GBERT: A hybrid deep learning model based on GPT-BERT for fake news detection.

The digital era has expanded social exposure with easy internet access for mobile users, allowing for global communication. Now, people can get to know what is going on around the globe with just a click; however, this has also resulted in the issue of fake news. Fake news is content that pretends to be true but is actually false and is disseminated to defraud. Fake news poses a threat to harmony, politics, the economy, and public opinion. As a result, bogus news detection has become an emerging research domain to identify a given piece of text as genuine or fraudulent. In this paper, a new framework called Generative Bidirectional Encoder Representations from Transformers (GBERT) is proposed that leverages a combination of Generative pre-trained transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT) and addresses the fake news classification problem. This framework combines the best features of both cutting-edge techniques-BERT's deep contextual understanding and the generative capabilities of GPT-to create a comprehensive representation of a given text. Both GPT and BERT are fine-tuned on two real-world benchmark corpora and have attained 95.30 % accuracy, 95.13 % precision, 97.35 % sensitivity, and a 96.23 % F1 score. The statistical test results indicate the effectiveness of the fine-tuned framework for fake news detection and suggest that it can be a promising approach for eradicating this global issue of fake news in the digital landscape.

Conditional spatial biased intuitionistic clustering technique for brain MRI image segmentation.

In clinical research, it is crucial to segment the magnetic resonance (MR) brain image for studying the internal tissues of the brain. To address this challenge in a sustainable manner, a novel approach has been proposed leveraging the power of unsupervised clustering while integrating conditional spatial properties of the image into intuitionistic clustering technique for segmenting MRI images of brain scans. In the proposed technique, an Intuitionistic-based clustering approach incorporates a nuanced understanding of uncertainty inherent in the image data. The measure of uncertainty is achieved through calculation of hesitation degree. The approach introduces a conditional spatial function alongside the intuitionistic membership matrix, enabling the consideration of spatial relationships within the image. Furthermore, by calculating weighted intuitionistic membership matrix, the algorithm gains the ability to adapt its smoothing behavior based on the local context. The main advantages are enhanced robustness with homogenous segments, lower sensitivity to noise, intensity inhomogeneity and accommodation of degree of hesitation or uncertainty that may exist in the real-world datasets. A comparative analysis of synthetic and real datasets of MR brain images proves the efficiency of the suggested approach over different algorithms. The paper investigates how the suggested research methodology performs in medical industry under different circumstances including both qualitative and quantitative parameters such as segmentation accuracy, similarity index, true positive ratio, false positive ratio. The experimental outcomes demonstrate that the suggested algorithm outperforms in retaining image details and achieving segmentation accuracy.

Situation analysis of the quality of primary health care services in Pakistan.

Background: Primary health care services to promote the mental and physical health of communities include preventive, promotive, curative, general hygiene, and nutritional elements. Aim: To assess the quality of service delivery at primary healthcare settings in Punjab, Pakistan. Methods: Quantitative surveys were conducted at 106 health facilities: 92 basic health units (BHUs) and 14 rural health centres (RHCs) across Punjab in 2020. Data from the survey were supplemented with information from observations by the researchers and all data were analysed using SPSS version 25. Findings: All the 7 district health authorities surveyed had monthly targets for number of normal deliveries and the outpatient department. Systems for safe transportation and storage of medicines were deficient except in 2 districts. Anti-venom and anti-rabies vaccines were either limited or not available at most of the health units visited. Some 14% of clinical equipment examined at the BHUs and RHCs were non-functional, and no BHU had ultrasonic machines to improve the quality of antenatal care. Sterilization of surgical instruments was unsatisfactory at most health units. Several key positions at BHU and RHC were vacant. Most health units did not have fence and their main buildings were in poor condition. Conclusions: Several gaps were identified at the primary healthcare level in Punjab that need to be addressed to improve the quality of service delivery.

Situation analysis of the quality of primary health care services in Pakistan

Background:Primary health care services to promote the mental and physical health of communities include preventive, promotive, curative, general hygiene, and nutritional elements.Aim:To assess the quality of service delivery at primary healthcare settings in Punjab, Pakistan.Methods:Quantitative surveys were conducted at 106 health facilities: 92 basic health units (BHUs) and 14 rural health centres (RHCs) across Punjab in 2020. Data from the survey were supplemented with information from observations by the researchers and all data were analysed using SPSS version 25.Findings:All the 7 district health authorities surveyed had monthly targets for number of normal deliveries and the outpatient department. Systems for safe transportation and storage of medicines were deficient except in 2 districts. Anti-venom and anti-rabies vaccines were either limited or not available at most of the health units visited. Some 14% of clinical equipment examined at the BHUs and RHCs were non-functional, and no BHU had ultrasonic machines to improve the quality of antenatal care. Sterilization of surgical instruments was unsatisfactory at most health units. Several key positions at BHU and RHC were vacant. Most health units did not have fence and their main buildings were in poor condition.Conclusions:Several gaps were identified at the primary healthcare level in Punjab that need to be addressed to improve the quality of service delivery.

