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Abstract: Herein, the cost-effective utilization of biosorption for toxic pollutant removal was investigated, with a specific emphasis on enhancing mushroom-based biosorbents for the decontamination of heavy metals-polluted aqueous environments. Firstly, Oyster mushrooms (Pleurotus ostreatus) collected from Bajaur, Khyber Pakhtunkhwa, Pakistan, underwent thorough cleaning, followed by air-drying for 2–3 weeks to produce a fine powder. The obtained powder was subjected to thermal modification through heating at 400°C/1 h, and a portion of it was additionally modified using potassium hydroxide (KOH). This process resulted in three distinct materials: untreated dried mushroom (AM), thermally modified mushroom (TMM), and chemically modified mushroom (CMM), and their chemical compositions were assessed using Fourier Transform Infrared (FTIR) spectroscopy. Subsequently, the resulting materials were employed as bioadsorbents for the elimination of toxic Fe(III) ions from aqueous media. Various experimental variables, including solution pH, temperature, contact time, pollutant concentration, and adsorbent amount were varied to investigate their impact on adsorption. Analysis using an atomic absorption spectrophotometer (AAS) demonstrated exceptional Fe(III) removal capacities of mushrooms, achieving a removal extent of 29.99 mg/g at 303 K, pH 6.0, and an adsorbent dosage of 0.1 g/L, resulting in a remarkable 99.97% removal efficiency. The type of adsorbent significantly affected the extent of adsorption. Furthermore, thermodynamic analysis revealed that adsorption was spontaneous and exothermic. The adsorption data were evaluated using Langmuir, Freundlich, and Temkin isotherms, with the pseudo-second-order reaction kinetics providing the best fit, suggesting that chemisorption predominates the surface adsorption of Fe(III). This study highlights the potential of mushroom-based biosorbents as eco-friendly and effective materials for purifying water contaminated with toxic pollutants.

Pavement maintenance presents significant financial challenges, driving researchers to explore innovative approaches that can prolong pavement service life and reduce costly interventions. The objective of this research is to examine the feasibility of utilizing polyvinyl alcohol (PVA) fibers to self-heal asphalt pavements. A comprehensive mechanical testing strategy was developed to evaluate the self-healing capability of several hot mix asphalt (HMA) mixtures at the micro- and macro-level. Moreover, to assess the healing behavior of macro fractures in PVA-fiber-reinforced HMA, semi-circular bending (SCB) specimens were loaded until peak load and then allowed to heal at varying time intervals and temperatures for several loading cycles. Healing indices developed based on SCB stiffness, strength, and beam fatigue technique were utilized to measure the self-healing ability of control and reinforced mixtures. By comparing different concentrations of PVA fibers with a control HMA, the optimal fiber dosage was determined based on newly developed healing indices. This study also investigated the impact of healing time and different temperatures on healing. The results demonstrated that PVA fibers expedited the healing process, effectively healing macro-, meso-, and micro-cracks. The most effective recovery occurred with 0.3% fiber content at 40°C for a 2 h duration. Incorporating PVA fibers into asphalt pavements presents a promising solution for extending pavement service life and can be helpful in reducing maintenance costs.

Glycoprotein (GP) VI (GPVI) plays a major role in thrombosis but not haemostasis, making it a promising antithrombotic target. The primary role of GPVI on the surface of platelets is a signalling receptor for collagen, which is one of the most potent thrombotic sub-endothelial components that is exposed by atherosclerotic plaque rupture. Inhibition of GPVI has therefore been investigated as a strategy for treatment and prevention of atherothrombosis, such as during stroke and acute coronary syndromes. A range of specific GPVI inhibitors have been characterized, and two of these inhibitors, glenzocimab and revacept, have completed Phase II clinical trials in ischaemic stroke. In this review, we summarize mechanisms of GPVI activation and the latest progress of clinically tested GPVI inhibitors, including their mechanisms of action. By focusing on what is known about GPVI activation, we also discuss whether alternate strategies could be used to target GPVI.

Background: Pain is a traumatic experience for most patients on hemodialysis. In this trial, we compared prilocaine/lidocaine cream with piroxicam gel for pain reduction during arteriovenous fistula needling.Methods: This randomized double-blind crossover clinical trial was done at dialysis unit of a tertiary care hospital from June to August 2022. Adult patients, aged 18–75 years, on maintenance hemodialysis through an arteriovenous fistula were selected randomly. Pain severity during needling of fistula was assessed during initial two hemodialysis sessions without application of any drug. Patients were then randomized into two groups receiving 5% prilocaine/lidocaine cream or 0.5% piroxicam gel 1 h before the next two hemodialysis sessions. After a 7-day washout period, patients crossed over to other groups for another two hemodialysis sessions. Pain was assessed on all these occasions. Primary outcome was reduction in pain with each intervention.Results: There were 32 patients aged 46.44 ± 11.58 years. Pain intensity was 6.69 ± 0.58, 3.13 ± 1.28, and 4.55 ± 1.95 without any medication, prilocaine/lidocaine cream and piroxicam gel respectively. There was greater pain reduction with prilocaine/lidocaine cream than piroxicam gel (3.56 ± 1.35 vs 2.14 ± 1.78; p= 0.001). Local redness with prilocaine/lidocaine cream was reported by one (3.13%) patient, whereas no side effects were seen with piroxicam gel (p= 1.000).Conclusion: Prilocaine/lidocaine cream provides better pain relief than piroxicam gel during arteriovenous fistula needling.

