Miracle-dz/UniFAR-FTUs huggingface dataset:
A Dual-Band Dual-Polarized Filtering Antenna

This letter presents and analyzes a dual-band, dual-polarized filtering antenna design. By inserting two pairs of vertical metal walls with different heights into a slot-coupled magneto-electric dipole (ME dipole) antenna, four radiation nulls are generated to ensure a high dual-band frequency selectivity. To analyze its working principle, a stub-loaded transmission line circuit is employed as the equivalent circuit to approximate the antenna structure. The ME dipole radiator, extended feedline and the two pairs of vertical metal walls can be regarded as four open-circuited stubs loaded on a transmission line. Based on the transmission line theory, when the length of each open-circuited stub equals a quarter wavelength of a specific frequency, a transmission zero is generated. Using this method, the antenna realizes a dual-band bandpass-type radiation response without adding intricate components. For demonstration, a dual-polarized prototype operating at 3.4 and 4.9 GHz is simulated, fabricated and measured. The measured in-band gains are about 6.5 dBi, and a better than 16 dB out-of-band radiation rejection level is achieved.
Real time in situ measurement of rumen methane concentration in the rumen of cattle.

Methane dominates agricultural greenhouse gas emissions in New Zealand (Leslie et al., 2008). Research efforts in New Zealand to date have been directed toward prediction of emissions, most often using the rumen gas marker sulphur hexafluoride (SF6) (Lassey, et al., 1997) to predict methane emissions in grazing livestock. However, the errors associated with this method necessitate the use of multiple day sampling programmes and large numbers of individuals to overcome animal variability (Pinares-Patino & Clark, 2008). As a consequence, reporting of methane emission profiles is limited to 24 hour periods, as measurement of sub-diurnal intervals is effectively excluded. The use of closed chamber measurements of total gas emissions can increase confidence in methane output estimations. There are however some differences between these and SF6 techniques (McGinn et al., 2006). The measurement of rumen specific output in sub-diurnal intervals is still not reliable, and important information on energy transfer due to changes in animal behaviour can be lost (Lassey, 2008). The use of artificial rumen replacements such as in vitro continuous or batch culture fermenters has not adequately simulated the in vivo rumen measurements of hydrogen disappearance (McAllister & Newbold, 2008). Because of these limitations, assessment of methane emissions, including mitigation assessments, have necessarily used mean methane yields per day. Mitigation of methane emissions in grazing ruminants has not been successful. However, grass based production systems, particularly in the New Zealand dairy industry, are characterised by periods of greater and lesser intake and rumen activity (Gibbs et al., 2007). An alternative mitigation approach would be to target specific windows of rumen activity within the diurnal cycle of ruminants. However, assessment of that approach is severely limited by existing rumen methane estimation methods. To date, little effort has been directed to understanding the rapid changes in environmental conditions within the rumen as a result of the metabolic activity of the rumen microorganisms, from which gas production arises. Consequently, predictions of rumen methane emissions are poor, as evidenced by a recent review reporting the strength of the correlation of predictors of methane yields as R <0.2 (Machmüller & Clark, 2006). The challenge is to better understand how the patterns of rumen function alter the utilisation of nutrients and the fate of hydrogen ions in the short term, rather than simply estimating the gas production over extended intervals. A method of estimating rumen methane production across short intervals suitable to create sub-diurnal ‘maps’ of rumen activity should greatly improve our understanding in this area, and open up new approaches to mitigation of methane emissions. This communication reports the use of a portable, in-situ rumen methane sensor in free grazing ruminants.
Multi-Level Optimization for Data-Driven Camera–LiDAR Calibration in Data Collection Vehicles

