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Innovation in the Information and Communication Technology (ICT) sector is critical for organizational growth and societal sustainability. However, the innovation capabilities of developing countries, particularly in the ICT sector, remain underexplored. This study is aimed at assessing the perceptions of senior leadership within Pakistan's ICT sector to identify the factors contributing to the limited scope of innovation. Through qualitative interviews with senior leaders from various ICT organizations, this research explores their views on the definition, types, and driving factors of innovation. Most interview participants reported that the major type of innovation practiced at the ICT sector of Pakistan is "product," the level is "partial replication," and the major driving source is "factors external to the organization." Based on these insights, we developed an innovation readiness framework (IRF) to help organizations enhance their innovation capacity. IRF is expected to provide actionable strategies for organizations to assess their current innovation practices, identify gaps, and adopt a structured approach to enhance both product development and operational processes. By addressing sector‐specific challenges such as limited resources and regulatory barriers, the IRF is aimed at empowering organizations to move from replication to more original, impactful innovations. Hence, implementing this framework is expected to improve the sector's competitiveness, drive sustainable growth, and contribute meaningfully to societal advancement by aligning with broader development goals. [ABSTRACT FROM AUTHOR]

The idea of a fault-tolerant metric dimension can be used in chemistry to examine a molecule's structural resistance to the failure of particular atoms or chemical bonds. This is especially important when creating molecules for specific uses, like drug candidates, as they may be exposed to biological systems or the environment, which could cause certain atoms or bonds to change or break. One such is the dialkyltin complex, which has two alkyl groups joined to a tin atom. The nitrogen atom of an N-salicylidene-L-valine molecule is coupled to the alkyl groups in the dialkyltin complex-1 of N-salicylidene-L-valine, making it a useful catalyst for numerous processes in organic synthesis. The purpose of this study is to identify the fault-tolerant metric dimension of the chain in N-salicylidene-L-valine dialkyltin complex-3. [ABSTRACT FROM AUTHOR]

Vehicular sensor networks (VSNs) are expected to revolutionize the transportation systems through automated decision‐making. These networks function on the communication between vehicles, traffic infrastructure, and people. One of the major challenges of VSNs is to deal with heterogeneous traffic due to the diverse nature of information and data generated by different types of sensors and devices within the vehicular environment. To ensure an effective operation of the network, it is crucial to offer a differentiated quality of service to each traffic class. Media access control (MAC) protocols offer an opportunity to provide differentiated channel access to the traffic of different priorities. This work is focused on offering comparison of recent MAC schemes in terms of their performance for heterogeneous traffic generated by VSNs. Two protocols FROG‐MAC and UrgMAC have been selected: FROG‐MAC is developed using a simple concept of packet fragmentation, where the packets of lower priority are fragmented to offer an early transmission opportunity to urgent traffic; on the other hand, urgMAC is designed using advanced cross‐layer techniques of two‐tiered service differentiation mechanism, adaptive data rate adjustment mechanism, urgency‐based contention window size adaptation, traffic type adaptive duty cycle, and multimedia message passing. It has been found that the FROG‐MAC outperforms urgMAC in terms of delay, energy consumption and packet delivery ratio. [ABSTRACT FROM AUTHOR]

Human fertility monitoring, whether for natural conception or artificial insemination and fertilization, presents a significant challenge for both physicians and patients. Detecting the presence of eggs in the fallopian tube and timely communication of this information to the healthcare team and the patient remains an elusive goal; in this paper, we propose a novel, hybrid communication architecture for this purpose. Our architecture combines an intrabody nano‐sensor network for detecting the presence of eggs in the female fallopian tube with a body area network (BAN) to receive information from the intrabody network and transmit it over‐the‐air to the required point (physician, nurse, or patient). To investigate the system's performance, we conducted preliminary simulations using a particle‐based stochastic simulator. The results revealed interesting relationships between information rates achievable, signal‐to‐noise ratio (SNR), distance, number of sensors, detection delay, viscosity, and temperature. Specifically, we found that increasing viscosity led to an increase in detection delay, while increasing temperature had the opposite effect. With data rates of up to 300 Mbps achievable at an SNR of 45, our proposed architecture ensures near‐zero latency, minimum energy consumption, and high throughput for efficient fertility monitoring. By integrating advanced communication technologies and sensor networks, our approach paves the way for improved fertility monitoring, enabling timely interventions and personalized care. These findings contribute to the development of a cooperative intra‐ to extra‐body network that enhances accuracy and enables real‐time monitoring for effective fertility management. [ABSTRACT FROM AUTHOR]

The G protein-coupled receptor 40 (GPR40) is known to exert a significant influence on neurogenesis and neurodevelopment within the central nervous system of both humans and rodents. Research findings indicate that the activation of GPR40 by an agonist has been observed to promote the proliferation and viability of hypothalamus cells in the human body. The objective of the present study is to discover new agonist compounds for the GPR40 protein through the utilization of machine learning and pharmacophore-based screening techniques, in conjunction with other computational methodologies such as docking, molecular dynamics simulations, free energy calculations, and investigations of the free energy landscape. In the course of our investigation, we successfully identified five unreported agonist compounds that exhibit robust docking score, displayed stability in ligand RMSD and consistent hydrogen bonding with the receptor in the MD trajectories. Free energy calculations were observed to be higher than control molecule. The measured binding affinities of compounds namely 1, 3, 4, 6 and 10 were -13.9, -13.5, -13.4, -12.9, and -12.1 Kcal/mol, respectively. The identified molecular agonist that has been found can be assessed in terms of its therapeutic efficacy in the treatment of neurological diseases. [ABSTRACT FROM AUTHOR]

