The rapid development and the expansion of Internet of Things (IoT)-powered technologies have strengthened the way we live and the quality of our lives in many ways by combining Internet and communication technologies through its ubiquitous nature. As a novel technological paradigm, this IoT is being served in many application domains including healthcare, surveillance, manufacturing, industrial automation, smart homes, the military, etc. Medical Internet of Things (MIoT), or the use of IoT in healthcare, is becoming a booming trend towards improving the health and wellbeing of billions of people by offering smooth and seamless medical facilities and by enhancing the services provided by medical practitioners, nurses, pharmaceutical companies, and other related government and non-government organizations. In recent times, this MIoT has gained higher attention for its potential to alleviate the massive burden on global healthcare, which has been caused by the rise of chronic diseases, the aging population, and emergency situations such as the recent COVID-19 global pandemic, where many government and non-government medical resources were challenged, owing to the rising demand for medical resources. It is evident that with this recent growing demand for MIoT, the associated technologies and its interconnected, heterogeneous nature adds new concerns as it becomes accessible to confidential patient data, often without patient or the medical staff consciousness, as the security and privacy of MIoT devices and technologies are often overlooked and undermined by relevant stakeholders. Hence, the growing security breaches that target the MIoT in healthcare are making the security and privacy of Medical IoT a crucial topic that is worth scrutinizing. In this study, we examined the current state of security and privacy of the MIoT, which has become of utmost concern among many security experts and researchers due to its rapid demand in recent times. Nevertheless, pertaining to the current state of security and privacy, we also examine and discuss a number of attack use cases, countermeasures and solutions, recent challenges, and anticipated future directions where further attention is required through this study.

An efficient feature extraction method for two classes of electroencephalography (EEG) is demonstrated using Common Spatial Patterns (CSP) with optimal spatial filters. However, the effects of artifacts and non-stationary uncertainty are more pronounced when CSP filtering is used. Furthermore, traditional CSP methods lack frequency domain information and require many input channels. Therefore, to overcome this shortcoming, a feature extraction method based on Online Recursive Independent Component Analysis (ORICA)-CSP is proposed. For EEG-based brain—computer interfaces (BCIs), especially online and real-time BCIs, the most widely used classifiers used to be linear discriminant analysis (LDA) and support vector machines (SVM). Previous evaluations clearly show that SVMs generally outperform other classifiers in terms of performance. In this case, Adaptive Support Vector Machine (A-SVM) is used for classification together with the ORICA-CSP method. The results are promising, and the experiments are performed on EEG data of 4 classes’ motor images, namely Dataset 2a of BCI Competition IV.

Reckless urbanization in developing regions is leading to the deterioration of the urban environment. The ensuing impacts can place a burden on urban ecology, urban infrastructure, and residents. This scenario requires a combination of avoidance measures and a detailed assessment of the ecological sustainability of the city. While monetary assessments are certainly conceivable, in this study, the contributions of urban environmental infrastructure are weighed financially. Semi-planned (Jhang) and planned (Faisalabad) urban settlements provided the context for this survey. The study uses the Benefit Transfer Method (BTM) to assess changes in the monetary value of urban ecosystem services (UES) from remote sensing data. This finding suggests that urbanization in Pakistan is devouring productive ecological land in urban areas. The assessment shows that between 1989 and 2019, the agricultural area in Faisalabad shrank (−17.38%), and the built-up area increased (16.05%). Likewise, in Jhang City, the built-up area (4.44%) and wasteland (3.10%) swelled. However, during this period (1989–2019), the proportion of agricultural land in Jhang City decreased (−8.93%). As a result, prime areas of UES are falling back into low-return areas. It also found that provisioning ecosystem services (PES) accounted for a significant portion (68.12%) of the UES produced in Faisalabad and Jhang (69.72%), respectively. In contrast, Cultural Ecosystem Services (CES) contributed the smallest share of UES in Faisalabad (1.63%) and Jhang (1.65%). However, the remaining two services, regulatory and support services, made significant contributions. The assessment shows the role of incoherence, inconsistency, resource constraints, and neglect in compromising the urban environmental integrity of these cities. This situation requires a comprehensive assessment and coordinated effort. For this, it is feasible and useful to combine socioeconomic information with land cover data through computerized equipment.

Gas-solid flow is used in the chemical industry, food industry, pharmaceuticals, vehicles, and power generation. The calculation of flow has aroused great interest in contemporary industry. In recent decades, researchers have been seeking to build an effective system to monitor and calculate gas-solid flow. Attempts have been extended from computational modeling to the creation of flow pattern visualization methods and mass flow (MFR) quantification. MFR is usually studied by volume flow concentration (VFC) and velocity distribution of solid particles. A non-invasive device is used for testing MFR, in which electronic and mechanical sensors are used to balance the shortcomings related to each other. This study investigates the simulation of flow patterns to demonstrate the behavior of solid particles as they pass through the channel. The particles are allowed to slide longitudinally in the insulated tending channel. This slippage is due to the influence of natural gravity. Electronic sensor components are used to measure the velocity distribution and concentration of volumetric flow. The load cell is used as an auxiliary sensor for measuring MFR. In addition, ANSYS fluent is used to analyze streaming queries. The experimental results are related to evaluating the accuracy and relative error of the data collected from various sensors under different conditions. However, the simulation results can help explain the movement of the gas-solid mixture and can understand the cause of pipeline blockage during the slow movement of solid particles.