Accurately calibrating camera–LiDAR systems is crucial for achieving effective data fusion, particularly in data collection vehicles. Data-driven calibration methods have gained prominence over target-based methods due to their superior adaptability to diverse environments. However, current data-driven calibration methods are susceptible to suboptimal initialization parameters, which can significantly impact the accuracy and efficiency of the calibration process. In response to these challenges, this paper proposes a novel general model for the camera–LiDAR calibration that abstracts away the technical details in existing methods, introduces an improved objective function that effectively mitigates the issue of suboptimal parameter initialization, and develops a multi-level parameter optimization algorithm that strikes a balance between accuracy and efficiency during iterative optimization. The experimental results demonstrate that the proposed method effectively mitigates the effects of suboptimal initial calibration parameters, achieving highly accurate and efficient calibration results. The suggested technique exhibits versatility and adaptability to accommodate various sensor configurations, making it a notable advancement in the field of camera–LiDAR calibration, with potential applications in diverse fields including autonomous driving, robotics, and computer vision.
Dimethyl Fumarate Attenuates Methotrexate Hepatotoxicity in Mice Via the Nrf2/HO-1/Anti-Apoptotic Signaling Pathway

Background: Methotrexate (MTX), a folate antagonist used to treat cancer and inflammatory diseases, is known to generate reactive oxygen species. Objectives: The research investigates the impact of dimethyl fumarate (DMF), a nuclear factor erythroid 2-related factor 2 (Nrf2) activator, on an MTX-induced mouse hepatotoxicity model. Methods: Forty-two mice were divided into 6 groups: control, MTX, DMF 120, and 3 groups of MTX co-treated with DMF 30, 60, and 120 mg/kg. Dimethyl fumarate was gavaged once daily for 10 days. On the fifth day, the animals received MTX 20 mg/kg intraperitoneally. On the eleventh day, the animals were sacrificed, and serum and liver samples were collected to assess the level of oxidative/anti-oxidative and apoptotic/anti-apoptotic markers. Results: Dimethyl fumarate prevented the increase of liver function enzymes, alanine aminotransferase (ALT), aspartate aminotransferase (AST), and alkaline phosphatase (ALP) induced by MTX (P < 0.001). It prevented the increase in AST and ALT levels, indicating liver recovery (P < 0.001). Furthermore, DMF restored antioxidant markers superoxide dismutase, catalase, glutathione peroxidase, and total thiol while reducing the level of thiobarbituric acid reactive substances (P < 0.001). Dimethyl fumarate also downregulated hepatic mRNA expression of caspase 3 and upregulated Bcl-2, heme oxygenase 1, and Nrf2 genes in MTX co-treated DMF groups. Conclusions: Dimethyl fumarate alleviates oxidative stress and apoptosis, which may be achieved by the Nrf2/HO-1 pathway. Therefore, DMF may be clinically effective in preventing or treating MTX-induced hepatotoxicity.
Reclassifying variations of unknown significance in diseases affecting Saudi Arabia’s population reveal new associations

Introduction: Physicians face diagnostic dilemmas upon reports indicating disease variants of unknown significance (VUS). The most puzzling cases are patients with rare diseases, where finding another matched genotype and phenotype to associate their results is challenging. This study aims to prove the value of updating patient files with new classifications, potentially leading to better assessment and prevention. Methodology: We recruited retrospective phenotypic and genotypic data from King Saud Medical City, Riyadh, Kingdom of Saudi Arabia. Between September 2020 and December 2021, 1,080 patients’ genetic profiles were tested in a College of American Pathologists accredited laboratory. We excluded all confirmed pathogenic variants, likely pathogenic variants and copy number variations. Finally, we further reclassified 194 VUS using different local and global databases, employing in silico prediction to justify the phenotype–genotype association. Results: Of the 194 VUS, 90 remained VUS, and the other 104 were reclassified as follows: 16 pathogenic, 49 likely pathogenic, nine benign, and 30 likely benign. Moreover, most of these variants had never been observed in other local or international databases. Conclusion: Reclassifying the VUS adds value to understanding the causality of the phenotype if it has been reported in another family or population. The healthcare system should establish guidelines for re-evaluating VUS, and upgrading VUS should reflect on individual/family risks and management strategies.
How Presidents Answer the Call of International Capital