Objective: This laboratory study evaluated the effect of Salvadora persica (S. persica) root extracts and Chlorhexidine Digluconate (CHX) on the antibacterial and surface hardness properties of glass ionomer cement (GIC). Methods: The in vitro experimental study was conducted at the Baqai Institute of Pharmaceutical Sciences of Baqai Medical University, Karachi, Pakistan, from October 2022 to March 2023. There were a total four experimental groups. The first group consisted of ethanol extract (GIC-SPEE) and second group consisted of hexane extract as (GIC-SPHE) both prepared from Salvadora persica root respectively, and mixed with liquid of GIC separately. The third group comprised chlorhexidine (GIC-CHX) that was also mixing into liquid portion of GIC and the last group was Control i.e. (cGIC). The GIC samples were prepared by using stainless steel metallic moulds with dimension (5mm x 2mm), following the manufacturer guidelines. Antibacterial activity against Streptococcus mutans was done by disc diffusion test (DDT), and surface hardness test was done by Vickers hardness tester. Statistical analysis was performed using One-Way ANOVA and Tukey’s post hoc tests (p<0.05). Results: The antibacterial activity against S. mutans reported that the maximum zone of inhibition was obtained at 3 wt% by the GIC-SPEE, when compared with other experimental groups. For surface hardness, the highest mean and standard deviation and significant findings was reported by the group GIC-SPEE. Conclusions: Considering the outcome of this in vitro study, it can be concluded that the addition of 3 wt% GIC-SPEE increased the surface hardness and antibacterial activity against Streptococcus mutans. [ABSTRACT FROM AUTHOR]

The present study focused on the physiological and biochemical aspects of Tricleocarpa fragilis, red seaweed belonging to the phylum Rhodophyta, along the South Andaman coast, with particular attention given to its symbiotic relationships with associated flora and fauna. The physicochemical parameters of the seawater at the sampling station, such as its temperature, pH, and salinity, were meticulously analyzed to determine the optimal harvesting period for T. fragilis. Seaweeds attach to rocks, dead corals, and shells in shallow areas exposed to moderate wave action because of its habitat preferences. Temporal variations in biomass production were estimated, revealing the highest peak in March, which was correlated with optimal seawater conditions, including a temperature of 34 ± 1.1 °C, a pH of 8 ± 0.1, and a salinity of 32 ± 0.8 psu. GC‒MS analysis revealed n-hexadecanoic acid as the dominant compound among the 36 peaks, with major bioactive compounds identified as fatty acids, diterpenes, phenolic compounds, and hydrocarbons. This research not only enhances our understanding of ecological dynamics but also provides valuable insights into the intricate biochemical processes of T. fragilis. The established antimicrobial potential and characterization of bioactive compounds from T. fragilis lay a foundation for possible applications in the pharmaceutical industry and other industries. [ABSTRACT FROM AUTHOR]

This meta-analysis examined the effectiveness of exercise interventions in reducing fatigue and depression among women undergoing chemotherapy for breast cancer. The study followed PRISMA guidelines and analysed seven randomized controlled trials between 2016 and 2022. The results showed that exercise can substantially reduce fatigue levels (MD: -0.40, CI: -0.66, -0.14, P: 0.003), a common side effect of chemotherapy. Although depression did not significantly change (MD: -0.39, CI: -0.98, 0.20, P: 0.19), this study highlights the positive impact of exercise on mental health outcomes. The control group also experienced decreased quality of life (MD: 0.18, CI: 0.01–0.35, P: 0.03), emphasizing the importance of incorporating exercise interventions to improve overall well-being during breast cancer treatment. In addition to primary outcomes, the study revealed that exercise positively affected secondary aspects such as cognitive fatigue, social function, physical function, constipation, and dyspnoea. [ABSTRACT FROM AUTHOR]

Introduction: Tracheal stenosis is a known complication following intubation. However, owing to its delayed presentation and symptoms of progressive wheezing and respiratory difficulty, it is often misdiagnosed as asthma. Case presentation: We present the case of a 10-year-old Asian boy who presented with cough, wheezing, and dyspnea. He was misdiagnosed with severe, uncontrolled asthma and respiratory failure, remaining unresponsive to initial asthma management. During his current admission, he had difficult intubation, necessitating an emergency tracheostomy. Further subsequent examination revealed grade III tracheal stenosis. Conclusion: This case highlights the importance of considering tracheal stenosis as a differential diagnosis in children presenting with dyspnea, cough, and wheezing, particularly if there is a history of prior intubation. The airway should be secured immediately in a controlled environment by an otolaryngologist or anesthetist. [ABSTRACT FROM AUTHOR]

This work integrates the Boussinesq approximation and magnetohydrodynamic effects to investigate the dynamics of incompressible triple diffusive fluid flow along a linearly stretched surface. Novel insights are revealed by contrasting instances with opposing and helpful flows. The partial differential equations are symmetrically reduced using Lie-group transformations, which make the Runge–Kutta shooting technique easier to solve. The effects of concentration buoyancy ratio, magnetic parameter, concentration parameter, and Lewis number on temperature, velocity, and concentration profiles are explained through graphical displays. Our results show that in both flow situations, the velocity distribution is decelerated by the magnetic parameter, and the salt concentration distributions are similarly affected by buoyancy ratio factors. Additionally, a higher Lewis number is associated with lower mass and heat transfer rates in the opposing and assisting fluid. [ABSTRACT FROM AUTHOR]

