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Purpose: Removal of clinically benign eyelid margin lesions is frequently desired for cosmetic concerns. Although surgical excision remains the standard therapy for such lesions, it carries the risk of unfavorable outcomes. Hence, this study aimed to evaluate a more aesthetic technique entailing a posterior lamellar wedge resection of lower eyelid margin lesions through an infraciliary incision. Methods: A retrospective review of patients who underwent posterior lamellar wedge resection for benign lower eyelid margin lesions between May 2017 and June 2022 was performed. Patient satisfaction using the visual analog score (VAS) and cosmetic outcomes using the Strasser score. Results: Forty-two patients met the inclusion criteria (61.9% female, mean age: 46.8 years). Nevus was the most frequent histologic diagnosis (57.1%). Furthermore, 66.7% of lesions extended to the back of the lid margin. The mean defect size was 5.8 mm (range: 3.5-8 mm). The mean follow-up interval was 24.1 months (range: 6-60 months). Thirty-two (76.2%) eyelids had scores indicating an excellent result, and 10 (23.8%) patients had a good result. The mean patient satisfaction VAS score for the eyelid appearance was 96 (range: 70-100). There were no cases of recurrence of resected lesions. No further surgery was performed on the operated eyelid for any reason during the follow-up period. Conclusion: Posterior lamellar wedge resection for lower eyelid margin lesions was associated with excellent cosmetic outcomes, high patient satisfaction, and minimal adverse events. Keywords: Eyelid margin lesion, infraciliary incision, lower eyelid, posterior wedge resection, Author(s): Mostafa M Diab (corresponding author) [1]; Richard C Allen [2]; Ahmed A Ismail [1]; Ahmed T S Saif [1] Lower eyelid margin lesions are frequently encountered in everyday ophthalmic [...]

Recent advances in path-planning algorithms have transformed robotics. The Rapidly exploring Random Tree (RRT) algorithm underpins autonomous robot navigation. This paper systematically examines the uses and development of RRT algorithms in single and multiple robots to demonstrate their importance in modern robotics studies. To do this, we have reviewed 70 works on RRT algorithms in single and multiple robot path planning from 2015 to 2023. RRT algorithm evolution, including crucial turning points and innovative techniques, have been examined. A detailed comparison of the RRT Algorithm versions reveals their merits, limitations, and development potential. The review’s identification of developing regions and future research initiatives will enable roboticists to use RRT algorithms. This thorough review is essential to the robotics community, inspiring new ideas, helping problem-solving, and expediting single- and multi-robot system development. This highlights the necessity of RRT algorithms for autonomous and collaborative robotics advancement.

The optimal operation of Distribution Networks (DNs) using network reconfiguration has become more critical in the modern power system due to the widespread use of Renewable Energy Sources (RESs) and the imbalance between load demand and energy provided by RESs. However, attaining the most efficient functioning while integrating RESs is a challenging endeavor due to the unpredictability of the electrical system and the complexities associated with network reconfiguration. Integrating Distribution Static Compensators (D-STATCOMs) with network reconfiguration is powerful for improving voltage deviation reducing overall costs, and minimizing active power losses, while successfully accommodating the fluctuating characteristics of renewable energy sources. To tackle these difficulties, we offer the Horned Lizard Optimization Algorithm (HLOA), a new approach for optimizing the operation of DNs. The efficacy of HLOA is showcased on the IEEE 33-bus DN, to minimize costs, voltage deviations, real power losses, and emissions in the presence of unpredictable factors like photovoltaic (PV) uncertainties, price changes, and load demand. The analysis encompasses three case studies: one focused on optimizing operation solely with PV integration, another using both PV and D-STATCOM integration, and a third incorporating PV, D-STATCOMs, and network reconfiguration. The results indicate that the combination of PV, D-STATCOMs, and network reconfiguration significantly decreases overall cost by 45.6 %, real power losses are reduced by 66.3 %, and voltage variations are improved by 71.04 %. Emissions are mitigated by 36.72 % compared to the base case.

In recent years, CFRP is the most popular material in strengthening RC members (columns, beams, slabs…etc.) as well as the textile-reinforced mortar (TRM) was also used as an alternative method for strengthening RC columns. The present study includes fifteen circular RC columns: One control column, ten columns strengthened by one or two layers of CFRP using EBR or EBROG methods, two columns was strengthened by textile-reinforced epoxy on grooves and last two columns were strengthened by textile reinforced mortar without grooves. The parameters of this investigation are type of strengthening technique (EBR, EBROG and TRM), number of CFRP layers, and the grooves configuration. The results showed that the columns strengthened longitudinally with one or two layers of CFRP strips using EBR technique enhanced the load-carrying capacity by 2 and 7.13%, respectively, when compared with control column. However, the load carrying capacity of RC columns strengthened by EBROG increased by the range of 11.65% to 71.4%. in comparison with control column. Finally, TRM strengthening method enhanced the load carrying capacity by about 21% to 58.5%. Also, the results showed that the EBROG and TRM approaches were improved the stiffness and ductility of the columns in comparison with EBR method.