How do governments manage expectations from international capital keen on pressuring them into adopting market-oriented economic policies during times of crises? Studying executive communication in 267 annual state-of-the-union speeches in 12 Latin American countries between 1980 and 2014 reveals two broad options for strategic position-taking on economic policies. First, when times are dire, presidents not only talk more about the economy and less about social policy, but they also attempt to repurpose other policies as an investment in development. Second, economic turmoil encourages presidents to signal policies, which are appealing to international capital owners. However, while currency crises exert more enduring pressure, the effects of loan crises are more fleeting. Our results are particularly relevant to all who seek to understand how governments use public statements to address pressures from financial markets.
Enhancing Zinc Uptake through Dual-Modification of Cicer arietinum Protein

Zinc is crucial for physiological processes; however, deficiency persists globally. Binding zinc to plant proteins enhances absorption, minimizing toxicity risks and offering a potential solution for deficiency. Mineral binding efficiency of the unmodified protein is limited; hence, dual modification (succinylation and ultrasonication) is potentially used to achieve higher binding efficiency. Enhancing zinc uptake is crucial for cellular health due to its vital roles in various biological processes including enzymatic activity, DNA repair, immune function, antioxidant defense, hormone regulation, brain function, signaling, growth, gene expression, and reproduction. Therefore, this research aimed to develop a chickpea protein-zinc complex and to evaluate the influence of dual modification on their physiochemical, bioavailability, and cellular mineral uptake attributes. Succinylation exhibited significant improvements in the water-holding capacity by 28.73%, oil-holding capacity by 34.09%, and solubility by 5.46% of the chickpea protein-zinc complex as compared to the native complex. Mineral bioavailability increased by 8.32%, and there were notable increases in cellular uptake of zinc by 2.10%, retention by 5.80%, and transport by 3.96%, respectively. Furthermore, the dual modification approach resulted in a notable decrease in the particle size of the chickpea protein-zinc complex, with a substantial reduction of 73.25% and an increased zeta potential value of −21 mV compared to the succinylated complex. As well, dual modification concurrently led to a substantial decline of 48.04% in the sulfhydryl (SH) content, coupled with a marked increase of 21.92% in the surface hydrophobicity. In addition, zinc bioavailability, cellular uptake, retention, and transport were further enhanced by 1.89, 3.34, and 4.8% through dual modification. Our findings highlight that the dual modification of the chickpea protein-zinc complex shows a promising strategy for enhancing the techno-functional characteristics, bioavailability, and cellular uptake of zinc, which could be a better platform for developing vegan foods.
WYA: A Novel Spatial Scene Classification Framework Based on Surrounding Object Detection

Spatial scene classification has long been a prominent area of research in the field of geographic information science. In the past, traditional approaches heavily relied on retrieval methods based on image features. However, given the rapid advancements in deep learning and artificial intelligence, the efficient classification of complex spatial scenes has become increasingly crucial. This paper presents a novel framework named WYA (Where You At) that combines surrounding object detection with knowledge graph to automate the process of spatial scene classification. Initially, the input images undergo processing using object detection techniques to identify key entities within the scenes. Subsequently, a knowledge graph, which encompasses various spatial scenes, entities, and their relationships, is utilized to identity spatial scene catogories. To validate the effectiveness of the framework, experiments were conducted using eight spatial scene categories as an example. The results demonstrated a high level of consistency with actual spatial types, thus affirming the efficacy of the framework and highlighting its potential application value in the domain of spatial scene classification.
Landscape Change in the Meander Valley: A Case Study for Monitoring and Reporting of Land Use Modification, Vegetation Condition and Biodiversity Loss.

