Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterizing and Eliminating the Inbreeding Load

Simple Summary Inbreeding, which is the mating of related animals, is a general phenomenon in closed populations. Because it is often associated with a decrease in performance, it has been studied extensively during the past 150 years. In this review, the authors cover the pedigree-based and genomic procedures that are available to characterize inbreeding load and determine whether the interaction between selection and inbreeding reduces the inbreeding load in a given population. The pedigree-based methods include ancestral inbreeding, the inbreeding–purging model, and expressed opportunity of purging. The various ancestral inbreeding coefficients show the proportion of the genome of an animal that is identical by descent to its ancestors or the part of the inbreeding coefficient that is identical by descent to its ancestors. The inbreeding–purging model calculates the purged inbreeding coefficient, which takes into account that the frequency of deleterious genes is decreased by the interaction of inbreeding and selection. Finally, the expressed opportunity of purging calculates the proportion of the reduction in the inbreeding depression due to purging. Relevant studies relying on genomic methods are also presented. According to the findings of the reviewed studies, it could be concluded that although the harmful effects could be eliminated under specific circumstances due to inbreeding-aided selection, the application of voluntary inbreeding cannot be recommended. Abstract The authors evaluated the relevant literature related to purging, which is the interaction between selection and inbreeding in which the population may eliminate its inbreeding load at least partially. According to the relevant literature, the inbreeding load and the process of purging were evaluated via pedigree methods based on ancestral inbreeding, the inbreeding–purging model, and expressed opportunity of purging, along with genomic methods. Most ancestral inbreeding-related studies were performed in zoos, where only a small proportion of the studied populations show signs of purging. The inbreeding–purging model was developed with Drosophila, and it was used to evaluate different zoo ungulates and Pannon white rabbits. Purging was detected in both studies. The expressed opportunity of purging was applied in Jersey cattle and Pannon white rabbits. In the Jersey cattle, it had an effect of 12.6% for fitness, while in the Pannon white rabbits, the inbreeding load was between 40% and 80% of its original value. The genomic studies also signalled purging, but they also made it clear that, contrary to the detected purging, the evaluated populations still suffered from inbreeding depression. Therefore, especially for domesticated animals, it can be concluded that deliberate inbreeding with the purpose of generating purging is not advocated.
Machine‐learning‐based prediction methods for path loss and delay spread in air‐to‐ground millimetre‐wave channels

The unmanned aerial vehicles (UAVs) have been widely applied in various fields due to their advantages like high mobility and low cost. Reliable communication is the premise to ensure the connectivity between UAV nodes. To provide reasonable references for the design, deployment, and operation of UAV communication systems, the precise prediction of radio channel parameters are required. In this study, the authors propose prediction methods for path loss and delay spread in air-to-ground millimetre-wave channels based on machine learning. Random forest and K-nearest-neighbours are the algorithms employed in the methods. Then, a feature selection scheme is proposed to further improve the prediction accuracy and generalisation performance of the machine-learning-based methods. Generally, machine learning algorithms require massive data for training purpose. However, measuring data is time-consuming and costly, especially when the scenario or frequency changes. Therefore, transfer learning methods are introduced to predict path loss with limited data. The proposed methods for path loss prediction are compared to Okumura-Hata and COST-231 Hata models. The lognormal distribution is the contrast model in delay spread prediction. Based on the data generated by ray-tracing software, the new methods have a smaller root mean square errors than contrast models.
Abstract Syntax Tree for Method Name Prediction: How Far Are We?

