Miracle-dz/UniFAR-FTUs huggingface dataset:
60 GHz Printed Yagi-Uda Beam-Deflector Integrated Refractive Index Metamaterial

An approach for deflection of the main radiation of the end-fire dipole antenna in a specific direction by changing the effective constitutive parameters of metamaterials are introduced. The concept of this phenomenon is creating a new medium that has different characteristics, in front of the electromagnetic waves to enforce it to propagate in a specific direction. This act has been done by embedding Modified Gradient Refractive Index Metamaterial (MGRIM) unit-cells (UCs) in front of the dipole-printed antenna. However, by changing the load dimensions of MGRIM, the permittivity and permeability of unit cells are going to change. Because the refractive index (n) of the medium changed, the phase of entered EM waves to this medium will be changed. So, the aim here is to create a new medium with different properties to provide a proper phase shift to tilt the end-fire radiation pattern. The challenge in this approach is are provide a high tilting beam angle, high gain, and low side lobe level simultaneously. Two unit cells are used in this paper to provide a high deflection angle and keep a high gain with a low side lobe level. First one to get medium has a gradient refractive index. The second one uses as a band stop structure in order to minimize the side lobe level (SLL) and enhance the gain. The simulation results show that by using the nonperiodicity arrangement of MGRIM UCs in front of a dipole-printed antenna, the main beam of the radiation pattern in the E-plane can be tilted 43oin the end-fire direction at 60 GHz. The proposed design exhibits an increasing gain of about 2.7dB and reduces the side lobe level to -11.2dB.
Performance Analysis of an Optimization-based Localization Algorithm for the 3D Spiral Deployed Wireless Sensor Network

Wireless sensor networks (WSNs) can be used to detect, collect, process, and transmit data. The majority of WSN applications revolve around a few issues of localization. A 3D node’s minimum transmission range is 25 meters. It has been found that node density has an impact on transmission range. The transmission range increases as node density rises. As a result, 3D space has a higher transmission distance than 2D space. The proposed approach is consequently applied in a 3D scenario. The disadvantages of the fundamental Distance Vector-Hop algorithm are minimized by the localization approach in this study, which is based on Class Topper Optimization (CTO). The results show that our suggested approach greatly outperforms certain already used methods in terms of lowering localization error (0.09%), lowering localization error variance (0.07%), and improving localization accuracy (0.06%).
An Examination of Virtualization Technologies for Enabling Intelligent Edge Computing

The Internet of Things (IoT) is the cutting edge of computing technology that connects things and integrate them into large ecosystem. Recently, the Edge Computing architecture is driving the rapid development of the Internet of Things (IoT). While the Edge Computing framework has its own drawbacks, these may be mitigated by the application of Artificial Intelligence (AI) and virtualization technologies. This research study examines a number of publications and research surveys on enabling virtual services in Intelligent Edge Computing. Few research studies are dedicated to increasing the inclusion of virtualization in Intelligent Edge Computing. This research study briefly reviews the virtualization technologies and also discusses about the future research directions to promote more research in this field.
Decentralized Control of Large-Scale Systems Under Asynchronous Denial-of-Service

This letter considers large-scale networked control systems with network communications undergoing denial-of-service (DoS) attacks. The local networks operate asynchronously and independently of each other in the presence of sampling instants and DoS attacks. We assume that the actuators refuse to work during DoS attack intervals. We design a piece-wise continuously differentiable auxiliary timer for each subsystem and use the graph-theoretic technique to deal with the crossing terms. Based on the auxiliary timer, we introduce a discontinuous Lyapunov functional (which is a mixture of vertex-Lyapunov functionals and vertex-Lyapunov functions) that does not grow at DoS on/off transitions. We obtain linear matrix inequality (LMI) conditions for finding the maximal allowable DoS duration that preserves stability. Finally, an example of coupled two inverted pendulums demonstrates our results and shows that the discontinuous Lyapunov functional improves the results compared with the continuous common one.
Study of Stochastic Analysis Optimization Algorithm in Information Retrieval

