Theoretical investigation of benzodithiophene-based donor molecules in organic solar cells: from structural optimization to performance metrics.

Achieving high power conversion efficiency (PCE) remains a significant challenge in the advancement of organic solar cells (OSCs). In the field of organic photovoltaics (OPVs), considerable progress has been made in optimizing molecular structures to improve the PCE. However, innovative material design strategies specifically aimed at enhancing PCE are still needed. Here, we have designed BDTS-2DPP-based molecules and propose a molecular design approach to develop donor materials that can significantly improve the PCE of OSCs. Density functional theory (DFT) and time-dependent DFT (TD-DFT) methods have been adopted in both gas and solvent phases. Our newly designed molecule M1 shows the highest absorption value (λ max = 846 nm), highest electron reorganization energy (λ e = 0.18 eV), and the lowest energy gap (E g = 1.81 eV) among all the designed molecules. M1 molecule also exhibits the highest dipole moment in both gas (10.62 D) and solvent phase (13.62 D), and their ground and excited state dipole moment difference is also higher (µ e - µ g = 2.99 D), which enhances its separation to make it a suitable candidate for charge transfer between HOMO-LUMO (97%). Newly designed molecule M3 is observed to have the highest voltage when the current is zero (V oc = 1.15 V) highest PCE value (21.90%) and highest fill factor (FF) value (89.42%). The lowest excitation binding energy is estimated by newly designed molecule M2 (E b = 0.30 eV), which indicates a higher rate of dissociation during the excitation as observed in transition density matrix (TDM) plots. Utilizing electron density difference maps, the newly designed molecules in dichloromethane solvent exhibited consistent intramolecular charge transfer (ICT). The designed molecules were evaluated against reference molecule R to determine if they exhibit superior optoelectronic capabilities. It is found that all designed molecules (M1-M5) exhibit reduced band gaps, are red-shifted in wavelength in comparison to a reference molecule R, and have remarkable charge motilities in terms of reorganisation energies.

On degree-based operators and topological descriptors of molecular graphs and their applications to QSPR analysis of carbon derivatives.

This work initiates a concept of reduced reverse degree based RR D M -Polynomial for a graph, and differential and integral operators by using this RR D M -Polynomial. In this study twelve reduced reverse degree-based topological descriptors are formulated using the RR D M -Polynomial. The topological descriptors, denoted as T D 's, are numerical invariants that offer significant insights into the molecular topology of a molecular graph. These descriptors are essential for conducting QSPR investigations and accurately estimating physicochemical attributes. The structural and algebraic characteristics of the graphene and graphdiyne are studied to apply this methodology. The study involves the analysis and estimation of Reduced reverse degree-based topological descriptors and physicochemical features of graphene derivatives using best-fit quadratic regression models. This work opens up new directions for scientists and researchers to pursue, taking them into new fields of study.

Evaluating a novel method for vitamin A analysis in an observational study of the UAE's obese population.

Vitamin A plays a critical role in various biological functions, including vision, cellular differentiation, and immune regulation. However, accurately assessing its status, particularly in obese individuals, presents challenges due to potential alterations in metabolism and distribution. This study utilized Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) methodology to precisely measure serum vitamin A concentrations in population of UAE. The methodology's reliability and precision, as demonstrated through validation procedures, underscore its potential utility in clinical settings. Employing the Multiple Reaction Monitoring mode of positive ion electrospray ionization, the LC-MS/MS system achieves a limit of detection (LOD) of 0.48 ng/mL in serum, while adhering to FDA-US regulations for accuracy and compliance. A key aspect of this study was the application of LC-MS/MS to assess vitamin A status in an obese population within UAE. By employing a diverse cohort of 452 Emirati participants, including 277 individuals from a randomized controlled trial who were assessed at baseline and at 6th month, and 175 healthy individuals aged 18-82 assessed at baseline, this study explores the relationship between obesity and vitamin A levels, shedding light on potential implications for health and well-being. It was an observational study based on a new vitamin A method and participants were asked to eat vitamin A rich foods. The robust performance of the LC-MS/MS methodology positions it as a valuable tool for clinical research. By accurately quantifying vitamin A levels in human serum, this methodology opens avenues for advancing our understanding of vitamin A physiology and its implications for health, particularly in obese populations. In summary, this LC-MS/MS methodology presents a potent tool for clinical studies, providing reliable, specific, and robust detection of vitamin A in human serum, thus, opening a new frontier for advancing our understanding of vitamin A related physiology and health in the obese population.

