Miracle-dz/UniFAR-FTUs huggingface dataset:
An assessment of groundwater quality for agricultural use: a case study from solid waste disposal site SE of Pune, India

Groundwater pollution around the improperly constructed landfill areas of the growing cities has always been in the rising trend and hence its effects on the environment warrant a thorough monitoring. The seasonal variations in the quality of groundwater from the dug wells surrounding the solid waste disposal site from the SE of Pune city (India) has been assessed by calculating the sodium adsorption ratio (SAR). The results indicate that the groundwater from the wells nearing the waste disposal site show consistent increase in the pollution from monsoon to summer through winter. The study further demonstrates that the wells near the site are severely polluted and the source is mainly the leachates emerging out of the decaying solid wastes. The recurrent addition of the solid waste in the dump site in the coming years would result in further exponential deterioration of the groundwater quality of the dug wells from the area and hence adequate steps are urgently needed to prevent further aggravation of the problem. Based upon the SAR values it is evident that most of the wells from the Hadapsar area have excellent groundwater for irrigation throughout the year; from Manjari area it is excellent to good; the Fursungi area has sub-equal proportions of excellent, good and fair groundwater, while in Mantarwadi, although most of the wells have excellent to good water, few wells have fair to poor quality water for irrigation purpose. In Uruli-Devachi about 50% wells have poor quality of water and hence can not be used for irrigation. Hence this study strongly suggests that most of the abstracted groundwater samples from the study area were suitable for irrigation except from Uruli Devachi area.
Recurrent paradoxical cerebral embolism caused by a thrombus entrapped in a patent foramen ovale: a case report

Introduction: A thrombus straddling the patent foramen ovale (PFO) is a very rare clinical entity. Optimal management remains unclear due to the availability of various therapeutic options; thus, an individualized approach is recommended. Case presentation: The authors describe a case presenting with recurrent cerebral embolism and a large and mobile thrombus straddled in the PFO. Historically, cardiac surgery, thrombolysis or anticoagulation were possible management options for this patient. However, there are no guidelines on what the optimal treatment is. Our patient was a poor surgical candidate because of advanced age and multiple comorbidities. Furthermore, she had a recent ischaemic stroke which was a relative contraindication to thrombolysis. After consulting the Heart Team, medical treatment alone with systemic anticoagulation was administered. Subsequent transthoracic echocardiography (TTE) after 1 week showed complete thrombus resolution. After 4 months, the PFO was successfully occluded with an Amplatzer device. The patient received rehabilitation therapy and had good functional recovery. Clinical discussion: Anticoagulant therapy alone was chosen because of high risk for surgery. Complete thrombus resolution was achieved after 1 week although the initial thrombus size was quite large. Nevertheless, this approach may not be applicable to every patient. In addition to TTE, multimodality imaging using transesophageal echocardiography and cardiac magnetic resonance is helpful to identify the mechanism of stroke, which in our case is a thrombus-straddled PFO, and to make early treatment decisions. Serial TTEs help assess the response to anticoagulation. An individualized approach should be made with a multidisciplinary Heart Team. Conclusion: Echocardiography plays an important role in the diagnosis and treatment evaluation for patients with a thrombus straddling a PFO. An individualized approach to manage the patient should be made with a multidisciplinary Heart Team.
MODELING THE IMPACT OF PROJECTED LAND COVER ON LYME DISEASE EMERGENCE

