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Pigeons are a cosmopolitan group of birds with abundant and large populations associated with human activities. This study focused on determining parasitic infections within domestic pigeons (Columba livia domestica). Forty-eight pigeons were examined for infections, of which 29.16% were infected with a nematode parasite, identified as Hadjelia truncata (Habronematidae), under the koilin layer of their gizzards. The population of nematodes in infected gizzards did not exceed 20 adult worms. DNA from the gizzard worms was extracted and subjected to PCR using primers that amplify the partial 18S rDNA and cytochrome C oxidase subunit I (COX I) regions. Identification of this parasite based on microscopic study revealed the presence of trilobed lips with cephalic papillae and amphidial pores, as well as other characteristic features. In males, spicules were unequal with the presence of six pedunculated pairs of caudal papillae (4 pre- and 2 post-anal) and a tail surrounded with caudal ala. In females, the vulva was a rounded aperture located in front of the posterior end of the esophagus and uteri, which was filled with numerous embryonated eggs. DNA Sequences from partial 18S rDNA were homologous to sequences obtained from H. truncata in GenBank with a high percentage of identity. DNA sequences from mitochondrial gene COX I, however, were unique, and they were the first sequenced for H. truncata, since no sequences for this taxon were previously available in GenBank. Histopathological examination revealed enlargement of infected gizzards in comparison to non-infected ones, with the presence of necrosis and interstitial infiltration in the koilin layer. Concentrations of heavy metals (Fe, Cu, Zn, Cd, Cr, and Co) were measured using inductivity-coupled plasma in tissues (liver, muscles, and gizzards) from infected and non-infected pigeons as well as their parasites. Results showed different affinities of metals to tissues. Recovered parasites can minimize element concentration from their pigeon tissues. In Saudi Arabia, this study was considered the first report identifying pigeon nematodes and evaluating of the effects of their pathogenicity on the animals’ welfare, as well as their application as a useful tool for monitoring environmental pollution.

Estimation of the partial pressure of carbon dioxide (pCO2) in the Bay of Bengal (BoB) region plays a crucial role in better understanding the air-sea CO2 fluxes. Complex physical and biogeochemical processes such as physical mixing, stratification, thermodynamic, and biological effects dominate the spatiotemporal variability of pCO2 concentration over the BoB. This is difficult to estimate through in-situ platforms alone due to the time-consuming, cost-effective, and intricacies involved in water sample collection during rough oceanic weather conditions. Alternatively, remote sensing technology provides governing control parameters with high spatiotemporal resolution over large synoptic scales. Since the BoB region is influenced by the Indian monsoon system and other complex processes, existing regional and global pCO2 algorithms are not adequate to estimate more accurate pCO2 fields. Hence, there is a need to develop a regional pCO2 algorithm over the BoB. To resolve this problem, in the present study, a Multi Parametric Regional Regression (MPRR) approach was developed over the BoB using satellite data such as sea surface temperature (SST), sea surface salinity (SSS), and chlorophyll- ${a}$ (Chl $a$ ) concentration. To train and validate the MPRR approach, required in-situ measurements were obtained from the open and coastal waters of the BoB. The validation results revealed that the present MPRR approach showed better performance with significant low errors (mean relative error (MRE) = 0.012, mean normalized bias (MNB) = 0.022, and root mean square error (RMSE) = 4.75 $\mu $ atm) and a high correlation coefficient (R2 = 0.92). Furthermore, the study demonstrated the spatiotemporal variability of pCO2and generated monthly, seasonal, and annual pCO2 maps over the BoB.

Abstract Background The novel coronavirus pandemic (COVID-19) has begun with a wave of misinformation and fear of infection. This may have led people to self-medicate inappropriately. The World Health Organization describes self-medication (SM) as utilizing medicines to relieve symptoms or health conditions without consulting a physician. Inappropriate drug use is a burden on both health resources and patient health in the Arab region. This study aimed to detect the prevalence and influencing factors of self-medication among the general Arab population during the COVID-19 pandemic. Methods A multinational cross-sectional study was conducted among the general population of ten Arab countries from early August to late October 2021. Participants aged 18 years or older could join the study via social media platforms. A convenience sampling technique was used. A developed and validated web-based questionnaire was used to collect data on self-medication practice, associated influencing factors, information sources, commonly used medications, and commonly treated conditions. Descriptive, univariate, and multivariate regression analyses were applied using IBM SPSS v 26 and R v 4.0.0 software. Results A total of 8163 participants completed the questionnaire, and 518 participants were excluded from the analysis due to inconsistencies in their data. Almost two-thirds (62.7%) of participants reported practicing self-medication during the COVID-19 pandemic. At the country level, Egypt had the highest prevalence of self-medication practice (72.1%), while Palestine had the lowest prevalence (40.4%). The most commonly used drugs were analgesics, antipyretics, and vitamins (86, 65.1, and 57.1%, respectively), while antitussives and antibiotics scored 47.6 and 43.3%, respectively. Experience with similar health conditions (74.6%) and urgency of the problem (47.2%) were the most frequent factors that led to self-medication. Additionally, 38.2% of the self-medicated participants (SMPs) used drugs as prophylaxis against COVID-19. Pharmacist consultation was the most common source of information about self-medication (66.7%). Multivariate analysis showed that predictors of self-medication were older age (p = 0.008), presence of chronic illness (p = 0.015), and having monthly income or medical insurance that does not cover the treatment cost (p = 0.001, p

