Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving the Deeplabv3+ Model with Attention Mechanisms Applied to Eye Detection and Segmentation

Research on eye detection and segmentation is even more important with mask-wearing measures implemented during the COVID-19 pandemic. Thus, it is necessary to build an eye image detection and segmentation dataset (EIMDSD), including labels for detecting and segmenting. In this study, we established a dataset to reduce elaboration for chipping eye images and denoting labels. An improved DeepLabv3+ network architecture (IDLN) was also proposed for applying it to the benchmark segmentation datasets. The IDLN was modified by cascading convolutional block attention modules (CBAM) with MobileNetV2. Experiments were carried out to verify the effectiveness of the EIMDSD dataset in human eye image detection and segmentation with different deep learning models. The result shows that the IDLN model achieves the appropriate segmentation accuracy for both eye images, while the UNet and ISANet models show the best results for the left eye data and the right eye data among the tested models.
Leveraging Representation Learning for the Construction and Application of a Knowledge Graph for Traditional Chinese Medicine: Framework Development Study

Background Knowledge discovery from treatment data records from Chinese physicians is a dramatic challenge in the application of artificial intelligence (AI) models to the research of traditional Chinese medicine (TCM). Objective This paper aims to construct a TCM knowledge graph (KG) from Chinese physicians and apply it to the decision-making related to diagnosis and treatment in TCM. Methods A new framework leveraging a representation learning method for TCM KG construction and application was designed. A transformer-based Contextualized Knowledge Graph Embedding (CoKE) model was applied to KG representation learning and knowledge distillation. Automatic identification and expansion of multihop relations were integrated with the CoKE model as a pipeline. Based on the framework, a TCM KG containing 59,882 entities (eg, diseases, symptoms, examinations, drugs), 17 relations, and 604,700 triples was constructed. The framework was validated through a link predication task. Results Experiments showed that the framework outperforms a set of baseline models in the link prediction task using the standard metrics mean reciprocal rank (MRR) and Hits@N. The knowledge graph embedding (KGE) multitagged TCM discriminative diagnosis metrics also indicated the improvement of our framework compared with the baseline models. Conclusions Experiments showed that the clinical KG representation learning and application framework is effective for knowledge discovery and decision-making assistance in diagnosis and treatment. Our framework shows superiority of application prospects in tasks such as KG-fused multimodal information diagnosis, KGE-based text classification, and knowledge inference–based medical question answering.
Financial Risk Early Warning Model of Industrial Listed Enterprises Based on Deep Learning

In this paper, the public financial indicators of 156 listed industrial enterprises in China Stock Market in T-2 year(2017) are used to predict the company financial status in T year(2019). Based on the feature extraction of Random Forest, a double LSTM financial risk early warning model is built through Keras framework. Machine learning algorithms including LR, SVM, KNN and NBC are set as baseline models. To reduce the influence of unbalanced data on the model, the G-mean is introduced as the comprehensive measure of the model. The result shows that G-mean of RF-LSTM on the test set is much higher than that of machine learning model which verifies the practicability of the RF-LSTM model.
What affects chronic lithium poisoning in psychiatric patients? - a case report

INTRODUCTION: Despite medical developments, drug poisoning still occurs. Lithium is a drug that has been used for more than 60 years as a mood stabilizer in the treatment and prevention of bipolar disorder, mania and recurrent depression. Lithium is the most commonly prescribed mood stabilizer. It has a narrow therapeutic index. Its target concentration in acute mania should be 0.6-1.5 mEq/L and 0.6-0.8 mEq/L for maintenance therapy. Because of this, there is a risk of lithium intoxication. We would like to present to you the case of a patient with chronic lithium poisoning. MATERIALS AND METHOD: Patient information was collected from hospital records available in the clinical toxicology department. In addition, we conducted a literature review on lithium treatment, its toxicity, side effects and pharmacokinetics using PubMed. CASE REPORT: The patient, 58, was referred for lithium poisoning and admitted to the Clinical Toxicology and Cardiology Department in Lublin for treatment. The patient had a previous diagnosis of bipolar affective disorder, hypertension and obesity. According to the history, the patient was admitted to the psychiatric ward due to deterioration of her mental state for a month. She was found to have a high lithium level -2.98 mmol/L and elevated renal function parameters-creatinine 2.13 mg/dl. During treatment with hemodialysis, these parameters returned to normal. CONCLUSION: It is important to remember that lithium has a narrow therapeutic range. Attention should be paid during lithium treatment to situations that increase the risk of adverse effects and the development of toxicity, and serum lithium concentrations should be monitored during treatment.
Eliminação Paralela de Termos Dominantes no Problema da Mochila

