Miracle-dz/UniFAR-FTUs huggingface dataset:
Host-Parasite Coevolution in Continuous Space Leads to Variation in Local Adaptation across Spatial Scales

Previous host-parasite coevolutionary theory has focused on understanding the determinants of local adaptation using spatially discrete models. However, these studies fall short of describing patterns of host-parasite local adaptation across spatial scales. In contrast, empirical work demonstrates that patterns of adaptation depend on the scale at which they are measured. Here, we propose a mathematical model of host-parasite coevolution in continuous space that naturally leads to a scale-dependent definition of local adaptation. In agreement with empirical research, we find that patterns of adaptation vary across spatial scales. In some cases, not only the magnitude of local adaptation but also the identity of the locally adapted species will depend on the spatial scale at which measurements are taken. Building on our results, we suggest a way to consistently measure parasite local adaptation when continuous space is the driver of cross-scale variation. We also describe a way to test whether continuous space is driving cross-scale variation. Taken together, our results provide a new perspective that can be used to understand empirical observations previously unexplained by theoretical expectations and deepens our understanding of the mechanics of host-parasite local adaptation.
Coping with Climate Shocks: Food Security in a Spatial Framework

: We develop a quantitative spatial general equilibrium model with heterogeneous house-holds and multiple locations to study households’ vulnerability to food insecurity from cli-mate shocks. In the model, households endogenously respond to negative climate shocks by drawing-down assets, importing food and temporarily migrating to earn additional income to ensure suﬃcient calories. Because these c oping strategies are most eﬀective when trade and migration costs are low, remote households are more vulnerable to climate shocks. Food insecure households are also more vulnerable, as their proximity to a subsistence requirement causes them to hold a sma ller capital buﬀer and more aggressively dissave in response to shocks, at the expense of future consumption. We calibrate the model to 51 districts in Nepal and estimate the impact of historical climate shocks on food consumption and welfare. We estimate that, on an annual basis, ﬂoods, landslides, droughts and storms combined generated GDP losses of 2.3 percent, welfare losses of 3.3 percent for the average household and increased the rate of undernourishment by 2.8 percent. Undernourished households experience roughly 50 percent larger welfare losses and those in remote locations suﬀer welfare losses that are roughly two times larger than in less remote locations (5.9 vs 2.9 percent). In counterfactual simulations, we show the role of better access to migration and trade in building resilience to climate shocks.
Design, Synthesis, and Neurotrophic Effect of Arg-Glu-Arg-Met-Ser-(3,5)-Dimethyladamantan-1-Amine In Vitro Evaluations as a Potential NMDAR Antagonist

Methyl-D-aspartate receptor (NMDAR) is an ionotropic glutamate receptor and plays an important role in neuronal degradation of Alzheimer’s disease (AD). According to molecular modeling docking studies, we have designed the compound Arg-Glu-Arg-Met-Ser-(3,5)-dimethyladamantan-1-amine (RERMS-MEM), consisting of an AβPP 5-mer peptide (RERMS) and memantine (MEM). This compound could dock into the active sites of N-methyl-D-aspartate receptor type 2B (NMDAR2B) with a −64.14 kcal/mol CDOCKER interaction energy. The stability of RERMS-MEM was evaluated through a 50 ns molecular dynamics simulation. The results revealed that the docked ligand-receptor complex was stable. Furthermore, surface plasmon resonance (SPR) revealed that the RERMS-MEM binding affinity to the NMDAR2B fragment exhibited over 15-fold enhancement compared to MEM. The SH-SY5Y cell assays showed that RERMS-MEM or RERMS at concentrations of 0.1, 1, 10, or 50 μM could enhance the metabolic rate, and MEM showed no difference compared to the control and indicated cytotoxic effects at 50 μM. RERMS-MEM at concentrations of 0.01, 0.1, 1, 10, or 50 μM increased the number of viable cells and reduced the release of lactate dehydrogenase (LDH). RERMS at concentrations of 10 or 50 μM was similar to RERMS-MEM for increasing viable cells, and MEM showed no difference compared to the control and decreased the number of viable cells at 50 μM. RERMS-MEM or RERMS at concentrations of 10 or 50 μM could antagonize Aβ25-35-induced cytotoxicity, and MEM at 50 μM strengthened the cytotoxicity effects. The results revealed that RERMS-MEM showed a strong NMDAR-blocking activity as a potential NMDAR antagonist, enhancing the neurotrophic effect and cellular growth in SH-SY5Y cells.
Forest restoration thinning accelerates development of old‐growth characteristics in the coastal Pacific Northwest, USA

