Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative brain-wide mapping of ketamine- and isoflurane-activated nuclei and functional networks in the mouse brain

Ketamine (KET) and isoflurane (ISO) are two widely used general anesthetics, yet their distinct and shared neurophysiological mechanisms remain elusive. In this study, we conducted a comparative analysis of the effects of KET and ISO on c-Fos expression across the brain, utilizing hierarchical clustering and c-Fos-based functional network analysis to evaluate the responses of individual brain regions to each anesthetic. Our findings reveal that KET activates a wide range of brain regions, notably in the cortical and subcortical nuclei involved in sensory, motor, emotional, and reward processing, with the temporal association areas (TEa) as a strong hub, suggesting a top-down mechanism affecting consciousness by primarily targeting higher-order cortical networks. In contrast, ISO predominantly influences brain regions in the hypothalamus, impacting neuroendocrine control, autonomic function, and homeostasis, with the locus coeruleus (LC) as a connector hub, indicating a bottom-up mechanism in anesthetic-induced unconsciousness. KET and ISO both activate brain areas involved in sensory processing, memory and cognition, reward and motivation, as well as autonomic and homeostatic control, highlighting their shared effects on various neural pathways. In conclusion, our results highlight the distinct but overlapping effects of KET and ISO, enriching our understanding of the mechanisms underlying general anesthesia.
How and When Generalized Reciprocity and Negative Reciprocity Influence Employees’ Well-Being: The Moderating Role of Strength Use and the Mediating Roles of Intrinsic Motivation and Organizational Obstruction

Although the literature has shown that generalized reciprocity and negative reciprocity as exchange norms can significantly influence employees’ outcomes, knowledge about how and when the two types of norms influence employees’ well-being is limited. Based on social exchange theory and self-determination theory, we built and investigated a model by conducting a large questionnaire survey with 551 employees and managers. The results of the structural equation model supported our hypotheses. First, generalized reciprocity is positively related to well-being, and negative reciprocity is negatively related to well-being. Both intrinsic motivation and perceived organizational obstruction can meditate roles in the above relationships. Moreover, strength use can enhance the relationship between generalized reciprocity and intrinsic motivation, and it can also weaken the relationship between negative reciprocity and perceived organizational obstruction. Our research represents a significant step towards better understanding the work-related implications of imbalanced reciprocity, highlighting the destructive influence of negative reciprocity on employees’ well-being.
Trends in Nursing Home Complaints: Prevalence and Variation Across Center for Medicare and Medicaid Services Regions

Resident complaints are vital to understanding nursing home (NH) quality. Using complaints as facility- and resident-level outcomes, this study replicates prior analyses (spanning 1998–2002 and 2007–2012) with 2013–2017 data, the phase prior to NH regulatory changes initiated in November, 2017. Advancing prior papers, we analyze the number of complaints, allegations, and deficiency citations separately. Between 2013–2017, 458,101 total complaints (5.9/NH/year) were identified, containing 949,466 allegations (12.2/NH/year), and resulting in the issuance of 156,135 deficiency citations (2.0/NH/year) in about 15,600 NHs across the country. Regarding the number of complaints, substantiated complaints, and deficiency citations, results show a steady increase compared to previous years. Furthermore, there are marked differences among the CMS survey regions on the prevalence of overall complaints, substantiated complaints, and deficiency citations. However, the average rate of substantiation of complaint allegations shows a decreasing trend over these years, suggesting that complaints may have increased due to higher care expectations.
Robust Scene Text Detection via Learnable Scene Transformations

Scene text detection based on deep neural networks has been extensively studied in the last few years. However, the task of detecting texts in complex scenes such as bad weather and image distortions has not received sufficient attentions in existing works, which is crucial for real-world applications such as text translation, autonomous driving, etc. In this paper, we propose a novel strategy to automatically search for the effective scene transformation polices to augment images in the training phase. In addition, we build a new dataset, Robust-Text, to evaluate the robustness of text detection methods in real complex scenes. Experiments conducted on the ICDAR2015, MSRA-TD500 and Robust-Text datasets demonstrate that our method can effectively improve the robustness of text detectors in complex scenes.
An Evaluation of a Diagnostic Wind Model (CALMET)

Abstract A U.S. Environmental Protection Agency (EPA)-approved diagnostic wind model [California Meteorological Model (CALMET)] was evaluated during a typical lake-breeze event under fair weather conditions in the Chicago region. The authors focused on the performance of CALMET in terms of simulating winds that were highly variable in space and time. The reference winds were generated by the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) assimilating system, with which CALMET results were compared. Statistical evaluations were conducted to quantify overall model differences in wind speed and direction over the domain. Below 850 m above the surface, relative differences in (layer averaged) wind speed were about 25%–40% during the simulation period; wind direction differences generally ranged from 6° to 20°. Above 850 m, the differences became larger because of the limited number of upper-air stations near the studied domain. Analyses implied th.
Atomistic Modelling of Segregation and Precipitation in Fe-Cr Alloys under Irradiation

