Miracle-dz/UniFAR-FTUs huggingface dataset:
Functional Brain Networks in Focal Dystonia and their Associations with Dystonic Behavior Short running title: Functional Networks in Dystonia

Multiple neuroimaging studies suggest that dystonia is a network-level brain disorder, but the key networks to target for brain therapy in dystonia remain poorly understood. This study identified impaired functional networks (FNs) in writer’s cramp (WC) dystonia while participants performed hand motor tasks in the scanner. Task-fMRI images were acquired from twelve WC and twelve healthy volunteers (HV) while they performed writing, tapping, and flexion-extension motor tasks. Group independent component analysis was used to derive FNs, and functional network connectivity (FNC) between the FNs was computed to evaluate their associations with dystonic behavior. Our approach revealed three novel findings: First, we found that the basal ganglia, orbitofrontal, and superior parietal FNs were aberrant during the on-block of the task and, interestingly, also during the off-block period reminiscent of the loss of network inhibitory response during writing in WC. Second, we found multiple impaired FNC patterns in WC primarily in these three FNs these results were further validated through non-parametric testing via Monte Carlo simulations. Third, using FNC deficits and FNC-writing legibility correlations, we characterized these FNC patterns as primary or secondary associations with the motor program for writing. Findings from our data-driven whole-brain analysis approach reveal new FNs disrupted in dystonia and they may serve as key targets for the treatment of this disorder. Impact Statement Dystonia is a rare and disabling brain disorder affecting motor function. In this study, novel functional network differences in dystonia were identified. Associations between functional network connectivity and dystonic behavior were found. Through a data-driven approach, this study reveals new targets for dystonia treatment.
Sub-kT/q subthreshold slope p-metal-oxide-semiconductor field-effect transistors with single-grained Pb(Zr,Ti)O3 featuring a highly reliable negative capacitance

A reliable on/off switching with an sub-kT/q subthreshold slope (38 mV/dec at room temperature) is experimentally demonstrated with using selectively nucleated laterally crystallized single-grain Pb(Zr,Ti)O3 (PZT) ferroelectric and ZrTiO4 paraelectric thin-film. The combination of ferroelectric and paraelectric thin-film is enabled to form a negative capacitance (NC) at the weak inversion region. However, the PZT grain-boundary easily degrades the NC properties after switching the on/off more than 108 times. It is found that the polarization of PZT is diminished from the path of grain-boundary. Here, we effectively suppress the degradation of NC MOS-FET which did not showed any fatigue even after 108 on/off switching.At the request of the authors this article is retracted due to duplication of figures and significant overlap with other publications by the authors and because of concerns about the accuracy of the description of the devices and materials from which the reported results were obtained. The au.
Modelling of Covid-19 spread in a supermarket

For the past 2 and half years, Covid -19 has been menacing to human society and has claimed numerous lives. Its impacts are not just limited to physical health but also the economy of society. Scientists, researchers, and governments have invested a lot of time and money to explore the determinant factors of transmission of Covid-19 to find strategies, and protocols to contain it. Geometric modelling of epidemic spread can help to mimic Covid-19 spread and analyse mitigation protocols to restrict it. In this paper, modelling of epidemic spread is done in a supermarket using 2 models, first an agent-based model for the customer to simulate realistic behaviour, and second, a virus transmission model to mimic covid -19 spread in the supermarket. The store layout, customer, and virus model simulation are all done using python libraries pyglet and networkx. A recommender system was created based on Machine learning. The recommender system would predict each customer’s path for the nth day based on his/her historic data. To make it realistic, each customer has a stationary time in each aisle they visit based on exponential distribution, simulating a customer spending some time to think whether to buy a particular item or not. To make sure customer entry into the supermarket is not disorganised. Gamma distribution was used. Once the initial setup was done, all mitigation protocols were implemented. To avoid any transmission of covid-19 through air particles, masks were introduced to block covid 19 droplets both in infected users’ mouths as well as susceptible users’ mouths. There is still a chance of transmission if 2 users are very close to each other, and thus social distancing was implemented. It is evident that the population following these protocols is important, hence both of these strategies were implemented as mandatory and non-mandatory. In the longer run, human beings need natural immunity against covid, and thus various vaccinations were made. In this simulation, users were provided either with 1st of dose or both doses. These protocols do help reduce transmission for each user but do not disinfect the surfaces around which gives the possibility of transmission from surfaces to hand and then later to mouth. Thus proper sanitization is as important as any other strategy. Alcohol and non-alcohol-based hand sanitizers were used by customers to sanitise their hands to protect themselves from covid-19. The results from these simulations are analysed and presented to help understand the effectiveness of each protocol against virus spread.
Operation of IR-UWB WBAN antennas close to human tissues

