bluuebunny/crossref_metadata_2025 huggingface dataset:
Segmented silicon-based solid-state detector with thin dead layer for superheavy element research
Authors: Kuramoto Kosaku , Morimoto Kouji , Kaji Daiya , Brionnet Pierre , Takeyama Mirei , Kosugi Kazumasa , Tokanai Fuyuki
Published: March 2023

Silicon-based solid-state detectors (SSDs) are crucial for investigating the properties of superheavy elements (SHEs), since they measure the energy of SHEs and charged particles that are emitted as successive decay events of the SHEs. We have developed a segmented SSD for SHE studies using the new gas-filled recoil ion separators (GARIS-II and GARIS-III) at RIKEN. It is based on a back-illuminated Si PIN photodiode in which the irradiation is through the N+ layer. The detector is introduced as a side one for the GARIS focal plane detection system. To investigate the characteristics of the SSD, 241Am α particles were irradiated by automatically moving and rotating the α source for each segmented portion of the SSD. A Monte Carlo simulation was also performed to estimate the dead layer thickness of the segmented SSD.
L’Ère du métaverse et autorégulation des plateformes en ligne
Authors:   Korean Constitutional Law Asscciation, JEON Hak-Seon
Published: March 2023

A l'ère du métaverse, divers problèmes apparaissent dans des domaines où la loi n'est pas appliquée. Dans le métaverse, divers problèmes juridiques tels que la liberté d'expression, les droits de la personnalité, la vie privée et les droits de propriété intellectuelle ont été mis en évidence, et de nombreuses discussions sont nécessaires sur la manière de les résoudre. La solution souhaitée est l'autorégulation par les opérateurs de plateformes Internet par consensus social, mais comme l'autorégulation n'est pas bien comprise, il existe de nombreuses revendications concernant l'autorégulation. L'autorégulation a des avantages et des inconvénients, et la réglementation légale a aussi des avantages et des inconvénients, il est donc nécessaire de les utiliser correctement. Si l'autorégulation est suffisamment en place, il n'y aura pas besoin d'une réglementation légale de l'État. Cependant, puisque l'autorégulation seule ne suffit pas à réglementer, la régulation légale de l'État doit être menée conjointement. Parmi les diverses formes d'autorégulation, l'autorégulation volontaire peut être qualifiée d'autorégulation pure, mais il est déraisonnable de s'attendre à une autorégulation uniquement volontaire. Par conséquent, on pense que le gouvernement a besoin d'une politique pour induire l'autorégulation des opérateurs de plateformes en ligne. En outre, lors de l'induction de l'autorégulation des opérateurs de plateformes en ligne, il est nécessaire de présenter un modèle d'autorégulation. Les opérateurs de plateformes en ligne manquant d'expertise sur le contenu et le mode de régulation, il est souhaitable que l'État présente un modèle d'autorégulation selon le type d'opérateur de plateforme en ligne, et pour les opérateurs de plateforme en ligne. Dans le secteur de l'Internet, en raison des changements environnementaux rapides, les réglementations légales ont des limites et l'autorégulation doit être appliquée conjointement. Pour l'autorégulation des plateformes en ligne, une promotion plus active et spécifique est nécessaire.
Ion Channels and Therapeutic Agents
Authors:   Georgian Technical University, Tsomaia Irma , Tabatadze Nino ,   Sokhumi State University, Tsintsadze Tamar ,   Georgian Technical University
Published: March 2023

Ion channels are parts of the nerve cell membrane that provide the selective permeability of ions, i.e. positively and negatively charged particles, and participate in the regulation of the body's vital processes. They are responsible for regulating the flow of ions in the plasma and membranes of all cells, controlling the intracellular concentrations of various ions. The alternating closing and opening of sodium, potassium and calcium ion channels ensures the spread of the nerve impulse. Blockage of ion channels causes serious changes in the body. In today's medicine, a number of diseases are explained precisely by the malfunction of ion channels. Widespread pathologies in the world population: angina pectoris, arterial hypertension, arrhythmia, epilepsy, diabetes, etc. This is a small list of diseases that are caused by malfunctioning of ion channels in the human body. Based on this, the diseases caused by ion channel malfunctions and the effective remedies for their treatment are an urgent issue for the world population.
Robust Decision Making for Autonomous Vehicles at Highway On-Ramps: A Constrained Adversarial Reinforcement Learning Approach
Authors: He Xiangkun , Lou Baichuan , Yang Haohan , Lv Chen
Published: March 2023

Reinforcement learning has demonstrated its potential in a series of challenging domains.

However, many real-world decision making tasks involve unpredictable environmental changes or unavoidable perception errors that are often enough to mislead an agent into making suboptimal decisions and even cause catastrophic failures.

In light of these potential risks, reinforcement learning with application in safety-critical autonomous driving domain remains tricky without ensuring robustness against environmental uncertainties (e.g., road adhesion changes or measurement noises). 

Therefore, this paper proposes a novel constrained adversarial reinforcement learning approach for robust decision making of autonomous vehicles at highway on-ramps.

Environmental disturbance is modelled as an adversarial agent that can learn an optimal adversarial policy to thwart the autonomous driving agent.

Meanwhile, observation perturbation is approximated to maximize the variation of the perturbed policy through a white-box adversarial attack technique.

Furthermore, a constrained adversarial actor-critic algorithm is presented to optimize an on-ramp merging policy while keeping the variations of the attacked driving policy and action-value function within bounds.