The stochastic optimization algorithm and the random signal nonlinear optimal filtering algorithm are typical representatives of intelligent optimization algorithms in the rapidly developing scientific research and engineering application technology fields. They are the two computing cores of high-performance intelligent calculation in intelligent control. In order to effectively improve the efficiency of information filtering and optimal quantization modeling of a class of partially observable random signals with massive characteristics in intelligent control, stochastic optimal control theory and methods are used to discuss in depth the mathematical construction of this type of random signal transmission system. Two optimal control mathematical models are obtained via the optimal calculation and random optimization analysis of the allowable coding function and decoding function under this type of signal. It provides an ideal mathematical processing method for improving the intelligent control and information management efficiency of the signal transmission system.
A Survey of VR-Based Neurofeedback Systems in Physiological Computing for Depression Treatment

Depression is one of the most significant mental or psychiatric disorders that impact millions of people worldwide. Commonly used solutions to treat depression are a combination of nondrug therapies such as psychotherapy and physical therapy, along with antidepressant medication prescriptions. However, therapy sessions are often terminated by patients losing interest in continuing due to the cost and time-consuming nature of the treatments, leading to unsatisfactory recovery progress. To assist in diagnosing and treating depression using advanced technologies, numerous researchers have explored and developed physiological computing techniques. One of the popular trends is the use of neurofeedback based on Virtual Reality (VR), which has grown in popularity. This current survey examines the hierarchy from medicine and therapy to software models, with a particular emphasis on neurofeedback and its significant use in conjunction with virtual reality, which is the spotlight for all researchers. While the search for more universally satisfactory approaches is still ongoing, the main goal of this review is to help researchers by providing a thorough overview of currently used techniques and identifying potential changes that could be made to improve the model’s performance.
Epileptic Seizure Detection Using Single-Channel EEG and Artificial Intelligence Techniques

Epilepsy is a chronic neurological disorder characterized by recurrent involuntary seizures which can lead to serious injuries and death. Clinicians diagnose epilepsy by examining the electroencephalograms (EEG) of patients for seizures. However, early-stage diagnosis of epilepsy is difficult due to the labor-intensive nature of the procedure, lack of trained professionals, and inherent variability in the EEG of different patients. In this paper, we propose an automatic seizure detection system with a patient-independent approach in which the system analyzes and detects seizures in single-channel EEG from multiple patients. The system classifies an EEG instance as seizure or non-seizure based on learned features from the training set. For this task, we combine a comprehensive set of features extracted from the time, frequency, and time-frequency domains to capture information about EEG signals' amplitude, frequency, and morphology. A significant amount of experimental work has been conducted to establish the best algorithms and parameters for preprocessing, feature selection, and classification. Which achieved satisfactory results where the highest accuracy and specificity exceeded 99% through a random forest classifier and the highest sensitivity of 98.9% was achieved through the RBF-SVM classifier. We also demonstrate through comparative results that feature sets selected by the minimum redundancy maximum relevance (MRMR) algorithm outperform the ones obtained through principal component analysis (PCA). Our results indicate that an accurate, rapid, and robust automatic detection of epileptic seizures is possible; which contributes significantly to the efficiency of the epilepsy diagnosis process and the care of epileptic patients.
Fishmeal replacement by mealworm (Tenebrio molitor) in diet of farmed Pacific white shrimp (Litopenaeus vannamei): effects on growth performance, serum biochemistry, and immune response

