Miracle-dz/UniFAR-FTUs huggingface dataset:
Take the Lead: Toward a Virtual Video Dance Partner

My work focuses on taking a single person as input and predicting the intentional movement of one dance partner based on the other dance partner’s movement. Human pose estimation has been applied to dance and computer vision, but many existing applications focus on a single individual or multiple individuals performing. Currently there are very few works that focus specifically on dance couples combined with pose prediction. This thesis is applicable to the entertainment and gaming industry by training people to dance with a virtual dance partner. Many existing interactive or virtual dance partners require a motion capture system, multiple cameras or a robot which creates an expensive cost. This thesis does not use a motion capture system and combines OpenPose with swing dance YouTube videos to create a virtual dance partner. By taking in the current dancer’s moves as input, the system predicts the dance partner’s corresponding moves in the video frames. In order to create a virtual dance partner, datasets that contain information about the skeleton keypoints are necessary to predict a dance partner’s pose. There are existing dance datasets for a specific type of dance, but these datasets do not cover swing dance. Furthermore, the dance datasets that do include swing have a limited number of videos. The contribution of this thesis is a large swing dataset that contains three different types of swing dance: East Coast, Lindy Hop and West Coast. I also provide a basic framework to extend the work to create a real-time and interactive dance partner.
Toward a Secure 5G-Enabled Internet of Things: A Survey on Requirements, Privacy, Security, Challenges, and Opportunities

5G and the Internet of Things (IoT) are a potent combination that offers a vast IoT infrastructure that can support billions of connected devices while maintaining reliability, affordability, and high-speed connectivity. Nevertheless, the integration of 5G-enabled IoT has received insufficient attention from security analysts, engineers, and researchers, resulting in a lack of information and viable solutions. This study investigates the benefits and issues associated with 5G-enabled IoT, including its privacy and security concerns as well as the technology drivers that enable its various layers. By incorporating 5G technology into IoT, several enhancements have been achieved, including enhanced reliability, simplicity, practicability, analysis, efficiency, agility, flexibility, and accessibility. To achieve the full potential of 5G for IoT, however, researchers must also address many research obstacles, such as designing the 5G-IoT architecture, managing committed machine interactions, and addressing security concerns. A promising strategy for overcoming these obstacles involves ensuring compatibility of operating systems with all devices, which will lead to the development of modern open-source standardization for smooth communication across diverse devices. This innovation will improve the security and privacy of 5G-enabled IoT devices by equipping their hardware with the intelligence to recognize, verify, and authorize processes with predetermined characteristics. This will provide authorized users with full autonomy and persistent connectivity, enhancing the overall user experience. However, 5G requires a thorough examination of its implications and difficulties. A safer and more efficient ecosystem for 5G-enabled IoT may be established by addressing these concerns and encouraging industry-wide collaboration.
The Metabolic Regulation of Amino Acid Synthesis Counteracts Reactive Nitrogen Stress via Aspergillus nidulans Cross-Pathway Control

Nitric oxide (NO) is a natural reactive nitrogen species (RNS) that alters proteins, DNA, and lipids and damages biological activities. Although microorganisms respond to and detoxify NO, the regulation of the cellular metabolic mechanisms that cause cells to tolerate RNS toxicity is not completely understood. We found that the proline and arginine auxotrophic proA5 and argB2 mutants of the fungus Aspergillus nidulans require more arginine and proline for normal growth under RNS stress that starves cells by accumulating fewer amino acids. Fungal transcriptomes indicated that RNS stress upregulates the expression of the biosynthetic genes required for global amino acids, including proline and arginine. A mutant of the gene disruptant, cpcA, which encodes the transcriptional regulation of the cross-pathway control of general amino acid synthesis, did not induce these genes, and cells accumulated fewer amino acids under RNS stress. These results indicated a novel function of CpcA in the cellular response to RNS stress, which is mediated through amino acid starvation and induces the transcription of genes for general amino acid synthesis. Since CpcA also controls organic acid biosynthesis, impaired intermediates of such biosynthesis might starve cells of amino acids. These findings revealed the importance of the mechanism regulating amino acid homeostasis for fungal responses to and survival under RNS stress.
A COMPREHENSIVE REVIEW ON NEUROTRANSMITTERS, ITS BIOLOGICAL IMPORTANCE AND METHODS OF ANALYSIS

Neurotransmitters being the signalling molecules are crucial for normal brain functions and their imbalance is associated with a number of mental health diseases. Therefore precise detection and measurement of these molecules are essential in diagnosis of different brain disorders and research studies. Although various techniques have been suggested for their analysis but their specific detection and measurement is a challenging task because of their presence in lower concentrations in central nervous system and mixing with other biochemical substances and it is still an area of ongoing research. This paper discusses the conventional methods and also most recent advances and developments in the detection techniques of neurotransmitters such as electrochemical and nano-object sensing techniques with the goal to provide selection guidelines for diagnosis of diseases and research.
Machine Learning Approach to Investigating the Relative Importance of Meteorological and Aerosol-Related Parameters in Determining Cloud Microphysical Properties