Lyme disease is a common tick borne disease in the US. Lyme disease emerged from the Northeast and in the past decade, Virginia has been witnessing a rapidly increasing trend in incidence. This thesis uses land cover projection data as a basis to look at the potential future trend of Lyme disease incidence in Virginia for the IPCC (Intergovernmental Panel on Climate change) scenarios of A1B and A2, which indicate a global and regional focus respectively. This study is a continuation of previous work done by an NSF funded research team at Virginia Tech, in exploring the variables affecting Lyme disease in Virginia. A Poisson point process is implemented in this thesis with land cover parameters (implemented land, water bodies, and edge metrics) and demographic parameters (population percentage and per capita income) as the spatial covariates. Lyme disease incidence data obtained from the Virginia Department of Health was used for model validation. The overall model was implemented using Python and its associated libraries while ArcGIS software was used for preliminary covariate analysis and data visualization. This thesis generates risk maps for A1B and A2 scenarios for each decade from 2010 through 2060. Spatial occurrence of disease incidence has been generated by the Poisson point process and the risk level of each county in Virginia has been calculated based on the incidence count predicted for it. Population and area at risk under each scenario for each decade was calculated. Results show that in A1B scenario 22.1% and 42.9% of the total population of Virginia are under high risk and in the A2 scenario, 21% and 33% of the total population of Virginia are under high risk of Lyme disease in 2010 and 2060 respectively. In terms of the area, A1B scenario has 28% under high risk in 2010 and 66% of the total area under high risk in 2060, while A2 scenario has 22.4% under high risk of Lyme disease in 2010 62.7% of the total area in Virginia is under high risk in 2060.
Omentin-1 ameliorates the progress of osteoarthritis by promoting IL-4-dependent anti-inflammatory responses and M2 macrophage polarization

Osteoarthritis (OA) is a prevalent joint disease commonly associated with aging and obesity, which can lead to pain, stiffness, joint dysfunction, and disability. Omentin-1 (also called intelectin-1) is a newly discovered adipokine, which plays a protective role in suppressing the secretion of pro-inflammatory cytokines. Based on data from the Gene Expression Omnibus (GEO) dataset and clinical samples obtained at our institution revealed, determined that omentin-1 and IL-4 (an anti-inflammatory cytokine) levels were significantly lower in OA patients than in normal controls. Omentin-1 was shown to induce IL-4-depedent anti-inflammatory responses and M2 macrophage polarization in OA synovial fibroblasts via the PI3K, ERK, and AMPK pathways. Administering omentin-1 was shown to block cartilage degradation and bone erosion resulting from anterior cruciate ligament transection by inhibiting the production of pro-inflammatory cytokines and promoting M2 macrophage polarization in vivo. Our findings indicate omentin-1 as a promising therapeutic avenue for the treatment for OA.
A Study of a Fractional Slot Concentrated Wound Permanent Magnet Synchronous Motor with Grain Oriented Electric Steel Lamination for Modular Stator Geometry

Nowadays the industry of electrical machines is looking for different procedures to reduce the effective cost of materials used in the manufacturing process, but at the same time to reach a good balance of efficiency. There might be many ways to obtain such a performance. It is obvious, that these machines should experience multiple adjustments to fulfill the desired requirements. The current study proposes an approach to reducing the iron core losses of Permanent Magnet Synchronous Motor (PMSM) with Fractional Slot Concentrated Wound (FSCW) by using a modular Grain-Oriented (GO) stator with steel lamination sheets. A modular structure means the yoke and teeth are distributed in multiple geometry assemblies that form together the motor stationary armature. It is known the fact that alternating current motors with an increased number of magnetic poles require a higher frequency to reach a high angular speed. At these high frequencies, the iron core loss is most significant. This study intends to provide several Finite Element Analyses (FEA) to draw different characteristics of the presented motor geometry. Finally, iron core losses are calculated, and the power balance is realized.
Shea nut meal effect on rumen fermentation parameters of sheep

