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Intelligent transportation systems (ITSs) derive significant advantages from advanced models like YOLOv8, which excel in predicting traffic incidents in dynamic urban environments. Roboflow plays a crucial role in organizing and preparing image data essential for computer vision models. Initially, a dataset of 1000 images is utilized for training, with an additional 500 images reserved for validation purposes. Subsequently, the Deep Simple Online and Real-time Tracking (Deep-SORT) algorithm enhances scene analyses over time, offering continuous monitoring of vehicle behavior. Following this, the YOLOv8 model is deployed to detect specific traffic incidents effectively. By combining YOLOv8 with Deep SORT, urban traffic patterns are accurately detected and analyzed with high precision. The findings demonstrate that YOLOv8 achieves an accuracy of 98.4%, significantly surpassing alternative methodologies. Moreover, the proposed approach exhibits outstanding performance in the recall (97.2%), precision (98.5%), and F1 score (95.7%), underscoring its superior capability in accurate prediction and analyses of traffic incidents with high precision and efficiency. [ABSTRACT FROM AUTHOR]

Visual clarity is significantly compromised, and the efficacy of numerous computer vision tasks is impeded by the widespread presence of haze in images. Innovative approaches to accurately minimize haze while keeping image features are needed to address this difficulty. The difficulties of current methods and the need to create better ones are brought to light in this investigation of the haze removal problem. The main goal is to provide a region-specific haze reduction approach by utilizing an Adaptive Neural Training Net (ANTN). The suggested technique uses adaptive training procedures with external haze images, pixel-segregated images, and haze-reduced images. Iteratively comparing spectral differences in hazy and non-hazy areas improves accuracy and decreases haze reduction errors. This study shows that the recommended strategy significantly improves upon the existing training ratio, region differentiation, and precision methods. The results demonstrate that the proposed method is effective, with a 9.83% drop in mistake rate and a 14.55% drop in differentiating time. This study's findings highlight the value of adaptable neural networks for haze reduction without losing image quality. The research concludes with a positive outlook on the future of haze reduction methods, which should lead to better visual clarity and overall performance across a wide range of computer vision applications. [ABSTRACT FROM AUTHOR]

The role of robotic systems in human assistance is inevitable with the bots that assist with interactive and voice commands. For cooperative and precise assistance, the understandability of these bots needs better input analysis. This article introduces a Comparable Input Assessment Technique (CIAT) to improve the bot system's understandability. This research introduces a novel approach for HRI that uses optimized algorithms for input detection, analysis, and response generation in conjunction with advanced neural classifiers. This approach employs deep learning models to enhance the accuracy of input identification and processing efficiency, in contrast to previous approaches that often depended on conventional detection techniques and basic analytical methods. Regardless of the input type, this technique defines cooperative control for assistance from previous histories. The inputs are cooperatively validated for the instruction responses for human assistance through defined classifications. For this purpose, a neural classifier is used; the maximum possibilities for assistance using self-detected instructions are recommended for the user. The neural classifier is divided into two categories according to its maximum comparable limits: precise instruction and least assessment inputs. For this purpose, the robot system is trained using previous histories and new assistance activities. The learning process performs comparable validations between detected and unrecognizable inputs with a classification that reduces understandability errors. Therefore, the proposed technique was found to reduce response time by 6.81%, improve input detection by 8.73%, and provide assistance by 12.23% under varying inputs. [ABSTRACT FROM AUTHOR]

Oral delivery, the most common method of therapeutic administration, has two significant obstacles: drug solubility and permeability. The challenges of current oral medicine delivery are being tackled through an emerging method that uses structures called polymeric micelles. In the present study, polymeric micelles were developed using conjugates of linoleic acid–carboxymethyl chitosan (LA-CMCS) for the oral delivery of paclitaxel (PCL). The developed micelles were evaluated by particle size, zeta potential, Fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), and thermogravimetric analysis (TGA). When PCL was contained within micelles, its solubility increased by almost 13.65 times (around 60 µg/mL). The micelles' zeta potentials were −29 mV, their polydispersity indices were 0.023, and their particle diameters were 93 nm. Micelles showed PCL loading and entrapment efficiencies of 67% and 61%, respectively. The sustained release qualities of the PCL release data from micelles were good. In comparison to the pure PCL suspension, the permeability of the PCL from micelles was 2.2 times higher. The pharmacokinetic data revealed that PCL with LA-CMCS micelles had a relative bioavailability of 239.17%, which was much greater than the PCL in the suspension. The oral bioavailability of PCL was effectively increased by LA-CMCS micelles according to an in vivo study on animals. The polymer choice, maybe through improved permeability, plays an essential role when assessing oral bioavailability enhancement and solubility improvement (13.65 times). The outcomes demonstrated that PCL's solubility and pharmacokinetics were improved in the micelles of the LA-CMCS conjugate. [ABSTRACT FROM AUTHOR]

Multi-level inverters are widely employed to generate new energy because of their huge capacity and benefits in sound control performance. One of the critical areas of study for multi-level inverters is control strategy research. In this study, the control strategy for a multi-level inverter—which is frequently employed in HVDC and FACTS systems—is designed. An asymmetrical D.C. voltage source is supplied to create the appropriate output voltage waveform with fewer total harmonic distortions (THDs) at the output voltage and current waveforms. In this work, the pulse width modulation techniques of POD (phase opposition disposition) and APOD (alternative phase opposition disposition) MC PWM are applied to a multi-level inverter to generate the seven-level output voltage waveform. This study presents an enhanced variable carrier frequency APOD control approach that can successfully lower the overall harmonic distortion rate. The design and completion of the phase-shifting POD and APOD control strategies are followed by an analysis and comparison of the THD situation under various switching frequencies and a simulation and verification of the control strategy using MATLAB simulation. The TI DSP-based control approach has been programmed. The APOD technique increases the output voltage's THD to 18.27%, while the output current waveform's THD is reduced to 15.67% by utilizing the APOD PWM technique. Using the POD PWM approach increases the total harmonic distortion (THD) of the voltage waveform by 18.06% and the output current waveform's THD by 15.45%. [ABSTRACT FROM AUTHOR]

Nanoslits have various applications, including localized surface plasmon resonance (LSPR)-based nanodevices, optical biosensors, superfocusing, high-efficiency refractive index sensors and chip-based protein detection. In this study, the effect of substrates on the optical properties of gold nanoslits placed in free space is discussed; for this purpose, glass BK7 and Al 2 O 3 are used as substrates and the wavelength of incident light is supposed to be 650 nm. The optical properties, power flow and electric field enhancement for gold nanoslits are investigated by using the finite element method (FEM) in COMSOL Multiphysics software. The effect of polarization of an incident electromagnetic wave as it propagates from a gold nanoslit is also analyzed. As special case, the effect of glass and alumina substrate on magnetic field, power flow and electric field enhancement is discussed. The goal of this research is to investigate the phenomenon of power flow and electric field enhancement. The study of power flow in gold nanoslits provides valuable insights into the behavior of light at the nanoscale and offers opportunities for developing novel applications in the field of nanophotonics and plasmonics. The consequences of this study show the significance of gold nanoslits as optical nanosensors. [ABSTRACT FROM AUTHOR]