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With the advancement of the robotics world, many path-planning algorithms have been proposed. One of the important algorithms is the Rapidly Exploring Random Tree (RRT) but with the drawback of not guaranteeing the optimal path. This paper solves this problem by proposing a Memorized RRT Optimization Algorithm (MRRTO Algorithm) using memory as an optimization step. The algorithm obtains a single path from the start point, and another from the target point to store only the last visited new node. The method for computing the nearest node depends on the position, when a new node is added, the RRT function checks if there is another node closer to the new node rather than that is closer to the goal point. Simulation results with different environments show that the MRRTO outperforms the original RRT Algorithm, graph algorithms, and metaheuristic algorithms in terms of reducing time consumption, path length, and number of nodes used.

Demand-side management (DSM) schemes play a crucial role in managing renewable energy generation and load fluctuations by utilizing demand–response programmes (DRPs). This paper aims to provide a detailed overview of DRPs that help microgrid operators to keep costs and reliability within acceptable ranges. Additionally, this review paper provides a detailed economic load model for DRPs based on initial load, demand–response (DR) incentive, DR penalty and elasticity coefficients. This article also aims to guide researchers in identifying research gaps in DSM applications in microgrids by comparing various DSM schemes from different countries and regions in terms of DSM strategies, objective functions and optimization techniques. Furthermore, this study analyses the impact of DRPs on microgrid configuration from the perspective of utilities and customers, considering technical and economic performance metrics. As a result, it can be concluded that none of the studied cases provides models or guidelines for choosing appropriate DSM schemes that consider different consumer interests or load-type features. Furthermore, a few researchers have addressed the features of a modern price-based DR strategy, renewable generation-based dynamic pricing DR, which offers higher customer satisfaction than traditional DRPs.This review provides a detailed overview of demand–response programmes (DRPs) that help microgrid operators to keep costs and reliability within acceptable ranges. A detailed economic load model for DRPs based on initial load, demand–response (DR) incentive, DR penalty and elasticity coefficients is presented.

Alkaline fuel cells (AFC) have proven to provide high power densities and can attain considerable lifetimes, thus making them a worthy competitor to proton exchange membrane fuel cells (PEMFC). The electrolytes are based on aqueous bases and are relatively inexpensive. The non-corrosive nature of their electrolyte facilitates diverse usage of various catalyst materials apart from platinum, thus enabling the mass production of low-cost fuel cells. This review provides a summarized overview of AFCs. It sheds light on its fundamental operational principles, the most recent research trends related to catalyst developments, its system components and stack designs, and also provides a brief performance analysis section that highlights how the performance of an AFC is impacted upon varying different system parameters (e.g. electrolyte concentration, electrolyte thickness, and operating temperatures). This parametric study has shown that increasing the electrolyte concentration from 1–61 mol/L results in an increase in the AFC’s peak power density from 72.9 to 169 W/m2. Similarly, increasing the cell’s operating temperatures from 243–283 K yields increased peak power densities from 112 to 140 W/m2. This is mainly attributed to the enhancement of the electrolyte’s specific conductivity and the cell’s overall electrochemical kinetics. Increasing the electrolyte thickness, however, decreases the performance of an AFC, where peak power densities fall from 123 to 66 W/m2when the thickness increases from 1 to 5 μm. This is a consequence of the increased internal resistances encountered by the hydroxyl ions as they migrate through the electrolyte.

ABSTRACT This paper investigates experimentally and analytically the shear performance of RC beams made of recycled coarse aggregate (RCA). Previous studies showed that the partial replacement of the virgin coarse [...]

The Coptic correspondences of Thebes are not only characterized by the variety of topics but also by some formulae that have become a distinctive feature of this region. The aim of the present paper is to publish four Coptic letters on ostraka; they are now kept in the Abou El-Goud storage magazine in Luxor. Although there is no information on their provenance, they are certainly written in the Theban area.