Although ruminants have a greater capacity to utilize various feed sources with lesser risks, the impact of non-conventional feed on rumen function should be understood. This study investigated the effects of Shea nut meal (SNM) different inclusion levels on rumen parameters of sheep fed 70% Rhodes grass hay and 30% maize bran basal diets. Five diets (D) containing SNM at different levels of D1=0% SNM, D2=5% SNM, D3=10% SNM, D4=15% SNM and D5=20% SNM were fed to fifteen ewes clustered into three, in a completely randomized design (CRD). Rumen samples were collected in the 10 th week of the feeding trial at hour 0, 3, 6, 9 and 12 post feeding and analyzed for pH, NH 3 -N, volatile fatty acids & protozoa count. SNM increased mean pH, and was in a range of (6.58-6.8) in SNM diets compared D1 (6.2). Mean NH 3 -N (mg/100ml) in SNM groups decreased with SNM increasing inclusion. Propionate (mmol/L) production was favored for SNM diets; 5% (1.379), 10% (1.375), 15% (1.377), 20% (1.365) compared to D1 (1.682) but total volatile fatty acids (mmol/L) ( p <0.05) reduced with SNM increased inclusion. Mean protozoa counts were similarly reduced with SNM increased inclusion; 5% (7.40), 10% (6.80), 15% (6.40), 20% (5.07) compared to D1 (8.53). It was concluded that SNM had no adverse effects on the rumen parameters but rather enhanced the rumen function through improved propionate production and exertion of ant protozoan properties, making it a safe supplement and/or additive source of essential oils and perhaps tannins when needed in formulation of ruminant feeds.
MSER-Vertical Sobel for Vehicle Logo Detection

Detecting a vehicle logo is the first step before the actual recognition of the logo. However, detecting logos can pose difficulties due to various factors, including logo variations, differing scales and orientations, background interference, varying lighting conditions, and partial obstruction. This paper presents a vehicle logo detection method using handcrafted features. We used a combination of Maximally Stable Extremal Region (MSER) and Vertical Sobel. We combine vertical Sobel with MSER to overcome MSER's limitation in recognizing objects of different sizes. These two features are merged using closing morphology operation to form blobs selected as logo candidate areas. Moreover, a Support Vector Machine (SVM) is implemented to choose a logo area by analyzing each candidate's Histogram of Oriented Gradient (HOG). The proposed method was compared to other methods by implementing them on the same dataset. The significant advantage of using MSER-Vertical Sobel is its fast computation time. It is faster than other approaches that use non-handcrafted features. The test results show that the MSER-Vertical Sobel can achieve high accuracy and the fastest computation time.
Variable Projection Support Vector Machines

Summary: We introduce an extension of the classical support vector machine classification algorithm with adaptive orthogonal transformations. The proposed transformations are realized through so-called variable projection operators. This approach allows the classifier to learn an informative representation of the data during the training process. Furthermore, choosing the underlying adaptive transformations correctly allows for learning interpretable parameters. Since the gradients of the proposed transformations are known with respect to the learnable parameters, we focus on training the primal form the modified SVM objectives using a stochastic subgradient method. We consider the possibility of using Mercer kernels with the proposed algorithms. We construct a case study using the linear combinations of adaptive Hermite functions where the proposed classification scheme outperforms the classical support vector machine approach. The proposed variable projection support vector machines provide a lightweight alternative to deep learning methods which incorporate automatic feature extraction.
Wildflower strips as a tool for conservation biological control and pollination service: Impact of functional diversity and mowing regime

▪ Mowing regime impacts flower mixes and thus the entomofauna, which can in fine affect conservation biological control and pollination service. Wildflower strips as a tool for conservation biological control and pollination service: Impact of functional diversity and mowing regime Séverin HATT 1, Roel UYTTENBROECK 1, Sarah GARRE 1, Bernard BODSON 2, Arnaud MONTY 3, Frédéric FRANCIS 4 1 AgricultureIsLife, University of Liège, Gembloux Agro-Biotech, Passage des déportés 2, 5030 Gembloux, Belgique 2 Crop Science Unit and Experimental Farm, University of Liège, Gembloux Agro-Biotech, Passage des déportés 2, 5030 Gembloux, Belgique 3 Biodiversity and Landscape Unit, University of Liège, Gembloux Agro-Biotech, Passage des déportés 2, 5030 Gembloux, Belgique 4 Functional and Evolutionary Entomology Unit, University of Liège, Gembloux Agro-Biotech, Passage des déportés 2, 5030 Gembloux, Belgique
Variational formalism for the Klein-Gordon oscillon

The variational method employing the amplitude and width as collective coordinates of the Klein-Gordon oscillon leads to a dynamical system with unstable periodic orbits that blow up when perturbed. We propose a multiscale variational approach free from the blow-up singularities. An essential feature of the proposed trial function is the inclusion of the third collective variable: a correction for the nonuniform phase growth. In addition to determining the parameters of the oscillon, our approach detects the onset of its instability.
VisPercep: A Vision-Language Approach to Enhance Visual Perception for People with Blindness and Low Vision

