Miracle-dz/UniFAR-FTUs huggingface dataset:
Impacts of Flowering Density on Pollen Dispersal and Gametic Diversity Are Scale Dependent

Pollen dispersal is a key evolutionary and ecological process, but the degree to which variation in the density of concurrently flowering conspecific plants (i.e., coflowering density) shapes pollination patterns remains understudied. We monitored coflowering density and corresponding pollination patterns of the insect-pollinated palm Oenocarpus bataua in northwestern Ecuador and found that the influence of coflowering density on these patterns was scale dependent: high neighborhood densities were associated with reductions in pollen dispersal distance and gametic diversity of progeny arrays, whereas we observed the opposite pattern at the landscape scale. In addition, neighborhood coflowering density also impacted forward pollen dispersal kernel parameters, suggesting that low neighborhood densities encourage pollen movement and may promote gene flow and genetic diversity. Our work reveals how coflowering density at different spatial scales influences pollen movement, which in turn informs our broader understanding of the mechanisms underlying patterns of genetic diversity and gene flow within populations of plants.
A Machine Learning Approach for Movement Monitoring in Clustered Workplaces to Control COVID-19 Based on Geofencing and Fusion of Wi-Fi and Magnetic Field Metrics

The ubiquitous existence of COVID-19 has required the management of congested areas such as workplaces. As a result, the use of a variety of inspiring tools to deal with the spread of COVID-19 has been required, including internet of things, artificial intelligence (AI), machine learning (ML), and geofencing technologies. In this work, an efficient approach based on the use of ML and geofencing technology is proposed to monitor and control the density of persons in workplaces during working hours. In particular, the workplace environment is divided into a number of geofences in which each person is associated with a set of geofences that make up their own cluster using a dynamic user-centric clustering scheme. Different metrics are used to generate a unique geofence digital signature (GDS) such as Wi-Fi basic service set identifier, Wi-Fi received signal strength indication, and magnetic field data, which can be collected using the person’s smartphone. Then, these metrics are utilized by different ML techniques to generate the GDS for each indoor geofence and each building geofence as well as to detect whether the person is in their cluster. In addition, a Layered-Architecture Geofence Division method is considered to reduce the processing overhead at the person’s smartphone. Our experimental results demonstrate that the proposed approach can perform well in a real workplace environment. The results show that the system accuracy is about 98.25% in indoor geofences and 76% in building geofences.
Context-Driven-Based Community Detection

Detecting cross-communities constructed based on the commonalities of their adaptive social traits is crucial for solving many settings of real-world problems, such as examining the dynamics of a social network, examining the behavior patterns that influence the outbreak and spread of disease, determining a criminal organization’s influential individuals, and amplifying a business potential. Unfortunately, investigating approaches that emphasize the detection of such cross-communities has been understudied. Moreover, few current approaches may detect cross-communities but without regard for their granularities. To overcome this, we introduce a novel methodology that analyses the overlapping among social traits to detect the most granular cross-communities with multisocial traits. The methodology is implemented in a working system called CDBCD. It can detect the most granular multisocial traits cross-communities, to which an active user (i.e., a context user) belongs. Such cross-communities are detected and selected by the system if they exhibit evidence of multisocial traits’ homophily among their users. We propose novel context-driven search techniques that infer the relationships among various social traits. We evaluated CDBCD by comparing it with ten methods. The results demonstrated that CDBCD can detect granular cross-communities with marked accuracy. The improvement of CDBCD over the ten methods combined is 23% and 19% in terms of ARI and F1-score, respectively.
Effects of Limb Dominance on Patellofemoral Joint Loading During Gait at 12 Weeks After Anterior Cruciate Ligament Reconstruction

