Miracle-dz/UniFAR-FTUs huggingface dataset:
A Photoactivatable Norepinephrine for Probing Adrenergic Neural Circuits

Norepinephrine (NE) is a critical neuromodulator that mediates a wide range of behavior and neurophysiology, including attention, arousal, plasticity, and memory consolidation. A major source of NE is the brainstem nucleus the locus coeruleus (LC), which sends widespread projections throughout the central nervous system (CNS). Efforts to dissect this complex noradrenergic circuitry have driven the development of many tools that detect endogenous NE or modulate widespread NE release via LC activation and inhibition. While these tools have enabled research that elucidates physiological roles of NE, additional tools to probe these circuits with a higher degree of spatial precision could enable a finer delineation of function. Here, we describe the synthesis and chemical properties of a photo-activatable NE, [Ru(bpy)2(PMe3)(NE)]PF6 (RuBi-NE). We validate the one-photon (1P) release of NE using whole-cell patch clamp electrophysiology in acute mouse brain slices containing the LC. We show that a 10 ms pulse of blue light, in the presence of RuBi-NE, briefly modulates the firing rate of LC neurons via α-2 adrenergic receptors. The development of a photo-activatable NE that can be released with light in the visible spectrum provides a new tool for fine-grained mapping of complex noradrenergic circuits, as well as the ability to probe how NE acts on non-neuronal cells in the CNS.
Antibiotic resistance alters the ability of Pseudomonas aeruginosa to invade the respiratory microbiome

The emergence and spread of antibiotic resistance in bacterial pathogens is a global health threat. One important unanswered question is how antibiotic resistance influences the ability of a pathogen to invade the host-associated microbiome. Here we investigate how antibiotic resistance impacts the ability of the opportunistic bacterial pathogen Pseudomonas aeruginosa to invade the respiratory microbiome, by measuring the ability of P. aeruginosa spontaneous antibiotic resistant mutants to invade pre-established cultures of commensal respiratory microbes. We find that commensal respiratory microbes tend to inhibit the growth of P. aeruginosa, and antibiotic resistance is a double-edged sword that can either help or hinder the ability of P. aeruginosa to overcome this inhibition. The directionality of this help or hinderance depends on both P. aeruginosa genotype and respiratory microbe identity. Antibiotic resistance facilitates the invasion of P. aeruginosa into Staphylococcus lugdunensis, yet impairs invasion into Rothia mucilaginosa and Staphylococcus epidermidis. Streptococcus species provide the strongest inhibition to P. aeruginosa invasion, and this is maintained regardless of antibiotic resistance genotype. Our study demonstrates how antibiotic resistance can alter the ability of a bacterial pathogen to invade the respiratory microbiome and suggests that attempts to manipulate the microbiome should focus on promoting the growth of commensals that can provide robust inhibition of both wildtype and antibiotic resistant pathogen strains.
ECG Stress Detection Model Based on Heart Rate Variability Feature Extraction

Stress usually causes changes in the status of the primary and secondary sympathetic nerves of the autonomic nervous system. This is also reflected in changes in indicators related to heart rate variability. Therefore, stress identification through the characteristics of heart rate variability is very effective. In this paper, Electrocardiographic Stress Detection (ESD) model based on heart rate variability is proposed to accurately identify stress from an objective perspective using physiological signals. Among them, the heart rate variability (HRV) in the ECG signal is the main feature involved in the ESD model, which is related to the response pattern of the human autonomic nervous system, and the changes of the relevant features represent the active state of the main sympathetic and parasympathetic nerves. Based on these patterns, we extracted the relevant HRV features and used a support vector machine (SVM) classifier for classification calculations with an accuracy of 94.8%.
Landmark Extraction and Matching for Robot Positioning Based on Intensity Differential of Magnetic Field

Localization is one of the basic capabilities of a movable unmanned system. Among many localization techniques, geomagnetic field-based localization, which does not require external infrastructure, has unique advantages in indoor localization, but its accuracy and precision still need to be further improved. In this paper, we propose a landmark named LIDMF based on intensity differential of magnetic field with better discrimination, study the extraction and matching methods of LIDMF, and apply LIDMF to robot localization, and experimentally prove the effectiveness of our landmark.
An optimized Hybrid Gauss-Seidel smoother In AMG solver of Hypre on Sunway Many-core Architecture

The multigrid algorithm is a commonly utilized iterative technique for resolving vast and sparse matrix equations in numerical computation. Hypre is an open-source library of high-performance preconditioners and solvers, incorporates multigrid methods and enables efficient resolution of large, sparse linear systems on parallel computers. We employed a master-slave acceleration model for Algebraic Multigrid method on sunway architecture. Address mapping and intervention strategies for CPE are adopted. Numerical experiments that we proposed optimized algorithm result in speedup of nearly nine times compared to the original version. This showcases the model’s effectiveness in accelerating the resolution of large, sparse linear systems.
Surface charge based rapid method for detection of microbial contamination in drinking water and food products