On statistical evaluation of reverse degree based topological indices for iron telluride networks.

In the context of graph theory and chemical graph theory, this research conducts a detailed mathematical investigation of reverse topological indices as they relate to iron telluride networks, clarifying their complex interactions. Graph theory is a branch of abstract mathematics that carefully studies the connections and structural features of graphs made up of edges and vertices. These theoretical ideas are expanded upon in chemical graph theory, which models molecular architectures with atoms acting as vertices and chemical bonds as edges. By extending these concepts, this work investigates the reverse topological indices in the context of Iron Telluride networks and outlines their significant effects on chemical reactivity, molecular topology and statistical modeling. By navigating intricate mathematical formalisms and algorithmic approaches, the analysis provides profound insights into the reactivity patterns and structural dynamics of Iron Telluride compounds, enhancing our knowledge of solid-state chemistry and materials science.

Statistical analysis of topological indices in linear phenylenes for predicting physicochemical properties using algorithms.

QSPR mathematically links physicochemical properties with the structure of a molecule. The physicochemical properties of chemical molecules can be predicted using topological indices. It is an effective method for eliminating costly and time-consuming laboratory tests. We established a QSPR between mev-degree and mve-degree-based indices and the physical properties of benzenoid hydrocarbons. To compute these indices, we designed a program using Maple software and the correlation between indices and physical properties was developed using the SPSS software. Our study reveals that the mve-degree-based sum-connectivity ( χ mve ) and atom bond connectivity ( A B C mve ) index, mev-degree-based Randic ( R mev ) and Zagreb ( M mev ) index are the three most significant parameters and have good prediction ability for the physicochemical properties. We examined that R mev predicts the molar refractivity and boiling point, χ mve predicts the LogP and enthalpy, A B C mve predicts the molecular weight, M mev predicts the Gibb's energy, Pie-electron energy and Henry's law. Moreover, we computed the indices for the linear [n]-phenylen.

On physical analysis of topological indices for iron disulfide network via curve fitting model.

In this study, we conduct a comprehensive physical analysis of topological indices for the Iron Disulfide (FeS 2 ) network using a curve-fitting model. Iron Disulfide is a cubic compound. In metamorphic rock, sedimentary rock, and quartz veins, it is typically found in combination with other sulfides or oxides. The numerical properties of molecular structures are referred to as topological indices. There are several different kinds of topological indices, including those that are based on distance, degree, or counting, among other factors. The real process of creating a topological index involves turning a chemical structure into a numerical value. In this paper, we calculate the iron disulfide network topological indices using the degrees of vertices in a chemical network of Iron Disulfide (FeS 2 ). Thereafter, we discovered the physical parameters of FeS 2 production, such as heat of formation. We then fitted curves between the thermodynamic properties and several indices. Several techniques based on rationality, linearity, and nonlinearity were used to fit curves in MATLAB. These quantitative results imply that a variety of thermodynamic characteristics of semiconducting materials may be accurately predicted by topological indices. These findings have significant ramifications as they provide the groundwork for the application of topological indices in semiconducting network design and optimization, which might result in more effective and economical material creation.

Efficient state of charge estimation of lithium-ion batteries in electric vehicles using evolutionary intelligence-assisted GLA-CNN-Bi-LSTM deep learning model.

The battery's performance heavily influences the safety, dependability, and operational efficiency of electric vehicles (EVs). This paper introduces an innovative hybrid deep learning architecture that dramatically enhances the estimation of the state of charge (SoC) of lithium-ion (Li-ion) batteries, crucial for efficient EV operation. Our model uniquely integrates a convolutional neural network (CNN) with bidirectional long short-term memory (Bi-LSTM), optimized through evolutionary intelligence, enabling an advanced level of precision in SoC estimation. A novel aspect of this work is the application of the Group Learning Algorithm (GLA) to tune the hyperparameters of the CNN-Bi-LSTM network meticulously. This approach not only refines the model's accuracy but also significantly enhances its efficiency by optimizing each parameter to best capture and integrate both spatial and temporal information from the battery data. This is in stark contrast to conventional models that typically focus on either spatial or temporal data, but not both effectively. The model's robustness is further demonstrated through its training across six diverse datasets that represent a range of EV discharge profiles, including the Highway Fuel Economy Test (HWFET), the US06 test, the Beijing Dynamic Stress Test (BJDST), the dynamic stress test (DST), the federal urban driving schedule (FUDS), and the urban development driving schedule (UDDS). These tests are crucial for ensuring that the model can perform under various real-world conditions. Experimentally, our hybrid model not only surpasses the performance of existing LSTM and CNN frameworks in tracking SoC estimation but also achieves an impressively quick convergence to true SoC values, maintaining an average root mean square error (RMSE) of less than 1 %. Furthermore, the experimental outcomes suggest that this new deep learning methodology outstrips conventional approaches in both convergence speed and estimation accuracy, thus promising to significantly enhance battery life and overall EV efficiency.