Accurate energy demand forecasting is critical for efficient energy management and planning. Recent advancements in computing power and the availability of large datasets have fueled the development of machine learning models. However, selecting the most appropriate features to enhance prediction accuracy and robustness remains a key challenge. This study proposes an ensemble approach that integrates a genetic algorithm with multiple forecasting models to optimize feature selection. The genetic algorithm identifies the optimal subset of features from a dataset that includes historical energy consumption, weather variables, and temporal characteristics. These selected features are then used to train three base learners: Long Short-Term Memory (LSTM), Bi-directional Long Short-Term Memory (BiLSTM), and Gated Recurrent Unit (GRU). The predictions from these models are combined using a stacking ensemble technique to generate the final forecast. To enhance model evaluation, we divided the dataset into weekday and weekend subsets, allowing for a more detailed analysis of energy consumption patterns. To ensure the reliability of our findings, we conducted ten simulations and applied the Wilcoxon Signed Rank Test to the results. The proposed model demonstrated exceptional precision, achieving a Root Mean Square Error (RMSE) of 130.6, a Mean Absolute Percentage Error (MAPE) of 0.38%, and a Mean Absolute Error (MAE) of 99.41 for weekday data. The model also maintained high accuracy for weekend predictions, with an RMSE of 137.41, a MAPE of 0.42%, and an MAE of 105.67. This research provides valuable insights for energy analysts and contributes to developing more sophisticated demand forecasting methods. [ABSTRACT FROM AUTHOR]

The 'Third Pole', home to numerous glaciers, serves as vital water reserves for a significant portion of the Asian population and has garnered global attention within the context of climate change due to their highly vulnerable nature. While a general decline in global glacial extent has been observed in recent decades, the pronounced regional imbalances across the Third Pole present a perplexing anomaly. To assess the impact of glacier mass changes in the Gangotri basin, we conducted a comprehensive analysis using remote sensing data to estimate spatially resolved mass changes from 2000 to 2023. Our glacier mass balance estimates were based on empirical models and the digital elevation model difference method. We also examine the relationship between glacier retreat and the variability of albedo and aerosols in the glacier ice. Analysis of the geodetic mass balance indicates that the glacier surface has decreased by 8.12 m, a loss of 0.49 m of water equivalent per annum (m.w.e. a−1) between 2000 and 2014. The estimates and results revealed from the accumulation area ratio (AAR) mass balance, and ice velocity measurements indicate a negative mass balance of − 0.28 m.w.e. a−1 for Gangotri between 2000 and 2023. Our analyses highlight both climatological and anthropogenic factors responsible for the accelerated rate of mass loss. Regional mass loss during the ablation season is primarily influenced by land surface temperature, yet the role of other factors, such as changes in surface albedo and light-absorbing impurities (LAIs), remains uncertain. Our analysis investigated temporal variations in mass balance values, while also considering changes in surface albedo and LAIs like black carbon (BC), organic carbon (OC) and dust concentrations. This analysis reveals that LAIs have an inverse relationship with albedo, where an increase in LAI concentration results in reduced albedo over the glacier. Consequently, as albedo decreases, the surface mass balance of the glacier also declines, which is further validated by the findings of this study. While this study highlights the detrimental effects of light-absorbing pollutants on the health of glacier, further investigation is necessary to comprehensively establish their role in reducing the albedo of the glacier surface and influencing associated mass loss. [ABSTRACT FROM AUTHOR]

In recent years, machine learning has gained substantial attention for its ability to predict complex chemical and biological properties, including those of pharmaceutical compounds. This study proposes a machine learning-based quantitative structure-property relationship (QSPR) model for predicting the physicochemical properties of anti-arrhythmia drugs using topological descriptors. Anti-arrhythmic drug development is challenging due to the complex relationship between chemical structure and drug efficacy. To address this, we applied machine learning techniques, specifically linear regression models combined with K-fold cross-validation, to predict critical properties such as Density, Boiling Point, Flash Point, Bioconcentration Factor (BCF), Organic Carbon Partition Coefficient (KOC), Polarizability, and Molar Volume. The models were developed using data from ten anti-arrhythmic drugs ( to ). We evaluated the models based on performance metrics such as R and and obtained significant results. Most accurate predictions are obtained for polarizability from models with H(G) and . [ABSTRACT FROM AUTHOR]

The hybrid nanofluid (HNF) flow consists of polymer/CNT matrix nanocomposite material (MNC) across coaxial cylinders is numerically described in this study. The HNF flow is inspected under the consequences of thermal radiation, exponential heat source/sink and viscous dissipation. The HNF is prepared by adding polymer/CNT MNC in water. MNCs are highly productive elements with unique designs and properties. The MNCs are widely used in biomedicine and electrical applications due to their exceptional thermophysical properties. Based on their exceptionally high electrical conductivity, CNT/polymer nanoparticles (NPs) are also utilized as shielding for electrostatic discharge and electromagnetic interference (EMI). The HNF flow is modeled with the help of energy, continuity, and momentum equations. MATLAB package bvp4c is used to numerically handle the modeled equations. It has been perceived that the intensifying numbers of polymer/CNT MNC will lessen the fluid velocity and temperature profile in cases of both nanofluid and HNF. [ABSTRACT FROM AUTHOR]