In this dissertation, the operation of planar impulse radio (IR) ultra wideband (UWB) antennas is examined for use in wireless body area networks (WBANs). The objective of the thesis is to consider electromagnetic phenomena due to coupling between an antenna and human body tissues and to analyse the challenges of wideband radiators. The aim is to understand the fundamental behaviour of an antenna in a WBAN, focusing on off-body and on-body communications. The thesis premises follow the international WBAN standard 802.15. 6-2012 by the Institute of Electrical and Electronics Engineering (IEEE) and the Federal Communications Commission’s (FCC) UWB regulations (3.1–10.6 GHz). In the examinations, the frequency-dependent modelling of human tissues is considered. The impact of the variation of tissue layer thickness on the performance of an antenna is depicted. The demonstration of the impact of antenna input power is given in terms of generation of heat in tissues and specific absorption rate (SAR). In order to theoretically contemplate the effect of reflections due to tissues on antenna patterns, the opportunities to influence polarization with an artificially anisotropic substrate are derived. With the proposed method, not earlier proposed for WBAN antennas, the smooth patterns without pattern minima can be achieved over the FCC UWB bandwidth. In addition, a theoretical two-path model is applied to the estimation of the antenna pattern shape close to tissues. Various antenna parameters are explored as a function of the use distance to tissue surface to demonstrate the behaviour in the vicinity of a body. The size of the reactive near-field is an important factor for the evaluation of satisfactory on-body performance. The proportions of absorption, mismatch and body losses are analysed close to a body. The connection between the complex input impedance and the dimensions of planar antennas is analysed by using lumped-element equivalent circuits. The impact of the actual width and length of the radiator on the impedance behaviour is presented for the first time. In addition, impedance is analysed in terms of capacitance, inductance and resistance within the reactive near-field for the first time. In order to understand the impact of tissues close to the antenna, the parasitic components for the stages in equivalents are proposed.
Nutritional composition, Phytochemical analysis and Antioxidant capacity of Ethanol extract of Picralima nitida fruit (bark and pulp)

Picralima nitida (Apocynaceae) is an African pepper tree known for its medicinal benefits. This paper aims to investigate the nutritional composition, phytochemical analysis, and Antioxidant capacity of Ethanol extract of Picralima nitida fruit (bark and pulp) using standard methods. The proximate analysis of the dried powdered fruit (bark and pulp) had high carbohydrate, crude fiber content, moderate ash content, low moisture content, and low crude fat composition. The metal composition analysis showed the presence of various essential and non-essential elements, with sodium, potassium, and iron having the highest concentrations. The qualitative phytochemical screening showed the presence of saponins, alkaloids, tannins, terpenoids, phenols, flavonoids, glycosides, steroids, and phytosterols in varying concentrations in the ethanol extract. The quantitative phytochemical analysis revealed that the total phenol, total flavonoid, total tannins, proanthocyanidin, and total saponin contents had concentrations of 92.45 mg GAE/g extract, 331.64 mg QE/g extract, 64.89 mg TAE/g extract, 129.00 mg AAE/g extract, and 213.79 mg DE/g extract respectively. The ethanol extract of P. nitida fruits exhibited strong antioxidant activities, with the FRAP assay showing the highest activity, followed by DPPH radical scavenging activities. These findings suggest that P. nitida stem bark has significant potential as a natural source of antioxidants and essential minerals, which could be useful in developing functional foods and nutraceuticals.
CONTROL OF NONLINEAR SYSTEMS USING A HYBRID APSO-BFO ALGORITHM: AN OPTIMUM DESIGN OF PID CONTROLLER

