Miracle-dz/UniFAR-FTUs huggingface dataset:
Convective Development Nowcasting Using Dual Polarization Radar Based on Cross Modal Fusion*

Due to its ability to effectively capture the evolution and spatial dynamic structure of convective weather systems, dual-polarization radar data has emerged as a critical component in achieving accurate predictions of heavy precipitation, thus garnering significant attention in weather forecasting research. This study aims to address the challenge of low prediction accuracy associated with convective weather using dual-polarization radar data within deep learning models by proposing a novel approach. Specifically, we propose a multi-branch feature extraction model that integrates horizontal reflectivity factor, differential reflectivity, and specific differential phase-shift multimodal data fusion strategies. This approach incorporates a convolutional neural network architecture for predicting convective weather. The model employs data fusion strategies both at the input stage and across modalities, resulting in a four-branch feature extraction network. Additionally, the robustness of the extracted features is enhanced using the squeeze and excitation algorithm. Finally, a convolutional neural network is utilized to generate long-term predictions of horizontal reflectivity factor, which are subsequently employed for heavy precipitation prediction. Through comprehensive ablation and comparative experiments, the proposed algorithm demonstrates promising results, achieving prediction accuracies of 46.28% and 28.68% for the probability of detection (POD) and critical success index (CSI) parameters, respectively, in 1-hour forecasts.
A Small Model Traffic Flow Count Algorithm Based on Improved YOLOv5n and DeepSORT

In order to solve the problem that the traffic flow count model has a large amount of calculation and cannot be used in actual industrial applications, this paper proposes a small model traffic flow count algorithm based on improved YOLOv5n and DeepSORT. In terms of detector, the small model YOLOv5n is used as the detector, and the C3 module of the YOLOv5n model is replaced by the C2f module. At the same time, in view of the poor re-identification effect of the tracker in vehicle tracking, the convolution structure of the DeepSORT algorithm is changed and retrained on the veri-wild vehicle re-identification dataset. We also share our approach to handling public datasets to make them more suitable for different model training. The experimental results show that the total number of parameters of the improved detector model is 2.33 million, and the MAP_0. 05 is 60.83%. Compared with the original model, the number of parameters is only increased by 0.57 million, and the mAP_0. 5 is increased by 4.01%. The improved detector model increases the detection accuracy of the model while maintaining the lightweight of the original model. The accuracy of the overall model is 6.25% higher than that of the original model. It can also detect traffic flow in real time on devices with poor computing power, and has a good accuracy.
Skeleton Timed Up and Go on MARIO Robot

Current robotic technology shows promise in improving medical monitoring routines. In this work, we present an example of automatic monitoring and analysis of human gait in the Timed Up and Go test, using the Microsoft Kinect 2 sensor. The approach was implemented in the mobile robot developed during the MARIO (Managing active and healthy Aging with use of caRing servIce rObots) project, with the aim to assess the risk of falling through daily measurements of temporal and spatial gait parameters. Thanks to the robot's capability to approach the user in an unstructured environment, the mobility measurements can be obtained passively, in normal daily activity, without any patient's instruction. We describe the technique used to extract gait parameters from the Kinect 2 data and present some of the performance results.
Experimental study on mechanical properties of anchoring fractured rock mass

Through the uniaxial compressive tests on cubic red sandstone samples preset with cracks of 30°,45° and 60°,the mechanical properties of rock samples with single crack under different anchoring conditions as well as the grouting and anchoring reinforcement effects were studied.The test results show that the peak strength of rock samples under grouting reinforcement is the smallest when the inclination angle of the crack is 45° and is the largest when the inclination angle of the crack is 30°. The peak strength of rock samples under anchoring reinforcement is the smallest when the inclination angle of the crack is 60° and is the largest when the inclination angle of the crack is 30°.
Forecasting Stock Price Index of Four Asian Countries During COVID-19 Pandemic Using ARMA-GARCH and RNN Methods

The COVID-19 pandemic has had a significant impact on the global economy, including the stock markets of many Asian countries. Forecasting stock prices is a challenging task, but it is essential for investors to make informed decisions. This study compares the performance of ARMA-GARCH and RNN models in forecasting the stock price index of four Asian countries (China, Indonesia, Japan, and South Korea) during the COVID-19 pandemic. The results show that the RNN model outperforms the ARMA-GARCH model in terms of forecasting accuracy, as measured by R-squared. The findings of this study suggest that RNN models are a more promising approach for forecasting stock prices during times of economic uncertainty. However, the methods used in this research can be further developed to improve model accuracy and precision. For example, future research could explore the use of longer time periods, other RNN variants such as LSTM, different data splitting techniques, and other hyperparameter tuning strategies.
A KPRN-Based Method for Weld Seam Point Recognition with Strong Robustness