Background: Patellofemoral joint complications have commonly been reported in long-term outcome studies for anterior cruciate ligament reconstruction (ACLR); however, the biomechanics in the early phases of rehabilitation that could be associated with the development of these abnormalities is unclear. Limb dominance may affect the biomechanics of the knee joint in patients after ACLR. Purpose: To compare knee joint loading between surgical and nonsurgical limbs at 12 weeks postoperatively in patients who underwent ACLR on either their dominant limb (ACL-D) or nondominant limb (ACL-ND). Study Design: Controlled laboratory study. Methods: Included were 54 patients (32 ACL-D and 22 ACL-ND). Peak and integrated patellofemoral joint stress (PFJS), peak patellofemoral joint reaction force (PFJRF), and peak knee extension moment (KEM) were assessed during the stance phase of gait while participants walked on a 10-m runway at a self-selected speed. Results: The surgical limb of the ACL-D group had significantly decreased peak PFJS (P < .001), integrated PFJS (P < .001), peak PFJRF (P < .001), and peak KEM (P < .001) compared to the nonsurgical limb. The surgical limb of the ACL-ND group demonstrated significantly increased peak PFJS (P = .001), integrated PFJS (P = .023), peak PFJRF (P < .001), and peak KEM (P = .001) compared to the nonsurgical limb. For the surgical limb, the ACL-ND group demonstrated significantly greater peak PFJS (P < .001), peak PFJRF (P < .001) , (PFJRF [P<.001]) and peak KEM (P < .001) than the ACL-D group. For the nonsurgical limb, the ACL-D group demonstrated greater peak PFJS (P < .001), integrated PFJS (P = .023), peak PFJRF (P = .003), and peak KEM (P < .001) than the ACL-ND group. Conclusion: Significantly larger knee joint loading on the surgical limb of the ACL-ND group and smaller knee joint loading on the surgical limb of the ACL-D group were observed compared to the contralateral nonsurgical limb, which suggests that limb dominance has a key role in loading at the knee joint during gait. Clinical Relevance: Altered knee joint loading during gait at 12 weeks after ACLR may lead to the development of patellofemoral joint abnormalities.
Experimental Investigation on the Potential Use of Magnetic Water as a Water Reducing Agent in High Strength Concrete

High-strength concrete is designed for a self-weight reduction structure and exhibits higher resistance to compressive loads. This paper proposes a novel technique to enhance concrete’s properties using Magnetic Field Treated Water (MFTW), describing the results of experimental studies to apprehend the fresh, hardened and microstructural behavior of concrete prepared with Magnetic Water (MW) using a permanent magnet with a field intensity of 0.9 Tesla. The novel scheme focuses on utilizing MW as a water-reducing agent instead of SP to improve the workability of fresh concrete with a 0.38 w/c ratio for achieving M40 grade concrete. Results show a 12% improvement in compressive strength and an 8.9% improvement in split tensile strength compared to normal water (NW) with 1% SP. At 30% cement volume reduction, Magnetic Water Concrete (MWC) performs better than Normal Water Concrete (NWC). Microstructure examination shows that a smaller Calcium Hydrate (CH) crystal is formed with MW and its mineral composition is observed through Energy Dispersive X-ray Analysis (EDAX).
Physics of ferroelectrics : a modern perspective

During the past two decades, revolutionary breakthroughs have occurred in the understanding of ferroelectric materials, both from the perspective of theory and experiment. First principles approaches, including the Berry phase formulation of ferroelectricity, now allow accurate, quantitative predictions of material properties, and single crystalline thin films are now available for fundamental studies of these materials. In addition, the need for high dielectric constant insulators and nonvolatile memories in semiconductor applications has motivated a renaissance in the investigation of these materials. This book addresses the paradigmatic shifts in understanding brought about by these breakthroughs, including the consideration of novel fabrication methods of single crystalline ferroelectric thin films and nanoscale applications of these materials, and new theoretical methods such as the effective Hamiltonian approach and density functional theory. A book for practicing scientists as well as graduate students.
Lethal and Sublethal Toxicity Assessment of Cyclosporin C (a Fungal Toxin) against Plutella xylostella (L.)