A Stream Cipher (SC) is a symmetric-key encryption type that scrambles each piece of data in clear text to conceal it from hackers. Despite its advantages, it has a substantial challenge. Correct handwriting of the script code for the cipher scheme is a challenge for programmers. In this paper, we propose a graphical Domain-Specific Modeling Language (DSML) to make it easier for non-technical users and domain specialists to implement an SC domain. The proposed language, SCLang, offers great expressiveness and flexibility. Six different methods of keystream generation are provided to obtain a random sequence. In addition, fifteen tests in the NIST suite are provided for random statistical analysis. The concepts of the SC domain and their relationships are presented in a meta-model. The evaluation of SCLang is based on qualitative analysis and is presented to demonstrate its effectiveness and efficiency.

Background: Bladder cancer (BC) has a particular importance in Egyptian patients due to aggressive behavior and absence of prognostic markers.Objective: To evaluate the expression of gene and protein expression of HER2 and epidermal growth factor (EGFR) in Egyptian patients with BC and ultimately to investigate their clinical implication and prognostic significance.Material and methods: The study was carried out on 46 patients with urothelial bladder BC. Tissue were obtained from transurethral resection (N= 22) and radical cystectomy (N= 24) specimens. The original hematoxylin and eosin slides were re-evaluated and the formalin fixed, paraffin-embedded (FFPE) tissues which had sufficient tumor tissue (>75%) and minimal or absent tumor necrosis were selected for immunohistochemistry (IHC) and RNA extraction. Furthermore, five control biopsies were obtained from patients with cystitis. Follow-up data were retrieved from the medical records which included the treatment regimen, disease recurrence and/or progression, and survival.Results: EGFR and HER2 protein were overexpressed in 35% and 46% of patients respectively. EGFR was correlated with the tumor size, grade and pathological stage, with a similar trend for HER2. The recurrence rate was higher in patients with expression of any of the markers. Gene expression was significantly higher (10.6-folds) for EGFR and (21-folds) for HER2 in patients with BC in comparison to control patients. Survival analysis showed lower median disease-free survival in association with HER2 protein overexpression.Conclusions: Our data highlighted the prognostic significance of EGFR and HER in BC and proposed their possible use as predictive markers and potential therapeutic targets.

A Ni-BiOx/SBA-15 catalyst synthesized using a co-impregnation method was utilized for the oxidative dehydrogenation (ODH) of n-butenes (1-butene and 2-butene) to 1,3-butadiene in a fixed-bed reactor. The layered structure of NiO/β-Bi2O3/Bi2SiO5/SBA-15 was confirmed using porosity, XRD, and H2-TPR measurements. The components of the structure interacted moderately to form an effectively active and selective NiO species as an ODH catalyst. Thermodynamic analysis results revealed the most favorable pathway for butene conversion; however, this catalyst kinetically favored the desired and efficient route of butene conversion via adsorption of butenes, abstraction of α-hydrogen, and the formation of highly selective 1,3-butadiene. The selectivity of the oxygen species in the metal oxide system along the efficient route was sustained by co-feeding gas phase oxygen and increasing the reaction temperature. The catalyst comprising 14 wt % Ni-10 wt % Bi-O/SBA-15 exhibited a 2-butene conversion of 83% and a 1,3-butadiene selectivity of 89% at 400 °C and O2/C4=ratio = 4.0 mol/mol.

Rapid urbanization and industrialization have created tremendous stress on fresh water sources. Moreover, discharge of industrial wastewater has left public health and environment under high potential risks. In this paper, chitosan-functionalized graphene oxide (GO-CH) was synthesized and incorporated into polysulfone (Psf) ultrafiltration membranes. The concentration of the nanocomposite and the ratio of GO to CH in the nanocomposite were varied to produce different GO-CH doped polysulfone membranes. The effect of GO-CH addition on the morphology and membrane performance was studied through water contact angle, scanning electron microscopy, atomic force microscopy, porosity, pore size, and permeation measurements as well as rejection and antifouling tests, while produced GO-CH nanocomposite was characterized using FTIR and XRD analyses. The GO-CH/Psf membrane exhibited higher water flux, bovine serum albumin (BSA) rejection rate, and water flux recovery ratio, and lower BSA solution flux attenuation rate than pristine Psf membrane. GO-CH/Psf membrane containing 0.085 wt% nanocomposite with GO to CH ratio of 1 to 0.01 had an improved permeate flux of 136%. Maintaining the same GO-CH composition of 0.085 wt% and changing GO to CH ratio to 1:1 resulted in better hydrophilicity (contact angle 68°) and increased reversible fouling from 23 to 33%. The best membrane was further tested with real industrial wastewater obtained from a GTL plant in Qatar showed 28% improvement in COD removal.