A century of industrial‐scale management has transformed vast swaths of forest land across the Pacific Northwest (PNW), USA, from ancient forests with complex structure and diverse habitats to young forests with simple structure and dominated by few species. Consequently, there have been calls to restore ecosystem integrity and resilience. Here, we apply data from a watershed‐scale experiment to determine if restoration treatments have achieved our management goal of accelerating the development of old‐growth forest characteristics. We provide empirical evidence of how restoration treatments have affected key old‐growth forest indicators resulting in larger trees, more complex vertical and horizontal forest structure, reduced stand density, and increased understory plant richness. Our study also demonstrates that some restoration indicators responded in counter‐intuitive ways contingent on interactions between stand age and restoration treatment. Through this work, we learned two important lessons: (1) more time and monitoring may be needed to fully understand the effects of restoration treatments and (2) a “one and done” approach of implementing restoration treatments may not achieve a full suite of old‐growth characteristics. Moreover, long‐term management for wildlife habitat and climate resilience will likely require an adaptive approach, with ongoing monitoring continually informing and adjusting management practices.
Adolescent dysmorphic disorder model research based on machine learning

Nowadays, dysmorphic disorder among contemporary adolescents has attracted more and more attention from people of all social circles. The purpose of this study is to provide a useful self-evaluation model of adolescent image for assessing adolescents’ dysmorphic disorder situations. 249 teenagers participated in this study and various machine learning algorithms have been developed and utilized for building the self-evaluation model, such as the K-Nearest Neighbor algorithm, Naïve Bayes algorithm, and Principal Component Analysis algorithm. The best self-evaluation model developed in this project gave the highest accuracy of 76.92% on the testing set. For predicting the trend of dysmorphic disorder among contemporary Chinese adolescents, ordinary least squares linear regression model has been created, and then the percentages of different age stages to carry out major plastic surgery in 2022, 2023, and 2024 have been predicted
Colour and wayfinding

Colour can be a powerful navigation tool to help people find their way around a building. For effective wayfinding people should be able to relate spaces to one another and see the basic underlying organisational principle of a building layout. The use of landmarks as mental anchors, provide unique points in space, which can also be helpful in wayfinding [1]. Colour can contribute to the legibility of architectural spaces both with its application to emphasize prominent features of the layout and with its usage on landmarks. Due to its easy manipulation in a variety of design materials, colours become ideal design elements for creating environments that support user’s wayfinding abilities [2]. This paper looks into the question of how colour could help wayfinding in buildings and which colours would be useful for this purpose. The paper concentrates on architectural design elements, namely layout of buildings and landmarks. Together with literature review and recent research, this study discusses colour contribution to these architectural wayfinding elements.
Phenomenologies in Hypersphere Soliton and Stringy Photon Models

We consider the Dirac quantization in the first-class formalism to investigate the hypersphere soliton model (HSM) defined on the S3 hypersphere. To do this, we construct the first-class Hamiltonian possessing the Weyl ordering correction. In the HSM, we evaluate the baryon physical quantities such as the baryon masses, magnetic moments, axial coupling constant and charge radii, most predicted values of which are in good agreement with the corresponding experimental data. Moreover, shuffling the baryon and transition magnetic moments, we find the model independent sum rules. In the HSM we also evaluate the baryon intrinsic frequencies such as ωN=0.87×1023s−1 and ωΔ=1.74×1023s−1 of the nucleon and delta baryon, respectively, to yield the identity ωΔ=2ωN. Next, making use of the Nambu-Goto string action and its extended rotating bosonic string theory, we formulate the stringy photon model to obtain the energy of the string configuration, which consists of the rotational and vibrational energies of the open string. Exploiting this total string energy, we evaluate the photon intrinsic frequency ωγ=9.00×1023s−1, which is comparable to the corresponding baryon intrinsic frequencies. We also predict the photon size ⟨r2⟩1/2(photon)=0.17fm, which is approximately 21% of the proton magnetic charge radius.
Low Detection Limit and High Sensitivity 2-Butanone Gas Sensor Based on ZnO Nanosheets Decorated by Co Nanoparticles Derived from ZIF-67