Um dos problemas mais conhecidos em otimização combinatória é o problema da mochila ilimitado. Devido a sua importância diversos autores buscaram formas eficientes de resolvê-lo. Por um lado, diversos estudos da paralelização deste problema foram feitos. Por outro lado, várias técnicas de eliminação de objetos também foram encontradas. Neste trabalho nós unimos as duas possibilidades apresentando algoritmos paralelos para a eliminação de objetos. Para validação dos algoritmos, os mesmos foram implementados em Java e executados em um cluster de computadores.
Seafood markets in transition

This special issue of Aquaculture Economics & Management features fives articles based on the 25 contributions presented at the International Association of Aquaculture Economics and Management (IAAEM) spon-sored section on economics and marketing at the Aquaculture America conference in San Antonio, August 11 – 14, 2021. This was a very good turn-out for one of the first aquaculture conference following the Covid-19 pandemic. The contributions covered topics on different farmed species groups and with a global scope. The section provided an arena to discuss issues related to risk management, regulations, trade, seafood certification, consumers ’ preferences, market integration, product differentiation, mari-culture tourism, food security, effects and contribution of aquaculture to the regional economy. The papers in this special issue reflect a variety of topics also seen in the section during the conference.
New ground state for quantum gravity

In this paper we conjecture the existence of a new "ground" state in quantum gravity, supplying a wave function for the inflationary Universe. We present its explicit perturbative expression in the connection representation, exhibiting the associated inner product. The state is chiral, dependent on the Immirzi parameter, and is the vacuum of a second quantized theory of graviton particles. We identify the physical and unphysical Hilbert sub-spaces. We then contrast this state with the perturbed Kodama state and explain why the latter can never describe gravitons in a de Sitter background. Instead, it describes self-dual excitations, which are composites of the positive frequencies of the right-handed graviton and the negative frequencies of the left-handed graviton. These excitations are shown to be unphysical under the inner product we have identified. Our rejection of the Kodama state has a moral tale to it: the semi-classical limit of quantum gravity can be the wrong path for making contact with reality (which may sometimes be perturbative but nonetheless fully quantum). Our results point towards a non-perturbative extension, and we present some conjectures on the nature of this hypothetical state.
TRANSIENT THERMAL ANALYSIS OF HUMAN EXPOSURE TO ELECTROMAGNETIC FIELDS

|The study of the thermal eﬀect caused by exposure to electromagnetic (cid:12)elds is a focus of this research. To quantify the induced current and temperature distribution in the human body an assessment tool for the frequency range of 50 Hz to 110 MHz has been developed. The major contribution consists of providing a quantitatively accurate and relatively simple model. The formulation of the problem is based on a simpli(cid:12)ed cylindrical representation de(cid:12)ned by the anatomical parameters of the human body. The bio-thermal modeling is carried out in two stages. Firstly, the electromagnetic analysis is based on the transmission lines (TL) theory. Secondly, a thermal modeling based on the thermal networks model (nodal method) is approached. This allows us to quantify the corresponding thermal gradients in the human body.
A Systematic Literature Review: University Strategic Management

The purpose of this research is to get an overview of the strategies management implemented by universities to achieve university goals. Higher education is being modernized to effectively coordinate the education system into the worldwide instructive surroundings. Nevertheless, the lion's share of institutions' administration these days shows a poor degree model of proficiency. This research utilized a systematic literature review approach to explore the concept of university strategic management and identified future research in university strategic management. The results showed that universities were particularly important for studying strategic management methods.
La calidad del servicio educativo y la fidelización de los alumnos de institutos privados Surco