Abstract: PVC-based nanocomposites with varying concentrations of zinc oxide (ZnO) nanoparticles are fabricated using the melt mixing technique and then subjected to compression molding to acquire desired shapes (circular) and thickness (1.5 mm). Conformational analysis is performed using scanning electron microscopy, Fourier transform infrared spectroscopy, X-ray diffractometry, and optical microscopy. The prepared samples are then exposed under UV light having an intensity of 5.11 mW/cm2for 5000 lab hours of aging. The effect of UV aging on the structural and thermal behavior of the nanocomposites is analyzed at every 1000 h. Structural degradation of more than 50% in the case of neat PVC has been observed to be reduced with the increase in filler concentration. The contact angle values for 2, 4 and 6 phr of PVC nanocomposites after 5000 h of aging are 69°, 93°, and 104° having hydrophobicity classes of HC3, HC2, and HC2, respectively. The detailed analysis to study the effect of UV aging on the thermal behavior of the nanocomposites is evaluated using differential scanning calorimetry in the temperature range of 60–220 °C. Finally, the life span of all the samples was calculated using statistical calculations and it was observed that PVC with 2 phr of ZnO showed a maximum lifetime of 17,750 lab hours whereas for PVC with 0 phr of ZnO 8693 lab hours were calculated. Graphical abstract:

Different strategies can be employed to enhance and adjust the overall characteristics and potential applications of the nanomaterials. Herein, ZnO–SiO2, Mn/ZnO–SiO2and ZnO–SiO2/PVA based nanocomposites were synthesized by sol-gel and in-situpolymerization methods by taking the Zn(CH3COO)·2H2O as ZnO, TEOS as the SiO2and Mn(SO4)2·7H2O as the Mn precursors. The present study investigates various aspects of ZnO–SiO2, Mn/ZnO–SiO2, and ZnO–SiO2/PVA nanomaterials, including electronic transition, surface morphology, elemental composition, chemical structure elucidation, thermal stability, and phase changes. To accomplish this, several techniques were employed. All the results confirmed the formation of the synthesized composite materials. Moreover, the catalytic and photocatalytic activities of the synthesized composites were studied through the degradation of Methylene blue (MB) dye, etc. The results confirmed that the synthesized nanocomposites exhibited good catalytic and photocatalytic activities towards removal of methylene blue (MB) dye removal. Further, the catalytic/photocatalytic activity of ZnO–SiO2, Mn/ZnO–SiO2and ZnO–SiO2/PVA nanocomposites were also compared towards the removal of methylene blue (MB) dye and it was verified that the Mn/ZnO–SiO2nanocomposite show high photocatalytic activity among the three nanocomposites i.e., ZnO–SiO2, Mn/ZnO–SiO2and ZnO–SiO2/PVA with removal efficiencies of 81 %, 77 % and 77 %, after 6 h, 5 h and 9 h respectively, under UV light illumination. Moreover, the photodegradation mechanism was also studied and finally, the biological activities like antioxidant and antileishmanial were also studied and compared.

AbstractLysosomal enzymes degrade cellular macromolecules, while their inactivation causes human hereditary metabolic disorders. Mucopolysaccharidosis IVA (MPS IVA; Moquio A syndrome) is one of the lysosomal storage disorders caused by a defective Galactosamine-6-sulfatase (GalN6S) enzyme. In several populations, disease incidence is elevated due to missense mutations brought on by non-synonymous allelic variation in the GalN6S enzyme. Here, we studied the effect of non-synonymous single nucleotide polymorphism (nsSNPs) on the structural dynamics of the GalN6S enzyme and its binding with N-acetylgalactosamine (GalNAc) using all-atom molecular dynamics simulation and an essential dynamics approach. Consequently, in this study, we have identified three functionally disruptive mutations in domain-I and domain-II, that is, S80L, R90W, and S162F, which presumably contribute to post-translational modifications. The study delineated that both domains work cooperatively, and alteration in domain II (S80L, R90W) leads to conformational changes in the catalytic site in domain-I, while mutation S162F mainly provokes higher residual flexibility of domain II. These results show that these mutations impair the hydrophobic core, implying that Morquio A syndrome is caused by misfolding of the GalN6S enzyme. The results also show the instability of the GalN6S-GalNAc complex upon substitution. Overall, the structural dynamics resulting from point mutations give the molecular rationale for Moquio A syndrome and, more importantly, the Mucopolysaccharidoses (MPS) family of diseases, re-establishing MPS IVA as a protein-folding disease.Communicated by Ramaswamy H. Sarma

ABSTRACTGreen practices and sustainability are mounting in business organizations around the world. All sized firms have concentrated on environmental and social strategies to benefit the societies that payback in sales and revenue. However, little is known about how Small and Medium Enterprises (SMEs) improve green strategies in emerging markets, especially in Pakistan. To contribute to the existing literature in small businesses, this research tests the influence of financial resources on green business strategy (environmental, social and economic) with a mediating role of competitive business strategy (differentiation and cost leadership). The results of the structural equation modeling based on the data set of 217 Pakistani SMEs indicate that financial resources do not directly influence green business strategy. However, the relationship between financial resources and green business strategy is significantly mediated by competitive business strategy. This research recommends SMEs utilize their financial resources efficiently and economically to practice green activities.