2-butanone has been certified to cause potential harm to the human body, environment, etc. Therefore, achieving a method for the high sensitivity and low limit detection of 2-butanone is of great significance. To achieve this goal, this article uses ZIF-67 prepared by a precipitation method as a cobalt source, and then prepares cobalt-modified zinc oxide nanosheets through a hydrothermal method. The microstructure of the materials was observed by SEM, EDS, TEM, HRTEM, XPS and XRD. The test data display that the sensor ZC2 can produce a high response (2540) to 100 ppm 2-butanone at 270 °C, which is 21 times higher than that of pure ZnO materials. Its detection limit is also optimized to 24 ppb. The sensor (ZC2) also excels in these properties: selectivity, repeatability and stability over 30 days. Further analysis indicates that the synergistic and catalytic effects of p-n heterojunction are the key sources for optimizing the performance of sensors for detecting 2-butanone.
Assessing the feasibility of assisted migration of corals in the Red Sea

Climate change, and in particular the unprecedented rapid global warming, presents a major threat to corals, with warming rates potentially exceeding the adaptive capacities of most coral species. Assisted gene flow, the human facilitated introduction of temperature resilience alleles from warmer to threatened colder populations via the movement of individuals (assisted migration) or their gametes (selective breeding), has been suggested as a tool to transfer thermal adaptations among populations. Due to its strong latitudinal temperature gradient and extreme temperature conditions, the Red Sea constitutes an ideal location to investigate the potential of this strategy. Here, we relocated Porites lobata colonies from three reefs along the Saudi Arabian Red Sea with different mean sea surface temperature summer maxima (ranging from 30.9 °C in Duba, 32.5 °C in Thuwal, to 33.8 °C in Jazan) to a common garden experiment in the intermediate central location. Five colonies from each location were fragmented and deployed in situ in early summer of 2018 to investigate physiological differences in bleaching, survival, and growth. Results showed significantly higher bleaching in fragments from Duba, followed by 65% mortality. Even though no bleaching was observed in fragments from Jazan, mortality rates of around 20% indicated that other environmental parameters besides temperature might influence coral health and survival. These results suggest that assisted gene flow via translocation alone may be restricted in its success due to a lack of local adaptations to environmental conditions other than temperature. However, strategies like inter-populational breeding may overcome these limitations as they might allow producing offspring with both increased thermal tolerance and local adaptations.
Deep Learning-Based Resource Allocation Scheme for Heterogeneous NOMA Networks

In this paper, we consider downlink power-domain non-orthogonal multiple access (NOMA) in heterogeneous networks (HetNets) and propose resource allocation algorithms for subchannels and transmit powers to improve the sum rate performance while satisfying a minimum data-rate requirement. The proposed subchannel allocation scheme is an iterative algorithm to achieve NOMA gain by selecting the best subchannel from the viewpoint of each user, without the constraint of the number of NOMA users on each subchannel. The proposed power allocation scheme for NOMA is a deep neural network (DNN)-based unsupervised learning algorithm, where the output of the subchannel allocation scheme is used, and unsupervised learning is adopted to reduce the training complexity, as compared to supervised learning. Through simulation, we show that the proposed subchannel allocation scheme provides better sum rates compared to the conventional two-sided matching scheme, and the proposed power allocation scheme achieves a comparable sum rate to the interior point method (IPM).
Recognition of Lower Limb Movements Using Machine Learning Methods and Bispectral Maps of Wireless sEMG Measurements