Waste glass (WG) and steel slag (SS) are industrial by-products with properties that benefit sustainable concrete production. This research aims to produce concrete with the highest compressive strength and lowest thermal conductivity for green buildings by using WG and SS as the cement and coarse aggregates, respectively. Such a combination is remarkable; furthermore, modeling and optimizing their effects on the thermal conductivity of concrete are an original contribution to this field. A response surface method (RSM) was used in the mix design by considering the most influencing factors (SS, WG, and w/b). The method assessed eighteen mixtures of multiple binders to examine the performance of SS and WG and their complementary roles in improving the desired properties. Substituting SS at high levels (up to 100%) significantly improved both desired properties. However, increasing WG and w/b positively influenced the thermal conductivity and negatively influenced the compressive strength, which decreased by more than 28% compared with the control sample. The implemented linear thermal conductivity effectively correlated the responses and considered factors with a high R-value of 88%. The multi-objective optimization approach determined that the combination of SS = 100%, WG = 30%, and w/b = 0.42 provides the highest compressive strength (71 MPa) and the lowest thermal conductivity (0.7 W/ m. K). Incorporating SS and WG into concrete improved its mechanical and thermal insulation properties and supported the green life strategies.

One of the ways to minimize fossil fuel consumption is to optimize the feedwater heaters of the power plants. The newly-built power plants work optimally at their design conditions; however, by aging, they often deviate from those conditions. Therefore, new optimal conditions are required to be determined for them. In this study, invoking a Predictive Model, the impact of different configurations of the feedwater heaters on the performance of the power plant is examined. It is shown that there are optimal values for the pressure of closed feedwater heaters. Following the optimization procedure of Conjugate Directions Method, the optimal pressures for bleeding off steam for all the closed feedwater heaters and the deaerator are achieved, simultaneously. The multi-variable optimization is carried out based on the maximal energy and exergy efficiencies as well as the maximal net delivered power of the cycle.

In this study, a novel algorithm is developed to solve the multi-level multiobjective fractional programming problems, using the idea of a neutrosophic fuzzy set. The co-efficients in each objective functions is assumed to be rough intervals. Furthermore, the objective functions are transformed into two sub-problems based on lower and upper approximation intervals. The marginal evaluation of pre-determined neutrosophic fuzzy goals for all objective functions at each level is achieved by different membership functions, such as truth, indeterminacy/neutral, and falsity degrees in neutrosophic uncertainty. In addition, the neutrosophic fuzzy goal programming algorithm is proposed to attain the highest degrees of each marginal evaluation goals by reducing their deviational variables and consequently obtain the optimal solution for all the decision-makers at all levels. To verify and validate the proposed neutrosophic fuzzy goal programming techniques, a numerical example is adressed in a hierarchical decision-making environment along with the conclusions.

Honeyword system is a successful password cracking detection system. Simply the honeywords are (False passwords) that are accompanied to the sugarword (Real password). Honeyword system aims to improve the security of hashed passwords by facilitating the detection of password cracking. The password database will have many honeywords for every user in the system. If the adversary uses a honeyword for login, a silent alert will indicate that the password database might be compromised. All previous studies present a few remarks on honeyword generation methods for max two preceding methods only. So, the need for one that lists all preceding researches with their weaknesses is shown. This work presents all generation methods then lists the strengths and weaknesses of 26 ones. In addition, it puts 32 remarks that highlight their strengths and weaknesses points. This research has proved that every honeyword generation method has many weaknesses points.

This study aimed to provide comprehensive diagnostic and candidate analyses in a pediatric rare disease cohort through the Genomic Answers for Kids program.

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.

Oil and gas exploration activities result in generation of large quantities of produced water. Globally, for each barrel of oil, three barrels of produced water is generated. The oil content in produced water can vary between 3 and 20% depending on the location and age of the hydrocarbon well. Due to their hydrophobic nature, conventional hydrophobic polymeric membranes struggle to effectively separate oil from produced water. In this work, an innovative strategy is suggested by employing a hydrophilic/super-oleophobic nanocomposite to develop novel polymeric membranes able to effectively separate oil content from produced water without negatively affecting the other membrane properties such as the total flux and fouling. Graphene oxide-chitosan-silicone oxide (GO-CH-SiO2) nanocomposite was synthesized by functionalizing graphene oxide (GO) with chitosan (CH) and silicon dioxide (SiO2). To improve the membrane flux, anti-fouling propensity, and oil rejection, the synthesized nanocomposites were doped in the polysulfone membranes matrix. The effect of GO-CH-SiO2concentration, GO:CH ratio, and GO-CH:SiO2ratio on the performances of developed membranes was experimentally assessed, and morphology of the synthesized membrane was investigated using appropriate characterization techniques. The experimental results showed that the membrane with GO:CH of 1:2 and GO-CH: SiO2ratio of 1:6.5 showed the highest pure water permeation flux of 28.35 LMH/bar with a comparable flux recovery rate of 76% and oil rejection efficiency of 98.5%. The study’s findings underscore the potential of GO-CH-SiO2nanocomposite membranes for oil–water separation research, presenting a promising solution for treating produced water in the oil and gas industry. Further research is needed to scale up this technology and improve membrane performance by optimizing the nanocomposite composition and conducting long-term performance tests.