On curve fitting between topological indices and Gibb's energy for semiconducting carbon nitrides network.

Quantitative structure relationships linked to a chemical structure that shed light on its properties and chemical reactions are called topological indices. This structure is upset by the addition of silicon (Si) doping, which changes the electrical and optical characteristics. In this article, we examine the connection between a chemical structure's Gibbs energy (GE) and K-Banhatti indices. In this article, we compute the K-Banhatti indices and then show the correlation between the indices and Gibb's energy of the molecule using curve fitting. Through the curve fitting, we see that there is a strong correlation between indices and Gibb's energy of a molecule. We use the polynomial curve fitting approach to see the correlation between indices and Gibb's energy.

The bacterial profile and antibiotic susceptibility in skin and soft tissue infections at a tertiary care hospital of Quetta, Pakistan.

Objectives: To determine the bacterial profile and antibiotic susceptibility in skin and soft tissue infections among patients in a tertiary care setting. METHODS: The cross-sectional cohort study was conducted at the Centre for Advanced Studies in Vaccinology and Biotechnology, University of Balochistan, Quetta, Pakistan, from June 2021 to May 2022, and comprised bacteriainfected skin samples that were collected from the Bolan Medical Complex Hospital, Quetta, and the Sandeman Provincial Hospital, Quetta. The swab samples were immediately cultured, and positive samples were evaluated for biochemical tests, antibiotic susceptibility test and polymerase chain reaction. Data was analysed using SPSS 22. RESULTS: Of the 800 samples, 598(74.7%) tested positive for pathogenic bacteria. Staphylococcus aureus accounted for 316(39.5%) infections, followed by clostridium perfringens 18.96(2.37%), escherichia coli 120(15.12%), pseudomonas aeruginosa 98(12.25%) and klebsiella pneumoniae 44(5.5%). Among all the infected samples, 380(47.5%) belonged to males, 218(27.25%) to patients aged 5-20 years, 448(56%) to the uneducated subjects, and 462(57.87%) to patients having lower socioeconomic status. Pseudomonas aeruginosa showed the highest level of resistance against all antibiotics. Conclusion: Regular surveillance and proper use of antibiotics should be encouraged in hospitals to limit the spread of antibiotic resistance against pathogenic bacteria.

Prevalence of De Quervain disease in infant caregivers and its association with risk factors.

De Quervain's disease (DQD) is commonly reported in mothers during pregnancy up to delayed postpartum period. A cross-sectional study was conducted to assess infant caregivers who visited the paediatric outpatient department or vaccination centre in two hospitals of Lahore, during the months of May and June, 2021. A total of 190 subjects were interviewed directly and assessed by applying Finkelstein's test on both hands. Data was collected using Numeric Pain Rating Scale (NPRS) and Patient Rated Wrist Evaluation (PRWE) from positive subjects. They were asked to report their pain and difficulty level of the affected hand with worsened symptoms. The results exhibited 26.8% prevalence of DQD in a sample size of 190. Infant's age, lifting frequency and hand dominance were proved significant risk factors. However, caregiver's age, history of wrist pain, infant weight and relationship with infant were proved insignificant. Mean PRWE pain and functional scores were 23.14±7.72 and 18.53±6.09, respectively.

On analysis of topological indices and heat of formation for benzyl sulfamoyl network via curve fitting model.

The study explores the intricate relationship between topological indices and the heat of formation in the benzyl sulfamoyl network. Topological indices of benzyl sulfamoyl networks are studied and also emphasize their properties statistically. The benzyl sulfamoyl has unique properties due to its crystalline structure and it is used in the form of artificial substance. We analyze the distributions and correlations of the benzyl sulfamoyl network with others by using statistical methods and also build a computational analysis for topological indices. The findings show a strong association between the variables, indicating that topological indices may be used to accurately predict thermodynamic characteristics and improve the effectiveness of molecular modelling and simulation procedures.

