Miracle-dz/UniFAR-FTUs huggingface dataset:
Usage Pattern and Nicotine Delivery during Ad Libitum Consumption of Pod E-Cigarettes and Heated Tobacco Products

Many different nicotine delivery products, such as e-cigarettes (e-cigs) or heated tobacco products (HTPs), are available on the market. To better understand these products, it is crucial to learn how consumers use them and how much nicotine they deliver. Therefore, a pod e-cig, an HTP, and a conventional cigarette (CC) were each used by 15 experienced users of the respective product category for 90 min without special use instructions (“ad libitum”). Sessions were video recorded to analyze usage patterns and puff topography. At defined time points, blood was sampled to determine nicotine concentrations, and subjective effects were inquired about using questionnaires. During the study period, the CC and HTP groups averaged the same number of consumption units (both 4.2 units). In the pod e-cig group, the highest number of puffs was taken (pod e-cig 71.9; HTP: 52.2; CC: 42.3 puffs) with the most extended mean puff duration (pod e-cig: 2.8 s; HTP: 1.9 s; CC: 1.8 s). Pod e-cigs were predominantly used with single puffs or in short clusters of 2–5 puffs. The maximum plasma nicotine concentration was highest for CCs, followed by HTPs, and then pod e-cigs with 24.0, 17.7, and 8.0 ng/mL, respectively. Craving was reduced by all products. The results suggest that the high nicotine delivery known for tobacco-containing products (CCs and HTPs) may not be needed for non-tobacco-containing products (pod e-cigs) to satisfy cravings in experienced users.
Higher-Order Link Prediction Via Learnable Maximum Mean Discrepancy

Higher-order link prediction (HOLP) seeks missing links capturing dependencies among three or more network nodes. Predicting high-order links (HOLs) can for instance reveal hyperlinks in the structure of drug substance and metabolic networks. Existing methods either make restrictive assumptions regarding the emergence of HOLs, or, they rely on reduced dimensionality models of limited expressiveness. To overcome these limitations, the HOLP approach developed here leverages distribution similarities across embeddings as captured by a learnable probability metric. The intuition underpinning the novel approach is that sets of nodes whose embeddings are less similar in distribution, are less likely to be connected by a HOL. Specifically, nonlinear dimensionality reduction is effected through a Gaussian process latent variable model that yields nodal embeddings, and also learns a data-driven similarity function (kernel). This kernel forms the core of a maximum mean discrepancy probability metric. Tests on benchmark datasets illustrate the potential of the proposed approach.
Blockchain‐based authentication and secure communication in IoT networks

The application of IoT covers various domains, including smart cities, smart homes, e‐health, autonomous vehicles and so forth. These are powered by IoT networks which consist of a collection of heterogeneous devices. These devices collect and analyze personal data and sometimes make decisions for us. Since IoTs are involved in the decision‐making process, the authenticity and privacy of such networks are critical. In this article, authenticated devices configuration protocol (ADCP) is proposed to handle the authentication and construct a secure overlay network in the existing IoT networks; authenticated device transmission protocol is also proposed to secure the communication within the overlay network. Besides the authentication, ADCP can also mitigate the zero‐day attack and achieve zero the zero‐day attack can also be mitigated and achieved 0 round trip time key exchange. These protocols utilize a distributed blockchain database to store the authentication records optimized for data integrity. These properties guarantee the integrity of the authentication records. Both protocols are compatible with the existing communication protocols and do not require reprogramming the current software. The formal analysis of the proposed scheme has proven its resilience against various possible attacks. In addition, a stochastic threat model is considered and numerical results give solid evidence of security enhancement.
Robust and Versatile Light Absorption at Near-Infrared Wavelengths by Plasmonic Aluminum Nanorods