An effective password cracking detection system is the honeyword system. The Honeyword method attempts to increase the security of hashed passwords by making password cracking easier to detect. Each user in the system has many honeywords in the password database. If the attacker logs in using a honeyword, a quiet alert trigger indicates that the password database has been hacked. Many honeyword generation methods have been proposed, they have a weakness in generating process, do not support all honeyword properties, and have many honeyword issues. This article proposes a novel method to generate honeyword using the meerkat clan intelligence algorithm, a metaheuristic swarm intelligence algorithm. The proposed generation methods will improve the honeyword generating process, enhance the honeyword properties, and solve the issues of previous methods. This work will show some previous generation methods, explain the proposed method, discuss the experimental results and compare the new one with the prior ones.

In this work, TiO nanostructure films were deposited via vacuum thermal evaporation at a temperature of 80°C. The TiO thin films were annealed under vacuum for 1 h at three different degrees (200, 300, and 400°C) in addition to the thin film prepared at 80°C. X-ray Diffraction (XRD) showed that all the deposited and annealed TiO films had anatase polycrystalline diffraction patterns with a predominant reflection of the (200) plane. As a result, the particle size increased with annealing temperature. Scanning Electron Microscopy (SEM) measurements showed that at the annealed temperature of 200°C, the shape of the TiO nanostructures began to change from a condensed cluster distribution to a conical shape. As the annealing temperature was increased to 400°C, all the conical shapes transformed into clear spherical shapes. The spherical shapes recorded 45 (nm) height and (20) (nm) base width. Optical measurements were performed using Ultraviolet-Visible spectroscopy (UV-Vis). The transmittance is reduced from 79.63% for the TiO sample prepared at 80°C to 71.91% for the TiO sample annealed at 400°C. The optical energy gap values decrease from 3.279 eV for the prepared TiO sample at 80°C to 3.115 eV for the TiO sample annealed at 400°C.

Brushless direct current motors (BLDC) are used in controlling the unmanned vehicles (UV) rotors dynamics. In this paper, speed control for BLDC motor via a set of adaptive intelligent control techniques is tackled. Adaptive neuro fuzzy inference systems (ANFIS), self-adaptive proportional - integral - derivative (SA-PID) and adaptive fuzzy sliding mode control (AFSMC) are applied to control the speed of the BLDC to maintain the required speed guaranteeing better performance, robustness and safety of the UV during mission. The main contribution in this paper lies in solving the speed control problem for a BLDC on board an UV via artificial intelligent control techniques ensuring the success of the UV in performing the required mission. The simulation results prove the success of designed controller to achieve the desired speed and the enhancement of the performance compared with traditional controllers.

Renal cell carcinoma, a form of kidney cancer, has responded poorly to all the chemotherapeutic agents and hormonal treatments that have so far been tested. Recently, the attention of researchers has turned towards substances such as interferon and interleukin-2 for the treatment of renal cell carcinoma. These substances exert powerful effects on the function of immune system cells, and may affect cancer cells directly, as well. The combination of interleukin-2 and interferon was evaluated in 15 patients with advanced renal cell carcinoma. The patients received intravenous infusion of interleukin-2 and intramuscular injection of interferon for four days each week. The protocol was repeated for four weeks. All patients in this study experienced moderate to severe side effects as a result of the treatment, including chills and fever, nausea and vomiting, anorexia, and fatigue. Thirteen of 15 patients experienced tachycardia (racing heart beat), and in 14 cases the patient's blood pressure dropped. However, complete responses to treatment were achieved by two patients, and partial responses by two more. Both patients who achieved complete remission had metastatic cancer; however, they continue to be free of disease at 14 and 24 months of follow-up. These results indicate that the combination of interleukin-2 and interferon shows promise in the treatment of renal cell carcinoma and should be evaluated further. (Consumer Summary produced by Reliance Medical Information, Inc.)