Lograr un servicio educativo de calidad es una prioridad para tener en cuenta en la formación de alumnos de los institutos peruanos de educación superior, ya que al hacerlo estas instituciones fidelizan a sus estudiantes para que terminen la carrera y contribuyan al desarrollo de la sociedad y en esta investigación se tuvo como objetivo determinar la calidad educativa superior técnica y la relación que tiene con la fidelización de los alumnos de la escuela de banca de institutos privados de Surco, asimismo se utilizó la herramienta de la encuesta en un grupo de 129 estudiantes de la mencionada escuela y los resultados evidenciaron que los niveles de fidelización se ven afectados por las dimensiones que componen la calidad del servicio educativo. Por otro lado, para la comprobación de hipótesis se utilizó la Prueba Chi Cuadrado de Pearson y la Prueba de correlación de Spearman, finalmente los resultados de estas pruebas mostraron una alta correlación significativa entre el servicio educativo de calidad y la fidelización de alumnos.
IFN-γ and LPS Induce Synergistic Expression of CCL2 in Monocytic Cells via H3K27 Acetylation

Background Overexpression of CCL2 (MCP-1) has been implicated in pathogenesis of metabolic conditions, such as obesity and T2D. However, the mechanisms leading to increased CCL2 expression in obesity are not fully understood. Since both IFN-γ and LPS levels are found to be elevated in obesity and shown to be involved in the regulation of metabolic inflammation and insulin resistance, we investigated whether these two agents could synergistically trigger the expression of CCL2 in obesity. Methods Monocytes (Human monocytic THP-1 cells) were stimulated with IFN-γ and LPS. CCL2 gene expression was determined by real-time RT-PCR. CCL2 protein was determined by ELISA. Signaling pathways were identified by using epigenetic inhibitors and STAT1 siRNA. Acetylation of H3K27 was analyzed by Western blotting. The acetylation level of histone H3K27 in the transcriptional initiation region of CCL2 gene was determined by ChIP-qPCR. Results Our results show that the co-incubation of THP-1 monocytes with IFN-γ and LPS significantly enhanced the expression of CCL2, compared to treatment with IFN-γ or LPS alone. Similar results were obtained using primary monocytes and macrophages. Interestingly, IFN-γ priming was found to be more effective than LPS priming in inducing synergistic expression of CCL2. Moreover, STAT1 deficiency significantly suppressed this synergy for CCL2 expression. Mechanistically, we showed that IFN-γ priming induced acetylation of lysine 27 on histone 3 (H3K27ac) in THP-1 cells. Chromatin immunoprecipitation (ChIP) assay followed by qRT-PCR revealed increased H3K27ac at the CCL2 promoter proximal region, resulting in stabilized gene expression. Furthermore, inhibition of histone acetylation with anacardic acid suppressed this synergistic response, whereas trichostatin A (TSA) could substitute IFN-γ in this synergy. Conclusion Our findings suggest that IFN-γ, in combination with LPS, has the potential to augment inflammation via the H3K27ac-mediated induction of CCL2 in monocytic cells in the setting of obesity.
Physics of magnetism

Paleomagnetism is the study of the magnetic properties of rocks. It is one of the most broadly applicable disciplines in geophysics, having uses in diverse fields such as geomagnetism, tectonics, paleoceanography, volcanology, paleontology, and sedimentology. Although the potential applications are varied, the fundamental techniques are remarkably uniform. Thus, a grounding in the basic tools of paleomagnetic data analysis can open doors to many of these applications. One of the underpinnings of paleomagnetic endeavors is the relationship between the magnetic properties of rocks and the Earth’s magnetic field. In this chapter, we will review the basic physical principles behind magnetism: what are magnetic fields, how are they produced, and how are they measured? Although many find a discussion of scientific units boring, much confusion arose when paleomagnetists switched from “cgs” to the Système International (SI) units, and mistakes abound in the literature. Therefore, we will explain both unit systems and look at how to convert successfully between them. There is a review of essential mathematical tricks in Appendix A, to which the reader is referred for help.
Interannual variabilities, long-term trends, and regulating factors of low-oxygen conditions in the coastal waters off Hong Kong

