Miracle-dz/UniFAR-FTUs huggingface dataset:
Photogrammetric reconstruction of homogenous snow surfaces in alpine terrain applying near-infrared UAS imagery

ABSTRACT Operating unmanned aerial systems (UAS) to record imagery of alpine snow cover for photogrammetric applications is challenging due to topographic and meteorological conditions. Furthermore, the homogenous snow surface encumbers the automated detection of matching points, which are required for generating accurate digital surface models (DSMs). On the other hand, there is rising demand for high spatial resolution snow depth and snow surface-type mapping as well as snow avalanche documentation. UAS have the potential to enable flexible, timely, and cost-efficient data acquisition, even in poorly accessible alpine terrain. This could be a major step forward for many applications in snow hydrology, avalanche research, mitigation measure planning, hazard zonation, and alpine ecology investigations. In this study, we investigate the applicability and performance of UAS-based structure-from-motion (SfM) photogrammetry on very homogenous snow surfaces, under suboptimal illumination conditions, at two alpine test sites in Tschuggen (2000 m a.s.l.) close to Davos, Switzerland, and Lizum (2000 m a.s.l.) near Innsbruck, Austria. We discuss the topographic and meteorological challenges for flying UAS missions in high-alpine terrain. Additionally, we compare DSMs calculated from the imagery acquired in the visual (VIS, λ = 400–700 nm) and near-infrared (NIR, λ = 700–830 nm) parts of the electromagnetic spectrum. We evaluate the resulting DSMs qualitatively and quantitatively by applying: a) differential Global Navigation Satellite System (GNSS) measurements at the Swiss test site, with an expected accuracy better than 0.1 m within x, y, and z directions; b) terrestrial laser scanning (TLS) at the Austrian test site, with an expected accuracy of ± 0.025 m (1σ), along with a distance-dependent error. The results of this study reveal the potential and limitations of UAS-based SfM photogrammetry for applications on snow-covered, alpine terrain in general and in particular the benefit of NIR imagery on the accuracy and precision of the results.
Investigating the Efficacy of Kidney-Protective Lactobacillus Mixture-Containing Pet Treats in Feline Chronic Kidney Disease and Its Possible Mechanism

Simple Summary Chronic kidney disease (CKD) is a vital issue waiting to be solved in the feline population. The efficacy of probiotics to prevent/alleviate CKD has been widely investigated in animal models. This pilot study combined a renal-protective functional Lactobacillus mixture (Lm) with pet feed as probiotic pet treats. CKD cats (stages 2 and 3) showed that kidney function and life quality were improved through modifying the composition of gut microbiota and metabolic patterns after administrating Lm pet treats daily for 8 weeks. This study clarified the possible mechanism of Lm in CKD cats and provided a possible novel way to treat cats with probiotics. Abstract Microbiota-based strategies are a novel auxiliary therapeutic and preventative way of moderating chronic kidney disease (CKD). Lactobacillus mixture (Lm) was previously demonstrated to exert a renal-protective function in the CKD mice model. The efficacy of probiotics in pet foods is a relatively new area of study, and thus verifying the potential health benefits is necessary. This study evaluated the efficacy of Lm treats in feline CKD and elucidated the mechanisms underlying host-microbe interactions. CKD cats (2 and 3 stages) were administrated probiotic pet treats daily (10 g) for 8 weeks. The results demonstrated that during the eight weeks of Lm administration, creatinine was reduced or maintained in all cats with CKD. Similarly, gut-derived uremic toxin (GDUT), indoxyl sulfate (IS), were potential clinical significance in IS after Lm treatment (confidence intervals = 90%). The life quality of the cats also improved. Feline gut microbiome data, metabolic functional pathway, and renal function indicator analyses revealed the possible mechanisms involved in modulating CKD feline microbial composition. Further regulation of the microbial functions in amino acid metabolism after Lm administration contributed to downregulating deleterious GDUTs. The current study provides potential adjuvant therapeutic insights into probiotic pet foods or treats for pets with CKD.
Hybrid total variance void-based noise removal in infrared images

An artifact known as an image is what makes the depiction of a thing or a person feasible. An image is a representation of visual perception and has a physical appearance that is analogous to that of the subject being portrayed. In situations when there is insufficient illumination, such as at night or when there is a lot of background noise, the use of infrared imagery can help improve the accuracy of object detection. Infrared images are able to account for a wide variety of noises, including those that are the result of sensor faults, lens distortion, software artifacts, blur, and other problems. It is difficult to do qualitative and quantitative analysis on thermal images due to the significant levels of noise that are present in these images. Eliminating noise in an infrared image by employing the total variance void (TVV) denoising technique while preserving the integrity of the image’s boundaries and texture. Denoising thermal images make use of a technique that is both efficient and reliable thanks to an integrated algorithm that combines TV denoising and Noise2Void (N2V). Strengths of the two methods, it is possible to produce denoised images of superior quality with improved retention of edge and texture detail.
3D printed osteochondral scaffolds: design strategies, present applications and future perspectives