Microbial contamination, most of which are fecal born in drinking water and food industry is a serious threat to humans. Escherichia coli is one of the most common and prevalent among them. We have developed a sensor for rapid and an early detection of contaminants, taking E.coli as a threat indicator organism. The sensor is based on co-polymerizations of aniline and formaldehyde in form of thin film over glass surface using the vacuum deposition technique. The particular doping combination of thin film with Fe-Al and Fe-Cu in different concentrations changes its non conducting properties to ptype semi conductor. This property is exploited to detect the different contaminants, believed to have the different surface charge. It was found through experiments that different microbes at same OD (0.600 at 600 nm) have different conductivity in solution. Also the doping concentration is found to be specific for attracting microbes on the basis of surface charge. This is a simple, cost effective and quick detection method which not only decreases the measurement time but also gives early warnings for highly contaminated samples. Keywords— Sensor, Vacuum deposition technique, thin film, E.coli detection, doping concentration.
Combinatorial Optimization with Policy Adaptation using Latent Space Search

Combinatorial Optimization underpins many real-world applications and yet, designing performant algorithms to solve these complex, typically NP-hard, problems remains a significant research challenge. Reinforcement Learning (RL) provides a versatile framework for designing heuristics across a broad spectrum of problem domains. However, despite notable progress, RL has not yet supplanted industrial solvers as the go-to solution. Current approaches emphasize pre-training heuristics that construct solutions but often rely on search procedures with limited variance, such as stochastically sampling numerous solutions from a single policy or employing computationally expensive fine-tuning of the policy on individual problem instances. Building on the intuition that performant search at inference time should be anticipated during pre-training, we propose COMPASS, a novel RL approach that parameterizes a distribution of diverse and specialized policies conditioned on a continuous latent space. We evaluate COMPASS across three canonical problems - Travelling Salesman, Capacitated Vehicle Routing, and Job-Shop Scheduling - and demonstrate that our search strategy (i) outperforms state-of-the-art approaches on 11 standard benchmarking tasks and (ii) generalizes better, surpassing all other approaches on a set of 18 procedurally transformed instance distributions.
Effects of Competitive Strategies on Financial Performance of Saccos in Kenya: The Case of SACCOs in NAROK Town

Background: There has been a productive development of SACCOs in Kenya to the degree that the World Council of Credit Unions distinguished Kenya's SACCO sub-area as the highest in Africa and the seventh quickest developing worldwide. The expanding number of contestants has prompted increased rivalry. Consequential studies have been directed on the impact of competitive strategies on the financial performance of Saccos. Most studies conducted in Kenya found out that dividend policy and membership had a constructive outcome while loan default had a negative impact. While past studies concentrated on factors affecting financial and non-financial performance and implementation of strategies in SACCOs, less has been said about the effect of low cost strategy on the financial performance of SACCOs. Materials and Methods: The study used causal and descriptive survey research designs. The target population of the study was the ten duly enlisted SACCOs in Narok town. The respondents were Managers of the SACCOs in Narok Town. The sampling method was based on a census of the target population. A sample size of 100 respondents was utilized with self-administered questionnaires issued to the respondent. Data were analyzed using descriptive statistics, where the means and standard deviations were computed. The multivariate regression model was used to assess the extent to which each competitive strategyexplains the variation of SACCOs’ financial performance. Results: The low-cost strategy was found to be significant with a p value of p=0.000 in explaining the financial performance of SACCOs. Conclusion: The study concluded that SACCOs should commit more to low-cost strategy in order to improve its financial performance.
Irregular tessellated surface model map algebras to define flow directions and delineate catchments using LiDAR bare earth sample points

ion that can degrade the data and compromise the quality of catchment boundary. Irregular tessellated surface models generate higher quality catchment boundaries, because the algorithms are devoid of additional data abstractions and transformations that degrade dataquality. Such catchments represent a more legally defensible delineation, and therefore may affect jurisdictional responsibilities with respect to water rights and other water resources-related issues. This thesis is divided into the following sections: a literature review, data sources, methods, results, discussion and conclusion. The literature review focuses on the LiDAR data 3 collection process and research into field of catchment delineations using raster-based surface models. The data section details the study area and data inputs. The methods section explaines the processing steps of ITSM Hydro, a Python-based series of algorithms that delineate flow direction lines and create catchment boundaries using LiDAR bare-earth sample points. The methods section also details the raster-based catchment delineation methods that used to compare and contrast the results of the Python algorithms and several different metrics used to assess rastersurface-model quality and quantify differences in area and shape between the tessellated surfacemodel catchment delineations and the raster-based catchment delineations. The results section contains maps of catchment delineations for four test areas, and the comparative metric results detailing the comparisons of catchment delineations for both raster and ITSMHydro methods. The discussion section highlights differences between the tessellated and the raster-based delineations, and the limitations and conditions of the ITSMHydro tool set. Finally, the conclusion and future work section is a discussion of suggested future enhancements and improvements to the tools developed for this research.
DT4I4-Secure: Digital Twin Framework for Industry 4.0 Systems Security