Checkpoint inhibitors are standard adjuvant treatment for stage IIB–IV resected melanoma, but many patients recur. Our study aimed to evaluate whether mRNA-4157 (V940), a novel mRNA-based individualised neoantigen therapy, combined with pembrolizumab, improved recurrence-free survival and distant metastasis-free survival versus pembrolizumab monotherapy in resected high-risk melanoma.

To review the outcomes of immune checkpoint inhibitor (ICI) treatment of advanced cutaneous squamous cell carcinoma (CSCC) outside clinical trials. Retrospective observational study; review of patient records in fifteen Australian institutions. All Australian adults with locally advanced or metastatic CSCC not amenable to curative surgery or radiotherapy treated with ICIs, 5 May 2017 – 23 May 2022, through a cemiplimab compassionate access scheme (Therapeutic Goods Administration Special Access Scheme) or who personally covered the cost of pembrolizumab prior to the start of the access scheme. Best overall response rate (ORR) according to standardised assessment criteria using the hierarchy: Response Evaluation Criteria in Solid Tumors (RECIST 1.1), the modified World Health Organization clinical response criteria, and the Positron Emission Tomography Response Criteria (PERCIST 1.0); overall and progression‐free survival. A total of 286 people with advanced CSCC received ICI therapy during May 2017 – May 2022 (cemiplimab, 270; pembrolizumab, 16). Their median age was 75.2 years (range, 39.3–97.5 years) and 232 were men (81%); median follow‐up time was 12.2 months (interquartile range, 5.5–20.5 months). Eighty‐eight people (31%) were immunocompromised, 27 had autoimmune disease, and 59 of 277 (21%) had ECOG performance scores of 2 or 3. The ORR was 60% (166 of 278 evaluable patients): complete responses were recorded for 74 (27%) and partial responses for 92 patients (33%). Twelve‐month overall survival was 78% (95% confidence interval [CI], 72–83%); progression‐free survival was 65% (95% CI, 58–70%). Poorer ECOG performance status was associated with poorer overall survival (per unit: adjusted hazard ratio [aHR], 3.0; 95% CI, 2.0–4.3) and progression‐free survival (aHR, 2.4; 95% CI, 1.8–3.3), as was being immunocompromised (overall: aHR, 1.8; 95% CI, 1.1–3.0; progression‐free: aHR, 1.8; 95% CI, 1.2–2.7). Fifty‐five people (19%) reported immune‐related adverse events of grade 2 or higher; there were no treatment‐related deaths. In our retrospective study, the effectiveness and toxicity of ICI therapy were similar to those determined in clinical trials. Our findings suggest that ICIs could be effective and well tolerated by people with advanced CSCC who are ineligible for clinical trials.

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In mobile adhoc networks for internet of things, the size of routing table can be reduced with the help of clustering structure. The dynamic nature of MANETs and its complexity make it a type of network with high topology changes. To reduce the topology maintenance overhead, the cluster based structure may be used. Hence, it is highly desirable to design an algorithm that adopts quickly to topology dynamics and form balanced and stable clusters. In this article, the formulation of clustering problem is carried out initially. Later, an algorithm on the basis of honey bee algorithm, genetic algorithm and tabu search (GBTC) for internet of things is proposed. In this algorithm, the individual (bee) represents a possbile clustering structure and its fitness is evaluated on the basis of its stability and load balancing. A method is presented by merging the properties of honey bee and genetic algorithms to help the population to cope with the topology dynamics and produce top quality solutions that are closely related to each other. The simulation results conducted for validation show that the proposed work forms balance and stable clusters. The simulation results are compared with algorithms that do not consider the dynamic optimization requirements. The GTBC outperform existing algorithms in terms of network lifetime and clustering overhead etc.

The widespread propagation of numerous rumors and fake news have seriously threatened the credibility of microblogs. Previous works often focused on maintaining the previous state without considering the subsequent context information. Furthermore, most of the early works have used classical feature representation schemes followed by a classifier. We investigate the rumor detection problem by exploring different Deep Learning models with emphasis on considering the contextual information in both directions: forward and backward, in a given text. The proposed system is based on Bidirectional Long Short-Term Memory with Convolutional Neural Network, effectively classifying the tweet into rumors and non-rumors. Experimental results show that the proposed method outperformed the baseline methods with 86.12% accuracy. Furthermore, the statistical analysis also shows the effectiveness of the proposed model than the comparing methods.