In the domain of automated welding, achieving accurate recognition of weld seam points is a formidable challenge due to the prevailing arc glare and spatter interferences. To address this challenge, a weld seam key point recognition method is proposed to improve the welding seam recognition ability of welding robots. Central to this approach is the development of an end-to-end key point regression network (KPRN), which leverages heatmaps to directly predict the coordinates of weld seam key points. The KPRN is designed with a lightweight feature extraction layer, tailored for efficient image processing in welding scenarios. Multi-scale feature fusion is employed to expand the receptive field of the network, thereby improving feature extraction. Moreover, noise interference is effectively addressed through spatial pyramid pooling (SPP) and squeeze-and-excitation (SE) modules. Notably, the network employs a heatmap as a loss function, enabling precise recognition of weld seam key points. The effectiveness of the proposed method is verified through experiments. The proposed method can accurately pinpoint weld seam key points in the presence of arc glare and spatter interference. The weld seam point recognition accuracy achieves greater than 98.6%.
A Neural Network-based Method for Tracking and Locating the Carrier-based Aircraft

In order to improve the efficiency and accuracy of information acquisition such as target position and azimuth of the Carrier-based aircraft, this paper proposes a target tracking and location method based on NeturalNetwork technology. This method uses deep learning technology to classify and locate the aircraft in the ship deck video, uses Hungarian algorithm to correlate and track targets, calculates target orientation, and uses BP neural network model to transform the physics coordinates. The experimental results show that this method can improve the real-time and accuracy of target position and azimuth information, and the accuracy of target recognition tracking and target orientation reaches 100%.
Analyzing Logistics 4.0's Impact on 3PL Performance During Pandemics: A South African Retail Perspective

The complexity and unpredictability of the contemporary business environment are expanding at an alarming rate for multinational corporations. As a result of the altering conditions caused by the global spread of the COVID-19 pandemic, there is now more competition for specific goods. This study investigated the feasibility of Logistics 4.0 in limiting the risks posed by pandemics on the main performance metrics of the third-party logistics (3PL) industry in the South African retail sector. This study employed a quantitative research methodology. Unfamiliar technologies are viewed with skepticism by industry specialists, as evidenced by the study's findings, which were gathered via a structured questionnaire. In addition, it was discovered that the deployment of IoT technology is regarded the most promising countermeasure against the pandemic's consequences on industry.
Partial Network Recovery for Large-Scale Failures in Smart Grid

Natural disaster or malicious attacks will cause a large-scale failure of communication network and interrupt communication services in smart grid. This paper addresses the problem of network recovery by selecting partial damaged links to be repaired so as to maximize the number of survival services. We formulate the problem as 0–1 programming model, which is NP-hard, and then propose a potential energy-based heuristic algorithm to solve the problem. Extensive experiments are carried out under the condition of the different topology, the different number of services and resources. The experiment results illustrate that the heuristic algorithm is time-efficient and is near-optimal.
The Role of Nicotinamide Mononucleotide Supplementation in Psoriasis Treatment

Psoriasis is one of several chronic inflammatory skin diseases with a high rate of recurrence, and its pathogenesis remains unclear. Nicotinamide mononucleotide (NMN), as an important precursor of nicotinamide adenine dinucleotide (NAD+), has been reported to be a promising agent in treating various diseases, its positive effects including those induced via its anti-inflammatory and antioxidant properties. For this reason, we have aimed to explore the possible role of NMN in the treatment of psoriasis. Psoriasis models were constructed with imiquimod (IMQ) stimulation for 5 days in vivo and with M5 treatment in keratinocyte cell lines in vitro. NMN treatment during the IMQ application period markedly attenuated excess epidermal proliferation, splenomegaly, and inflammatory responses. According to GEO databases, Sirtuin1 (SIRT1) levels significantly decreased in psoriasis patients’ lesion tissues; this was also the case in the IMQ-treated mice, while NMN treatment reversed the SIRT1 decline in the mouse model. Moreover, NMN supplementation also improved the prognoses of the mice after IMQ stimulation, compared to the untreated group with elevated SIRT1 levels. In HEKa and HaCaT cells, the co-culturing of NMN and M5 significantly decreased the expression levels of proinflammation factors, the phosphorylation of NF-κB, stimulator of interferon genes (STING) levels, and reactive oxygen species levels. NMN treatment also recovered the decrease in mitochondrial membrane potential and respiration ability and reduced mtDNA in the cytoplasm, leading to the inhibition of autoimmune inflammation. The knockdown of SIRT1 in vitro eliminated the protective and therapeutic effects of NMN against M5. To conclude, our results indicate that NMN protects against IMQ-induced psoriatic inflammation, oxidative stress, and mitochondrial dysfunction by activating the SIRT1 pathway.
Corals and Carbon: The physiological response of a protected deep-sea coral (Solenosmilia variabilis) to ocean acidification