Secondary metabolites/toxins produced by Purpeocillium lilacinum (Hypocreales; Phiocordycipitaceae), a well-known insect pathogen, can be used for the management of different insect pests. We report the lethal and sublethal effects of cyclosporin C (a toxin produced by P. lilacinum) against a major vegetable pest, Plutella xylostella, at specific organismal (feeding rate, larval growth, adult emergence, fecundity, and adult longevity) and sub-organismal levels (changes in antioxidant and neurophysiological enzyme activities). The toxicity of cyclosporin C against different larval instars of P. xylostella increased with increasing concentrations of the toxin and the maximum percent mortality rates for different P. xylostella larval instars at different times were observed for the 300 µg/mL cyclosporin C treatment, with an average mortality rate of 100% for all larval instars. The median lethal concentrations (LC50) of cyclosporin C against the first, second, third, and fourth larval instars of P. xylostella 72 h post-treatment were 78.05, 60.42, 50.83, and 83.05 μg/mL, respectively. Different concentrations of cyclosporin C caused a reduction in the average leaf consumption and average larval weight. Different life history parameters, such as the pupation rate (%), adult emergence (%), female fecundity, and female longevity were also inhibited when different concentrations of cyclosporin C were applied topically. The cyclosporin C concentrations inhibited the activities of different detoxifying (glutathione S-transferase, carboxylesterase, and acetylcholinesterase) and antioxidant enzyme (superoxide dismutase, catalase, and peroxidase) activities of P. xylostella when compared to the control. These findings can serve as baseline information for the development of cyclosporin C as an insect control agent, although further work on mass production, formulation, and field application is still required.
High Resolution Treatment Effects Estimation: Uncovering Effect Heterogeneities with the Modified Causal Forest

There is great demand for inferring causal effect heterogeneity and for open-source statistical software, which is readily available for practitioners. The mcf package is an open-source Python package that implements Modified Causal Forest (mcf), a causal machine learner. We replicate three well-known studies in the fields of epidemiology, medicine, and labor economics to demonstrate that our mcf package produces aggregate treatment effects, which align with previous results, and in addition, provides novel insights on causal effect heterogeneity. For all resolutions of treatment effects estimation, which can be identified, the mcf package provides inference. We conclude that the mcf constitutes a practical and extensive tool for a modern causal heterogeneous effects analysis.
A Stochastic Approximation-Langevinized Ensemble Kalman Filter Algorithm for State Space Models with Unknown Parameters

Abstract Inference for high-dimensional, large scale and long series dynamic systems is a challenging task in modern data science. The existing algorithms, such as particle filter or sequential importance sampler, do not scale well to the dimension of the system and the sample size of the dataset, and often suffers from the sample degeneracy issue for long series data. The recently proposed Langevinized ensemble Kalman filter (LEnKF) addresses these difficulties in a coherent way. However, it cannot be applied to the case that the dynamic system contains unknown parameters. This article proposes the so-called stochastic approximation-LEnKF for jointly estimating the states and unknown parameters of the dynamic system, where the parameters are estimated on the fly based on the state variables simulated by the LEnKF under the framework of stochastic approximation Markov chain Monte Carlo (MCMC). Under mild conditions, we prove its consistency in parameter estimation and ergodicity in state variable simulations. The proposed algorithm can be used in uncertainty quantification for long series, large scale, and high-dimensional dynamic systems. Numerical results indicate its superiority over the existing algorithms. We employ the proposed algorithm in state-space modeling of the sea surface temperature with a long short term memory (LSTM) network, which indicates its great potential in statistical analysis of complex dynamic systems encountered in modern data science. Supplementary materials for this article are available online.
Macroeconomic Effects of Energy Price: New Insight from Korea?