The Chiltan formation is a potential hydrocarbon-producing reservoir in the Indus Basin, Pakistan. However, its diagenetic alterations and heterogeneous behavior lead to significant challenges in accurately characterizing the reservoir and production performance. This manuscript aims to utilize six carbonate core samples of the Chiltan limestone to conduct an in-depth analysis of the diagenetic impacts on reservoir quality. The comprehensive formation evaluation was carried out through thin-section analysis, SEM-EDS, and FTIR investigation, as well as plug porosity and permeability measurements under varying stress conditions. In result, petrography revealed three microfacies of intraclastic packestone (MF1), bioclastic pelliodal packestone (MF2), and bioclastic ooidal grainstone (MF3), with distinct diagenetic features and micro-nano fossil assemblages. The MF1 microfacies consist of bioclasts, ooids, pellets, and induced calcite, while the MF2 microfacies contain micrite cemented peloids, algae, and gastropods. Although, the MF3 grainstone microfacies contains key features of bioclasts, milliods, bivalves, echinoderms, and branchiopods with intense micritization. Diagenesis has a significant impact on petrophysical properties, leading to increased reservoir heterogeneity. The specified depositional environment exposed the alteration of the Chiltan formation during distinct diagenetic phases in marine, meteoric, and burial settings. Marine diagenesis involves biogenic carbonates and micro-nano fossils, while meteoric diagenesis involves mineral dissolution, reprecipitation, secondary porosity, compaction, cementation, and stylolite formation. Pore morphology and mineralogy reveal a complex pore network within the formation, including a micro-nano pore structure, inter–intra particle, moldic, vuggy, and fenestral pores with variations in shape, connectivity, and distribution. Various carbonate mineral phases in the formation samples were analyzed, including the calcite matrix and dolomite crystals, while silica, calcite, and clay minerals were commonly observed cement types in the analysis. The core samples analyzed showed poor reservoir quality, with porosity values ranging from 2.02% to 5.31% and permeability values from 0.264 mD to 0.732 mD, with a standard deviation of 1.21. Stress sensitivity was determined using Klinkenberg-corrected permeability at increasing pore pressure conditions, which indicated around 22%–25% reduction in the measured gas permeability and 7% in Klinkenberg permeability due to increasing the net confining stress. In conclusion, the Chiltan formation possesses intricate reservoir heterogeneity and varied micropore structures caused by diagenesis and depositional settings. The formation exhibits nonuniform pore geometry and low petrophysical properties caused by the diverse depositional environment and various minerals and cement types that result in a low-quality reservoir. Stress sensitivity further decreases the permeability with varying stress levels, emphasizing the need of stress effects in reservoir management. The results of this study provide a solid foundation in reservoir characterization and quality assessment that has implications for predicting fluid flow behavior, providing insight into geological evolution and its impact on reservoir quality and leading to improving resource exploration and production strategies. [ABSTRACT FROM AUTHOR]

Minerals play a dynamic role in plant growth and development. However, most of these mineral nutrients are unavailable to plants due to their presence in fixed forms, which causes significant losses in crop production. An effective strategy to overcome this challenge is using mineral solubilizing bacteria, which can convert insoluble forms of minerals into soluble ones that plants can quickly assimilate, thus enhancing their availability in nutrient-depleted soils. The main objective of the present study was to isolate and characterize mineral solubilizing rhizobacteria and to assess their plant growth-promoting potential for Rhodes grass. Twenty-five rhizobacterial strains were isolated on a nutrient agar medium. They were characterized for solubilization of insoluble minerals (phosphate, potassium, zinc, and manganese), indole acetic acid production, enzymatic activities, and various morphological traits. The selected strains were also evaluated for their potential to promote the growth of Rhodes grass seedlings. Among tested strains, eight strains demonstrated strong qualitative and quantitative solubilization of insoluble phosphate. Strain MS2 reported the highest phosphate solubilization index, phosphate solubilization efficiency, available phosphorus concentration, and reduction in medium pH. Among tested strains, 75% were positive for zinc and manganese solubilization, and 37.5% were positive for potassium solubilization. Strain MS2 demonstrated the highest quantitative manganese solubilization, while strains MS7 and SM4 reported the highest solubilization of zinc and potassium through acidifying their respective media. The strain SM4 demonstrated the most increased IAA production in the presence and absence of L-tryptophan. The majority of strains were positive for various enzymes, including urease, catalase protease, and amylase activities. However, these strains were negative for coagulase activity except strains SM7 and MS7. Based on 16S rRNA gene sequencing, six strains, namely, SM2, SM4, SM5, MS1, MS2, and MS4, were identified as Bacillus cereus, while strains SM7 and MS7 were identified as Staphylococcus saprophyticus and Staphylococcus haemolyticus. These strains significantly improved growth attributes of Rhodes grass, such as root length, shoot length, and root and shoot fresh and dry biomasses compared to the uninoculated control group. The present study highlights the significance of mineral solubilizing and enzyme-producing rhizobacterial strains as potential bioinoculants to enhance Rhodes grass growth under mineral-deficient conditions sustainably. [ABSTRACT FROM AUTHOR]

Salinity and cadmium (Cd) contamination of soil are serious environmental issues threatening food security. This study investigated the role of salicylic acid (SA) and potassium (K) in enhancing the resilience of quinoa against the combined stress of salinity and Cd. Quinoa plants were grown under NaCl (0, 200 mM) and Cd (0, 100 µM) stress, with the addition of 0.1 mM SA and 10 mM K, separately or in combination. The joint stress of Cd and NaCl caused >50% decrease in plant growth, chlorophyll contents, and stomatal conductance compared to the control plants. The higher accumulation of Na and Cd reduced the uptake of K in quinoa tissues. The joint stress of salinity and Cd caused an 11-fold increase in hydrogen peroxide and 13-fold increase in thiobarbituric acid reactive substances contents, and caused a 61% decrease in membrane stability. An external supply of 0.1 mM SA and 10 mM K helped plants to better adapt to salinity and Cd stress with less of a reduction in plant biomass (shoot 19% and root 24%) and less accumulation of Na and Cd in plant tissues. The activities of superoxide dismutase (SOD), catalase (CAT), peroxidase (POD), and ascorbate peroxidase (APX) were enhanced by 11-fold, 10-fold, 7.7-fold, and 7-fold, respectively, when SA and K were applied together to the plants subjected to the joint stress of Cd and salinity. Based on the values of the bioconcentration factor (>1), the translocation factor (<1), and the higher tolerance index, it was clear that Cd-contaminated, salty soils could be stabilized with quinoa under the combined supply of SA and K. [ABSTRACT FROM AUTHOR]

Depression is the major mental illness which causes along with loss of interest in daily life, a feeling of hopelessness, appetite or weight changes, anger and irritability. Due to the hepatic first-pass metabolism, the absolute bioavailability of fluvoxamine (FVM) after oral administration is about 50%. By avoiding the pre-systemic metabolism, nasal delivery would boost bioavailability of FVM. Additionally, the absorption is anticipated to occur more quickly than it would via the oral route because of the existence of microvilli and high vasculature. A nonionic surfactant, cholesterol and an arachidonic acid-carboxymethyl chitosan (AA-CMCS) conjugate were used to develop FVM-loaded novasomes. To investigate the effects of surfactant concentration, AA-CMCS conjugate concentration and stirring speed on the novasomes' characteristics, a Box–Behnken design was used. The dependent variables chosen were zeta potential, polydispersity index and particle size. The AA-CMCS conjugate was confirmed by 1H-NMR and FTIR. Using Design Expert software (version 7; Stat-Ease Inc., Minneapolis, MN, USA), novasomes were further optimized. The chosen optimal formulation (NAC8) was made up of AA-CMCS conjugate, Span 60 and cholesterol. Particle size, zeta potential and PDI values for NAC8 formulation were 101 nm, −35 mV and 0.263, respectively. The NAC8 formulation's DSC and TGA analysis demonstrated that the medication had been uniformly and amorphously distributed throughout the novasomes. The NAC8 formulation showed 99% and 90% FVM release and permeation, respectively, and the novasome adherence time was 24 h. An improved antidepressant effect along with five-fold increase in bioavailability of FVM was observed after trans-nasal administration of NAC8 formulation compared to the reference commercially available Flumin® tablets. FVM-loaded novasomes administered via the nasal route may therefore constitute an advancement in the management of depression. [ABSTRACT FROM AUTHOR]

