Miracle-dz/UniFAR-FTUs huggingface dataset:
C-LLDP-monitoring: Latency Monitoring across Large-scale Software Defined Networks

This paper presents a network-wide latency monitoring method, called C-LLDP-monitoring, for large-scale Software Defined Networks (SDNs) with multiple controllers constructing a hierarchical control plane. C-LLDP-monitoring first uses each local controller in the lower layer to repeatedly inject Link Layer Discovery Protocol (LLDP) packets into its managed network domain to obtain the round-trip time (RTT) of links within the domain, and uses the root controller to receive the packets from the border switch in the neighboring domain to obtain the RRT of links between two neighboring domains, and then calculates the total network-wide path latency over the entire network. It avoids uncertainties introduced by the control channel and clock frequencies of different devices, requires no dedicated network infrastructure, and achieves continuous and accurate network-wide latency monitoring across large-scale multi-domain SDNs.
Research on Vehicle Routing Problem Based on Heuristic Neural Network

Vehicle routing problem is a classical problem in combinatorial optimization, which has been repeatedly studied by scholars. Recently, deep learning frameworks based on reinforcement learning have become the mainstream deep learning frameworks for vehicle routing problems. This paper proposes a heuristic neural network algorithm. The heuristic algorithm generates a subset of nodes through a destruction operator, and then the repair operator reconstructs the sequence based on the principle of minimum cost. In the network, the encoder consists of a graph neural network with an attention mechanism at one node, and the decoder consists of a GRU with a pointer network, which is trained by an actor-critic framework Experimental results show that the model in this paper outperforms the classical heuristic algorithm.
Optimizing HEV Performance with Fuzzy Logic-Based PID Gain Tuning

This paper focuses on optimizing the performance of hybrid electric vehicles (HEVs) through an integrated approach of fuzzy logic-based PID gain tuning. The paper examines a broad range of factors that influence the system's operation, considering the dynamics of the engine, drivetrain, rotational wheel, and the applied load. For handling the optimal performance of HEVs, a unique approach is introduced. This approach utilizes fuzzy logic to fine-tune gains for proportional integral derivative (PID) controllers, aiming to enhance their efficiency. The proposed method aims to achieve an optimal balance between acceleration, speed, and range of travel while improving controller quality and response. The findings suggest that the proposed approach has the potential to enhance the performance of HEVs, and the approach outperforms existing control techniques.
Additive Attention for Medical Visual Question Answering

Medical visual question answering is a prominent research area, presenting significant challenges within the domain of visual question answering. In traditional medical visual question answering, the initial step commonly involves employing Convolutional Neural Network (CNN) for image information extraction. Subsequently, bilinear attention mechanisms are employed to merge textual question characteristics with image visual features. However, the method of extracting visual attributes through convolutional neural networks often overlooks the global contextual within the image, which is crucial for answering questions accurately. Consequently, this paper introduces an Additive Attention Network (AANet) to capture comprehensive image information features. Specifically, CNN is employed to obtain local visual features of images, while the additive attention mechanism serves to acquire global contextual features of images. These components complement each other, enhancing the representation of visual features and augmenting the model’s global contextual awareness capability. The proposed method demonstrated superior performance on the VQA-RAD dataset, achieving an overall accuracy of 72.5%, especially for closed questions, achieving an accuracy of 81.9%.
Research on optimization selection of radar high-resolution range target recognition features

A set of feature combinations with high recognition rate is proposed for the identification of vehicle targets and corner reflectors in this article. In the process of target recognition based on high-resolution range profiles (HRRP) from radar; extracting target features and selecting the appropriate features are crucial. The stability and effectiveness of the selected features directly impact the quality of recognition. This paper proposes 15 identification features and determines the optimal feature combination for specific problems. These feature combinations are validated using a neural network classifier, resulting in a satisfactory verification outcome.
A new attack detection scheme based on ellipsotope

This paper considers the abnormal intrusion detection issue for linear systems with unknown-but-bounded (UBB) noises which belong to ellipsotope set. The ellipsotope combine the advantages of ellipsoid and zonotope. Then, in order to detect the malicious cyber attacks, the ellipsotope-based approach is designed and the strategy is characterized by the residue always remains inside the ellipsotope set when the system is attack free. In addition, compared with the zonotope-based detection approach, our method have better sensitivity. Finally, a quadrotor UAV control system is employed to illustrate the effectiveness of the main results.
25.3 Toward Exponential Growth of Therapeutic Neurotechnology

Bioelectronic devices that manipulate and record human neural activity could help patients suffering from debilitating health conditions not effectively treated by drugs. Here we discuss the exponential growth in both neuroscience and neurotechnology that provides the ingredients to fuel exponential growth of implantable neurotechnology as therapeutic options, and the implications this has on circuit design for neurotech applications.
In vitro fermentation with inoculated goat rumen to evaluate the degradation of diets supplemented with three levels of protected palmitic acid

