Miracle-dz/UniFAR-FTUs huggingface dataset:
Green workers of Himalayas: evidence of transformation induced regeneration

PurposeThe investigation of the relationship between the future of humanity and the future of ecology is a pertinent issue. In this context, the study aimed to explore people's travel experience in the Himalayan region of India for transformation and well-being and how transformation induces their involvement in regenerative practices. The authors investigated if transformations contribute to the well-being of all living beings and the environment and induce involvement in regenerative practices? Design/methodology/approachThe authors preferred qualitative research design and selected narrative inquiry as a research approach to bring individuals' life stories to the centre stage for examination. Accordingly, diachronic data was collected, and a paradigmatic type of narrative inquiry was applied that uses paradigmatic analytical procedures to produce thematic categories and taxonomies from the database.FindingsNine themes were identified and discussed in the light of existing literature. Transformational tourism promotes well-being and reduces mental health anomalies. The study participants used words like calm, compose, stress-free, and compassion, indicating enhanced consciousness. They also reported satisfaction and induced environmentally friendly behaviour after serving the community and environment at tourist destinations situated in Himachal Pradesh, Uttarakhand, and the Union territory of Ladakh of the Himalayan region of India.Originality/valueThe paper collates evidence to establish a correlation between transformation and regeneration. There is a suggestion that transformative travel leads to participation in regenerative activities. The paper is based on people's experiences described as green workers of the Indian Himalayas and provides empirical conclusions that support the argument of transformational induced regenerative tourism.
A Novel TCP/IP Header Hijacking Attack on SDN

Middlebox is primarily used in Software-Defined Network (SDN) to enhance operational performance, policy compliance, and security operations. Therefore, security of the middlebox itself is essential because incorrect use of the middlebox can cause severe cybersecurity problems for SDN. Existing attacks against middleboxes in SDN (for instance, middleboxbypass attack) use methods such as cloned tags from the previous packets to justify that the middlebox has processed the injected packet. Flowcloak as the latest solution to defeat such an attack creates a defence using a tag by computing the hash of certain parts of the packet header. However, the security mechanisms proposed to mitigate these attacks are compromise-able since all parts of the packet header can be imitated, leaving the middleboxes insecure. To demonstrate our claim, we introduce a novel attack against SDN middleboxes by hijacking TCP/IP headers. The attack uses crafted TCP/IP headers to receive the tags and signatures and successfully bypasses the middleboxes.
Target Detection Based on Improved Hausdorff Distance Matching Algorithm for Millimeter-Wave Radar and Video Fusion

The intelligent transportation system (ITS) is inseparable from people’s lives, and the development of artificial intelligence has made intelligent video surveillance systems more widely used. In practical traffic scenarios, the detection and tracking of vehicle targets is an important core aspect of intelligent surveillance systems and has become a hot topic of research today. However, in practical applications, there is a wide variety of targets and often interference factors such as occlusion, while a single sensor is unable to collect a wealth of information. In this paper, we propose an improved data matching method to fuse the video information obtained from the camera with the millimetre-wave radar information for the alignment and correlation of multi-target data in the spatial dimension, in order to address the problem of poor recognition alignment caused by mutual occlusion between vehicles and external environmental disturbances in intelligent transportation systems. The spatio-temporal alignment of the two sensors is first performed to determine the conversion relationship between the radar and pixel coordinate systems, and the calibration on the timeline is performed by Lagrangian interpolation. An improved Hausdorff distance matching algorithm is proposed for the data dimension to calculate the similarity between the data collected by the two sensors, to determine whether they are state descriptions of the same target, and to match the data with high similarity to delineate the region of interest (ROI) for target vehicle detection.
Oriented Matroids in Terms of Order Functions