We investigate the far-field and near-field properties of aluminum nanorods fabricated by electron beam lithography and exhibiting plasmonic resonance in the near-infrared region. First, we show that plasmonic modes within nanorod arrays can be tuned by geometrical parameters, allowing one to control the system transparency. Next, the light absorption in this structure is closely examined, and we demonstrate that aluminum has great potential due to its unique interband transition at 800 nm. The roles of the dielectric confinement and the coupling between plasmonic resonance and the interband transition are particularly emphasized, as their adjustment can be used to switch from highly scattering particles to absorbing particles without a significant modification of the plasmonic resonance position. Finally, we image the plasmon-generated local field distribution in the aluminum nanostructures and observe, for the first time, the effect of the interband transition on the near-field behavior. The effect of t.
Kefir Prevents Adipose Tissue Growth Through the Induction of Apoptotic Elements in High-Fructose Corn Syrup-Fed Rats

Consumption of high-fructose corn syrup (HFCS) in the diet is a causal factor in the development of abdominal obesity; however, the molecular mechanism behind this association is still up for debate. This study evaluated the metabolic disturbances that are caused by HFCS on adipose tissue as well as the possibility of kefir as a therapy to prevent these metabolic disturbances. Male Wistar rats were divided into four groups: control, kefir, HFCS, and HFCS+kefir. HFCS (20%, w/v ) was given in drinking water and kefir (1 mL/100 g body weight) by gastric gavage daily for 8 weeks. Levels of insulin signaling, inflammation, and apoptosis-associated proteins of adipose tissues were determined with Western blot and immunohistochemical techniques. Gene expressions were evaluated with semi-quantitative real-time polymerase chain reaction (qRT-PCR). The indirect terminal deoxynucleotidyl transferase dUTP nick-end labeling (TUNEL) method was used to assess changes in apoptotic cells, and hematoxylin/eosin staining to determine adipocyte number and diameter. Accordingly, HFCS boosted protein kinase B (Akt) and p-Akt while reducing nuclear factor κB (NF-κB), and tumor necrosis factor alpha (TNFα) levels and kefir treatment restored Akt induction in HFCS-treated rats despite raising NF-κB, and TNFα. Increased expression of Akt and B-cell lymphoma-2 gene ( Bcl2 ) was contrasted with decreased expression of Nfkb, Tnfa, tumor protein 53 gene ( p53 ), and caspase-8 gene ( Casp8 ). Furthermore, while there was a marked reduction in TUNEL-positive cells in the HFCS group, the number of such cells was greater in the HFCS+kefir group. These results show that HFCS intake suppresses apoptosis in adipose tissues, which may be responsible for tissue development and abdominal obesity and may be reversed with kefir administration due to the activation of apoptosis-associated genes and proteins.
From Necessary Chances to Biological Laws

In this article, I propose a new way of thinking about natural necessity and a new way of thinking about biological laws. I suggest that much of the lack of progress in making a positive case for distinctively biological laws is that we’ve been looking for necessity in the wrong place. The trend has been to look for exceptionlessness at the level of the outcomes of biological processes and to build one’s claims about necessity off of that. However, as Beatty (1995) observed, even when we are lucky enough to find a biological ‘rule’ of some sort, that rule is apt to be a victim of ‘the rule-breaking capabilities of evolutionary change’. If indeed no distinctively biological generalization—even an exceptionless one—is safe, we need to locate necessity elsewhere. A good place to start is, I think, precisely the point at which Beatty sees the possibility of lawhood as breaking down—namely, at the level of chances. 1 The ‘Necessity’ Objection to Biological Laws 2 Necessary Chances 2.1 Necessary chances: random drift 2.2 Necessary chances: fitness 3 But is it Biological? 4 Conclusion 1 The ‘Necessity’ Objection to Biological Laws 2 Necessary Chances 2.1 Necessary chances: random drift 2.2 Necessary chances: fitness 2.1 Necessary chances: random drift 2.2 Necessary chances: fitness 3 But is it Biological? 4 Conclusion
On a numerical solution of the Laplace equation