Target detection in high-contrast, multi-object images and movies is challenging. This difficulty results from different areas and objects/people having varying pixel distributions, contrast, and intensity properties. This work introduces a new region-focused feature detection (RFD) method to tackle this problem and improve target detection accuracy. The RFD method divides the input image into several smaller ones so that as much of the image as possible is processed. Each of these zones has its own contrast and intensity attributes computed. Deep recurrent learning is then used to iteratively extract these features using a similarity measure from training inputs corresponding to various regions. The target can be located by combining features from many locations that overlap. The recognized target is compared to the inputs used during training, with the help of contrast and intensity attributes, to increase accuracy. The feature distribution across regions is also used for repeated training of the learning paradigm. This method efficiently lowers false rates during region selection and pattern matching with numerous extraction instances. Therefore, the suggested method provides greater accuracy by singling out distinct regions and filtering out misleading rate-generating features. The accuracy, similarity index, false rate, extraction ratio, processing time, and others are used to assess the effectiveness of the proposed approach. The proposed RFD improves the similarity index by 10.69%, extraction ratio by 9.04%, and precision by 13.27%. The false rate and processing time are reduced by 7.78% and 9.19%, respectively. [ABSTRACT FROM AUTHOR]

Metallic trace elements toxicity has been associated with a wide range of morphological abnormalities in fish, both in natural aquatic ecosystems and controlled environments. The bioaccumulation of metallic trace elements can have devastating effects on several aspects of fish health, encompassing physiological, reproductive, behavioural, and developmental functions. Considering the significant risks posed by metallic trace elements-induced toxicity to fish populations, this review aims to investigate the deleterious effects of prevalent metallic trace elements toxicants, such as mercury (Hg), cadmium (Cd), chromium (Cr), lead (Pb), arsenic (As), and copper (Cu), on the neurological, reproductive, embryonic, and tissue systems of fish. Employing diverse search engines and relevant keywords, an extensive review of in vitro and in vivo studies pertaining to metallic trace elements toxicity and its adverse consequences on fish and their organs was conducted. The findings indicate that Cd was the most prevalent metallic trace elements in aquatic environments, exerting the most severe impacts on various fish organs and systems, followed by Cu and Pb. Moreover, it was observed that different metals exhibited varying degrees and types of effects on fish. Given the profound adverse effects of metallic trace elements contamination in water, immediate measures need to be taken to mitigate water pollution stemming from the discharge of waste containing metallic trace elements from agricultural, industrial, and domestic water usage. This study also compares the most common methods for treating metallic trace elements contamination in water. [ABSTRACT FROM AUTHOR]

Multilevel inverters, known for their low switching loss and suitability for medium- to high-power applications, often create a heavy computational overhead for the controller. This paper addresses the aforementioned limitation by presenting a novel approach to Direct Model Predictive Control (DMPC) for a grid-tied 289-level ladder multilevel inverter (LMLI). The primary objective is to achieve perfect inverter current control without enumeration. The proposed control method provides a single best solution without complete exploration of the search space. This generalized method can be applied to any multilevel inverter (MLI), enabling them to be used in the grid-tied mode without the computational burden due to a large number of switching states. The DMPC of LMLI with 289-level output and corresponding 289 control inputs, utilizes a discrete model to predict the future state of the state variable. In order to alleviate the enumeration burden, virtual sectors on a linear scale are introduced, and a general formula is provided to identify the single best state among the 289 states, reducing the time required to find the best optimal state per sampling period. Moreover, the proposed control scheme is independent of objective evaluation. [ABSTRACT FROM AUTHOR]

Leflunomide is an isoxazole immunomodulating drug used to treat rheumatoid arthritis (RA). It is adopted as a metal-containing molecule to proceed with saturated salts of essential and detected metals; it amends the pharmacokinetic and pharmacodynamics activity of leflunomide to provide [M(Lef)4]X2-type complexes. Earlier it has been reported that after forming complexes with metals, leflunomide anti-arthritic activity was significantly altered in an acute arthritic model. In the present study, we evaluated the possible modification in anti-arthritic activities of leflunomide–metal complexes (Mg+2, Ca+2, Fe+2, Zn+2) with and without an anti-depressant drug, i.e., fluoxetine (10 mg/kg) in a chronic AIA model. Rats (n = 5) were administered with 0.1 mL of CFA into the right hind paw while treated groups received leflunomide and its metal complexes orally (3.2 mg/kg) for 24 days. On the final day of experiment, rats were sacrificed; a specific rat immunoassay ELISA kit was used to assess TNF-α in serum samples and read at 450 nm; a tissue sample of a paw was homogenized in a phosphate buffer using DCFH-DA dye for binding to assess ROS. A rat's brain sample was homogenized and evaluated for tryptophan, serotonin (5-HT), and HIAA by RP-HPLC with EC detector. The overall TNF production was altered in treated rats. In addition, a decreased ROS was observed in all categories, except lef+Mg+2 group. Moreover, depletion in the brain indolamine levels were found in treated groups; an upraised level of these indolamines was observed when fluoxetine was added. It is concluded that metals affect leflunomide activity on complexation and simultaneous administration of fluoxetine cope up with the depression in arthritic-induced rats. [ABSTRACT FROM AUTHOR]

Wastewater generation is a major concern, as most of it goes untreated. Industries, urban areas, and agriculture are the major contributors to wastewater. Phytoremediation is an effective method of wastewater treatment. However, the potential of local aquatic species for hyper-accumulation of heavy metals remains elusive. This study focuses on evaluating the native macrophyte Eichhornia crassipes for phytoremediation potential in different source-based water environments: freshwater (FW), industrial (IW), and urban wastewater (UW). Physico-chemical analysis was conducted on water samples (five samples from each source) along with the corresponding E. crassipes plants for assessing physiological, nutritional, and heavy metal parameters. The results showed distinct characteristics among the water sources. The FW had a high pH, and the IW exhibited elevated levels of electrical conductivity (EC: 1746 μS cm−1), total dissolved solids (TDS: 864 mg L−1), chloride (Cl−: 557.83 mg L−1), sulfate (SO4−: 137.27 mg L−1), and calcium (Ca++: 77.83 mg L−1) ions. The UW exhibited high bicarbonate (HCO3−: 123.38 mg L−1), sodium (Na+: 154 mg L−1), and potassium (K+: 37.12 mg L−1) ions. The Cd contamination exceeded World Health Organization (WHO) limits (0.003 mg L−1) in the FW (0.05 mg L−1 in FW-5) and UW (0.05 mg L−1 in UW-3); Cr contamination was higher than the permissible limits of the WHO, National Environmental Quality Standards (NEQS), and the European Union (EU) (0.05 mg L−1) in FW, IW, and UW; arsenic (As) in IW exceeded the WHO, United States Environmental Protection Agency (USEPA), and EU limits of 10 μg L−1, and Pb in UW exceeded the WHO (0.01 mg L−1), NEQS (0.05 mg L−1), and EU (0.01 mg L−1) limits. E. crassipes displayed different traits depending on the water sources. FW-grown plants had a higher biomass and chlorophyll-b content, while UW-grown plants had higher photosynthesis rates and chlorophyll-a content. Shoots accumulated more Na+, K+, and Ca++ ions than roots. Metal translocation from roots to shoots followed specific patterns for each source: the TFs of Zn = 3.62 in FW > Cd = 2.34 in UW > Cr = 1.61 and Pb = 1.29 in IW and BCFs were found in ascending order: Zn > Ni > Cd > As > Pb in FW, Cd > Zn > Ni > Cr > Pb > As in IW, and Cd > Ni > Pb > Cr > Zn > As in UW. The bioconcentration factor was higher in the roots than in the shoots. These findings suggest that E. crassipes shows promise as a phytoremediation option for heavy metal-contaminated wastewater due to its ability to thrive in harsh wastewater conditions with a higher TF > 1 and BCF > 1. Therefore, the utilization of these macrophytes holds potential for wastewater treatment. [ABSTRACT FROM AUTHOR]