Abstract. The summertime low-oxygen conditions in the Pearl River Estuary (PRE) have experienced a significant spatial expansion associated with notable deoxygenation in recent decades. Nevertheless, there is still a lack of quantitative data on the long-term trends and interannual variabilities in oxygen conditions in the PRE as well as on the driving factors. Therefore, the long-term deoxygenation in a subregion of the PRE (the coastal waters off Hong Kong) was comprehensively investigated in this study using monthly observations during 1994–2018. To evaluate the changes in scope and intensity of oxygen conditions, an indicator (defined as the low-oxygen index, LOI) that integrates several metrics related to low-oxygen conditions was introduced as the result of a principal component analysis (PCA). Moreover, primary physical and biogeochemical factors controlling the interannual variabilities and long-term trends in oxygen conditions were discerned, and their relative contributions were quantified by multiple regression analysis. Results showed that the regression models explained over 60 % of the interannual variations in LOI. Both the wind speeds and concentrations of dissolved inorganic nitrogen (DIN) played a significant role in determining the interannual variations (by 39 % and 49 %, respectively) and long-term trends (by 39 % and 56 %, respectively) in LOI. Due to the increasing nutrient loads and alterations in physical conditions (e.g., the long-term decreasing trend in wind speeds), coastal eutrophication was exaggerated and massive marine-sourced organic matter was subsequently produced, thereby resulting in an expansion of intensified low-oxygen conditions. The deteriorating eutrophication has also driven a shift in the dominant source of organic matter from terrestrial inputs to in situ primary production, which has probably led to an earlier onset of hypoxia in summer. In summary, the Hong Kong waters have undergone considerable deterioration of low-oxygen conditions driven by substantial changes in anthropogenic eutrophication and external physical factors.
Health Condition Monitoring and Control of Vibrations of a Rotating System through Vibration Analysis

This paper presents condition monitoring, fault detection, and control of vibration of a rotating system using vibration analysis. The system includes ball bearings, pulleys, impellers, shafts, and an electric motor. The experimental approach generated different levels and forms of vibration that were measured using a vibration analyzer (CSI 2140). A vibration analyzer is used for determining the characteristics and nature of vibration of the experimental system. System vibrations were measured in terms of amplitude, phase angle, and frequency during the experimental operating conditions. After that, the experimental results were compared with the ideal working conditions mentioned in the ISO standard for the determination of machine health and condition monitoring. This paper also presents a passive control technique to enhance the machine’s health condition. The generated faults of the system have been detected and solved by using an exact measurement of polar plot analysis. This approach of condition monitoring, fault detection, and improving operating conditions can be used to visualize and analyze vibration data in any rotating system. Furthermore, this technique would be a feasible and effective way of finding strategies for the maintenance of rotary machines.
Delay Wreaks Havoc on Your Smart Home: Delay-based Automation Interference Attacks

With the proliferation of Internet of Things (IoT) devices and platforms, it becomes a trend that IoT devices associated with different IoT platforms coexist in a smart home, demonstrating the following characteristics. First, a smart home may use more than one platform to support its devices and automation. Second, IoT devices of a home may transmit messages over different paths. By selectively delaying IoT messages, our study finds that two issues, inconsistency and disorder, can be exacerbated by attackers significantly. We then explore how these issues can be exploited and present seven types of exploitation, collectively referred to as Delay-based Automation Interference (DAI) attacks. DAI attacks cause home automation to yield incorrect interaction results, placing the IoT devices and smart home in insecure, unsafe, or unexpected states. It is worth highlighting that DAI attacks do not depend on any IoT implementation vulnerabilities or leaked keys/tokens, and they do not trigger alarms at any layers of the IoT protocol stack. To demonstrate and evaluate the new attacks, we set up two real-world testbeds, where commercial IoT devices and apps are deployed. The week-long experiments from both testbeds show that an attacker has adequate opportunities to launch DAI attacks that cause security or safety issues.
HUBBLE SPACE TELESCOPE AND HI IMAGING OF STRONG RAM PRESSURE STRIPPING IN THE COMA SPIRAL NGC 4921: DENSE CLOUD DECOUPLING AND EVIDENCE FOR MAGNETIC BINDING IN THE ISM