The rapid adoption of automation in the Cyber-Physical Systems (CPS) is triggering Industry 4.0 (I4.0), integrating cloud computing, machine learning (ML), artificial intelligence (AI), and universal network connectivity into traditionally isolated systems. These I4.0 changes are optimizing the performance of Smart Manufacturing (SM) systems at the cost of increased complexity, exposing I4.0 systems to more cyberattacks than ever before. To address these challenges, this work presents DT4I4-Secure: A Digital Twin Framework for Industry 4.0 Security. The DT4I4-Secure presents a framework to create Digital Twins (DT) for I4.0 systems using a combination of models (including physics and data-based models). This paper showcases the use of the DT framework to detect attacks on I4.0 systems by comparing observations with future predictions from the DT. This paper evaluates the performance of the DT4I4-Secure for a Computer Numerical Control (CNC) turning process manufacturing a metallic spool, wherein the experimental results show the model can predict normal operations with a mean absolute error (MAE) of 0.005081. This work also explores using an Exponentially Weighted Moving Average (EWMA) based dynamic threshold instead of a traditional static threshold for attack detection when the CNC turning process is under three separate attack scenarios. The DT4I4-Secure combined with the dynamic threshold shows a 3.46 times improvement in F1-Scores over all three attack scenarios for instantaneous attack detection while having 100% accuracy during the manufacturing cycle.
Geomorphologic hazards for Vanyar Dam with emphasis on the reactivation of Tabriz fault, northwest Iran

Shahid Madani or Vanyar Dam is being construction on Aji Cahy River, to the north of Tabriz. It has been built on the upper red formation of Miocene. This lithology generally consists of sandstones, conglomerates, clays, and gypsum. Salt domes are also located underneath the reservoir area behind the dam. The slopes beside the reservoir are not stable. Unexpected events, such as subsidence, could occur after the reservoir is filled causing destruction to infrastructure. However, the most serious event could be the infiltration of water into the Tabriz fault. This fault has not been reactivated for over 220 years and it is thought that the infiltration of water from the reservoir could cause the two sides of the fault plane to move. It is thought that the displacement of the fault could cause an earthquake of more than 6.5 on the Richter scale. To try and prevent this fault reactivating, filling of reservoir is proposed to be undertaken gradually. Resume: Shahid Madani ou barrage de Vanyar est construction sur le fleuve d'Aji Cahy, au nord de Tabriz. Il a ete construit sur la formation rouge superieure de miocene. Cette lithologie se compose generalement des gres, conglomerats, argiles, et gypse. Des domes de sel sont egalement situes dans le reservoir du barrage. Les pentes pres du reservoir ne sont pas stables. Quelques evenements inattendus, comme l'affaissement des terres, pourrait se produire apres que le reservoir soit rempli causant la destruction a l'infrastructure. Cependant, l'evenement le plus dangereux est l'infiltration de l'eau dans le defaut de Tabriz. Ce defaut n'a pas ete reactive pendant plus de 220 annees. On le pense que l'infiltration de l'eau du reservoir pourrait causer les deux cotes de l'avion de defaut au mouvement. On le pense que le deplacement du defaut pourrait causer un tremblement de terre plus de de 6.5 sur l'echelle de Richter. Pour essayer et empecher ce defaut dangereux reactivant le remplissage du reservoir est propose d'etre entrepris graduellement.
Grid Integrated Transformerless Interleaved Hybrid Converter with Reduced Common-Mode Leakage Current

Grid integration of a transformerless interleaved hybrid converter (TLIHC) and its behavior during dynamic conditions for solar photovoltaic (PV) applications is proposed in this article. The controller design for the TLIHC and its dynamic performance is also discussed in this article. Further, the detailed analysis of grid side filter inductors $(L_{g1}$ and $L_{g2})$ and phase-locked loop (PLL) of the proposed TLIHC are discussed. Also, the closed loop control strategy of grid current $(I_{g})$ is discussed in this article. As, the DC power processing part of the proposed TLIHC is a derived output form of a conventional boost converter, the performance of TLIHC is studied when both $I_{g}$ and duty ratio (D) are varying. Further, the controlled switching pulses of the proposed TLIHC are well enough to minimize the high frequency common-mode voltage across the PV to ground parasitic capacitor. So, the harmful common mode leakage current is reduced in the system. In addition, as the shoot-through issue in the inverter part is resolved in this topology, the reliability of the proposed TLIHC is improved. Simulation results for the performance verifications of the grid integrated proposed TLIHC is also presented in this article.
Performance-Guaranteed Dimensionality Reduction of Large-Scale Data for Adaptive Filtering