This paper proposes a novel hybrid algorithm namely APSO-BFO which combines merits of Bacterial Foraging Optimization (BFO) algorithm and Adaptive Particle Swarm Optimization (APSO) algorithm to determine the optimal PID parameters for control of nonlinear systems. To balance between exploration and exploitation, the proposed hybrid algorithm accomplishes global search over the whole search space through the APSO algorithm whereas the local search is performed by BFO algorithm. The proposed algorithm starts with APSO algorithm. In the proposed APSO, every particle dynamically adjusts inertia weight according to feedback taken from particles best memories. In this case, APSO algorithm is used to enhance global search ability and to increase convergence speed. When the change in fitness value is smaller than a predefined value, the searching process is switched to BFO to accelerate the search process and find an accurate solution. In this way, this hybrid algorithm may find an optimum solution more accurately. To demonstrate the effectiveness of the proposed algorithm, its results are compared with those obtained by Basic PSO (BPSO), Standard BFO (SBFO), BFO with PSO (PSO-BFO), BFO with GA (GA-BFO) and Differential Evolution with BFO (DEBFO). The numerical simulations are shown the potential of proposed algorithm.
The Impact of Economic Narratives on Household Debt: Evidence from Religious Sermons

We investigate whether economic narratives in religious sermons influence household economic decisions. Leveraging geographic variation in membership density of The Church of Jesus Christ of Latterday Saints, we examine whether regions with more Church members exhibit less indebtedness when Church leaders speak about debt avoidance in worldwide religious broadcasts. We show that the household debtto-income ratio is lower in regions with more Church members during years with debt-avoidance sermons, and several additional analyses corroborate our conclusions. Our models suggest that this effect is economically meaningful, emphasizing the importance of considering narratives alongside traditional determinants when studying economic decisions and outcomes.
Microbes in Models: Integrating Microbes into Earth System Models for Understanding Climate Change

Climate change is altering the planet and threatens humanity. Earth system models simulate the planet's physical, chemical, and biological processes to help scientists understand current environmental changes and make projections for Earth's future, which can inform society's responses to combat and mitigate climate change's negative effects. Climate change will fundamentally change life on Earth, including microorganisms. Microbes will also influence climate change by driving biogeochemical cycles through the consumption and production of greenhouse gasses. Thus, explicitly including microbial processes into Earth system models can improve model projections. However, fully understanding the feedbacks between climate change and microbes, and then including those processes into Earth systems models, is a major challenge. This report is based on the deliberations of experts who participated in a virtual colloquium on 6 and 8 December, 2022, organized by the American Academy of Microbiology, which is the honorific leadership group and think tank within the American Society for Microbiology. At the colloquium, these experts from the climate and microbial sciences attempted to clearly articulate current knowledge gaps of the two fields. As a result, the participants compiled a list of top ten challenges to better incorporate microbial processes into Earth system models. Solving these challenges requires new thinking and approaches. Transdisciplinary efforts have the potential to propel science—and society—towards combating climate change.
Effects of seasons and indole-3-buteric acid doses on the propagation of some native rhododendron species by air layering technique in their natural habitats

This research was conducted to determine to the effects of indole-3-butyric acid (IBA) doses and propagation season on rooting rates and some root parameters of Rhododendron species, R. ponticum L. , R. luteum Sweet, R. ungernii Trautv, and R. caucasicum Pallas. Air layering technique and IBA doses (0, 500 ppm, and 1,500 ppm) were applied to each Rhododendron species in their natural habitat in autumn and spring. In the present investigation, dry root weight (mg), root length (cm), root volume (cm3), and root diameter (mm) were investigated as root quality parameters. The highest rooting efficiency (100%) was obtained from R. caucasicum and R. ungernii species. The rooting efficiencies of R. ponticum (between 6.67% to 66.67%) and R. luteum (between 0% to 53.33%) species were lower than the other two species. The heaviest dry root (266.10 mg), the largest root volume (1.49 cm3), and the thickest root diameter (0.05 cm) were obtained in autumn and 1,500 ppm IBA dose from R. caucasicum species. The longest roots, 349.21 cm, were formed in the R. ungernii species in the spring, during the air layering, in which 500 ppm IBA dose was applied.
Cooperation Learning in Time-Varying Multi-Agent Networks