This research employed machine learning approaches to classify acceptable or non-acceptable particulate matter (PM2.5) concentrations using a dataset that was obtained from the Nairobi expressway road corridor. The dataset contained air quality data, traffic volume, and meteorological data. The Boruta Algorithm (BA) was utilized in conjunction with the Random Forests (RF) classifier to select the most appropriate features from the dataset. The findings of the BA analysis indicated that humidity was the most influential factor in determining air quality. This was closely followed by the variables of ‘day_of_week’ and the volume of traffic bound for Nairobi. The temperature of the site was determined to have a lower significance. The comparison among different machine learning classifiers for the classification of acceptable and unacceptable PM2.5concentrations revealed that the Extreme Gradient Boosting (XGBoost) classifier displayed superior performance in terms of Sensitivity (0.774), Specificity (0.943), F1-Score (0.833), and AU-ROC (0.874). The Binary Logistic Regression (BLR) model demonstrated comparatively poorer performance in terms of Sensitivity (0.244), Specificity (0.614), F1-Score (0.455), and AU-ROC (0.508) when compared to other ML models. The prediction of PM2.5has the potential to provide valuable insights to transport policymakers in their deliberations on urban transport policy formulation.

The industrial environment augments resource-constrained devices to bring services closer to autonomous devices. However, over time, these devices get overburdened due to computational workload, which results in degraded network performance. Therefore, the devices are programmed to share resources with nearby devices. However, owing to real-time collaboration, there is the possibility that the device moves to an undefined state and starts behaving maliciously. This can impact the entire collaborative environment laid to meet the industrial product deadline. In this article, we propose an industrial simulation framework that enables the resource-sharing environment and identifies the undefined device behavior. Furthermore, our detection scheme is based on an intelligent model trained on device behavior through the machine-in-a-loop mechanism and deployed at network intersections, i.e., edge nodes. The proposed technique improves the efficiency of the collaborative network by 30%.

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The integration of environmental considerations into business strategies is vital for organizational sustainability. The adoption of cleaner production technologies remains a complex issue in emerging economies. The majority of the literature still focuses on developed nations. This study aims to fill this gap by providing empirical insights into CPT adoption among small and medium enterprises (SMEs) in Malaysia. Utilizing the technology-organization-environment (TOE) framework and multiple regression analysis, data from 437 SME managers reveal that factors such as relative advantage, regulatory pressure, government support, customer pressure, environmental uncertainty, human resources, top management support, compatibility, and complexity significantly influence CPT adoption. Interestingly, perceived cost was not a significant predictor. Both research and managerial implications are discussed.

Transit-oriented development is a need of the modern world. It not only has the potential to solve traffic-related problems but also needs to comply with sustainability and equity. As a case study, bus rapid transit of the twin cities Rawalpindi and Islamabad in Pakistan is examined. The study utilizes a GIS-based analysis that quantifies the impact of street network attributes surrounding transit stations on pedestrian accessibility to optimize the station location in an urban environment. A GIS application is used to model the actual pedestrian network around transit stations, where street network attributes like density, connectivity and configuration are evaluated. Walking accessibility indices are estimated based on street network attributes. Results revealed that there exists a trend in accessibility index values and street network attributes, where network configuration is the most significant aspect in explaining accessibility. The actual network-based analysis demonstrated in this study can assist planners and policy makers in the strategic positioning of future transit stations while selecting network attributes that are crucial for walking accessibility.

Modern robotic systems are required to operate in challenging environments, which demand reliable localization under challenging conditions. LiDAR-based localization methods, such as the iterative closest point (ICP) algorithm, can suffer in geometrically uninformative environments that are known to deteriorate point cloud registration performance and push optimization toward divergence along weakly constrained directions. To overcome this issue, this work proposes: 1) a robust fine-grained localizability detection module and 2) a localizability-aware constrained ICP optimization module, which couples with the localizability detection module in a unified manner. The proposed localizability detection is achieved by utilizing the correspondences between the scan and the map to analyze the alignment strength against the principal directions of the optimization as part of its fine-grained LiDAR localizability analysis. In the second part, this localizability analysis is then integrated into the scan-to-map point cloud registration to generate drift-free pose updates by enforcing controlled updates or leaving the degenerate directions of the optimization unchanged. The proposed method is thoroughly evaluated and compared to state-of-the-art methods in simulated and real-world experiments, demonstrating the performance and reliability improvement in LiDAR-challenging environments. In all the experiments, the proposed framework demonstrates accurate and generalizable localizability detection and robust pose estimation without environment-specific parameter tuning.

This article surveys recent progress and discusses future opportunities for simultaneous localization and mapping (SLAM) in extreme underground environments. SLAM in subterranean environments, from tunnels, caves, and man-made underground structures on Earth, to lava tubes on Mars, is a key enabler for a range of applications, such as planetary exploration, search and rescue, disaster response, and automated mining, among others. SLAM in underground environments has recently received substantial attention, thanks to the DARPA Subterranean (SubT) Challenge, a global robotics competition aimed at assessing and pushing the state of the art in autonomous robotic exploration and mapping in complex underground environments. This article reports on the state of the art in underground SLAM by discussing different SLAM strategies and results across six teams that participated in the three-year-long SubT competition. In particular, the article has four main goals. First, we review the algorithms, architectures, and systems adopted by the teams; particular emphasis is put on light detection and ranging (LIDAR)-centric SLAM solutions (the go-to approach for virtually all teams in the competition), heterogeneous multirobot operation (including both aerial and ground robots), and real-world underground operation (from the presence of obscurants to the need to handle tight computational constraints). We do not shy away from discussing the “dirty details” behind the different SubT SLAM systems, which are often omitted from technical papers. Second, we discuss the maturity of the field by highlighting what is possible with the current SLAM systems and what we believe is within reach with some good systems engineering. Third, we outline what we believe are fundamental open problems, which are likely to require further research to break through. Finally, we provide a list of open-source SLAM implementations and datasets that have been produced during the SubT challenge and related efforts and constitute a useful resource for researchers and practitioners.