An oriented matroid of rank r is described as a pair M = (E;) where E is a nonempty set and is a chirotope of rank r on E. Relating the chirotope to an order function ! : (H; a; b) 7 ! (Hjab) according to the rule (Hjab) = (P; a) (P; b) where H is a hyperplane, P is a base of H and a and b are points with a; b 2 E n H, ! is shown to be harmonic and strict. The presented theorem gives a geometric characterization of oriented matroids in terms of order functions.
Modeling and prototyping of a soft closed-chain modular gripper

Purpose This paper aims to present a soft closed-chain modular gripper for robotic pick-and-place applications. The proposed biomimetic gripper design is inspired by the Fin Ray effect, derived from fish fins physiology. It is composed of three axisymmetric fingers, actuated with a single actuator. Each finger has a modular under-actuated closed-chain structure. The finger structure is compliant in contact normal direction, with stiff crossbeams reorienting to help the finger structure conform around objects. Design/methodology/approach Starting with the design and development of the proposed gripper, a consequent mathematical representation consisting of closed-chain forward and inverse kinematics is detailed. The proposed mathematical framework is validated through the finite element modeling simulations. Additionally, a set of experiments was conducted to compare the simulated and prototype finger trajectories, as well as to assess qualitative grasping ability. Findings Key Findings are the presented mathematical model for closed-loop chain mechanisms, as well as design and optimization guidelines to develop controlled closed-chain grippers. Research limitations/implications The proposed methodology and mathematical model could be taken as a fundamental modular base block to explore similar distributed degrees of freedom (DOF) closed-chain manipulators and grippers. The enhanced kinematic model contributes to optimized dynamics and control of soft closed-chain grasping mechanisms. Practical implications The approach is aimed to improve the development of soft grippers that are required to grasp complex objects found in human–robot cooperation and collaborative robot (cobot) applications. Originality/value The proposed closed-chain mathematical framework is based on distributed DOFs instead of the conventional lumped joint approach. This is to better optimize and understand the kinematics of soft robotic mechanisms.
Sideband Vibro-Acoustic Responses and Improvements With Different Pulsewidth Modulation Strategies in Permanent Magnet Synchronous Motor for Electric Vehicle

This article presents a comprehensive investigation into the sideband vibro-acoustics with continuous and discontinuous pulsewidth modulation (PWM) strategies in permanent magnet synchronous motor (PMSM). The primary motivation for the evaluation reveals the frequency distributions of the sideband components and the magnitudes suppression with the space-vector PWM (SVPWM) and the discontinuous PWM (DPWM). The principle and implementation of the conventional SVPWM and six basic DPWM modes are firstly presented. The sideband harmonic components are provided by the analytical derivations, in which the frequency distributions are established by coupling the temporal features between the current harmonics and radial electromagnetic force. To evaluate and compare the sideband current harmonics and vibro-acoustic responses among the PWM schemes, the experimental tests are presented in a study case of 12/10 PMSM. Significant deterioration is observed in each of the basic DPWMs compared with that in SVPWM. Developed with periodic dithering modulation, notable sideband harmonics suppression and vibro-acoustic improvements in DPWM schemes are observed. This work is expected to provide a guidance for a hybrid modulation strategy in electric vehicles (EVs).
InertiEAR: Automatic and Device-independent IMU-based Eavesdropping on Smartphones

IMU-based eavesdropping has brought growing concerns over smartphone users’ privacy. In such attacks, adversaries utilize IMUs that require zero permissions for access to acquire speeches. A common countermeasure is to limit sampling rates (within 200 Hz) to reduce overlap of vocal fundamental bands (85-255 Hz) and inertial measurements (0-100 Hz). Nevertheless, we experimentally observe that IMUs sampling below 200 Hz still record adequate speech-related information because of aliasing distortions. Accordingly, we propose a practical side-channel attack, InertiEAR, to break the defense of sampling rate restriction on the zero-permission eavesdropping. It leverages IMUs to eavesdrop on both top and bottom speakers in smartphones. In the InertiEAR design, we exploit coherence between responses of the built-in accelerometer and gyroscope and their hardware diversity using a mathematical model. The coherence allows precise segmentation without manual assistance. We also mitigate the impact of hardware diversity and achieve better device-independent performance than existing approaches that have to massively increase training data from different smartphones for a scalable network model. These two advantages re-enable zero-permission attacks but also extend the attacking surface and endangering degree to off-the-shelf smartphones. InertiEAR achieves a recognition accuracy of 78.8% with a cross-device accuracy of up to 49.8% among 12 smartphones.
Addressing Non-response Bias in Urban Forest Inventories: An Estimation Approach