Iron-chromium alloys are the basis for ferritic and ferritic-martensitic steels that will be used in future fission (generation IV) and fusion nuclear reactors. With Cr content between typically 8 to 12% [1], or even 14% in the matrix of some oxide dispersion-strengthened steels [2], one can expect the precipitation of a Cr-rich α’ phase that can be strongly accelerated under irradiation, due to point defect supersaturation. This precipitation can cause hardening and embrittlement. Radiation-induced segregation (RIS) is another important technological problem. It can lead to a Cr depletion at grain boundaries and therefore to a loss of corrosion resistance and again, to embrittlement. RIS in austenitic steels is well known and presents almost systematic trends: depletions of Cr and enrichments of Ni at grain boundaries. In ferritic steels, the experimental situation is far from being so clear: depletions and enrichments of Cr have been observed, without clear correlation with the irradiation conditions and the materials properties [3].
Validation of the Non-Motor Symptoms Scale for Parkinson's Disease of Persian Version

Objective We aimed to assess the validity and reliability of the Persian version of the NonMotor Symptoms Scale (NMSS) in Iranian patients with PD. Methods This cross-sectional study was conducted in patients with PD. After the cross-cultural adaptation of the NMSS, the acceptability, reliability, precision, and validity of the Persian NMSS were evaluated. For this purpose, in addition to NMSS, we used the following measures: Scales for Outcomes in Parkinson's Disease (SCOPA)-Autonomic (SCOPA-AUT), SCOPA-Sleep, Beck's Depression Inventory (BDI) questionnaire, Parkinson's Disease Questionnaire-8 questions (PDQ–8), SCOPA-Motor, SCOPA-Psychiatric Complications (SCOPA-PC), SCOPA-Cognition (SCOPA-COG), Mini-Mental State Examination (MMSE), Hoehn and Yahr Staging (H and Y), and Unified Parkinson Disease Rating Scale (UPDRS). Results 186 patients were enrolled (mean age 64.46 ± 9.9 years; disease duration 5.59 ± 3.99 years; 118 (63.4%) male; mean NMSS score 52.01 ± 38.54). Neither the floor effect (2.7%) nor the ceiling effect (0.5%) was seen in NMSS total score. Cronbach's alpha of total NMSS was 0.84. The test-retest reliability was 0.93 for the NMSS total and 0.81–0.96 for domains. The standard error of measurement (SEM) was lower than half of the standard deviation for NMSS total and all domains. NMSS total showed a high correlation with UPDRS I (rs = 0.84), UPDRS II (rs = 0.58), PDQ-8 (rs = 0.61), BDI (rs = 0.71), SCOPA-sleep (rs = 0.60), and SCOPA AUT (rs = 0.66). NMSS has an acceptable discriminative validity based on disease duration and severity of disease according to H and Y staging. Conclusion The Persian NMSS is a valid and reliable measure for evaluating the burden of nonmotor symptoms in Iranian patients with PD.
Insect-inspired minimal model for active vision 1 A neuromorphic model of active vision shows spatio-temporal 1 encoding in lobula neurons can aid pattern recognition in bees 2

15 Bees possess remarkable cognitive abilities in on-the-fly visual learning, making them an ideal model for 16 studying active information acquisition and representation. In this study, we investigated the minimal circuitry 17 required for active vision in bees by considering their flight behaviours during visual pattern scanning. By 18 developing a neural network model inspired by the insect visual system, we examined the influence of 19 scanning behaviour on optic lobe connectivity and neural activity. Through the incorporation of non20 associative learning and exposure to diverse natural images, we obtained compelling results that align with 21 neurobiological observations. Our findings reveal that active scanning and non-associative learning 22 dynamically shape the connectivity within the visual lobe, resulting in an efficient representation of visual 23 input. Interestingly, we observed self-organization in orientation-selective neurons in the lobula region, 24 characterized by sparse responses to orthogonal bar movements. These dynamic orientation-selective cells 25 cover various orientations, exhibiting a bias towards the speed and contrast of input sampling. To assess the 26 effectiveness of this spatiotemporal coding for pattern recognition, we integrated our model with the 27 mushroom body circuitry underlying associative learning. Notably, our model demonstrated impressive 28 performance across several pattern recognition tasks, suggesting a similar coding system within the bee visual 29 system. Overall, this study integrates behavioural experiments, neurobiological findings, and computational 30 models to reveal how complex visual features can be condensed through spatiotemporal encoding in the 31 lobula neurons, facilitating efficient sampling of visual cues for identifying rewarding foraging resources. Our 32 findings have broader implications for understanding active vision in diverse animals, including humans, and 33 offer valuable insights for the application of bio-inspired principles in the design of autonomous robots. 34
Tactile Imagery Affects Somatosensory Processing