Few studies evaluate the impact of a lipid-rich diet with palmitic acid (PA) in ruminal fermentation. The objective was to evaluate the in vitro bath culture of the protected PA on the ruminal fermentative variables. Four diets were used: a) without protected PA (nPA), b) inclusion of protected PA at three levels: PA3 = 3%, PA6 = 6%, and PA9 = 9% dry matter (DM). The results indicated that protected PA decreased gas production (p ≤ 0.05). DM and organic matter (OM) degradation after 72h of incubation linearly decreased (p ≤ 0.05) with a high level of protected PA. Neutral and acid detergent �ber degradation had no signi�cant differences between treatments (p > 0.05). PA concentration in ruminal �uid had a linear effect with an increase from PA3 to PA9. Propionate had a quadratic effect (p ≤ 0.05) from nPA to PA3, and total volatile fatty acids had a linear decrease (p ≤ 0.05) from nPA to PA9. In conclusion, supplements with protected PA at 3 and 6% increased PA availability, and the results indicate good bene�ts of protected PA on fermentative variables. The doses of 3 and 6% of protected PA are recommended for use and evaluation in lactating goats.
Calyptraeidae from the northeast Pacific (Gastropoda: Caenogastropoda)

Calyptraeids are sedentary suspension-feeding caenogastropods, which have garnered attention for their protandrous sex change and the propensity of some species to become invasive. The upwelling areas in the northeast Pacific support a particularly diverse calyptraeid fauna, including many poorly known species. This taxonomic review, based primarily on material in the collections of the Natural History Museum of Los Angeles County provides descriptions and photographs of three species of Calyptraea Lamarck, 1799, 13 Crepidula Lamarck, 1799, three Crepipatella Lesson, 1830, and one species each of Bostrycapulus Olsson & Harbison, 1953, Crucibulum Schumacher, 1817, and Grandicrepidula McLean, 1995. Three new species, Bostrycapulus decorus n. sp. , Crepidula huertae n. sp. and Crepidula wolfae n. sp. are described.
Applied Thermal Engineering

Mobile, small-scale refrigeration applications for last-mile deliveries have gained increased importance in recent years. As they face disturbances and frequent door openings more extensively than long-distance transport, reliable temperature information is crucial for control concepts to comply with temperature regulations and increase efficiency. For desired ruggedness, sensors are typically integrated within the cooling unit, yielding substantial deviations between actual air temperature in the cooling chamber and measured one — particularly in periods of altered airflow conditions resulting from fan switching and the actual door opening status. This article introduces and compares two hybrid, online estimation procedures to overcome this issue: firstly, a Kalman-filter approach based on a simple lumped physical heat transfer model, and secondly, a gray-box-model approach resulting from a realizable inversion of the physical model. Experimental investigations of typical operating profiles provide 14h of real-world data to parameterize (11.75h data length), validate (2.25h data length), and compare both estimators. The proposed concepts are based on a sensor setup available in state-of-the-art system architectures and provide satisfactory temperature estimates with more than 83% overall fit. As the algorithms provide comprehensive process insight independent of the actual operating condition, sophisticated control schemes can be built upon the concept.
e-Publications@Marquette e-Publications@Marquette

As climate change is becoming an important global issue, more and more people are beginning to pay attention to reducing greenhouse gas emissions. To measure personal or household carbon dioxide emission, there are already plenty of carbon footprint calculators available on the web. Most of these calculators use quantitative models to estimate carbon emission caused by a user's activities. Although these calculators can promote public awareness regarding carbon emission due to an individual's behavior, there are concerns about the consistency and transparency of these existing CO 2 calculators. Apart from a small group of smart phone based carbon footprint calculator applications, most of the existing CO 2 calculators require users to input data manually. This not only provides a poor user experience but also makes the calculation less accurate. The use of a standard framework for various carbon footprint application developments can increase the accuracy of overall calculations, which in turn may increase energy awareness at the individual human level. We aim for developing a carbon footprint calculation framework that can serve as a platform for various carbon footprint calculator applications. Therefore, in this paper, we propose a platform-agnostic Open Carbon Footprint Framework ( OCFF ) that will provide the necessary interfaces for software developers to incorporate the latest scientific knowledge regarding climate change into their applications. OCFF will maintain a clouded knowledge base that will give developers access to a dynamic source of computational information that can be brought to bear on real-time sensor data. Based on the OCFF platform, we developed a Ubiquitous Carbon Footprint Calculator application ( UCFC ) that allows the user to be aware of their personal carbon footprint based on their ubiquitous activity and act accordingly. The major contribution of this paper is the presentation of the quantitative model of the platform along with the entire design and implementation of UCFC application. We also present the results, analysis, and findings of an extensive survey that has been conducted to find users’ awareness of increased carbon footprint, feature requirements, and expectations and desires to alleviate CO 2 emissions by using a footprint calculator. The design of UCFC application incorporates the analysis and inferences of the survey results. We are also developing a fuel efficient mobile GPS application for iPhone suggesting the greenest/most fuel efficient route to the user. In this paper, we also point out some important features of such an application.
Knowledge Management Process in Pharmaceutical Companies: Role of Innovation on Organizational Performance