Although, most researchers focus on some of the key components of good digital wireless communications which are the Bit Error Rate (BER) versus Signal to Noise Ratio (SNR) of modulation schemes. Energy consumption optimization is necessary for enhancing the performance of a wireless communication system as it offers numerous advantages to the system and the users. Therefore, this research focuses on analyzing the efficiency in the performance of the various QAM Modulation Schemes (4QAM, 16QAM, 32QAM & 64QAM) as they travel over noise/fading channels with the quest to obtain an energy-efficient scheme which will enhance system performance in terms of system runtime and quality of service. The efficiency of any given process, operation, or device is rated per the energy it consumes in carrying out an activity per unit output. Hence, the objective of this research work is to study and analyze comparatively the efficiency of these modulation schemes and to conclude with the most efficient scheme over the various channels. The evaluation of the Bit Error Rate (BER) versus energy per bit to noise spectral density (EbNo) for each communication scenario was carried out in MATLAB.

The ability to control the function and structure of some promising nanosystems using an external stimulus is attractive research to develop programmable and reconfigurable intelligent nanomaterials. The focal point of this review is the silicon-based nanoporous materials, and particularly the mesoporous silica materials (MSMs) class (pore size: 2–50 nm), due to their important intrinsic properties, such as high surface area, highly ordered nanostructure, narrow pore size distribution, various dimensions (one-dimensional, two-dimensional, and three-dimensional), and easily functionable. One of the most essential organic components that can be incorporated in MSMs is organic photochromic molecules (OPMs), such as azobenzene, stilbene, dithienylethenes, and spiropyrans. OPMs can be incorporated into MSMs, to form photochromic mesoporous organosilica materials (PMOMs), in two different ways: physical (non-covalent immobilization) or chemical (covalent immobilization) binding. PMOMs are considered smart nanomaterials because they have the ability to undergo reversible changes in the solid state when exposed to an external electromagnetic radiation. PMOMs have been the subject of many research studies during the last decade due to their potential applications, especially as chemosensors. This review discusses the main families of OPMs, their incorporation into MSMs using different methods, and the applications of some PMOMs as chemosensors.

Extensive research efforts have been made over the last few years to proffer solution to the high recombination rate and stability issues associated with Sn-based perovskite solar cells. In line with this, we modeled cesium tin iodide (CsSnI3)–based perovskite solar cell (PSC) with titanium (IV) oxide (TiO2) and copper thiocyanate (CuSCN) as the electron and hole transport materials respectively by employing one-dimensional solar cell capacitance simulator (1D-SCAPS). For the CsSnI3-based PSC, n-i-p planar configuration was employed and previously published relevant data were used for the simulation. The results obtained for the initially modeled PSC compared well with similar devices in the literature. Based on the established relationship between charge carrier lifetime and power conversion efficiency (PCE) of a solar cell, we varied defect density in the cesium tin iodide (CsSnI3) from 1013to 1017cm−3and studied its influence on the performance parameters of the modeled CsSnI3-based PSC. We find that the diffusion length and lifetime of charge carriers are significantly increased with decreasing defect density of the CsSnI3absorber. In this study, we report that it is possible to significantly reduce the dominated Shockley–Read–Hall (SRH) high recombination rate occurring in CsSnI3perovskite layer even at a low defect density of 1013cm−3by using a combination of SnCl2and Br−as additive and dopant respectively for CsSnI3enrichment. This strategy ensured a reduced concentration of Sn4+vacancy (VSn) in the CsSnI3absorber and an improved carrier lifetime beyond 0.05 ns as obtained for the initially modeled CsSnI3-based PSC by a magnitude of the order of 103while the diffusion length was improved from 1.1 µm by a magnitude of the order of 102for the enriched CsSnI3-based PSC. For the optimized CsSnI3-based PSC, we recorded an open-circuit voltage (VOC) of magnitude 1.289 V, short-circuit current density (JSC) value 32.60 mA∙cm−2, fill factor (FF) 83.56%, and PCE 35.12%.

