Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection and Localization of Albas Velvet Goats Based on YOLOv4

Simple Summary We proposed a target detection algorithm based on the channel attention mechanism SENet, the GeLU activation function and layer normalized ShallowSE. We refined and simplified the PANet part and the YOLO Head part in YOLOv4 to obtain the Custom_YOLO target detection module. We designed a 3D coordinate regression algorithm for three fully connected networks in order to predict the goats’ coordinates. We combined the improved YOLOv4 target detection algorithm and coordinate regression algorithm to achieve goat localization. Abstract In order to achieve goat localization to help prevent goats from wandering, we proposed an efficient target localization method based on machine vision. Albas velvet goats from a farm in Ertok Banner, Ordos City, Inner Mongolia Autonomous Region, China, were the main objects of study. First, we proposed detecting the goats using a shallow convolutional neural network, ShallowSE, with the channel attention mechanism SENet, the GeLU activation function and layer normalization. Second, we designed three fully connected coordinate regression network models to predict the spatial coordinates of the goats. Finally, the target detection algorithm and the coordinate regression algorithm were combined to localize the flock. We experimentally confirmed the proposed method using our dataset. The proposed algorithm obtained a good detection accuracy and successful localization rate compared to other popular algorithms. The overall number of parameters in the target detection algorithm model was only 4.5 M. The average detection accuracy reached 95.89% and the detection time was only 8.5 ms. The average localization error of the group localization algorithm was only 0.94 m and the localization time was 0.21 s. In conclusion, the method achieved fast and accurate localization, which helped to rationalize the use of grassland resources and to promote the sustainable development of rangelands.
Assessing attentional task-related electroencephalogram signal variations by using mobile electroencephalogram technology: An experimental study

Background: A better understanding of the network responses of cortical activities during rest and cognitive tasks is necessary. Therefore, in this study, we aimed to evaluate cerebral activities during attentional tasks by using mobile electroencephalography, identifying the types of attentional components and brain waves. Methods: In this experimental study, we enrolled 12 healthy young adults. The attentional tasks comprised parts A and B of the Trail-Making Test (TMT). Nineteen electroencephalography electrodes were placed over various brain regions. The Wilcoxon signed-rank test was used to examine the differences in power levels between the rest and TMT conditions. Results: During TMT part A, the electroencephalography power level of the delta waves was significantly higher in the right frontal, left central, left occipital, left inferior frontal, right mid-temporal, right posterior temporal, and middle parietal areas (P < .05) than those during the resting state; that of the alpha waves was significantly lower in the left posterior temporal area (P = .006); and that of the high gamma waves was significantly lower in the left parietal (P = .05) and left occipital (P = .002) areas. During TMT part B, the electroencephalography power level of the beta waves was significantly higher in the right frontal area (P = .041) than that during the resting state, and that of the low gamma waves was significantly higher in the left frontal pole, right frontal, and right inferior frontal areas (P < .05). During the focused attentional task, the power level of the delta waves increased and that of the alpha waves decreased, and during the alternating attentional task, those of both the beta and gamma waves increased. The delta waves were related to the whole brain, the alpha and high gamma waves to the left posterior lobe, and the beta and low gamma waves to both frontal lobes. Conclusion: These findings contribute to the basic knowledge necessary to develop new attentional assessment methods for clinical situations.
Tidal forcing on David Glacier and Drygalski Ice Tongue

Summary During the 2005-06 Austral Summer, we carried out a joint observational campaign in the area of the DavidGlacier, South Victoria Land, with the aim of collecting simultaneous time series of geodetic and seismological data.We installed 7 temporary seismographic stations on rock outcrops surrounding the glacier and 3 temporary geodeticstations both on flowing ice and on rock. The seismic network registered a significant low-energy seismic activity,principally originated by ice creeping and basal stress at the interface between the ice and the bedrock. The geodeticstations allowed us to survey the glacier kinematics forced by the Ross Sea tides, and to infer the grounding linelocation. Here we show some details about data analysis and preliminary results. Citation: Casula G. , S. Danesi, M. Dubbini and L. Vittuari (2007), Tidal forcing on David Glacier and Drygalski Ice Tongue in the 10 th ISAES XOnline Proceedings, in Antarctica: A Keystone in a Changing World – Online Proceedings of the 10
Identifying Serum Metabolomic Markers Associated with Skin Disease Activity in Patients with Psoriatic Arthritis