Finally, the proposed robust highway on-ramp merging decision making method of autonomous vehicles is evaluated in three stochastic mixed traffic flows with different densities, and its effectiveness is demonstrated in comparison with the competitive baselines.
On the Stability and Homogeneous Ensemble of Feature Selection for Predictive Maintenance: A Classification Application to Tool Condition Monitoring in Milling
Authors: Assafo Maryam , Städter J. Philipp , Meisel Tenia , Langendörfer Peter
Published: March 2023

Feature selection (FS) represents an essential step for many machine learning-based predictive maintenance (PdM) applications, including various industrial processes, components, and monitoring tasks. The selected features do not only serve as inputs to the learning models, but also they can influence further decisions and analysis, e.g., sensor selection, understandability of the PdM system. Hence, before deploying the PdM system, it is crucial to examine the reproducibility and robustness of the selected features under variations in the input data. This is particularly critical for the real-world datasets with a low sample-to-dimension ratio (SDR). However, to the best of our knowledge, stability of the FS methods under data variations has not been considered yet in the field of PdM. This paper addresses this issue with an application to tool condition monitoring in milling, where classifiers based on support vector machine and random forest were employed. We used a 5-fold cross-validation to evaluate three popular filter-based FS methods, namely Fisher score, maximum relevance minimum redundancy (mRMR), and ReliefF, in terms of both stability and macro-F1. Further, for each method, we investigated the impact of the homogeneous FS ensemble on both performance indicators. To gain broad insights, we used four (2:2) milling datasets generated from our experiments and NASA’s repository, which differ in the operating conditions, sensors, SDR, number of classes, etc. Among the findings: 1) Different FS methods can yield comparable macro-F1, yet considerably different FS stability values. 2) Fisher score (single and/or ensemble) is the superior in most of the cases. 3) mRMR’s stability is overall the lowest, the most variable over different settings, e.g., sensor(s), subset cardinality, and the one most benefiting from the ensemble. 
Multidimensional Hosting Capacity Region in Dutch MV Grid Congestion Management
Authors: Gong Sicheng , Cuk Vladimir , Cobben Sjef
Published: March 2023

The hosting capacity determines the grid ability to host integrated installations, where mutual-limitations among points of connection (POCs) are important and formulate a combinational feasible region. Intended to assess such interdependence for defining the region, this paper proposes a multidimensional hosting capacity region derivation scheme in a radial grid. As this scheme can be designed conservative, from a risk-averse viewpoint, it gains significant acceptance in the industrial sector. This multidimensional region not only exploits grid power delivery potential, but also aids in making grid congestion management decisions. A typical 10.5kV Dutch grid case has been tested accordingly, and corresponding results have confirmed the conservative property preservation of the region. The case study demonstrates that compared to the original hosting capacity concept, the region hypervolume gain ratio remains higher than 1.94. With proper measure selection, the estimated region occupation ratio can keep up to 92.50%. The congestion management decision computation time can also be reduced by 58.0% compared to that using the monolithic model. The effectiveness of the proposed scheme and its advantages in grid congestion management have been successfully confirmed. 
Navigating the Ethical Landscape of AI in Healthcare: Insights from a Content Analysis
Authors: Wu Yin , Liu Xiang Michelle
Published: March 2023

Rapid development of artificial intelligence (AI) has permeated various aspects of our lives. Its usage in the field of healthcare has shown significant improvement in the efficiency and efficacy of numerous aspects of healthcare services. However, wide applications of AI technologies in healthcare are also joined by ethical challenges threatening the privacy and security of public health. This study conducts a content analysis on primary organization and government documents related to ethical issues of AI in healthcare. While the transparency and explainability of AI technologies have been granted a great amount of focus, more region-specific discussions are in need. Inclusiveness and equity of access to AI technology in healthcare should also be considered more by regulators and practitioners when proposing future standards and legislation frameworks.     
Ill-posedness and the bias-variance tradeoff in residual stress measurement inverse solutions
Authors: Beghini Marco , Grossi Tommaso , Prime Michael B. , Santus Ciro
Published: March 2023

Background: Relaxation methods determine residual stresses by measuring the deformations produced by incremental removal of a subdomain of the specimen.

Measured strains at any given increment, determined by the cumulative effect of the relieved stresses, appear as an integral equation, which must be inverted to obtain residual stresses. In practice, stress distributions are discretized by a finite-dimensional basis, to transform the integral equations into a linear system of equations, which is often ill-conditioned.

Objective: This article demonstrates that the problem is actually ill-posed and comes with an inherent bias-variance tradeoff.

Methods: The hole drilling method is used as an example application, and the practical effects of ill-posedness are illustrated.

Results: Traditional regularization of the solution by limiting the resolution of the discretization reduces solution variance (noise) at the expense of increased bias

and often results in the ultimately harmful practice of taking fewer data points. A careful analysis including the alternate Tikhonov regularization approach shows

that the highest number of measurements should always be taken to reduce the variance for a given regularization scheme. Unfortunately, the variability of a regularized solution cannot be used to build a valid confidence interval, since an unknown bias term is always present in the true overall error.

Conclusions: The mathematical theory of ill-posed problems provides tools to manage the bias-variance tradeoff on a reasonable statistical basis, especially

when the statistical properties of measurement errors are known. In the long run, physical arguments that provide constraints on the true solution would be of utmost importance, as they could regularize the problem

without introducing an otherwise unknown bias. Constraining the minimum length scale to some physically meaningful value is one promising possibility.
A Novel Pumping Principle for a Total Artificial Heart
Authors: Bierewirtz Tim , Narayanaswamy Krishnaraj , Giuffrida Rosario , Rese Tim , Bortis Dominik , Zimpfer Daniel , Kolar Johann Walter , Kertzscher Ulrich , Granegger Marcus
Published: March 2023

Objective: Total artificial hearts (TAH) are used as a temporary treatment for severe biventricular heart failure. Long-term cardiac replacement is hampered by limited durability and complication rates, which may be attributable to the modus operandi of state-of-the-art pumping systems. The aim of this study was to assess the feasibility of a novel valveless pumping principle for a durable pulsatile TAH (ShuttlePump). 