Hazardous liquids, such as nitroglycerin, are now preferred over traditional explosives in modern terrorist attacks. Their inconspicuous nature poses an identification challenge, necessitating urgent large-scale security inspections to prevent terrorism in public spaces. However, conventional liquid detection methods face obstacles in terms of cost, accuracy, and scalability, hindering their widespread use. This article introduces a platform that combines radio frequency (RF) sensing based on software-defined radio (SDR) technology and state-of-the-art machine learning (ML) algorithms to detect and classify suspicious and nonsuspicious liquids. The detection method utilizes fine-grained samples of orthogonal frequency division multiplexing (OFDM) to acquire channel state information (CSI) of the liquids present in the environment at operating frequencies 900 MHz and 2.45 GHz. ML algorithms are employed for classification purposes based on liquids’ dielectric properties, and their effectiveness is evaluated based on accuracy, prediction speed, and training time. The analysis of experimental results demonstrated that our method successfully classified over 95% of both suspicious and nonsuspicious liquids. It also identified more than 97% of suspicious liquid types and classified up to 98% of nonsuspicious liquids. These findings confirm the efficacy of the proposed system. The software-defined RF sensing system is versatile, portable, adaptable, and cost-efficient.

Nonlinear propagation of a highly intense circularly polarized electromagnetic wave through a 3-component relativistic quantum plasma (RQP) is studied. The plasma is composed of relativistic degenerate electrons, dynamic-degenerate ions, and a beam of non-relativistic degenerate electrons. The relativistic degenerate electrons are modeled by a Klein–Gordonequation (KGE), whereas the degenerate ions and beam of electrons are described by Schrödingerequations (SEs). The dynamics of the CPEM wave through the plasma is governed by the Maxwelland Poissonequations. Four modes have been observed through the linear analysis. It has been observed that the opacity of the plasma increases with an increase in the beam electron concentration. Stimulated Raman scattering, modulational instability, and stimulated Brillouin scattering have been studied, and an optimum value of the CPEM wave intensity has been found for the growth of these scattering instabilities. The growth rates of the SRS and SBS have been found to drop with increase in the quantum parameter (associated with the density) of the plasma. It has also been observed that the scattering spectra in both the SRS and SBS get restricted to very small wave number regions. The spectrum and the growth rate of the MI also show dependence on the quantum parameter.

Objectives: Novel coronavirus-19 (COVID-19) has led to over 6 million fatalities globally. An estimated 75% of COVID-19 patients who require critical care admission develop acute respiratory distress syndrome (ARDS) needing invasive mechanical ventilation (IMV) and/or extracorporeal membrane oxygenation (ECMO). Due to prolonged ventilation requirements, these patients often also require tracheostomy. We performed a review of clinical outcomes in COVID-19 patients on ECMO at a high-volume tertiary care center in Hamilton, Ontario, Canada.Methodology: We performed a retrospective case series, including 24 adult patients diagnosed with COVID-19 who required IMV, veno-venous (ECMO), and tracheostomy. All patients were included from April to December 2021. We extracted demographic and clinical variables pertaining to the tracheostomy procedure and ECMO therapy. We performed descriptive statistical analyses. This study was approved by the Hamilton Integrated Research Ethics Board (14217-C).Results: We included 24 consecutive patients with COVID-19 who required tracheostomy while undergoing ECMO therapy. The mean age was 49.4 years [standard deviation (SD): 7.33], the majority of patients were male (75%), with mean body mass index of 32 (SD: 8.81). Overall mortality rate was 33.3%. Percutaneous tracheostomy was performed most frequently (83.3%) and, similar to open tracheostomy, was associated with a low rate of perioperative bleeding complications. Within surviving patients, the mean time to IMV weaning and decannulation was 60.2 (SD: 24.6) and 49.4 days (SD: 21.8), respectively.Conclusion: Percutaneous tracheostomy appears to be safe in COVID-19 patients on ECMO and holding anticoagulation 24 hours prior to and after tracheostomy may limit bleeding events in these patients.

The study’s prime objective was to systematically verify maize’s phyto-hazardous tolerance for bisphenol A (BPA). BPA is an emerging pollutant of plastics that may stimulate plant stress reactions. Various levels of BPA (0, 1, 10, 30, 60, and 100 mg L−1) were applied. The effects on germination, physiological and metabolic features, and BPA accumulation were examined. Maize germination indices and IC50values revealed severe inhibition. Low levels of BPA promoted maize growth and photosynthetic activity, but high levels inhibited them. Evans’ blue staining and reduction of triphenyl tetrazolium chloride (TTC) demonstrate cells are impaired by hydrogen peroxide (H2O2) and malondialdehyde (MDA). Activation of superoxide dismutase (SOD), peroxidase (POD), catalase (CAT), and ascorbate peroxidase (APX) increased at high BPA levels. The principal component analysis (PCA) revealed a random distribution of oxidants, antioxidant enzymes, and BPA levels across shoots and roots and a separation in regulating oxidative and antioxidative elements. Cluster analysis revealed the separated grouping of lower to higher levels of BPA under antioxidant enzyme and oxidant regulations in both roots and shoots. A random correlation of antioxidant enzymes and oxidants at different levels of BPA indicated variations in maize seedlings’ root/shoot tolerance to stress. BPA inhibited the synthesis of auxin. Anthocyanin production indicated that maize is sensitive to BPA. Finally, these phytotoxic effects resulted from BPA accumulation. According to the study results, BPA has a greater impact on roots than on shoots. The study reveals BPA’s phytotoxic effects and maize resistance to BPA pollution.