: In the dynamic landscape of the pharmaceutical industry, knowledge management plays a pivotal role in shaping the organizational performance of companies. This paper explores the multifaceted relationship between knowledge management processes and innovation, highlighting their profound influence on the overall performance of pharmaceutical organizations. It delves into the intricate mechanisms through which knowledge is acquired, shared, and leveraged within these companies, fostering innovation, and ultimately driving enhanced performance. The research highlights how innovation is sparked by knowledge management, serving as a bridge between the wealth of information available in the industry and its transformation into novel drugs, therapies, and business strategies of selected pharma companies through knowledge management and innovation. This research study investigates the dynamic interplay between knowledge acquisition, dissemination, application, innovation, and organizational performance within selected pharmaceutical companies. In the context of the ever-evolving pharmaceutical industry, where scientific breakthroughs and novel therapies are paramount, the role of knowledge in encouraging innovation and, consequently, influencing organizational performance is of particular interest.
GNU Radio realization of Waveform Co-design for Joint Radar-Communications system using SDRs

Combining Software Defined Radios (SDRs) with the GNU Radio software development toolkit can enable rapid prototyping of integrated sensing and communications (ISACs) systems. In this paper, we conduct a Hardware-In-The-Loop (HWIL) Over-The-Air (OTA) experiment on a low-cost SDR platform testbed to demonstrate the feasibility of cooperative waveform de-sign for a joint radar-communications system. We implement the system using USRP (Universal Software Radio Peripheral) B210s with GNU Radio acting as command software for the SDRs. A joint radar-communications node acts as a mono-static radar trying to detect a target in the environment, while also acting as a communications relay. A separate SDR transmits a complete BPSK or QPSK modulated signal that is implemented completely in GNU Radio. The joint radar-communications SDRs, equipped with high gain horn antennas, simultaneously performs radar processing to detect an emulated target in the environment and decoding of a communications message. Different modulation schemes for the communications transmitter, such as BPSK and QPSK are also implemented to verify the soundness of the joint radar-communications system.
Comparison Between the Use of Advanced Platelet-Rich Fibrin (A-PRF) and Connective Tissue Graft in the Treatment of Cairo Type 1 Gingival Recession Associated with the Coronally Advanced Flap

: The effects of Advanced Platelet Rich Fibrin (A-PRF) are promising in periodontal surgeries because of the high number of cells and growth factors due to slower centrifugation. Then, the aim of the present study was to evaluate the efficacy of A-PRF in combination with Coronally Advanced (CAF) and compare it with the use of Connective Tissue Graft (CTG) also associated with CAF in Cairo type 1 gingival recession. The study was approved by the Human Research Ethics Committee of the University and patients signed the informed consent. Forty gingival recessions were selected, with 20 belonging to the CAF + A-PRF group (test group) and 20 to the CAF + CTG group. The height of the gingival recession, height, and thickness of the attached gingiva, probing depth, level of clinical insertion, and tooth sensitivity were evaluated at the beginning and 6 months after surgery. GraphPad Prism 8.0 program® was used for statistical analysis. The normality was assessed through Shapiro-Wilk. T-student was performed for the parametric data and MANN-WHITNEY test for the non-parametric. The significance level was set at 5%. There was a gain in gingival height of 1.45±0.96 mm in the CAF + CTG group and 1.04±0.95 mm in the CAF + A-PRF group, resulting in a coverage of 54.3% in the test group and 73% in the control group. Complete coverage (100%) of the recessions was found at 7 sites (35%) in the group with A-PRF and 10 sites (50%) with the CTG. In both groups, there was a significant improvement in tooth sensitivity (p<0.05). Regarding the other clinical parameters evaluated, there was no statistically significant difference; however, slightly higher rates of gum gain were observed in both height and thickness in the control group. After 180 days, both techniques showed significant improvement in the evaluation + ed periodontal parameters, concluding that both can be used for treating Cairo type I gingival recessions.
Understanding and Securing Unmanned Aerial Vehicle (UAV) Services: A Comprehensive Tutorial