Methods: With a rotating and linearly shuttling piston within a cylindrical housing with 2 in- and outlets, the pump features only one single moving part and delivers pulsatile flow to both systemic and pulmonary circulation. The pump and actuation system were designed iteratively based on analytical and in silico methods, utilizing finite element methods (FEM) and computational fluid dynamics (CFD) Pump characteristics were evaluated experimentally in a mock circulation loop mimicking the cardiovascular system, while hemocompatibility related parameters were calculated numerically. 

Results: Pump characteristics cover the entire required operating range for a TAH (2.5 - 9L/min at 50 - 160mmHg arterial pressures) at stroke frequencies of 1.5 - 5Hz while balancing left and right atrial pressures. FEM analysis showed a mean overall copper losses of 8.84W, resulting in local blood temperature rise of < 2k. The CFD results of normalized index of hemolysis was 3.57 mg/100L and 95% of the pumps blood volume was exchanged after 1.42s. 

Conclusion: This study indicates feasibility of a novel pumping system for a TAH with numerical and experimental results substantiating further development of the ShuttlePump.
Probabilistic Navigation with Quantum Circuits
Authors: Delgado Alberto
Published: March 2023

A probabilistic controller, for robot navigation with behaviors, is designed using the basic mathematical framework of quantum computing. The controller changes behavior probabilities following sensor readings, behaviors depend on arena and platform properties, not just obstacles, also controller gains can be programmed by direct external signals.
The Effect of Coil to Cortex Distance and Induced Electric Field Strength on Resting Motor Thresholds in Schizophrenia Patients
Authors: Lewis Connor , Cheng Emily , Mehta Urvakhsh Meherwan , Pandurangi Anand , Hadimani Ravi
Published: March 2023

Transcranial magnetic stimulation is a noninvasive neuromodulation technique utilized to treat several neuropsychiatric disorders. Schizophrenia patients typically experience positive and negative symptoms, which are commonly treated with antipsychotic medications. Repetitive (r)TMS is explored to alleviate these symptoms by modulating cortical excitability. TMS stimulation strength is determined by the resting motor threshold (RMT), which is determined from single pulse stimulation to the precentral gyrus region of the brain. In this study, 62 schizophrenia and 41 healthy participants’ magnetic resonance imaging (MRI) were used to create anatomically accurate head models. Head models were then used to calculate the coil-to-cortex distance (CCD), gray matter volume (GMV), and finite element analysis was performed to calculate the induced electric field strength (EFS) and depolarized gray matter volume (DGMV). These MRI-derived parameters were then evaluated to establish their relationship with clinically collected RMT in both healthy and schizophrenia populations. In both populations, RMT correlated with CCD, EFS, and DGMV. Our hypothesis that the presence of schizophrenia would have a negative impact on the influence of MRI model-driven parameters was largely supported. Future investigations into RMT variability should incorporate structural connectivity using white matter-based fiber tractography and functional connectivity.
Stock market prediction based on machine learning and social sentiment analysis
Authors: Zaman Noor , Ghazanfar Mustansar Ali , Anwar Madiha , Lee Sin Wee , Qazi Nadeem , Karimi Amin , Javed Ali
Published: March 2023

Precise stock market prediction is crucial for investors, but the volatility of the stock market is influenced by multiple factors such as public sentiments, business news, and related product volatility. While several algorithms have been proposed to predict the stock exchange index based on historical data, they are not ideal as external factors play a critical role in market volatility. To address this issue, we proposed a machine learning model that incorporates historical data with external factors such as social media sentiments, oil and gold trends, and financial news data to enhance prediction accuracy. Our study used HPQ, IBM, ORCL, and MSFT stock market datasets to validate the effectiveness of the proposed model, including an analysis of the impact of Covid19 on companies. Our experimental results showed the highest accuracy of 87.2% using oil and sentiment datasets. Additionally, we identified that social media significantly affects IBM stocks, and the GBM (Gradient Boosting Classifier) classifier produced consistent results. 
Fourier-Based Multi-Agent Formation Control to Track Evolving Closed Boundaries
Authors: Zhang Bin , Zhi Hui , Romero Jose Guadalupe , Labazanova Luiza , Duan Anqing , Li Xiang , Navarro-Alarcon David
Published: March 2023

The automatic monitoring/tracking of environmental boundaries by multi-agent systems is a fundamental problem which has many practical applications. In this paper, we address this problem with formation control techniques based on parametric curves that represent the boundary’s feedback shape. For that, we approximate the curve with truncated Fourier series, whose finite coefficients are utilized to characterize the curve’s shape and to automatically distribute the agents along it. These feedback Fourier coefficients are exploited to design a new type of formation controller that drives the agents to form desired curves. A detailed stability analysis is provided for the proposed control methodology, considering both fixed and switching multi-agent topologies. The reported numerical simulation study demonstrates the performance and feasibility of our new method to track closed boundaries of different shapes.
FitFun: A Modelling Framework for Successfully Capturing the Functional Form and Noise of Observed Traffic Flow-Density-Speed Relationships
Authors: Bramich Daniel
Published: March 2023