The current study focused on the fabrication of a well-designed, biocompatible, physically stable, non-irritating and highly porous gelatin scaffold loaded with controlled-release triamcinolone acetonide (TA) and econazole nitrate (EN) co-loaded into mesoporous silica nanoparticles (EN-TA-loaded MSNs) to provide a better long-lasting antifungal therapeutic effect with minimal unfavorable effects. Optimization of the MSNs-loaded scaffold was performed using central composite rotatable design (CCRD), where the effect of gelatin concentration (X1), plasticizer (X2) and freezing time (X3) on the entrapment of EN (Y1) and TA (Y2) and on the release of EN (Y3) and TA (Y4) from the scaffold were studied. The significant compatibility of all formulation ingredients with both drugs was established from XRD, DSC and FT-IR spectra analyses while SEM and zeta studies represented a very precise unvarying distribution of the loaded MSNs in the porous structure of the scaffold. The stability of the optimized scaffold was confirmed from zeta potential analysis (−16.20 mV), and it exhibited higher entrapment efficiency (94%) and the slower (34%) release of both drugs. During in vitro and in vivo antifungal studies against Candida albicans, the MSNs-loaded scaffold was comparatively superior in the eradication of fungal infections as a greater zone of inhibition was observed for the optimized scaffold (16.91 mm) as compared to the pure drugs suspension (14.10 mm). Similarly, the MSNs-loaded scaffold showed a decreased cytotoxicity because the cell survival rate in the scaffold presence was 89% while the cell survival rate was 85% in the case of the pure drugs, and the MSNs-loaded scaffold did not indicate any grade of erythema on the skin in comparison to the pure medicinal agents. Conclusively, the scaffold-loaded nanoparticles containing the combined therapy appear to possess a strong prospective for enhancing patients' adherence and therapy tolerance by yielding improved synergistic antifungal efficacy at a low dose with abridged toxicity and augmented wound-healing impact. [ABSTRACT FROM AUTHOR]

Recently, different metaheuristic techniques, their variants, and hybrid forms have been extensively used to solve economic load dispatch (ELD) problems with and without valve point loading (VPL) effects. Due to the randomization involved in these metaheuristic techniques, one has to perform extensive runs for each experiment to get an optimal solution. The process may sometimes become laborious and time-consuming to converge to an optimal solution. On the other hand, advanced calculus-based techniques, being deterministic, perform iteration systematically and come up with the same solution on each run of the experiment. Since ELD problems are constrained optimization problems, we are proposing the constrained (deterministic) optimization algorithm for their solutions. Various 13-unit, 38-unit, and 40-unit thermal test systems are considered. Valve point loading (VPL) effects are also considered in some cases. Computer-based numerical results depict that the constrained optimization algorithm shows evidence of being almost as competitive in a total fuel cost as the metaheuristic optimization techniques, especially for the less-constrained ELD problems but with far reduced computation time. This finding validates the application of the constrained optimization technique to solve the economic dispatch problem. [ABSTRACT FROM AUTHOR]

The exponential growth of unpredictable renewable power production sources in the power grid results in difficult-to-regulate reactive power. The ultimate goal of optimal reactive power dispatch (ORPD) is to find the optimal voltage level of all the generators, the transformer tap ratio, and the MVAR injection of shunt VAR compensators (SVC). More realistically, the ORPD problem is a nonlinear multi-objective optimization problem. Therefore, in this paper, the multi-objective ORPD problem is formulated and solved considering the simultaneous minimization of the active power loss, voltage deviation, emission, and the operating cost of renewable and thermal generators. Usually, renewable power generators such as wind and solar are uncertain; therefore, Weibull and lognormal probability distribution functions are considered to model wind and solar power, respectively. Due to the unavailability and uncertainty of wind and solar power, appropriate PDFs have been used to generate 1000 scenarios with the help of Monte Carlo simulation techniques. Practically, it is not possible to solve the problem considering all the scenarios. Therefore, the scenario reduction technique based on the distance metric is applied to select the 24 representative scenarios to reduce the size of the problem. Moreover, the efficient non-dominated sorting genetic algorithm II-based bidirectional co-evolutionary algorithm (BiCo), along with the constraint domination principle, is adopted to solve the multi-objective ORPD problem. Furthermore, a modified IEEE standard 30-bus system is employed to show the performance and superiority of the proposed algorithm. Simulation results indicate that the proposed algorithm finds uniformly distributed and near-global final non-dominated solutions compared to the recently available state-of-the-art multi-objective algorithms in the literature. [ABSTRACT FROM AUTHOR]

Recently, unmanned aerial vehicles (UAVs) have emerged as a viable solution for data collection from remote Internet of Things (IoT) applications. However, the successful implementation in this regard necessitates the development of a reliable and energy-efficient routing protocol. This paper proposes a reliable and an energy-efficient UAV-assisted clustering hierarchical (EEUCH) protocol designed for remote wireless sensor networks (WSNs) based IoT applications. The proposed EEUCH routing protocol facilitates UAVs to collect data from ground sensor nodes (SNs) that are equipped with wake-up radios (WuRs) and deployed remotely from the base station (BS) in the field of interest (FoI). During each round of the EEUCH protocol, the UAVs arrive at the predefined hovering positions at the FoI, perform clear channel assignment, and broadcast wake-up calls (WuCs) to the SNs. Upon receiving the WuCs by the SNs' wake-up receivers, the SNs perform carrier sense multiple access/collision avoidance before sending joining requests to ensure reliability and cluster-memberships with the particular UAV whose WuC is received. The cluster-member SNs turn on their main radios (MRs) for data packet transmission. The UAV assigns time division multiple access (TDMA) slots to each of its cluster-member SNs whose joining request is received. Each SN must send the data packets in its assigned TDMA slot. When data packets are successfully received by the UAV, it sends acknowledgments to the SNs, after which the SNs turn off their MRs, completing a single round of the protocol. The proposed EEUCH routing protocol with WuR eliminates the issue of cluster overlapping, improves the overall performance, and increases network stability time by a factor of 8.7. It also improves energy efficiency by a factor of 12.55, resulting in a longer network lifespan compared to Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. Moreover, EEUCH collects 5.05 times more data from the FoI than LEACH. These results are based on simulations in which the EEUCH protocol outperformed the existing six benchmark routing protocols proposed for homogeneous, two-tier, and three-tier heterogeneous WSNs. [ABSTRACT FROM AUTHOR]