People with blindness and low vision (pBLV) encounter substantial challenges when it comes to comprehensive scene recognition and precise object identification in unfamiliar environments. Additionally, due to the vision loss, pBLV have difficulty in accessing and identifying potential tripping hazards on their own. In this paper, we present a pioneering approach that leverages a large vision-language model to enhance visual perception for pBLV, offering detailed and comprehensive descriptions of the surrounding environments and providing warnings about the potential risks. Our method begins by leveraging a large image tagging model (i.e., Recognize Anything (RAM)) to identify all common objects present in the captured images. The recognition results and user query are then integrated into a prompt, tailored specifically for pBLV using prompt engineering. By combining the prompt and input image, a large vision-language model (i.e., InstructBLIP) generates detailed and comprehensive descriptions of the environment and identifies potential risks in the environment by analyzing the environmental objects and scenes, relevant to the prompt. We evaluate our approach through experiments conducted on both indoor and outdoor datasets. Our results demonstrate that our method is able to recognize objects accurately and provide insightful descriptions and analysis of the environment for pBLV.
Selective induction of Rab9-dependent alternative mitophagy using a synthetic derivative of isoquinoline alleviates mitochondrial dysfunction and cognitive deficits in Alzheimer's disease models

Rationale: Promotion of mitophagy is considered a promising strategy for the treatment of neurodegenerative diseases including Alzheimer's disease (AD). The development of mitophagy-specific inducers with low toxicity and defined molecular mechanisms is essential for the clinical application of mitophagy-based therapy. The aim of this study was to investigate the potential of a novel small-molecule mitophagy inducer, ALT001, as a treatment for AD. Methods: ALT001 was developed through chemical optimization of an isoquinolium scaffold, which was identified from a chemical library screening using a mitophagy reporter system. In vitro and in vivo experiments were conducted to evaluate the potential of ALT001 as a mitophagy-targeting therapeutic agent and to investigate the molecular mechanisms underlying ALT001-induced mitophagy. The therapeutic effect of ALT001 was assessed in SH-SY5Y cells expressing mutant APP and mouse models of AD (5×FAD and PS2APP) by analyzing mitochondrial dysfunction and cognitive defects. Results: ALT001 specifically induces mitophagy both in vitro and in vivo but is nontoxic to mitochondria. Interestingly, we found that ALT001 induces mitophagy through the ULK1-Rab9-dependent alternative mitophagy pathway independent of canonical mitophagy pathway regulators such as ATG7 and PINK1. Importantly, ALT001 reverses mitochondrial dysfunction in SH-SY5Y cells expressing mutant APP in a mitophagy-dependent manner. ALT001 induces alternative mitophagy in mice and restores the decreased mitophagy level in a 5×FAD AD model mouse. In addition, ALT001 reverses mitochondrial dysfunction and cognitive defects in the PS2APP and 5×FAD AD mouse models. AAV-mediated silencing of Rab9 in the hippocampus further confirmed that ALT001 exerts its therapeutic effect through alternative mitophagy. Conclusion: Our results highlight the therapeutic potential of ALT001 for AD via alleviation of mitochondrial dysfunction and indicate the usefulness of the ULK1-Rab9 alternative mitophagy pathway as a therapeutic target.
Artifacts of the Yamashita-cho Cave Site

Artifacts excavated at the Yamashita-cho Cave Site were classified as stone and bone artifacts. Deer bone artifacts were rich in quantity, whereas stone implements were few. The tradition of the Yamashitacho culture differs from those of the Sampoeng (Indonesia), the Hoabinhian (Indonesia) or the changpinian (Formosa), and the traditional relationsip of ti, . Yamashita-rho rnltiira is not rlanr.
Late Ordovician Glaciation Initiated by Early Land Plant Evolution and Punctuated by Greenhouse Mass Extinctions