Measurements of the average properties of vehicular traffic are inherently noisy. The distributions of flow and speed measurements at any particular density are non-Gaussian with density-dependent variance, skewness, and kurtosis. Previous studies have failed to properly account for these complicated noise properties. In remediation, we present FitFun, a general framework for modelling any observed flow-density-speed relationship. Models specified within FitFun incorporate components for both the functional form and the noise. We define three flexible noise model components and we fit 200 different models to a high-quality sample of 10,150 observed urban flow-occupancy relationships. We compare the fits using information criteria and assess fit quality through analysis of the residuals. We find that the non-parametric Sun model for the functional form component combined with a Skew Exponential Power Type III noise component significantly outperforms all of the other models. Interestingly, we find that the city, country, road topology, and detector location have virtually no impact on model performance and fit quality, which is very convenient for model selection. The only factor of relevance from those that we studied is the effective occupancy coverage of the data. We conclude that certain models specified judiciously within FitFun can successfully capture the functional form and noise of observed flow-density-speed relationships without the need to discard data taken during non-stationary conditions. This is particularly advantageous for urban data where stationary traffic conditions are rarely observed.

Accepted by Transportation Research Part C on 16th Feb 2023.
Inherently Decoupled Dc-Link Capacitor Voltage Control of Multilevel Neutral-Point-Clamped Converters
Authors: Garcia-Rojas Gabriel , Busquets-Monge Sergio , Griñó Robert , Campos-Salazar José M.
Published: March 2023

This letter derives and discusses the superiority of a simple dc-link capacitor voltage control configuration for multilevel neutral-point-clamped converters with any number of levels. The control involves n−2 control loops regulating the difference between the voltage of neighbor capacitors. These control loops are inherently decoupled; i.e., they are independent and the control action of one loop does not affect the others. The good performance of such control is confirmed through simulations and experiments. 
ESRNN: Effective Residual Self-Attention Recurrent Neural Network for Sound Event Location
Authors: Zhang Bin
Published: March 2023

Sound event location is a momentous subtask of two-dimensional direction-of-arrival (2D-DOA) estimation to forecast the azimuth and elevation angle from each active sound event class of an audio fragment using multi-label regression in the 3D Cartesian coordinates. The main problem with the traditional multi-signal classification (MUSIC) algorithm and the existing baseline convolution recurrent neural network (BCRNN) are lower precision and huge computation in weaker signal-noise-ratio (SNR) environment. In particular, MUSIC algorithm will cause complete distortion when the SNR is lower than -5 dB. We thus design effective residual self-attention recurrent neural network (ESRNN) not only to overcome the distortion of traditional MUSIC algorithm under lower SNR but also further reduce predicted 2D-DOA error in different SNR reverberation environments. Two different filter structures, ESRNN-L and ESRNN-G, are designed to improve the SNR of our model when the SNR is above 0 dB or below -5 dB in a targeted manner respectively. To verify the efficiency and robustness, we train and test our model using TAU Spatial Sound Events 2019 datasets extracted the phase and magnitude spectrogram with different synthetic SNRs ranging from -10 dB to 30 dB. The experimental results show that the optimal 2D-DOA error of our ESRNN-L is 21% lower than BCRNN when the SNR is less than -5 dB, and ESRNN-G is 15% error lower with 10% parameters reduction when the SNR is higher than 0 dB.
Ultrasound Concave 2D Ring Array for Retinal Stimulation
Authors: Lu Jian-yu , Lu Gengxi , Thomas Biju , Humayun Mark , Zhou Qifa
Published: March 2023

This paper developed an ultrasound concave 2D ring array transducer for retinal visual stimulation of the brain to restore vision in blind people. Theory, simulation, and experiment were presented. 
PURIFYING ADVERSARIAL IMAGES USING ADVERSARIAL AUTOENCODERWITH CONDITIONAL NORMALIZING FLOWS
Authors: Ji Yi , Le Trung-Nghia , Nguyen Huy H. , Echizen Isao
Published: March 2023

We present a target-agnostic adversarial autoencoder with conditional normalizing flows specifically designed to, given any unlabeled image dataset, purify adversarial samples into clean images, i.e., remove adversarial noise from the images while preserving their visual quality. In our model interpretation, samples are processed by manifold projection in which the encoder brings the sample back into a posterior data distribution in latent space so that the sample is less likely to be irregular to the learned representation of any target classifier. Normalizing flows conditioned on top of our hybrid network structure and walk-back training are used to deal with common drawbacks of generative model and autoencoder-based approaches: not only the trade-off between compression loss and over-fitting on training data but also the structural model dependency on dataset classes and labels. Experiments demonstrated that our proposed model is preferable to existing target-agnostic adversarial defense methods particularly for large and unlabeled image datasets.
Prediction of Nocturnal Hypoglycaemia in Adults with Type 1 Diabetes using Machine Learning Classifiers
Authors: Afentakis Ioannis , Unsworth Rebecca , Herrero Pau , Oliver Nick , Reddy Monica , Georgiou Pantelis
Published: March 2023

1st dataset

6-months randomized controlled, cross-over study

37 participants wearing CGM sensors and using MDI regimen

smartphone app where to record administered insulin and meal macronutrient information

diagnosed with T1D for more than 3 years

2nd dataset

head-to-head glucose monitoring study, comparing CGM and flash glucose monitoring

40 high- risk adult participants using MDI for their insulin treatment 
Enhancing Machine Learning Algorithms using GPT Embeddings for Binary Classification
Authors: SATHVIK MSVPJ
Published: March 2023

