Miracle-dz/UniFAR-FTUs huggingface dataset:
Synaptic Communication Engineering for Future Cognitive Brain–Machine Interfaces

Disease-affected nervous systems exhibit anatomical or physiological impairments that degrade processing, transfer, storage, and retrieval of neural information, leading to physical or intellectual disabilities. Brain implants may potentially promote clinical means for detecting and treating neurological symptoms by establishing direct communication between the nervous and artificial systems. Current technology can modify the neural function at the supracellular level as in Parkinson’s disease, epilepsy, and depression. However, recent advances in nanotechnology, nanomaterials, and molecular communications have the potential to enable brain implants to preserve the neural function at the subcellular level, which could increase effectiveness, decrease energy consumption, and make the leadless devices chargeable from outside the body or by utilizing the body’s own energy sources. In this paper, we focus on understanding the principles of elemental processes in synapses to enable diagnosis and treatment of brain diseases with pathological conditions using biomimetic synaptically interactive brain–machine interfaces (BMIs). First, we provide an overview of the synaptic communication system, followed by an outline of brain diseases that promote dysfunction in the synaptic communication system. Then, we discuss the technologies for brain implants and propose future directions for the design and fabrication of cognitive BMIs. The overarching goal of this paper is to summarize the status of engineering research at the interface between the technology and the nervous system and direct the ongoing research toward the point where synaptically interactive BMIs can be embedded in the nervous system.
Survey of isometric scapulothoracic muscles strength in Mazandaran University of Science and Technology athlete’s student

Purpose: Shoulder girdle muscles are important for stabilizing the scapula and orienting the glenoid for upper-extremity motion in student athletes. Scapular strength deficits have been linked to shoulder dysfunction. Material: study the data of scapulothoracic musculature in student athletes using a handheld dynamometer. Cohort study. 66 subjects with varying levels of overhead activity. A handheld dynamometer was used to test the upper, middle, and lower trapezius, rhomboids, and serratus anterior. A one way-factor ANOVA was performed for each of the muscles by activity level analyses. 2-factor ANOVA was performed for each of the muscles by activity level and unilateral ratio by activity-level analyses. Post hoc analysis included multiple pairwise comparisons, using the Dunn-Bonferroni correction method. Results: Activity level did not significantly affect the unilateral ratios: Elevation: depression was 2.47:1, upward: downward rotation was 1.23:1, and protraction: retraction was 2.35:1. A rank order from strongest to weakest was established through significant comparisons. Conclusions: The unilateral ratios along with the rank order should be considered when discussing scapula rehabilitation protocols. Assessment of the upper and lower trapezius and serratus anterior muscles and should be part of any shoulder examination.
Plasmon excitation of gold split-ring array: spectral studies and numerical simulation

Strongly focused femtosecond laser pulses at a wavelength of 515 nm were used for the single-pulse ablative nano-texturing of a 50 nm thick gold film using a diffractive optical element, which enables the production of nanoelements in the form of a U-split-ring. The optical properties of such U-shaped split-ring arrays and their optimal conditions for the near-field enhancement, depending on the shape and size of the individual element, were investigated using optical microspectroscopy and numerical simulations.
The Impact of Woody Biochar on Microbial Processes in Conventionally and Organically Managed Arable soils

ABSTRACT Although environmental impacts of biochar are well characterized, impacts on soil quality, nutrient availability and crop productivity, still remain a challenge due to the diverse response of different soil types to different types of biochar, namely those obtained at low temperature. The impact of an alkaline woody biochar (two doses 5% and 10%) obtained at 280°C, on soil enzyme activity, soil microbial respiration rate, mineral nitrogen (N) availability and ammonia volatilization was studied in one conventionally and one organically managed soils, with and without the addition of urea or composted farmyard manure. Biochar additions had different effects on soil enzyme activity in both soils, suggesting lower decomposing microbial activity processes promoted by biochar. Both soils showed a similar decreasing trend regarding soil respiration rates for all treatments, and significant relationships were observed between the treatments with different rates of applied biochar, but not constant for the entire incubation period. Urea application increased soil mineral N concentrations, especially nitrate concentrations when biochar was applied as well. Biochar decreased ammonia volatilization from conventionally managed soil fertilized with urea, but did not have a significant effect when compost was added to the organically managed soil. Biochar altered microbial behavior in soil, and was affected by previous soil management. So, the impact of biochar produced at low temperatures on soil biological processes is similar to those obtained at high temperature, thus proving that there is no need to increase the energy expenditure to produce biochar, to obtain a good product.
Quantum Micro–Nano Devices Fabricated in Diamond by Femtosecond Laser and Ion Irradiation