Aerosol effects on cloud properties are notoriously difficult to disentangle from variations driven by meteorological factors. Here, a machine learning model is trained on reanalysis data and satellite retrievals to predict cloud microphysical properties, as a way to illustrate the relative importance of meteorology and aerosol, respectively, on cloud properties. It is found that cloud droplet effective radius can be predicted with some skill from only meteorological information, including estimated air mass origin and cloud top height. For ten geographical regions the mean coefficient of determination is 0.41 and normalised root-mean square error 24%. The machine learning model thereby performs better than a reference linear regression model, and a model predicting the climatological mean. A gradient boosting regression performs on par with a neural network regression model. Adding aerosol information as input to the model improves its skill somewhat, but the difference is small and the direction of the influence of changing aerosol burden on cloud droplet effective radius is not consistent across regions, and thereby also not always consistent with what is expected from cloud brightening.
Performance Evaluation of Deep Learning Techniques in The Detection of IOT Malware

Internet of Things (IoT) equipment is rapidly being used in a variety of businesses and for a variety of reasons (for example, sensing and collecting data from the environment in both public and military settings). Because of their expanding involvement in a wide range of applications and their rising computational and processing capabilities, they are a viable attack target for malware tailored to infect specific IoT devices. This study investigates the potential of detecting IoT malware using different deep learning techniques: the classic feedforward neural network (FNN), convolutional neural networks (CNN), long short-term memory (LSTM), and recurrent neural networks (RNN). The proposed method analyses the execution operation codes of IOT app sequences using modern NLP (natural language processing) methods. The current work utilized an IoT application dataset with 500 malware (collected from the IOTPOT dataset) and 500 goodware samples to train the proposed algorithms. The trained model is tested against 2971 fresh IoT malware and goodware samples. The samples were input into deep learning models, and performance metrics were obtained. The results demonstrate that the RNN model had the best accuracy (99.19%) in detecting fresh malware samples. On the other hand, the results were compared by the time required for training; the CNN model shows that it could achieve high accuracy (98.05%) with less training time. A comparison with various deep learning classifiers demonstrates that the RNN and CNN techniques produce the best results.
Succession planning in higher education: A systematic literature review (2012–2022)

This research investigates the dynamic landscape of succession planning (SP) strategies in higher education, with a focus on synthesizing existing literature to guide improvements in presidential succession practices. The intense global competition in higher education has led to imbalances in the quantity and composition of potential successors, hindering institutions’ rapid advancement and affecting their competitiveness on the global stage. The study addresses critical challenges such as attracting, retaining, and nurturing successors in key positions beyond material incentives. Employing a literature analysis methodology, the research comprehensively examines the existing body of literature related to succession planning, offering recommendations to promote stability in leadership, foster continuous talent development, and mitigate talent crises. The study evaluates the current state of succession planning in higher education, identifying issues and their root causes. It provides a summary and analysis of ongoing research efforts related to successor quality, team formation, and cultivation models. Despite advancements through national talent cultivation policies, persistent challenges like talent scarcity, the absence of gender-inclusive succession plans, a lack of originality, and inconsistent staff flow hinder progress. The research attributes these challenges to traditional personnel systems and university administrators. Proactive measures are proposed, including creating awareness of succession planning, advocating for personnel mechanism reform, establishing a comprehensive training system, and developing a scientifically-grounded succession plan. Though the study aims to contribute to leadership development and address pressing issues faced by higher education institutions, with only a limited number utilizing mixed techniques, it restricted the comprehensive inclusion of social context knowledge and evidence regarding the motivations, beliefs, and experiences of individuals in this investigation.
Discussion on the Petrogenic Time of Longshoushan Group, Gansu Province