Psoriatic arthritis (PsA) is a chronic, systemic, immune-mediated inflammatory disease causing cutaneous and musculoskeletal inflammation that affects 25% of patients with psoriasis. Current methods for evaluating PsA disease activity are not accurate enough for precision medicine. A metabolomics-based approach can elucidate psoriatic disease pathogenesis, providing potential objective biomarkers. With the hypothesis that serum metabolites are associated with skin disease activity, we aimed to identify serum metabolites associated with skin activity in PsA patients. We obtained serum samples from patients with PsA (n = 150) who were classified into mild, moderate and high disease activity groups based on the Psoriasis Area Severity Index. We used solid-phase microextraction (SPME) for sample preparation, followed by data acquisition via an untargeted liquid chromatography—mass spectrometry (LC-MS) approach. Disease activity levels were predicted using identified metabolites and machine learning algorithms. Some metabolites tentatively identified include eicosanoids with anti- or pro-inflammatory properties, like 12-Hydroxyeicosatetraenoic acid, which was previously implicated in joint disease activity in PsA. Other metabolites of interest were associated with dysregulation of fatty acid metabolism and belonged to classes such as bile acids, oxidized phospholipids, and long-chain fatty acids. We have identified potential metabolites associated with skin disease activity in PsA patients.
Subsidence related to underground nuclear explosions, Nevada Test Site

abstract The U. S. Geological Survey is investigating the mechanism of collapse of nuclear explosion cavities for its value in containment, with the hope of eventually determining those geologic environments most susceptible to early collapse or most adaptable to the incitation of collapse. This is a progress report on a part of these studies. The investigations to date have been confined largely to the subsidence of the ground surface in the desert alluvium of Yucca Flat by collapse of volcanic rocks and (or) alluvium into the large cavities formed by nuclear explosions. The sinks thus formed are as much as 60 m (meters) deep and 250 m in radius; they have forms that are commonly variations of inverted cones or sectors of spheres. The outer half of each sink is an inward slide of slightly disoriented but relatively undisturbed alluvium. The major physical disturbance within the slide mass is by subsidiary spoon-shaped cracks and slide surfaces along which individual blocks have adjusted within the glide mass. Each individual block moves a net distance that is small relative to the total movement of the main slide. The cracks or glide surfaces are assumed to flatten with depth within the slide and to join the major surface of failure at the base of the main slide mass. The spoon-shaped surfaces, where they intersect the top surface of the slide, dip steeply to vertically. In plan they make a pattern of curving, intersecting cracks. The depth of burial of the explosion cavity in alluvium relative to its size affects the profile of the sinks, the height of chimneys for collapses not extending to the surface, and, to an extent, the likelihood that later surface subsidence will occur. A working model of collapse includes: (a) maintenance of the explosion cavity until main collapse occurs; (b) a two-phase collapse taking place in several seconds or several tens of seconds, culminating in surface subsidence if the cavity is sufficiently close to the surface. The first phase of collapse in alluvium, at least, consumes 70-90 per cent of the collapse period; collapse propagates upward at rates of 50-80 ft/sec. The second phase shows faster rates of upward propagation, a lesser degree of particulation of the collapse material, and ends with the drop of a central mass of alluvial material that initiates formation of the sink. The shape, size, and distance of vertical drop of this alluvial plug control the size and form the resulting sink. Acceleration of this mass ranges from 0.35 to 0.8 g.
Searching for Swedish LGBTQI fiction: the librarians' perspective

PurposeThis article aims to help ensure high-quality subject access to Swedish lesbian, gay, bisexual, transgender, queer and intersexual (LGBTQI) fiction, and aims to identify challenges that librarians consider important to address, on behalf of themselves and end users.Design/methodology/approachA web-based questionnaire comprising 35 closed and open questions, 22 of which were required, was sent via online channels in January 2022. By the survey closing date, 20 March 2022, 82 responses had been received. The study was intended to complement an earlier study targeting end users.FindingsBoth this study of librarians and the previous study of end users have painted a dismal image of online search services when it comes to searching for LGBTQI fiction. The need to consult different channels (e.g. social media, library catalogues and friends), the inability to search more specifically than for the broad LGBTQI category and suboptimal search interfaces were among the commonly reported issues. The results of these studies are used to inform the development of a dedicated Swedish LGBTQI fiction database with an online search interface.Originality/valueThe subject searching of fiction via online services is usually limited to genre with facets for time and place, while users are often seeking characteristics such as pacing, characterization, storyline, frame/setting, tone and language/style. LGBTQI fiction is even more challenging to search because indexing practices are not really being standardized or disseminated worldwide. This study helps address this important gap, in both research and practical applications.
Unraveling the role of disulfidptosis-related LncRNAs in colon cancer: a prognostic indicator for immunotherapy response, chemotherapy sensitivity, and insights into cell death mechanisms

