Miracle-dz/UniFAR-FTUs huggingface dataset:
Structural studies for the prediction of karst in the Kuala Lumpur limestone

This paper reports the results of research currently being undertaken into the geological structure of Kuala Lumpur Limestone and its relationship to karst beneath the city of Kuala Lumpur. Reference to the existing geological map permits the basic geology of the area to be related to river patterns. From this relationship an indication can be obtained of the direction of fractures expected in the limestone and hence the direction in which karst is likely to be readily developed. These data are presented; two directions appear to be special interest: N040 to 070 and N300 to 330. The evidence for this is given and the methods used for these studies are described.
Some observations on precipitation measurement on forested experimental watersheds

Measurement of precipitation on forested experimental watersheds presents difficulties other than those associated with access to and from the gages in all kinds of weather. For instance, the tree canopy must be cleared above the gage. The accepted practice of keeping an unobstructed sky view of 45" around the gage1 involves considerable tree cutting. On a level area, trees 30 feet tall around a gage require an opening of about 0.06 acre, and trees 60 feet tall, 0.25 acre; on slopes, the openings required can be much greater. Once cleared, the opening immediately begins to close almost surreptitiously so that periodic maintenance is necessary. Still another problem is the effect of heavy cutting, as a watershed treatment, on precipitation catch. Other concerns are the effect of slope on the catch and whether or not the use of tilted gages for slope correction is justified. Some recent experiences along lines of these last two problems are described below. They are based on work at the Fernow Experimental Forest in West Virginia and at the Hubbard Brook Experimental Forest in New Hampshire.
Catatan Pertama Distribusi Tridacna noae (Röding, 1798) dari Perairan Kepulauan Kei, Maluku

The giant clams are the biggest living bivalves that not only play a vital role in coral reef ecosystem, but also as a food provider for coastal inhabitant. One of these species was Tridacna noae which is a crpyctic species due to morphoplogial characteristic similarity and habitat preferences with Tridacna maxima. Tridacna noae was recorded at the first time found in Kei Islands on November and December 2017 based on morphological characteristic and DNA barcoding analysis. This research found 8 individu of T. Noae in total, 7 individu was found in Kur Island and one individu from Ohoidertom at 2-3 m depth. The mantle pattern of T. noae was very unique and can be used to distinguished from T. maxima. Special feature of ormanent mantle of T. noae indicated with oval-shape patch with white line on the edge, has hyaline organ scattered along the margin of mantle and the presence of papillae that clearly seen. The result on Blasting from GenBank NCBI for COI sequence of giant clam sample revealed that 99.79%-100% was identical with T. noae species. Furthermore, result on genetic distance analysis of T. noae sample from Kei Island compare with T.noae from GenBank was 0.00-0.051, while genetic distance value compare with T. maxima was 0.159 – 0.205. The new finding of T. noae in Kei Island has added the number of giant clam species recorded previously become 7 species. This finding was also has enhanced the information on biogeographical distribution of T.noae in Indonesia. Kerang kima merupakan kelompok bivalvia berukuran paling besar yang memlilki peranan penting di ekosistem terumbu karang serta menjadi sumber nutrisi bagi masyarakat peisir. Salah satu species kima adalah Tridacna noae yang bersifat cryptic spesies karena memiliki banyak kesamaan karakteristik morfologi dan preferensi habitat dengan T. Maxima. T. noae tercatat pertama kali ditemukan di Perairan Kepulauan Kei pada bulan November dan Desember 2017 berdasarkan pengamatan karekteristik morfologi dan aplikasi DNA barcoding. Hasil peneltian menemukan delapan individu T. noae, tujuh individu ditemukan di Perairan Pulau Kur, satu individu ditemukan di Perairan Ohoidertoom dengan kedalaman perairan 2-3 m. Ornamen mantel T. noae sangat khas dan dapat digunakan untuk membedakan dari T. maxima. Ornamen mantel ditandai dengan patch berbentuk oval terpisah dengan garis putih di tepi, memiliki organ hialin yang tersebar di sepanjang margin mantel, dan kehadiran papila yang terlihat jelas. Hasil blasting di GenBank NCBI untuk sekuens COI sampel kima menghasilkan tingkat kemiripan 99.79%-100% (identik) dengan T. noae. Hasil analisis jarak genetik sampel T. noae asal Kepulauan Kei dengan T. noae Genbank adalah sebesar 0.00 hingga 0.051, sedangkan jarak genetik dengan T. maxima adalah 0.159 hingga 0.205. Temuan T. noae di Kepulauan Kei menambahkan jenis kima yang tercatat sebelumnya di Kepulauan Kei menjadi 7 jenis. Temuan ini juga menambahkan lebih banyak informasi tentang distribusi geografis T. noae di Indonesia
Both sides of irrigation management: An empirical analysis of smallholders' perception of co‐management of irrigation systems