Method name prediction (MNP) aims to recommend a proper name for a method given by the developer, which can ease the programming task and improve programmer productivity. Due to the excellent expressiveness of code representation, abstract syntax trees (AST) have been widely exploited by MNP techniques. However, it is a complex process to manipulate AST, including AST parsing, AST preprocessing, and AST encoding, of which a change in the scheme may change the AST embeddings and thus affects the performance of MNP. In this paper, we first conduct a comprehensive empirical study to systematically investigate the impact of the sub-processes of AST usage on MNP performance. The empirical findings of this study unmistakably demonstrate that AST has a positive impact on promoting MNP. Moreover, the selection of schemes for AST parsing, preprocessing, and encoding exerts a profound influence on the effectiveness of MNP. Properly combining AST (e.g., using JDT, AST Pathfull, and code2seq as AST parsing, preprocessing, and encoding methods, respectively) can improve MNP performance by 164% in terms of F1-score compared to using only code tokens.
A Graph Attention Network-Based Link Prediction Method Using Link Value Estimation

Link prediction in complex networks is a critical process aimed at uncovering hidden or potential connections among nodes. This technique is widely utilized in areas such as knowledge graphs. Current Graph Neural Networks (GNNs) often focus exclusively on determining whether nodes are connected or assessing the strength of these links by leveraging node attributes. They typically use network structure and attributes to develop node representations through neighborhood aggregation. However, these methods often overlook the intrinsic importance of the links themselves. This paper thoroughly examines the significance of link value based on network structure and introduces an innovative approach for estimating this value, and proposes a method that incorporates link value into both the formulation and training of a link prediction graph attention network. This integration not only boosts the accuracy of link predictions but also provides a theoretical basis for understanding the prediction results. We conducted extensive experiments in link prediction employing widely recognized benchmark datasets. The findings reveal that our proposed framework for link prediction exhibits commendable performance and generalization capabilities, and overall performance improved by an average of 1.2%, thereby establishing it as an effective baseline model.
Coarse-Grained Monte Carlo Simulations with Octree Cells for Geopolymer Nucleation at Different pH Values

Geopolymers offer a potential alternative to ordinary Portland cement owing to their performance in mechanical and thermal properties, as well as environmental benefits stemming from a reduced carbon footprint. This paper endeavors to build upon prior atomistic computational work delving deeper into the intricate relationship between pH levels and the resulting material’s properties, including pore size distribution, geopolymer nucleate cluster dimensions, total system energy, and monomer poly-condensation behavior. Coarse-grained Monte Carlo (CGMC) simulation inputs include tetrahedral geometry and binding energy parameters derived from DFT simulations for aluminate and silicate monomers. Elevated pH values may can alter reactivity and phase stability, or, in the structural concrete application, may passivate the embedded steel reinforcement. Thus, we examine the effects of pH values set at 11, 12, and 13 (based on silicate speciation chemistry), investigating their respective contributions to the nucleation of geopolymers. To simulate a larger system to obtain representative results, we propose the numerical implementation of an Octree cell. Finally, we further digitize the resulting expanded structure to ascertain pore size distribution, facilitating a comparative analysis. The novelty of this study is underscored by its expansion in both system size, more accurate monomer representation, and pH range when compared to previous CGMC simulation approaches. The results unveil a discernible correlation between the number of clusters and pores under specific pH levels. This links geopolymerization mechanisms under varying pH conditions to the resulting chemical properties and final structural state.
Application of Noise-Assisted Multivariate Data Analysis for Hour-Ahead GHI Forecasting

This paper proposed a hybrid model based on a noise-assisted data analysis technique and a less time-complex stacked (ensemble) model for f-h ahead GHI forecasting. Data analysis techniques reduce the non-stationary issues of time series data. Noise-assisted multivariate empirical mode decomposition (NA-MEMD) proposed in this work is an updated form of the standard MEMD technique. The former can overcome the drawback (mode mixing) of the latter technique. The ensemble model combines the prediction of three simple ML models (decision tree regressor, k-nearest neighbor, and ridge regressor). The results are analyzed for two locations in India under different climate zones. The results show that after replacing MEMD with NA-MEMD, the forecasting error has further reduced. The corresponding reduced values in terms of %RMSE (%MAE) are as follows: 23.87% (26.84%) at Delhi and 20.65% (25.64%) at Nainital. Interestingly, MEMD took around 232 sec, whereas NA-MEMD consumed around 180 sec to decompose the time series. Further, the proposed model's performance is visualized against some popular and complex ML algorithms. The minimum %RMSE reduction of 58.85%, 61.04%, 59.76%, and 63.36% has been reported after replacing stacked model LSTM, RF, ANN, and (kNN-DTR-Ridge), respectively, with the proposed NA-MEMD-stacked model.
Parallel Clustering with Resolution Variation