Diamond has attracted great interest as a quantum technology platform thanks to its optically active nitrogen vacancy (NV) center. The NV's ground state spin can be read out optically, exhibiting long spin coherence times of ≈1 ms even at ambient temperatures. In addition, the energy levels of the NV are sensitive to external fields. These properties make NVs attractive as a scalable platform for efficient nanoscale resolution sensing based on electron spins and for quantum information systems. Diamond photonics enhance optical interactions with NVs, beneficial for both quantum sensing and information. Diamond is also compelling for microfluidic applications due to its outstanding biocompatibility, with sensing functionality provided by NVs. However, it remains a significant challenge to fabricate photonics, NVs, and microfluidics in diamond. In this Progress Report, an overview is provided of ion irradiation and femtosecond laser writing, two promising fabrication methods for diamond‐based quantum technological devices. The unique capabilities of both techniques are described, and the most important fabrication results of color center, optical waveguide, and microfluidics in diamond are reported, with an emphasis on integrated devices aiming toward high performance quantum sensors and quantum information systems of tomorrow.
Nutritional Properties, Antioxidant and Anti-Acetylcholinesterase Activities of Pleurotus ostreatus

Pleurotus ostreatus, the worldwide edible mushroom, has a potent economic values and medicinal properties. In this study, nutritional properties, antioxidant, and anti-acetylcholinesterase activities of P. ostreatus were investigated. The extracts were prepared by maceration and soxhlet extraction with ethanol and decoction with distilled water. Nutritional analysis showed that P. ostreatus has high content of fibers (45.5%), proteins (20.8%) and carbohydrates (68.4%) with low content of lipids (0.56%). HPLC analysis revealed that P. ostreatus has high content of the potential health promoting of β-glucan (42.8%). Acetylcholinesterase (AChE) is a key enzyme in nervous system. Inhibition of this enzyme is used for the symptomatic treatment of Alzheimer’s disease (AD), the most common disease in aging population. Ellman colorimetric method was used to determine Acetylcholinesterase inhibitory (AChEI) activity. All extracts found containing high AChEI activity (IC50 = 1.75-5.91 mg/ml). Oxidative stress plays a major role in the pathogenesis of AD, thus antioxidant activity was also performed in this study. DPPH assay exhibited all P. ostreatus extracts had high antioxidant activity (IC50 = 0.59-5.47 mg/ml). The results suggested that P. ostreatus should be beneficial for AD treatment and the most efficient nutraceutical and functional foods.
International Capital Flows and the Independence of China's Monetary Policy

This article estimates the quantitative relationship between international capital flows and China's monetary policy. A major innovation over most previous studies is taking into consideration the effects of changes in the reserve requirement ratio. Employing the method of the Federal Reserve Bank of St. Louis, this article translates the change in the reserve requirement ratio into the change in the monetary base, while the money multiplier remains unchanged. The change in the reserve requirement ratio is combined with the other monetary policies that directly affect the monetary base to construct an integrated measure of monetary policy. We then construct a modified offset and sterilization coefficients model. We find that the failure to take reserve requirement ratio changes into account leads to an underestimation of both the degree of sterilization and of capital mobility as measured by the offset coefficients. The results show that China's capital flows are quite limited, while China's monetary policy is able to almost completely sterilize monetary base fluctuations caused by international capital flows. Thus, we find that China has been able to maintain a great deal of domestic monetary policy independence despite a limited degree of exchange rate flexibility.
FACTORS INFLUENCING FARMERS DECISION IN COMMUNITY-BASED FOREST MANAGEMENT PROGRAM, KPH CIAMIS, WEST JAVA

Community Based Forest Management program through Pengelolaan Hutan Bersama Masyarakat (PHBM) scheme has been implemented in Perhutani forest in Java since 2001. The program has been developed to alleviate rural poverty and deforestation as well as to tackle illegal logging. However, there was very limited information and evaluation on activities of the program available especially in remote area/regencies, including Ciamis. This paper studies the socio-economic, geographical and perceptional factors influencing farmers decision to join PHBM program, farmers selection criteria for the crops used in the program, and farmer decision to allocate their time in the program. It also examines the costs and income related to the program and how the program land was allocated between different farmers groups and within the farmers groups as well as the perceptions of the state company’s (Perhutani) staff members on the program. Deductive approach was used with quantitative and qualitative methods. Quantitative data were collected through questionnaires from 90 respondents at three farmer groups from 3 villages, 30 respondents of each group respectively. Cross tabulation and descriptive statistical analysis were used to analyse quantitative data. Qualitative data were collected through interviewing of 9 key informants, three informants of each farmer group respectively, and two Perhutani’s staff. Results showed that PHBM program contributed to about 26.9% to community’s monthly income. The program introduced benefit-sharing system and accommodated community initiatives. Perhutani’s support was illustrated by freedom of choice of community in selecting the sharing area (land allocated for farmer to manage) and the planted crops. Factor influencing farmers’ decision in selecting the sharing area was geographic conditions, in selecting the crops was farmer skills, and in allocating working time was farmers’ priority.
A probabilistic fusion of a support vector machine and a joint sparsity model for hyperspectral imagery classification