Due to the limitations of the initial rapidly exploring random tree (RRT) algorithm, robotics faces challenges in path planning. This study proposes the integration of the metaheuristic salp swarm algorithm (SSA) to enhance the RRT algorithm, resulting in a new algorithm termed IRRT-SSA. The IRRT-SSA addresses issues inherent in the original RRT, enhancing efficiency and path-finding capabilities. A detailed explanation of IRRT-SSA is provided, emphasizing its distinctions from the core RRT. Comprehensive insights into parameterization and algorithmic processes contribute to a thorough understanding of its implementation. Comparative analysis demonstrates the superior performance of IRRT-SSA over the basic RRT, showing improvements of approximately 49, 54, and 54% in average path length, number of nodes, and number of iterations, respectively. This signifies the enhanced effectiveness of the proposed method. Theoretical and practical implications of IRRT-SSA are highlighted, particularly its influence on practical robotic applications, serving as an exemplar of tangible benefits.

Analyzing the impact of measurement noise on space vehicle dynamics is vital for understanding its implications in proximity missions. Despite researcher's focus on addressing position and velocity challenges during cooperative operations, few studies explore measurement noise effects in dynamic environments. This study introduces a methodology, primarily utilizing the Model Predictive Control-Based Moving Horizon Estimator (MPC-MHE) with Euclidean navigation constraints, to optimize the estimation of unmeasured state variables in space vehicle dynamics. Results show the efficacy of the moving-horizon estimator inaccurate estimation, outperforming LQ-MPC and IDVD in assessing unmeasured states with minimal control effort under measurement noise. The proposed technique is further compared with LQ-MPC/DH, LQ-MPC/RH, and NMPC in three scenarios, revealing superior performance in time to dock and control effort metrics. The method demonstrates an ARPI of 42.33  and 41.70  in Case A, 34.51  and 33.65  in Case B, and 32.90  and 41.25  in Case C for time to dock and control effort, respectively. Moreover, the moving horizon estimator exhibits a 95  coefficient of determination for unmeasured state estimates compared to ground truth data, highlighting its effectiveness in mitigating measurement noise challenges in space vehicle dynamics.

•Multiple sclerosis cortical lesions are areas of demyelination and neuroaxonal loss.•Optical coherence tomography (OCT) can measure the thinning of peripapillary retinal nerve fibre layer (pRNFL), a marker of axonal loss, and ganglion cell and inner plexiform layer (GCIPL), a marker of neuronal loss.•Higher total cortical and leukocortical lesion volumes correlated with thinner pRNFL at the onset of multiple sclerosis.•The association between cortical lesions and axonal damage in the optic nerve reinforces the role of neurodegenerative processes in multiple sclerosis pathogenesis at onset.

Experiments in several mouse models of human cancer suggest that synergy occurs with the combination of interleukin-2 (IL-2) and alpha-interferon (alpha IFN). That is, the combination of the two agents is more effective than the sum of their individual anticancer activities. A clinical trial was conducted to determine the maximum dose of the combination that could be tolerated by human patients with a variety of different cancers. The toxic effects of fatigue, diarrhea, low blood pressure, fluid retention, and anorexia limited the dose of the combination to 5 million units per meter squared of body area of interleukin-2 per day, and 6 million units per meter squared of alpha interferon per day. One patient suffered a cardiac arrest, which is considered to represent Grade IV toxicity. Less than half the patients experienced Grade III toxicity; 33 percent had non-life-threatening Grade III toxicity, and 13 percent developed life-threatening Grade III complications in the lungs or nervous system. The drug combination was found to have no measurable beneficial effect on melanoma, lymphoma, and colorectal cancer. However, 4 of 18 patients with kidney cell cancer showed a partial response to the combination treatment. (Consumer Summary produced by Reliance Medical Information, Inc.)

IMPORTANCE: Although magnetic resonance imaging (MRI) is useful for monitoring disease dissemination in space and over time and excluding multiple sclerosis (MS) mimics, there has been less application of MRI to progressive MS, including diagnosing primary progressive (PP) MS and identifying patients with relapsing-remitting (RR) MS who are at risk of developing secondary progressive (SP) MS. This review addresses clinical application of MRI for both diagnosis and prognosis of progressive MS. OBSERVATIONS: Although nonspecific, some spinal cord imaging features (diffuse abnormalities and lesions involving gray matter [GM] and ≥2 white matter columns) are typical of PPMS. In patients with PPMS and those with relapse-onset MS, location of lesions in critical central nervous system regions (spinal cord, infratentorial regions, and GM) and MRI-detected high inflammatory activity in the first years after diagnosis are risk factors for long-term disability and future progressive disease course. These measures are evaluable in clinical practice. In patients with established MS, GM involvement and neurodegeneration are associated with accelerated clinical worsening. Subpial demyelination and slowly expanding lesions are novel indicators of progressive MS. CONCLUSIONS AND RELEVANCE: Diagnosis of PPMS is more challenging than diagnosis of RRMS. No qualitative clinical, immunological, histopathological, or neuroimaging features differentiate PPMS and SPMS; both are characterized by imaging findings reflecting neurodegeneration and are also impacted by aging and comorbidities. Unmet diagnostic needs include identification of MRI markers capable of distinguishing PPMS from RRMS and predicting the evolution of RRMS to SPMS. Integration of multiple parameters will likely be essential to achieve these aims.