(Abstracted from JAMA2023;330(16):1547–1556Worldwide, about 2.93 million pregnancies are complicated by gestational diabetes mellitus (GDM) annually. GDM is associated with an increased risk of maternal adverse outcomes, such as weight gain, cesarean delivery (CD), and preeclampsia, as well as fetal adverse outcomes, such as admission to the neonatal intensive care unit (NICU) due to birth injuries and respiratory disease.

The objective of this work was to determine the effect of visibility-related factors and some environmental and human factors on the severity of pedestrian-vehicle crashes. It was hypothesized that decreasing visibility, contributed to by factors such as lighting, number of lanes, speed limit, and weather, are associated with an increase in injury severity. Some of the key results of the final model indicate that higher speed limits, less light conditions, and no traffic controls were significantly correlated with increased pedestrian injury severity when roadway visibility factors were under consideration. Dusk and dark with or without lighting were found to be factors correlated with increased pedestrian injury severity, while inclement weather was found to be correlated with lower pedestrian injury severity when environmental visibility-related factors were considered. Furthermore, a spatial autocorrelation revealed a high concentration of pedestrian–vehicle crashes in the Nashville and Memphis areas. This work is similar to prior works in their goal to study factors that affect pedestrian injury severity. While other models have looked at a large range of possible factors that may affect pedestrian injury severity, the model developed in this work focuses on visibility factors, environmental factors, and human-related factors. Another contribution is the data and modeling of the data. This study utilizes a dataset from Tennessee with more categories recorded for the visibility-related factors and applies a multinomial logistic regression model to the data.

Purpose: This study aims to explore the mechanism by which intellectual capital and corporate social responsibility (CSR) influence the sustainable competitive performance of small and medium-sized enterprises (SMEs), with the mediating role of organizational innovation in an emerging economy. Design/methodology/approach: The data collection was conducted through a survey completed by 208 owners and top managers operating in the service, trading and manufacturing sector SMEs, positioned within twin cities of Pakistan. Structural equation modeling (SEM) was utilized for data analysis. Findings: The results of the study suggest that intellectual capital and CSR have a markedly positive influence on the sustainable competitive performance of SMEs. The organizational innovation appears to mediate these relationships. Originality/value: This study pioneers research on the links between intellectual capital, CSR organizational innovation and sustainable competitive performance of SMEs. The current research contributes to the literature by defining intellectual capital and CSR as an antecedent and organizational innovation as an intervening variable for the sustainable competitive performance of SMEs. In addition, this study underlines the significance of intellectual capital and CSR activities as valuable intangible assets for the achievement of sustainable competitive performance of SMEs. [ABSTRACT FROM AUTHOR]

In the contemporary world, wearable electronics and smart textiles/fabrics are galvanizing a transformation of the health care, aerospace, military, and commercial industries. However, a major challenge that exists is the manufacture of electronic circuits directly on fabrics. In this work, we addressed the issue by developing a sequential manufacturing process. First, the target fabric was coated with a customized ink containing lignin. Next, a desired circuit layout was patterned by laser burning lignin, converting it to carbon and establishing a conductive template on the fabric. At last, using an in-house-designed printer, a devised localized hydrogen evolution-assisted (HEA) copper electroplating method was applied to metalize the surface of the laser-burned lignin pattern to achieve a very low resistive circuit layout (0.103 Ω for a 1 cm long interconnect). The nanostructure and material composition of the different layers were investigated via scanning electron microscopy, energy-dispersive X-ray spectroscopy (EDX), Raman spectroscopy, and Fourier-transform infrared spectroscopy (FTIR). Monitoring the conductivity change before and after bending, rolling, stretching, washing, and adhesion tests presented remarkable mechanical stability due to the entanglement of the copper nanostructure to the fibers of the fabric. Furthermore, the HEA method was used to solder a light-emitting diode to a patterned circuit on the fabric by growing copper at the terminals, creating interconnects. The presented sequential printing method has the potential for fabricating reliable wearable electronics for various applications, particularly in medical monitoring.