Empowering the Vehicular Network with RIS Technology: A State-of-the-Art Review.

Reconfigurable intelligent surfaces (RIS) are expected to bring about a revolutionary transformation in vehicular networks, thus paving the way for a future characterized by connected and automated vehicles (CAV). An RIS is a planar structure comprising many passive elements that can dynamically manipulate electromagnetic waves to enhance wireless communication by reflecting, refracting, and focusing signals in a programmable manner. RIS exhibits substantial potential for improving vehicle-to-everything (V2X) communication through various means, including coverage enhancement, interference mitigation, improving signal strength, and providing additional layers of privacy and security. This article presents a comprehensive survey that explores the emerging opportunities arising from the integration of RIS into vehicular networks. To examine the convergence of RIS and V2X communications, the survey adopted a holistic approach, thus highlighting the potential benefits and challenges of this combination. In this study, we examined several applications of RIS-aided V2X communication. Subsequently, we delve into the fundamental emerging technologies that are expected to empower vehicular networks, encompassing mobile edge computing (MEC), non-orthogonal multiple access (NOMA), millimeter-wave communication (mmWave), Artificial Intelligence (AI), and visible light communication (VLC). Finally, to stimulate further research in this domain, we emphasize noteworthy research challenges and potential avenues for future exploration.

Analysis of IoT-Related Ergonomics-Based Healthcare Issues Using Analytic Hierarchy Process Methodology.

The objective of the present work is for assessing ergonomics-based IoT (Internet of Things) related healthcare issues with the use of a popular multi-criteria decision-making technique named the analytic hierarchy process (AHP). Multiple criteria decision making (MCDM) is a technique that combines alternative performance across numerous contradicting, qualitative, and/or quantitative criteria, resulting in a solution requiring a consensus. The AHP is a flexible strategy for organizing and simplifying complex MCDM concerns by disassembling a compound decision problem into an ordered array of relational decision components (evaluation criteria, sub-criteria, and substitutions). A total of twelve IoT-related ergonomics-based healthcare issues have been recognized as Lumbago (lower backache), Cervicalgia (neck ache), shoulder pain; digital eye strain, hearing impairment, carpal tunnel syndrome; distress, exhaustion, depression; obesity, high blood pressure, hyperglycemia. "Distress" has proven itself the most critical IoT-related ergonomics-based healthcare issue, followed by obesity, depression, and exhaustion. These IoT-related ergonomics-based healthcare issues in four categories (excruciating issues, eye-ear-nerve issues, psychosocial issues, and persistent issues) have been compared and ranked. Based on calculated mathematical values, "psychosocial issues" have been ranked in the first position followed by "persistent issues" and "eye-ear-nerve issues". In several industrial systems, the results may be of vital importance for increasing the efficiency of human force, particularly a human-computer interface for prolonged hours.

Intelligent Reflecting Surfaces Enhanced Mobile Edge Computing: Minimizing the Maximum Computational Time.

Intelligent reflecting surfaces (IRS) and mobile edge computing (MEC) have recently attracted significant attention in academia and industry. Without consuming any external energy, IRS can extend wireless coverage by smartly reconfiguring the phase shift of a signal towards the receiver with the help of passive elements. On the other hand, MEC has the ability to reduce latency by providing extensive computational facilities to users. This paper proposes a new optimization scheme for IRS-enhanced mobile edge computing to minimize the maximum computational time of the end users' tasks. The optimization problem is formulated to simultaneously optimize the task segmentation and transmission power of users, phase shift design of IRS, and computational resource of mobile edge. The optimization problem is non-convex and coupled on multiple variables which make it very complex. Therefore, we transform it to convex by decoupling it into sub-problems and then obtain an efficient solution. In particular, the closed-form solutions for task segmentation and edge computational resources are achieved through the monotonical relation of time and Karush-Kuhn-Tucker conditions, while the transmission power of users and phase shift design of IRS are computed using the convex optimization technique. The proposed IRS-enhanced optimization scheme is compared with edge computing nave offloading, binary offloading, and edge computing, respectively. Numerical results demonstrate the benefits of the proposed scheme compared to other benchmark schemes.

An Intelligent Model-Based Effective Approach for Glycemic Control in Type-1 Diabetes.