We introduce a novel approach for parallel data clustering with resolution variation. Conventional graph clustering is typically governed by a function defined over all possible cluster configurations but at a fixed value of the resolution hyperparameter denoted as $\gamma$. Such clustering suffers from issues related to the so-called resolution limit or requires resolution tuning. This has been changed by recent theories and algorithms for graph clustering with resolution variation. The requirement for specifying or tuning the $\gamma$ -hyperparameter is effectively removed, and the clustering function becomes or transforms to a functional form with $\gamma$ as an internal resolution variable. We address a standing and significant challenge in parallel clustering with resolution variation. We identify and remove the key bottlenecks in search operations confined to a specific $\gamma$ value, and reduce and minimize redundant search operations at different $\gamma$ values. We show impressive performance achieved with our parallel approach on real-world datasets.
Improving Reliability of PV-Powered Highway With Electric Vehicle Charging Services

Due to growing concerns related to greenhouse gas emissions, the growing utilization of sustainable energy sources such as photovoltaic (PV) powered electric vehicle (EV) charging stations in the highway transportation sector can be considered as one of the climate change adaptation measures. Reliability and economic considerations are two critical factors that need to be considered in design and operation of PV-powered EV charging stations. This paper develops an approach for the evaluation of reliability for PV-powered charging services. The costs associated with providing reliable services are also assessed. The developed methodology is applied to PV-powered charging stations operating with or without battery energy storage systems (BESS) along a highway to showcase the effect of varying PV system capacity and different inter-station distances on charging service reliability and associated costs. The results demonstrate that the cost-effectiveness of charging stations equipped with BESS becomes increasingly evident as the reliability level improves.
Linear Time-Varying Parameter Estimation: Maximum A Posteriori Approach via Semidefinite Programming

We study the problem of identifying a linear time-varying output map from measurements and linear time-varying system states, which are perturbed with Gaussian observation noise and process uncertainty, respectively. Employing a stochastic model as prior knowledge for the parameters of the unknown output map, we reconstruct their estimates from input/output pairs via a Bayesian approach to optimize the posterior probability density of the output map parameters. The resulting problem is a non-convex optimization, for which we propose a tractable linear matrix inequalities approximation to warm-start a first-order subsequent method. The efficacy of our algorithm is shown experimentally against classical Expectation Maximization and Dual Kalman Smoother approaches.
Quantifying the Resilience of a Healthcare System: Entropy and Network Science Perspectives

In this study, we consider the human body and the healthcare system as two complex networks and use theories regarding entropy, requisite variety, and network centrality metrics with resilience to assess and quantify the strengths and weaknesses of healthcare systems. Entropy is used to quantify the uncertainty and variety regarding a patient’s health state. The extent of the entropy defines the requisite variety a healthcare system should contain to be able to treat a patient safely and correctly. We use network centrality metrics to visualize and quantify the healthcare system as a network and assign the strengths and weaknesses of the network and of individual agents in the network. We apply organization design theories to formulate improvements and explain how a healthcare system should adjust to create a more robust and resilient healthcare system that is able to continuously deal with variations and uncertainties regarding a patient’s health, despite possible stressors and disturbances at the healthcare system. In this article, these concepts and theories are explained and applied to a fictive and a real-life example. We conclude that entropy and network science can be used as tools to quantify the resilience of healthcare systems.
ISSUES AND CHALLENGES IN DEVELOPING SHARIAH GOVERNANCE AND SHARIAH AUDIT IN ISLAMIC COOPERATIVES