Calcifying corals provide important habitat complexity in the deep-sea and are consistently associated with a biodiversity of fish and other invertebrates. Little is understood about how deep-sea corals may respond to predicted scenarios of ocean acidification (OA), but any predicted changes will have wider impacts on the ecosystem. Colonies of Solenosmilia variabilis, a species of deep-sea coral found in the waters surrounding New Zealand, were collected during a cruise in March 2014 from the Louisville Seamount chain. Over 12-months, coral samples were maintained in temperature controlled (~3.5°C) continuous flow-through tanks. A control group of coral colonies was held in seawater with pH 7.88 and a treatment group in pH 7.65. These two pH levels were designed to reflect current pH conditions and end-of-century conditions, respectively. In addition to investigating changes in growth and morphology, measurements of respiration and intracellular pH (pHi) were taken after a mid-term (6 months for respiration; 9 months for pHi) and long-term (12 months for both respiration and pHi) exposure period. An established method used in measuring the pHi of shallow water corals was adapted for use with deep-sea corals for the first time. pHi was independent from the seawater treatment and ranged from 7.67 – 8.30. Respiration rate was not influenced by the reduced seawater pH tested here. Respiration rates were highly variable, ranging from 0.065 to 1.756 μg O2 g-1 protein h-1 and pHi ranged from 7.67 – 8.30. Yearly growth rates were also variable, ranging from 0.53 to 3.068 mm year-1, and again showed no detectable difference between the treatment and control colonies. However, a loss in the colouration of coral skeletons was observed in the treatment group and was attributed to a loss of tissue. This could indicate a reallocation of energy, allowing for the maintenance of those other physiological parameters measured here (e.g. growth and respiration rates). If this is indeed occurring, it would be consistent with the idea of phenotypic plasticity, where corals can alternate between soft-bodied and fossilizing forms, allowing them to survive past periods of environmental stress. This research is an important first step towards understanding the sensitivity of deep-sea corals to OA and the potential for acclimation, and suggests that in many respects, S. variabilis might not be susceptible to end-of-century projections of OA. Nevertheless, the observed tissue loss is interesting and warrants further investigation to assess its long-term implications. Furthermore, the impacts of greater levels of OA, and the interactive effects of other ecological parameters such as food availability, need to be tested.
Adversarial attacks via Sequential Quadratic Programming

Deep neural networks (DNN) achieve state-of-the-art performance in many machine learning tasks and in various types of applications. Their efficiency in solving complex problems has led to apply deep learning techniques in safety-critical tasks such as autonomous driving or medicine. However their sensitivity to adversarial attacks, rounding errors, or quantization processes raises concerns and has lead to high interest in finding new approaches to make them more robust. In this work we propose a novel approach for the construction of adversarial attacks which relies on a local Sequential Quadratic Programming (SQP) strategy. These attacks use second order information to achieve competitive performance compared with existing state-of-the-art approaches. We present numerical results that support our theoretical findings and illustrate the relevance of our approach on well-known datasets.
Design and Implementation of Integrated Sensing and Communication Waveform Based on LFM-CPM

The integrated waveform of sensing and communication is to overlap the sensing waveform and the communication waveform in the time and frequency domains, and respectively perform radar signal processing and communication signal processing at the receiving end. The integrated waveform obtained by superimposing the linear frequency modulated (LFM) signal and the continuous phase modulated (CPM) signal has the property of constant envelope, which can effectively avoid the signal distortion caused by the nonlinearity of the power amplifier, so it has better sensing performance and communication performance. In this paper, a new multi-segment frame structure design is proposed to solve the problem of spectral spreading of LFM-CPM waveforms, and comprehensively considers synchronization acquisition, the estimation and recovery of frequency offset and phase offset. In addition, we propose an efficient implementation of integrated waveforms based on FPGA, which can reduce resource consumption and improve quantization accuracy. Finally, we complete the designed integrated system on the hardware platform, and the experimental results show that the sensing and communication performances are outstanding.
EROSS+ GROUND DEMONSTRATION FROM NUMERICAL MODEL TO HARDWARE IN THE LOOP VALIDATION

This paper introduces the tuning and validation process followed to increase the maturity of a Guidance, Navigation and Control software fed by an Image Processing software processing cameras images during an orbital rendezvous scenario. This validation process and the resulting experiments were conducted during the EROSS+ project, for which a mission and system design review are proposed before describing into more details the validation results. They are based on the Model/Software/Processor/Hardware-in-the-Loop approaches, led sequentially and iteratively to converge the On-Board Software tuning towards the flight hardware configuration and characteristics, like the impact of processing delays, numerical errors, or high uncertainty. Eventually, the performances of both the Image Processing and the Guidance, Navigation and Control are illustrated on a Hardware-in-the-Loop experiments simulating a Vbar approach of the Servicer vehicle towards a Client. Analyses and recommendations are made on the validation process followed and on its potential improvements to increase the software development and validation along with the measurement chain characterisation for performance model derivation.
the wisdom of the crowd: a comparative analysis of innovation strategies in four European crowdfunding platforms