Reducing the use of fishmeal (FM) in shrimp feed means significant savings in the amount of FM consumed globally and subsequently reducing production costs and environmental impacts. Insect meal (IM) is one of the protein sources to replace FM in aquafeeds. In this regard, this study was conducted with the aim of investigating the effect of replacing FM with mealworm (MW, Tenebrio molitor) on the growth performance, haemolymph biochemical responses, and innate immunity of Litopenaeus vannamei. Shrimps with a mean weight of 7.41 ± 0.13 gram were cultured in 300-liter fiberglass tanks (with a useful drainage volume of 200 liters) with a density of 20 shrimp per tank over a period of 60 days. Dietary treatments, including the control treatment (no mealworm; T0), 15% (T15), 30% (T30), 60% (T60), and 100% (T100) level of replacing FM with mealworm (MW), each with three replications, were investigated in the form of a randomized design. The results of this study showed a significant difference in body weight gain (BWG), feed efficiency (FE), feed conversion ratio (FCR), and hepatopancreas index (HPI) among the treatments (P < 0.05). With the increase of the replacement of FM with MW up to 30%, BWG, FE, and HPI were significantly increased then reduced. The levels of cholesterol (Chol), triglycerides (Tg), and glucose (Glu) showed a decreasing trend with increasing replacement of FM with MW and revealed a significant difference with the control treatment at high levels of replacement (P < 0.05). Besides, the results showed that replacing FM with MW had a significant effect on the activities of superoxidase dismutase (SOD), phenol oxidase (PO), lysozyme (LZM), acid phosphatase (ACP), alkaline phosphatase (ALP) and the total count of hemocytes (THC) in the practical diets compared to the control group (P < 0.05). Overall, the findings suggest that MW is a promising alternative protein source for L. vannamei, as it enhances both growth performance and the immune system. The study recommends the use of MW in the diet of farmed species in the aquaculture industry, given its lack of adverse impacts on growth performance and its potential to reduce environmental consequences resulting from its production. The results also underscore the importance of exploring alternative protein sources to reduce dependence on FM and enhance sustainability in the aquaculture industry.
Plan Modi(cid:12)cation versus Plan Generation: A Complexity-Theoretic Perspective (cid:3)

The ability of a planner to modify a plan is considered as a valuable tool for improving e(cid:14)-ciency of planning by avoiding the repetition of the same planning e(cid:11)ort. From a computational complexity point of view, however, it is by no means obvious that modifying a plan is computationally as easy as planning from scratch if the modi(cid:12)cation has to follow the principle of \conservatism," i.e., to reuse as much of the old plan as possible. Indeed, considering propositional STRIPS planning, it turns out that conservative plan modi(cid:12)cation is as hard as planning and can sometimes be harder than plan generation. Furthermore, this holds even if we consider modi(cid:12)cationproblems where the old and the new goalspeci(cid:12)cation are similar. We put these results into perspective and discuss the relationship to existing plan modi(cid:12)cation systems.
Serratus anterior plane block versus thoracic epidural block in patients undergoing thoracotomy: a randomized double-blind trial

Background & Objective: Standard pain management technique after thoracic surgery is thoracic epidural analgesia, parentral analgesics and nerve blocks. Thoracic epidural analgesia (TEA) has many known complications, including respiratory depression and urinary retention. Serratus anterior plane block (SAPB) is a simple procedure, which provides postoperative pain relief by blocking the lateral cutaneous branches of T2–T9 spinal neurons. We compared the analgesic efficacy of both of these techniques in thoracotomy patients in this randomized trial. Methodology: The study involved 74 cancer patients scheduled for thoracotomy. The patients were randomly divided into two groups. The patients in Group SAPB underwent ultrasound-guided serratus anterior plan block (SAPB) with catheter insertion. The second group (Group TEA) underwent TEA with preoperative indwelling catheter insertion. In both groups blocks were activated before induction of routine general anesthesia (GA) by a bupivacaine bolus dose, then continued as bupivacaine infusion in ICU for postoperative analgesia. Hemodynamic monitoring was started. Intraoperative fentanyl consumption, postoperative morphine consumption, and time to first request for analgesic were noted including MAP in the two groups. Results: Both groups were statistically comparable regarding intraoperative fentanyl consumption, postoperative morphine consumption, and time to first request for analgesia in the two groups. Hypotensive episodes were significantly more frequent (P < 0.001) in the TEA group (n = 17; 45.9%) compared to the Group SAPB (n = 2; 5.4%). Ramsey sedation scores (RSS) were comparable in the two groups immediately postoperatively and after 2 h. After 4 h after recovery all patients in both groups had an RSS of 2. Conclusion: Ultrasound-guided serratus anterior plane block is associated with reduced intraoperative and postoperative fentanyl consumption, but the Ramsey sedation scores were equivalent in the two groups after recovery and upto 4h. Abbreviations: GA: General Anesthesia; PTPS: Post-Thoracotomy Pain Syndrome: RSS: Ramsey Sedation Score; SAPB: Serratus Anterior Plane Block; TEA: Thoracic Epidural Analgesia; VATS: Video-Assisted Thoracoscopic Surgery Key words: Pain, Postoperative; Serrataus plane block; Thoracotomy; Ultrasound; Thoracic epidural Citation: Gamal RM, Bossily SS, Ali AR, Samy SF, Seif NE, Ahmed HI. Serratus anterior plane block versus thoracic epidural block in patients undergoing thoracotomy: a randomized double-blind trial. Anaesth. pain intensive care 2023;27(3):389−395. DOI: 10.35975/apic.v27i3. 2243 Received: March 06, 2023; Reviewed: March 31, 2023; Accepted: April 01, 2023
Surface Toppling on Cylindrical and Polyhedral Sandpiles