Purpose: This study aims to investigate the effect of servant leadership on employee organizational identification and career satisfaction through the mediating lens of leader-member-exchange (LMX). Furthermore, this study also examines whether perceived organizational support (POS) strengthens the positive effect of servant leadership on LMX and subsequently, on employee organizational identification and career satisfaction. Design/methodology/approach: Survey data were collected from 314 respondents working in hotels in United States of America (USA). Structural equation modeling (SEM), hierarchical moderation analysis and bootstrapping were used to test the study hypotheses. Findings: Servant leadership was found to positively influence employee organizational identification and career satisfaction. Further, analysis revealed that LMX partially mediated the positive relationship between servant leadership and employee career satisfaction and fully mediated the positive relationship between servant leadership and organizational identification. However, although POS moderated the indirect relationship between servant leadership and employee’ career satisfaction, it did not moderate the indirect relationships between servant leadership and organizational identification. Practical implications: This study provides insight into the nexus of servant leadership and organizational support in hospitality industry to foster the employee organizational identification and career satisfaction which are extremely needed for competitive advantage in hotel industry. Originality/value: This study addresses recent calls for future researchers to investigate the important of servant leadership in the hospitality industry.

Concern exists regarding the performance of concrete containing a higher percentage of recycled aggregates. There is an ongoing effort to improve the strength and durability characteristics of such concrete in various ways. As part of that effort, this study investigated the laboratory performance of geopolymer concrete containing a higher dosage of reclaimed asphalt pavement (RAP) as a pavement construction material. A series of laboratory investigations were performed to evaluate the mechanical, durability, and morphological characteristics of RAP-geopolymer concrete (GPC). The mechanical properties of RAP-geopolymer concrete were also compared with RAP-Portland cement concrete (PCC) produced using ordinary Portland cement (OPC). Results indicated that RAP-geopolymer concrete exhibited higher compressive, flexural, split tensile strength as compared to RAP-cement concrete. Further, RAP-geopolymer concrete revealed satisfactory results in terms of durability regarding wet–dry cycles and water absorption (sorptivity). Morphological and microstructural investigation using scanning electron microscopy (SEM), X-Ray diffraction (XRD), and Fourier-transform infrared spectroscopy (FTIR) also discovered the formation of geopolymer compounds in RAP-geopolymer concrete which explains the better mechanical and durability properties of such concrete mixtures.

ABSTRACT In recent years, 'smart structures' experienced significant popularity in civil engineering. Such structures, referred to as intelligent structures, are built with combination of smart and conventional materials and have [...]

ABSTRACT Polymer modified asphalts are being used frequently in almost every part of the world, especially in developed countries. Higher initial cost of polymer modified asphalts in terms of material, [...]

The use of natural colorants in modern dye factories is a potential green chemistry idea that should be widely promoted to minimize the dependency of wool dyeing on some hazardous and non-biodegradable synthetic colors. In this study, an effort was undertaken to see if Rheum emodi(rhubarb) extract might be used as a natural dye for wool dyeing for the replacement of synthetic dyes. The dyeing of wool fabric was carried out using microwave (MW) ray treatment. By combining several mordants, a stunning color palette of shades of varying hues and tones was created. Comparative evaluation of the effects of various chemical mordants (aluminum salt, iron salt, tannic acid and cream of tartar) and bio-mordants (pomegranate extract and pine nut hull extract) on the characteristics of dyed wool samples was carried out to choose the best mordant for each application. It was found that MW-treated wool fabric using a bio-mordant showed a higher colorfastness value of 4/5 to 5 and a color strength value of approximately 10–20 than using metallic mordant. Scanning electron microscopy photographs and Fourier transform infrared spectroscopy analyses revealed the difference between irradiated and unirradiated wool fabrics. The employment of MW rays and bio-mordants in the natural coloring of wool fabric is encouraged due to their biocompatibility and non-toxicity when combined with MW treatment of wool fabric, as well as their high colorfastness and color strength performance. As a result, naturally extracted dyes from rhubarb can be a replacement for synthetic dyes for the coloration of wool fabric in the textile industry due to their environmental friendliness.

Objective: COVID-19 resulted in Regional tiered restrictions being introduced across the UK with subsequent implications for planned and emergency surgical care. Specific to Merseyside, Tier 4, Tier 2 and Tier 5 restrictions were introduced in late 2020 and early 2021. The purpose of this study was to examine the nature and workload of emergency urological procedures during three different national lockdown Tiers in the North West of England.Method: A 3-month prospective study examining all emergency urological activity was conducted from November 2020 when Tier 4 restrictions were introduced and included Tier 2 restrictions in December and then concluded at the end of January 2021 when Tier 5 restrictions were in place. Data was obtained by identifying patients using the electronic theatre listing system.Results: A total of 71 emergency cases were performed (24 in November (Tier 4), 28 in December (Tier 2), 19 in January 2021 (Tier 5)) with 15 different types of procedures performed. The most frequently performed procedure was stent insertion (36), followed by scrotal exploration (10). The least commonly performed procedure was suprapubic catheter insertion under general anaesthesia (1). One patient required transfer to a different hospital. In total 6 calls were made by general surgery and 3 by gynaecology for urgent urological assistance in theatre. Three urology patients returned to the theatre as emergencies following elective procedures.Conclusion: Unlike the Spring lockdown, acute urological presentations requiring operative intervention still presented daily. Of the 71 cases performed, most occurred in Tier 2. Stent insertion was the most commonly performed procedure, with the majority of the cases performed by registrars.