The Language Model Models (LLMs) have demonstrated their ability to process and understand natural language inputs accurately. This indicates that LLMs are capable of superior natural language processing capabilities. The GPT embeddings are words generated from the GPT backend of the ChatGPT developed by OpenAI, which can produce accurate outputs for user inputs. In this paper, we generate GPT embeddings and apply machine learning algorithms for fake news prediction and sentiment analysis. The proposed method produces remarkable results with an improvement of approximately 12.59% in accuracy compared to traditional embeddings. For fake news detection, GPT embeddings with SVM outperformed LSTM with Glove embeddings. We evaluated 10 machine learning models using 4 versions of GPT embeddings, namely Ada, Babbage, Curie, and Davinci. The sentiment analysis execution resulted in an impressive accuracy of 98.6%. We made our embeddings publicly available for both datasets. We believe that this is a valuable contribution since generating such embeddings requires access to GPT, which is not freely available to researchers.
The Effects of Exoskeleton Assistance at the Ankle on Sensory Integration during Standing Balance
Authors: Canete Santiago , Wilson Elizabeth , Wright W. Geoffrey , Jacobs Daniel A
Published: March 2023

Exoskeleton devices can reduce metabolic cost, increase walking speed, and augment load-carrying capacity. However, little is known about the effects of powered assistance on the sensory information required to achieve these tasks. To learn how to use an assistive device, humans must integrate novel sensory information into their internal model, but under certain sensory conditions, this may present a challenge. We investigated the exoskeleton-induced changes in balance performance and the sensory system adaptations during quiet standing. We asked 11 unimpaired adults to perform a virtual reality-based test of sensory integration in balance (VRSIB) on two days while wearing the exoskeleton either unpowered, using proportional myoelectric control, or with regular shoes. We measured postural sway, equilibrium scores, and sensory ratios. In addition, we measured postural control strategy, joint kinematics and kinetics, and muscle activity. Results showed improvement in balance performance when wearing the exoskeleton on firm ground. The opposite occurred when standing on an unstable platform with eyes closed or when the visual information was non-veridical. The balance performance was equivalent when the exoskeleton was powered versus unpowered in all conditions except when both the support surface and the visual information were altered. We argue that in stable ground the passive stiffness of the device dominates the postural task. In contrast, when the ground becomes unstable the passive stiffness negatively affects balance performance. Furthermore, when the visual input to the user is non-veridical, exoskeleton assistance can magnify erroneous muscle inputs and negatively impact the user's postural control.
Tandem Neural Network based Design of Multi-band Antennas
Authors: Gupta Aggraj , Karahan Emir , Bhat Chandan , Sengupta Kaushik , Khankhoje Uday
Published: March 2023

We present a deep neural network-based framework for designing multi-band microstrip antennas given a desired impedance matching spectrum. The approach enables a design methodology that generates the desired antenna structures rapidly (under a second) through an effective deep learning- enabled search of a large design space and eliminates the need for extensive domain knowledge of antenna design. The framework is built on our innovations in tandem neural networks consisting of two cascaded neural networks. Our structures are parameterized in an exponentially large design space of discrete variables (pixels), leading to the realization of nonintuitive structures. This end-to-end synthesis in terms of discrete variables is enabled by introducing a new type of “smooth thresholding” activation function, which, along with crucial regularization terms in the network loss function, aids in designing our structures. We perform extensive neural network optimizations and study various trade-offs in the design process. We demonstrate the efficacy of our methods by generating single and dual-band resonant structures, which can be up to 50% more compact in terms of area, and up to 18 % thinner in terms of substrate height than conventional structures, while retaining competitive performance parameters in terms of gain, polarization properties, radiation efficiency, and fractional bandwidth. 
MySQL Select Query Optimization Using Self-Join
Authors: Jain Vedant
Published: March 2023

 The challenge of query execution in MySQL involves the efficient and accurate processing of SQL queries to retrieve, manipulate, and store data in a database. It requires optimizing the query execution plan, identifying and resolving performance issues, and ensuring data integrity and security. The challenge lies in balancing the trade-offs between query complexity, database size, system resources, and user requirements. Choosing the right approach depends on the specific characteristics of the data and workload. Therefore, in this paper, we have applied various techniques to get optimized results. 
Document Analysis and Recognition: A survey
Authors: NIGAM SHIVANGI , Verma Shekhar , Nagabhushan P
Published: March 2023

The journey of research for Document Analysis and Recognition (DAR) started with the problem of automatic character recognition. Today, it has covered a vast span of recognition tasks such as text recognition, script identification, writer identification, word spotting, signature verification etc., in scripts like Roman, Arabic, Chinese, Japanese, Brahmi etc. Extensive advancements in deep learning techniques have achieved state-of-the-art results for various DAR tasks. This work explores the challenges from different perspectives and reviews the techniques for online/offline and printed/handwritten DAR tasks. We examine the research works with the view of script-related challenges. Various datasets for DAR are categorized into historic, printed and handwritten datasets. We present a comprehensive survey of challenges, techniques, datasets, evaluation metrics, script-related perspectives and potential future directions for DAR.
التجريب في قصيدة النثر المفهوم والمصطلح
Authors: الطائي حسن , عبد رفعت
Published: March 2023