Type-1 diabetes mellitus (T1DM) is a challenging disorder which essentially involves regulation of the glucose levels to avoid hyperglycemia as well as hypoglycemia. For this purpose, this research paper proposes and develops control algorithms using an intelligent predictive control model, which is based on a UVA/Padova metabolic simulator. The primary objective of the designed control laws is to provide an automatic blood glucose control in insulin-dependent patients so as to improve their life quality and to reduce the need of an extremely demanding self-management plan. Various linear and nonlinear control algorithms have been explored and implemented on the estimated model. Linear techniques include the Proportional Integral Derivative (PID) and Linear Quadratic Regulator (LQR), and nonlinear control strategy includes the Sliding Mode Control (SMC), which are implemented in this research work for continuous monitoring of glucose levels. Performance comparison based on simulation results demonstrated that SMC proved to be most efficient in terms of regulating glucose profile to a reference level of 70 mg/dL compared to the classical linear techniques. A brief comparison is presented between the linear techniques (PID and LQR), and nonlinear technique (SMC) for analysis purposes proving the efficacy of the design.

Preparing Wi-Fi 7 for Healthcare Internet-of-Things.

The healthcare Internet of Things (H-IoT) is an interconnection of devices capable of sensing and transmitting information that conveys the status of an individual's health. The continuous monitoring of an individual's health for disease diagnosis and early detection is an important application of H-IoT. Ambient assisted living (AAL) entails monitoring a patient's health to ensure their well-being. However, ensuring a limit on transmission delays is an essential requirement of such monitoring systems. The uplink (UL) transmission during the orthogonal frequency division multiple access (OFDMA) in the wireless local area networks (WLANs) can incur a delay which may not be acceptable for delay-sensitive applications such as H-IoT due to their random nature. Therefore, we propose a UL OFDMA scheduler for the next Wireless Fidelity (Wi-Fi) standard, the IEEE 802.11be, that is compliant with the latency requirements for healthcare applications. The scheduler allocates the channel resources for UL transmission taking into consideration the traffic class or access category. The results demonstrate that the proposed scheduler can achieve the required latency for H-IoT applications. Additionally, the performance in terms of fairness and throughput is also superior to state-of-the-art schedulers.

Dual Band and Dual Diversity Four-Element MIMO Dipole for 5G Handsets.

The increasing popularity of using wireless devices to handle routine tasks has increased the demand for incorporating multiple-input-multiple-output (MIMO) technology to utilize limited bandwidth efficiently. The presence of comparatively large space at the base station (BS) makes it straightforward to exploit the MIMO technology's useful properties. From a mobile handset point of view, and limited space at the mobile handset, complex procedures are required to increase the number of active antenna elements. In this paper, to address such type of issues, a four-element MIMO dual band, dual diversity, dipole antenna has been proposed for 5G-enabled handsets. The proposed antenna design relies on space diversity as well as pattern diversity to provide an acceptable MIMO performance. The proposed dipole antenna simultaneously operates at 3.6 and 4.7 sub-6 GHz bands. The usefulness of the proposed 4×4 MIMO dipole antenna has been verified by comparing the simulated and measured results using a fabricated version of the proposed antenna. A specific absorption rate (SAR) analysis has been carried out using CST Voxel (a heterogeneous biological human head) model, which shows maximum SAR value for 10 g of head tissue is well below the permitted value of 2.0 W/kg. The total efficiency of each antenna element in this structure is -2.88, -3.12, -1.92 and -2.45 dB at 3.6 GHz, while at 4.7 GHz are -1.61, -2.19, -1.72 and -1.18 dB respectively. The isolation, envelope correlation coefficient (ECC) between the adjacent ports and the loss in capacity is below the standard margin, making the structure appropriate for MIMO applications. The effect of handgrip and the housing box on the total antenna efficiency is analyzed, and only 5% variation is observed, which results from careful placement of antenna elements.

piRDisease v1.0: a manually curated database for piRNA associated diseases.

In recent years, researches focusing on PIWI-interacting RNAs (piRNAs) have increased rapidly. It has been revealed that piRNAs have strong association with a wide range of diseases; thus, it becomes very important to understand piRNAs' role(s) in disease diagnosis, prognosis and assessment of treatment response. We searched more than 2500 articles using keywords, such as `PIWI-interacting RNAs' and `piRNAs', and further scrutinized the articles to collect piRNAs-disease association data. These data are highly complex and heterogeneous due to various types of piRNA idnetifiers (IDs) and different reference genome versions. We put considerable efforts into removing redundancy and anomalies and thus homogenized the data. Finally, we developed the piRDisease database, which incorporates experimentally supported data for piRNAs' relationship with wide range of diseases. The piRDisease (piRDisease v1.0) is a novel, comprehensive and exclusive database resource, which provides 7939 manually curated associations of experimentally supported 4796 piRNAs involved in 28 diseases. piRDisease facilitates users by providing detailed information of the piRNA in respective disease, explored by experimental support, brief description, sequence and location information. Considering piRNAs' role(s) in wide range of diseases, it is anticipated that huge amount of data would be produced in the near future. We thus offer a submitting page, on which users or researches can contribute in to update our piRDisease database.