Miracle-dz/UniFAR-FTUs huggingface dataset:
Land use changes associated with declining honey bee health across temperate North America

Urbanization and agricultural intensification continue to reshape landscapes, altering the habitat available to wildlife and threatening species of both economic and conservation concern. The honey bee, Apis mellifera, is a pollinator of economic importance to North American agriculture yet managed colonies are burdened by poor health and high annual mortality. Understanding the factors influencing this species is critical for improving colony health and supporting crop production. We used a nationwide cohort of 638 managed Canadian colonies to study the dominant drivers of colony health and overwintering mortality. We found that fall colony weight—a major predictor of overwintering survival—was strongly associated with landscape composition. Among four broadly defined land cover types, we discovered that urban and forested land covers were the least valuable sources of habitat for colonies, as inferred from fall colony weight measurements. Agricultural land appeared to provide habitat quality of slightly greater value, while herbaceous land cover was most strongly positively associated with fall colony weight. Herbaceous land cover also exhibited an associational effect size which was strongly statistically distinguishable from those of urban and forested land. Our research indicates that recent and ongoing land-use changes exacerbate modern apicultural challenges, and suggests variation in nutrition or floral resource availability plays a major role in modulating honey bee health. Our work highlights the need for additional research investigating whether land use change-associated alterations in floral resource availability increase the potential for resource competition between pollinator species.
An Improved Method of Hyperspectral Remote Sensing Data Adaptive Band Selection

In comparison with multispectral remote sensing,the characteristics of hyperspectral remote sensing data with more channels,narrower bandwidth and larger amount of data have caused many problems to further management.In order to solve these problems,this paper introduced an improved adaptive band selection method of dimensional reduction,which can't only obtain the bands with the high information content and the low correlativity,but also the bands with the longer divisibility distance between the continous spectrals of all ground objects.It shows that the divisibility distance between the spectrals of the arbitrary two ground objects is more long,these two kinds of ground objects appear more clearly.At first,the ideal band subsets are choiced,from which the optimal three bands combination(not unique) with high information content,smaller correlation coefficient and longer divisibility distance are further choosed based on the lowest correlation coefficient sum and the lowest mean square deviation.In order to testify the effect of the improved adaptive band selection method,the spectral angle mapping classification method is implemented on the false color synthesis image of the channels 45、75 and 85.The results of the classification show that the total classification accuracy percentage of the choiced bands image is 91.7%,Kappa coefficient 0.82.
Positive College Experiences Moderate the Association Between Resilience and Anxiety Symptoms among Underrepresented College Students

Abstract Generalized anxiety disorders are among the most prevalent mental health concerns for college students. Resilience, one’s ability to recover from adversity, is a critical component in reducing anxiety. The association between resilience and anxiety may be strengthened by positive college experiences of institutional support and experiential learning, particularly among underrepresented students. This study explored whether positive college experiences (Gallup “Big Six”) moderated the association between resilience (Brief Resilience Scale) and anxiety symptoms (Generalized Anxiety Disorder-7) among underrepresented college students (N = 430; 73.5% first year; 38.1% first generation) at a large public institution. Controlling for demographic variables, greater resilience was associated with lower anxiety ( β = -.42, p < .001). Positive college experiences were not directly associated with anxiety ( β = -.04, p = .45); however, positive college experiences moderated the negative association of resilience and anxiety ( β = -.14, p < .01) such that the association between greater resilience and lower anxiety was stronger for students reporting more positive college experiences. The final model accounted for 21% of the variance in anxiety symptoms (F 11,418 = 10.22, p < .001). Findings support a beneficial role of positive college experiences on the association between resilience and anxiety.
Design, Modeling and Experiments of a Variable Stiffness Soft Robotic Glove for Stroke Patients With Clenched Fist Deformity

Soft wearable devices for hand rehabilitation can assist activities of daily living. While existing soft robotic gloves can help with finger flexion, they are less suitable for patients who cannot extend their fingers due to poststroke increased muscular tension. In this letter, we proposed a variable stiffness pneumatic actuator that effectively increases the elasticity of the actuator to facilitate finger extension. The actuator consists of a fiber-reinforced actuator and a multi-stage articulated elastomer. It bends and extends by pneumatic actuation, with multi-stage articulated elastomer providing stiffness adjustment to accommodate varying joint resistance. The variable stiffness mechanism was achieved by the multi layered discrete metal plates which can gracefully fit the unique structure of human fingers. Analysis models were established to quantify the actuator's air pressure, vacuum pressure, and bending angle, also to predict the actuator's stiffness under different vacuum pressures. Experiments and finite element simulation were carried out to evaluate the bending performance of the actuator. Meanwhile, the output force and variable stiffness characteristic of the actuator were tested. The results show that the actuator can achieve 1.54 N fingertip output force and 2.4 times stiffness change, which satisfy the requirement of stroke patient. In addition, a clenched artificial hand with similar damper forces to human hand was applied to further verify the extending effect of the variable stiffness actuator. Finally, a rehabilitation glove integrated with multiple actuators was tested to assess typical rehabilitation gestures and the ability to grasp daily objects.
Analysis of Green Supply Chain Management Application in Indonesian Automotive Company Using Integrated Green SCOR and AHP Method