Researchers are interested in the non-Newtonian fluid flow with mixed convection because of its extensive use in industry and manufacturing. Additionally, thermal radiation in convective heat transfer is critical for thermal transmission regulation. As a result, the authors provide an in-depth study of how mixed convective effects on concentration and temperature impact mass, heat, and non-Newtonian Casson fluid flow. A transverse magnetic field and vertical permeable stretched sheet affect the fluid. Nonlinear thermal radiation, Brownian motion, thermophoresis, velocity slip, and temperature slip are all examined. The governing nonlinear partial differential equations (PDEs) can be changed into especially nonlinear coupled ordinary differential equations (ODEs) with the right similarity transformation. We use the RK-45 technique in Mathematica to solve the system to accommodate different physical attributes. The data are analyzed graphically. This study shows that increasing the free convection parameters Gr and Gc improves the velocity profile. However, the Casson parameter, magnetic field, velocity slip, and mass suction parameter lower it. Increasing Gc, Nb, Nt, Rd , and θ w parameters lead to a higher temperature profile, whereas β , Gr , S , and ε parameters have the opposite. Increased concentration is shown with β and Nt parameters, whereas Gc, Gr, CR, Le, and Sc have the opposite impact. Skin friction increases against δ and Gr and reduces for S and M Heat transfer increases for β ,  Gc, Gr, and S whereas reduces for ε  Nb, Nt,Rd, and θ w. Mass transfer increases for CR, Gr, Gc, Le, Nb, and Sc and reduces for Nt and β. [ABSTRACT FROM AUTHOR]

The body's immune response plays a critical role in defending against external or foreign antigens while also preserving tolerance to self‐antigens. This equilibrium, referred to as immune homeostasis, is paramount for overall health. The regulatory mechanisms governing the maintenance of this delicate immune balance are notably complex. It is currently accepted that immune tolerance is a dynamic outcome regulated by multiple factors, including central and peripheral mechanisms. Its induction or elimination plays a significant role in autoimmune diseases, organ transplantation, and cancer therapy, markedly impacting various major diseases in modern clinical practice. Overall, our current understanding of immune tolerance is still very limited. In this review article, we summarized the main mechanisms that have been known to mediate immune tolerance so far, including endogenous immune tolerance, adaptive immune tolerance, other immune tolerance mechanisms, and the homeostasis of immune tolerance, identified the key factors that regulate immune tolerance, and provided new clues for immune system recovery in many autoimmune diseases, organ transplantation, and tumor therapy. [ABSTRACT FROM AUTHOR]

In this article, we present the concept of extended Seidel energy by employing a generalized extended matrix to study various molecular properties, including the Kovats retention index, boiling point, enthalpy of formation, entropy, acentric factor, and octanol-water partition coefficient. Our research broadens the scope of energy matrices in graph theory, with a particular emphasis on Sombor energy, reduced Sombor energy, average Sombor energy, Banhatti Sombor energy and reduced Banhatti Sombor energy. We examined the correlation of these graph-based energy descriptors with the thermodynamic properties of Benzenoid hydrocarbons (BHC), uncovering strong relationships between these indices and different molecular attributes. These results provide valuable insights into the predictive potential of graph energies in modeling the thermodynamic behavior of polycyclic aromatic hydrocarbons. [ABSTRACT FROM AUTHOR]

Visual attribution in medical imaging seeks to make evident the diagnostically-relevant components of a medical image, in contrast to the more common detection of diseased tissue deployed in standard machine vision pipelines (which are less straightforwardly interpretable/explainable to clinicians). We here present a novel generative visual attribution technique, one that leverages latent diffusion models in combination with domain-specific large language models, in order to generate normal counterparts of abnormal images. The discrepancy between the two hence gives rise to a mapping indicating the diagnostically-relevant image components. To achieve this, we deploy image priors in conjunction with appropriate conditioning mechanisms in order to control the image generative process, including natural language text prompts acquired from medical science and applied radiology. We perform experiments and quantitatively evaluate our results on the COVID-19 Radiography Database containing labelled chest X-rays with differing pathologies via the Frechet Inception Distance (FID), Structural Similarity (SSIM) and Multi Scale Structural Similarity Metric (MS-SSIM) metrics obtained between real and generated images. The resulting system also exhibits a range of latent capabilities including zero-shot localized disease induction, which are evaluated with real examples from the cheXpert dataset. [ABSTRACT FROM AUTHOR]

In the past decade, the use of immune checkpoint therapy (ICT) has increased across many malignancies, including metastatic renal cell carcinoma as an option for frontline and subsequent lines of therapy. Despite the many therapeutic benefits of ICT, its use is complicated by the potential risk of immune‐related adverse events (irAEs). One rare but potentially life‐threatening irAE is hemophagocytic lymphohistiocytosis (HLH). HLH is a systemic inflammatory disorder resulting in multiorgan failure. The diagnosis of HLH is a challenge due to nonspecific symptoms and overlap with other systemic conditions, which can lead to delays in receiving appropriate treatment and potentially poor patient outcomes. This case illustrates the management of HLH caused by nivolumab plus ipilimumab combination therapy through the use of corticosteroids and tocilizumab in a patient with metastatic clear cell renal cell carcinoma. [ABSTRACT FROM AUTHOR]

Background & Objective: Determination of axillary lymph-node status plays a pivotal role in decision making for breast cancer treatment. Biopsy is the current standard of care but hold risks of complications as well. We aimed to find out the correlation of sonographic features of lymph node and histo-pathological findings, to predict axillary lymph-node metastasis in breast cancer patients. Method: This retrospective observational study included 176 breast cancer patients at a private tertiary care hospital from January 2019 to December 2023. The study calculated sensitivity, specificity and accuracy of ultrasound (US) in identifying ALN metastasis. Also, binary logistic regression analysis was used to demonstrate the association between suspicious findings on axillary US with pathology report. Patients who never had undergone axillary surgery or with insufficient data, were excluded from our study. Results: In our study Axillary US was found to be 84.2% sensitive, 48.1% specific, and 67.6% accurate in identifying nodal metastases. In this context, ALN metastases was strongly and independently correlated with cortical thickness > 3 mm and the absence of a fatty hilum (P <.05). Conclusion: Ultrasound was found to be highly sensitive but not specific in predicting metastatic lymph nodes in patients with breast cancer. [ABSTRACT FROM AUTHOR]

