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NO2 is a toxic gas that can damage the lungs with prolonged exposure and contribute to health conditions, such as asthma in children. Detecting NO2 is therefore crucial for maintaining a healthy environment. Carbon nanotubes (CNTs) are promising materials for NO2 gas sensors due to their excellent electronic properties and high adsorption energy for NO2 molecules. However, conventional CNT-based sensors face challenges, including low responses at room temperature (RT) and slow recovery times. This study introduces a memristor-based NO2 gas sensor comprising CNT/ZnO/ITO decorated with an N-[3-(trimethoxysilyl)propyl] ethylene diamine (en-APTAS) membrane to enhance room-temperature-sensing performance. The amine groups in the en-APTAS membrane increase adsorption sites and boost charge transfer interactions between NO2 and the CNT surface. This modification improves the sensor’s response by 60% at 20 ppm compared to the undecorated counterpart. However, the high adsorption energy of NO2 slows the recovery process. To overcome this, a pulse-recovery method was implemented, applying a −2.5 V pulse with a 1 ms width, enabling the sensor to return to its baseline within 1 ms. These findings highlight the effectiveness of en-APTAS decoration and pulse-recovery techniques in improving the sensitivity, response, and recovery of CNT-based gas sensors.

IntroductionChile pepper (Capsicum annuum L.) mechanization is a promising alternative to traditional hand harvesting due to the costs associated with manual harvest, as well as the increasing unavailability of skilled manual chile harvesters. This study aimed to identify single nucleotide polymorphisms (SNPs) associated with mechanical harvestability (MH) and yield-related traits using multi-locus genome-wide association mapping approaches in a C. annuum association mapping population.MethodsA C. annuum association mapping panel for mechanical harvest was manually direct seeded in an augmented block design in two locations. After filtration, imputation, and quality control 27,291 single nucleotide polymorphism (SNP) markers were used for association analyses. Six multi-locus GWAS models were implemented to identify marker trait association.Results and DiscussionMulti-locus GWAS models identified 12 major SNP markers (R2 > 10) across nine chromosomes associated with plant architecture, easy destemming traits, and yield parameters. The presence of a major QTL in chromosome P2, dstem2.1, identified recently to be associated with destemming force, was confirmed. Mature green and mature red yield shared three SNP markers mapped on chromosome P3, P5, and P6 explaining 11.94% to 25.15% of the phenotypic variation. Candidate gene analysis for the significant loci identified 19 candidate genes regulating different phytohormone biosynthesis/signaling, metabolic processes, transcription, methylation, DNA repair/replication, and RNA splicing, with potential roles in controlling plant architecture and morphology. The diverse positions of the associated SNPs suggest the complex nature of these quantitative traits, involvement of multiple genetic factors, and novel significant marker-trait associations. Results from this study will be relevant for genetic improvement of mechanical harvestability traits in New Mexican chile pepper using molecular markerassisted breeding and selection.

There has been increasing growth in incorporating fly ash as a supplementary cementitious material in concrete mixtures due to its potential to enhance the durability and strength properties of concrete. However, there is a lack of research on predicting the depth of wear of fly ash-based concrete. The laboratory methods available for estimating the depth of wear often involve destructive and expensive tests. Therefore, to avoid costly and laborious tests, this study utilized two machine learning methods, including multi-expression programming (MEP) and gene expression programming (GEP), to predict the depth of wear of fly ash-modified concrete. A comprehensive dataset of 216 experimental records was compiled from published studies for model training and validation. This extensive dataset encompasses the depth of wear as the target variable, along with nine explanatory parameters, namely fly ash, cement content, fine and coarse aggregate, water content, plasticizer, age of concrete, air-entraining agent, and testing time. The models were trained with 70% of the data, and the remaining 30% of data was used for validating the models. The models were developed by a continuous trial-and-error process and iterative refinement of hyperparameters until optimal results were achieved. The efficacy of the models was assessed via multiple statistical indicators. Furthermore, the SHapley Additive exPlanation (SHAP) was utilized for the interpretability of the model prediction from both global and local perspectives. The GEP model exhibited excellent accuracy with a correlation coefficient (R) of 0.989 (training) and 0.992 (validation). Similarly, the MEP model provided prediction accuracy with R values of 0.965 and 0.968 for training and validation sets, respectively. In addition, the MEP and GEP models outperformed the traditional multi-linear regression model. The SHAP interpretation revealed that testing time and age have a higher contribution in determining the depth of wear. The findings of this study can assist practitioners and designers in avoiding costly and laborious tests for durability assessment and promoting sustainable use of fly ash in the construction sector.