Background: Colon cancer, a prevalent and deadly malignancy worldwide, ranks as the third leading cause of cancer-related mortality. Disulfidptosis stress triggers a unique form of programmed cell death known as disulfidoptosis, characterized by excessive intracellular cystine accumulation. This study aimed to establish reliable bioindicators based on long non-coding RNAs (LncRNAs) associated with disulfidptosis-induced cell death, providing novel insights into immunotherapeutic response and prognostic assessment in patients with colon adenocarcinoma (COAD). Methods: Univariate Cox proportional hazard analysis and Lasso regression analysis were performed to identify differentially expressed genes strongly associated with prognosis. Subsequently, a multifactorial model for prognostic risk assessment was developed using multiple Cox proportional hazard regression. Furthermore, we conducted comprehensive evaluations of the characteristics of disulfidptosis response-related LncRNAs, considering clinicopathological features, tumor microenvironment, and chemotherapy sensitivity. The expression levels of prognosis-related genes in COAD patients were validated using quantitative real-time fluorescence PCR (qRT-PCR). Additionally, the role of ZEB1-SA1 in colon cancer was investigated through CCK8 assays, wound healing experiment and transwell experiments. Results: disulfidptosis response-related LncRNAs were identified as robust predictors of COAD prognosis. Multifactorial analysis revealed that the risk score derived from these LncRNAs served as an independent prognostic factor for COAD. Patients in the low-risk group exhibited superior overall survival (OS) compared to those in the high-risk group. Accordingly, our developed Nomogram prediction model, integrating clinical characteristics and risk scores, demonstrated excellent prognostic efficacy. In vitro experiments demonstrated that ZEB1-SA1 promoted the proliferation and migration of COAD cells. Conclusion: Leveraging medical big data and artificial intelligence, we constructed a prediction model for disulfidptosis response-related LncRNAs based on the TCGA-COAD cohort, enabling accurate prognostic prediction in colon cancer patients. The implementation of this model in clinical practice can facilitate precise classification of COAD patients, identification of specific subgroups more likely to respond favorably to immunotherapy and chemotherapy, and inform the development of personalized treatment strategies for COAD patients based on scientific evidence.
Closure of "Real Time Routing of Flood Hydrographs in Storm Sewers"

A rapid computation of routed flood hydrographs in circular sewers was required for use in the evaluation of the effect of storm flooding in the Minneapolis-St. Paul Interceptor sewers. The method of characteristics is used to provide the accurate assessment of the routed hydrograph but this method requires a comparatively large amount of computer time. The progressive average-lag method is used to carry out the evaluation in real time. The routing constants in the latter method are determined by comparing the results from the former with those of the latter until good agreement is obtained. It has not been possible to determine the routing constants in the latter method analytically.
The birth of brand: 4000 years of branding

This article seeks to show that brands and branding are as old as known civilisation. We derive evidence of branding, in various forms, from historical periods beginning 2250 BC in the Indus Valley, through to 300 BC in Greece. This evidence is compared with modern research directed toward developing a meaning of ‘brand’. We observe a gradual transition from a more utilitarian provision of information regarding origin and quality to the addition of more complex brand image characteristics over time, including status/power, added value and finally, the development of brand personality.
Experimental evaluation of a latency-aware routing and spectrum assignment mechanism based on deep reinforcement learning

The introduction of futuristic and challenging use cases of 5G and 6G communications will demand strict requirements in terms of high bandwidth and low latency. Optical backbone networks need to tackle these new network scenarios by offering highly efficient, flexible, and scalable technologies and solutions. In this context, elastic optical networks (EONs) have been recognized as a promising technological transport infrastructure for the future Internet since they can manage the optical spectrum with enhanced flexibility and efficiency. The service provisioning in EONs is a challenging issue to tackle since the routing and spectrum assignment (RSA) is characterized by a high degree of complexity. This work presents an approach for RSA in EONs leveraging the advantages of deep reinforcement learning (DRL) solutions. The devised approach jointly considers the constraints imposed by the optical technologies and the demanded connectivity service requirements (i.e., guaranteed bandwidth and maximum end-to-end latency) when computing and selecting the optical path and spectral resources. We first evaluate our approach through simulation experiments considering two reference network topologies, demonstrating its effectiveness in reducing the bandwidth blocking ratio, the path computation time, and the number of rejected connectivity services requiring lower latencies when compared to a baseline k-shortest path routing and first-fit spectrum allocation algorithm. Then, the trained DRL agent is integrated within a real proof of concept to attain an ML-assisted SDN control plane in the CTTC ADRENALINE testbed. The attained performance improvements highlight the potential benefits brought by using DRL mechanisms and its feasible integration within production EON transport infrastructures.
Conjoint choice models for urban tourism planning and marketing