When conducting urban forest inventories, complete or partial inaccessibility of sample plots results in non-response for a portion of the selected sample. Unfortunately, the non-response is rarely random and thus a potential bias may be imparted in the sample and associated data analyses. In this study, results from an alternative estimation method that employs response homogeneity groups (RHGs) appeared to be more robust to non-random non-response when compared to those of a standard estimation method. Across the six cities studied, the total non-response rates varied from 8.0 to 20.4%. Percent differences between the two methods in estimated number of trees ranged from −0.7 to 12.6%; whereas 1.4 to 14.8% differences were found for tree biomass density. While these differences only approximate the amount of non-response bias present under standard estimation methods, there is a clear indication that misleading results may be obtained if non-response bias is not adequately addressed. By implementing methods that mitigate potential non-response bias, urban forest inventory practitioners would increase the reliability of information used by city planners to make effective management and policy decisions.
Joint Communication and Sensing Enabled Cooperative Perception Testbed for Connected Automated Vehicles

To overcome the bottleneck of sensing ability improvement in a single automated vehicle, the joint communication and sensing (JCS), as one of the potential sixth-generation wireless communication technologies, can support the efficient raw perception data sharing among connected automated vehicles in the millimeter wave frequency band. We propose a weighted mean accuracy based sensing data fusion algorithm to enhance the target positioning performance by sharing the perception data from two time division based JCS systems. Field test results prove that the proposed algorithm using two cooperative JCS systems can reduce the target positioning root mean square error by 31% compared to a single JCS system.
Slack Equalization Algorithm: Precise Slack Distribution for Low-Level Synthesis and Optimization

With the growing popularity of personal computing and communication devices, the high-performance as well as low power consumption is much desirable in the synthesis of VLSI circuits and systems. Over the years, much research progress has been made towards the timing, area and power optimization [1-3]. However, when the designers look into the possibil ity of further improvement over circuit area/power, the timing constraints turn out to be a strong limiting factor. This trend can be exacerbated at lower levels (such as gate-level and physical-level) where relatively accurate timing models are available. Therefore, the tradeoff between delay and area/power has to be made, in general, to achieve the result with reasonably good quality. Since the timing performance of a circuit depends on critical paths, the logic gates on non-critical paths have typically large timing slacks (i.e. the difference between required time and arrival time). The problem is thus translated into how to exploit these excessive slacks effectively during the process of optimization. Most of prior efforts do not deal directly with the slacks.
Effect Evaluation of Norepinephrine on Cardiac Function in Patients with Sepsis by Cardiac Ultrasound Imaging

In order to investigate the therapeutic effect of norepinephrine on patients with sepsis and the effect of echocardiography on the diagnosis of cardiac function in patients with sepsis, 86 patients with sepsis were selected as research objects and randomly divided into two groups. Patients in the control group (N = 43 cases) received conventional treatment (1~15 μg/kg∗min dopamine), and those in the experimental group (N = 43 cases) received conventional treatment+norepinephrine therapy (0.05~0.5 μg∗kg−1/min). The clinical efficacy, cardiac ultrasonography results, and hemodynamic indexes of patients between the two groups were analyzed and compared. The results showed that the total effective rate of treatment in the experimental group (97.7%) was significantly higher than that in the control group (81.4%) (P < 0.05). The maximum, minimum, and average values of mitral valve E peak flow velocity (VEpeak) and left ventricular outflow tract peak flow velocity (Vpeak), respiratory variability of mitral valve E peak flow velocity (ΔVEpeak), and respiratory variability of peak flow velocity (ΔVpeak) were all significantly greater than those of the control group (P < 0.05). The area under the receiver operating characteristic curve (AUC) of ΔVEpeak and ΔVpeak for predicting positive volume response in patients with sepsis was 0.934 and 0.913, respectively; the sensitivity was 0.828 and 0.827; the specificity was 0.936 and 0.893; and the Youden indices were 0.765 and 0.712, respectively. In short, norepinephrine had a high total response rate in patients with sepsis, and echocardiography can well evaluate the effect of norepinephrine on cardiac function in patients with sepsis, which is worthy of further promotion.
Integrating traditional taxonomy and DNA barcoding for the identification of some bivalves in the Northern Red Sea, Egypt

