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Introduction: The usage of Quick Response (QR) Codes has become widely popular in recent years, primarily for immense electronic transactions and industry uses. The structural flexibility of QR Code architecture opens many more possibilities for researchers in the domain of the Industrial Internet of Things (IIoT). However, the limited storage capacity of the traditional QR Codes still fails to stretch the data capacity limits. The researchers of this domain have already introduced different kinds of techniques, including data hiding, multiplexing, data compression, color QR Codes, and so on. However, the research on increasing the data storage capacity of the QR Codes is very limited and still operational. Objectives: The main objective of this work is to increase the data storage capacity of QR Codes in the IIoT domain. Methods: In the first part, we have introduced a 4-State-Pattern-based encoding technique to generate the proposed 4-State QR (4SQR) Code where actual data are encoded into a 4SQR Code image which increases the data storage capacity more than the traditional 2-State QR Code. The proposed 4SQR Code consists of four types of patterns, including Black Square Box (BSB), White Square Box (WSB), Triangle, and Circle, whereas the traditional 2-State QR Codes consist of BSB and WSB. In the second part, the 4SQR Code decoding module has been introduced using the adaptive YOLO V5 algorithm where the proposed 4SQR Code image is decoded into the actual data. Results: The proposed model is tested in a Digital Twin (DT) framework using randomly generated 3000 testing samples for the encoding module that converts into 4SQR Code images successfully and similarly for the decoding module that decodes the 4SQR Code images into the actual data. Conclusion: Experimental results show that this proposed technique offers increased data storage capacity two times than traditional 2-State QR Codes.

Background: Acute malnutrition is a serious public health problem reaching to epidemic proportions in certain regions of the world. It has been reported that children with severe malnutrition have weakened immunity and are prone to developmental delays, with a higher mortality risk from common childhood illnesses such as diarrhea, pneumonia and malaria. Objective: To determine the frequency and associated factors of severe acute malnutrition in children aged 6-59 months at District Headquarter Hospital, Qambar Shahdadkot, Sindh. Methods: A cross-sectional study was conducted from November 2021 to February 2023 among children aged 6-59 months at District Headquarter Hospital, District Qamber Shahdadkot, Sindh, Pakistan. Data were collected by means of interviews using the study questionnaire. Height was measured using a height board (Stadio Meter), weight was measured using SECA digital weighting scale while mid upper arm circumference was measured using MUAC tape. Data were analyzed by SPSS version 20. Crude and adjusted odd ratios with 95% confidence interval were computed using binary logistic regression to determine the association of socio-demographic characteristics with the nutritional status of children. Results: The study results showed that the mean age of study children was 26.58±14.54 months, 101 (50.5%) of them were less than 2 years old, 109 (54.5%) were male, 149 (74.5%) were exclusively breastfed for 6 months, only 19 (9.5%) of them started weaning between the age of 4 to 6 months and 147 (73.5%) were completely immunized. Moreover, 26 (13.0%) of them had severe acute malnutrition. On the multivariable model, only child age was found to be significantly associated with the nutritional status of children (aOR: 4.07, 95% CI 1.14-14.48, p=0.030). Conclusion: Among the study sample, 13% of children had severe acute malnutrition. Moreover, among the socio-demographic characteristics studied, only the age of child was found to be significantly associated with the nutritional status of a child.

Abstract Lotus (Nelumbo nucifera, G.) is a perennial aquatic plant, which is utilized for a variety of therapeutic and food applications. Muffins are a kind of baking product that is typically consumed for breakfast or as a snack. Currently, many baked goods in the market use fat replacers to lower the overall fat content, while conserving consumer approval. The study's objectives were to produce low-fat muffins and to characterize the types of quality changes that occurred in the muffins. Lotus rhizome was sliced, dried and converted into powder, which used as fat substitute in muffins, at 0, 5, 10, 15 and 20% replacement levels, with names as M0, M1, M2, M3 and M4, respectively. Composite flours having these proportions of lotus Rhizome Powder (LRP) were analyzed for proximate and rheological parameters. Increasing levels of LRP caused increased water absorption and dough development time of the composite flours. Proximate analyses of muffins showed highest value of moisture (25.05%), ash (2.43%), fiber (0.99%), protein (11.89%) and NFE (58.45%) in M4. Whereas, fat content and energy value were significantly reduced in M4, as highest fat was found to be in M0 (36.65%) and highest calories in M0 (576.5 kcal), which were significantly reduced to 26.24% and 514.5 kcal, respectively in M4. High yeast and mold count was of M0 at first day as 98 CFU/g, which was increased to 129 CFU/g at day 7, whereas M4 presented this count to be 44 CFU/g at day one, which was increased to 70.33 CFU/g at day 7. Texture analysis showed softest texture of M4 as firmness value was 1887 g, which was significantly high in M0 as 2285 g. The research revealed that M3 best performed out of all treatments as 15% replacement of fat with LRP significantly improved the rheological properties of the flour, ash and fiber content were high, whereas fat contents were significantly low in these muffins. Textural and sensory analysis also supported the M3 due to softer texture and significantly high color, appearance, flavor, and overall acceptability scores. From the results of this study, it could be concluded that a 15% replacement of shortenings with LRP could be considered as optimum for developing good quality baked muffins. Graphical Abstract

