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Abstract This research takes on a crucial task- exploring the optimal placement of Renewable Distributed Generators such as Solar Photovoltaic, wind turbines and Electric Vehicles into the Radial Distribution System (RDS). This is a strategic move aimed at minimising power loss (PLoss) and improving the voltage profile and stability index. The RDGs are integrated into RDS with and without considering the uncertainty of the different load demands for 24 h. The probability function of Beta and Weibull distribution functions are employed to attain the solar irradiance and wind speed in a particular region. In addition, EVs are also integrated into RDS, employing meta-heuristic algorithms intended to reduce power loss (PLoss) and improve the voltage profile. The study uses an Indian 28-bus test system mimicking a balanced radial distribution network to integrate distributed generators (DGs) and EV charging stations. The simulated results demonstrate that integrating DGs into power systems has offered considerable benefits, including reduced PLoss, heightened efficiency, decreased dependency on centralised generation, and improved environmental sustainability. It is discovered that the Multi-population Evolution Whale Optimization Algorithm (MEWOA) produces better results than other methods in the literature and is valuable and practical for handling these nonlinear optimisation situations.

Abstract Background Early diagnosis of large vessel occlusion (LVO) in acute stroke often requires CT angiography (CTA). Automated CT perfusion (CTP) software, which identifies blood flow abnormalities, enhances LVO diagnosis and patient selection for endovascular thrombectomy (EVT). This study evaluates the sensitivity of automated CTP images in detecting perfusion abnormalities in patients with acute ischemic stroke (AIS) and LVO or medium vessel occlusion (MeVO), compared to CTA. Methods We screened acute ischemic stroke patients presenting within 24 h who underwent CT, CTA, and CTP as per institutional protocol. RAPID AI software processed CTP images, while neuroradiologists reviewed CTA for intracranial arterial occlusions. Sensitivity, specificity, and accuracy of automated CTP maps in detecting occlusions were assessed. Results Of 790 screened patients, 31 were excluded due to lack of RAPID CTP data or poor‐quality scans, leaving 759 for analysis. The median age was 71 years (IQR: 61–81), with 47% female. Among them, 678 had AIS, and 81 had AIS ruled out. CTA identified arterial occlusion in 562 patients (74%), with corresponding CTP abnormalities in 537 patients (Tmax > 6 sec). In the 197 without occlusion, CTP was negative in 161. Automated CTP maps had a sensitivity of 95.55% (CI 95: 93.50–97.10%), specificity of 81.73% (CI 95: 75.61–86.86%), negative predictive value of 98.22% (CI 95: 97.39–98.79%), positive predictive value of 63.54% (CI 95: 56.46–70.09%), and overall accuracy of 85.18% (CI 95: 82.45–87.64%). Conclusions Automated CTP maps demonstrated high sensitivity and negative predictive value for LVOs and MeVOs, suggesting their usefulness as a rapid diagnostic tool, especially in settings without expert neuroradiologists.

Abstract Reduction of fossil fuel usage, clean energy supply, and dependability are all major benefits of integrating distributed energy resources (DER) with electrical utility grid (UG). Nevertheless, there are difficulties with this integration, most notably accidental islanding that puts worker and equipment safety at risk. Islanding detection methods (IDMs) play a critical role in resolving this problem. All IDMs are thoroughly evaluated in this work, which divides them into two categories: local approaches that rely on distributed generation (DG) side monitoring and remote approaches that make use of communication infrastructure. The study offers a comparative evaluation to help choose the most efficient and applicable IDM, supporting well-informed decision-making for the safe and dependable operation of distributed energy systems within electrical distribution networks. IDMs are evaluated based on NDZ outcomes, detection duration, power quality impact, multi-DG operation, suitability, X/R ratio reliance, and efficient functioning.