We propose a Multi-Agent Systems coordination framework for complex and dynamic environments, where agents’ neighbors vary over time. We consider an agent-agent communication network, where we represent time-varying interactions as weighted network edges, and propose a heat kernel mechanism to compute those time-varying edge weights. We represent our network as a graph and build a heat diffusion kernel using the graph Laplacian. The key idea of our proposed approach is to model time-varying edge weights using the heat diffusion approach. Our proposed approach Cooperation Learner in MultiAgent Networks (CooLMAN) has a number of features: (1) it captures information ﬂow in a dynamic environment using temporal indexing, (2) agents can achieve optimal policy and stability by the system-enabled timed interaction and coordination, thereby (3) providing trained weights that can be deployed to larger swarms in a scalable manner. We demonstrate the effectiveness of our proposed method using a cooperation task, where our model signiﬁcantly outperforms state-of-the-art method.
Robotic Fastening with a Manual Screwdriver

The robotic hand is still no match for the human hand on many skills. Manipulation of hand tools, which usually requires sophisticated finger movements and fine controls, not only poses a clear technical challenge but also carries a great potential for enabling the robot to assist humans in a wide range of tasks accomplishable using tools. This paper takes a first step to investigate how a robotic arm mounts a rigidly attached screwdriver onto a screw (pre-mounted in a tapped hole) and then tightens it using the tool. Mounting begins with sliding the screwdriver tip on the screw head along preplanned paths to search for the drive and follows with rotating the screwdriver to drop the tip into the drive. Prevention of a slip off the screw head is achieved via impedance control to install a “virtual fence” along its boundary. Turning of the screw is conducted via hybrid position/admittance control based on modeling the reaction force between the screw and the substrate. Simulation results with a KUKA Arm demonstrate the smoothness of the entire action.
The Study on Chinese Cosmetics Repurchase Intention: A Survey Based on the Florasis Brand, Guangxi Province, China

In the development of cosmetics industry, the most critical aspect is the consumer segment, focusing on the factors that affect cosmetics consumers in the consumption process will enable companies to have an better understanding of the needs of cosmetics consumers and develop corresponding marketing strategies, thus improving the competitiveness of Chinese Mainland cosmetics companies. The purpose of this study was to investigate the factors of university students in Guangxi province repurchase Florasis products. In this study, a questionnaire with 23 questions created by the researcher was used. The statistical population consisted of Florasis brand consumers and repurchase consumers, and 370 people were selected by non-probability sampling to answer the research questionnaire. By using multiple research methods and combining the data obtained from the survey, factors that influence consumers to repurchase Florasis were derived. In addition, this study aims to provide some ideas for marketers.
Spasers with retardation and gain saturation: electrodynamic description of fields and optical cross-sections

Realistic spasers are numerically modeled within classical electrodynamics scattering framework using intensity-dependent Lorentzian dielectric function. Quantum mechanical effects are accounted for via saturation broadening. Spasers based on silver nano-shells and nanorods with strong field inhomogeneity and retardation are studied in detail. Fields and optical cross-sections are exhaustively analyzed upon variation of three control parameters: the amplitude of the gain Lorentzian, the detuning of the driving frequency from the spaser generation frequency, and the strength of the external E-field. An externally driven spaser demonstrates bistability for E-fields and optical cross-sections, while a freely generating spaser corresponds to the limiting case of vanishing external field. Gain saturation removes singularities and unphysical post-threshold behavior frequently reported with linear simulations. A small shift of the spaser generation frequency with increasing available gain level is observed.
ESTIMATION OF SOIL HYDRAULIC PARAMETERS WITH THE HELP OF ROSETTA PROGRAM