Under the double pressure of the Ukrainian–Russian war and the COVID-19 pandemic, the global energy crisis has also engulfed the Korean economy. Based on this context, this article examines the macroeconomic implications of energy prices, using Korea as an example. According to an empirical study using the impulse response function, the results show that an energy price shock causes a decline in production, labor supply, capital stock, and energy consumption, as well as an increase in consumption, wages, the goods price level, inflation, and the deposit interest rate. Meanwhile, variance decomposition findings indicate that the energy price shock has a greater impact on the Korean macroeconomy than other shocks. In addition, the findings of three types of robustness tests validate the reliability and accuracy of the conclusions reached in this work. In conclusion, the information presented in this study may aid Korean policymakers in implementing appropriate countermeasures against macroeconomic volatility caused by the energy price shock.
Smart Building Technologies in Response to COVID-19

The COVID-19 pandemic has had a huge impact on society. Scientists are working to mitigate the impact in many ways. As a field closely related to human life, building engineering can make a great contribution. In this article, we started with the concept of the smart building as our guide. The impact of COVID-19 on daily energy consumption, information and communication technology, the ventilation of the interior environment of buildings, and the higher demand for new energy technologies such as electric vehicles is an entry point. We discuss how the concept of the smart building and related technologies (refrigeration, measurement, sensor networks, robotics, local energy generation, and storage) could help human society respond to the pandemic. We also analyze the current problems and difficulties that smart buildings face and the possible future directions of this technology.
Trust-Based Decision-Making in the Health Context Discriminates Biological Risk Profiles in Type 1 Diabetes

Theoretical accounts on social decision-making under uncertainty postulate that individual risk preferences are context dependent. Generalization of models of decision-making to dyadic interactions in the personal health context remain to be experimentally addressed. In economic utility-based models, interactive behavioral games provide a framework to investigate probabilistic learning of sequential reinforcement. Here, we model an economic trust game in the context of a chronic disease (Diabetes Type 1) which involves iterated daily decisions in complex social contexts. Ninety-one patients performed experimental trust games in both economic and health settings and were characterized by a multiple self-report set of questionnaires. We found that although our groups can correctly infer pay-off contingencies, they behave differently because patients with a biological profile of preserved glycemic control show adaptive choice behavior both in economic and health domains. On the other hand, patients with a biological profile of loss of glycemic control presented a contrasting behavior, showing non-adaptive choices on both contexts. These results provide a direct translation from neuroeconomics to decision-making in the health domain and biological risk profiles, in a behavioral setting that requires difficult and self-consequential decisions with health impact. Our findings also provide a contextual generalization of mechanisms underlying individual decision-making under uncertainty.
Gambaran Pengetahuan Mahasiswa Tingkat 1 tentang BHD di STIKes Santa Elisabeth Medan Tahun 2022

Latar Belakang : Bantuan Hidup Dasar (BHD) adalah tindakan pertolongan pertama yang dilakukan atau diberikan saat kita menemukan pasien yang perlu diberikan pertolongan karena sudah tidak sadarkan diri atau mengalami sumbatan jalan nafas. Pertolongan Bantuan Hidup Dasar ini sangat diperlukan karena dapat mencegah keadaan yang lebih parah. Dalam melakukan pertolongan pengetahuan sangat dibutuhkan untuk menolong jiwa pasien, pengetahuan sangat dibutuhkan karena kemungkinan semua orang dapat berada di kondisi yang memerlukan pertolongan pertama.Metode : Penelitian ini menggunakan metode kuantitatif degan jenis penelitian deskriptif. Penelituian ini menggunakan kuesioner dengan jumlah populasi penelitian sebanyak 168 orang. Pengambilan sampel dilakukan dengan teknik total sampling.Hasil : Hasil penelitian menunjukkan bahwa pengetahuan mahasiswa tingkat 1 di STIKes Santa Elisabeth Medan dalam kategori baik sebanyak 45 responden (26,8%) dengan kategori cukup sebanyak 121 responden (72%) dan dengan kategori kurang sebanyak 2 responden (1,2%). Kesimpulan : Mayoritas mahasiswa tingkat 1 dikategori “cukup” dalam mengetahui tentang bantuan hidup dasar. Pengetahuan yang cukup ini didukung oleh adanya latihan tanggap darurat yang rutin dilaksanakan oleh TIM tanggap darurat STIKes Santa Elisabeth Medan.
Effect of a 16-Week Yoga Program on Cobb’s Angle in Female Patients with Scoliosis

