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Abstract We present a case of atypical recurrent optic neuritis. A man in his 50s presented with right optic neuritis and profound visual loss, associated with elevated inflammatory markers. Lymph‐node biopsy was consistent with sarcoidosis. Aquaporin‐4 antibodies were also present. Three months following corticosteroid treatment, his right optic neuritis relapsed, again with raised inflammatory markers. He was started on azathioprine and prednisolone with good effect. A dual diagnosis of sarcoidosis and neuromyelitis optica with aquaporin‐4 antibodies is very rare. Long‐term immunosuppression is required. The case highlights the importance of identifying the features and cause of atypical optic neuritis.

This paper presents a design and analysis for the PV system with a DC-DC boost converter controlled by the Fractional Order PI controller (FOPI). The study includes obtaining the optimal parameters for the PV model and the operating parameters for the FOPI controller. The first part’s objective function is to search a five-parameter model based on the data-sheet given by commercial PV modules for a single-diode (SD) model. In the second part of the study, a detailed comparison is executed versus P, PI, PID, and FOPI controllers showing the effect of implementing Fractional order controllers on the results. A new application of the flower pollination algorithm (FPA) and water cycle algorithm (WCA) is presented to identify the models parameters of PV modules and the FOPI controller’s tuning. The booster converter model’s simulations demonstrate that the proposed optimal FOPI controller can improve the feedback system’s transient response by reducing its control effort with the optimal PID controller. The FOPI controller also increases the robustness of the boost converter to trace in input voltage. The results show that FPA demonstrated better performance in two aspects, finding the PV model’s parameters and obtaining the optimal parameters for the FOPI controller.

Transportation is among the most prevalent, challenging, and complex sectors that significantly influence energy consumption, environmental sustainability, and economic dynamics. So, it is critical to identify effective strategies to lessen its detrimental consequences. The conventional signalized intersections, which use pre-programmed schedules to control traffic flow at junctions, are one of the most used systems and one of the biggest high Energy consumption rates creators. The study focuses on reducing fuel consumption and enhancing traffic flow by utilizing advanced technologies such as intelligent transportation systems (ITS), communication technologies, adaptive traffic systems, and coordination techniques. The study incorporates manually collected real-world data with an average time step of 15 min through various simulation scenarios. The aim is to develop adaptive coordination strategies for signalized intersections using intelligent communication technologies (ICT) and uncover the importance of coordination techniques combined with ICT. Additionally, the study investigates the effects of these technologies on traffic flow, energy consumption, and air pollution emissions. The results show that using adaptive traffic lights only reduces fuel consumption and emissions by 8%, while applying coordination techniques can increase the percentage up to 25%. In addition, by implementing ICT combined with coordination technique, fuel consumption and emissions, average queue length, and waiting time decreased by around 48%, 63%, and 41%, respectively while increase the average vehicle speed by 46% compared to the pretimed isolated intersections. The findings highlight the developed strategy capability in enhancing traffic flow, energy efficiency, environmental sustainability, leading to a cleaner environment, and transportation sector cost savings. Highlights: Adaptively coordinated traffic lights combined with V2X outperformed other regulation strategies. Proposed strategy decreases fuel consumption and emissions, average queue length, and waiting time by 48%, 63%, and 41%, respectively. Proposed strategy enhances the vehicle average speed by 46% compared to isolated intersections. Adaptively coordinated traffic lights combined with V2X presents a sustainable solution for traffic management. [ABSTRACT FROM AUTHOR]

This paper presents Fuzzy Time Series (FTS) forecasting technique using Hidden Markov Model (HMM) optimized by Particle Swarm Optimization (PSO) and Genetic algorithm (GA). One of the major limitations of HMM is the lack of efficient method for HMM parameter estimation. Traditional methods like Baum Welch Algorithm (BWA) have been used to address the associated problem with HMM model. The BWA in itself does not truly capture the fuzziness in natural data resulting in the HMM algorithm into local minima. To address this challenge, this paper formulates the HMM parameter estimation problem into an optimization problem optimized by GA and PSO algorithms. To solve the problem of insufficiency in data allied with the HMM model, we adopted a method called smoothing to reduce the occurrence of zero in the observation events. Monte Carlo simulation is used at the end of the forecast to maintain stability and efficiency of the developed approach. The performance of developed model is evaluated using data of daily average temperature and cloud density of Taipei Taiwan, the Internet traffic data of Ahmadu Bello University (ABU) and Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX). Experimental results showed that the proposed forecasting models has a good forecasting accuracy when compared with existing models.