Drilling bits are essential downhole hardware that facilitates drilling operations in high-pressure, high-temperature regions and in most carbonate reservoirs in the world. While the drilling process can be optimized, drilling operators and engineers become curious about how drill bits react during rock breaking and penetration. Since it is experimentally expensive to determine, the goal of the study is to maximize the rate of penetration by modeling fluid interactions around the roller cone drilling bit (RCDB), specifying a suitable number of jet nozzles and venturi effects for non-Newtonian fluids (synthetic-based muds), and examining the effects of mud particles and drill cuttings. Ansys Fluent k-epsilon turbulence viscous model, a second order upwind for momentum, turbulent kinetic energy, and dissipation rate, were used to model the specified 1000 kg/m3 non-Newtonian fluid around the roller cone drill bit. The original geometry of the nozzles was adapted from a Chinese manufacturer whose tricone had three jet nozzles. The results of our six redesigned jet nozzles (3 outer, 39.12 mm, and 3 proximal, 20 mm) sought to offer maximum potential for drilling optimization. However, at a pressure of 9.39 × 104 Pa, the wellbore with particle sizes between 0.10 mm and 4.2 mm drill cuttings observed an improved rate of penetration with a rotation speed of 150 r/min. [ABSTRACT FROM AUTHOR]

Plug-in hybrid electric vehicles (PHEVs) and plug-in electric vehicles (PEVs) have gained enormous attention for their ability to reduce fuel consumption in transportation and are, thus, helpful in the reduction of the greenhouse effect and pollution. However, they bring up some technical problems that should be resolved. Due to the ever-increasing demand for these PHEVs, the simultaneous connection of large PEVs and PHEVs to the electric grid can cause overloading, which results in disturbance to overall power system stability and quality and can cause a blackout. Such situations can be avoided by adequately manipulating power available from the grid and vehicle power demand. State of charge (SoC) is the leading performance parameter that should be optimized using computational techniques to charge vehicles efficiently. In this research, an efficient metaheuristic algorithm, accelerated particle swarm optimization (APSO), and its five variants were applied to allocate power to vehicles connected to the grid intelligently. For this, the maximization of average SoC is considered a fitness function, and each PHEV can be connected to the grid once a day so that the maximum number of cars can be charged daily. To statistically compare the performance of these six algorithms, one-way ANOVA was used. Simulation and statistical results obtained by maximizing this highly non-linear objective function show that accelerated particle swarm optimization with Variant 5 achieved some improvements in terms of computational time and best fitness value. The APSO-5 solution has a considerable percentage increase compared with the solution of other variants of APSO for the four PHEV datasets considered. Moreover, after 30 trials, APSO 5 gives the highest possible fitness value among all the algorithms. [ABSTRACT FROM AUTHOR]

This study explores the association of novel COVID-19 with the dominant financial assets, global uncertainty, commodity prices, and stock markets of the top ten corona-affected countries. We employ a wavelet coherence technique to unearth this linkage using daily data of COVID-19 deaths and reported cases from 1 January 2020 until 26 February 2021. The study finds a weak coherence between COVID-19 and global uncertainty variables in the short and medium term, while a strong positive correlation has been witnessed in the long run. The COVID-19 cases impact the stock markets in the short and medium term, while no significant impact is reported in the long run. On the other hand, a substantial impact of the COVID-19 outbreak has also been found on the exchange rate. In addition, the real asset market, such as gold, remains more stable during the COVID-19 outbreak. Thus, the study recommends that investors and portfolio managers should add such assets to their investment options to safeguard the excessive risk and downside momentum of the equity market. The study also has implications for regulators who are concerned with the neutrality of the COVID-19 effect and market stability. [ABSTRACT FROM AUTHOR]

In the present paper, strong deflection gravitational lensing is studied in a conformal gravity black hole. With the help of geometric optics limits, we have formulated the light cone conditions for the photons coupled to the Weyl tensor in a conformal gravity black hole. It is explicitly found that strong deflection gravitational lensing depends on the coupling with the Weyl tensor, the polarization directions, and the black hole configuration parameters. We have applied the results of the strong deflection gravitational lensing to the supermassive black holes S g r A * and M 87 * and studied the possibility of encountering quantum improvement. It is not practicable to recognize similar black holes through the strong deflection gravitational lensing observables in the near future, except for the possible size of the black hole's shadow. We also notice that by directly adopting the constraint of the measured shadow of M 87 * , the quantum effect demands immense care. [ABSTRACT FROM AUTHOR]

Hydro generation is the simplest and oldest method of electricity generation, with a century of successful operation. Using a pump as a turbine (PAT) is an optimal solution for minimizing the cost, particularly in low-head and small-scale hydro plants. Commercially available centrifugal pumps have become a popular solution for small-scale hydro and pumped-hydro facilities owing to their simple geometry, ease of operation, maintenance, and abundant availability in local markets. Variations in the water flow in hydro facilities, such as pumped-hydro and small-scale hydro facilities, are common; however, a PAT is unable to respond to variable flows because it is a fixed-speed device. To overcome this problem, different techniques have been suggested by researchers: (a) a system of parallel PATs; (b) geometrical modifications in the impeller of the PAT; and (c) power electronics-based variable frequency drives. All the aforementioned techniques have limitations, such as low output, high cost, complexity, transportation, and operation and maintenance. In this study, a simple and economical technique is proposed to smooth the output of a PAT on variable/decreasing water flow profiles. In the proposed technique, water columns connected in parallel (PWCs) are used to produce a pressurized water flow, as they have a convergent nozzle at the outlet. The PWC creates more space for water, and this additional mass of water boosts the water flow at the outlet. In this manner, the PWC technique maintains the flow at the inlet of the turbine. A serial integration of five PWCs with the same dimensions was conducted to inject the additional flow into the existing PAT penstock, governing a 37-kW generator. The design flow was maintained at 192.1 L/s without any additional power usage at the inlet of the PAT, and the output was smoothed even at the minimum water flow/head. Pump design and computational fluid dynamics simulations were performed using ANSYS software, whereas generator simulations were performed using MATLAB/Simulink software. [ABSTRACT FROM AUTHOR]

Soil salinity, drought, and increasing temperatures are serious environmental issues that drastically reduce crop productivity worldwide. Quinoa (Chenopodium quinoa Willd) is an important crop for food security under the changing climate. This study examined the physio-biochemical responses, plant growth, and grain yield of four quinoa genotypes (A7, Titicaca, Vikinga, and Puno) grown in pots containing normal (non-saline) or salt-affected soil exposed to drought and elevated-temperature treatments. Combinations of drought, salinity, and high-temperature stress decreased plant growth and yield more than the individual stresses. The combined drought, salinity, and heat stress treatment decreased the shoot biomass of A7, Puno, Titicaca, and Vikinga by 27, 36, 41, and 50%, respectively, compared to that of control plants. Similar trends were observed for grain yield, chlorophyll contents, and stomatal conductance. The combined application of these three stresses increased Na concentrations but decreased K concentrations in roots and shoots relative to control. Moreover, in the combined salinity, drought, and high-temperature treatment, A7, Puno, Titicaca, and Vikinga had 7.3-, 6.9-, 8-, and 12.6-fold higher hydrogen peroxide contents than control plants. All four quinoa genotypes increased antioxidant enzyme activities (CAT, SOD, and POD) to overcome oxidative stress. Despite A7 producing the highest biomass under stress, it did not translate into increased grain production. We conclude that Puno and Titicaca are more tolerant than Vikinga for cultivation in salt-affected soils prone to drought and heat stress. [ABSTRACT FROM AUTHOR]