The Laplace Equation in three variable can be reduced to three ODEs by means of the Fourier method. For the cases when the exact solution does not exist, or it is complicated,we apply the wavelet-Galerkin method to the ODEs. We use suitable wavelets or scaling functions that allow finding the numerical solutions of the three equations, which will form the solution of the Laplace equation.
A DNN-Based Hearing-Aid Strategy For Real-Time Processing: One Size Fits All

Although hearing aids (HAs) can compensate for elevated hearing thresholds using sound amplification, they often fail to restore auditory perception in adverse listening conditions. To achieve robust treatment outcomes for diverse HA users, we use a differentiable framework that can compensate for impaired auditory processing based on a biophysically realistic and personalisable auditory model. Here, we present a deep-neural-network (DNN) HA processing strategy that can provide individualised sound processing for the audiogram of a listener using a single model architecture. The DNN architecture was trained to compensate for different audiogram inputs and was able to enhance simulated responses and intelligibility even for audiograms that were not part of training. Our multi-purpose HA model can be used for different individuals and can process audio inputs of 3.2 ms in <0.5 ms, thus paving the way for precise DNN-based treatments of hearing loss that can be embedded in hearing devices.
Translation Animal Models of Diabetic Kidney Disease: Biochemical and Histological Phenotypes, Advantages and Limitations

Abstract Animal models play a crucial role in studying the pathogenesis of diseases, developing new drugs, identifying disease risk markers, and improving means of prevention and treatment. However, modeling diabetic kidney disease (DKD) has posed a challenge for scientists. Although numerous models have been successfully developed, none of them can encompass all the key characteristics of human DKD. It is essential to choose the appropriate model according to the research needs, as different models develop different phenotypes and have their limitations. This paper provides a comprehensive overview of biochemical and histological phenotypes, modeling mechanisms, advantages and limitations of DKD animal models, in order to update relevant model information and provide insights and references for generating or selecting the appropriate animal models to fit different experimental needs.
Improving a method for segmenting optical-electronic images acquired from space observation systems based on the firefly algorithm

The object of research is the process of segmentation of optoelectronic images acquired from space observation systems. The method of segmentation of optoelectronic images acquired from space observation systems based on the firefly algorithm, unlike known ones, involves the following: – the pre-selection of brightness channels of the Red-Green-Blue color space in the original image; – calculation of the level of luminosity for each firefly; – assigning each firefly with the neighboring firefly, within a certain radius, whose level of luminosity is higher than the natural level of luminosity of the firefly; – determination of the coordinate of the updated position of the firefly in each brightness channel. An experimental study into the segmentation of optoelectronic images acquired from space observation systems based on the firefly algorithm was carried out. It is established that the improved segmentation method based on the firefly algorithm allows for the segmentation of optoelectronic images acquired from space observation systems. The quality of segmentation of optoelectronic images by the method based on the firefly algorithm was evaluated in comparison with methods based on the particle swarm algorithm and the Sine-Cosine algorithm. It was found that the improved method based on the firefly algorithm reduces the segmentation error of the first kind by an average of 11 % and the segmentation error of the second kind by an average of 9 %. This becomes possible by using the firefly algorithm. Methods of image segmentation can be implemented in software and hardware systems for processing optoelectronic images acquired from space surveillance systems. Further studies may focus on comparing the quality of segmentation method based on the firefly algorithm with segmentation methods based on genetic algorithms.
Storage of Extended Boar Semen at 5 °C Inhibits Growth of Multi-Drug Resistant Serratia marcescens and Klebsiella oxytoca while Maintaining High Sperm Quality

Multi-drug antibiotic resistance of Serratia (S.) marcescens and Klebsiella (K.) oxytoca in boar semen is an emerging threat to pig reproduction and the environment. The aim of this study is to examine the efficiency of a novel hypothermic preservation method to inhibit the growth of these bacterial species in extended boar semen and to maintain the sperm quality. The semen samples extended in an antibiotic-free Androstar Premium extender were spiked with ~102 CFU/mL of S. marcescens or K.oxytoca. Storage at 5 °C for 144 h inhibited the growth of both bacterial species and maintained the sperm quality, whereas bacterial counts increased to more than 1010 CFU/mL in the 17 °C samples used as positive controls. This was accompanied by an increase in the sperm agglutination and the loss of motility and membrane integrity. We conclude that hypothermic storage is a promising tool to combat resistant bacteria in boar semen and to contribute to the One Health approach.
On Expectation Correlate System and Chaotic Dynamics in Time-Series