Abelian Sandpiles are commonly investigated on unbounded two-dimensional square lattices. Less explored is the behavior of sandpiles on three-dimensional surfaces. In this paper I investigate partially and fully closed sandpile graphs that can be wrapped around cylinders and cubes. Aside from attractive symmetries not possible on a uniform flat lattice, I am interested in the ways that bending the sandpile in on itself this way can produce novel interactions. I study how the quantity and placement of drop spots affected the final stabilized pattern aesthetically and the density of sand throughout the sandpile.
Baroclinic Adjustment and Dissipative Control of Storm Tracks

The steady-state response of a midlatitude storm track to large-scale extratropical thermal forcing and eddy friction is investigated in a dry general circulation model with a zonally symmetric forcing. A two-way equilibration is found between the relative responses of the mean baroclinicity and baroclinic eddy intensity, whereby mean baroclinicity responds more strongly to eddy friction whereas eddy intensity responds more strongly to the thermal forcing of baroclinicity. These seemingly counterintuitive responses are reconciled using the steady state of a predator–prey relationship between baroclinicity and eddy intensity. This relationship provides additional support for the well-studied mechanism of baroclinic adjustment in Earth’s atmosphere, as well as providing a new mechanism whereby eddy dissipation controls the large-scale thermal structure of a baroclinically unstable atmosphere. It is argued that these two mechanisms of baroclinic adjustment and dissipative control should be used in tandem when considering storm-track equilibration.
Visual Coding for Machines

In this thesis, we study different approaches to visual data coding for machines and develop new methods to address some issues in this area. We mainly focus on the Image and Video signals processed by a Deep Neural Network (DNN)-based computer vision model. Our proposed methods are designed to be utilized in DNN-based machines deployed collaboratively on the edge and cloud. This framework is called Collaborative Intelligence (CI), in which a DNN model is split into two parts such that the edge device runs the first few layers, and the remaining layers are executed on the cloud. To that end, the intermediate feature tensors need to be coded and transferred to the cloud. This research explicitly attempts to provide solutions for efficient coding of these tensors, considering challenges such as motion estimation and compensation for videos in the latent space, and privacy for images.
QUANTITATIVE ETHNOBOTANICAL DOCUMENTATION OF THE MEDICINAL PLANTS USED BY THE INDIGENOUS MARING TRIBE OF CHANDEL DISTRICT OF MANIPUR, INDIA.