The Cascaded Short-Term Hydrothermal Scheduling (CSTHTS) problem is a highly non-linear, multi-modal, non-convex, and NP-hard optimization problem that has been solved by conventional and metaheuristic algorithms in the past. As the CSTHTS problem falls under the category of applied operational research, therefore, the work is still on-going to find new algorithms and variants of the existing algorithms that would better approximate the optimal global solution in a shorter computational time. This article proposes a novel deterministic thermal economic dispatch method embedded with the improved Accelerated Particle Swarm Optimization (APSO) algorithm to infinitesimally reduce the Big O time complexity for the standard benchmark test case of the CSTHTS optimization problem. Then, it discusses and presents the importance of performing standard statistical tests to establish the supremacy of one metaheuristic algorithm over the other one in solving the CSTHTS problem. The results obtained are better than the results of the many state-of-the-art algorithms applied to solve the considered test case of the CSTHTS problem in the literature, and the superiority of the improved APSO algorithm has been established statistically using the parametric independent samples t-test and the non-parametric Mann–Whitney U-test over the other metaheuristic algorithms such as particle swarm optimization in solving the chosen test case of the CSTHTS problem. [ABSTRACT FROM AUTHOR]

The increasing penetration of Distributed Generators (D.G.) into the existing power system has brought some real challenges regarding the transient response of electrical systems. The injection of D.G.s and abrupt load changes may cause massive power, current, and voltage overshoots/undershoots, which consequently affects the equilibrium of the existing power system and deteriorate the performance of the connected electrical appliances. A robust and intelligent control strategy is of utmost importance to cope with these issues and enhance the penetration level of D.G.s into the existing power system. This paper presents a Modified Particle Swarm Optimization (MPSO) algorithm-based intelligent controller for attaining a desired power-sharing ratio between the M.G. and the main grid with an optimal transient response in a grid-tied Microgrid (M.G.) system. The proposed MPSO algorithm includes an additional parameter named best neighbor particles (rbest) in the velocity updating equation to convey additional information to every individual particle about all its neighbor particles, consequently leading to the increased exploration capability of the algorithm. The MPSO algorithm optimizes P.I. parameters for transient and steady-state response improvement of the studied M.G. system. The main dynamic response evaluation parameters are the overshoot and settling time for active and reactive power during the D.G. connection and load change. Furthermore, the performance of the proposed controller is compared with the PI-PSO-based MG controller, which validates the effectiveness of the proposed M.G. control scheme in maintaining the required active and reactive power under different operating conditions with minimum possible overshoot and settling time. [ABSTRACT FROM AUTHOR]

The reconfigurable intelligent surfaces (RIS) is a new technology that can be utilized to provide security to vehicle-to-vehicle (V2V) communications at the physical layer. In this paper, we achieve a higher key generation rate for V2V communications at lower cost and computational complexity. We investigate the use of a passive RIS as a relay, to introduce channel diversity and increase the key generation rate (KGR), accordingly. In this regard, we consider the subsets of consecutive reflecting elements instead of the RIS as a whole in a time slot, i.e., instead of a single reflector, the subsets of reflectors are utilized to redirect the signal to the receiver via passive beam forming. Simulations are conducted for different sizes of RISs and subsets of reflectors per RIS. From the results obtained, it can be seen that when we consider a subset of reflectors instead of the RIS as a single entity, it becomes increasingly difficult to intercept the signal at the eavesdropper. In the proposed scheme, the KGR reaches up to 6 bps per time slot. [ABSTRACT FROM AUTHOR]

In recent decades, the rapid rise in electricity demand has compelled transmission and distribution systems to operate at almost their maximum capacity. This can pose numerous technical challenges such as excessive power losses, voltage and transient instabilities, as well as reduced power quality and reliability. Employment of Flexible Alternating Current Transmission System (FACTS) devices can be an effective approach to obviate such challenges and reinforce the power system functionality. Nevertheless, FACTS devices require a high initial investment, and hence their optimal allocation in terms of various aspects such as type, size and location is of utmost importance. This cannot be achieved without the deployment of optimization techniques. The aim of this paper is to provide a comprehensive review of the existing proposals for the enhancement of power system performance adopting FACTS devices. Adhering to that, an in-depth analysis is carried out, in which the most pertinent options are classified into specific groups based on their optimization objectives. Finally, a comparative analysis is accomplished in which the main attributes and drawbacks of each optimization technique are presented. [ABSTRACT FROM AUTHOR]

Interference has been a key roadblock against the effectively deployment of applications for end-users in wireless networks including fifth-generation (5G) and beyond fifth-generation (B5G) networks. Protocols and standards for various communication types have been established and utilised by the community in the last few years. However, interference remains a key challenge, preventing end-users from receiving the quality of service (QoS) expected for many 5G applications. The increased need for better data rates and more exposure to multimedia information lead to a non-orthogonal multiple access (NOMA) scheme that aims to enhance spectral efficiency and link additional applications employing successive interference cancellation and superposition coding mechanisms. Recent work suggests that the NOMA scheme performs better when combined with suitable wireless technologies specifically by incorporating antenna diversity including massive multiple-input multiple-output architecture, data rate fairness, energy efficiency, cooperative relaying, beamforming and equalization, network coding, and space–time coding. In this paper, we discuss several cooperative NOMA systems operating under the decode-and-forward and amplify-and-forward protocols. The paper provides an overview of power-domain NOMA-based cooperative communication, and also provides an outlook of future research directions of this area. [ABSTRACT FROM AUTHOR]

In developing countries, many areas are deprived of electrical energy. Access to cleaner, more affordable energy is critical for improving the poor's living conditions in developing countries. With the advent of smart grid technology, the integration and coordination of small grids, known as nanogrids, has become very easy. The purpose of this research is to propose a nanogrid model that will serve the purpose of providing the facility of electrical power to the poor rural community in Pakistan using hybrid renewable energy sources. This paper targets the electrification of a poor rural community of Akora Khatak, a small district located in Pakistan. The mathematical modeling of solar and wind energy, neural network-based forecasting of solar irradiance and wind velocity, and the social analysis to calculate the payback period for the community have been discussed in this paper. [ABSTRACT FROM AUTHOR]

Low cycling stability is one of the most crucial issues in rechargeable batteries. Herein, we study the effects of a simple ultrasound treatment of graphite for the reversible (de)intercalation of a ClO4− anion from a 2.4 M Al(ClO4)3 aqueous solution. We demonstrate that the ultrasound-treated graphite offers the improved reversibility of the ClO4− anion (de)intercalation compared with the untreated samples. The ex situ and in situ Raman spectroelectrochemistry and X-ray diffraction analysis of the ultrasound-treated materials shows no change in the interlayer spacing, a mild increase in the stacking order, and a large increase in the amount of defects in the lattice accompanied by a decrease in the lateral crystallite size. The smaller flakes of the ultrasonicated natural graphite facilitate the improved reversibility of the ClO4− anion electrochemical (de)intercalation and a more stable electrochemical performance with a cycle life of over 300 cycles. [ABSTRACT FROM AUTHOR]