In this paper, the mean-square performance of the normalized least-mean-square (NLMS) learning algorithm is elaborately analyzed to carefully adjust the sampling size of large-scale data. Based on the performance analysis of the NLMS learning algorithm, a steady-state criterion is derived to determine whether its learning performance reaches the steady state or not. From this criterion, a performance-guaranteed adjusting method of the sampling size is proposed to reduces the redundant computations at the steady state. Furthermore, a fast matrix inverse method also proposed with respect to the NLMS algorithm for large-scale data.
Tensile property of printed paper sensor with disposable property

Recently, wearable sensors have attracted attention in the medical field. Additionally, the demand for disposable medical devices from infectious and public health viewpoints is increasing. In this paper, we propose a fabrication method for highly stretchable printed sensors with disposable properties. The proposed printed sensor is mounted by inkjet printing on an origami corrugated structure. We conducted tensile tests to investigate the tensile properties of the printed sensor. Moreover, we measured the resistance change and investigated the electrode printing pattern with higher sensitivity. The fabricated printed sensor is disposable because it is made of paper and can be adaptable in accordance with body movements. We expect the printed sensor to be applied as a wearable sensor in the medical field.
A Novel Concentric Winding Axial-Flux Permanent Magnet Machine with High Winding Factor

Axial-flux permanent magnet (AFPM) machines are welcomed widely in many applications thanks to the high torque density and compactness. In the AFPM machine, the concentrated winding and distributed winding have different characteristics. In order to increase the winding factor and reduce the winding ends, a novel concentric winding AFPM machine is proposed in the paper. The stator of the proposed machine is divided into three circles from the inner side to the outer side. Then, the three-phase windings are arranged on the three circles, respectively. The phase angle difference is achieved through the mechanical offset between the teeth of three circles. Thus, the winding factor can be kept as 1 with a few winding ends by adjusting the pole-slot combination. In addition, the staggered teeth between the three circles can also reduce the cogging torque. In the optimization, the back EMFs of the three phases are nearly balanced. 3D finite element analysis also shows that the machine has a strong overload capability.
A 3D Fusion U-Net with Dual CNN and Transformer Encoders for Lung Airway Segmentation

The health of the lungs is significant importance to human well-being. As a vital respiratory organ, lung diseases have a high incidence and mortality rate worldwide. Accurate segmentation of the lung airways, including the trachea and bronchi, is crucial for the diagnosis of lung diseases. The recent coronavirus has further shown the importance of accurately distinguishing the lung airways from the lung parenchyma to assist in diagnosis. Therefore, developing an automated segmentation method that can accurately segment the lung airways from lung CT images is essential. Despite extensive research in this field, challenges remain. The unique tree-like structure of the lung airways and the fine-grained details at the terminal bronchioles pose difficulties for achieving precise segmentation using existing methods. In this study, we propose a novel dual-encoder network for accurate lung airways segmentation. Our method can efficient capture the local and global features using the combination of Convolutional Neural Networks (CNNs) and Transformer network. We evaluate our method on a lung CT dataset provided by Shandong University and demonstrate its superiority over existing methods.
Dual Balancing of SoC/SoT in Smart Batteries Using Reinforcement Learning in Uppaal Stratego

Battery packs in electric vehicles are managed by battery management systems that influence the state of charge among the cells in the pack, where such systems have received much attention in research. More recently, balancing the temperature among the cells has become a research topic. In our work, we consider a dual-balancing problem where we aim to balance both the parameters of the state of charge and temperature. We consider a Smart Battery Pack, where individual cells can be bypassed, meaning that no current is going to or from the cell, which allows the cell to cool off while the cell does not charge or discharge. Moreover, a smart battery pack can estimate each cell's characteristics, which, in turn, can be used to define a model of cell and battery pack behavior. We conduct experiments using the model of a battery pack where each cell differs in its configuration as an effect of aging. For such a pack with heterogeneous cells, we use Q- Learning in U ppaal Stratego to synthesize a controller that maximizes the time spent in a balanced state, meaning that all cells' states are within a specific range of each other. We show significant improvements in two aspects compared with two threshold-based controllers that balance either state of charge or temperature. The synthesized controllers are only unbalanced with the state of charge between 1-4% of the time and for temperature between 15-20% of the time. The threshold-based controllers are either unbalanced for the state of charge for as much as 37 % of the time or for temperature for as much as 44 % of the time. Finally, the maximum variations of state of charge and temperature among the cells are decreased.