Graph operations play a significant role in constructing new and valuable graphs and capturing intermolecular forces between atoms and bonds of a molecule. In mathematical chemistry and chemical graph theory, a topological invariant is a numeric value extracted from the molecular graph of a chemical compound using a mathematical formula involving vertex degrees, distance, spectrum, and their combination. An intriguing problem in chemical graph theory is figuring out the lower and the upper bound on pertinent topological indices among a particular family of graphs. The first Gourava index for a graph Γ is denoted and defined as GO 1 (Γ) = ∑ x y ∈ E (Γ) [ Γ (x) + Γ (y) + Γ (x) Γ (y) ]. Recently, Kulli studied and derived formulas of the first Gourava index for four graph operations. We proved with the help of counter-examples that the results provided by Kulli produce inaccurate values when compared with exact values. In this paper, we determined the exact formulas and bounds of the first Gourava index for Ψ -sum graphs. Besides, we presented diverse examples to support our results. [ABSTRACT FROM AUTHOR]

Graphene nanoribbons (GNRs) with precise structural properties have become attractive options for next-generation nanoelectronics because of their adaptable electrical topologies and their uses in quantum electronics, photovoltaics, and transistors. These interesting properties have recently collected significant attention in the field. Cove-edged graphene nanoribbons (CGNRs) are especially interesting because of their distinctive electromagnetic properties. This type of nanoribbon features asymmetric edges composed of sequential hexagons, exhibiting remarkable mechanical and electrical properties. In this paper, we find the new Zagreb-type topological indices and the corresponding entropy measures of graphene nanoribbons with curved edges. We establish correlation analysis between the Zagreb-type indices and entropy measures through the curve-fitting model and the Pearson correlation coefficient. The results of this study may reduce the amount of work needed to investigate the physicochemical characteristics of graphene nanoribbons with curved edges. [ABSTRACT FROM AUTHOR]

Underwater wireless sensor networks (UWSNs) have gained global attention in recent years due to their numerous applications and the vast amount of the world’s surface area covered by water. However, the security of UWSNs is a critical concern in order to protect the advantages of this technology and its various applications. This paper presents an investigative study and simulations using ns-3 and aquasim-ng to identify the factors that challenge the security of UWSNs and their applications. The study explores the UWSN architecture, vulnerabilities, and potential attacks, as well as various scenarios including normal, limited, and special conditions. The results of the study and simulations are analyzed to determine the factors that contribute to the security challenges faced by UWSNs. Finally, the paper concludes with a discussion of the factors or components that can assist in overcoming these security challenges and the need to address and consider them in future work. This research aims to provide a deeper understanding of the security issues facing UWSNs and to identify potential solutions for addressing these challenges. Finally, the research concludes with recommendations for addressing and overcoming the challenges to UWSN security in the future. [ABSTRACT FROM AUTHOR]

Purpose: Methylglyoxal (MG) is the most potent precursor during the formation of the advanced glycation end products (AGEs). MG-dependent glycative stress contributes to pathogenesis of diabetes, age-related disorders, and cancer. There is a great need to study the reduction process of glycative stress for effective management of metabolic disorders. From natural compounds to synthetic drugs, each element contributes to the reduction of glycative stress. Previously, it was established that the lowering of uric acid, low-density lipoprotein cholesterol, and urine albumin excretion rate, as well as reducing total oxidative stress, were all achieved more effectively with a levothyroxine regimen. Still, there is no such study found that supports the MG-dependent glycative stress reduction with thyroid hormone compound. Our study aims to investigate the effects of T3 and T4 on MG-dependent glycative stress. Methods: The antiglycation effect was assayed through NBT assay, DNPH assay, ELISA, and fluorescence spectrophotometer. The intracellular reduction in reactive oxygen species (ROS) has been estimated through confocal microscopy. Results: The results revealed an effective reduction in the formation of AGEs adducts and intracellular ROS formation. Conclusion: The investigation concludes AGEs formation was suppressed using these compounds, although in vivo and rigorous clinical trials are required in order to verify these findings. [ABSTRACT FROM AUTHOR]

This study examines the static and time-varying cointegration between Mexico, Indonesia, Nigeria, and Turkey (MINT), and developed stock markets in USA and Japan by applying conventional cointegration methodology and rolling multivariate trace statistics over the period of 10 years. Overall, the findings of bivariate cointegration test suggested that MINT stock markets do not have significant long running relationship with Japan stock market. Hence, the scope of diversification exists accordingly. However, rolling trace statistics revealed the time-varying nature of integration between the markets considered in the study. The varying degree of cointegration is more likely to be caused by major economic, financial, and political events. Hence, the presence of equilibrium relationship among the markets in the sub-periods may limit the potential portfolio diversification benefits. Thus, lack of consistency in the findings of the static and dynamic cointegration analysis highlights the limitation of the static assessment. On the short-term linkage, the examination of dynamic conditional correlation (DCC) for each MINT-developed market pair suggested that Indonesia exhibits least correlation with USA and Japan and hence, offering highest portfolio avenues. [ABSTRACT FROM AUTHOR]