Molluscs comprise the marine domain's second-largest animal phylum (Sharabati, 1984; Rusmore-Villaume, 2008; Carpenter and De Angelis, 2016). Within any aquatic ecosystem, molluscs play significant parts both economically and ecologically (Coen and Bishop, 2015). Economically, some industries of beneficial aquaculture and wild harvest depend on molluscs. Ecologically, their populations participate in the recycling of nutrients, determine the structure of benthic communities and maintain water quality by decreasing the levels of some heavy metals in environments. Moreover, higher trophic levels organisms, such as fishes, depend on it as a resource of food. Bivalvia is the second-largest molluscan class, with around 7500 species inhabiting a variety of marine ARTICLE INFO ABSTRACT Article History: Received: June 6, 2022 Accepted: June 13, 2022 Online: June 16, 2022 _______________
Research on Ambient Backscatter Communication Signal Detection Algorithm Based on Digital Terrestrial Multimedia Broadcast

Digital Terrestrial Multimedia Broadcast (DTMB) has high signal coverage and strong stability, so it can be used in Ambient Backscatter Communication (AmBC). To solve the problem of signal detection performance degradation of traditional algorithm in high backscattering rate scenes, this paper presents a signal detection algorithm based on DTMB frame structure. By introducing preamble, the receiver can not only detect the reflected link signal from the received signal, but also perceive the corresponding relationships between the information symbol and the DTMB signal frame; by introducing training symbol, the receiver can get the decision threshold of the information symbol, and then complete the demodulation of the information symbol. Simulation results show that the proposed algorithm significantly improves the signal detection performance.
Simulation of consumers and markets towards real time demand response

The use of Real Time Locating Systems is gradually spreading in different areas of society. Multiple sectors, such as industry, security or healthcare are already taking advantage of the benefits of knowing the exact position of people, animals or objects. Energy consumption can also be improved by the use of RTLS and thanks to these systems we can model the habits of users both at home and in public buildings. This information will create consumption maps based on the use of different electronic devices or the use of different spaces. Thus, the election of the best energy tariff or when the best moment is to use appliances will be an easier process.
Micro-Expression-Based Emotion Recognition Using Waterfall Atrous Spatial Pyramid Pooling Networks

Understanding a person’s attitude or sentiment from their facial expressions has long been a straightforward task for humans. Numerous methods and techniques have been used to classify and interpret human emotions that are commonly communicated through facial expressions, with either macro- or micro-expressions. However, performing this task using computer-based techniques or algorithms has been proven to be extremely difficult, whereby it is a time-consuming task to annotate it manually. Compared to macro-expressions, micro-expressions manifest the real emotional cues of a human, which they try to suppress and hide. Different methods and algorithms for recognizing emotions using micro-expressions are examined in this research, and the results are presented in a comparative approach. The proposed technique is based on a multi-scale deep learning approach that aims to extract facial cues of various subjects under various conditions. Then, two popular multi-scale approaches are explored, Spatial Pyramid Pooling (SPP) and Atrous Spatial Pyramid Pooling (ASPP), which are then optimized to suit the purpose of emotion recognition using micro-expression cues. There are four new architectures introduced in this paper based on multi-layer multi-scale convolutional networks using both direct and waterfall network flows. The experimental results show that the ASPP module with waterfall network flow, which we coined as WASPP-Net, outperforms the state-of-the-art benchmark techniques with an accuracy of 80.5%. For future work, a high-resolution approach to multi-scale approaches can be explored to further improve the recognition performance.
The use of pre-trained deep learning models for the photographic assessment of donor livers for transplantation