Crowdfunding is a business model where diverse stakeholders operate in a context considered an example of innovation in value co-creation. Based on literature about service ecosystems and value co-creation this study adopts the platformization of society as critical theoretical approach and analytical framework to study the relations between human and technological agents within four crowdfunding ecosystems in Europe. Conceiving Crowdfunding Service Providers (CSPs) as specialized organizations, regulated to operate multi-sided markets and to serve roles as ecosystems ’ coordinators, we aim to identify the innovation strategies adopted by CSPs platforms from Italy, France, Spain and Portugal to support their core functions related to governance mechanisms, business models and infrastructural components. To understand the motives for innovation implementation and to compare the competitive adaptation from an ecosystemic perspective, we triangulate qualitative data from multiple sources to explain how and why these strategic choices affect the dynamics of value co-creation. The multiple cases-study systematizes and discusses similarities and differences, particularly as concerns the capacity of CFPs to orchestrate their governance and ICT components to innovate and take advantage of algorithms and AI implementation, collecting more data and money from user interactions. Conclusions highlight the non-neutrality of CSPs, which act as data analysts and translators, adopting more preventive, proactive or reactive strategies of usage and redistribution of the value, created, acquired and aggregated through data. Each CSP can be characterized by its infrastructure and data management approach, metaphorically being more eco-centred or ego-centred.
University of Birmingham Crisis in the UK National Health Service

There have been more-or-less continual suggestions that the UK National Health Service (NHS) has been suffering from one kind of crisis or another since its creation in 1948. If we are to understand the problems the NHS faces, then we need to empirically investigate what kinds of crises it has faced, if such crises have patterns to them, and whether or not they tend to lead to policy change. This article considers NHS crisis in terms of academic accounts of its history, as well in occurrences of the term ‘ NHS crisis ’ (and its synonyms) in national newspaper headlines from the 1980s up to 2020 through the application of topic modelling. The combination of these two sources of data leads to the construction of a typology of NHS crises. Having constructed this typology, we can then examine the timing and frequency of NHS crises, and consider the relationship between crises and periods of policy change, as well as to the wider economic and social context in which crises occur through the notion of the ‘ NHS spatio-temporal fix ’ .
Roles of Myokines and Muscle-Derived Extracellular Vesicles in Musculoskeletal Deterioration under Disuse Conditions

Prolonged inactivity and disuse conditions, such as those experienced during spaceflight and prolonged bedrest, are frequently accompanied by detrimental effects on the motor system, including skeletal muscle atrophy and bone loss, which greatly increase the risk of osteoporosis and fractures. Moreover, the decrease in glucose and lipid utilization in skeletal muscles, a consequence of muscle atrophy, also contributes to the development of metabolic syndrome. Clarifying the mechanisms involved in disuse-induced musculoskeletal deterioration is important, providing therapeutic targets and a scientific foundation for the treatment of musculoskeletal disorders under disuse conditions. Skeletal muscle, as a powerful endocrine organ, participates in the regulation of physiological and biochemical functions of local or distal tissues and organs, including itself, in endocrine, autocrine, or paracrine manners. As a motor organ adjacent to muscle, bone tissue exhibits a relative lag in degenerative changes compared to skeletal muscle under disuse conditions. Based on this phenomenon, roles and mechanisms involved in the communication between skeletal muscle and bone, especially from muscle to bone, under disuse conditions have attracted widespread attention. In this review, we summarize the roles and regulatory mechanisms of muscle-derived myokines and extracellular vesicles (EVs) in the occurrence of muscle atrophy and bone loss under disuse conditions, as well as discuss future perspectives based on existing research.
The Challenge in Neutralizing Shadow IT: A Literature Review

In this essay, we would like to explore the potential challenge when organizations try to solve the shadow Information Technology (IT) phenomenon. Shadow IT refers to technologies or systems employees use without the IT department's acknowledgement to overcome current system limitations or speed up their work. In company policy, this violates work rules if employees use this technology. It can potentially undermine an organization's performance structure and have profound consequences if allowed to continue. However, preventing shadow IT is difficult because of several challenges and outcomes. In this essay, we want to explore what challenges will be faced if organizations want to neutralize shadow IT. In doing so, we collected workaround papers from major Information Systems (IS) databases such as EBSCO, Emerald, IEEE, Web of Sciences, AISeL, and the IS basket of eight journals. Afterwards, we develop our research question and visualize it.