Energy auditing is crucial for both emerging and developed nations, focusing on aspects such as energy efficiency, quality and intensity. This becomes especially significant in the industrial sector, where major operating costs involve materials, machinery, personnel and energy. A key objective is identifying energy consumption in the Continuous Miner Machine, boasting a 1050 KW connected load, to explore opportunities for energy savings and improved quality. The execution of an energy audit promises increased efficiency, enhanced power quality, reduced costs and prevention of energy wastage. This research study has meticulously examined the electrical equipment supplying power to Continuous Miner Machines, proposing modifications to boost production through energy-efficient methods. Comprehensive assessments of primary substations, including Incomers, Transformers, Feeders, etc., involve tests for voltage, current, power factor, harmonics and AC waveform. Thermal imaging is employed to analyze the operational temperature of the electrical equipment. The graphical representations of test outcomes have highlighted a significant recommendation: installing an Automatic Power Factor Controller on the inductive load side of transformers could lead to a notable 3.85% reduction in energy costs. [ABSTRACT FROM AUTHOR]

The article offers information on clinical practice guidelines for addressing cognitive impairment in treatable dementias. Topics discussed include the causes and clinical course of potentially reversible dementias; diagnostic investigations for identifying treatable conditions; and treatment strategies for specific causes like vitamin B12 deficiency and obstructive sleep apnea.

Cholecystectomy is one of the most performed surgical procedures. The safety of this surgery notwithstanding, the sheer volume of operations results in a notable incidence of post-cholecystectomy complications. Early and accurate diagnosis of such complications is essential for timely and effective management. Imaging techniques are critical for this purpose, aiding in distinguishing between expected postsurgical changes and true complications. This review highlights current knowledge on the indications for cholecystectomy, pertinent surgical anatomy and surgical technique, and the recognition of anatomical variants that may complicate surgery. The article also outlines the roles of various imaging modalities in identifying complications, the spectrum of possible postsurgical anatomical changes, and the implications of such findings. Furthermore, we explore the array of complications that can arise post-cholecystectomy, such as biliary system injuries, gallstone-related issues, vascular complications, and the formation of postsurgical collections. Radiologists should be adept at identifying normal and abnormal postoperative findings to guide patient management effectively. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic changed public awareness of the importance of high-quality race and ethnicity data for identifying and redressing widely documented racial and ethnic health inequity. This article emphasizes the importance of high-quality race and ethnicity data in health equity research, as highlighted by the COVID-19 pandemic. The article defines what constitutes high-quality race and ethnicity data, discusses challenges in using these data, and provides 2 cases that illustrate the role of these data in identifying and redressing health inequity. Finally, this article advocates for the use of accurate, standardized, and granular data and highlights the need for community engagement and trust building to improve data quality and research outcomes. [ABSTRACT FROM AUTHOR]

During drilling operations, abnormal pore pressures pose significant risks including blowouts, wellbore collapse, and pipe sticking, emphasizing the need for accurate pore pressure estimation in geoscience exploration and drilling projects. However, current prediction methods, whether mathematical or statistical, have limitations. Manual mathematical approaches are prone to human error, while statistical methods excel in inferring variable relationships but fall short in predictive accuracy. In response, artificial intelligence (AI), particularly deep learning (DL), offers promising solutions. This study proposes an innovative framework leveraging AI and DL for efficient pore pressure prediction solely from 3D seismic data. The framework includes end-to-end strategies, from preprocessing seismic data to predicting pressure zones. Data sourced from the Roosevelt, Utah field comprising 375 traces is employed, labelled using Eaton's pore pressure calculation and Gardner's bulk density approximation. A DL model based on a simple deep neural network is integrated into the framework, incorporating techniques like training set resampling and hyperparameter optimization to enhance performance. Comparative analysis with state-of-the-art machine learning models validates the superiority of the proposed DL model. Results demonstrate its state-of-the-art performance, achieving 95.5% accuracy, 96.6% precision, 94.2% recall, and an F1-score of 95.3% in predicting pressure zones. This research showcases the potential of AI and DL in revolutionizing pore pressure prediction, offering advanced and cost-effective solutions to mitigate drilling-related risks in the oil and gas industry. [ABSTRACT FROM AUTHOR]

In this study, we have synthesized two zinc(II)‐based metal‐organic frameworks (MOFs) designated as [Zn(4‐nvp)(bdc)] ⋅ (MeOH) (1) and [Zn2(4‐nvp)2(bpdc)2] ⋅ (DMF) (2) [4‐nvp=4‐(1‐naphthylvinyl) pyridine, H2bdc=1,4‐benzendicarboxylic acid and H2bpdc=4,4′‐biphenyldicarboxylic acid]. Single‐crystal X‐ray diffraction (SCXRD) of both compounds unveiled an interesting paddle‐wheel [Zn2(O2C−C)4] secondary building block (SBB) composed of dinuclear Zn (II) centers and four dicarboxylate groups with a (4,4) square grid topology. These SBBs are interconnected giving rise to an infinite 2D layer architecture. Notably, the grid structure is composed of MeOH molecules in compound 1 and DMF molecules in compound 2, both of them arranged in a free lattice. In both compounds, 3D supramolecular architecture is ultimately formed through the stacking of 2D layers. Since the length of the bpdc ligand is higher than that of the bdc ligand, the solvent‐accessible void volume is comparatively higher for compound 2. To corroborate all non‐bonded interactions, Hirshfeld analysis was carried out for synthesized compounds. DNA binding application was extensively investigated through docking study. Results indicated that the synthesized compounds have strong affinities towards DNA via DNA groove binding. Henceforth, the synthesized compounds 1 and 2 would open the door for their potential applications as particular protein binders and bioactive substances. [ABSTRACT FROM AUTHOR]