Articular osteochondral (OC) defects are a global clinical problem characterized by loss of full-thickness articular cartilage with underlying calcified cartilage through to the subchondral bone. While current surgical treatments can relieve pain, none of them can completely repair all components of the OC unit and restore its original function. With the rapid development of three-dimensional (3D) printing technology, admirable progress has been made in bone and cartilage reconstruction, providing new strategies for restoring joint function. 3D printing has the advantages of fast speed, high precision, and personalized customization to meet the requirements of irregular geometry, differentiated composition, and multi-layered boundary layer structures of joint OC scaffolds. This review captures the original published researches on the application of 3D printing technology to the repair of entire OC units and provides a comprehensive summary of the recent advances in 3D printed OC scaffolds. We first introduce the gradient structure and biological properties of articular OC tissue. The considerations for the development of 3D printed OC scaffolds are emphatically summarized, including material types, fabrication techniques, structural design and seed cells. Especially from the perspective of material composition and structural design, the classification, characteristics and latest research progress of discrete gradient scaffolds (biphasic, triphasic and multiphasic scaffolds) and continuous gradient scaffolds (gradient material and/or structure, and gradient interface) are summarized. Finally, we also describe the important progress and application prospect of 3D printing technology in OC interface regeneration. 3D printing technology for OC reconstruction should simulate the gradient structure of subchondral bone and cartilage. Therefore, we must not only strengthen the basic research on OC structure, but also continue to explore the role of 3D printing technology in OC tissue engineering. This will enable better structural and functional bionics of OC scaffolds, ultimately improving the repair of OC defects.
INVESTIGATING THE IMPACT OF TAPER AND ASPECT RATIO ON A STALLING WING USING A CORRECTED VORTEX LATTICE METHOD

INVESTIGATING THE IMPACT OF TAPER AND ASPECT RATIO ON A STALLING WING USING A CORRECTED VORTEX LATTICE METHOD Ryan Anderson Mechanical Engineering Bachelor of Science The advent of new technologies such as eVTOL vehicles is exciting. The development of fast aerodynamics models incorporating stall is one small step on the road to realizing such concepts by improving the speed of design optimization algorithms. To this end, a modified vortex lattice model was developed based on one reported by dos Santos and Marques [4]. The model was validated against experimental data found in the literature. Making use of its stall capabilities, a sample study was performed to test for any coupling effect between aspect ratio and taper ratio. This was done by calculating the lift curve through stall for three situations: first varying aspect ratio, second varying taper ratio, and third varying aspect ratio and taper ratio simultaneously. The superposition of effects of independent sweeps is compared to the effects of the coupled sweep. Substantial disparity between the two would signify coupling. No significant coupling effect was discovered between aspect and taper ratios on lift slope, though some coupling was observed to affect max lift coefficient. Significant time savings were observed when compared to CFD. Fast models like rVLMk may be used to speed up design optimization for eVTOL vehicle design.
Technology Use for Nonwork Purposes at Work: A Behavior-Focused Integrative Review

Technology use for nonwork-related purposes at work — defined as technology-facilitated behaviors primarily unrelated to job tasks that occur during working hours — is a prevalent phenomenon. However, the study of these behaviors is fragmented across three communities: counterproductive work behaviors (i.e., “ cyberslacking ” ); recovery (i.e., “ microbreaks ” ); and a descriptive category that equates behaviors with constructs (e.g., “ social media use ” ). Our review of 135 studies reveals the same behaviors are studied across all three communities but with critical shortcomings: distinctive areas of focus within each community, including theoretical divides; overlapping conceptualizations with ambiguous measurements; insufficient consideration of nuances in use behaviors; and an underexplained parity of positive and negative effects on key work and nonwork outcomes. Given these shortcomings, we identify five review-driven themes centered around the emergence of a 2 3 2 framework that pinpoints concrete behaviors as ranging from passive to active (i.e., how much effort is associated with use) and intrapersonal to relational (i.e., how much interpersonal interaction is involved). Our framework provides guidance for future research and practice on how best to identify precise patterns and situations regarding when specific use behaviors occur and whether they result in positive or negative outcomes.
A Transfer Learning Approach towards Zero-training BCI for EEG-Based Two Dimensional Cursor Control