Aim: Hepatic steatosis is a recognised major risk factor for primary graft failure in liver transplantation. In general, the global fat burden is measured by the surgeon using a visual assessment. However, this can be augmented by a histological assessment, although there is often inter-observer variation in this regard as well. In many situations the assessment of the liver relies heavily on the experience of the observer and more experienced surgeons will accept organs that more junior surgeons feel are unsuitable for transplantation. Often surgeons will err on the side of caution and not accept a liver for fear of exposing recipients to excessive risk of death. Methods: In this study, we present the use of deep learning for the non-invasive evaluation of donor liver organs. Transfer learning, using deep learning models such as the Visual Geometry Group (VGG) Face, VGG16, Residual Neural Network 50 (ResNet50), Dense Convolutional Network 121 (DenseNet121) and MobileNet are utilised for effective pattern extraction from partial and whole liver. Classification algorithms such as Support Vector Machines, k-Nearest Neighbour, Logistic Regression, Decision Tree and Linear Discriminant Analysis are then used for the final classification to identify between acceptable or non-acceptable donor liver organs. Results: The proposed method is distinct in that we make use of image information both from partial and whole liver. We show that common pre-trained deep learning models can be used to quantify the donor liver steatosis with an accuracy of over 92%. Conclusion: Machine learning algorithms offer the tantalising prospect of standardising the assessment and the possibility of using more donor organs for transplantation.
Management of antithrombotic therapy in patients at high bleeding risk after percutaneous coronary intervention for acute coronary syndromes: a case report

Abstract Background Choosing antithrombotic therapy for patients at high bleeding risk, particularly those requiring long-term anticoagulant therapy, who have acute coronary syndromes (ACS) and/or undergoing percutaneous coronary intervention (PCI) is becoming increasingly complex. Case summary A 78-year-old women was hospitalized with chest pain and a diagnosis of non-ST-elevation ACS was made. It was decided that the patient should undergo coronary angiogram with a view for angioplasty. Subsequently, she underwent successful PCI to the left anterior descending artery. Shortly after PCI, she was noted to be in atrial fibrillation. Furthermore, she had per rectal bleeding and acute kidney injury, which were managed conservatively. Aspirin and ticagrelor were stopped and she was discharged on dual antithrombotic therapy with clopidogrel and apixaban. Discussion Available evidence, driven mainly from expert consensus documents, advocates a case-by-case comprehensive evaluation that integrates patient- and procedure-related factors to assess patients for thrombotic and bleeding tendencies to identify those who may gain most net clinical benefit of antithrombotic combination therapy. In general, if thrombotic drivers prevail, an augmented antithrombotic regime with a view for a longer duration should be planned, and if bleeding drivers prevail, a de-escalated regime with a view for a shorter duration should be sought.
Anti-Inflammatory Effects of Natural Products on Cerebral Ischemia

Cerebral ischemia with high mortality and morbidity still requires the effectiveness of medical treatments. A growing number of investigations have shown strong links between inflammation and cerebral ischemia. Natural medicine’s treatment methods of cerebral ischemic illness have amassed a wealth of treatment experience and theoretical knowledge. This review summarized recent progress on the disease inflammatory pathways as well as 26 representative natural products that have been routinely utilized to treat cerebral ischemic injury. These natural products have exerted anti-inflammatory effects in cerebral ischemia based on their inflammatory mechanisms, including their inflammatory gene expression patterns and their related different cell types, and the roles of inflammatory mediators in ischemic injury. Overall, the combination of the potential therapeutic interventions of natural products with the inflammatory mechanisms will make them be applicable for cerebral ischemic patients in the future.