This paper suggests a new system of time-series called Expectation Correlate System (ECS) that are good at detecting the behavior of dynamical systems (both deterministic and stochastic systems) and the dependence on initial values. A new measure on sensitivity to initial values can be monitored by the newly defined Lyaponov Correlate, so ECS can be a signal to chaotic property. In a stochastic systems, small shifts in some initial value can lead to error in prediction, this property and a
Trial by Fire: Support for Mitigation and Adaptation Policy after the 2020 Oregon Wildfires

The September 2020 Oregon wildfires were unprecedented in terms of their geographic scope and the number of communities affected by smoke and wildfire. Though it is difficult to directly attribute the event to climate change, scientists have noted the strong connection between warmer and drier conditions in the Western U.S. – conditions that are linked to climate change – and increasing wildfire risk. These wildfires thus had the potential to act as a “focusing event,” potentially strengthening public support for climate change policy. Political ideology is a well-known driver of public support for climate change mitigation policies in the U.S., but few studies have examined adaptation policy support. Moreover, other factors shaping post-event support for the two “pillars” of climate change policy—adaptation and mitigation—have rarely been compared. We conducted a survey of Oregonians within 6 months of the 2020 wildfires (n=1,308) to understand post-event support for climate mitigation and adaptation policies. We found that the magnitude of the association between political ideology and policy support was lower for adaptation policies than mitigation policy and there was no association with support for forest management changes. In contrast, selected socio-demographic characteristics played a more important role in support for selected adaptation policies than mitigation policy.
Robot Sound-In-Interaction

Sound is an important interaction modality in human interaction, which robot design is only starting to tap into. Drawing on insights about how human sounds support coordination of bodily activities, this work focuses on how robots can communicate through sound in concrete interactions in the wild. My work contributes a focus on how users make sense of sound in everyday interaction, promotes re-consideration of what HRI is designing for and stimulates the development of new HRI design methods.
Pathophysiological Association of Alzheimer’s Disease and Hypertension: A Clinical Concern for Elderly Population

Abstract Alzheimer’s disease (AD), the most common cause of dementia and the fifth leading cause of death in the adult population has a complex pathophysiological link with hypertension (HTN). A growing volume of published literature on a parallel elevation of blood pressure (BP), amyloid plaques, and neurofibrillary tangles formation in post-middle of human brain cells has developed new, widely accepting foundations on this association. In particular, HTN in elderly life mediates cerebral blood flow dysfunction, neuronal dysfunction, and significant decline in cognitive impairment, primarily in the late-life populace, governing the onset of AD. Thus, HTN is an established risk factor for AD. Considering the impact of AD, 1.89 million deaths annually, and the failure of palliative therapies to cure AD, the scientific research community is looking to adopt integrated approaches to target early modified risk factors like HTN to reduce AD burden. The current review highlights the significance and impact of HTN-based prevention in lowering the AD burden in the elderly by providing a comprehensive overview of the physiological relationship between AD and HTN with an in-detail explanation of the role and applications of pathological biomarkers in this clinical association. The review will gain worth in presenting new insights and providing inclusive discussion on the correlation between HTN and cognitive impairment. It will increase across a wider scientific audience to expand understanding of this pathophysiological association.
Time-reversal algorithms in viscoelastic media†