Disclaimer: The views expressed in this document are those of the Author and are not necessarily shared by any of the partner organisations. Acknowledgments I would like to acknowledge the contribution of all the partner organisations in providing inputs into this case study: CSIRO, for her work on the vegetation classification. Special thanks to Kiowa Rieck for helping me overcome MS Word and formatting the document for me. All the work undertaken for this study was made possible by the Natural Heritage Trust. Cover Images Clockwise: recently cleared native forest ready for plantations, training site ID 9; heavily gorse infested eucalypt forest, training site ID 1, terrain illumination corrected Landsat TM for 23 rd November 2000; Air photo 1998 the Huntsman Valley an area experiencing rapid landscape change and; woody – non woody change Meander Valley 1991 – 2000.
The Effect of Organic Manures on Soil Fertility and Microbial Biomass Carbon, Nitrogen and Phosphorus under Maize-cowpea Intercropping System

Soil microbial biomass carbon, nitrogen and phosphorus and general soil fertility were examined under different organic manures in a field experiment conducted at the Department of Crop and Soil Sciences, Faculty of Agriculture of the Kwame Nkrumah University of Science and Technology, Kumasi, Ghana. The experimental design used was randomized complete block design with three replications where both cattle and poultry manures were applied 4t/ ha and inorganic fertilizer (NPK 15- 15-15) at 90- 60- 60 kg/ ha. The effect of the amendment on the microbial biomass and soil fertility were tested under maizecowpea intercropping system. Results indicated that the manures had no significant effect on soil microbial biomass C, N, P and soil fertility. However, available P and microbial P showed considerable increase following amendment compared to the initial values recorded before the study was conducted. Plant height was similarly influenced by the organic manures but differed significantly (P<0.05) from values recorded under chemical fertilizer amendment.
First Stage Evaluation of ISSuFiRs: Partners’ Financial Report Monitoring System by Islamic Financial Cooperatives (BMT) in Indonesia

Most MSMEs, especially micro-enterprises, do not have good business accounting records, making it difficult for owners/managers to measure the profitability of their companies. Internal control management has a significant influence in reducing fraud in Micro Enterprises. The more principles-based accounting rules, the shorter the delay in annual reporting disclosures. It is important to have a good financial reporting system that can be utilized by Micro Enterprises and its cooperation partners who provide and distribute funds. ISSuFiRs is a web-based application that enables Islamic Financial Cooperatives to track the business progress of their financing partners. With this application, micro businesses can disclose their business activities in these financial records in accordance with the SAK-ETAP national accounting standards. Reporting on this system includes balance sheet, profit and loss, cash flow, sources, use of ZIS funds, and comments on financial reports. This study presents a prototyping framework that enables user value perception, evaluation, and continuous improvement of ISSuFiR design quality through co-creation methods. It is hoped that a simple and effective financial reporting system, ISSuFiRs, will be useful for Micro Enterprises and Islamic Financial Cooperatives to monitor business developments. This study proposes a prototyping framework that enables the user's perception of value. The framework is expected to be implemented in industrial product service systems, evaluated and then concluded. This research is ongoing, with a new prototype being rebuilt based on the evaluation results.
Three-Dimensional Information Modeling Based on Incomplete Data for Anchor Engineering

Intelligent design is a trend in geotechnical engineering. However, a middle link between demand identification and intelligent design is missing in anchor engineering, resulting in lower efficiency in terms of the interaction between the anchor components, geological body and modeling information. To address this challenge, this paper proposes a method for modeling a three-dimensional anchor engineering information model based on sparse boreholes. This method includes three parts: modeling the geological body, integrated modeling of the anchor components and the geological body, and multiple information assignments based on the anchor model. The feasibility was validated in actual engineering scenarios by using neural networks and rendering engines. The results showed that the prediction accuracy of the strata improved by 17.69%, the modeling efficiency increased by double, and engineering information was fit to the model well. This paper has the application potential to achieve intelligent design in anchor engineering.
Main geomagnetic field models

Modeling the main geomagnetic field is a kind of all-around research work,concerning with the research on the main geomagnetic filed theory,magnetic survey,data reduction,model expression,and model interpretation and application.In this article,we present a brief summary of more than ten main geomagnetic field models that have been put forward by many workers in German,Denmark,U.S.A.and Britain over the last five decades.The progresses of the studies on the main field models are briefly reviewed.Finally,the model description,the theoretical basis and construction methods are summarized.
Exploring the dimensions of traditional breakfast experience: A netnography study