The development and competition in the automotive industry have become one of the biggest contributors to various pollution and waste caused by the movement of the industrial supply chain in Indonesia. Companies in the automotive industry are required to implement green supply chain management. Therefore, it is necessary to analyze the application of green supply chain management in companies in the automotive industry. The study was conducted at a manufacturing company located in Cikarang, Jawa Barat, which is engaged in the field of car assembly, which is a company with fairly large car production in the Indonesian automotive industry. The population in this study are green procurement, green manufacturing and green logistics (inbound). The flow of the research was carried out with the initial steps of primary and secondary data collection, then the KPI design which would later be validated by an expert. After that, the AHP is weighted and then the performance is calculated using the Green SCOR method. The results showed there were 22 KPIs which became the basis for calculating Green SCOR. The results of the Green SCOR for GSCM obtained amounted to 65,819 where according to the monitoring system table the work indicators are included in the "Average" category. Therefore, there are still lot of room for improvement. Improving the performance of green supply chain management by implementing proposed improvements will have a good impact on green supply chain management as a whole.
A method for predicting aerosol optical thickness based on kernel principal component analysis using geographically and temporally weighted regression model

The spatial and temporal distribution of atmospheric aerosols closely affects climate change, air quality, environmental pollution and human health. Exploring and predicting the spatial and temporal characteristics of regional atmospheric aerosols is beneficial to the monitoring and assessment of regional atmospheric environmental quality. Taking the Qinghai-Tibet Plateau and its surrounding areas as an example, this study considers the spatial and temporal non- stationarity of aerosol optical thickness (AOD) and its multiple driving factors, and proposes a geographically and temporally weighted regression method based on kernel principal component analysis (KPCA-GTWR) is proposed. The method eliminates the multicollinearity among the driving factors after the multicollinearity test, extracts the principal components with a cumulative contribution rate greater than 95% as the input of GTWR, and improves the prediction accuracy of GTWR. Finally, the method compared with the prediction results of the conventional VIF-GTWR method and PCA-GTWR method. The results found that (1) there are correlations between AOD and its multiple drivers, as well as linear and nonlinear correlations between the drivers. (2) In comparison, the KPCA-GTWR method has the highest prediction accuracy. Compared with the conventional VIF-GTWR and PCA-GTWR methods, the predicted AOD with MERRA-2 AOD 10-fold cross validated R 2 improved from 0.764, 0.861 to 0.914 , RMSE decreased from 0.059, 0.05 to 0.044 , and MAE decreased from 0.043, 0.037 to 0.033, respectively. (3) Comparing the results in June, July, August in 2020, the spatial distribution of AOD and MERRA-2 AOD predicted using this method in and around the Tibetan Plateau is consistent and shows large spatial differences. The low values of both predicted AOD and MERRA-2 AOD are located in the main part of the Tibetan Plateau, around 0.25 or less, while the high values are found in the Tarim Basin, the Ganges Basin of India and the Sichuan Basin, up to 0.75 or more.
Inducing sparsity via the horseshoe prior in imaging problems

A problem typically arising in imaging applications is the reconstruction task under sparsity constraints. A computationally efficient strategy to address this problem is to recast it in a hierarchical Bayesian framework coupled with a Maximum A Posteriori (MAP) estimation approach. More specifically, the original unknown is modeled as a conditionally Gaussian random variable with an unknown variance. Here, the expected behavior on the variance is encoded in a half-Cauchy hyperprior. The latter, coupled to the conditioned Gaussian prior, yields the horseshoe shrinkage prior, particularly popular within the statistics community and here introduced into the context of imaging problems. The arising non-convex MAP estimation problem is tackled via an alternating minimization scheme for which the global convergence to a stationary point is guaranteed. Experimental results prove that the derived hypermodel is competitive with classical variational methods as well as with other hierarchical Bayesian models typically employed for sparse recovery problems.
Swing-up and Stabilization of Inverted Pendulum by Nonlinear Optimal Control

In this paper, the problem of swing up and stabilization of an inverted pendulum by a single feedback control law is considered. The problem is formulated as an optimal control problem including input saturation and is solved via the stable manifold approach which is recently proposed for solving the Hamilton-Jacobi equation. In this approach, the problem is turned into the enhancement problem of the domain of validity to include the pend-ing position. After a ﬁnite number of iterations, an optimal feedback control law is obtained and its eﬀectiveness is veriﬁed by experiments. It is shown that the stable manifold approach can be applied for systems including practical nonlinearities such as saturation by directly deriving a controller satisfying the input limitation of the experimental setup. It is also reported that this system is an example in which non-unique solutions for the Hamilton-Jacobi equation exist.
Strain‐Induced Distortions Modulate the Optoelectronic Properties of Epitaxial BiVO4 Films