BACKGROUND Long-term postural imbalances can promote the development of diseases such as scoliosis or pain in the neck, shoulders, arms, and lumbar spine. Yoga combines exercises that target muscular strength and flexibility, suggesting that it can aid in attenuating structural and postural imbalances without the need for invasive procedures. The present study aimed to investigate the effect of a 16-week yoga program on Cobb’s angle and other postural indicators in patients with scoliosis.METHODS Ten middle-aged women living in South Korea who were judged to have scoliosis based on the results of posture tests participated in a 16-week yoga program.It consisted of two 70-min sessions per week during weeks 1–4, two 80-min sessions per week during weeks 5–9, and two 90-min sessions per week during weeks 10–16. The heights of the ilium and ischium were measured to check for pelvic imbalances. Shoulder height and Cobb’s angle in the spine were also assessed on radiography both before and after the intervention.RESULTS Following the 16-week program, Cobb’s angle had significantly decreased from 10.50±4.26 to 7.05±3.37 (p<0.001). However, there were no statistically significant differences between pre- and postintervention shoulder height (pre: 2.85±1.76 mm; post: 1.58±0.99 mm;p=0.124). In contrast, iliac height had decreased from 2.62±1.33 mm to 1.78±0.80 mm (p=0.048), and ischial height had decreased from 1.72±0.92 mm to 1.17±0.80 mm (p=0.043), and both differences were significant.CONCLUSIONS These results suggest that regular participation in yoga is effective for maintaining or improving postural indicatorsand attenuating imbalances in middle-aged women with scoliosis.
Note sur la convergence de méthodes de directions conjuguées

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Recent Advances in Nanostructured Inorganic Hole-Transporting Materials for Perovskite Solar Cells

Organic-inorganic halide perovskite solar cells (PSCs) have received particular attention in the last decade because of the high-power conversion efficiencies (PCEs), facile fabrication route and low cost. However, one of the most crucial obstacles to hindering the commercialization of PSCs is the instability issue, which is mainly caused by the inferior quality of the perovskite films and the poor tolerance of organic hole-transporting layer (HTL) against heat and moisture. Inorganic HTL materials are regarded as promising alternatives to replace organic counterparts for stable PSCs due to the high chemical stability, wide band gap, high light transmittance and low cost. In particular, nanostructure construction is reported to be an effective strategy to boost the hole transfer capability of inorganic HTLs and then enhance the PCEs of PSCs. Herein, the recent advances in the design and fabrication of nanostructured inorganic materials as HTLs for PSCs are reviewed by highlighting the superiority of nanostructured inorganic HTLs over organic counterparts in terms of moisture and heat tolerance, hole transfer capability and light transmittance. Furthermore, several strategies to boost the performance of inorganic HTLs are proposed, including fabrication route design, functional/selectively doping, morphology control, nanocomposite construction, etc. Finally, the challenges and future research directions about nanostructured inorganic HTL-based PSCs are provided and discussed. This review presents helpful guidelines for the design and fabrication of high-efficiency and durable inorganic HTL-based PSCs.
An Investigation into the Methods and Applications of Deep Learning in Smart Grid

The manuscript represents the state-of-the-art review of the deep learning methods for smart grid applications. This paper reviews novel applications of deep learning algorithms in smart grid. The deep learning based three algorithms i.e., Long-Short Term Memory, recurrent neural network, convolution neural network found to be most useful in smart grid application. These algorithms are found to be most useful for forecasting, cyber-attack, anomaly detection and electricity theft in smart grid. This paper briefly surveys the most usable deep learning algorithms for making the smart is resilience, accurate, and safe. The review result shows that the mentioned deep learning algorithms give an excellent results over other deep learning algorithm. Therefore, these three algorithms are widely acceptable for the evaluation of smart grids.