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The semiconductor industry is actively looking for an all-encompassing memory solution that incorporates the advantageous aspects of current technology. This features non-volatility, like that of Flash memory, high scalability, like that of both Dynamic Random Access Memory (DRAM) and Flash, quick operation, like that of Static RAM (SRAM), and durability, like that of both DRAM and SRAM. Ferroelectric thin films, which have electrically switchable bi-stable polarization, are one prospective technology that has the potential to revolutionize memory storage. However, due to difficulties with scalability and dependable industrial manufacturing, Ferro-Electric (FE) memory technology has not been able to effectively compete with DRAM and Flash. Research in this area has accelerated after the recent discovery of resistive switching in ferroelectric tunnel junctions (FTJs). For FTJs to be successful, it is important to overcome some obstacles, such as preserving bi-stability in ferroelectric thin films over the critical thickness. Additionally, the existence of interfacial layers, sometimes known as a “dead layer”, between the electrode and the film can affect its characteristics. The article gives an overview of semiconductor memories with an emphasis on emerging technologies having the potential for future applications. It then goes into detail on the benefits of FTJ and its non-destructive reading capacity. The article also discusses the potential uses for FTJs in resistive switching while acknowledging their drawbacks and constraints.

This work considers using a novel heuristic population-based evolutionary algorithm [viz., the moth flame optimization (MFO) algorithm] to regulate the conventional controller installed in an autonomous power system (APS). The moth flame optimization algorithm intends to produce the optimal magnitudes of the proportional-integral-derivative plus second derivative (PIDD2) controller parameters along with its first- and second-order low-pass filter constraints (installed in the investigated autonomous power system). The present task includes a comparison of the voltage response profiles of the investigated system obtained by the proposed moth flame optimization-based proportional-integral-derivative plus second derivative controller and those obtained by other algorithms (conveyed in current state-of-the-art literature) based on a proportional-integral controller. A fast-acting Sugeno fuzzy logic (SFL) technique is used to achieve the dynamic online results of the investigated autonomous power system model for online, off-nominal operational circumstances. Under step perturbations, the time-domain transient investigation in reference to voltage and/or mandate of load for the proposed autonomous power system model is inspected. Additionally, the robustness of the proposed moth flame optimization-based proportional-integral-derivative plus second derivative controller is investigated to test its behavior. An investigation has been provided by varying the model components of the studied autonomous power system model. It may be reported, as per the results obtained from the simulation, that the proposed moth flame optimization-based proportional-integral-derivative plus second derivative controller is an effective control strategy for the autonomous power system. The current research effort indicates that the proposed moth flame optimization algorithm, along with Sugeno fuzzy logic, may be useful for the actual time process of an autonomous power system.

Thermo-hydraulic performance of a Double Pass Parallel Flow Solar Air Heater (DPPFSAH) roughened with perforated multi-V ribs has been studied experimentally in a prior study and the effect of variation in open area ratio and change in relative roughness width has been analysed and reported. The current work builds upon the aforementioned earlier study by discussing in detail the methodology and various steps involved in the development of a correlation for variable parameters with Nusselt number and friction factor for DPPFSAH. The outcomes show that perforations in the multi-V ribs lead to a considerable rise in the Nusselt number, a 9.66 times increase in the thermo-hydraulic performance parameter and nearly a four times increase in friction factor compared to multi-V ribs with smoothed walls. Empirical correlations for the Nusselt number and friction factor were obtained for the double pass parallel flow solar air heater with perforated multi-V ribs established with ±14% and ±7%.

Abstract Mobile Stroke Unit (MSU) expedites the delivery of intravenous thrombolysis in acute stroke patients. We further evaluated the functional outcome of patients shipped to a tertiary care centre or repatriated to local hospitals after triage by MSU in acute stroke syndrome in rural northern Alberta. Consecutive patients with suspected acute stroke syndrome were included. On the basis of neurology consultation and, Computed Tomography findings, patients, who were thrombolysed or needed advanced care were transported to the Comprehensive stroke center (CSC) (Triage to CSC group). Other patients were repatriated to local hospital care (Triage to LHC group). A total of 156 patients were assessed in MSU, 73 (46.8%) were female and the mean age was 66.6 ± 15 years. One hundred and eight (69.2%) patients, including 41 (26.3%) treated with thrombolysis were transported to the CSC (Triage to CSC group) and 48 (30.8%) were repatriated to local hospital care. The diagnosis made in MSU and final diagnosis were matching in 88% (95) and 91.7% (44, p = 0.39) in Triage to CSC and Triage to LHC groups respectively. Prehospital triage by MSU of acute stroke syndrome can reliably repatriate patients to the home hospital. The proposed model has the potential to triage patients according to their medical needs by enabling treatment in home hospitals whenever reasonable.