Transition metal oxide (TMO) photoabsorbers are expected to play an important role in the development of renewable solar‐to‐fuel devices. Modest efficiencies have been demonstrated with devices based on TMO photoabsorbers, and further progress will likely rely on material property control beyond conventional bulk chemistry or nanostructuring strategies. To this end, model TMO photoabsorbers such as single crystalline monoclinic bismuth vanadate (BiVO4) are beneficial to advance the understanding of structure‐functionality relationships with minimal convoluted effects inherent in polycrystalline systems. Here, the authors reveal for the first time the effects of strain modulation strategies on the optoelectronic properties of epitaxial BiVO4 films synthesized by alternate‐target layer‐by‐layer pulsed laser deposition. Through a combination of high‐resolution X‐ray diffraction methods and optical and photoluminescence spectroscopies, the correlation between anisotropic, uniaxial strain‐driven bandgap widening and deviatoric strains associated with volume‐preserving lattice distortions is established. Broad polaronic photoluminescence signals are detected in epitaxial BiVO4, and its redshift is attributed to the structural distortion in BiO8 dodecahedra. Overall, the relationship between the structural and optoelectronic properties revealed in this study suggests that strain modulation and engineering of local distortion in complex transition metal oxides may be exploited as a viable strategy for the development of efficient photoabsorbers.
Corporate Social Responsibility and Reputation: A Study on Top 100 Companies Operating in India

The corporate social responsibility (CSR) performance of a company enhances a company’s credibility, thereby influencing the perception of its stakeholders in improving the corporate reputation of a company. This study examines this claim by empirically investigating the CSR performance of India’s top 100 companies and its impact on their reputational status. We have employed the panel-corrected standard error model by controlling companies’ financial performance, size, age, and market risk to analyze the impact of CSR performance on reputation. The result of the analysis is contradictory to the common belief that CSR positively impacts reputation building. Although this study is not novel in nature, it is incremental to the current literature as this study is conducted from an Indian perspective (where no study has been conducted as per our knowledge). This study uses data that are more objective and concrete than those of previous studies, making this study another valuable addition to the extant literature as it is an improvement over the previous study.
FISTULA ARTERI KORONER

Fistula arteri koroner (CAF) adalah struktur anatomi yang tidak biasa dengan adanya hubungan langsung dari satu atau lebih arteri koroner ke bilik jantung atau pembuluh darah besar. Insidensi CAF tidak dapat diketahui secara pasti karena kemungkinan ada terdapat banyak kasus yang tidak terdiagnosis dan baru terdiagnosis ketika pasien melakukan angiografi koroner. CAF seringkali tidak bergejala, perkembangan penyakitnya bergantung dengan keparahan CAF yang biasa dapat dinilai dari ukuran fistula, perubahan hemodinamik pada ekokardiografi, adanya iskemia pada pencitraan perfusi miokard, dan anomali jantung terkait. Prinsip-prinsip pengelolaan CAF tergantung pada apakah pasien bergejala dan usia saat diagnosis.
Hysteresis free carbon nanotube thin film transistors comprising hydrophobic dielectrics

We present two examples of carbon nanotube network thin film transistors with strongly hydrophobic dielectrics comprising either Teflon-AF or a poly(vinylphenol)/poly(methyl silsesquioxane) (PVP/pMSSQ) blend. In the absence of encapsulation, bottom gated transistors in air ambient show no hysteresis between forward and reverse gate sweep direction. Device threshold gate voltage and On-current present excellent time dependent stability even under dielectric stress. Furthermore, threshold gate voltage for hole conduction is negative upon device encapsulation with PVP/pMSSQ enabling much improved current On/Off ratio at 0 V. This work addresses two major challenges impeding solution based fabrication of relevant thin film transistors with printable single-walled carbon nanotube channels.
A High-Efficiency Capacitor-Based Battery Equalizer for Electric Vehicles