Remarkable dust extinction features in the deep Hubble Space Telescope (HST) V and I images of the face-on Coma cluster spiral galaxy NGC 4921 show in unprecedented ways how ram pressure strips the ISM from the disk of a spiral galaxy. New VLA HI maps show a truncated and highly asymmetric HI disk with a compressed HI distribution in the NW, providing evidence for ram pressure acting from the NW. Where the HI distribution is truncated in the NW region, HST images show a well-defined, continuous front of dust that extends over 90° and 20 kpc. This dust front separates the dusty from dust-free regions of the galaxy, and we interpret it as galaxy ISM swept up near the leading side of the ICM–ISM interaction. We identify and characterize 100 pc–1 kpc scale substructure within this dust front caused by ram pressure, including head–tail filaments, C-shaped filaments, and long smooth dust fronts. The morphology of these features strongly suggests that dense gas clouds partially decouple from surrounding lower density gas during stripping, but decoupling is inhibited, possibly by magnetic fields that link and bind distant parts of the ISM.
Robust Traffic Speed Inference With Ensemble Learning

Traffic speed inference enables many applications that are essential for everyday life. Most traffic-prediction approaches assume that a constant number of sensors are deployed on the roads, whether they are either stationary loop detectors or vehicles equipped with Global Positioning System (GPS) tracking devices. The static nature of those fixtures limits their ability to adapt to scenarios that are more dynamic. Rather than relying on several fixed sensors to detect changes and infer traffic, we use crowdsourcing to judiciously select individuals and then make predictions. Our solution consists of three core components: dynamic seed selection, regional cluster building and ensemble traffic prediction. In the first phase, we employ Efficient Transition Probability (ETP) to evaluate candidate seed sets. Road clusters are then formed using hierarchical clustering that is tweaked by a dynamic programming technique. This method assesses the eccentricity of every cluster to bond every road within each cluster more closely. Subsequently, we develop an ensemble-learning strategy in conjunction with Lasso regression to forecast traffic. The strength of our ensemble approach is its ability to manage absent seeds, a condition that has never been investigated, to our knowledge. Substantial experimental evaluation indicates that our claim of dynamic updates is valid and effective. Our solution outperforms the state-of-the-art techniques by a wide margin, in terms of prediction accuracy.
Do field collected immature stages of Aedes mosquitoes exhibit correspondence of the life-history traits? Observations from Kolkata, India

ABSTRACT Entomological monitoring of dengue vectors Aedes aegypti (Linnaeus, 1762) and Ae. albopictus (Skuse, 1894) (Diptera: Culicidae) provides crucial information on the life-history traits (LHTs). An assessment of the LHTs of Aedes mosquitoes was accomplished using randomly collected immature stages from various larval habitats of Kolkata, India. Out of 104 larval habitats ~43% were exclusive for Ae. aegypti, ~31% were exclusive for Ae. albopictus and ~26% were common for both the species. Both female and male Ae. aegypti were bigger than the equivalents of Ae. albopictus in terms of pupal weight, adult weight and wing length. All the LHTs were significantly (P < 0.05) correlated, and the sexual dimorphism was evident for both Aedes mosquitoes. In both the species, the fecundity (ln-transformed value; y) increased with the wing length (x) [for Ae. aegypti y = 1.012x+1.023, R2 = 0.455, and for Ae. albopictus y = 1.022x+1.083, R2 = 0.154]. A correspondence of the larval development and the adult traits was evident for both the Aedes mosquitoes. Incorporation of the life-history trait data with pupal productivity in the surveillance of the dengue vectors will enable precise predictions about the population abundance and possibility of disease transmission.