إن التجريب عملية فنية فكرية إبداعية خلاقة تحقق معرفة أرقى، متجددة غالباً ما تحمل صفات المغامرة الإنسانية وخصائصها، والقدرة على فض المجهول واستيعاب الجديد، وجعل من قصيدة النثر العراقية شعلة إبداعية تساهم في خلق تجربة خلاقة تفيد المجتمع، وتتفاعل مع الحياة اليومية. ويتسم التجريب بالمقصدية، للولوج إلى عالم جديد حداثوي يتعصرن مع استمرار الحياة، ولا يستطيع أن يخوض مضماره الهاوي أو المبتدئ ؛ أو المتشعرن، إلا من احكمته الخبرة وهذبته الموهبة الشعرية التي تمتد لسنين طويلة، وارتشف من منابع الطبع منذ نعومة أظفاره بعيداً عن التصنع. ويتأطر ويضع قوانينه يوماً بعد الآخر، ويرتقي لينظم نفسه، محاولاً الخروج عن النموذج الثابت متجاوزاً الأعمال التقليدية، ويبحث عن سبل الانفتاح، وقد ينسلخ في بعض الأحيان عن كل موروث أدبي، كما تجلت عوامله في قصيدة النثر العراقية بعد عام 2003م.
الأديب البيشاوَري وانعكاسات الثورة الدستوريّة في شعره العربي
Authors: كريمي مديحة , مرعشي حسين
Published: March 2023

في المنتصف الثاني من القرن التاسع عشر الميلادي، أدّت عوامل حديثة إلي حُدوث تغيير في رؤي المتنوّرين وأفكارهم في إيران؛ يُضاف إلي ذلك الآراء السياسيّة الغربيّة التي أدّت إلي حركات وطنيّة وبالتالي أَنتجت الشعر الوطني في هذا العصر. وأُولي التغيّرات التي شهدَها الشعر في هذا العصر كان في حقل الفكرة حيثُ إنّ التطرّقَ إلي المسائل الاجتماعيّة ومَطالب الشعب والتحرّر من الظلم والتعسّف أخذ حيّزًا كبيرًا من اهتمام الشعراء. والسيّد أحمد الرضَوي الملقّب بالأديب البيشاوَري تطرّقَ أكثر من غيره من الشعراء إلي الشعر السياسي، ويمكن عَدُّه رائدَ الشعر السياسي الفارسي والعربي في إيران المعاصرة. مِن هذا المنطلق، وعبرَ دراستنا لأهمّ الأغراض والموضوعات التي تطرّقَ إليها الأديب البيشاوَري في أشعاره العربيّة، وبيان مَوقفه بالنسبة إلي الحوادث التي وقعت أيّام الثورة الدستوريّة، تتّضح لنا الظروفُ السياسيّة والاجتماعيّة المسيطرة علي إيران في ذلك العصر وكيفيّةُ ظهور غرض شعري عربيّ جديد في إيران أي الشعر السياسي. فبالتالي، يسعي البحث عن طريق المنهج التأريخي ودراسة المصادر المختلفة، الوُصولَ إلي النتائج المطلوبي، وأهمّها: يري الأديب البيشاوَري أنّ مُبتغي الثورة الدستوريّة هو تحقيق مَصالح بريطانيا ويَعُدّ دعمَها ذنبًا كبيرًا. فهو يَدعو أنصار هذه الثورة وحتّي الآخوند الخراساني الذي كان من زُعمائها الدينيّين، كفّارًا وزَنادقة. ويَعتبر الآخوند الخراساني المسؤولَ عن المُجون والتغيّر الناشئ في زَيّ النساء بعد الثورة الدستوريّة.
A Morpho-Syntactic Approach to Translating English Verb Phrases in Literary Texts into Arabic
Authors: Meteab Waleed , Hazem Ali
Published: March 2023

Translation is a wonderful area of study especially the literary one. Unlike other kinds of renditions, literary translation needs a lot of cognitive processing on the part of translators in that the selecting of one of the multi grammatical forms available in Arabic is not as easy as a cake considering the implied meaning that syntactic or morphological structure suggests. It could be stated that the main problem of this research is the multi syntactic and morphological structures in Arabic and how translators decide on which is the most suitable equivalent for the English verb to be translated. It is hypothesized that translators involved in literary rendering must be fully- equipped with the various morphological and syntactic devices English and Arabic have and how to accurately employ them to produce good renditions. This research aims at providing literary translators with a sort of practical knowledge and how to achieve accurate equivalence greatly based on meaning on one hand and the precision of Arabic syntactic and morphological structures on the other. It can be concluded that it is not always suitable and accurate to render the English verbs into Arabic verbs. In the current study, however, the verb phrase can be successfully rendered into nominal, prepositional, or adverbial phrases
Assessment of Potassium Bromate, Calcium Sulfate and Synergistic Toxic Effects in Male Mice
Authors: Ali Imarah Ameer , Ahmed Najm Rana
Published: March 2023

