdisciplinary research and exploring underrepresented areas. This study provides a comprehensive overview of the research landscape, offering insights to steer future studies and inform policy interventions aimed at mitigating land degradation and bolstering the resilience of communal rangelands in southern Africa.

Research on short-term optimization and scheduling of multi-energy complementary systems based on forecast scenario dynamic correction

The inherent unpredictability and instability of renewable energy sources, such as wind and solar power, hinder the precise execution of power generation plans in complementary systems, posing significant challenges to their integration into power grids. Therefore, this study proposes a dynamic correction method for wind and solar output forecast scenarios in the short-term scheduling of wind-solar-hydro complementary systems. The method utilizes statistical analysis of forecast errors in wind and solar power outputs to characterize uncertainty patterns across different forecast levels and constructs a typical forecast scenario set based on single-day forecasts. This approach probabilistically models each scenario according to the temporal migration patterns of wind and solar power outputs and develops a neural network-based dynamic correction fusion model to refine the forecasts. Application of this method in a case study of the Yalong River Basin demonstrated that, after applying dynamic correction to the forecast scenarios, the mean absolute error in total wind and solar output predictions during the wet and dry seasons was reduced by 50.73 % and 47.95 %, respectively. Additionally, the dynamic correction reduced the maximum residual load on typical wet and dry days by 82.70 % and 62.37 %, respectively, and decreased the total intraday residual electricity by 91.17 % and 73.24 %, compared to single-day forecasts. The study concludes that the proposed dynamic correction method enhances power system stability and improves power generation efficiency and reliability.

Phase-modulation-induced reconfigurable rotating photonic lattices in atomic vapors.

We propose a method to prepare optically induced rotating hexagonal and honey-comb photonic lattices by employing the phase modulated three-beam interference in atomic vapors with electromagnetically induced transparency. The phase differences among the three beams are dynamically elaborated to synthesize the circular motion (in transverse dimensions) of waveguides in the photonic lattices. Further, we verify this model experimentally in the case of low-speed modulation. A weak Gaussian probe field is sent into the constructed helical photonic lattices to image their structures under electromagnetically induced transparency (EIT). The motion trajectories of the sites on the discretized output patterns exhibit repeated circles, advocating the formation of rotating lattices. By introducing phase modulations to involved beams, we provide a continent way for producing transverse motions in waveguide arrays with reconfigurability in rotational direction, radius, and speed. This work looks forward to promising applications in topological photonics with great popularity.

Two-dimensional black phosphorus/platinum catalase-like nanozyme-based Fenton reaction-mediated dual-mode immunoassays for the detection of enrofloxacin.

Hydrogen peroxide-based Fenton reaction can effectively degrade many small-molecule fluorescent dyes, leading to notable alterations in fluorescence signals. Additionally, the two-dimensional black phosphorus/platinum nanocomposite (BP/Pt) demonstrates exceptional catalase (CAT) characteristics. Based on these, a colorimetric-fluorescence dual-mode signal output pattern based on BP/Pt-Fenton reaction-rhodamine B tandem reaction system isreported. Thephysical adsorption property of the BP/Pt nanozymes was utilized to couple with antibodies, thus constructing a novel dual-mode nanozyme-based immuno-sensing assay (NISA). By using the migratory antibiotic enrofloxacin (ENR) as the target, the NISA providedhighly sensitive detection with the detection limits of 0.058ng/mL for colorimetric-mode and 0.025ng/mL for fluorescence-mode and achieved accurate quantitative detection in environmental water and crucian carp samples. This work provides an innovative design for monitoring antibiotics in the environment and broadens the idea for the application of nanozymes and Fenton systems in immunosensing assays.