250 words maximum, including citations) Although imagery of tactile sensations is not so well studied compared to other types of mental imagery, it is potentially very useful for brain computer interfaces (BCIs) where it could produce neural modulations needed for BCI operations. Here we assessed neural modulations associated with tactile imagery (TI) by comparing its effects on cortical responses to the effects of actual vibrotactile stimulation of the fingertip. We found that both TI and vibrostimulation evoked event-related frequency changes of the electroencephalographic (EEG) activity. Moreover, TI affected somatosensory evoked potentials (SEPs) evoked by short pulses of vibration. EEG data were collected in 29 participants trained to perform tactile imagery task. Responses to vibratory pulses were measured with and without TI. These SEPs consisted of three prominent components: a P100 response in the centro-parietal regions, a P200 response in the frontal region, and a P300 response in the central regions. The TI consistently resulted in an increase in ipsilateral P100, ipsiand contralateral P300 and frontal P200. Moreover, TI strengthened the θ-band ERS in the frontal region that occurred in response to vibration. These findings suggest that TI not only modulates EEG patterns by itself but also affects cortical processing of physical somatosensory stimuli. Such conjoint processing of both real and imagined somatic sensations could be utilized in BCIs, particularly in clinically relevant BCI that strive to restore somatosensory processing by combining centrally-induced and peripheral activities.
Integration of Decentralized BlockChain with Cloud & IoT Based SCM

A decentralized blockchain (BC) technology is becoming the disruptive era of the next generation that takes a major role in versatile applications. This integrates with two broad connecting environs cloud computing and the Internet of Things (IoT). It is a promising solution for assuring security for all types of data exchanged in the particular application. The provisioning of security is allowed based on access control mechanisms, user authentication, data authentication, and encrypting data. The development of decentralized blockchain technology toward global use cases has multiple challenges and issues. In this paper, the concept of the cloud of things i.e. BCOT model is studied based on a variety of applied scenarios. It includes a discussion about the participation of blockchain in supply chain management applications. The key challenges in this integration of blockchain with cloud and IoT are detailed, along with the extension of research directions.
Spatiotemporal dynamics of slum populations in Caracas, Venezuela

Knowledge on the quantity of slum populations is often nonexistent or contains uncertainties. Adding to these uncertainties, the temporal dynamics of slum populations are known to be high. However, these temporal dynamics have rarely been addressed by research studies. We analyse the temporal and spatial dynamics of slums in the South American city of Caracas. To do this, we record the spatial extent of slums at two points in time and estimate the population using globally available datasets. By combining the resulting datasets with data on infrastructures and topography, we are able to verify statements made in the literature about the growth of slums.
Influence of the Apex Angle of Cone-Shaped Tubes on Particulate Fouling of Heat Exchangers

A two-dimensional (2D) cone shape has been added to the normal circular tubes of heat exchangers to minimize the area of stagnation and to streamline the air flow around the heat exchanger tubes. An experimental setup has been developed to study the influence of the apex angle of the cone-shaped tubes on particulate fouling of heat exchangers. Fouling experiments have been performed in which calcium carbonate particles are injected during the experiments and the deposition of particles on the tubes of the heat exchanger is monitored. Four sets of experiments have been performed, in which normal cylindrical tubes and coned tubes with an apex angle of 60°, 90°, and 120° are examined. It was found that particulate fouling ceased if the apex angle of the cone-shaped tubes is smaller than 90°. The attached cones enhance the flow around the tubes of the heat exchanger, by minimizing the stagnation area and keeping the flow attached to the tubes starting from the tip of the attached cone until separation, such that particles that deposit on the top of the tubes of the heat exchanger can be removed by the air flow.
Topological Properties of Interacting Fermionic Systems

This thesis is a study of three categories of problems in fermionic systems for which topology plays an important role: (i) The properties of zero modes arising in systems of fermions interacting with a bosonic background, with a special focus on Majorana modes arising in the superconductor state. We propose a method for counting Majorana modes and we study a mechanism for controlling their number parity in lattice systems, two questions that are of relevance to the protection of quantum bits. (ii) The study of dispersionless bands in two dimensions as a platform for correlated physics, where it is shown the possibility of stabilizing the fractional quantum Hall effect in a flat band with Chern number. (iii) The extension of the hierarchy of quantum Hall fluids to the case of time-reversal symmetric incompressible ground states describing a phase of strongly interacting topological insulators in two dimensions.
Accurate Determination of Spin-Exchange Relaxation in a Self-Compensated Atomic Comagnetometer