Purpose: Oxidative stress and sperm DNA fragmentation (SDF) are potential contributing factors for idiopathic male infertility. Coenzyme Q10 (CoQ10) have been reported to be effective in the treatment of idiopathic male infertility, in general, owing to its antioxidant properties. Thus, the present study intends to investigate the effects of CoQ10 therapy on semen parameters, oxidative stress markers and SDF in infertile men, specifically with idiopathic oligoasthenozoospermia (OA). Materials and Methods: In this case-control study, sixty-five infertile patients with idiopathic OA and forty fertile men (control) were included. All participants underwent semen analysis based on the World Health Organization guidelines (5th edition, 2010). Patients received CoQ10 at the dose of 200 mg/d orally for three months. Seminal plasma CoQ10, total antioxidant capacity (TAC), total reactive oxygen species (ROS), glutathione peroxidase (GPx), and SDF levels were measured in controls (baseline) and infertile patients pre- and post-CoQ10 treatment. Results: CoQ10 treatment for three months significantly improved sperm concentration (p

Objective: To determine the prevalence of Charcot triad, Reynolds pentad, and Tokyo Guidelines criteria and clinical outcomes among patients with cholangitis across different age groups. Patients and Methods: We conducted a retrospective analysis of 257 consecutive hospitalized adult patients with acute cholangitis due to endoscopic retrograde cholangiopancreatography–confirmed choledocholithiasis between January 1, 2015, and December 31, 2019. Patients were divided into 3 age groups: less than 65 years, 65 to 79 years, and 80 years or older. Symptoms, vital signs, and laboratory data on admission were collected. Outcomes included length of hospitalization, intensive care unit stay, and 3-month mortality. Nominal variables were tested with the Pearson χ2 test, and continuous variables were tested with the Wilcoxon rank sum test. Results: Charcot triad decreased with older ages. In the group that was age 80 years or older, malaise was the most common symptom; 33.6% (37 of 110) presented with altered sensorium, 9.1% (10 of 110) had no pain, fever, or jaundice, and positive blood culture results were more frequent. Tokyo cholestasis criterion was present in 96.0% (247 of 257), while inflammation (considered essential for diagnosis) was present in 75.9% (195 of 257). Patients 80 years or older had significantly higher mean length of hospital stay (P

Mitral regurgitation can have varying hemodynamic parameters dependent on factors such as pressure gradients, exercise, and/or provocative maneuvers. We present a case of unusual dynamic mitral regurgitation resolved by coughing in a patient with hypertrophic cardiomyopathy. (Level of Difficulty: Intermediate.)

Abstract Selective serotonin reuptake inhibitors (SSRIs) are the first-line treatment for major depressive disorder (MDD), yet their mechanisms of action are not fully understood and their therapeutic benefit varies among individuals. We used a targeted metabolomics approach utilizing a panel of 180 metabolites to gain insights into mechanisms of action and response to citalopram/escitalopram. Plasma samples from 136 participants with MDD enrolled into the Mayo Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS) were profiled at baseline and after 8 weeks of treatment. After treatment, we saw increased levels of short-chain acylcarnitines and decreased levels of medium-chain and long-chain acylcarnitines, suggesting an SSRI effect on β-oxidation and mitochondrial function. Amines—including arginine, proline, and methionine sulfoxide—were upregulated while serotonin and sarcosine were downregulated, suggesting an SSRI effect on urea cycle, one-carbon metabolism, and serotonin uptake. Eighteen lipids within the phosphatidylcholine (PC aa and ae) classes were upregulated. Changes in several lipid and amine levels correlated with changes in 17-item Hamilton Rating Scale for Depression scores (HRSD17). Differences in metabolic profiles at baseline and post-treatment were noted between participants who remitted (HRSD17 ≤ 7) and those who gained no meaningful benefits (