The development of single-phase symmetrical bipolar voltage gain matrix converters (MC) is growing rapidly as they find their application in power systems for dynamic restoration of line voltages, high voltage AC–DC converters, and variable frequency controllers for many industrial processes. However, the existing trend in matrix converter technology is a buck–boost operation that has inherently serious issues of high voltage and current surges or stresses. This is a big source of the high voltage and current rating of semiconductor switching devices. There is also a problem of high ripples both for voltage as well for current, requiring large size of filtering capacitors and inductors. The non-symmetrical control of the voltage gain increases the control complication. A large count of operating transistors is critical regarding their cost, size, and power conversion losses, as the space and cost required by their gate control circuits are much larger than the size and cost of the switching transistors. Thus, in this research work, a new single-phase MC is introduced only employing six fully controlled switching devices, ensuring similar operation or outputs as is obtained from the existing topologies that require the use of eight or more fully controlled switching devices, and the reduction by two or more switching transistors helps to compact the overall size and lower the overall cost. The separation in its voltage buck and boost operation enables smooth control of the voltage gain through duty cycle control. The low values of the voltage and current surges reduce the power rating and losses of the switching devices. The flow of the current in the filtering inductor is kept unidirectional to avoid the current interruption and reversal problem once the operation of the converter is abruptly switched from inverting to non-inverting and vice versa. All these factors are comprehensively detailed through the circuit's description and comparative analysis. Simulation and practical results are presented to confirm the effectiveness of the developed circuit topology. [ABSTRACT FROM AUTHOR]

We conduct molecular dynamics simulations of model heterogeneous membranes and their interactions with a 24-amino acid peptide—NAF-144–67. NAF-144–67 is an anticancer peptide that selectively permeates and kills malignant cells; it does not permeate normal cells. We examine three membranes with different binary mixtures of lipids, DOPC–DOPA, DOPC–DOPS, and DOPC–DOPE, with a single peptide embedded in each as models for the diversity of biological membranes. We illustrate that the peptide organization in the membrane depends on the types of nearby phospholipids and is influenced by the charge and size of the head groups. The present study sheds light on early events of permeation and the mechanisms by which an amphiphilic peptide crosses from an aqueous solution to a hydrophobic membrane. Understanding the translocation mechanism is likely to help the design of new permeants. [ABSTRACT FROM AUTHOR]

Present research was planned to assess the in vitro and in vivo anti-arthritic potential of Caralluma tuberculata N. E. Brown. methanolic (CTME) and aqueous (CTAQ) extracts. Chemical characterization was done by high-performance liquid chromatography and gas chromatography–mass spectrometry analysis. The Complete Freund's Adjuvant (CFA) was injected in left hind paw of rat at day 1 and dosing at 150, 300 and 600 mg/kg was started on the 8th day via oral gavage in all groups except normal and disease control rats (which were given distilled water), whereas methotrexate (intraperitoneal; 1 mg/kg/mL) was administered to standard control. The CTME and CTAQ exerted significant (p < 0.01–0.0001) in vitro anti-arthritic action. Both extracts notably reduced paw edema, and restored weight loss, immune organs weight, arthritic score, RBCs, ESR, platelet count, rheumatoid factor (RF), C-reactive protein, and WBCs in treated rats. The plant extracts showed significant (p < 0.05–0.0001) downregulation of tumor necrosis factor-α, Interleukin-6, -1β, NF-κB, and cyclooxygenase-2, while notably upregulated IL-4, IL-10, I-κBα in contrast to disease control rats. The plant extracts noticeably (p < 0.001–0.0001) restored the superoxide dismutase and catalase activities and MDA levels in treated rats. Both extracts exhibited significant anti-arthritic potential. The promising potential was exhibited by both extracts probably due to phenolic, and flavonoids compounds. [ABSTRACT FROM AUTHOR]

The short-term hydrothermal scheduling (STHTS) problem has paramount importance in an interconnected power system. Owing to an operational research problem, it has been a basic concern of power companies to minimize fuel costs. To solve STHTS, a cascaded topology of four hydel generators with one equivalent thermal generator is considered. The problem is complex and non-linear and has equality and inequality constraints, including water discharge rate constraint, power generation constraint of hydel and thermal power generators, power balance constraint, reservoir storage constraint, initial and end volume constraint of water reservoirs, and hydraulic continuity constraint. The time delays in the transport of water from one reservoir to the other are also considered. A supervised machine learning (ML) model is developed that takes the solution of the STHTS problem without PDZ, by any metaheuristic technique, as input and outputs an optimized solution to STHTS with PDZ and valve point loading (VPL) effect. The results are quite promising and better compared to the literature. The versatility and effectiveness of the proposed approach are tested by applying it to the previous works and comparing the cost of power generation given by this model with those in the literature. A comparison of results and the monetary savings that could be achieved by using this approach instead of using only metaheuristic algorithms for PDZ and VPL are also given. The slipups in the VPL case in the literature are also addressed. [ABSTRACT FROM AUTHOR]

In vehicular ad hoc networks (VANETs), helpful information dissemination establishes the foundation of communication. One of the significant difficulties in developing a successful dissemination system for VANETs is avoiding traffic fatalities. Another essential success metric is the transfer of reliable and secure warning messages through the shortest path, particularly on highways with high mobility. Clustering vehicles is a general solution to these challenges, as it allows warning alerts to be re-broadcast to nearby clusters by fewer vehicles. Hence, trustworthy cluster head (CH) selections are critical to decreasing the number of retransmissions. In this context, we suggest a clustering technique called Optimal Path Routing Protocol for Warning Messages (OPRP) for dissemination in highway VANETs. OPRP relies on mobility measured to reinforce cluster creation, evade transmission overhead, and sustain message authenticity in a high mobility environment. Moreover, we consider communication between the cluster heads to reduce the number of transmissions. Furthermore, the cluster head is chosen using the median technique based on an odd or even number of vehicles for a stable and lengthy cluster life. By altering traffic densities and speeds, OPRP is compared with prominent schemes. Simulation results revealed that OPRP offers enhanced throughput, end-to-end delay, maximizing packet delivery ratio, and message validity. [ABSTRACT FROM AUTHOR]

Harmonics worsen the quality of electrical signals, hence, there is a need to eliminate them. The test objects under discussion are single-phase versions of cascaded H-bridge (CHB) multilevel inverters (MLIs) whose switching angles are optimized to eliminate specific harmonics. The Dragonfly Algorithm (DA) is used to eradicate low-order harmonics, and its statistical performance is compared to that of many other optimization techniques, including Particle Swarm Optimization (PSO), Accelerated Particle Swarm Optimization (APSO), Differential Evolution (DE), and Grey Wolf Optimization (GWO). Various scenarios of the algorithms' search agent population for inverters with seven, nine, and eleven levels of output voltages are comprehensively addressed in this research. No algorithm shows total dominance in every scenario. The DA is least impacted by the change in dimensions of the narrated problem. [ABSTRACT FROM AUTHOR]