Abstract Positron emission tomography (PET) offers the study of biochemical, physiological, and pharmacological functions at a cellular and molecular level. The performance of a PET study mostly depends on the used radiotracer of interest. However, the development of a novel PET tracer is very difficult, as it is required to fulfill a lot of important criteria. PET radiotracers usually encounter different chemical modifications including redox reaction, hydrolysis, decarboxylation, and various conjugation processes within living organisms. Due to this biotransformation, different chemical entities are produced, and the amount of the parent radiotracer is declined. Consequently, the signal measured by the PET scanner indicates the entire amount of radioactivity deposited in the tissue; however, it does not offer any indication about the chemical disposition of the parent radiotracer itself. From a radiopharmaceutical perspective, it is necessary to quantify the parent radiotracer’s fraction present in the tissue. Hence, the identification of radiometabolites of the radiotracers is vital for PET imaging. There are mainly two reasons for the chemical identification of PET radiometabolites: firstly, to determine the amount of parent radiotracers in plasma, and secondly, to rule out (if a radiometabolite enters the brain) or correct any radiometabolite accumulation in peripheral tissue. Besides, radiometabolite formations of the tracer might be of concern for the PET study, as the radiometabolic products may display considerably contrasting distribution patterns inside the body when compared with the radiotracer itself. Therefore, necessary information is needed about these biochemical transformations to understand the distribution of radioactivity throughout the body. Various published review articles on PET radiometabolites mainly focus on the sample preparation techniques and recently available technology to improve the radiometabolite analysis process. This article essentially summarizes the chemical and structural identity of the radiometabolites of various radiotracers including [11C]PBB3, [11C]flumazenil, [18F]FEPE2I, [11C]PBR28, [11C]MADAM, and (+)[18F]flubatine. Besides, the importance of radiometabolite analysis in PET imaging is also briefly summarized. Moreover, this review also highlights how a slight chemical modification could reduce the formation of radiometabolites, which could interfere with the results of PET imaging. Graphical abstract

The present study describes the single-step transesterification method of biodiesel production from high free fatty acid (FFA) waste cooking oil blended with algae oil using a homogeneous base catalyst. Due to high FFA contents, two step transesterification is needed to convert oil into biodiesel and therefore the high FFA content of waste cooking oil is decreased by blending it with low FFA content algae oil, which would further lead only to single step transesterification of low FFA oil. The design and optimization studies were conducted using Response Surface Methodology (RSM). The box-Behnken design technique is applied to optimize the three process parameters, i.e., catalyst concentration (0–2 wt%), methanol concentration (v/v) (20–60%) and reaction time (60–180 min) at a uniform reaction temperature of 50 °C. The result of the current study indicates that an effective biodiesel yield of 92% can be obtained at the optimized condition of catalyst concentration of 1.5% (w/w), methanol/oil ratio of 21:1 and reaction time of 110 min at a constant reaction temperature of 50 °C. This analysis clearly shows that this study can resolve the storage problem of high FFA oils from different feedstock and RSM can be successfully used to model the reaction to maximize the biodiesel yield.

Energy harnessing from renewable energy sources has become more flexible with power electronic technologies. Recent advancements in power electronic technologies achieve converter efficiency higher than 98%. Today, reliable power electronic devices are needed to design a PV-based energy converter (inverter) to reduce the risk of failure and maintenance costs during operation. Wide-bandgap SiC devices are becoming more common in power electronic converters. These devices are designed to reduce switching loss and improve the efficiency of the system. Nevertheless, the cost of SiC devices is a major concern. Hence, to improve the reliability of the PV inverter while considering the economic aspects, this paper develops a highly reliable PV inverter with a hybrid Si/SiC power module that consists of a Si-IGBT with a SiC anti-parallel diode. A test case of a 3 kW PV inverter is considered for reliability analysis. The loading of the PV inverter is done under uncertain environmental conditions by considering the yearly Mission Profile (MP) data related to Ambient Temperature (AT) and Solar Irradiance (SI) at the India and Denmark locations. The effectiveness of the proposed hybrid Si/SiC power module is tested by comparing it with a conventional IGBT power module. The results showcase the marked improvement in PV inverter reliability with the proposed hybrid power module.