Energy scarcity in various regions worldwide not only adversely affects people's quality of life but also hinders overall development. Pakistan is among the nations grappling with energy shortages, with high consumption and limited generation, resulting in a substantial energy shortfall of 2500 MW. Floating photovoltaic (FPV) systems present an attractive solution for harnessing solar energy, particularly where land availability is constrained. These systems offer benefits such as conserving water and land while delivering higher power output compared to conventional terrestrial PV systems. While the advantages of FPV systems are generally recognized, research on their specific characteristics remains limited. This paper aims to address this gap by providing a comprehensive review of FPV technology and its potential applications, particularly in Pakistan. By comparing ground-mounted and FPV systems, the study explores the economic, technological, and environmental implications of FPV adoption in regions with similar geographical profiles. The analysis highlights not only the cooling impact of water, which enhances the efficiency of FPV systems, but also other key aspects such as cost-effectiveness, site preparation, and environmental impact. Output power of FPV and land based obtained are 390 kW and 370 kW at irradiances of 180kW/m2and 170 kW/m2 at temperature from 30 to35°C respectively. The findings demonstrate the viability of FPV systems and emphasize that by leveraging the characteristics of water temperature, it becomes feasible to more accurately predict the electricity generation potential of FPV systems. This synthesis offers new insights and underscores the potential for FPV adoption in areas facing similar energy challenges.

Background: Accidental parathyroidectomy during thyroid surgery can lead to temporary or permanent hypocalcemia and serious morbidity. Proper identification of parathyroid glands during surgery can be an effective way to prevent this complication. Methods: The keywords “parathyroid gland,” “preserve,” “protect,” “inadvertent or accidental parathyroidectomy,” “surgery,” and “dissection” were used to search Medline and Embase databases. A total of 133 articles were selected after preliminary review, of which 80 indexed papers were reviewed for subject relevance. Results: Imaging techniques, such as the use of carbon nanoparticles or indocyanine green angiography, and operative techniques, such as meticulous capsular dissection of the thyroid gland, were associated with significantly improved rates of avoidance of inadvertent parathyroidectomy. Conclusion: During thyroid surgery, the use of imaging and careful operative techniques can prevent parathyroid injury. This, in turn, can prevent complications resulting from hypoparathyroidism such as tetany, ECG changes, and neurological sequelae.

Background: Clinical laboratories perform antimicrobial susceptibility testing (AST) primarily by determining the minimum inhibitory concentration (MIC) for an organism–antimicrobial combination and comparing it with established breakpoints to generate interpretations. The Antimicrobial Resistance (AR) Option of CDC’s National Healthcare Safety Network (NHSN) permits hospitals to submit clinical isolate AST data, including test values and interpretations (Figure 1). The Clinical and Laboratory Standards Institute (CLSI) periodically revises breakpoints, but their adoption by clinical laboratories can be delayed, potentially affecting national AR surveillance data accuracy. Using MIC values, instead of clinical laboratory interpretations, can improve surveillance data accuracy and overcome misclassification due to delayed uptake of revised breakpoints. We evaluated the completeness and consistency of MIC data submitted to the AR Option for fluoroquinolone-resistant Escherichia coli and methicillin-resistant Staphylococcus aureus (MRSA). Methods: We included data on (1) E. coli isolates tested for ciprofloxacin or levofloxacin susceptibility and (2) S. aureus isolates tested for oxacillin or cefoxitin susceptibility in 2022 and reported by October 1, 2023. We evaluated completeness among isolates reporting a final AST interpretation as the proportion of isolates reporting both an MIC value and interpretation. We evaluated consistency using percent agreement comparing the laboratory’s MIC interpretation (classified as resistant or not resistant) with the interpretation derived by applying 2021 CLSI M100 breakpoints to the MIC values reported for the same isolate. Results: Across 974 hospitals, fluoroquinolone MICs and interpretations were reported for 172,012/393,359 E. coli isolates (43.7%), and oxacillin or cefoxitin MICs and interpretations were reported for 38,519/79,372 S. aureus isolates (48.5%). Of isolates with both MIC values and interpretations, 157,902 (91.8%) E. coli and 7,808(79.7%) S. aureus isolates had MICs that could be classified as resistant or non-resistant (i.e., intermediate or susceptible) per CLSI breakpoints (Figure 2). The remaining MICs were unclassifiable (reported as intervals spanning CLSI breakpoints, e.g., ≤1 μg/ml ciprofloxacin for E. coli). Among isolates with classifiable MICs, the agreement between the clinical laboratory and CLSI-based interpretation was 99.5% for E. coli and 99.7% for S. aureus. Conclusion: MIC values and interpretations were available for