The spin-exchange (SE) relaxation of the electron spins limits the sensitivity of the self-compensated atomic comagnetometer. In this article, a novel method for measuring the SE relaxation based on the pulse response of the comagnetometer is proposed. The SE relaxation is measured from the difference between the transverse and longitudinal relaxation of the electron spins, which are synchronously extracted from the responses along the probe and pump laser directions to a series of pulsed magnetic fields. Furthermore, we investigate the variation of the SE relaxation with different pump laser power densities and cell temperatures based on this method. The results indicate that there is a maximum of the SE relaxation with the increase of the pump laser power density, and the pump laser power density corresponding to the maximum increases with the cell temperature. The theory and method presented here not only shed light on the way to suppress the SE relaxation in the self-compensated comagnetometer, but also have the potential to be applied to other optically pumped atomic sensors.
Flexible and Diverse Contrastive Learning for Steel Surface Defect Recognition With Few Labeled Samples

As samples of steel defects are industrially limited, it is challenging for most deep learning methods that rely on ample labeled data to identify steel surface defects. Recently, contrastive learning has achieved good performance in natural image classification tasks with few labeled samples, yet two obstacles prevent its effective application to steel surface defect images. One is that due to the presence of inter-class and intra-class similar samples in steel surface defect, the fixed contrast strength in contrastive learning will destroy the potential semantic information of defect samples. Another is that contrastive learning requires a large amount of unlabeled data, whereas steel surface defect samples are insufficient. To overcome the above-mentioned problems, a novel framework named flexible and diverse contrastive learning (FDCL) is proposed. This framework consists of two parts, flexible contrast (FiCo) and diverse generative adversarial networks (DGANs). Diverse images generated by the DGAN and real images are fed into FiCo for representation learning. In the FiCo, the contrast strength among samples is flexibly adjusted by the proposed variable temperature discrimination and feature reconstruction (FR). In addition, the output features (OF) of FiCo will be used as input to the DGAN generator to improve image quality, thus further facilitating representation learning. The proposed FDCL is implemented on four standard steel surface defect data sets, and the experimental results demonstrated that it achieves superior performance over state-of-the-art methods. Our code is available at: https://github.com-/jiacongc/FDCL.
Evaluation of Infrastructure-Assisted Cooperative Tracking of Vehicles Using Various Motion Models

Vehicle positioning and tracking is a key component of Intelligent Transportation Systems (ITS). Cooperative positioning techniques through vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) information sharing can improve the existing proprioceptive based positioning systems such as Global Navigation Satellite System (GNSS) which are prone to errors due to urban canyons, signal jamming, etc. V2V-based positioning might not fulfill all positioning needs, given the low Connected and Automated Vehicle (CAV) penetration in today's traffic. In these scenarios, infrastructure sensors can assist the vehicles in estimating the state of the traffic through I2V communication. The state estimation requires fusion between the infrastructure and the on-board sensor measurements which are often multi-rate and asynchronous in nature. Moreover, the measurements from the infrastructure might be delayed and not time-synchronized with other sensors. Hence, it is imperative to address the practical problems while designing a sensor fusion framework for fusing multiple sensor measurements in a real world scenario. This paper aims at evaluating the improvement in vehicle tracking by fusing roadside LiDAR measurements with the on-board GPS position measurements. Various motion models for the vehicle are studied and implemented with a sequential Kalman filter for estimating the vehicle states.
Characterizing the Number of Subgroups of Prime Index

In group theory, one of the most significant properties of normal subgroups is that they allow the formation of quotient groups. More specifically, if H IJ G, then G{H is a group. Thus is natural to wonder how often do normal subgroups appear in a group, where say we only know the order of the group. In this paper we characterize the possible number of normal subgroups of a specific index in any group. We follow the work in How Rare are subgroups of Index 2 by Jean Nganou, in which the number of subgroups of index 2 is characterized, and ultimately generalize the result to subgroups of index p. There are obstacles along the way in generalizing to index p, but we circumvent the obstacles by using a result from On The Number of Subgroups of Index Two by Crawford and Wallace, as well as some of our own. Ultimately, we obtain an expression representing the possible quantities for subgroups of index p.
Use of Digital Word Games as a Tool for Improving Vocabulary Skills

With the advancement in technology and availability in the last few decades, it has become extremely convenient for us to use computer software and mobile apps to learn new things and improve our existing skills. One such way to improve our vocabulary and spelling skills is using digital word games. Word games have always been a great way to learn and improve language skills like vocabulary and spelling. Digital word games like Hangman, spelling quiz, synonym-matching, and many more help people enhance their vocabulary in a fun way. With the high popularity of mobile and computer games in the last few decades, digital word games have also seen a tremendous increase in their acceptance by people. It is not a surprise that even parents and teachers tend to encourage the use of these learning activity games for students, especially for young ones. This paper discusses the importance and scope of these digital games in the education sector. This paper also details the designing, working, and development implementation of a theme-based computer game that can be used as a tool to improve language skills using an experiment to evaluate and compare the effectiveness of such digital word games.