This paper presents FTS forecasting model using hidden Markov model (HMM) and genetic algorithm (GA). Over the years, traditional methods such as Baum Welch algorithm (BWA) have been employed significantly for HMM parameter estimation. This method does not usually capture effectively the fuzziness in natural data leading the HMM algorithm into local minima. To address this limitation, we formulate an objective function representing the HMM parameter estimation problem and optimise the formulated objective function using GA. The insufficiency in data associated with the HMM model, was addressed using smoothing technique. Monte Carlo simulation was employed at the end of the forecast to ensure stability and efficiency of the forecasting outcome of the developed approach. The model was tested on daily average temperature and cloud density of Taipei, Taiwan and internet traffic data of Ahmadu Bello University (ABU). In verifying the performance of the developed using the Taiwan temperature and ABU internet traffic datasets, we employed the mean square error (MSE) and average forecasting error percentage (AFEP) as performance metric. Experiment results showed that the new forecasting method has an improved forecasting accuracy compared to existing methods.

ABSTRACTThis study aimed to utilize cholesterol conjugation of 5-fluorouracil (5-FUC) and liposomal formulas to enhance the partitioning of 5-FU into low density lipoprotein (LDL) to target hepatocellular carcinoma (HCC). Thus, 5-FU and 5-FUCwere loaded into liposomes. Later, the direct loading and transfer of 5-FU, and 5-FUC from liposomes into LDL were attained. The preparations were characterized in terms of particle size, zeta potential, morphology, entrapment efficiency, and cytotoxicity using the HepG2 cell line. Moreover, the drug deposition into the LDL and liver tissues was investigated. The present results revealed that liposomal preparations have a nanosize range (155 − 194 nm), negative zeta potential (- 0.82 to – 16 mV), entrapment efficiency of 69% for 5-FU, and 66% for 5-FUC. Moreover, LDL particles have a nanosize range (28–49 nm), negative zeta potential (- 17 to −27 mV), and the entrapment efficiency is 11% for 5-FU and 85% for 5-FUC. Furthermore, 5-FUC loaded liposomes displayed a sustained release profile (57%) at 24 h compared to fast release (92%) of 5-FU loaded liposomes. 5-FUC and liposomal formulas enhanced the transfer of 5-FUC into LDL compared to 5-FU. 5-FUC loaded liposomes and LDL have greater cytotoxicity against HepG2 cell lines compared to 5-FU and 5-FUC solutions. Moreover, the deposition of 5-FUC in LDL (26.87ng/mg) and liver tissues (534 ng/gm tissue) was significantly increased 5-FUC liposomes compared to 5-FU (11.7 ng/g tissue) liposomal formulation. In conclusion, 5-FUC is a promising strategy for hepatic targeting of 5-FU through LDL-mediated gateway.

BackgroundMultiple sclerosis (MS) is a chronic disabling, demyelinating disease of the central nervous system and is often associated with psychiatric comorbidities. Some studies suggest increased prevalence of bipolar disorder (BD) in MS.ObjectiveTo conduct a systematic review and meta-analysis assessing the prevalence of BD in adults with MS.MethodsWe registered this review with PROSPERO and searched electronic databases (Ovid MEDLINE, Central, Embase, PsycINFO and Scopus) for eligible studies from earliest inception to October 2020. Prevalence data of BD in adult patients with MS were extracted. Meta-analysis was conducted using random-effects model.FindingsOf the 802 articles that were screened, 23 studies enrolling a total of 68 796 patients were included in the systematic review and meta-analysis. The pooled prevalence rate of BD in patients with MS was 2.95% (95% CI 2.12% to 4.09%) with higher prevalence in the Americas versus Europe. The lifetime prevalence of BD was 8.4% in patients with MS. Subgroup analysis showed a higher prevalence of BD in MS in females (7.03%) than in males (5.64%), which did not reach statistical significance (p=0.53).ConclusionsThis meta-analysis suggests a high lifetime prevalence of BD in patients with MS. Patients with MS should be routinely screened for BD. Further assessment of bipolar comorbidity in MS through prospective studies may help in developing effective management strategies and may improve treatment outcomes in patients with MS.