Abstract Background The main anesthetic goal in the postoperative period of functional endoscopic sinus surgery (FESS) is early and pain-free recovery, with return of protective airway reflex. The aim of this study is to evaluate the role of dexmedetomidine as an adjuvant to regional blocks in patients undergoing functional endoscopic sinus surgery. Results Group bupivacaine + dexmedetomidine (BD) showed statistically significantly lower postoperative pain measurements than group bupivacaine (B), longer time to the first request of rescue analgesia, less patients needing rescue analgesia, and less rescue morphine analgesic requirements. Other measurements were insignificantly different between both groups. Conclusions Dexmedetomidine can be used as an adjuvant to regional nerve block in patients undergoing functional endoscopic sinus surgery. It effectively prolonged postoperative analgesia, decreased postoperative opioid analgesic requirements, and reduced the number of patients needing rescue analgesia.

The global reliance on electric vehicles (EVs) has been rapidly increasing due to the excessive use of fossil fuels and the resultant CO2 emissions. Moreover, EVs facilitate using alternative energy sources, such as energy storage systems (ESSs) and renewable energy sources (RESs), promoting mobility while reducing dependence on fossil fuels. However, this trend is accompanied by multiple challenges related to EVs' traction systems, storage capacity, chemistry, charging infrastructure, and techniques. Additionally, the requisite energy management technologies and the standards and regulations needed to facilitate the expansion of the EV market present further complexities. This paper provides a comprehensive and up-to-date review of the state of the art concerning EV-related components, including energy storage systems, electric motors, charging topologies, and control techniques. Furthermore, the paper explores each sector's commonly used standards and codes. Through this extensive review, the paper aims to advance knowledge in the field and support the ongoing development and implementation of EV technologies. [ABSTRACT FROM AUTHOR]

The control of energy storage systems (ESSs) within autonomous microgrids (MGs) is critical for ensuring stable and efficient operation, especially when incorporating renewable energy resources (RESs) such as photovoltaic (PV) systems. This paper addresses managing a standalone DC microgrid that combines PV generation and a battery energy storage system (BESS). We propose a hybrid control strategy that combines a Recurrent Neural Network (RNN) with Proportional-Integral (PI) controllers to improve the performance of the bidirectional converter that connects the BESS to the microgrid. The RNN processes the voltage error and derivative into a reference current, which a PI controller refines to determine the best duty cycle for the converter's switches. This hybrid control scheme provides superior adaptability and performance in various load conditions, including pulsed power load (PPL) demands. Simulation results show that the proposed control method exceeds traditional PI-PI control algorithms, particularly in improving the transient stability of the DC bus voltage and optimizing BESS performance. We conducted extensive hardware experiments to verify the robustness and effectiveness of the developed control algorithm. The experimental results confirmed the superior performance of the hybrid RNN-PI control scheme, demonstrating its ability to maintain system stability and efficiency across a wide range of real-world scenarios. This experimental validation reflects the reliability and effectiveness of the proposed control strategy in improving microgrid operations. [ABSTRACT FROM AUTHOR]

Microgrids (MGs) are the new paradigm of decentralized networks of renewable energy sources, loads, and storage devices that can operate independently or in coordination with the primary grid, incorporating significant flexibility and supply reliability. To increase reliability, traditional individual MGs can be replaced by networked microgrids (NMGs), which are more dependable. However, when it comes to operation and control, they also pose challenges for cyber security and communication reliability. Denial of service (DoS) is a common danger to DC microgrids with advanced controllers that rely on active information exchanges and has been recorded as the most frequent cause of cyber incidents. It can disrupt data transmission, leading to ineffective control and system instability. This paper proposes digital twin (DT) technology as an integrated solution, with new, advanced analytics technology using machine learning and artificial intelligence to provide simulation capabilities to predict and estimate future states. By twinning the cyber-physical dynamics of NMGs using data-driven models, DoS attacks targeting cyber-layer agents will be detected and mitigated. A long short-term memory (LSTM) model data-driven digital twin approach for DoS attack detection and mitigation is implemented, tested, and evaluated. [ABSTRACT FROM AUTHOR]