Food additives are the basis of the modern food industry, and play an important role in improving the color, smell, and taste of food, altering its nutritional structure, perfecting its processing conditions, and extending its shelf life. Twenty four adult male Swiss albino mice were used in our experiment : Group (1) mice were Intraperitoneally injected of normal saline 0.5 ml (as control). Group (2) mice were intraperitoneally injected of well dispersed Potassium Bromate 320 mg/kg of body weight for two months (twice weekly). Group (3) mice were faded Calcium sulfate as chew of food 0.03% w/w for two months (daily intake). Group (4) mice were intraperitoneally injected of well dispersed Potassium Bromate 320 mg/kg of body weight for two months (twice weekly) and also given Calcium sulfate as chew of food 0.03% W/W for two months (daily intake). After the end of project protocol the mice had been sacrificed and blood collocated and this parameters was evaluated white blood cells (WBC) , red blood cells (RBC) ,platelet (PLT),hematocrit (HCT), and hemoglobin concentration (Hgb). The result showed there is highly significant decrease for WBC for the (group 2 and group 4) , Red blood cell count show that there is highly significant decrease of (group 2 and4), In contrast the result for Hemoglobin concentration show there were highly significant increase in group 2 and 4 by comparing with control group , also highly significant increase for Hct percentage for the (group 2 and 4) by comparing with control group and group 3. Finally platelets count result show that there is highly significant decrease in group 2 and group 4. From current study We can conclude that there are different harmfully effects of KBrO3 and the synergistic effect with CaSo4 on the animal model and according to that on human due to the daily uptake of this compounds in our life style ,according to the hematological indices (WBC),(RBC), (Hgb), (Hct), and finally (PLT). Thus the using of KBrO3 and CaSo4 should be limited and used specific documented concentration to reduce its unfavorable effects
Effectiveness of Bosentan VS Sildenafil for the Treatment of Persistent Pulmonary Hypertension of the Newborn
Authors: Tasnim Dr. Galiba , Khanam Dr. Wahida , Begum Dr. Shahana , Hossain Dr. Umme Saima , Sharifa Dr. Tanjina , Mia Dr. Md. Moshiur Rahman , Parvin Dr. Rubiya , Rahman Dr. Md. Mozibur
Published: March 2023

Background: Persistent pulmonary hypertension of the newborn (PPHN) is a serious disease among newborns. Pulmonary vasodilators such as Sildenafil, Bosentan and Milrinone are important treatment modalities to treat persistent pulmonary hypertension of the newborn, especially in resource limited centers where inhaled nitric oxide is not available. Objective: To assess and compare the effectiveness of Bosentan and Sildenafil in persistent pulmonary hypertension of the newborn. Method: This randomized controlled trial was done in the NICU, ICMH, Matuail, Dhaka during July 2017 to July 2019. Following echocardiographic diagnosis, a total of 76 patients were randomly assigned into group A and group B (by lottery). Bosentan was given to group A (1mg/kg/body wt) 12 hourly for 7 days and Sildenafil was given to group B (2mg/kg/body wt.) 8 hourly for 7 days via oro-gastric tube/orally. Then heart rate, respiratory rate, cyanotic changes, SPO2 was recorded every day for 7 days and finally on the 8th day echocardiographic findings were obtained. Effects of two groups were compared. Results: The age of presentation of persistent pulmonary hypertension of newborn was < 24 hours and male newborns were predominant in both groups. Almost 100 % newborns had cyanosis, tachypnea and SPO2 <80% on admission. After intervention for 7 days in both groups no newborn had cyanosis or tachypnea. The mean pulmonary artery systolic pressure (PASP) on echocardiography before and after intervention was found (53.16 ± 9.263 and 35.55 ± 6.41) mm of Hg in group A. The mean PASP on echocardiography before and after intervention was found (53.24 ±11.012 and 35.45 ± 7.43) mm of Hg respectively in group B. In both groups the mean PASP was decreased significantly after intervention. But while comparing the PASP between two groups, there was no significant (p>0.05) difference. The mean length of stay in hospital was 13.05 ± 2.96 days for group A and 13.34 ± 2.86 days for group B. The mean length of ......
Safety and Efficacy of Brolucizumab Injection among Patients with Diabetic Retinopathy & DME in a Tertiary Care Hospital– A Prospective Study
Authors: Mukta Dr. Ziaul Ahsan , Khatun Dr. Dilara , Khanom Dr. Tasnim , Rahman Dr. H N Ashequr , Yousuf Dr. Md. Harun Bin , Bashir Dr. Mashrur Al , Nawaz Dr. Al- , Dutee Dr. Nabila Noor , Siddique Dr. Kamrun Nahar
Published: March 2023

Introduction: Diabetic Retinopathy (DR) is the leading cause of vision loss in adults aged 20–74 years. From 1990–2010, DR ranked as the fifth most common cause of preventable blindness and fifth most common cause of moderate to severe visual impairment. Previous research has implicated vascular endothelial growth factor (VEGF) in the pathogenesis of diabetic retinopathy (DR). Antivascular endothelial growth factor (anti-VEGF) therapy has become the treatment of choice for retinal vascular disorders such as diabetic macular edema (DME). Aim of the Study: The aim of this study was to evaluate the safety and efficacy of brolucizumab injection in patients with diabetic retinopathy. Methods: This was a prospective observational study and was conducted in the Department of Ophthalmology of Bangladesh Eye Hospital & Institute Ltd, Dhaka, Bangladesh during the period from February, 2022 to February, 2023. In our study we took 300 patients with diabetic retinopathy & DME. All of them were given 6mg IVI brolucizumab injection. Result: In our study we found majority (35%) of our patients were aged 55-64 years and most of our patients were male (60%).We found the mean age was 56.53± 9.74 years and mean blood glucose was 98.8 ± 1.4 mg/dL. All patients had Type 2 diabetes. We found 20% NPDR, 47.33% PDR & 32.67% DME patients. The mean eye vision of our patients was 66.02± 4.02 and the mean kept reducing significantly till 18th week. Before treatment the mean of OCT was 427.04 ± 18.07 and after treatment we found 413.21 ± 11.12, 384.03 ± 14.02 & 254.19 ± 9.02 at 6th, 12th & 18th week respectively. Conclusion: In our study, we found a few complications of brolucizumab like no improvement of vision, nephropathy, cardiovascular disease was present among our patients. Although there were more adverse effects of brolucizumab but in our study we only found mild uveitis. The administration of anti-VEGF medicines in PDR is an emerging field. Although PRP is considered the first line .....
The Maternal Risk Factor for HIE Progression in Infants
Authors: Rahman Dr. Mst. Ruzina , Noor Dr. Reshma , Das Dr. Ashith Chandra , Azad Dr. Md. Tarek , Naznin Dr. Halima , Bhadra Dr. Mousumi
Published: March 2023