Growing water imbalance requires a systematic development process to enable the agriculture sector to address what it faces as challenges. However, the overall longevity and efficiency of developed infrastructure become doubtful if water users are not involved in the development process. Thus, the assessment of the impact of irrigation improvement on all sides of the management, including smallholders, is fundamental. The paper investigates the impact of irrigation improvement on farmers' satisfaction with transferred water services in the Old Lands in Egypt. A counterfactual analysis, relying on four alternative estimation methods, is introduced to measure the average treatment effect of irrigation improvement on farmers' perceptions of reliability, flexibility and equity of the water service. Such indicators' selection was planned under the assumption that combined social and engineering‐based approaches to impact assessments are more suitable to represent the perspectives of all stakeholders. The results of the analysis are consistent and show that increases in perceptions are positive and significant in each case, as the result of the improvement. The paper argues that modern infrastructure framed in successful co‐management can work towards a mutual interest.
Evolving Clinical–Translational Investigations of Cerebroprotection in Ischemic Stroke

Ischemic stroke is a highly morbid disease, with over 50% of large vessel stroke (middle cerebral artery or internal carotid artery terminus occlusion) patients suffering disability despite maximal acute reperfusion therapy with thrombolysis and thrombectomy. The discovery of the ischemic penumbra in the 1980s laid the foundation for a salvageable territory in ischemic stroke. Since then, the concept of neuroprotection has been a focus of post-stroke care to (1) minimize the conversion from penumbra to core irreversible infarct, (2) limit secondary damage from ischemia-reperfusion injury, inflammation, and excitotoxicity and (3) to encourage tissue repair. However, despite multiple studies, the preclinical–clinical research enterprise has not yet created an agent that mitigates post-stroke outcomes beyond thrombolysis and mechanical clot retrieval. These translational gaps have not deterred the scientific community as agents are under continuous investigation. The NIH has recently promoted the concept of cerebroprotection to consider the whole brain post-stroke rather than just the neurons. This review will briefly outline the translational science of past, current, and emerging breakthroughs in cerebroprotection and use of these foundational ideas to develop a novel paradigm for optimizing stroke outcomes.
UAV Camera Re-localization Based on Image Retrieval

Camera relocalization is a challenging problem in computer vision, particularly for unmanned aerial vehicles (UAVs) due to their mobility, limited resources, and harsh conditions. Although several methods exist for camera relocalization, they suffer from limitations such as indoor bias, limited accuracy and range, high computational cost, and single-camera assumption. This highlights the need for more efficient and robust camera relocalization methods for UAVs. To address this challenge, We propose a realtime UAV camera relocation system that utilizes an unsupervised deep learning framework to learn image relationships. Transfer learning from large-scale classified data enables faster training and higher accuracy. The system employs image retrieval and a convolutional neural network to match features from a single RGB image with location information in a database. It achieves strong rotational invariance, enhancing its performance and versatility in various scenarios. The system has demonstrated high accuracy and robustness in large outdoor scenes, with each image taking only 0.6 seconds to compute.
$\mathrm{H}_{\infty}$ Control of One Class of Bilinear Positive Continuous-Time Systems

This paper focuses on the quadratic stabilization of one class of bilinear positive continuous-time systems subject to system disturbances. Using the quadratic Lyapunov function, new control design conditions are proposed as a set of linear matrix inequalities, which reflect the principle of diagonal stabilization and guarantee the quadratic stability and positivity of the closed-loop system. Taking into account an upper bound for the $\mathrm{H}_{\infty}$ norm of the disturbance transfer function matrix, the task is redefined as a convex problem and it is shown that quadratic stabilization of the bilinear systems can be achieved by state feedback control. The key properties are illustrated by the detailed construction of the required system parameter structures in the example.
Lightweight Histological Tumor Classification Using a Joint Sparsity-Quantization Aware Training Framework

Cancer decision-making is a complex process that can be exacerbated by the limited availability of oncological expertise. This is particularly true in rural areas and settings with fewer resources. Recently, there has been an interest in the potential of artificial intelligence in reliable computer-aided diagnosis tools in such settings. Nevertheless, the majority of deep learning algorithms are resource hungry in terms of data and storage requirements. In this work, we propose a novel lightweight deep learning model for histological tumor classification through a Joint Sparsity-Quantization Aware Training framework. Extensive experiments were conducted to evaluate the proposed framework. Promising performance has been achieved compared to the most relevant state-of-the-art work with a classification accuracy of 94.26% and an average $5\times $ reduction in the memory footprint. This work aims at opening doors toward efficient point-of-care diagnostic devices suitable for environments with limited resources.
A New Approach for Human Body Orientation Detection Using Camera Footage

This study contributes to the field of orientation detection from camera footage, showcasing the advancements in computer vision techniques and their practical applications. The insights gained from this study can be leveraged in various domains, including surveillance, security, activity recognition, and pose estimation, fostering the development of more efficient and accurate orientation detection systems. Impressive results are obtained, with high AUROC values achieved for classifying different orientations: 0.91 for “right,” 0.91 for “left,” 0.95 for “fore,” and 0.93 for “back. ” These results underline the models' ability to accurately estimate human body orientations as fore, back, right, and left from camera footages with a high degree of accuracy.
Isn't Climate Change Affecting Wheat Productivity in India?