Longshoushan Group(LGr) is located in a narrow zone with NW-SE direction in Longshoushan area (Fig.1), northwestern Gansu Province. It is suspended that LGr belongs to Archean or Preterozoic since there was no precise and reliable isotopic data. Currently an accurate isotopic age of 2015±16 Ma (Fig.3) with single zircon U-Pb was obtained from trondjemite in Longshoushan area. The location of sample D10 is in Shuangjizi, about 30 kilometers northwest to Jinchang City; and the zicorns that we had analyzed were selected from the gray deformed trondjemite in so-called Longshoushan Group. After Fig.4, the under intersection age: 452±16 Ma, stands for the thermal-tectonic event of collision in Longshoushan area in Caledonian; and the upper intersection age: 2015±16 Ma, reflects the petrogenic time of the trondjemite. The trondjemite might intrude the upper crustal rocks although it is adjacent to the rocks of LGr with fault. So the petrogenic time of Longshoushan Group should be more than 2015 Ma. Combined with Sm-Nd, Rb-Sr , other single zircon U-Pb ages (Table 1) and regional geology correlation, we thought that the rocks of LGr mainly contributed to Paleoproterozoic. And these ages also implied that there was another important Precambrian thermal-tectonic event besides two main activities of 2500 and 1800 Ma in North China Platform.
Nine-Month Continuous Fremanezumab Prophylaxis on the Response to Triptans and Also on the Incidence of Triggers, Hypersensitivity and Prodromal Symptoms of Patients with High-Frequency Episodic Migraine

Objective: To investigate whether the incidence of triggers, prodromal symptoms, hypersensitivity symptoms accompanying headache and responses to triptans were modified during a continuous 9-month fremanezumab therapy for migraine prophylaxis. Patients and methods: We studied 63 patients with high-frequency episodic migraine (HFEM). Enrolled patients received fremanezumab for nine consecutive months before defining the response rates and being stratified into treatment responders (≥50–74% reduction in monthly headache days (MHDs)), super responders (≥75%), partial non-responders (<50%) and super non-responders (<30%). Through headache diaries, patients provided data in order to document the impact of fremanezumab on the incidence of triggers, associated symptoms followed by headache and response to triptans (the use of the migraine treatment optimization questionnaire-4 (mTOQ-4)) during the 9-month treatment period. Results: Fremanezumab had early (after 3 monthly cycles) beneficial effects on the response to triptans in the majority of responders with relevant increases in mTOQ-4 scoring, but also in half of partial non-responders. A significant reduction in median days with migraine-associated symptoms was seen in responders after 6 months of therapy with fremanezumab, mostly for osmophobia, photophobia, phonophobia and nausea/vomiting, but partial non-responders also benefited. Likewise, the incidence of self-reported prodromal symptoms was significantly reduced in responders and was modestly diminished in partial non-responders. Triggers remained unaffected in both responders and non-responders. Conclusions: Fremanezumab given for at least 6–9 months may exert neuromodulatory effects in the migraine brain. These effects could result both in the inhibition of migraine chronification, but also in the diminishing of the magnitude of migraine-associated symptoms, mostly in responders and in partial non-responders.
Offshore Oil Spill Detection Based on CNN, DBSCAN, and Hyperspectral Imaging

Offshore oil spills have the potential to inflict substantial ecological damage, underscoring the critical importance of timely offshore oil spill detection and remediation. At present, offshore oil spill detection typically combines hyperspectral imaging with deep learning techniques. While these methodologies have made significant advancements, they prove inadequate in scenarios requiring real-time detection due to limited model detection speeds. To address this challenge, a method for detecting oil spill areas is introduced, combining convolutional neural networks (CNNs) with the DBSCAN clustering algorithm. This method aims to enhance the efficiency of oil spill area detection in real-time scenarios, providing a potential solution to the limitations posed by the intricate structures of existing models. The proposed method includes a pre-feature selection process applied to the spectral data, followed by pixel classification using a convolutional neural network (CNN) model. Subsequently, the DBSCAN algorithm is employed to segment oil spill areas from the classification results. To validate our proposed method, we simulate an offshore oil spill environment in the laboratory, utilizing a hyperspectral sensing device to collect data and create a dataset. We then compare our method with three other models—DRSNet, CNN-Visual Transformer, and GCN—conducting a comprehensive analysis to evaluate the advantages and limitations of each model.
Wideband Biconical Antenna With Embedded Band-Notch Resonator

A volumetric resonant structure is embedded inside a wideband 7–34.7 GHz omnidirectional biconical (bicone) antenna to achieve controllable, narrowband rejection within the wide instantaneous bandwidth for use in electronic support measures (ESMs). Design curves are shown for the impacts of various parameters of the resonant slot with respect to notch frequency, bandwidth, and impact on radiation characteristics. Physical realizations are demonstrated with a notch response of 3 dB or lower return loss formed between 15.3 and 16.9 GHz. The provided design curves and corresponding parametric studies exemplify the control of the band-reject region. Further examination on the steering of radiation pattern null in elevation is shown by varying the resonant slot structure between the top and bottom halves of the bicone. The biconical antenna is designed for fabrication in stereolithography (SLA) followed by electroplating. For this manufacturing approach, the band-reject bicone is compared to the corresponding base version. The prototype structures successfully validate the proposed approach.
Filamentous prophage Pf4 promotes genetic exchange in Pseudomonas aeruginosa