The primary aim of this paper is to explore the dimensions of traditional breakfast experience of international tourists visiting Istanbul. To achieve this aim, the netnography approach, which is a qualitative study method, was performed using the User-Generated Content (UGC) technique. The data were gathered in July 2022 from TripAdvisor platform and were sequenced from the latest to the earliest reviews (n=1184). The results of this study demonstrated that the traditional breakfast experience has six major components: authenticity, memorability, novelty, local hospitality, food attributes, togetherness, and social interactions. Significant theoretical and managerial implications were discussed.
Permian fragmentation, accretion and subsequent translation of a low-latitude Tethyan seamount to the high-latitude east Gondwana margin: evidence from detrital zircon age data

Ion microprobe analyses of detrital zircons in the Te Akatarawa Terrane, New Zealand, reveal that the age of unfossiliferous turbidites overlying a fusuline- and coral-bearing limestone block olistostromal mélange is no older than 255±4 Ma (Late Permian). This is approximately 15 m.y. younger than the Kungurian age of the fusulinid limestone. We interpret this to indicate collapse of a Permian oceanic seamount as it entered a subduction zone along the Pacific margin of Gondwana. These turbidites differ markedly in composition from adjoining Permian to Middle Triassic sand-stones of the Torlesse Terrane. Detrital zircon age data indicate predominantly Permian and Carboniferous ages for source rocks supplying the Te Akatarawa turbidites, but also reveal significant earlier Palaeozoic and Proterozoic components, ranging back to 1.9 Ga. The warm-water setting of limestone blocks and the short 15 m.y. time period between sedimentation and accretion onto a continental margin require the limestone to have formed in a low-latitude position probably off the northeast Australian (New Guinea) margin of Gondwana. Zircons within the sample underwent re-crystallization at around 230±11 Ma which may be related to alteration during accretion in a subduction zone environment. Over a period of 100 to 150 m.y. from 255 Ma the terrane underwent more than 5000 km translation along the continental margin southward to its current location as an exotic mini-terrane enclosed within the New Zealand Torlesse Terrane.
Identification and validation of a pyroptosis-related signature in identifying active tuberculosis via a deep learning algorithm

Introduction Active tuberculosis (ATB), instigated by Mycobacterium tuberculosis (M.tb), rises as a primary instigator of morbidity and mortality within the realm of infectious illnesses. A significant portion of M.tb infections maintain an asymptomatic nature, recognizably termed as latent tuberculosis infections (LTBI). The complexities inherent to its diagnosis significantly hamper the initiatives aimed at its control and eventual eradication. Methodology Utilizing the Gene Expression Omnibus (GEO), we procured two dedicated microarray datasets, labeled GSE39940 and GSE37250. The technique of weighted correlation network analysis was employed to discern the co-expression modules from the differentially expressed genes derived from the first dataset, GSE39940. Consequently, a pyroptosis-related module was garnered, facilitating the identification of a pyroptosis-related signature (PRS) diagnostic model through the application of a neural network algorithm. With the aid of Single Sample Gene Set Enrichment Analysis (ssGSEA), we further examined the immune cells engaged in the pyroptosis process in the context of active ATB. Lastly, dataset GSE37250 played a crucial role as a validating cohort, aimed at evaluating the diagnostic prowess of our model. Results In executing the Weighted Gene Co-expression Network Analysis (WGCNA), a total of nine discrete co-expression modules were lucidly elucidated. Module 1 demonstrated a potent correlation with pyroptosis. A predictive diagnostic paradigm comprising three pyroptosis-related signatures, specifically AIM2, CASP8, and NAIP, was devised accordingly. The established PRS model exhibited outstanding accuracy across both cohorts, with the area under the curve (AUC) being respectively articulated as 0.946 and 0.787. Conclusion The present research succeeded in identifying the pyroptosis-related signature within the pathogenetic framework of ATB. Furthermore, we developed a diagnostic model which exuded a remarkable potential for efficient and accurate diagnosis.