Background: The number of advanced practice providers (APPs)—nurse practitioners (NPs) and physician assistants (PAs)—continues to expand across the United States. Several studies suggest differences in antibiotic prescribing rates and appropriateness by APPs compared to physicians. The objective of this analysis is to characterize population- and provider-specific outpatient antibiotic prescribing rates among physicians and APPs nationally, by state, and within urban versus rural counties. Methods: We estimated outpatient oral antibiotic prescription rates for 2011 and 2022 using county-level prescription dispensing data from IQVIA Xponent® (numerator) and population census estimates (denominator). Provider specialty denominators were provided by IQVIA, based on data from the American Medical Association. Counties were classified as urban or rural per the 2013 National Center for Health Statistics classification. National and state-level prescription volume, rates per 1000 population, and average number of prescriptions per provider were calculated for physicians, NPs, and PAs. We assessed the degree to which provider-specific rates explained the variance of the overall rate by state, using the coefficient of determination (r2) from Pearson’s correlation. Results: Between 2011 and 2022, overall U.S. antibiotic prescribing declined from 877 to 709 per 1000 population, a 19.2% relative reduction. The provider-specific proportion of the overall prescribing rate relatively decreased by 32% for physicians but increased by 157% for APPs (NPs 229%, PAs 86%; Figure 1). State-level antibiotic prescribing rates varied by provider type for both years, shifting towards proportionally greater APP prescribing in 2022 (Figure 2). For 2011 and 2022, physician prescribing rate strongly correlated with the overall state rate (r2 = 0.83 in 2011 versus 0.80 in 2022), whereas the correlation of the NP prescribing rate increased (r2 = 0.20 in 2011 versus 0.76 in 2022). A total of 60,327 (7.2%) physicians practiced in rural settings in contrast to 42,876 (12%) NPs and 14,495 (9.4%) PAs in 2022. Providers in rural counties prescribed more antibiotics per provider on average compared to urban counties; rural physicians prescribed 57% more antibiotics per provider (207 vs 132 antibiotics per provider), rural NPs prescribed 115% more (284 vs 132), and rural PAs prescribed 53% more (289 vs 189). Conclusions: The relative contribution of APPs to outpatient antibiotic prescriptions more than doubled over the past decade, accounting for 1 in 3 prescriptions in 2022. This contribution was especially prominent among NPs in rural counties. Further evaluation of antibiotic prescribing appropriateness among APPs and integration of APPs into antibiotic stewardship efforts in various settings.

In this study, we have synthesized S-substituted benzothiazole derived thiazole bearing bis-Schiff base derivatives (1–19) and characterized via different spectroscopic techniques including NMR and HR-EIMS and then screened against α-amylase and α-glycosidase. All the analogues show excellent inhibitory capability. Analogues 1 (IC50 = 3.18 ± 0.72 µM and 2.30 ± 1.80 µM) and 4 (IC50 = 1.40 ± 0.59 µM and 2.10 ± 0.78 µM) were found to be the strongest among the all in contrast with standard drug acarbose (IC50 = 4.30 ± 0.18 and 6.45 ± 1.84 µM) for α-amylase and α-glycosidase. Some derivatives show good potency against both the enzymes while few were found moderately potent. For all the molecules structure–activity relationship was carried to determine the decrease/increase in potency due to steric hindrance, bulky nature, position, type, size, quantity of the substituent/s on phenyl rings. Molecular docking and ADME analysis were carried out to signify the binding interactions of the most potent derivatives with the active site of enzymes.