Abstract Objective Patients with drug‐resistant epilepsy (DRE) pose considerable management challenges for patients, their families, and providers. Both the vagus nerve stimulator (VNS) and the ketogenic diet (KD) have been shown to be safe and effective in treating DRE. Nevertheless, information is lacking regarding treatment with combination of both modalities. This study reports the efficacy and tolerability of combining VNS and KD in a pediatric cohort with intractable epilepsy. Methods This is a retrospective review of 33 patients (0‐17 years) with DRE treated with VNS and KD at a single pediatric level IV epilepsy center. We compared seizure reduction rates for each patient at baseline and at every clinic visit for 24 months after adding the second nonpharmacological therapy. The frequency of adverse events on the combined therapy was collected to assess safety and tolerability. Results There were a total of 170 visits for all patients while on the combined therapy. At 88% (95% CI: 83%‐93%) of the visits, patients reported some reduction in seizure frequency. The proportion of patients reporting a greater than 50% seizure reduction over all visits was 62% (95% CI: 55%‐69%). The proportion of a patient's visits with at least a greater than 50% reduction in seizure frequency had a median of 71% (IQR 33%‐100%). Continued improvement was seen over time of combined treatment; for every one‐unit time unit change (one month), there was a 6% increase in the odds of having a reduction in seizure frequency of >50% (OR = 1.06, 95% CI: 1.01‐1.11). Significance This study shows that combining the VNS and KD in patients with drug‐resistant epilepsy is well tolerated and reduces seizure frequency more than either one modality used alone and that the benefits in terms of seizure reduction continue to increase with the length of treatment.

The ion emission properties of laser-produced plasmas as a function of laser intensities between 4–50 GW cm − 2 and varying angles with respect to the target normal were investigated. The plasmas were produced by focusing 1064 nm, 6 ns pulses from an Nd:YAG laser on various metal targets. The targets used for this study include Ti, Mo, and Gd (Z = 22 , 42 , 64). It is noted that all ion profiles are composed of multiple peaks—a prompt emission peak trailed by three ion peaks (ultrafast, fast, and thermal). Experimentally, it is shown that each of these ion peaks follows a unique trend as a function of laser intensity, angle, and distance away from the target. Theoretically, it is shown that simple analytical models can be used to explain the properties of the ions. The variations in the ion velocity and density as a function of laser intensity are found to be in good agreement with theoretical models of sheath acceleration, isothermal self-similar expansion, and ablative plasma flow for various ion peaks. [ABSTRACT FROM AUTHOR]

The transition to Electric Vehicles (EV) in place of traditional internal combustion engines is increasing societal demand for electricity. The ability to integrate the additional demand from EV charging into forecasting electricity demand is critical for maintaining the reliability of electricity generation and distribution. Load forecasting studies typically exclude households with home EV charging, focusing on offices, schools, and public charging stations. Moreover, they provide point forecasts which do not offer information about prediction uncertainty. Consequently, this paper proposes the Long Short-Term Memory Bayesian Neural Networks (LSTM-BNNs) for household load forecasting in presence of EV charging. The approach takes advantage of the LSTM model to capture the time dependencies and uses the dropout layer with Bayesian inference to generate prediction intervals. Results show that the proposed LSTM-BNNs achieve accuracy similar to point forecasts with the advantage of prediction intervals. Moreover, the impact of lockdowns related to the COVID-19 pandemic on the load forecasting model is examined, and the analysis shows that there is no major change in the model performance as, for the considered households, the randomness of the EV charging outweighs the change due to pandemic.