Hyperspectral imagery (HSI) is now in use for a wide range of applications such as land cover classification, climate change studies and environmental monitoring; however, the acquisition of HSI is still costly, and the curse of dimensionality i.e. the phenomenon that the amount of required training samples increases exponentially as the dimensionality increases linearly, makes it difficult to exploit the full potential of machine learning in HSI classification. To resolve the problem, we propose a novel framework for spatial-spectral HSI classification in this article. A soft support vector machine (SVM) and a probabilistic joint sparsity model (JSM) are proposed to compute a posteriori probabilities of the test pixels, respectively; and the probability scores are then fused by a linear opinion pool. Furthermore, a Markov random field (MRF) model is used as a maximum a posteriori (MAP) segmentation method for further regularization of the neighbor information to derive the labels for pixels. Extensive experiments conducted on three commonly-used benchmarking data sets show that the proposed probabilistic fusion method outperforms a number of well-known spatial-spectral HSI classification techniques.
Adaptive Neural Network Usage in Computer Go

For decades, computer scientists have worked to develop an artificial intelligence for the game of Go intelligent enough to beat skilled human players. In 2016, Google accomplished just that with their program, AlphaGo. AlphaGo was a huge leap forward in artificial intelligence, but required quite a lot of computational power to run. The goal of our project was to take some of the techniques that make AlphaGo so powerful, and integrate them with a less resource intensive artificial intelligence. Specifically, we expanded on the work of last year’s MQP of integrating a neural network into an existing Go AI, Pachi. We rigorously tested the resultant program’s performance. We also used SPSA training to determine an adaptive value function so as to make the best use of the neural network.
Rain erosion-resistant coatings for wind turbine blades: A review

Wind blades are the most expensive parts of wind turbines made from fibre-reinforced polymer composites. The blades play a critical role on the energy production, but they are prone to damage like any other composite components. Leading edge (LE) erosion of the wind turbine blades is one of the common damages, causing a reduction in the annual energy production especially in offshore wind turbine farms. This erosion can be caused by rain, sand and flying solid particles. Coating the blade against erosion using appropriate materials can drastically reduce these losses and hence is of great interest. The sol–gel technique is a convenient method to manufacture thin film coatings, which can protect the blades against the rain erosion, while having negligible effect on the weight of the blades. This article provides an extensive review of the liquid erosion mechanism, water erosion testing procedures and the contributing factors to the erosion of the LE of wind turbine blades. Techniques for improving the erosion resistance of the LE using carbon nanotubes and graphene nano-additives are also discussed.
Efficiency in warehouse-scale computers: a datacenter tax study

Computation has been steadily migrating from isolated on-premise deployments to the datacenters of a small number of large-scale cloud providers. The datacenters powering the cloud, also known as warehouse-scale computers (WSCs), have a unique set of design constraints, balancing efficiency at scale with ever-growing application needs for performance. Designing next-generation server platforms forWSCs after the end ofDennard scaling is one of themost important challenges for computer architects. In order to guide such future designs, we performed the first (to the best of our knowledge) longitudinal profiling study of a live productionWSC.Our performancemeasurements span tens of thousands of machines over several years, while these machines serve the requests of billions of users. Even though we observe significant diversity, both in applications and architectural behaviors, patterns begin to emerge. We identify the “datacenter tax” – a set of shared low-level software components that comprise almost 30% of all processor cycles in production datacenters. The constituents of this “tax” – the necessary components to do distributed computation (data serialization, compression, etc.) – are also prime candidates for optimization, both in software and through specialized hardware. The latter case has especially high potential upside, but requires hardware accelerators that are markedly different from traditional designs. These new “broad” accelerators face a unique set of challenges: because calls to tax routines tend to be frequent, fast, and interspersed inside other application code, accelerators must be optimized for latency rather than throughput, and because each one accelerator
Recent Modeling and Analytical Advances in Hospital Inpatient Flow Management