The development of Shariah governance and audit in Islamic Cooperatives in Malaysia is one of the primary concerns since some issues were raised throughout the process. Islamic cooperatives in Malaysia are regulated by Suruhanjaya Koperasi Malaysia (SKM), which aims to encourage the growth of cooperatives that comply with Shariah. Islamic cooperatives operate their business under supervision from SKM regarding governance and Shariah perspective. The development of Shariah governance and Shariah audit support the Shariah compliance system that already operates in most Islamic institutions. During the Shariah governance and audit process, some issues are addressed during the development, which involves guidelines, Shariah auditor competency, staff skills, and governance and Shariah audit of the cooperative sector. The study’s approach included an interview with practitioners from cooperatives, auditors, and experts in the industry. This paper aims to identify the issues and challenges of Shariah governance and Shariah audit in the Islamic cooperative sector. The study will contribute to one of the key critical elements in strengthening the Shariah governance and Shariah audit in the cooperative sector.
Chip-Interleaved DSSS for Energy-Efficient Physical Layer Encryption

Traditional methods for ensuring secure communications typically rely on encryption, which necessitates some form of coordination between the transmitter and the receiver. The Direct Sequence Spread Spectrum modulation can introduce some security at the physical layer but this technique must be combined with other technologies. Physical layer encryption techniques offer an alternative approach by taking advantage of transmit arrays to introduce beamformed noise. These techniques guarantee a certain level of confidentiality without requiring neither a heavy coordination nor losing time for secret key establishment, which make these techniques very attractive in particular for military and secured communications. However, the proposed solutions in the literature suffer from several limitations, and in particular, poor energy efficiency. To address this issue, we propose an original solution for physical layer encryption that does not spoil any energy with a signal mask by taking advantage of the wiretap channel inherent degradation introduced by the spatial chip interleaving over the transmitter antennas. Our simulations confirm that our proposal guarantees a considerable level of confidentiality without any energy loss.
Bioanalytical and validation high-performance liquid chromatography method for simultaneous quantification cefotaxime and ciprofloxacin in human plasma

This study aims to obtain a validated method for simultaneously determining cefotaxime and ciprofloxacin levels in spiked human plasma. The chromatographic system used a Luna Phenomenex ® C18 column (250 × 4 mm i.d; 5 µm), and a mobile phase consisting of an isocratic mixture of 0.02 M phosphate buffer (pH 3.0), acetonitrile (ACN), and methanol (80:12:8 v / v ), flow rate 1.0 ml/minute, 280 nm. Extraction of human plasma was carried out using 100 µl of acetic acid 10% and ACN, and extraction was repeated twice, the supernatant obtained was collected and then evaporated to dryness. The dried sample was dissolved with 500 µl of mobile phase before being injected into the system. The method was validated by evaluating the selectivity, linearity, accuracy, precision, and stability parameters. This method was declared valid for the simultaneous determination of cefotaxime and ciprofloxacin levels in human plasma and it is stated to be linear with the value of the correlation coefficient of 0.9986 for cefotaxime and 0.9981 for ciprofloxacin. The average percent recovery of cefotaxime was in the range of 89.13 and 112.81. Meanwhile, ciprofloxacin was in the range of 94.33 and 107.98. The developed analytical method is stated to fulfill the validity requirements.
Net Zero Water Strategies and Impacts for PV Manufacturing

Net zero water is a sustainable development strategy for manufacturing in water-stressed locations. A case study in Tamil Nadu, India shows that sustainable net zero water PV manufacturing can be achieved by a) utilizing on-site wastewater treatment and zero liquid discharge units to maximize the usage of onsite reclaimed water b) using offsite reclaimed water to meet the remaining water demand and c) implementing continuous improvement in water conservation. Net zero water can be combined with net zero electricity to reduce the life cycle water footprint of PV modules by ~half while also reducing the life cycle carbon footprint by ~40%. While crucial for managing local water and energy resources, net zero strategies have a relatively small (~15%) impact on reducing the total multi-criteria product footprint. Adding a third strategy of high value recycling with semiconductor recovery can achieve up to ~65% reduction in the multi-criteria PV module product environmental footprint covering health, ecosystem, and natural resource impact categories.
Effect of Moisture Conservation Methods and Plant Density on the Productivity of Two Maize (Zea mays L.) Varieties under Semi-Arid Tropics of Hamelmalo, Eritrea