The purpose of this research is to develop an efficient single-phase grid-connected PV system using a better performing asymmetrical multilevel inverter (AMI). Circuit component reduction, harmonic reduction, and grid integration are critical criteria for better inverter efficiency. The proposed inverter’s optimized topology requires seven unidirectional switches, three symmetric dc sources, and three diodes to produce an asymmetric fifteen level output; whereas, the same configuration will generate 7, 11, and 15-level output with an appropriate choice of dc source magnitudes. It is possible to reduce inverter losses and boost efficiency by decreasing the number of switches used. The integration of an asymmetric 15-level inverter with a grid-connected solar photovoltaic system is discussed in this article. A grid-connected solar photovoltaic (GCSPV) system is modelled and simulated using an asymmetric 15-level inverter. The dc sources of the 15-level inverter are replaced with PV sources. The results were analyzed with different operating temperatures and solar irradiance conditions. The GCSPV system is controlled by a closed-loop control system using Particle Swarm Optimization (PSO), Harris Hawk Optimization (HHO), and Hybrid Particle Swarm Optimization-Genetic Algorithm (PSOGA) based Proportional plus Integral (PI) controllers. Grid voltage, grid current, grid power, and total harmonic distortion (THD) of grid currents were analyzed. The performance of the 15-level asymmetric inverter was evaluated by comparing the THD of the grid current and the efficiency of the grid-connected photovoltaic system.

In this paper production inventory (EPQ) model by means of incorporate cost diminution release strategy is considered. Production and demand both are simultaneous process in the real world Production and demand rate are considered to be time- linked. Two models (i) production inventory and (ii) manufacture inventory model in the midst of amalgamating cost decline delivery procedure are discussed. Mathematical formulations are provided to find optimal solution for both models. The objective of this study is to obtain optimal order measure to examine the outcome of lessening for cost release plan in the EPQ model. Best possible fabrication lot size model is developed that diminishes total cost. The sensitivity analysis is discussed for variation of different parameters.

Recent advancements in green building technologies (GBTs) have grown substantially, as an outcome of the environmental, economic and societal benefits. It has the potential to move toward sustainable development, specifically related to climate change. In GBTs, the main objective is to use energy, water and other resources in a balanced way, without using them extensively. This will improve the environmental conditions. Green buildings (GBs) are beneficial when it comes to energy consumption and emissions; low maintenance and operation costs; boosting health and productivity; etc. There is a lack of a critical review of the past or present research work in the area of the Green Building Technology (GBT) sector to identify the future roadmap for sustainable green building technologies. A critical review, with the help of proper research methodology, was identified. The scope of this study is to analyze the existing work on different issues, and find different key issues in green building research, which has minimal use of natural resources, is cost-effective and is designed and constructed for a longer duration, considering future prospects. This paper examines the state of green building construction today and makes recommendations for further study and development which will be necessary for a sustainable future. In order to encourage research, this study also identified a few possible future research directions in sustainable development.

This paper proposes an effective approach to solve renewable distributed generators (RDGs) and electric vehicle charging station (EVCS) allocation problems in the distribution system (DS) to reduce power loss (PLoss) and enhance voltage profile. The RDGs considered for this work are solar, wind and fuel cell. The uncertainties related to RDGs are modelled using probability distribution functions (PDF). These sources’ best locations and sizes are identified by the voltage stability index (VSI) and political optimization algorithm (POA). Furthermore, EV charging strategies such as the conventional charging method (CCM) and optimized charging method (OCM) are considered to study the method’s efficacy. The developed approach is studied on Indian 28 bus DS. Different cases are considered, such as a single DG, multiple DGs and a combination of DGs and EVs. This placement of multiple DGs along with EVs, considering proper scheduling patterns, minimizes PLoss and considerably improves the voltage profile. Finally, the proposed method is compared with other algorithms, and simulated results show that the POA method produces better results in all aspects.