Microwave (MW)‐assisted catalytic pyrolysis represents a promising method for transforming petroleum‐based plastic waste into valuable chemicals, offering a pathway towards more sustainable circular economy. In this study, catalytic pyrolysis of low‐density polyethylene (LDPE) was conducted under MW irradiation. The influence of various catalyst types (HZSM‐5, Ga/ZSM‐5, Ga/Ni/ZSM‐5, Ga/Co/ZSM‐5, and Ga/Cu/ZSM‐5) on product yield and distribution was examined. The results revealed that the Ga/ZSM‐5 catalyst yielded the maximum liquid oil, approximately 41%. Ga/Ni/ZSM‐5 performed excellently in the production of long‐chain olefins, constituting about 27% of the liquid fraction. However, Ga/Co/ZSM‐5 led to the production of heavy pyrolysis oil containing nearly 25% long‐chain paraffins, rendering it unsuitable for producing high‐value chemicals. Conversely, the Ga/Cu/ZSM‐5 catalyst yielded an aromatic‐rich pyrolysis oil, with benzene derivatives constituting approximately 90% of the liquid oil fraction, thus proving to be a suitable catalyst for the intended application. The liquid product distribution was compared with a petroleum assay by SimDist, and this suggested that utilizing the HZSM‐5 catalyst could yield an 86.4% naphtha fraction. The study also revealed that the Ga/Cu/ZSM‐5 catalyst generated the largest amounts of hydrogen and syngas, as determined by a MicroGC analysis of the gas products. This catalyst also exhibited the maximum coke deposition (1.35%) postreaction, which was attributed to its high aromatic hydrocarbon content in the pyrolysis oil and maximal hydrogen release. A comparison of fresh and spent catalyst properties was conducted to gain insights into catalyst activity and to correlate the effects of metal doping on product distribution. These findings underscore the potential of MW‐assisted catalytic pyrolysis, particularly with the Ga/Cu/ZSM‐5 catalyst, for the efficient conversion of plastic waste into valuable chemicals, thereby contributing to sustainable resource utilization and environmental conservation. [ABSTRACT FROM AUTHOR]

Rapid visual screening (RVS) is a practically viable tool implemented all over the world to rank structures based on their seismic vulnerability. Seismic assessment of hospital buildings is essential in seismic-prone regions to ensure their structural integrity and readiness to provide emergency care and medical treatment after significant seismic events. Therefore, in the current study, an approach is developed that is similar to the proven methodologies reported in the literature, for the seismic evaluation of structural and non-structural elements of hospital buildings in a metropolitan city like Karachi. Karachi is located in the southwest of Pakistan and is vulnerable to earthquake and tsunami hazards. To this end, an android-based RVS system has been developed for large-scale seismic risk mapping of hospital buildings. The proposed system acquires the physical status of the building features in real time and identifies the building performance obtained from the calculated seismic risk index. Moreover, three case study hospital buildings were employed and analysed through the proposed RVS system. Results illustrate the performance of case study hospital buildings at organisational, structural and non-structural levels based on seismic risk indices. [ABSTRACT FROM AUTHOR]

Replacing conventional solvents with deep eutectic solvents (DES) has shown promising effects on the extraction yield of (poly)phenols. DES can be combined with ultrasound-assisted extraction (UAE) to further increase the extraction efficiency of (poly)phenols from natural resources compared to conventional methods. This review discusses the factors associated with DES (composition, solvent-to-sample ratio, extraction duration, and temperature) and UAE (ultrasound frequency, power, intensity, and duty cycle) methods that influence the extraction of (poly)phenols and informs future improvements required in the optimization of the extraction process. For the optimum (poly)phenol extraction from natural resources, the following parameters shall be considered: ultrasound frequency should be in the range of 20–50 kHz, ultrasound intensity in the range of 60–120 W/cm2, ultrasound duty cycle in the range of 40–80%, ultrasound duration for 10–30 minutes, and ultrasound temperature for 25–50 °C. Among the reported DES systems, choline chloride with glycerol or lactic acid, with a solvent-to-sample mass ratio of 10–30:1 shown to be effective. The solvent composition and solvent-to-sample mass ratio should be selected according to the target compound and the source material. However, the high viscosity of DES is among the major limitations. Optimizing these factors can help to increase the yield of extracted (poly)phenols and their applications. [ABSTRACT FROM AUTHOR]

Autism spectrum condition (ASC) is characterized by atypical attention to eyes and faces, but the onset and impact of these atypicalities remain unclear. This prospective longitudinal study examined face perception in infants who develop ASC (N = 22, female = 5, 100% White) compared with typically developing infants (N = 131, female = 65, 55.6% White), tracking social‐cognitive and ASC development through age seven. Reduced interest in direct gaze and eyes during infancy correlated with atypical development of adaptive behavior at age four and theory of mind at age seven. Principal component analyses revealed less integrated processing of facial features and eye‐gaze information in ASC infants, potentially impacting their childhood social functioning. These findings highlight the intertwined nature of social‐cognitive development and ASC. [ABSTRACT FROM AUTHOR]

Agriculture is one of the fundamental components of human civilization, contributing not only to food production but also to economic growth. Early identification of diseases in plants presents a significant challenge, as timely identification is crucial to increasing agricultural production. Incorporating the latest technology, such as artificial intelligence (AI) techniques, in fields can reduce major losses for farmers and also improve productivity. In this paper, we have proposed a Deep Vision transformer and an Explainable AI (XAI)-based technique to address various diseases in plants. Here, we have used the dataset “Plant Village" as a case study that focuses on two majorly grown crops like: potatoes and tomatoes. In addition, we have analysed nine diseases that affect these crops around the world, with tomatoes showing six different conditions and potatoes affected by three bacterial diseases. The proposed DVTXAI framework incorporates the Deep vision transforms to identify plant diseases at an early stage. In the experiments, the proposed model achieves an accuracy of 93.56% for tomatoes and 99.95% for potatoes. Additionally, to make model ‘Trustworthy’, we make model Explainable, which reveals the transparency mechanism that helps the farmer to make the right decision at an early stage of the crop. [ABSTRACT FROM AUTHOR]