Non-invasive Brain Computer Interface (BCI) has attained substantial achievements in a number of areas such as rehabilitation and neuroprosthetics. However, the costly and time consuming process of acquiring subject-specific data and model calibration limit the expansion of BCI utilization. Moreover, due to the large variability of inter-subject and inter-trial data patterns, we cannot expect a satisfactory result from classifier on a new subject. In this study, we introduced a novel offline BCI platform using imagined body kinematics paradigm for computer cursor control in which the issue of inter-subject and inter-session model variability were addressed. We present a method to transfer parameters extracted from a pool of labeled recorded data of individuals (source) and generate a tailored classifier for a new subject (target) with minimal calibration. Due to disparity of the source and the target in terms of parameter distribution and variations in electroencephalography (EEG) response, part of the source data contributed to learn the target model while there were other parts harmful to the learning phase. Consequently, a measure was introduced to partake only the suitable part of the source data. Despite the fact that the BCI paradigm we resorted to is historically more prone to suffer from subject variability, the results showed encouraging figures in terms of both predictability and saving the number of target-specific samples. The lagged samples of subject's EEG from the whole brain activity contributed to a regression model. A measure of likelihood between the observed cursor velocity and the corresponding predicted velocity has been introduced. This measure not only was responsible for pulling out a satisfactory set of subjects' trials in order to construct a pool of data, but also defined how source data would impact the target model. Figure 1 depicts the average likelihood of the subject-specific model and the corresponding transferred model for horizontal cursor velocity over all participants.
Indian Classical Music Recognition using Deep Convolution Neural Network

A divine approach to communicate feelings about the world occurs through music. There is a huge variety in the language of music. One of the principal variables of Indian social legacy is classical music. Hindustani and Carnatic are the two primary subgenres of Indian classical music. Models have been trained and taught to distinguish between Carnatic and Hindustani songs. This paper presents Indian classical music recognition based on multiple acoustic features (MAF) consisting of various statistical, spectral, and time domain features. The MAF provides the changes in intonation, timbre, prosody and pitch of the musical speech due to different ragas. The lightweight DCNN is used to improve the representation of the raga sound and to provide higher order abstract level features. The overall performance of the raga type is estimated using various performance metrics, including accuracy, precision, recall and F1-score. The proposed DCNN achieves an accuracy, precision, recall, and F1-score of 89.38%, 0.89, 0.89, and 0.89, respectively, for eight raga classifications. The extensive experimentation on eight classical ragas has shown a noteworthy improvement over the traditional state of art.
Insights into Household Electric Vehicle Charging Behavior: Analysis and Predictive Modeling

In the era of burgeoning electric vehicle (EV) popularity, understanding the patterns of EV users’ behavior is imperative. This paper examines the trends in household charging sessions’ timing, duration, and energy consumption by analyzing real-world residential charging data. By leveraging the information collected from each session, a novel framework is introduced for the efficient, real-time prediction of important charging characteristics. Utilizing historical data and user-specific features, machine learning models are trained to predict the connection duration, charging duration, charging demand, and time until the next session. These models enhance the understanding of EV users’ behavior and provide practical tools for optimizing the EV charging infrastructure and effectively managing the charging demand. As the transportation sector becomes increasingly electrified, this work aims to empower stakeholders with insights and reliable models, enabling them to anticipate the localized demand and contribute to the sustainable integration of electric vehicles into the grid.
Effect of energy to protein ratio using alternative feed ingredients on growth performance and nutrient digestibility in broilers

Present study was conducted to evaluate the different levels of energy to protein ratios (EPR) using food waste and black soldier fly larvae meal (FWBSFL) on growth performance and nutrient digestibility of broilers. A total of 160 one-day old broiler chicks were divided randomly to four groups and each group had 8 replicates with 5 chicks per replicate. The control diet was formulated using conventional feed ingredients with EPR of 154 for the starter period and 167 for the finisher period. The other treatments were diets with normal, low, and high EPR (154,143, and 166 for the starter period; 167, 155, and 177 for the finisher period) using FWBSFL. Feed consumption and body weight gain as well as digestibility of crude protein, crude fiber, and fat were decreased due to feeding of FWBSFL diets. Different levels of EPR using FWBSFL decreased growth performance and nutrient digestibility of broilers.
Sudocetaxel Zendusortide (TH1902) triggers the cGAS/STING pathway and potentiates anti-PD-L1 immune-mediated tumor cell killing