LTP-assisted fabrication of laccase-like Cu-MOF nanozyme-encoded array sensor for identification and intelligent sensing of bioactive components in food

Nanozymes are potential candidates for constructing sensors due to their adjustable activity, high stability, and high cost-effectiveness. However, due to the lack of reasonable means, designing and preparing efficient nanozymes remains challenging. Herein, inspired by the property of natural laccase, we applied the novel and facile low-temperature plasma (LTP) technology to fabricate a series of different base-ligand Cu metal organic framework (MOF) nanozymes (namely, A-Cu, G-Cu, C-Cu and T-Cu nanozymes) with laccase-like activity successfully. Owing to the different catalytic capacities of four types of base-Cu-MOF nanozymes in the response to five common effective bioactive substances, we constructed the nanozyme-encoded array sensor for the identification of different bioactive compounds. As a result, the four-channel colorimetric sensor array was constructed, in which four laccase-like nanozymes were utilized as the sensing units, achieving high-throughput, high-sensitivity and rapid detection/identification of five common bioactive compounds in the concentration range of 1.5–150 μg mL−1 through different color output patterns. It is worth noting that the as-prepared sensor array can successfully distinguish the natural bioactive compounds in a variety of real samples. Furthermore, with the assistance of smartphones, we also designed a portable smart sensing approach for detecting the bioactive compounds effectively in food. This study has therefore not only provided an effective way for preparation highly effectively nanozymes, but also established a new sensing platform for intelligent sensing of bioactive components in food.

Blended learning research: Identifying research output patterns in Scopus (2000-2023)

Blended learning research: Identifying research output patterns in Scopus (2000-2023)

Investigating the Visual Utility of Differentially Private Scatterplots.

Increasingly, visualization practitioners are working with, using, and studying private and sensitive data. There can be many stakeholders interested in the resulting analyses-but widespread sharing of the data can cause harm to individuals, companies, and organizations. Practitioners are increasingly turning to differential privacy to enable public data sharing with a guaranteed amount of privacy. Differential privacy algorithms do this by aggregating data statistics with noise, and this now-private data can be released visually with differentially private scatterplots. While the private visual output is affected by the algorithm choice, privacy level, bin number, data distribution, and user task, there is little guidance on how to choose and balance the effect of these parameters. To address this gap, we had experts examine 1,200 differentially private scatterplots created with a variety of parameter choices and tested their ability to see aggregate patterns in the private output (i.e. the visual utility of the chart). We synthesized these results to provide easy-to-use guidance for visualization practitioners releasing private data through scatterplots. Our findings also provide a ground truth for visual utility, which we use to benchmark automated utility metrics from various fields. We demonstrate how multi-scale structural similarity (MS-SSIM), the metric most strongly correlated with our study's utility results, can be used to optimize parameter selection.

Nonlinear optical encoding enabled by recurrent linear scattering

AbstractOptical information processing and computing can potentially offer enhanced performance, scalability and energy efficiency. However, achieving nonlinearity—a critical component of computation—remains challenging in the optical domain. Here we introduce a design that leverages a multiple-scattering cavity to passively induce optical nonlinear random mapping with a continuous-wave laser at a low power. Each scattering event effectively mixes information from different areas of a spatial light modulator, resulting in a highly nonlinear mapping between the input data and output pattern. We demonstrate that our design retains vital information even when the readout dimensionality is reduced, thereby enabling optical data compression. This capability allows our optical platforms to offer efficient optical information processing solutions across applications. We demonstrate our design’s efficacy across tasks, including classification, image reconstruction, keypoint detection and object detection, all of which are achieved through optical data compression combined with a digital decoder. In particular, high performance at extreme compression ratios is observed in real-time pedestrian detection. Our findings open pathways for novel algorithms and unconventional architectural designs for optical computing.

Bibliometric Analysis and Knowledge Mapping of Research Trends in Brugada Syndrome

Background: Brugada Syndrome (BrS) has been extensively studied over the last 30 years, warranting a review of its research landscape, including prevalent themes and potential future directions. Objective: By employing bibliometric analysis, this work seeks to map the current state and trajectory of BrS research, highlighting key contributors and unexplored areas while suggesting avenues for future studies which could propel the field forward. Methods: This paper analyzed BrS-related articles from 1992 to 2023, extracted from the Web of Science core collection, using tools like CiteSpace, VOSviewer, Pajek, and Scimago Graphica to examine research output, geographies, authors, affiliations, keywords, and citation patterns. Results: Out of 3,713 BrS publications, the US has been the most prolific, with the Netherlands producing the highest caliber work, and China ranking fourth in output. The University of Amsterdam emerged as the leading institution. Pedro Brugada topped the author list. The journal Circulation led in citations, with an impact factor of 37.8, indicative of its JCR Q1 status and elite ranking. Keyword analysis revealed ‘Brugada syndrome’ (2756), ‘Sudden death’ (1387), and ‘St-segment elevation’ (1200) as common terms, with ‘Management,’ ‘Guidelines,’ ‘Consensus conference,’ and ‘Genetics’ as up-and-coming topics. Conclusions: Stable research funding and publication rates indicate a mature phase for BrS research, with genomics, proteomics, biomarkers, clinical prediction models, and gene therapy poised as future focal points.