We study the theoretical and simulated shielding properties of 69P2O5–10Gd2O3–10BaO–10ZnO–1Er2O3and 69P2O5–10GdF3–10BaO–10ZnO-1Er2O3glasses. We exploit advanced simulation (MCNPX) and Phy-X, an online Photon Shielding and Dosimetry software, in the 0.001–15 MeV energy range, to calculate the linear attenuation coefficient and mass attenuation coefficients of the samples. The obtained mass attenuation coefficient is then used to calculate the critical radiation shielding characteristics, such as the half-value layer, mean free path, tenth value layer, and effective atomic number. The calculated LAC values range from 108.759 to 0.097 and from 84.124 to 0.087 cm−1while HVL values range from 0.006 to 7.164 and from 0.008 to 8.013 cm, and MFP values vary from 0.009 to 10.335 and from 0.012 to 11.561 cm for ZBaOEr and ZBaFEr, respectively, with energy ranging from 0.015 to 15 MeV. We find that the results obtained from MCNPX and Phy-X are consistent with each other, based on the computed relative difference. The glass sample containing Gd2O3exhibits better shielding properties than the one with GdF3, over the entire energy range. Our examined glass samples are thick enough to substantially lessen the intensity of incident X-rays and gamma rays.

Purpose: This paper aims to examine the effects of perceived desirability for entrepreneurship (PDE) and perceived self-efficacy (PSE) on the entrepreneurial intentions (EI) of business students, with the moderation effect of entrepreneurial knowledge (EK) in the context of the theory of planned behaviour. Design/methodology/approach: We used a self-administered survey of 200 students registered in the business schools of Pakistani universities. The causal association between the variables was estimated through SmartPLS by using hierarchal linear modelling. Findings: The study findings indicate that PDE and PSE significantly influence EIs. Furthermore, EK significantly strengthens the nexus between PDE and EIs and between PSE and EIs. Those students who had already acquired self-efficacy and exhibited desirability for the business venture were more inclined towards entrepreneurship if they had acquired some EK and vice versa. Research limitations/implications: This study reveals that a model of EIs is needed to configure the students’ goals and motivations. Also, using new education programmes will help students acquire new knowledge for business startups. Further implications are also discussed. Originality/value: This research fills a gap by using the moderating role of EK on the nexus between PDE, PSE and EIs, which has remained untouched in the educational sector.

An analytic parahermitian matrix admits in almost all cases an eigenvalue decomposition (EVD) with analytic eigenvalues and eigenvectors. We have previously defined a discrete Fourier transform (DFT) domain algorithm which has been proven to extract the analytic eigenvalues. The selection of the eigenvalues as analytic functions guarantees in turn the existence of unique one-dimensional eigenspaces in which analytic eigenvectors can exist. Determining such eigenvectors is not straightforward, and requires three challenges to be addressed. Firstly, one-dimensional subspaces for eigenvectors have to be woven smoothly across DFT bins where a non-trivial algebraic multiplicity causes ambiguity. Secondly, with the one-dimensional eigenspaces defined, a phase smoothing across DFT bins aims to extract analytic eigenvectors with minimum time domain support. Thirdly, we need to check whether the DFT length, and thus the approximation order, is sufficient. We propose an iterative algorithm for the extraction of analytic eigenvectors and prove that this algorithm converges to the best of a set of stationary points. We provide a number of numerical examples and simulation results, in which the algorithm is demonstrated to extract the ground truth analytic eigenvectors arbitrarily closely.

Traffic signal control and cooperation are extremely important to alleviate traffic congestion in a large traffic network. This study develops a higher-order conflict graph approach for network-wide traffic signal control and cooperation. A conflict graph is applied to model the traffic signal configurations, which identifies the conflict and unconflicted movements for each intersection. In conflict graph, the node represents each movement. The weight of each node can be defined as traffic volume, queue length, fuel consumption, or any weighted combinations of these measurements. The calculation of the optimal green light duration and green light sequence (for different movements) is equivalent to sequentially finding the maximum weight independent set (MWIS) in the conflict graph. The conflict graph also provides a uniform and efficient way to connect traffic signal operations among nearby intersections spatially. Then, we introduced the concept of the $k$ -th order neighborhood to model the degree of connectivity between each movement to the movements at upstream or downstream intersections. The weight of each node in the higher-order conflict graph not only represents its own congestion level, but also relates to the traffic conditions of nearby intersections. Through this approach, the cooperation of multiple intersections can be realized by incorporating their spatial connectivity into conflict graph and solving the MWIS problem. A simulation network is built in SUMO to test the effectiveness of the proposed method. Results suggested that the proposed model outperformed other state-of-the-art signal control methods. Also, the scheme maintains good performance under varying traffic demands.

This paper addresses the effectiveness of real-time and post-trip interventions from the H2020 i-DREAMS naturalistic driving project. The project aims to setup a framework for the definition, development and validation of a context-aware ‘safety tolerance zone (STZ)’ for driving. A range of sensors are used to collect a large variety of data, which enables assessment of the STZ, and help designing interventions. Effectiveness evaluation is based on the outcome and process evaluation using the COM-B and RE-AIM frameworks, respectively. Preliminary results for a sample of 27 car drivers from Belgium and 26 car drivers from UK are presented. Overall, the interventions were found effective in improving driving behaviour. UK drivers have performed significantly better, while Belgian car drivers showed mixed results, mainly due to the changing mobility patterns during COVID19 pandemic.