The anticancer efficacy of Sudocetaxel Zendusortide (TH1902), a peptide-drug conjugate internalized through a sortilin-mediated process, was assessed in a triple-negative breast cancer-derived MDA-MB-231 immunocompromised xenograft tumor model where complete tumor regression was observed for more than 40 days after the last treatment. Surprisingly, immunohistochemistry analysis revealed high staining of STING, a master regulator in the cancer-immunity cycle. A weekly administration of TH1902 as a single agent in a murine B16-F10 melanoma syngeneic tumor model demonstrated superior tumor growth inhibition than did docetaxel. A net increase in CD45 leukocyte infiltration within TH1902-treated tumors, especially for tumor-infiltrating lymphocytes and tumor-associated macrophages was observed. Increased staining of perforin, granzyme B, and caspase-3 was suggestive of elevated cytotoxic T and natural killer cell activities. Combined TH1902/anti-PD-L1 treatment led to increases in tumor growth inhibition and median animal survival. TH1902 inhibited cell proliferation and triggered apoptosis and senescence in B16-F10 cells in vitro, while inducing several downstream effectors of the cGAS/STING pathway and the expression of MHC-I and PD-L1. This is the first evidence that TH1902 exerts its antitumor activity, in part, through modulation of the immune tumor microenvironment and that the combination of TH1902 with checkpoint inhibitors (anti-PD-L1) could lead to improved clinical outcomes.
Derivation and applications of probabilistic measures of class membership from the maximum-likelihood classification

The maximum-likelihood classification of remotely sensed data involves considerable computational effort, in the process calculating a large amount of information on the class membership characteristics for each case (e.g., pixel). Little of this information, however, is made available in the conventional output, which consists simply of the most likely class of membership for each case. More of the information generated in the classification can be output, specifically the a posteriori probabilities and typicalities of class membership.
Dietary proteins: from evolution to engineering

Because of the indispensable amino acids dietary proteins are the most important macronutrients. Proper growth and body maintenance depends on the quantity and quality of protein intake and proteins have thus been most crucial throughout evolution with hominins living in quite diverse food ecosystems. Developments in agriculture and food science have increased availability and diversity of food including protein for a rapidly growing world population while nutrient deficiencies resulting in stunting in children for example have been reduced. Nevertheless, the developing world and growing population needs more protein of high quality – with around 400 million tons per annum estimated for 2050. In contrary, protein consumption in all developed countries exceeds meanwhile the recommended intakes considerably with consequences for health and the environment. There is a growing interest in dietary proteins driven by the quest for more sustainable diets and the increasing food demand for a growing world population. This brings new and novel sources such as algae, yeast, insects or bacteria into play in delivering the biomass but also new technologies such as precision fermentation or in vitro meat/fish or dairy. What needs to be considered when such new protein sources are explored is that proteins need to provide not only the required amino acids but also functionality in the food produced thereof. This review considers human physiology and metabolism in the context of protein intake from an evolutionary perspective and prospects on future protein production.
The sensitivity of water availability to changes in the aridity index and other factors—A probabilistic analysis in the Budyko space

One of the pending questions in the context of global change is whether climatic drivers or other factors have stronger influences on water availability. Here we present an approach that allows to estimate the probability that changes in the aridity index have a larger effect on water availability than equal relative changes in other factors. The analysis builds upon a probabilistic extension of the Budyko framework, which is subject to an analytical sensitivity assessment. The results show that changes in water availability are only dominated by changes in the aridity index in very humid climates. This implies that projected intensifications of aridity in drylands may have less influence on water availability than commonly assumed. Instead, other climatic or nonclimatic factors are dominating. The analysis does hence allow to map regions in which water availability is more sensitive to equal relative changes in either the aridity index or all other factors.
A Practical and Energy-efficient System to Discover Wi-Fi Hotspots Using Cellular Signals

In mobile devices, to discover Wi-Fi hotspots accurately while Wi-Fi is turned off is an important but a difficult problem to solve. Solutions to this problem have many practical applications (e.g., energy-efficient Wi-Fi offloading). We present PRiSM, a practical system to solve this problem, using the detailed statistical properties of cellular signals alone. Cellular signals are received at no extra cost in mobile devices and hence PRiSM is highly energy efficient. PRiSM is a lightweight client-side only implementation and needs no prior knowledge on site plans or RF infrastructure. It is very robust and in general can be used for any location determination systems. We implement PRiSM on Android phones and perform extensive evaluation using both trace-based simulation and practical observation. PRiSM demonstrates average prediction accuracy of up to 98% and average energy savings of up to 16.5% of the total battery capacity which is very significant. The energy savings are equivalent to extending the battery lifetime by up to 6.6hrs in typical usage scenarios.