Temporal patterns of food control outputs and outcomes and effects of targeted control interventions on compliance in food service establishments in Finland

Food control inspections are an essential control measure to ensure compliance, and it is important to understand possible patterns within inspections and their outcomes when developing food control. In this study, we analysed food control inspection reports (n = 86 223) concerning Finnish food service establishments from 2016 to 2021 to reveal temporal patterns in food control outputs and outcomes and the possible reasons behind them, and to evaluate the effects of targeted control interventions on inspection grades. The analyses revealed that the proportions of non-compliances were high throughout the study period in several inspected issues, such as temperature management (7.1–8.1%) and cleanliness (4.4–5.3%). Moreover, the inspection grades improved in only one of sixteen inspection subsections, but in five subsections the proportion of non-compliances increased significantly within the study period (linear regression analysis) and in seven subsections the inspection grades were inferior during the COVID-19 pandemic compared to the pre-pandemic years. In addition to the long-term variation in the level of non-compliances, we observed seasonal variation, with higher proportions of non-compliances in summer compared to other times. This study also showed that targeted food control interventions may influence both the number of inspections and the level of observed non-compliances. We also observed that almost half of the annual pre-planned inspections were made in the proximity of the inspection time in the previous year (44.6% within 30 days), decreasing the surprise factor of the inspections. These results can be used in developing the effectiveness of food control.

Multiplexed All‐Optical Permutation Operations Using a Reconfigurable Diffractive Optical Network

AbstractLarge‐scale and high‐dimensional permutation operations are important for various applications in, for example, telecommunications and encryption. Here, all‐optical diffractive computing is used to execute a set of high‐dimensional permutation operations between an input and output field‐of‐view through layer rotations in a diffractive optical network. In this reconfigurable multiplexed design , every diffractive layer has four orientations: , , , and . Each unique combination of these layers represents a distinct rotation state, tailored for a specific permutation operation. Therefore, a K‐layer rotatable diffractive design can all‐optically perform up to independent permutation operations. The original input information can be decrypted by applying the specific inverse permutation matrix to output patterns. The feasibility of this reconfigurable multiplexed diffractive design is demonstrated by approximating 256 randomly selected permutation matrices using = 4 rotatable diffractive layers. To further enhance its multiplexing capability, input polarization diversity is also utilized. Additionally, this reconfigurable diffractive design is experimentally validated using terahertz radiation and 3D‐printed diffractive layers, providing a decent match to numerical results. The presented rotation‐multiplexed diffractive processor is particularly useful due to its mechanical reconfigurability, offering multifunctional representation through a single fabrication process.

Information-hiding cameras: Optical concealment of object information into ordinary images.

We introduce an information-hiding camera integrated with an electronic decoder that is jointly optimized through deep learning. This system uses a diffractive optical processor, which transforms and hides input images into ordinary-looking patterns that deceive/mislead observers. This information-hiding transformation is valid for infinitely many combinations of secret messages, transformed into ordinary-looking output images through passive light-matter interactions within the diffractive processor. By processing these output patterns, an electronic decoder network accurately reconstructs the original information hidden within the deceptive output. We demonstrated our approach by designing information-hiding diffractive cameras operating under various lighting conditions and noise levels, showing their robustness. We further extended this framework to multispectral operation, allowing the concealment and decoding of multiple images at different wavelengths, performed simultaneously. The feasibility of our framework was also validated experimentally using terahertz radiation. This optical encoder-electronic decoder-based codesign provides a high speed and energy efficient information-hiding camera, offering a powerful solution for visual information security.