In this letter, an attempt is made to develop a movement recognition framework for the classification of five locomotion activities using bispectral representation of wireless surface electromyography (sEMG) measurements and machine learning classifiers. For this purpose, wireless sEMG signals are recorded during locomotive activities, namely, level ground walking, ramp up, ramp down, stair up, and stair down from six muscles of the lower limbs, namely gastrocnemius, semitendinosus, rectus femoris, biceps femoris, tibialis anterior, and vastus lateralis. Nine features, namely variance, root mean square, integrated EMG, mean absolute value, waveform length, Willison amplitude, skewness, entropy, and zero-crossing, are extracted from the recorded sEMG. Tree-based machine learning approaches namely random forest and extreme gradient boosting (XG Boost) are employed to classify lower limb movements. Moreover, we explore the synergy of bispectral maps from sEMG data with deep learning models to enhance the discrimination of lower limb activities. The proposed approach effectively discriminates various lower limb movements. The evaluation of machine learning models demonstrates promising results, with random forest and XG Boost achieving notable accuracy rates. Notably, the convolutional neural network model outperforms others with a testing accuracy of 91.33%, closely followed by the visual geometry group with 16 layers model at 89.9%. The results indicate the potential utility of the proposed approach in leg-machine interface development and rehabilitation for lower limb amputees. This research contributes to the advancement of movement recognition technology, offering valuable insights for biomedical engineering and rehabilitation sciences.
A disturbance rejection adaptive filtering approach for human motion tracking

This article studies the human motion tracking problem using the disturbance rejection adaptive filter with an inertial sensor. Due to the complexity of human motion, conventional inertial methods easily suffer from motion accelerations (MAs) and ferromagnetic disturbances (FDs). Here, a disturbance rejection adaptive filter is presented to segregate the exterior disturbances from the inertial sensor observations before attitude estimation. This method estimates the MA and FD by Kalman filters. According to disturbance intensity, Sage–Husa adaptive strategies based on fuzzy rules are designed to adjust noise covariance. Moreover, gravity and geomagnetic field estimation are applied as multiplicative extended Kalman filter observations to solve external disturbances problems. Finally, the effectiveness and superiority of the proposed method are verified by an example of human motion tracking.
Altered cardiovascular health in COVID-19 patients

Mortality due to cardiac cause is common amongst COVID-19 patients. Myocarditis, decompensated heart failure, acute coronary syndrome, sinus tachycardia, atrial fibrillation and right bundle branch block (RBBB) were noted2 in many hospitalized COVID patients. Studies conducted time to time, in various countries and hospitals registered various numbers and percentages (%) of patients suffering from myocardial injury ranging from 8% to70% among the COVID victims. Infection by COVID-19 causes fever and inflammation in many organs and may cause serious myocarditis, even in those with no previous clinical history of cardiovascular disease.4 This infection may cause cardiomyopathy, altered rhythm, and reduction in optimum cardiac output.
Influence of environmental impact assessment on decision making for hazardous waste management in Mexico

It has been acknowledged in Mexico that there is a need to increase the infrastructure for the adequate management of hazardous wastes, and therefore hazardous waste management decision-making needs to be improved. Environmental Impact Assessment (EIA) is a component for granting permission for, and the licensing of, waste treatment and disposal facilities in many countries. In Mexico, EIA is considered an important instrument of environmental policy; however, its practice and effectiveness in decision making has been questioned. Studies about the influence of EIA in decision making have analysed effects of EIA on decisions prior to formal application and during the EIA process concluding that EIA has limited or weak influence in decision making. EIA was founded on the rational model of decision making, which assumes that EIA is a process for generating, organising, and communicating information. However, it has been advanced that environmental assessment based on the rational model of decision making is inadequate to determine EIA’s influence. Thus, research regarding the different models of EIA and its influence could provide guidance to increase the effect of EIA on decision making. Therefore, the objective of this thesis is to critically evaluate the influence of EIA on the decision-making process for Hazardous Waste Management Facilities (HWMF) in Mexico. A case study research design and a qualitative research strategy (semi-structured interviews and documents) were used. During the different stages of decision making for HWMF different theoretical models of EIA and potential outcomes were identified. The theoretical models of EIA identified include the information processing, participation, institutional, political economy, and symbolic politics. The findings in this research indicate that the rational decision making model was limited at explaining how decision making occurred. Rather, decision making for HWMF was similar to the bounded rationality, incrementalism, and mixed-scanning theoretical model. Thus, recommendations are made for increasing the opportunity for EIA to influence decision making and for improving the Mexican EIA system. Chapter 1: Introduction and overview 1 1.1 Objective and aims of the thesis 1 1.2 Thesis outline 2 Chapter 2: Hazardous wastes, hazardous waste management, and hazardous wastes in Mexico 5 2.
A Review on the Incorporation of Nanoparticles in Polymer based Optoelectronic Devices