Though change in climate over a long period of time is a natural phenomenon, human activities in the last few decades have hastened the process of climate change. Several studies including the International Panel on Climate Change (IPCC) have observed increasing trends in the global surface temperature and sea levels; many of these studies have also found significant changes in precipitation trend across regions, countries, continents and globe (IPCC, 2007). Indian Metrological Department (IMD) also observed increase in temperature and changes in the pattern of rainfall across space and time in India (Government of India 2010). The above changes in climatic factors directly affect physiology, phrenology and morphology of plants. The changes in climatic factors indirectly affect the soil fertility, irrigation availability and also natural calamities like floods and droughts. The climate change in short affects plant, plant environment and society at large. Several studies report that agriculture and human well-being will be negatively affected by the climate change (Nelson et al., 2009). Studies to assess the impact of climate change on agriculture in the Indian context can broadly be classified into two groups; first, the experiment-based simulation studies conducted mostly by agricultural scientists, and second the actual field-based studies undertaken mostly by the social scientists. The studies based on controlled agricultural experiments in a laboratory-like settings, reflect the optimal outcomes with only minimal adaptation to change. The results from such studies often differ from the realised outcomes at the farm level. Some of the important experiment based simulation studies are Saseendran et al., 2000; Mall and Agrawal, 2002; Pathak et al., 2003; Aggarwal et al., 2006; Aggarwal, 2008; Kalra et al., 2008; Srivastava et al., 2010; and Boomiraj et al., 2010. Most of the recent studies have found adverse effect of climate change on crop yield. The effect of climate change on agriculture using field level data were studied by Kumar et al., 1998; Kumar et al., 2001; Sanghi et. al., 1998; Kumar and Kavi, 2009. Most of these studies are based on the Ricardian approach (Mendelsohn et al., 1994)
Particle Filter-Based Prognosis and Health Monitoring of Electromechanical Actuators

Given the important role of Electromechanical Actuators (EMAs) in the aviation industry, this paper aims to develop Prognosis and Health Monitoring (PHM) solutions for EMAs. We begin by analyzing the general configuration and architecture of EMAs and demonstrate that load torque oscillation induces amplitude modulation in the stator current. We also propose a relationship between two faults, namely spiral bevel gear and flex spline wear. Next, we model an EMA, including a brushless DC motor, inverter, gearbox, mechanical load, and other units. As a prerequisite for fault prediction, we address the estimation of two states: stator current and motor speed. We use a Particle Filter-based (PF) methodology to estimate these states and perform predictions. The prediction scheme involves forming an auxiliary state corresponding to fault degradation, based on which the remaining useful life (RUL) of the system is computed. Finally, we present extensive simulation results of the proposed methodology corresponding to various scenarios.
A U-Net Approach for InSAR Phase Unwrapping and Denoising

The interferometric synthetic aperture radar (InSAR) imaging technique computes relative distances or surface maps by measuring the absolute phase differences of returned radar signals. The measured phase difference is wrapped in a 2π cycle due to the wave nature of light. Hence, the proper multiple of 2π must be added back during restoration and this process is known as phase unwrapping. The noise and discontinuity present in the wrapped signals pose challenges for error-free unwrapping procedures. Separate denoising and unwrapping algorithms lead to the introduction of additional errors from excessive filtering and changes in the statistical nature of the signal. This can be avoided by joint unwrapping and denoising procedures. In recent years, research efforts have been made using deep-learning-based frameworks, which can learn the complex relationship between the wrapped phase, coherence, and amplitude images to perform better unwrapping than traditional signal processing methods. This research falls predominantly into segmentation- and regression-based unwrapping procedures. The regression-based methods have poor performance while segmentation-based frameworks, like the conventional U-Net, rely on a wrap count estimation strategy with very poor noise immunity. In this paper, we present a two-stage phase unwrapping deep neural network framework based on U-Net, which can jointly unwrap and denoise InSAR phase images. The experimental results demonstrate that our approach outperforms related work in the presence of phase noise and discontinuities with a root mean square error (RMSE) of an order of magnitude lower than the others. Our framework exhibits better noise immunity, with a low average RMSE of 0.11.
How Many Are Too Many? Analyzing Dockless Bike-Sharing Systems with a Parsimonious Model