Permanent magnet (PM) machines with fractional slot concentrated windings (FSCW) constitute a notably remarkable proposition for electric vehicles. Additionally, an integrated onboard battery charger (IOBC) provides another superiority as it exploits the components of the powertrain to charge the battery without any additional components. Interior permanent magnet (IPM) motor arises as a credible choice due to its high torque density, resulting from the high saliency ratio. The optimal design of an IPM motor has been extensively presented from different perspectives, but the optimal design of a motor employed for IOBC application for both propulsion and charging modes has not been studied extensively. In this paper, the design and optimization of a 12-slot/10-pole IPM motor with IOBC are studied under both propulsion and charging modes. A finite-element-based optimization with the aid of a genetic algorithm technique is proposed to obtain the optimal machine by maximizing the average torque and minimizing the torque ripple, core losses, and magnet size.

5-(1H-Indol-2-yl)-4-phenyl-2,4-dihydro-3H-1,2,4-triazole-3-thione 1a and 4-(4-chlorophenyl)-5-(1H-indol-2-yl)-2,4-dihydro-3H-1,2,4-triazole-3-thione 1b were galactosylated in the presence of NaHCO3 in ethanol to produce S-galactosides 3,4, whereas, in the presence of K2CO3 in acetone they produced a mixture of S- and N-galactosides 3-6 with a higher yield of S-galactosides over the respective N-galactosides. Improvement in the yields of N-galactosides was produced by thermal migration of the galactosyl moiety from sulfur to nitrogen using fusion. β-Stereoselectivity of galactosylation was determined using the coupling constant value 3J1,2, which exceeded 9.0 Hz in all prepared galactosides. The precursors 1a and 1b alkylated with 3-bromopropan-1-ol 7 in K2CO3 and acetone produced the S-alkylated products 8 and 9, respectively. Structural determinations of new compounds 5 and 9 are presented. The phenyl and indole moieties were found to be twisted from the triazole ring mean in both compounds. For compound 5, the twist angles were 66.24° and 18.86°, respectively, while the corresponding values for 9 were in the ranges of 73.15–77.29° and 13.96–20.70°, respectively. Hence, the crystal system of 9 is triclinic while the space group is P-1. Detailed analysis of the intermolecular interactions in the crystal structure of 5 is presented using Hirshfeld calculations. The O…H, N…H, C…H, and S…H contacts appeared as red spots in the dnorm Hirshfeld surface indicating short distance intermolecular interactions. Their percentages were estimated based on the decomposition of the fingerprint plot to be 25.6, 2.4, 14.0, and 6.3%, respectively.

Wound healing is regulated by complex crosstalk between keratinocytes and other cell types, including stem cells. In this study, a 7-day direct co-culture model of human keratinocytes and adipose-derived stem cells (ADSCs) was proposed to study the interaction between the two cell types, in order to identify regulators of ADSCs differentiation toward the epidermal lineage. As major mediators of cell communication, miRNome and proteome profiles in cell lysates of cultured human keratinocytes and ADSCs were explored through experimental and computational analyses. GeneChip® miRNA microarray, identified 378 differentially expressed miRNAs; of these, 114 miRNAs were upregulated and 264 miRNAs were downregulated in keratinocytes. According to miRNA target prediction databases and the Expression Atlas database, 109 skin-related genes were obtained. Pathway enrichment analysis revealed 14 pathways including vesicle-mediated transport, signaling by interleukin, and others. Proteome profiling showed a significant upregulation of the epidermal growth factor (EGF) and Interleukin 1-alpha (IL-1α) compared to ADSCs. Integrated analysis through cross-matching the differentially expressed miRNA and proteins suggested two potential pathways for regulations of epidermal differentiation; the first is EGF-based through the downregulation of miR-485-5p and miR-6765-5p and/or the upregulation of miR-4459. The second is mediated by IL-1α overexpression through four isomers of miR-30-5p and miR-181a-5p.