Assessment of biomass material as valuable electrode for high energy performance in microbial fuel cell with biodegradation of organic pollutant

Microbial fuel cells (MFC) have becomea more popular bio-electrochemical approach due to their environmental friendliness and sustainability. The anode is a vital component of the MFC, and its unstable material remains one of the primary causes of poor electron transportation throughout the reactor operations. This study seeks to improve MFC electron transport and naphthalene biodegradation by employing modified palm kernel shell (PKS)-based anode electrodes. These anode fabrications were successfully developed through the combination of PKS-based rGO hybridized into metal oxide modifiers (ZnO and TiO2). The anode electrodes were subsequently assigned PKS-rGO, PKS-rGO/TiO2, and PKS-rGO/ZnO. The power density displayed for PKS-rGO was 35.1 mW/m2, whereas the power density for the PKS-rGO/TiO2 composite anode was found to be 38.2 mW/m2. On the other hand, PKS-rGO/ZnO had a maximum power density of 45.4 mW/m2. The maximum current density (CD) in the anode PKS-rGO/ZnO was 114.0 mA/m2, indicating a comparable pattern of power output. Bacillus sp. and Niallia sp. were identified as exo-electrogens in the microbial study. In the MFC operation, the highest biodegradation efficiency was 85.50 % in the PKS-rGO settings, 92.80 % in PKS-rGO/TiO2, and94.30 % in PKS-rGO/ZnO. SEM and microbiological studies revealed that our anodes were highly biocompatible with microbes. Furthermore, this result proposes future research directions for the mortality ofanodic microbial communitiesin MFC.

Author productivity analysis in Q1 of authors in journal library and information science Q1 journals using with Lotka’s Law

Introduction. The purpose of this study is to use Lotka's law to determine the pattern of author output in the field of Library Science in the Library Science Journal Q1 indexed by Scopus. Data Collection Methods. The quantitative descriptive method was conducted, with a bibliometric approach to explore aspects of scientific articles. Data Analysis. The research data was acquired from the Scopus International Journal, which can be viewed at www.scopus.com from 2018 to 2022, with article types. Results and Discussion. The research results found that the number of library science journals indexed by Scopus Q1 was 61. Based on the analysis of Lotka’s law calculations, it is found that the value of C is 0,518 and the value of n was 1,717. The productivity equation for SCOPUS-indexed authors in Library Science journals for 2018–2022 was 0,518, which results in the number of author contributions in publishing one article being 51,84%. In addition, the results of the analysis show that Dmax was 0.280 with a critical value of 0,009. Conclusion. These findings indicate that Dmax is bigger than the crucial value and it does not follow Lotka's law.

Multiple paternity, fertilization success, and male quality: Mating system variation in the eelgrass, Zostera marina.

Genetic diversity can modulate a population's response to a changing environment and plays a critical role in its ecological function. While multiple processes act to maintain genetic diversity, sexual reproduction remains the primary driving force. Eelgrass (Zostera marina) is an important habitat-forming species found in temperate coastal ecosystems across the globe. Recent increases in sea surface temperatures have resulted in shifts to a mixed-annual life-history strategy (i.e., displaying characteristics of both annual and perennial meadows) at its southern edge-of-range. Given that mating systems are intimately linked to standing levels of genetic variation, understanding the scope of sexual reproduction can illuminate the processes that shape genetic diversity. To characterize edge-of-range eelgrass mating systems, developing seeds on flowering Z. marina shoots were genotyped from three meadows in Topsail, North Carolina. In all meadows, levels of multiple mating were high, with shoots pollinated by an average of eight sires (range: 3-16). The number of fertilized seeds (i.e., reproductive success) varied significantly across sires (range: 1-25) and was positively correlated with both individual heterozygosity and self-fertilization. Outcrossing rates were high (approx. 70%) and varied across spathes. No clones were detected, and kinship among sampled flowering shoots was low, supporting observed patterns of reproductive output. Given the role that genetic diversity plays in enhancing resistance to and resilience from ecological disturbance, disentangling the links between life history, sexual reproduction, and genetic variation will aid in informing the management and conservation of this key foundation species.