Optimization of Production Parameters for Fabrication of Gum Arabic/Whey Protein-Based Walnut Oil Loaded Nanoparticles and Their Characterization.

The encapsulation of fatty acids, including walnut oil, within complexes is a promising strategy to address challenges, for instance, low water solubility and susceptibility to oxidation while incorporating these oils into food products. Additionally, encapsulation can effectively mask undesirable odor and flavor. The current study focuses on the optimization of walnut oil nanoparticles (WON) using complexes fabricated from gum arabic and whey protein by applying a response surface methodology. The impact of three different independent variables were determined, such as surfactant mixture (33-66%), walnut oil (5-25%), and sonication time (60-300 s), under three distinct desired conditions (low, medium, and high) on four different responses, i.e., particle size, polydispersity index (PDI), moisture level, and encapsulation efficiency (EE). The findings of the present study indicate that the point prediction-based WON resulted in significantly low particle size (82.94 nm), PDI (0.19), moisture content (3.49%), and high EE (77.26%). Fourier transform infrared spectroscopy (FTIR) study demonstrated the successful encapsulation of walnut oil and wall material into nanocapsules. Differential scanning calorimetry (DSC) verified the improved thermal stability property of WON after incorporation, and scanning electron microscopy (SEM) indicated that the WON had relatively fragile and smooth surfaces, along with the presence of few porous structures. The recorded experimental data from the existing study showed that the developed formulation of WON was potentially useful as a value-added ingredient for food industries.

Applications of magnesium iodide structure via modified-polynomials.

A relatively recent approach in molecular graph theory for analyzing chemical networks and structures is called a modified polynomial. It emphasizes the characteristics of molecules through the use of a polynomial-based procedure and presents numerical descriptors in algebraic form. The Quantitative Structure-Property Relationship study makes use of Modified Polynomials (M-Polynomials) as a mathematical tool. M-Polynomials used to create connections between a material's various properties and its structural characteristics. In this study, we calculated several modified polynomials and gave a polynomial description of the magnesium iodide structure. Particularly, we computed first, second and modified Zagreb indices based M-polynomials. Randic index, and inverse Randic indices based M-polynomials are also computed in this work.

Topological indices and patterns in iron telluride networks.

This paper explores the complex interplay between topological indices and structural patterns in networks of iron telluride (FeTe). We want to analyses and characterize the distinct topological features of (FeTe) by utilizing an extensive set of topological indices. We investigate the relationship that these indicators have with the network's physical characteristics by employing sophisticated statistical techniques and curve fitting models. Our results show important trends that contribute to our knowledge of the architecture of the (FeTe) network and shed light on its physiochemical properties. This study advances the area of material science by providing a solid foundation for using topological indices to predict and analyses the behavior of intricate network systems. More preciously, we study the topological indices of iron telluride networks, an artificial substance widely used with unique properties due to its crystal structure. We construct a series of topological indices for iron telluride networks with exact mathematical analysis and determine their distributions and correlations using statistical methods. Our results reveal significant patterns and trends in the network structure when the number of constituent atoms increases. These results shed new light on the fundamental factors that influence material behavior, thus offering a deeper understanding of the iron telluride network and may contribute to future research and engineering of these materials.

Hybrid constructed wetlands and filamentous fungi for treatment of mixed sewage and industrial effluents.

Developing countries face multifaceted problems of water pollution and futile measures to combat water pollution. This study was conducted to explore the potential application of sustainable nature-based solutions, hybrid constructed wetlands, and the application of filamentous fungi to treat polluted river water that receives sewage and industrial wastewater. A pilot-scale hybrid constructed wetland design comprising two types of floating plants in distinct tanks along with a floating wetland and a free-water surface wetland connected in series was commissioned and tested. The system successfully removed organic pollution (BOD 94% and COD 90%), nutrients (NH4-N and NO3-N 67% and PO4-P 81%), and heavy metals (Cr 75%, Ni 56%, and Fe 79%) in 40 h and showed a high buffering capacity to cope with the varying pollutant loads. Metagenomics analysis of treated and untreated samples of river water revealed a diversified spatial bacterial community with ~ 25% sequences related to sulfur-metabolizing bacteria, genus Sulfuricurvum. The application of an immobilized strain of A. niger as a mycoremediation technique was also tested. It successfully removed pollutants in the combined sewage and industrial wastewater present in river water: COD (96%), TSS (97%), NH4-N (65%), NO3-N (67%), and PO4-P (78%). This study demonstrated that hybrid constructed wetlands and mycoremediation can be used as sustainable wastewater treatment options in the local context and also in developing countries where most of the conventional wastewater treatment plants do not operate.