In this thesis a methodology is developed to measure and model urban tourists' preferences for combinations of alternatives. Models and experimental designs are introduced that allow researchers to measure the influence that various attributes of urban tourism activities have on the probability that these activities will be undertaken. The method applied in the studies reported in this thesis is based on an extension of traditional conjoint choice modeling techniques. It proposes models and experimental designs that allow one to incorporate urban tourists' choices between combinations of several activities. It is especially relevant to know urban tourists' preferences for different possible combinations of alternatives in urban tourism planning and marketing, because previous research bas shown that urban tourists' typically like to combine several activities when visiting a city. Also, urban tourism behavior often takes place in highly complex urban environments that incorporate multiple tourism facilities and services. Therefore, models that allow one to model between combinations of alternatives can significantly improve the precision of predicted changes in tourism demand, in ex-ante evaluations of the impact of new urban tourism project developments. The proposed approach was applied and tested for three important types of choices in urban tourism research: (i) participation choice, (ii) destination choice, and (iii) activity choice. The results of the empirical studies show that the proposed methodology effectively supports measuring and modeling effects that are specifically related to choices between combinations of alternatives. They also show that the proposed models lead to significantly better predictions of urban tourists' choices than the traditional models for single choices. Therefore, it can be concluded that the proposed approach offers a way to more precisely evaluate the potential impact of urban tourism planning and marketing strategies on urban tourism demand.
A New Coding Scheme for UMP and JSCC in Short-Packet Communications

This letter proposes a new coding scheme for short-packet communications, where the messages are encoded successively at the transmitter by a classified enumerative (CE) coding, a cyclic redundancy check (CRC) coding and a polar coding, and then recovered reliably at the receiver by a triple-check hypothesis testing decoding. The proposed scheme can be applied not only in an unequal message protection (UMP) scenario, where different classes of messages have different reliability requirements, but also in a joint source-channel coding (JSCC) system. To evaluate the performance of the JSCC, we present the weighted dependence testing (DT) bound, which is then exploited as a criterion for classifying messages. Simulation results show that: 1) Our UMP coding scheme can provide more error protection for low-entropy messages; 2) Our JSCC scheme can outperform the double polar JSCC scheme and is close to the JSCC DT bound.
Focused Ultrasound-Mediated Delivery of Anti-Programmed Cell Death-Ligand 1 Antibody to the Brain of a Porcine Model

Immune checkpoint inhibitor (ICI) therapy has revolutionized cancer treatment by leveraging the body’s immune system to combat cancer cells. However, its effectiveness in brain cancer is hindered by the blood-brain barrier (BBB), impeding the delivery of ICIs to brain tumor cells. This study aimed to assess the safety and feasibility of using focused ultrasound combined with microbubble-mediated BBB opening (FUS-BBBO) to facilitate trans-BBB delivery of an ICI, anti-programmed cell death-ligand 1 antibody (aPD-L1) to the brain of a large animal model. In a porcine model, FUS sonication of targeted brain regions was performed after intravenous microbubble injection, which was followed by intravenous administration of aPD-L1 labeled with a near-infrared fluorescent dye. The permeability of the BBB was evaluated using contrast-enhanced MRI in vivo, while fluorescence imaging and histological analysis were conducted on ex vivo pig brains. Results showed a significant 4.8-fold increase in MRI contrast-enhancement volume in FUS-targeted regions compared to nontargeted regions. FUS sonication enhanced aPD-L1 delivery by an average of 2.1-fold, according to fluorescence imaging. In vivo MRI and ex vivo staining revealed that the procedure did not cause significant acute tissue damage. These findings demonstrate that FUS-BBBO offers a noninvasive, localized, and safe delivery approach for ICI delivery in a large animal model, showcasing its potential for clinical translation.
ConvLSTM-based drought prediction using vegetation health index (VHI)

The temperature rise due to climate change is expected to increase the frequency of droughts. These droughts not only pose a significant threat to agriculture and food security, but also increase the risk of additional disasters such as wildfires. Therefore, accurate drought monitoring and prediction are crucial. Predicted drought information can help strengthen policies related to agriculture and water management and prepare for drought response. Generally, the surface drought condition is monitored through satellite-based drought indices. Among various drought indices, the vegetation health index (VHI) which comprehensively combines temperature and vegetation status, is mainly used. In this study, we propose a model for predicting VHI time series data using Convolutional Long Short-Term Memory (ConvLSTM). ConvLSTM is a model that combines Convolutional Neural Network (CNN) and LSTM and can learn both temporal and spatial characteristics of time series data while preserving its spatial features. Therefore, it is being used in various fields such as image and video processing, and weather forecasting, where local features need to be considered. The study area is South Korea, and long-term weekly VHI data provided by NOAA were used for short-term prediction. The proposed model can be useful for drought prediction considering local features.