The need for energy resources is continuously increasing worldwide and raises the need for environmentally benign, low-cost processes for energy generation. Membraneless microbial fuel cell (ML-MFC) has emerged as a promising solution for electricity production. MFCs are bioelectrical devices that harness the metabolism of electrogenic bacteria (EB) for generation of electrical energy. In the present investigation, Bacillus subtilis (BS) and Pseudomonas aeruginosa (PA) were utilized to catalyze transformation of carbon sources in chicken manure as source of renewable energy. The study was carried out by setting the experimental conditions with the ambient temperature (34°C ± 1°C) pH (12), electrode distance (3 cm), initial moisture amount (57% vol/wt) set as constant. Results highlighted BS and PA performance in an ML-MFC system for 7 to 14 days incubation periods. The specific growth recorded 0.0262 g(Lh−1) BS strain and 0.0141 g(Lh−1) PA strain for 7 days and the specific growth 0.0057 g(Lh−1) BS strain and 0.00163 g(Lh−1) PA strain was recorded after 14 days incubation. ML-MFCs were conducted for 14 days incubation period and significant effect of growth of BS and PA were reflected on voltage and power production. Maximum voltage (BS, 0.06 V; PA, 0.043 V) and power density (BS, 391 mW/m2; PA, 591 mW/m2) were recorded after 14 days of incubation. A series of ML-MFC contributed a promising performance for BS, which was 0.05 V and 9.05 mW/m2, compared with PA, which was 0.022 V and 9.05 mW/m2. Moreover, reduction in macronutrients and micronutrients was analyzed through inductively coupled plasma-optical emission spectroscopy (ICP-OES), chemical oxygen demand (COD) removal, and Fourier-transform infrared spectroscopy (FTIR) analysis. Thus, analysis on scanning electron microscope (SEM) and FTIR bands showed that these two strains are compatible for biofilm formation and degradation of the chicken manure into simple bands. The study concluded that BS showed better performance in terms of EB growth (21.7%; 14 days), COD removal (2.1%; 14 days), and power density generation (54.7%; 14 days) as compared with the PA strain. [ABSTRACT FROM AUTHOR]

A neurodegenerative illness is a disorder in which the brain and/or spinal cord’s neurons, or nerve cells, gradually deteriorate and disappear. These illnesses often get worse with time and can seriously affect movement, cognition, and other neurological functions. Psoriasis is a long-term autoimmune skin condition marked by fast skin cell growth that results in red, elevated areas coated in silvery-white scales. It can affect several body parts, such as the elbows, knees, scalp, and lower back, and it is not communicable. The build-up of amyloid beta [Aβ] protein is linked to elevated levels of reactive oxygen species (ROS) (Kim et al. 2020). These ROS can trigger multiple pathways, including MAPK, NFkB, JAK/STAT, and interleukin 1 beta (IL-1β), ultimately playing a role in the development of neurodegenerative illnesses like Alzheimer’s disease (AD) and psoriasis. People who have psoriasis are more likely to acquire AD, as psoriasis is a chronic inflammatory skin condition that is genetically connected. Because of the antioxidants and anti-inflammatory properties of citrus fruits neurodegenerative and psoriasis disease may be prevented. The neuroprotective action of bioactives in citrus fruits involves the inhibition of inflammation through the control of p38 mitogen-activated protein kinase (MAPK) and the activation of nuclear factor erythroid 2-related factor 2 (Nrf2). Due to their immunomodulatory and anti-inflammatory qualities, polyphenols may be able to control the immune response in psoriasis. We performed a thorough review in order to investigate for the first time to understand the role of citrus fruits in comorbid neurodegenerative disorders associated with psoriasis. For better understanding into the possible applications of citrus fruits in treating psoriasis and neurodegenerative disease would require additional studies focusing directly on the relationship between citrus fruits consumption in managing neurodegenerative and psoriasis disease. [ABSTRACT FROM AUTHOR]

The presence of 17-β-estradiol sulfate residue in water is of great concern due to its hazardous nature (endocrine disruptor and carcinogen). For this, chitosan was used with graphene oxide to prepare chitosan graphene oxide nanocomposite. This material was characterized and used as an adsorbent for the removal of 17-β-estradiol sulfate residue in water. This adsorbent showed a rapid, efficient, and environmentally friendly method of 17-β-estradiol sulfate removal from water. The optimal conditions for maximum removal were 93.6% (4.68 mg/g) with 2500 µg/L of 17-β-estradiol sulfate, 30 min of agitation, 0.50 g/L of graphene oxide chitosan nanocomposite, a pH of 7.0, and temperature of 25 °C. The thermodynamic parameters confirmed the spontaneity and exothermic nature of the 17-β-estradiol sulfate uptake process. The kinetic study revealed a first-order reaction, with the mechanism of uptake attributed to liquid film diffusion. The supra-molecular interaction between β-estradiol and graphene oxide chitosan nanocomposite was elucidated, highlighting the pivotal role of two hydrogen bonds with binding affinities of −6.80 and −7.03 kcal/mole for the first and second hydrogen bonds. This method is speedy, efficacy, and environmentally friendly; making it applicable for the removal of β-estradiol from water in diverse water systems. [ABSTRACT FROM AUTHOR]