—Unmanned Aerial Vehicles (UAVs), commonly known as drones, have found applications in various fields in recent years. However, along with their proliferation, security and privacy attacks have also increased. Nearby or even remote attackers may interfere with their electronic systems to take control of UAVs or alter their operations. In addition, UAVs equipped with sensors may be abused by their operators to trigger privacy attacks, such as spying or monitoring of individuals. The aim of this tutorial is to assist the reader in gaining a fundamental understanding and practical knowledge of security and privacy threats, attacks and mitigation controls for UAVs. Initially, we provide a description of the underlying technologies, architectures and applications of UAVs. Then we dive into security and privacy attacks and countermeasures. We analyze the various risks and cyberattacks that may arise, as well as the possible ways through which organizations and individuals may address the challenges presented by the malicious use of UAVs. As our main goal is to provide the reader with both theoretical and technical knowledge on UAV security, we describe a step-by-step security assessment methodology for UAV applications. The methodology which is based on well-known security standards may help the interested reader in gaining a holistic view of a security assessment, starting from threat modeling and going through penetration testing to security management. Finally, to showcase the methodology, we apply it in a practical, hands-on scenario, demonstrating the identification of attack paths against UAVs and the implementation of appropriate mitigation controls.
Managing Expectations: Signalling effects of Federal Reserve Bank announcements over inflation expectations and expected real interest rates

Communication is an important aspect of modern monetary policy. Since the popularization of the inflation targeting framework, communication has been used to manage expectations and maintain credibility. In recent years, forward guidance as a signaling strategy has been recognized as an important tool of monetary policy. Can communication be considered an effective monetary policy tool? Using data from the United States, in a sample from 1990 to 2019, a Bai Perron test for multiple breakpoints is conducted to identify changes in both inflation expectations, term structure of the expectation, and expected real interest rates. We find evidence that changes in expectations for both inflation and real interest rates are associated with changes in central bank’s signaling strategy.
Swapping-Centric Neural Recording Systems

Neural interfaces read the activity of biological neurons to help advance the neurosciences and offer treatment options for severe neurological diseases. The total number of neurons that are now being recorded using multi-electrode interfaces is doubling roughly every 4-6 years [5]. However, processing this exponentially-growing data in real-time under strict power-constraints puts an exorbitant amount of pressure on both compute and storage within traditional neural recording systems. Existing systems deploy various accelerators for better performance-per-watt while also integrating NVMs for data querying and better treatment decisions. These accelerators have direct access to a limited amount of fast SRAM-based memory that is unable to manage the growing data rates. Swapping to the NVM becomes inevitable; however, naive approaches are unable to complete during the refractory period of a neuron – i.e., a few milliseconds – which disrupts timely disease treatment. We propose co-designing accelerators and storage, with swapping as a primary design goal, using theoretical and practical models of compute and storage respectively to overcome these limitations.
The Hoechst Side Population Cells can be Eﬀectively Detected by 375nm and 405nm Lasers

The side population (SP) cells are identiﬁed through Hoechst 33342 staining and analyzed using ﬂow cytometry (FCM). The Hoechst SP method is utilized for the isolation of stem cells based on the dye eﬄux properties of ATP-binding cassette (ABC) transporters. This method was initially employed for the identiﬁcation and isolation of hematopoietic stem cells (HSCs), but it has now evolved to primarily focus on the identiﬁcation and isolation of cancer stem cells (CSCs). In this study, the capability of the 375 nm and 405 nm lasers in detecting SP cells was assessed and compared with that of the 355nm laser. Our ﬁndings demonstrate a remarkable similarity between the SP cells detected by the 355nm laser and the 405nm laser on the CytoFLEX system of FCM. These results suggest that the high-power 375 nm and 405 nm lasers can serve as a viable alternative to the 355 nm laser for SP cell detection.
Understanding Coordinated Communities Through The Lens Of Protest-centric Narratives: A Case Study on #CAA Protest

Social media platforms, particularly Twitter, have emerged as vital media for organizing online protests worldwide. During protests, users on social media share different narratives, often coordinated to share collective opinions and obtain widespread reach. In this paper, we focus on the communities formed during a protest and the collective narratives they share, using the protest on the enactment of the Citizenship Amendment Act (#CAA) by the Indian Government as a case study. Since #CAA protest led to divergent discourse in the country, we first classify the users into opposing stances, i.e., protesters (who opposed the Act) and counter-protesters (who supported it) in an unsupervised manner. Next, we identify the coordinated communities in the opposing stances and examine the collective narratives they shared. We use content-based metrics to identify user coordination, including hash-tags, mentions, and retweets. Our results suggest mention as the strongest metric for coordination across the opposing stances. Next, we decipher the collective narratives in the opposing stances using an unsupervised narrative detection framework and found call-to-action, on-ground activity, grievances sharing, questioning, and skepticism narratives in the protest tweets. We analyze the strength of the different coordinated communities using network measures, and perform inauthentic activity analysis on the most coordinated communities on both sides. Our findings suggest that coordinated communities, which were highly inauthentic, showed the highest clustering coefficient towards a greater extent of coordination.