The increased popularity of small-scale DER has replaced the well-established concept of conventional generating plants around the world. In the present energy scenario, a significant share of energy production now comes from the grid integrated DERs installed at various consumer premises. These DERs are being renewable-based generates only intermittent power, which in turn makes the scheduling of electrical dispatch a tough task. The Virtual Power Plant (VPP) is a potential solution to this challenge, which coordinates and aggregates the DERs generation into a single controllable profile. In this paper, a modified PSO-based multi-objective optimization is proposed for the VPP scheduling in distribution network applications such as energy cost minimization, peak shaving, and reliability improvement. For feasibility analysis of the VPP, a case study of state power utility is taken, which includes a 90 bus industrial feeder with grid integrated PVs as DER. The optimized results are computed in both grid-connected and autonomous mode reveal that the operating cost, peak demand, and EENS are declined by 31.70%, 23.59%, and 62.30% respectively. The overall results obtained are compared by the results obtained from other well-established optimization techniques and it is found that the proposed technique is comparatively more cost-effective than others.

Abstract With primitive approaches, the diagnosis and therapy were operated at the cellular, molecular, or even at the genetic level. As the diagnostic techniques are more concentrated towards molecular level, multi modal imaging becomes specifically essential. Multi-modal imaging has extensive applications in clinical as well as in pre-clinical studies. Positron Emission Tomography (PET) has flourished in the field of nuclear medicine, which has motivated it to fuse with Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) for PET/CT and PET/MRI respectively. However, the challenges in PET/CT are due to the inability of simultaneous acquisition and reduced soft tissue contrast, which has led to the development of PET/MRI. Also, MRI offers the better soft tissue contrast over CT. Hence, fusion of PET and MRI results in combining structural information with functional image from PET. Yet, it has many technical challenges due to the interference between the modalities. Also, it must be resolved with various approaches for addressing the shortcomings of each system and improvise on the image quantification system. This review elaborates on the various challenges in the present PET/MRI system and the future directions of the hybrid modality. Also, the different data acquisition and analysis techniques of PET/MRI system are discussed with enhanced details on the software tools.

Recent trends in the photovoltaic (PV) technology industry are moving towards utilizing bifacial PV panels. Unlike traditional PV panels, bifacial PV panels can yield energy from both sides of the panel. Manufacturers specify that bifacial PV panels can harness up to 30% more energy than traditional PV panels. Hence, bifacial PV panels are becoming a common approach at low solar irradiance conditions to yield more energy. However, a bifacial PV panel increases PV inverter loading. The PV inverter is the most unreliable component in the entire PV system. This results in a negative impact on PV system reliability and cost. Hence, it is necessary to anticipate the inverter’s reliability when used in bifacial PV panels. This paper analyzes the reliability, i.e., lifetime, of PV inverters, considering both monofacial and bifacial PV panels for the analysis. Results showed that the increase in bifacial energy yield could significantly affect PV inverter reliability performance, especially in locations where the average mission profile is relatively high.

The synergy of perceptual psychology, technology, and neuroscience can be used to comprehend how virtual reality affects cognition of human brain. Numerous studies have used neuroimaging modalities to assess the cognitive state and response of the brain with various external stimulations. The virtual reality-based devices are well known to incur visual, auditory, and haptic induced perceptions. Neurophysiological recordings together with virtual stimulations can assist in correlating humans’ physiological perception with response in the environment designed virtually. The effective combination of these two has been utilized to study human behavior, spatial navigation performance, and spatial presence, to name a few. Moreover, virtual reality-based devices can be evaluated for the neurophysiological correlates of cognition through neurophysiological recordings. Challenges exist in the integration of real-time neuronal signals with virtual reality-based devices, and enhancing the experience together with real-time feedback and control through neuronal signals. This article provides an overview of neurophysiological correlates of cognition as revealed by virtual reality experience, together with a description of perception and virtual reality-based neuromodulation, various applications, and existing challenges in this field of research.