Recently, deep learning and the Internet of Things (IoT) have been widely used in the healthcare monitoring system for decision making. Disease prediction is one of the emerging applications in current practices. In the method described in this paper, lung cancer prediction is implemented using deep learning and IoT, which is a challenging task in computer-aided diagnosis (CAD). Because lung cancer is a dangerous medical disease that must be identified at a higher detection rate, disease-related information is obtained from IoT medical devices and transmitted to the server. The medical data are then processed and classified into two categories, benign and malignant, using a multi-layer CNN (ML-CNN) model. In addition, a particle swarm optimization method is used to improve the learning ability (loss and accuracy). This step uses medical data (CT scan and sensor information) based on the Internet of Medical Things (IoMT). For this purpose, sensor information and image information from IoMT devices and sensors are gathered, and then classification actions are taken. The performance of the proposed technique is compared with well-known existing methods, such as the Support Vector Machine (SVM), probabilistic neural network (PNN), and conventional CNN, in terms of accuracy, precision, sensitivity, specificity, F-score, and computation time. For this purpose, two lung datasets were tested to evaluate the performance: Lung Image Database Consortium (LIDC) and Linear Imaging and Self-Scanning Sensor (LISS) datasets. Compared to alternative methods, the trial outcomes showed that the suggested technique has the potential to help the radiologist make an accurate and efficient early lung cancer diagnosis. The performance of the proposed ML-CNN was analyzed using Python, where the accuracy (2.5–10.5%) was high when compared to the number of instances, precision (2.3–9.5%) was high when compared to the number of instances, sensitivity (2.4–12.5%) was high when compared to several instances, the F-score (2–30%) was high when compared to the number of cases, the error rate (0.7–11.5%) was low compared to the number of cases, and the computation time (170 ms to 400 ms) was low compared to how many cases were computed for the proposed work, including previous known methods. The proposed ML-CNN architecture shows that this technique outperforms previous works.

The present synthetic strategy involves the synthesis of indolyl-triazolo-thiadiazole heterocyclic ring systems 8–13 from the condensation of 4-amino-5-(1H-indol-2-yl)-3H-1,2,4-triazole-3-thione 1 with the aromatic carboxylic acid derivatives 2–7 in presence of POCl3 for 1 h. All compounds were obtained in very good yields and have been well-characterized using spectroscopic techniques. Exclusively, good quality crystals from the target organic hybrid 8-(1H-indol-2-yl)-5-(p-tolyl)-[1,2,4]triazolo [3,4-b][1,3,4]thiadiazole 9 were obtained and found suitable for X-ray single crystal diffraction measurement, which is used to confirm and analyze the molecular and supramolecular structure aspects of 9. The solid-state structure of the synthesized molecule 9 agrees very well with other characterizations. The packing of 9 is dominated by the N…H, S…H, C…C and S…C non-covalent interactions, which agree with the Hirshfeld surface analysis. The percentages of these contacts are calculated to be 20.3%, 5.4%, 9.4% and 4.3%, respectively.

The interest in hydrogen is rapidly expanding because of rising greenhouse gas emissions and the depletion of fossil resources. The current work focuses on employing affordable Al alloys for hydrogen production and storage to identify the most efficient alloy that performs best in each situation. In the first part of this work, hydrogen was generated from water electrolysis. The Al alloys that are being examined as electrodes in a water electrolyzer are 1050-T0, 5052-T0, 6061-T0, 6061-T6, 7075-T0, 7075-T6, and 7075-T7. The flow rate of hydrogen produced, energy consumption, and electrolyzer efficiency were measured at a constant voltage of 9 volts to identify the Al alloy that produces a greater hydrogen flow rate at higher process efficiency. The influence of the electrode surface area and water electrolysis temperature were also studied. The second part of this study examines these Al alloys' resistance to hydrogen embrittlement for applications involving compressed hydrogen gas storage, whether they are utilized as the primary vessel in Type 1 pressure vessels or as liners in Type 2 or Type 3 pressure vessels. Al alloys underwent electrochemical charging by hydrogen and Charpy impact testing, after which a scanning electron microscope (SEM) was used to investigate the fracture surfaces of both uncharged and H-charged specimens. The structural constituents of the studied alloys were examined using X-ray diffraction analysis and were correlated to the alloys' performance. Sensitivity analysis revealed that the water electrolysis temperature, electrode surface area, and electrode material type ranked from the highest to lowest in terms of their influence on improving the efficiency of the hydrogen production process. The 6061-T0 Al alloy demonstrated the best performance in both hydrogen production and storage applications at a reasonable material cost. [ABSTRACT FROM AUTHOR]