Abstract Filamentous prophages are widespread among bacteria and play crucial functions in virulence, antibiotic resistance, and biofilm structures. The filamentous Pf4 particles, extruded by an important pathogen Pseudomonas aeruginosa, can protect producing cells from adverse conditions. Contrary to the conventional belief that the Pf4-encoding cells resist reinfection, we herein report that the Pf4 prophage is reciprocally and commonly exchanged within P. aeruginosa colonies, which can repair defective Pf4 within the community. By labeling the Pf4 locus with antibiotic resistance and fluorescence markers, we demonstrate that the Pf4 locus is frequently exchanged within colony biofilms, in artificial sputum media, and in infected mouse lungs. We further show that Pf4 trafficking is a rapid process and capable of rescuing Pf4-defective mutants. The Pf4 phage is highly adaptable and can package additional DNA doubling its genome size. We also report that two clinical P. aeruginosa isolates are susceptible to the Pf4-mediated exchange, and the Pf5 prophage can be exchanged between cells as well. These findings suggest that the genetic exchanging interactions by filamentous prophages may facilitate defect rescue and the sharing of prophage-dependent benefits and costs within the P. aeruginosa community.
Blank predetermination in the Iberian Acheulean. Insight from the cleaver on flake assemblage of Casal do Azemel site (Leiria, Portugal) by a Geometric Morphometric approach

Over the last decades, the increase of data available for the study of the archaeological topic in the Iberian Peninsula during the Middle Pleistocene has favoured the understanding of the technological trends of the Iberian Acheulean assemblages. These have features of a Large Flake Acheulean (LFA), displaying, among other traits, a significant presence of cleavers on flake, a specific tool type that is of great cultural and technological value. Particularly, these artefacts are privileged to discuss the importance of blank predetermination in the Acheulean techno-complex. Following this reason, in the present work we aimed to explore this topic through the 2D Geometric Morphometric Analysis of the cleaver on flake assemblage from Casal do Azemel (Leiria, Portugal), an example of a paradigmatic Iberian Acheulean site that has one of the largest collections of this type of tools in Western Europe. The results obtained revealed that no significant morphological differences were found according to the technological solutions applied to the acquisition of the blank and its secondary transformation. Considering that in most of the cases these tools display a low degree of secondary transformation, these data suggest that underlying the production of Casal do Azemel’s cleaver on flake assemblage was not only a technological and cognitive flexibility (given its typological composition), but also a conceptual, structural, and morphological standardisation. These observations allowed us to discuss the significance of blank predetermination in the Acheulean, implying the existence of greatly structured technical and cognitive prerequisites.
An Egocubemap Based Algorithm for Quadrotors Obstacle Avoidance Using a Single Depth Camera

A fast obstacle avoidance algorithm is a necessary condition to enable safe flights of Unmanned Aerial Vehicles (UAVs) eventually at high-speed. Large UAVs usually have a lot of sensors and available computational resources which allow complex algorithms to run fast enough to navigate safely. On the contrary, small UAVs gather many difficulties, like computation and sensors limitations, forcing algorithms to retain only a few keys points of their environment. This paper proposes an obstacle avoidance algorithm for quadrotor using a single depth camera. Taking advantage of the possibilities offered by embedded GPUs, a cubic world representation centered on the robot-called Egocubemap-is used while the whole obstacle detection and avoidance algorithm is light enough to run at 10 Hz on-board. Numerical and experimental validations are provided.
The Contributions of Knee Extension Strength and Hand Grip Strength to Factors Relevant to Physical Frailty: The Tanno-Sobetsu Study

Sarcopenia is the core factor of frailty. This study specifically focused on lower limb muscle strength and examined muscle indices that indicate the risk of frailty or pre-frailty in older adults. The study included 327 community-dwelling individuals aged ≥65 years (43.7% male) who participated in the cohort. Frailty was defined based on five symptoms: weight loss, low activity level, exhaustion, weakness and slowness. Participants were classified into frail (three or more applicable), pre-frail (one to two applicable) and non-frail groups. Muscle strength (knee extension strength, toe grip strength and hand grip strength) were assessed, and appendicular muscle mass was assessed via a bioelectrical impedance analysis. The adjusted odds ratio (OR) of muscle indices for with frailty (frail group vs. pre-frail group) or pre-frailty (pre-frail group vs. non-frail group) were calculated. The prevalence of frail and pre-frail was 7% and 40%, respectively. Adjusted for age, sex, albumin and medical history, knee extension strength was significantly associated with frailty (odds ratio 0.95, 95% CI 0.92–0.98), while hand grip strength was associated with pre-frailty (odds ratio 0.92, 95% CI 0.88–0.97) but not with other muscle indices. This study is significant for identifying knee extension strength as a factor relevant to frailty in older adults considered pre-frailty, emphasizing the importance of this specific muscle measure in predicting and managing frailty.