Inpatient flow management plays a critical role in care delivery, patient outcomes, and hospital operational and financial costs. Modeling and performance analysis of inpatient flow present unique features and challenges that differ from operations in other service industries. In this study, we review recent modeling and analytical advances in the setting of inpatient flow management, with a particular focus on service time models motivated from the observations of inpatient discharges. We first compare two new service time models developed to capture the time‐of‐day inpatient flow dynamics, and reveal interesting connections between the two models. We then review analytical methods developed to analyze systems with the new service time models. Based on one method, which is amenable to a one‐dimensional exact analysis under certain conditions, we further introduce its approximations that have explicit analytical forms and enable efficient computations in large systems. In particular, we showcase how to leverage a powerful tool, Stein’s method framework, in the hospital setting for steady‐state approximations and characterizing error bounds. We conclude this study by a literature review on other important aspects in inpatient flow management and propose future research directions, from both the modeling and analytical perspectives.
PractIcal IntroductIon to landMarK-based geoMetrIc MorPhoMetrIcs

—Landmark-based geometric morphometrics is a powerful approach to quantifying biological shape, shape variation, and covariation of shape with other biotic or abiotic variables or factors. The resulting graphical representations of shape differences are visually appealing and intuitive. This paper serves as an introduction to common exploratory and confirmatory techniques in landmark-based geometric morphometrics. The issues most frequently faced by (paleo)biologists conducting studies of comparative morphology are covered. Acquisition of landmark and semilandmark data is discussed. There are several methods for superimposing landmark configurations, differing in how and in the degree to which among-configuration differences in location, scale, and size are removed. Partial Procrustes superimposition is the most widely used superimposition method and forms the basis for many subsequent operations in geometric morphometrics. Shape variation among superimposed configurations can be visualized as a scatter plot of landmark coordinates, as vectors of landmark displacement, as a thin-plate spline deformation grid, or through a principal components analysis of landmark coordinates or warp scores. The amount of difference in shape between two configurations can be quantified as the partial Procrustes distance; and shape variation within a sample can be quantified as the average partial Procrustes distance from the sample mean. Statistical testing of difference in mean shape between samples using warp scores as variables can be achieved through a standard Hotelling’s T2 test, MANOVA, or canonical variates analysis (CVA). A nonparametric equivalent to MANOVA or Goodall’s F-test can be used in analysis of Procrustes coordinates or Procrustes distance, respectively. CVA can also be used to determine the confidence with which a priori specimen classification is supported by shape data, and to assign unclassified specimens to pre-defined groups (assuming that the specimen actually belongs in one of the pre-defined groups). Examples involving Cambrian olenelloid trilobites are used to illustrate how the various techniques work and their practical application to data. Mathematical details of the techniques are provided as supplemental online material. A guide to conducting the analyses in the free Integrated Morphometrics Package software is provided in the appendix.
An Indirect Testing Method for the Torque Ripple of Multiunit Permanent Magnet Synchronous Machines

The torque ripple of the permanent magnet synchronous machine (PMSM) is always cared in the machine system, since the torque ripple mainly determines the vibration and stability of the machine. This paper presents an indirect testing strategy for the torque ripple of multiunit PMSMs without using a mechanical load. Through the proposed unit-motor configuration, the curve of the torque can be obtained in the situation that some unit motors are running as generators and some unit motors are running as motors. Then, the additional torque ripple in this situation will be calculated based on the measured parameters. Finally, the torque ripple can be obtained by subtracting the additional torque ripple from the tested torque ripple in the situation without mechanical load.
Computation of Moments in the Trellis

Decisions on sources with memory transmitted over independent channels can be taken by employing trellis calculations. In this paper, it is shown that for a certain class of functions their moments can be computed in the trellis, too. This is done by generalizing the forward/backward recursion known from the BCJR algorithm [1]. In analogy to the symbol probabilities, by introducing a constraint at a certain depth in the trellis we obtain symbol moments. These moments are required for an efficient implementation of the discriminated belief propagation algorithm in [2], and can furthermore be utilized to compute conditional entropies in the trellis. The moment computation algorithm has the same asymptotic complexity as the BCJR algorithm. It is applicable to any commutative semi-ring, thus also providing a generalization of the Viterbi algorithm [3].
Non-Equilibrium ϕ4 theory for networks: towards memory formations with quantum brain dynamics

We investigate the time evolution of quantum fields in neutral scalar ϕ4 theory for open systems with the central region and the multiple reservoirs (networks) as a toy model of quantum field theory of the brain. First we investigate the Klein–Gordon (KG) equations and the Kadanoff–Baym (KB) equations in open systems in d + 1 dimensions. Next, we introduce the kinetic entropy current and provide the proof of the H-theorem for networks. Finally, we solve the KG and the KB equations numerically in spatially homogeneous systems in 1 + 1 dimensions. We find that decoherence, entropy saturation and chemical equilibration all occur during the time evolution in the networks. We also show how coherent field transfer takes place in the networks.