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As technology advances and develops, the need for strong and simple authentication mechanisms that can help protect data intensifies. The contemporary approach to giving access control is through graphical passwords comprising images, patterns, or graphical items. The objective of this review was to determine the documented security risks that are related to the use of graphical passwords, together with the measures that have been taken to prevent them. The review was intended to present an extensive literature review of the subject matter on graphical password protection and to point toward potential future research directions. Many attacks, such as shoulder surfing attacks, SQL injection attacks, and spyware attacks, can easily exploit the graphical password scheme, which is one of the most widely used. To counter these security threats, several measures have been suggested, but none of the security attacks can be completely overcome. Each of the proposed measures has its pros and cons. This study begins by elucidating some of the graphical password schemes studied between 2012 and 2023, delving into potential threats and defense mechanisms associated with these schemes. Following a thorough identification and selection process, five of the reviewed papers explain the threat of shoulder surfing and spyware attacks on graphical password schemes, while two explain the threat of brute force attacks. One paper focuses on dictionary attacks, while four other papers address social engineering, SQL injection attacks, and guessing attacks as potential threats to graphical password schemes. In addition, the papers recognize other forms of attacks, such as video recording attacks, filtering attacks, reverse engineering attacks, multiple observation attacks, key/mouse logger attacks, insider attacks, computer vision attacks, image gallery attacks, sonar attacks, reply attacks, data interception attacks, and histogram manipulation attacks. These attacks are examined in three, three, eight, one, four, one, one, one, one, one, one, and one papers, respectively. Moreover, out of all such countermeasures, most of them are based on three categories—randomization, obfuscation, and password space complexity—which are the most commonly employed strategies for improving graphical password schemes. [ABSTRACT FROM AUTHOR]

In this work, new glass systems with composition xV2O5–20CuO–60B2O3–15CaO–(5−x)ZnO which is coded as BCuCa–(5−x)Zn:xV where (0, 1, 2.5 mol.%) were fabricated using the melt-quenching technique with melting temperature 1200 °C to serve as competitive candidates for γ-ray and neutron shielding. XRD and FT-IR were used to characterize the assembled glass samples. The density values of synthesized glasses were increased almost linearly from 2.807 to 2.834 g cm−3, whereas molar volume decreased from 54.894 to 54.174 cm3 mol−1, depending on the addition of V2O5. The γ-ray shielding properties of the prepared glass samples were tested experimentally using P-type HPGe detector in the photon energy range of 0.356–1.332 MeV and theoretically with the help of Monte Carlo (MC) simulations and Phy-X/PSD software in the photon energy range of 0.015–15 MeV. Various nuclear radiation shielding parameters such as linear attenuation coefficient (µ), half value layer (HVL), tenth value layer (TVL), mean free path (MFP), effective atomic number (Zeff), radiation protection efficiency (RPE), and fast neutron removal cross-section (FNRCS) for the prepared glasses have been evaluated at the selected range of photon energies. The experimental data of μ for all the tested glass systems is comparable to that obtained from a theoretical calculation. Also, Monte Carlo calculations were in a fairly good agreement with the Phy-X/PSD data, so that the maximum difference between them was about 2.716%. The outlined results indicated that the glass sample with 2.5-mol % V2O5 (BCuCa–2.5Zn:2.5V) exhibits optimal µ, Zeff, HVL, TVL, and MFP values. BCuCa–2.5Zn:2.5V was also found to have the best value of the FNRCS (about 0.1 cm−1). [ABSTRACT FROM AUTHOR]

This article discusses a case of narrow complex tachycardia with alternating atrial activation. The patient had previously undergone an ablation procedure for pre-excited atrial fibrillation but experienced recurrent episodes of palpitations. The article explores the mechanism behind the alternating atrial activation and suggests that it may be due to conduction block across the mitral isthmus. The left lateral pathway was successfully ablated, resulting in the resolution of the tachycardia. Further research and mapping techniques are recommended to improve the accuracy of ablation procedures. [Extracted from the article]