In a detailed investigation of lichen diversity in different regions of Pakistan, utilizing molecular and morphological techniques, we identified and reported three species – Dimelaena altissima, Lecanora chlarotera, and Usnea lapponica – as new records for Pakistan. Furthermore, nrITS sequences for Evernia prunastri, Lecanora horiza, L. hybocarpa, Peltigera polydactylon, and P. praetextata were generated for the first time from Pakistan. Detailed morpho-anatomical descriptions, habitat information, distribution data, and results of biochemical analyses are also provided for a better understanding of the lichen diversity and ecology in this region.

Abstract Background Bigleaf hydrangea (Hydrangea macrophylla) is a widely cultivated ornamental plant species. Leaf spot disease, caused by Corynespora cassiicola, poses a significant threat to the ornamental quality and economic value of hydrangeas. However, the disease resistance breeding of hydrangea is limited due to the lacking of resistant varieties and genes. Results This study evaluated ten hydrangea varieties for their resistance to leaf spot disease. Among them, ‘White Angel’ and ‘Ocean Heart’ were screened out as representative varieties for resistance and susceptibility, respectively, on the basis of evaluation. Physiological and biochemical indices, phytohormones, and transcriptomic changes were measured in the leaves of both varieties at 0 and 24 h post inoculation with C. cassiicola. The results showed that C. cassiicola infection significantly increased abscisic acid (ABA) contents in both varieties; however, the increase was significantly higher in the susceptible variety ‘Ocean Heart’ compared to the resistant variety ‘White Angel’ (p

Abstract The study of genotype × environment interaction is crucial for plant breeders to introduce new cultivar(s) with improved and stable yield performance. The productivity of chickpea crops is very low in Pakistan. It requires the selection of genotypes with optimal productivity for diverse environmental conditions. Fourteen different chickpea genotypes were assessed using the linear mixed model to evaluate genotypes across four diverse chickpea growing regions, including Attock (Punjab), Bhakkar (Punjab), Karak (Khyber Pakhtunkhwa), and Larkana during the year 2017–19. The environmental effect was pronounced and contributed significantly to variation (25.8%) in grain yield. Analysing genotype × environmental interactions at multiple locations facilitates ranking superior genotypes that excel in specific or diverse environments. Notably, the genotypes, viz., Fakhr-e-Thal and Bhakkar-2011, demonstrated superior performance in terms of overall grain yield. Utilising a multitrait stability index, Bittal-2016 and Thal-2006 exhibited the most stable genotypes across various environments and were observed suitable for diverse growing regions. While, for specific environmental conditions, genotypes, i.e., KK-1, Noor-2013 and Fakhr-e-thal, exhibited higher yields and stability. This showed their adaptability to the particular environment. The present study revealed that Larkana as the mega environment was conducive to higher yield, while Attock, Bhakkar, and Karak sites were less favorable for KK-3, DG-89, and Dasht. The BLUP outperformed the AMMI in our analysis of evaluating multi-location trials. The integration of WAASBY and MTSI indices proved useful in differentiating between high yielding genotypes and those with stable performance. These tools are essential for breeders aiming to select genotypes that will perform consistently across diverse environmental conditions.

Abstract This study aimed to evaluate the adaptation and effectiveness of telemedicine with the diffuse-dual-channel system (DDS) and tiered-gatekeeper system (TGS) across different tiers of the healthcare system on technical-organizational-environmental (TOE) framework. The telemedicine services were extended as Tiered-gatekeeper system (TGS) by Primary and Secondary Healthcare (PSHC) and Diffuse-dual-channel system (DDS) by King Edward Medical University (KEMU) in 2020 benefiting 2605 and 21,905 patients, respectively. This cross-sectional survey is based on a structured questionnaire conducted on 172 healthcare practitioners (HCP) from KEMU and 76 from PSHC selected by purposive sampling and analysis is conducted through descriptive analysis and the Boruta features selection method. The diffuse-dual-channel system is found to be flexible, easy to implement, and impactful due to innate compatibility with prevailing healthcare practices, however, it is a parallel effort to the existing healthcare system. The tiered-gatekeeping system is found to be complex in adaptation initially, but is more organized, deeply integrated, accountable and channelizes the flow of patients, resultantly aiding the preservation of resources.

Abstract Self-adaptive thermoregulation, the mechanism living organisms use to balance their temperature, holds great promise for decarbonizing cooling and heating processes. This functionality can be effectively emulated by engineering the thermal emissivity of materials to adapt to background temperature variations. Yet, solutions that marry large emissivity switching ( $$\varDelta \epsilon$$ Δ ϵ ) with scalability, cost-effectiveness, and design freedom are still lacking. Here, we fill this gap by introducing infrared dipole antennas made of tunable thermochromic materials. We demonstrate that non-spherical antennas (rods, stars and flakes) made of vanadium-dioxide can exhibit a massive (~200-fold) increase in their absorption cross-section as temperature rises. Embedding these antennas in polymer films, or simply spraying them directly, creates free-form thermoregulation composites, featuring an outstanding $$\varDelta \epsilon \sim 0.6$$ Δ ϵ ~ 0.6 in spectral ranges that can be tuned at will. Our research paves the way for versatile self-adaptive heat management solutions (coatings, fibers, membranes, and films) that could find application in radiative-cooling, heat-sensing, thermal-camouflage, and other.