Single-phase AC–AC converters with a direct power conversion approach are receiving rapid development as they have the ability to produce the regulated non-inverting and inverting form of the input voltage at the output. This feature enables them to correct the line voltage profile once they are used as dynamic voltage restorers if there is an issue of voltage sag or swell in the power distribution system. The regulated non-inverting and inverting form of the output voltage may also be used to obtain the step change in the output frequency, as it is required in many industrial drive systems. However, the realization of such existing circuits needs a large number of components and semiconductor devices, especially switching transistors. The operating control (on and off) of the transistors is directly associated with the use of gate control circuits. The count of such circuits is critical as their volume and cost are much greater than the operating transistors. The number of conducting semiconductor devices in the existing converters is also a big source of high conversion losses, thus leading to lower efficiency. This article introduces a new circuit topology realized only with the use of one full bridge of four IGBTs and a full bridge of four diodes. The use of four switching transistors only requires four gate control circuits that drastically reduce the overall volume and size. All the operating modes of the proposed topology require the conduction of fewer semiconductor devices, which helps to lower the conduction losses. Detailed analysis and description were carried out to validate the attractive features of the developed circuit once compared with the existing circuit topologies. For validation purposes, the computer simulation was carried out on Simulink software. The results obtained from this environment were compared with the real results gained from a practically developed laboratory test bench. The voltage regulation characteristics of the output voltage by employing pulse width modulation (PWM) were confirmed for two values of the non-inverting and inverting outputs. [ABSTRACT FROM AUTHOR]

Recent developments in communication and information technologies, especially in the internet of things (IoT), have greatly changed and improved the human lifestyle. Due to the easy access to, and increasing demand for, smart devices, the IoT system faces new cyber-physical security and privacy attacks, such as denial of service, spoofing, phishing, obfuscations, jamming, eavesdropping, intrusions, and other unforeseen cyber threats to IoT systems. The traditional tools and techniques are not very efficient to prevent and protect against the new cyber-physical security challenges. Robust, dynamic, and up-to-date security measures are required to secure IoT systems. The machine learning (ML) technique is considered the most advanced and promising method, and opened up many research directions to address new security challenges in the cyber-physical systems (CPS). This research survey presents the architecture of IoT systems, investigates different attacks on IoT systems, and reviews the latest research directions to solve the safety and security of IoT systems based on machine learning techniques. Moreover, it discusses the potential future research challenges when employing security methods in IoT systems. [ABSTRACT FROM AUTHOR]

In smart grids (SGs), the systematic utilization of consumer energy data while maintaining its privacy is of paramount importance. This research addresses this problem by energy theft detection while preserving the privacy of client data. In particular, this research identifies centralized models as more accurate in predicting energy theft in SGs but with no or significantly less data protection. Current research proposes a novel federated learning (FL) framework, namely FedDP, to tackle this issue. The proposed framework enables various clients to benefit from on-device prediction with very little communication overhead and to learn from the experience of other clients with the help of a central server (CS). Furthermore, for the accurate identification of energy theft, the use of a novel federated voting classifier (FVC) is proposed. FVC uses the majority voting-based consensus of traditional machine learning (ML) classifiers namely, random forests (RF), k-nearest neighbors (KNN), and bagging classifiers (BG). To the best of our knowledge, conventional ML classifiers have never been used in a federated manner for energy theft detection in SGs. Finally, substantial experiments are performed on the real-world energy consumption dataset. Results illustrate that the proposed model can accurately and efficiently detect energy theft in SGs while guaranteeing the security of client data. [ABSTRACT FROM AUTHOR]

The proportional integral derivative (PID) controller is one of the most robust and simplest configuration controllers used for industrial applications. However, its performance purely depends on the tuning of its proportional (KP), integral (KI) and derivative (KD) gains. Therefore, a proper combination of these gains is primarily required to achieve an optimal performance of the PID controllers. The conventional methods of PID tuning such as Cohen-Coon (CC) and Ziegler–Nichols (ZN) generate unwanted overshoots and long-lasting oscillations in the system. Owing to the mentioned problems, this paper attempts to achieve an optimized combination of PID controller gains by exploiting the intelligence of the whale optimization algorithm (WOA) and one of its recently introduced modified versions called improved whale optimization algorithm (IWOA) in an automatic voltage regulator (AVR) system. The stability of the IWOA-AVR system was studied by assessing its root-locus, bode maps, and pole/zero plots. The performance superiority of the presented IWOA-AVR design over eight of the recently explored AI-based approaches was validated through a comprehensive comparative analysis based on the most important transient response and stability metrics. Finally, to assess the robustness of the optimized AVR system, robustness analysis was conducted by analyzing the system response during the variation in the time constants of the generator, exciter, and amplifier from −50% to 50% range. The results of the study prove the superiority of the proposed IWOA-based AVR system in terms of transient response and stability metrics. [ABSTRACT FROM AUTHOR]

In this study, the suitability of four earthen, seawater ponds located in the Thatta district of Sindh province (Pakistan) was evaluated for the purpose of semi-intensive mariculture, which remains to be a severely underdeveloped branch of the agricultural industry of this populous Asian country. Initial pond soil probes were promising, as they showed a high clay and silt content. Monthly water samples were obtained in the year 2019 (from January to December), which allowed for the monitoring of water parameters, as well as the identification and relative quantification of planktic populations. As a result, the monthly variations of basic water parameters were found within optimal ranges for planktic growth (water temperature, salinity, pH, transparency, and dissolved oxygen). Bacillariophyta was the largest phytoplanktic group, with the most dominant species being Sundstroemia setigera, followed by the cyanobacteria Oscillatoria limosa. Copepoda was the most numerous group of identified zooplankton, followed by tintinnids and foraminiferans. Total suspended solids (TSS) calculations indicated up to nine-fold month-to-month reductions of planktic biomass, observed in the form of diminishing Bacillariophyta (December) and Copepoda (June and December). In conclusion, the studied ponds appear to be suitable for semi-intensive mariculture activity due to the abundance of diverse planktic forms (mainly Copepoda—preferable natural food for commercially important fish species), which was achieved even without the use of fertilizers. However, significant drops of planktic biomass may still occur, which implies the need for regular water monitoring procedures, which would in turn allow fish producers to implement periodical adjustments to the administered feeding rates with artificial diets. [ABSTRACT FROM AUTHOR]

This scientific paper aims to increase the voltage source converter (VSC) control efficiency in a multi-terminal high voltage direct current (MTDC) network during dynamic operations. In the proposed study, the Mayfly algorithm (MA) is used to modify the control parameters of VSC stations. Traditional strategies that modify VSC control settings using approximate linear models fail to produce optimal results because VSCs are nonlinear characteristics of the MTDC system. Particle swarm optimization (PSO) may produce optimal outcomes, but it is prone to becoming stuck in a local optimum. To modify the proportional-integral (P.I.) control parameters of the VSC station, the Mayfly algorithm, a modified form of PSO, is used. The suggested algorithm's objective function simultaneously optimizes both the outer and inner control layers. A four-terminal MTDC test system is developed in PSCAD/EMTDC to assess the benefits of the proposed algorithm. For VSCs, a comparison of classical, PSO, and proposed MA-based tuned parameters is carried out. The integral of time multiplied by absolute error (ITAE) criterion is used to compare the performance of classical, PSO, and a proposed algorithm for VSC controller parameters/gains. With an ITAE value of 6.8521 × 10−6, the MA-based proposed algorithm computes the optimal values and outperforms its predecessor to adjust the VSCs controller gains. For (i) wind farm power variation, (ii) AC grid load demand variation, and (iii) ultimate permanent VSC disconnection, steady-state and dynamic performances are evaluated. According to the results, the proposed algorithm based MTDC system performs well during transients. [ABSTRACT FROM AUTHOR]