Background Bronchial artery embolization (BAE) is the established first-line treatment for patients presenting with massive haemoptysis, a life-threatening condition that can occur because of numerous underlying diseases. BAE is a relatively safe procedure with control of haemorrhage achieved in 77%–90% of cases and rare occurrence of complications. Spinal cord infarction is one such rare complication, which can have severe implications in terms of morbidity.Case presentations We present a case of a 70-year-old man who developed paraplegia with loss of pain and temperature sensation as well as sphincteric involvement following BAE for hemoptysis. MRI of the spine was suggestive of an ischaemic event involving anterolateral spinal cord segment T4–T6, so a diagnosis of anterior spinal artery syndrome post BAE was made. The patient was given corticosteroids, dual antiplatelet medications, pregabalin, supportive management and regular physiotherapy. Follow-up of the patient at 3 and 6 months failed to show any significant improvement in neurological function, although the patient did not report problem of significant hemoptysis afterward.Conclusion Spinal cord infarct is a rare and disabling complication of BAE despite it being a safe procedure with good long-term outcomes. Detailed knowledge about the anatomy of bronchial arteries and spinal arteries with detailed preprocedure investigations may lower the risk of this disabling complication.

The current study aims to examine how and why actors contest the business education field through rankings. Inspired by the field structuration process and field level change, we argue that categorization systems, in this case, rankings, construct boundaries of the business education field and redefine authority and elites for the field. Ten highly ranked Pakistani business schools were selected through a purposive sampling method. For the thematic analysis adopted in the current study, we collected empirical evidence mainly through interviews with the directors of accreditation and ranking bodies, deans, and marketing directors of business schools. The interview data was also supplemented by secondary data such as internal student surveys, business school's annual reports, and other relevant sources of data. The current study showed institutional work in the business education field. Through categorization systems, actors use their authority to challenge the existing social order and define the uncontested space of the business education field in developing countries. Actors then populate the uncontested space with new members thus legitimizing new rules and standards for the field and promoting new elites for the field thus creating new social order.

The rollout of China Pakistan Economic Corridor (CPEC) has benefited Pakistan in getting a faster pace in terms of economic and financial stability. Pakistan has a significant geographical location in the Asian continent. It possesses an enormous renewable energy sources (RES) potential, primarily wind, in its Sindh, Balochistan, and Khyber Pakhtun-Khuwa (KPK) provinces. This study concentrates on the availability of wind power potential at the selected twelve sites located in three different provinces of the country, using the Weibull probability distribution (WPD) technique. Five parametric estimation techniques were simulated on the data sets of the selected wind sites to find the optimal results. The obtained results showed that the annual wind energy density was found to be 3668.28 kWh/m2 at Lucky Energy, 3297.21 kWh/m2 at Sujawal, 3180.03 kWh/m2 at Tando Ghulam Ali, 2836.76 kWh/m2 at Sanghar, 2821.09 kWh/m2 at Baburband, 2684.32 kWh/m2 at Ketibandar, 2618.78 kWh/m2 at Umerkot, 1759.2 kWh/m2 Hawksby, 1253.76 kWh/m2 at Gwadar, 896.41 kWh/m2 at Quetta, 558.59 kWh/m2 at Haripur and 376.07 kWh/m2 at Peshawar respectively. From the results, it is found that sites present in Sindh province are suitable for commercial-scale wind farm development and the selected wind sites of Balochistan are appropriate for utility-scale wind power development. In contrast, the selected wind sites of Khyber Pakhtunkhwa province are unsuitable for commercial scale due to low wind speeds around the year.

Air pollution poses significant public health risks, necessitating accurate and efficient monitoring of particulate matter (PM). These organic compounds may be released from natural sources like trees and vegetation, as well as from anthropogenic, or human-made sources including industrial activities and motor vehicle emissions. Therefore, measuring PM concentrations is paramount to understanding people’s exposure levels to pollutants. This paper introduces a novel image processing technique utilizing photographs/pictures of Do-it-Yourself (DiY) sensors for the detection and quantification of PM10 particles, enhancing community involvement and data collection accuracy in Citizen Science (CS) projects. A synthetic data generation algorithm was developed to overcome the challenge of data scarcity commonly associated with citizen-based data collection to validate the image processing technique. This algorithm generates images by precisely defining parameters such as image resolution, image dimension, and PM airborne particle density. To ensure these synthetic images mimic real-world conditions, variations like Gaussian noise, focus blur, and white balance adjustments and combinations were introduced, simulating the environmental and technical factors affecting image quality in typical smartphone digital cameras. The detection algorithm for PM10 particles demonstrates robust performance across varying levels of noise, maintaining effectiveness in realistic mobile imaging conditions. Therefore, the methodology retains sufficient accuracy, suggesting its practical applicability for environmental monitoring in diverse real-world conditions using mobile devices.