Using a parsimonious model, this paper analyzes a dockless bike-sharing (DLB) service that competes with walking and a generic motorized mode. The DLB operator chooses a fleet size and a fare schedule that dictate the level of service (LOS) as measured by the access time or the walking time taken to reach the nearest bike location. The market equilibrium is formulated as a solution to a nonlinear equation system over which three counterfactual design problems are defined to maximize (i) profit, (ii) ridership, or (iii) social welfare. The model is calibrated with empirical data collected in Chengdu, China, and all three counterfactual designs are tested against the status quo. We show the LOS of a DLB system is subject to rapidly diminishing returns to the investment on the fleet. Thus, under the monopoly setting considered herein, the current fleet cap set by Chengdu can be cut by up to three quarters even when the DLB operator aims to maximize ridership. This indicates the city’s fleet cap decision might have been misguided by the prevailing conditions of a competitive yet highly inefficient market. For a regulator seeking to influence the DLB operator for social good, the choice of policy instruments depends on the operator’s objective. When the operator focuses on profit, limiting fare is much more effective than limiting fleet size. If, instead, it aims to grow market share, then setting a limit on fleet size becomes a dominant strategy. We also show, both analytically and numerically, that the ability to achieve a stable LOS with a low rebalancing frequency is critical to profitability. A lower rebalancing frequency always rewards users with cheaper fares and better LOS even for a profit-maximizing operator. Funding: This research was partially supported by the U.S. National Science Foundation [Grant CMMI 1922665] and the National Natural Science Foundation of China [Grant 71971044]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022. 0304 .
Exploring the religious and spiritual trajectory of cathedral choristers in England

ABSTRACT The present paper reports findings from interviews with thirty ex-choristers from cathedrals and collegiate chapels in England, aged from eighteen to eighty. These interviews explored choristers’ religious commitments before entering choir school and factors for staying with, leaving, or later returning to religious practice. The findings suggest that the attitudes to religion among ex-choristers mirror those of the wider population and confirms the trend that ex-choristers may be seeking spirituality but not religion. The study concludes that, although choristers are not invited into the spiritual community on a level that allows them to engage cognitively with organised religion, they remain open to its emotional connotations as expressed in music. They might, nonetheless, subsequently find faith if they get help to explore the meaning of liturgy and music within it. However, there have been accounts where this faith is forced upon them in a way that pushes them away.
A Novel Stochastic Decoder for Extended BCH Code Based Turbo Product Codes

Turbo product codes (TPC) have received recent attention as a competitive forward error correction scheme in high-speed communication protocols and Extended BCH based TPC has been accepted by several optical transmission standards. Decoding complexity is one of the major challenges for TPC. This paper proposes a novel stochastic TPC decoder that processes the decoding in a bit-wise iterative manner to reduce the decoding complexity. Further, a hard-decision decoding temporary frozen strategy is proposed to further speed up the convergence and reduce the decoding complexity. The evaluation results demonstrate significant complexity reduction with error correction performance improvements compared to the existing works, making it a promising candidate for practical application.
Soil suitability and crop versatility assessment using fuzzy analysis at a farm scale.

There is the need to explore simultaneous soil suitability analysis for several viable crops to determine the overall suitability or versatility of a given area or a farm. The main objectives of this paper are to: fit fuzzy membership functions (FMFs) to determine soil suitability for multiple crops; determine the diversity of multiple suitability at each location using the Shannon’s Index and equitability equation; and determine the overall versatility at the farm scale, and establish a new versatility equation by combining the previous versatility methodology with a diversity index. To understand the overall versatility of the study area suitability analysis was carried out for each of the several cropscanola, barley, field pea, Lucerne and wheat. The multiple suitability analysis demonstrated subtle differences in the trend or patterns of the individual crop maps. The development of the improved versatility map, incorporating Shannon’s index, yielded important information for management decisions. The results indicate that the Northern and Southern paddocks of the study area exhibited higher versatility than the rest of the farm and would be highly suited for multiple crop rotations. Another pertinent point is that the areas of low versatility could be studied further to determine which of the crops are better suited to the soil.
A Model Selection Criterion to Use Gap Metric, Stability Margin, and Model Order Reduction Tools in Control of Nonlinear Systems

How to deal with high order nonlinear systems has led to the proposal of reduced multiple model control design in which three tools such as gap metric, stability margin, and model order reduction have been employed. High order nonlinear systems suffer from complexities in terms of both order and nonlinearity. On the one hand, model order-reduction methods can be applied to reduce the order complexity, and on the other hand, multiple models approach in which gap metric and stability margin are used, could face the nonlinearity. The priority in calling multiple models and model order-reduction methods and the selected reduction method leads to creation of four reduced multiple model control design schemes. Advanced model selection criterion is a tool to predict the best one from the closed-loop point of view. Heating, ventilating, and air conditioning system is considered to illustrate the effectiveness of the proposed approaches.