Soil hydraulic parameters are necessary for many studies but, most of the time they cannot be measured because of the practice and financial restraints. A wide range of methods exists currently for measuring soil hydraulic parameters. Using mathematics models to solve problems that involves transport and flow processes it became very important. Soil hydraulic functions can be directly measured, or they can be estimated indirectly more easier using soil data, such as texture and bulk density. This methods have an important role in understanding transport processes happening in soils. Models of this kind can be named pedotransfer functions. This pedotransfer functions have been developed over time. Pedotransfer functions estimates indirectly some soil attributes and they are known from the beginning of modern soil science. By applying this we can conclude some of the soil properties based on some other properties, more easily to find and with lower costs. Rosetta program can estimate many properties as: water retention parameters according to van Genuchten(1980), saturated hydraulic conductivity and unsaturated hydraulic conductivity parameters according to van Genuchten (1980) and Mualem (1976). Rosetta have five pedotransfer functions that allows us to predict hydraulic properties. The hierarchical approach has a great importance because it permits optimal use of available input data. In this context, this paper presents some characteristics of the program. Also the program Rosetta is used to determine hydraulic properties for one type of soil. This properties were used later in a modeling program.
On the Fundamental Issues of Spatial Similarity Relations in Multi-scale Maps

Spatial similarity relations are put forward with the introduction of automated map generalization in the construction of multi-scale map databases.Then the definition of spatial similarity relations is presented based on set theory and the characteristics of spatial similarity are discussed in detail,including reflexivity,symmetry,non-transitivity,self-similarity in multi-scale spaces,and scale-dependence.Finally the classification systems for spatial similarity relations in multi-scale map spaces are discussed from the viewpoints of graphic similarity and attribute similarity.This research may be useful to automated map generalization and spatial similarity retrieval.
Patient-reported outcomes after posterior surgical stabilization for thoracolumbar junction fractures: A pilot study with combined patient-reported outcome measure methodology

Background: Thoracolumbar junction fractures (TLJFs) attract controversy for several parameters, including surgery versus conservative treatment, fusion versus stabilization, open versus percutaneous surgery, construct length, and downstream metalwork extraction. Aims and Objectives: The aim of this pilot study was to assess the effectiveness of surgical treatment in patients with burst (AO Classification Type A4) TLJFs using patient-reported outcome measures (PROMs) and evaluate and compare different PROMs in this clinical scenario. Materials and Methods: Patient records of consecutive patients who underwent posterior stabilization surgery for TLJFs were retrospectively reviewed. Data were collected on demographics, medical and social history, neurological examination, and postoperative complications. Telephone interviews and a combined PROM methodology (Numerical Rating Scale [NRS], EuroQol [EQ]-5D-5L, and Oswestry Disability Index [ODI]) were utilized to assess the effectiveness of intervention. Descriptive statistics were used to analyze exposure variables and outcome measures. Spearman's rank correlation was used for the outcome measures. Results: Thirteen patients were included. The mean age was 42 ± 16 years; the male: female ratio was 8:5; the mean follow-up was 18.9 ± 6.4 months. The mean NRS score was 3.3 ± 2.5, in line with a median score of 2 (2) on EQ-5D-5L pain/discomfort scale. Statistically significant correlations were found between several PROMs: pain-EQ-5D-5L and NRS (rs = 0.8, P = 0.002), pain-EQ-5D-5L and ODI (rs = 0.8, P = 0.001), usual anxiety/depression-EQ-5D-5L, and ODI (rs = 0.7, P = 0.008). Conclusion: A combined PROM methodology showed supportive evidence for safety and efficacy in the surgical stabilization of burst TLJFs. This alleviated significant pain and prevented neurological deficit and major disability. The preliminary widespread correlation between these PROMs supports further larger studies of their combined use in clinical practice, to measure the outcomes of spine trauma patients.