Organic solar cells (OSCs) and polymer-based optoelectronic devices have been a topic of focus for the researcher to meet the growing need for clean and economical energy. Polymer-based solar cells have been very prominent in the study of the production of organic solar cells. P3HT: PCBM has been a combination of polymers mostly used for manufacturing solar cells because of their high efficiency and accessibility. Some added assistance of metal nanoparticle coating on the solar cell has helped optimize saturation current (Jsc) in them. There has been a multitude of device structures tried for the optimum performance of the solar cells, with bulk heterojunction layer (BHJ) being the most fruitful in the results acquired. Incorporating metal NPS also helps stabilize the OSCs to make a more robust panel. Different studies have been carried out on the specimen used in OSCs with different advantages in the functioning of the device. This article gives an extensive overview of different OSCs and devices tried and manufactured to obtain an optimal device for commercial use.
FAKTOR-FAKTOR YANG MEMPENGARUHI MINAT PENGGUNAAN QUICK RESPONSE INDONESIAN STANDARD (QRIS) BANK SYARIAH DENGAN PENDEKATAN TECHNOLOGY ACCEPTANCE MODEL 3

Digitalisasi mendorong Bank Indonesia mengembangkan infrastruktur sistem pembayaran yang terintegrasi yaitu QRIS, agar transaksi menjadi lebih mudah, cepat, dan terjaga keamanannya. Penelitian ini bertujuan untuk mengetahui minat penggunaan QRIS dengan pendekatan Technologi Acceptance Model 3. Populasi penelitian ini adalah generasi Z yang berdomisili di Yogyakarta. Penelitian ini memperoleh sampel yang sesuai dengan kriteria sebanyak 100 responden. Hasil dari pengumpulan data kemudian diolah menggunakan aplikasi SmartPLS 4.0. Berdasarkan hasil pengujian menunjukkan hampir semua variabel berpengaruh positif dan signifikan, kecuali pada variabel norma subjektif yang tidak berpengaruh signifikan terhadap persepsi manfaat
MODEL DETEKSI SERANGAN SSH-BRUTE FORCE BERDASARKAN DEEP BELIEF NETWORK

Deep Belief Networks are deep learning models that utilize stacks of Restricted Boltzmann Machines (RBM) or sometimes Autoencoders. Autoencoder is a neural network model that has the same input and output. The autoencoder learns the input data and attempts to reconstruct the input data. The solution in this study can provide several tests on DBN such as detecting recall accuracy and better classification precision. By using this algorithm, it is hoped that we as users can overcome problems that occur quite often such as brute force attacks in our accounts and within the company. And the results obtained from this DBN experiment are with an accuracy value of 90.27%, recall 90.27%, precession 91.67%, F1-score 90.51%. The results of this study are the data values of accuracy, recall, precession, and f1-score data used to detect brute force attacks are quite efficient using the deep model of the deep belief network.
Utilisation of Social Media in Market Research and Business Decision Analysis

In recent years, social media has experienced significant growth and become a very popular platform for individuals and companies. Social media such as Facebook, Twitter, Instagram, and LinkedIn have become important places for people to interact, share information, and express their opinions. This study aims to conduct a review of current literature on the utilisation of social media in market research and business decision analysis. The focus of this study is primarily qualitative. Methods for gathering data include paying close attention to detail while viewing and recording data, and then using analytical techniques such as data reduction, visualisation, and inference to draw conclusions. The study concluded that utilising social media in market research and business decision analysis can provide significant benefits for companies in understanding consumers, identifying market trends, and making smarter business decisions. With direct access to real-time data and user insights, companies can unearth valuable information about their consumers' preferences, behaviours, and needs.