An ethnobotanical programme was conducted in Maring inhabited areas of Chandel district of Manipur state of North-eastern India with an aim to document the indigenous knowledge of the community. Since, the community is mostly located in hilly areas they depend heavily on plant based medicines for curing their diseases. The collected data was quantitatively analyzed through the methods of Use Value (UV), Informant Consensus Factor (Fic) and Fidelity level (FL %). In this study, a total of 144 plant species belonging to 66 families were collected which is used by the tribal community in curing their ailments. The ailments were grouped into 18 disease categories. Skeleton muscular system disorder (SMSD), Oral care (OC) and Circulatory system disorder (CSD) had the highest Fic of 0.91, 0.89 and 0.84 respectively. The highest use reports were recorded from Genito-urinary disorders (GUD) with 176 use reports & 32 plant species, Circulatory system disorder (CSD) 126 use report
Procrastination Preventor using YOLO

According to research, procrastination is a frequent and damaging type of self-regulation failure. With so many deadlines and pressure to complete their work, students struggle with academic procrastination. As a result, even feeling unwell can delay coursework and affect academic progress. Not even Students do procrastinate as if they have to do something, employees and other people do the same. This delay affects many things. It affects our mental health and leads to stressful lives. To reduce this procrastination, a software model is developed called “Procrastination Preventer using YOLO” that helps to avoid procrastination, reduce stress and help complete a specific task on time. This model also has a pomodoro timer which will also help in managing time efficiently. The intriguing thing about it is that, it has integrated text-to-speech technology. It means that in addition to notifying the user by displaying the text, the timer will also verbally announce it at the same time. This model is built using Python, Jupyter Notebook, OpenCV, Pomodoro timer, Text to Speech converter.
A new direction in mental health: purposeful life engagement

Historically, mental health research and practice has focused primarily on mental illness or distress. In such a framework, health usually means the absence of the negative. It is increasingly recognized, however, that mental health must also encompass the presence of the positive. One aspect of positive functioning receiving growing attention is purposeful life engagement: perceiving that one ’ s life has direction and meaning. The conceptual and philosophical foundations of this aspect of well-being are examined, followed by a look at select empirical ﬁ ndings, including how purpose in life is linked in bene ﬁ cent ways to multiple aspects of health (biological risk factors, preventive health behaviors, brain-based regulation of emotion, morbidity
Your Mind Online: The Influence of Contingent Self-Esteem on Confirmation Bias

Scholars have speculated on the effects of social media on our self-esteem, but research has resulted in mixed findings (e.g., Valkenburg et al., 2006). One area that has yet to be investigated thoroughly is how social media use relates to self-esteem pursuit (Crocker & Park, 2004). Self-esteem pursuit has negative outcomes for learning, relatedness, and autonomy (Crocker & Park, 2004), but research has yet to determine whether pursuing self-esteem on social media results in similar negative outcomes. The current study investigated whether social media engage users in self-esteem pursuit by priming existing self-esteem contingencies. Additionally, we investigated whether these contingencies lead to preoccupation with the self or impact rational information processing. Using a selective exposure paradigm, we assessed whether heightened awareness of self-esteem contingencies on social media motivates one to bolster their self-view by engaging in biased motivational reasoning (i.e., confirmation bias) and whether the proposed relationship can be explained by an increase in self-focused attention. Results showed that engaging with Instagram (relative to Wikipedia) did not produce increases in contingent self-esteem or biased motivational reasoning behavior. These findings suggest that brief exposure to varied content on social media does not prime existing self-esteem contingencies. Implications of these results with respect to improving the current methods for future investigation of this research question are discussed.
Self-completeness and the generalized uncertainty principle Self-completeness and the generalized uncertainty principle

: The generalized uncertainty principle discloses a self-complete characteristic of gravity, namely the possibility of masking any curvature singularity behind an event horizon as a result of matter compression at the Planck scale. In this paper we extend the above reasoning in order to overcome some current limitations to the framework, including the absence of a consistent metric describing such Planck-scale black holes. We implement a minimum-size black hole in terms of the extremal conﬁguration of a neutral non-rotating metric, which we derived by mimicking the eﬀects of the generalized uncertainty principle via a short scale modiﬁed version of Einstein gravity. In such a way, we ﬁnd a self-consistent scenario that reconciles the self-complete character of gravity and the generalized uncertainty principle.