Background: Perinatal asphyxia, or more accurately, hypoxic-ischemic encephalopathy (HIE), remains a devastating illness, producing considerable death and long-term morbidity despite great breakthroughs with sophisticated monitoring technologies and understanding of fetal and neonatal pathology. Objective: In this study our main goal is to evaluate the maternal risk factor for HIE progression in infants. Method: This case control study was conducted in the Department of the Paediatrics, Jalalabad Ragib Rabeya Medical College Hospital, Sylhet during the period from 1 st January 2014 to 30 th June 2014. Ninety-six normal birth weight term babies with HIE were selected as case; Ninety six sex and birth weight matched, term singleton baby without HIE born in Jalalabad Ragib Rabeya Medical College Hospital, Sylhet were taken as control. Results: Maternal age (<20 years) [15 (15.6%) versus 6 (6.2%); OR=3.165; 95% of CI=1.176-8.518; p=0.037]; maternal height (<145 cm) [37 (38.5%) versus 24 (25.0%); OR=1.881; 95% of CI=1.014-3.492; p=0.044), primiparity [45 (46.9%) versus 31 (32.3%); OR=1.850; 95% of CI=1.029-3.325; p=0.039], irregular or no antenatal care [41 (42.7%) versus 27 (28.1%); OR=1.905; 95% of CI=1.044-3.476; p=0.035]; pregnancy induced hypertension [38 (39.6%) versus 22 (22.9%);OR=2.204; 95% of CI=1.777-4.128; p=0.013]. Conclusion: Maternal risk factors of development of hypoxic ischemic encephalopathy are maternal age (& lt;20 years), primigravida mother, absence or irregular antenatal care, pregnancy induced hypertension is associated with HIE.
Optimal Scheduling of DC Microgrids based on MOPSO Algorithm
Authors: Hang Zhao Zi
Published: March 2023

The DC microgrid system contains a variety of distributed power sources, but the distributed power sources are uncertain and intermittent. In order to improve the economy of DC microgrid operation, mainly to reduce the microgrid operation cost and environmental management cost, and at the same time the largest proportion of scenery consumption, the optimization algorithm is effective for microgrid for optimal scheduling. A multi-objective optimal scheduling model for microgrids is established. A microgrid model with wind, photovoltaic, micro gas turbine and energy storage is constructed and solved using a multi-objective particle swarm algorithm. The multi-objective particle swarm algorithm is invoked for a typical 24h daily isolated micro-grid system in a certain region. The simulation results show that the optimal dispatching scheme can effectively improve the economy of the microgrid, reduce its operating costs as well as the ability to consume renewable energy scenery power.
Asian Summit on Robotic Surgery 2022 gynecology tract meeting report
Authors: Wong Jeslyn , Ng Joseph , Chung Hyewon , Cho Chi-Heum , Kim Mee-Ran , Kim Tae-Joong , Singson Rebecca , Paek Jiheum , Sinha Rooma , Jose Jennifer , Tse Ka-Yu , Noh Joseph , Lai Hung-Cheng , Lee Keun Ho
Published: March 2023

The second Asian Summit on Robotic Surgery (ASRS) was convened in Singapore, 12 November 2022. This meeting was well attended by speakers and participants from around the region. The Gynecology tract of the ASRS 2022 was supported by the Asian Society for Gynecology Robotic Surgery. Co-chaired by Dr. Jeslyn Wong of Singapore, and Dr. Hyewon Chung of South Korea, the Gynecology program saw experts share their experiences, tips and surgical pearls with enthusiastic participants from the region. This article summarizes the key topics that were discussed during the meeting.
Single centre experience in India for benign gynecological robotic surgery with da Vinci Si system: A real world data analysis of one decade
Authors: Rooma Sinha , Rupa Bana , Reddy Nikitha
Published: March 2023

Objective: To analyse real time data over a decade of robotic surgery for benign Gynecological conditions using the da Vinci Si system.Methods: This is a retrospective analysis of prospectively collected data of da Vinci Si robotic surgery between August 2012 and July 2021 at single medical centre by a single surgeon.Results: Of the total 452 cases, the procedures were broadly categorised into myomectomy (RM-204), hysterectomy (RH-150), endometriosis excision (RE-80). The mean age of 46.12±6.77 and the mean BMI 28.89±5.02 kg/m2 in RH group was higher in RH group when compared to RM and RE groups. The mean docking time over 10 years was 10.59 minutes and mean console time was 96.35±47.77 minutes and both were significantly lower in the last 5 years when compared to first 5 years. 49.9% patients did not require intravenous analgesics beyond 24 hours and only 7.2% needed beyond 48 hours. 44.7% had less than 24 hours of hospital stay and 1.1% stayed beyond 48 hours. The mean hemoglobin drop was 1.109±0.7531 g%, significantly higher in RM group. The mean specimen weight in RM group was 323.8 g (200–1,372 g) and 84 cases had multiple leiomyomas. The commonest indication of robotic surgery was fibroid uterus.Conclusion: Robotic surgery for benign gynecologic surgery feasible and safe. It has equivalent outcomes when compared to laparoscopic surgery and superior when compared with laparotomy. Using robots to reduce open and repeat surgery for injury corrections could translate to reductions in the over-all economic burden of treatment.