Late Ordovician (Hirnantian) glaciation, indicated by periglacial paleosols, tillites, and glacial pavements in Saharan Africa, has been attributed to advances in weathering and carbon sequestration due to evolution of early land plants, in the same way that Devonian-Permian glaciation has been attributed to evolution of forests and Quaternary glaciations to evolution of grasslands. Two problems for carbon cycle explanations of this glaciation are an Hirnantian CO2 greenhouse estimated at 4035 ppmv from mass balance models and possibly related Hirnantian mass extinctions. This estimate of high CO2 is a 10-m.yr. average, here evaluated against high-resolution sequences of paleosols that give estimates not only of CO2 but also of paleoclimate and vegetation through the Late Ordovician. Katian paleosols of the eastern United States show increases in depth and in degree of carbon consumption and storage comparable with those preceding glaciation of the Devonian-Permian and Quaternary. Early thalloid land plants with rhizoids may have drawn down atmospheric CO2 to only 166 ± 83 ppmv during the Hirnantian, as estimated here from a pedogenic CO2 paleobarometer. Ordovician paleosols also reveal a short-term greenhouse spike to as much as 4670 ppmv immediately before Hirnantian glaciation. This greenhouse spike may be due to thermogenic methane release from a large igneous province and is comparable with greenhouse spikes during other mass extinctions, such as those of the Late Permian and Late Jurassic.
Representativeness as a Forgotten Lesson for Multilingual and Code-switched Data Collection and Preparation

Multilingualism is widespread around the world and code-switching (CSW) is a common practice among different language pairs/tuples across locations and regions. However, there is still not much progress in building successful CSW systems, despite the recent advances in Massive Multilingual Language Models (MMLMs). We investigate the reasons behind this setback through a critical study about the existing CSW data sets (68) across language pairs in terms of the collection and preparation (e.g. transcription and annotation) stages. This in-depth analysis reveals that \textbf{a)} most CSW data involves English ignoring other language pairs/tuples \textbf{b)} there are flaws in terms of representativeness in data collection and preparation stages due to ignoring the location based, socio-demographic and register variation in CSW. In addition, lack of clarity on the data selection and filtering stages shadow the representativeness of CSW data sets. We conclude by providing a short check-list to improve the representativeness for forthcoming studies involving CSW data collection and preparation.
Finding a Maximum Restricted t-Matching via Boolean Edge-CSP

The problem of finding a maximum $2$-matching without short cycles has received significant attention due to its relevance to the Hamilton cycle problem. This problem is generalized to finding a maximum $t$-matching which excludes specified complete $t$-partite subgraphs, where $t$ is a fixed positive integer. The polynomial solvability of this generalized problem remains an open question. In this paper, we present polynomial-time algorithms for the following two cases of this problem: in the first case the forbidden complete $t$-partite subgraphs are edge-disjoint; and in the second case the maximum degree of the input graph is at most $2t-1$. Our result for the first case extends the previous work of Nam (1994) showing the polynomial solvability of the problem of finding a maximum $2$-matching without cycles of length four, where the cycles of length four are vertex-disjoint. The second result expands upon the works of B\'{e}rczi and V\'{e}gh (2010) and Kobayashi and Yin (2012), which focused on graphs with maximum degree at most $t+1$. Our algorithms are obtained from exploiting the discrete structure of restricted $t$-matchings and employing an algorithm for the Boolean edge-CSP.
Human Action Recognition Based on Residual Networks with Improved Attention Mechanisms

For the current deep learning methods in human movement recognition, there are problems of high number of parameters and insufficient feature extraction. , this paper proposes a residual network incorporating improved attention mechanism for human behavior, which introduces an attention mechanism into the residual network and the multilayer vector machine for the channel attention module was replaced using a one-dimensional convolutional, which reduces the feature loss and the parameter number at the same time, and replaces the convolution for the spatial attention module with a dilated convolution, which expands the sensory field. The recognition accuracies of this paper are 66.7% and 96.5% on the public datasets HMDB51 and UCF101, respectively, and the recognition accuracies are improved by 7.3% and 8.5%, respectively, compared with the traditional deep behavior recognition method TwoStream.