The productivity of maize in Eritrea in general and under semi-arid tropics of Hamelmalo in particular is low because of moisture stress. The low moisture content is ascribed to the low and erratic distribution of rainfall, high temperature, lack of suitable varieties, and competition by weeds and low soil fertility. To overcome some of these problems, a field experiment was carried out to assess the effect of moisture conservation methods (MCM) and plant density on the productivity of two maize (Zea mays L.) varieties under semi-arid tropics of Hamelmalo, Eritrea. The experiment was conducted in split-split plot design with three MCM viz tied ridge, ridge and furrow and flat-bed in main plots; two maize varieties viz early local and 04sadve hybrid in sub plots and three plant densities by manipulating the plant to plant distance viz 35 cm, 25 cm and 15 cm at a fixed 75 cm row spacing in sub-sub plots, each replicated thrice. The experiment was focused in addressing the effective moisture conservation techniques, optimum plant density to each variety thus to improve productivity. The crop experienced 10°C to 34.8°C minimum and maximum temperature, respectively and received 429.1 mm total rainfall. The results of the experiment indicated that among all the combinations, 04sadve hybrid variety sown at 75 cm × 25 cm spacing in ridge and furrow method or at 75 cm × 15 cm spacing in tied ridge or flat-bed method and early local variety sown at 75 cm × 15 cm spacing in flat-bed being statistically at par resulted in significantly higher moisture conservation and consequently higher grain yield (4509 kg·ha-1) and higher water use efficiency. It is, therefore, recommended that tied ridge or flat-bed of moisture conservation method at 15 cm plant spacing and 04sadve is preferable to optimize productivity in Hamelmalo area, Eritrea.
Oxy-Fuel Combustion: A Threat or an Opportunity for Solar?

The rapid growth of solar, as well as other variable renewable energies, is key for achieving decarbonization targets. However, their integration to the grid and increased curtailment have become a major challenge for their further development in the most successful markets. In this study, the impacts of introducing an oxy-fuel combustion resource in California's energy grid are evaluated. To do so, we use RESOLVE, a capacity expansion model, to predict the energy grid mix for 2030, 2035, 2040, and 2045. Our results indicate that the model chooses to build the oxy-combustion resource when it is available at relatively low costs. While the introduction of oxy-fuel combustion, even at limited scale, reduces the selected operational capacity of solar PV and lithiumion batteries, it substantially reduces the curtailment of the solar that $is$ installed. Far from being a threat, this reduction could be an opportunity for solar to continue with robust growth, retaining its economic and environmental value even when the procurement of storage might otherwise limit its adoption rate.
Estimation of Sediment, Organic Carbon, and Phosphorous Loads from Pasikhan River into Anzali Wetland, Iran

Pasikhan River into the Wetland. The estimation of sediment delivery to the Anzali Wetland was updated based on the suspended load measurements in sedimentological stations for nine major entering rivers. Total sediment load was estimated at 683.5 × 10 3 ton y -1 ; this shows a significant increase from previous estimations. This indicates that soil erosion is an important problem in the Anzali watershed. Water sampling was performed at Pasikhan River’s Nokhaleh Hydrometric Station, since Pasikhan is considered the most important river entering the Anzali Wetland during one year. The samples were analyzed for phosphorous fractions and organic carbon content of suspended sediment. P concentration and OC showed monthly changes. Both were high in wet months (September to March) and lower in dry months. This pattern also emphasizes the importance of soil erosion on the conditions of the Anzali Wetland. Total OC and P load from Pasikhan River into the Wetland were estimated to be 2710 ton y -1 and 245 ton y -1 , respectively.