Computation of molecular description of supramolecular Fuchsine model useful in medical data.

Supramolecular chemistry is a fascinating field that explores the interactions between molecules to create higher-order structures. In the case of the supramolecular chain of Fuchsine acid, which is a type of dye molecule, several chemical applications are possible. Fuchsine acid helps to make better medicine carriers that deliver drugs where they're needed in the body, making treatments more effective and reducing side effects. It also helps create smart materials like sensors and self-fixing plastics, which are useful in electronics, keeping our environment clean, and making new materials. In sensing and detection, the supramolecular chain of Fuchsine acid utilizes as a sensor or detector for specific analyzes. In drug delivery, the supramolecular chains of Fuchsine acid incorporated into drug delivery systems. In recent years, a common method is linking a graph to a chemical structure and using topological descriptors to study it. This technique is becoming increasingly important over time. Topological descriptors gives very useful information while studying the topology of chemical graph. In this paper, we have computed the 3D structure of supramolecular graph of Fuchsine acid. We have computed an explicit expressions of ABC index, GA index, General Randi c ´ index, first and second Zagreb index, hyper Zagreb index, H-index and F-index of supramolecular structure of Fushine acid.

Pharmacological interventions to improve sleep in people with Alzheimer's disease: a meta-analysis of randomized controlled trials.

INTRODUCTION: This systematic review and meta-analysis evaluates the evidence from randomized controlled trials (RCTs) involving pharmacological interventions for improving sleep in people with Alzheimer's disease (AD). METHODS: A systematic literature search in eight databases from January 2000 to July 2023 focusing on RCTs that compared a pharmacological intervention with a placebo for enhancing sleep in people with AD. The authors registered the study protocol at Prospero, followed the PRISMA guidelines, and produced the pooled estimates using random-effect or IVhet models. RESULTS: Eight different interventions and 29 different sleep outcomes were examined in 14 RCTs included in this review. Eszopiclone positively affected sleep efficiency, as did orexin antagonists. However, there was no difference when melatonin was used. The interventions demonstrated low discontinuation rates and a few adverse drug reactions. CONCLUSION: Although melatonin was the most investigated intervention, the evidence for its efficacy is inconclusive. On the other hand, trazodone and orexin receptor antagonists showed promising results; however, more RCTs are needed for definite answers.

Serological and epidemiological investigation of Mycobacterium avium subspecies paratuberculosis in bovines in Pakistan.

OBJECTIVE: This study aimed to investigate the prevalence of paratuberculosis in cattle and buffaloes at twelve public dairy farms in Punjab, Pakistan. METHODS: A total of 2,181 more than two-year-old animals (1,242 cattle and 939 buffaloes) were tested by avian tuberculin, i.e., killed purified protein derivative of Mycobacterium avium paratuberculosis and indirect enzyme linked immunosorbent assay (ELISA). Blood and fecal samples were collected from tuberculin positive animals. These samples were further processed by indirect ELISA. The data were analyzed using frequency analysis and logistic analysis procedures. RESULTS: The prevalence of paratuberculosis at public dairy farms was 3.8%, as determined by tuberculin+ELISA test. It varied from 0.71% to 13.5% with a 100% herd prevalence. Multivariate logistic regression analysis revealed that species, milk production, total animals, total small ruminants, and total buffaloes were significantly associated with the occurrence of paratuberculosis. Odd ratio analysis revealed that with a one-kilogram increase in body weight, there will be a 0.006% increase in disease occurrence. With the increase in one animal in small ruminants and buffaloes, there will be 0.008% and 0.42% greater chances of developing paratuberculosis, respectively. Bivariate logistic regression analysis of cattle and buffaloes revealed that farm number, age, and total number of cattle were significantly associated with the occurrence of paratuberculosis. A one-month increase in lactation length increases the chance of tuberculosis by 0.004%; similarly, a one-liter increase in milk production increases the chance of disease by 10%. With each additional buffalo in the herd, there will be a 0.007% greater chance for the occurrence of paratuberculosis. CONCLUSION: This study concluded that tuberculin testing can be used in conjunction with ELISA to screen animals for paratuberculosis in countries with scarce resources, such as Pakistan. The efficacy of disease diagnosis can be improved by combining multiple tests.