In this paper we consider the problem of reconstructing sources in a homogeneous viscoelastic medium from wavefield measurements. We first present a modified time-reversal imaging algorithm based on a weighted Helmholtz decomposition and justify mathematically that it provides a better approximation than by simply time reversing the displacement field, where artifacts appear due to the coupling between the pressure and shear waves. Then we investigate the source inverse problem in an elastic attenuating medium. We provide a regularized time-reversal imaging which corrects the attenuation effect at the first order. The results of this paper yield the fundamental tools for solving imaging problems in elastic media using cross-correlation techniques.
Properties of Quaternion Fourier Transforms

With the recent popularity of quaternion and quaternion Fourier transforms (QFTs) technique in physics and engineering applications, there tends to be an inordinate degree of interest placed on the properties of QFTs. Due to the noncommutativity of multiplication of quaternions, there are different types of QFTs and we focus on the right-sided QFT (RQFT) and two-sided QFT (SQFT). In this paper, we begin by considering the behavior of the RQFT on the space L1(R2, H). The energy-preserved property deduces to the extension of RQFT on L2(R2, H). We are largely exploiting the relation between RQFT and SQFT and some elementary properties of RQFT can be transformed to SQFT. As a generalization of QFTs, the properties of quaternion linear canonical transforms (QLCTs) are also studied on the basis of existing properties of QFTs.
ClassA Entropy for the Analysis of Structural Complexity of Physiological Signals

Despite the recent theoretical boom in Sample Entropy based algorithms for the analysis of physiological and pathological systems, the major issue which prevents their more widespread use remains that of large computational load, particularly in the studies of quantification of structural richness in data. This issue becomes even more prohibitive when it comes to large data sizes and real-time processing. To this end, a new Classification Angle (ClassA) Entropy is introduced for the quantification of structural complexity of real world signals, based on an improved Second Order Difference Plot and Shannon Entropy. In comparison with existing nonlinear techniques, including Real Sum Angle index, Phase Entropy, Gridded Distribution Entropy and Cosine Similarity Entropy, the proposed method offers the advantages of a minimal number of tunable parameters, lower requirement of data size, wider application field and large relaxation of computational load. Simulations on real world physiological data support the approach.
Improved Wifi-Based Respiration Tracking via Contrast Enhancement

Respiratory rate tracking has gained more and more interest in the past few years because of its great potential in exploring different pathological conditions of human beings. Conventional approaches usually require dedicated wearable devices, making them intrusive and unfriendly to users. To tackle the issue, many WiFi-based respiration tracking systems have been proposed because of WiFi’s ubiquity, low-cost, and most importantly, contactlessness. However, most existing works are of limited coverage and inflexible deployment, which greatly hinders their applications. In this paper, we propose WiResP, a practical and innovative WiFi-based respiration tracking system that utilizes a contrast enhancement technique to improve the detection of respiration. This approach combines both instantaneous and time-domain information, resulting in better recognition of breaths and identification of breath patterns. Extensive experiments under different settings show that WiResP can well capture respiratory rate during sleep under flexible deployments. Moreover, it remarkably increases the sensing coverage compared with the existing methods, making it a potential candidate toward real-world applications.
Quantum wireless communication network model and performance analysis

A quantum routing scheme is proposed to teleport a quantum state from one quantum device to another wirelessly,even though the two devices do not share EPR pairs mutually.The scheme is based on entanglement swapping and quantum teleportation.The model of quantum wireless communication network and correlative communication protocol is presented.In order to be compatible with the conventional wireless communication network,the network architecture of a twotier database structure is proposed.In terms of qubit transmission time and security,performance analysis results show that the proposed scheme is superior to quantum relay routing scheme.The proposed scheme has high scalability and practicability.
Manifold learning and manifold alignment based on coupled linear projections

Traditional manifold learning mainly deals with data dimensionality reduction problems from a single manifold.Through further development,manifold learning focusing on the multi-manifold has drawn the attention of many scholars.An algorithm of manifold alignment based on coupled projections was proposed.The proposed method using the coupled linear projections dealt with an out-of-sample data set without re-training.Compared to other linear methods,it handled some real problems of manifold alignment with flexibility since the proposed method did not need any assumptions about the relationship of affine transformation between the manifolds.