Technology in electric vehicles has increased substantially in the past decade. Moreover, it is projected to grow at record highs in the coming years since these vehicles are needed to reduce the contamination related to the transportation sector. One of the essential elements of an electric car is its battery, due to its cost. Batteries comprise parallel and series-connected cell arrangements to meet the power system requirements. Therefore, they require a cell equalizer circuit to preserve their safety and correct operation. These circuits keep a specific variable of all cells, such as the voltage, within a particular range. Within cell equalizers, capacitor-based ones are very common as they have many desirable characteristics of the ideal equalizer. In this work, an equalizer based on the switched-capacitor is proposed. A switch is added to this technology that allows the disconnection of the capacitor from the circuit. In this way, an equalization process can be achieved without excess transfers. Therefore, a more efficient and faster process can be completed. In addition, it allows another equalization variable to be used, such as the state of charge. This paper studies the operation, power design, and controller design of the converter. Moreover, the proposed equalizer was compared to other capacitor-based architectures. Finally, simulation results were presented to validate the theoretical analysis.
A 510 $\mu$W 0.738-mm$^{2}$ 6.2-pJ/SOP Online Learning Multi-Topology SNN Processor With Unified Computation Engine in 40-nm CMOS

Implementing neural networks (NN) on edge devices enables AI to be applied in many daily scenarios. The stringent area and power budget on edge devices impose challenges on conventional NNs with massive energy-consuming Multiply Accumulation (MAC) operations and offer an opportunity for Spiking Neural Networks (SNN), which can be implemented within sub-mW power budget. However, mainstream SNN topologies varies from Spiking Feedforward Neural Network (SFNN), Spiking Recurrent Neural Network (SRNN), to Spiking Convolutional Neural Network (SCNN), and it is challenging for the edge SNN processor to adapt to different topologies. Besides, online learning ability is critical for edge devices to adapt to local environments but comes with dedicated learning modules, further increasing area and power consumption burdens. To alleviate these problems, this work proposed RAINE, a reconfigurable neuromorphic engine supporting multiple SNN topologies and a dedicated trace-based rewarded spike-timing-dependent plasticity (TR-STDP) learning algorithm. Sixteen Unified-Dynamics Learning-Engines (UDLEs) are implemented in RAINE to realize a compact and reconfigurable implementation of different SNN operations. Three topology-aware data reuse strategies are proposed and analyzed to optimize the mapping of different SNNs on RAINE. A 40-nm prototype chip is fabricated, achieving energy-per-synaptic-operation (SOP) of 6.2 pJ/SOP at 0.51 V, and power consumption of 510 $\mu$W at 0.45 V. Finally, three examples with different SNN topologies, including SRNN-based ECG arrhythmia detection, SCNN-based 2D image classification, and end-to-end on-chip learning for MNIST digit recognition, are demonstrated on RAINE with ultra-low energy consumption of 97.7nJ/step, 6.28 $\mu$J/sample, and 42.98 $\mu$J/sample respectively. These results show the feasibility of obtaining high reconfigurability and low power consumption simultaneously on a SNN processor.
Spectral theory of differential operators

The spectral theory of operators in a finite-dimensional space first appeared in connection with the description of the frequencies of small vibrations of mechanical systems (see Arnol’d et al. 1985). When the vibrations of a string are considered, there arises a simple eigenvalue problem for a differential operator. In the case of a homogeneous string it suffices to use the classical theory of Fourier series. For an inhomogeneous string it becomes necessary to consider the general Sturm-Liouville problem, which is the eigenvalue problem for a simple one-dimensional differential operator with variable coefficients. Failing to be explicitly soluble, the problem calls for a qualitative and asymptotic study (see Egorov and Shubin 1988a, §9). When considering the vibrations of a membrane or a three-dimensional elastic body, we arrive at the eigenvalue problems for many-dimensional differential operators. Such problems also arise in the theory of shells, hydrodynamics, and other areas of mechanics. One of the richest sources of problems in spectral theory, mostly for Schrodinger operators, is quantum mechanics, in which the eigenvalues of the quantum Hamiltonian, and, more generally, the points of the spectrum of the Hamiltonian, are the possible energy values of the system.
Incentive Compatible Pricing for Enhancing the Controllability of Price-Based Demand Response

Price-based demand response is widely implemented by the load aggregators (LAs) to exploit the flexibility of massive demand-side resources. However, a key problem in real-life implementation is the controllability of price-based demand response, viz, whether the price can guide the demand to the desired value. To bridge this gap, this paper proposes a close-loop reverse Stackelberg game-based pricing method to better regulate the demand response. In the hierarchical game structure, the non-cooperative competition of heterogeneous demand-side resources in participating demand response constitutes the lower-level, while the determination of pricing policy by LA makes the upper-level. Towards a computationally efficient solution, the mean-field game is adopted to approximate the Nash equilibrium of the large-scale lower level competition, and the indirect formulation of close-loop reverse Stackelberg game is adopted. The accuracy of the approximation is proved to be the $(\epsilon _{1}, \epsilon _{2})$ -hierarchical equilibrium of the original game. Finally, a decentralized solution algorithm with sequential linear programming as outer-loop and mean-field iteration as inner-loop is proposed to price the demand response. Numerical tests on a retail electricity market containing 15,000 heterogeneous demand-side flexible resources validate the effectiveness of the proposed pricing scheme.