Abstract The root architecture, more seminal roots, and Deeper roots help the plants to uptake the resources from the deeper soil layer to ensure better growth. The Gibberellic acid-sensitive (GA-sensitive) Rht genes are well known for increasing drought tolerance in wheat. Much work has been performed on the effect of these genes on the plant agronomic traits and little work has been done on the effect of Rht genes on seminal roots and root architecture. This study was designed to evaluate 200 wheat genotypes under normal and osmotic stress. The genotypes were sown in the solution culture and laid under CRD factorial arrangement with three replications and two factors i.e., genotypes and treatments viz. normal and osmotic stress (20% PEG-6000) applied one week after germination. The data was recorded for the root traits. Results demonstrated that out of 200 genotypes, the GA-sensitive Rht13 gene was amplified in 21 genotypes with a fragment length of 1089 bp. In comparison, the GA-insensitive Rht1 gene was amplified in 24 genotypes with a band size of 228 bp. From 200 wheat genotypes, 122 genotypes produced 5 seminal roots, 4 genotypes 4 seminal roots, and 74 genotypes 3 seminal roots. The genotypes G-3 (EBW11TALL#1/WESTONIA-Rht5//QUAIU#1), G-6 (EBW01TALL#1/SILVERSTAR-Rht13B//ROLF07) and G-8 (EBW01TALL#1/SILVERSTAR-Rht13B//NAVJ07) produced 5 seminal roots and have longer coleoptile (> 4.0 cm), root (> 11.0 cm) and shoot (> 17 cm) under normal and osmotic stress. Furthermore, Ujala 16, Galaxy-13, and Fareed-06 produced 3 seminal roots and have short coleoptile (

Climate change remains a critical issue worldwide, and balancing economic growth with sustainable development is of paramount importance. Our study explores the complex relationship between globalization, environmental sustainability, and urbanization in selected developing countries. By utilizing a simple panel regression technique and data from 40 developing countries from 1994 to 2018, the study examines how these factors influence environmental stability. We find that with a strong focus on environmental sustainability, economic growth can be enhanced. Additionally, we find that globalization, along with GDP per capita and access to electricity, significantly impacts environmental sustainability. Conversely, the effect of urbanization on environmental stability appears to be minimal.

Abstract The integration of Fintech is revolutionizing global financial engagement, moving away from traditional payment systems. This study, thus, explores the interaction between communication dynamics, Fintech use, intentions, cashless transactions adoption, financial literacy, personal innovativeness, and socioeconomic development in Pakistan. The study employs a dual methodology, encompassing “PLS-SEM and Probit Model alongside Propensity Score Matching technique.” The survey targeted small business owners and individuals involved in informal sectors, encompassing roles such as fruit and vegetable vendors, small shop owners, taxi drivers, and auto rickshaw operators. A total of 394 responses were analyzed. The findings highlight the facilitating role of individuals’ intentions to use cashless transactions, acting as a mediator through which Fintech use fosters actual behavior. Moreover, the study finds financial literacy and personal innovativeness as critical moderating factors. Adopting cashless transactions emerges as a pathway to enhanced financial inclusion, particularly impactful for small business owners. The achieved financial inclusion has the potential for profound financial inclusion implications, as evidenced by its positive effects on sustainable livelihoods, living conditions, and social development among small business owners and individuals operating within the informal sector.

Abstract Wheat commands attention due to its significant impact on culture, nutrition, the economy, and the guarantee of food security. The anticipated rise in temperatures resulting from climate change is a key factor contributing to food insecurity, as it markedly reduces wheat harvests. Terminal heat stress mostly affects spike fertility in wheat, specifically influencing pollen fertility and anther morphology. This research especially focuses on the shape of anthers and examines the effects of heat stress. The DinoLite Microscope’s high-resolution images are used to measure the length and width of wheat anthers. By using object identification techniques, the research accurately measures the length and width of each anther in images, offering valuable insights into the differences between various wheat varieties. Furthermore, Deep Learning (DL) methodologies are utilized to enhance agriculture, specifically employing record categorization to advance plant breeding management. Given the ongoing challenges in agriculture, there is a belief that incorporating the latest technologies is crucial. The primary objective of this study is to explore how Deep Learning algorithms can be beneficial in categorizing agricultural records, particularly in monitoring and identifying variations in spring wheat germplasm. Various Deep Learning algorithms, including Convolution Neural Network (CNN), LeNet, and Inception-V3 are implemented to classify the records and extract various patterns. LeNet demonstrates optimized accuracy in classifying the records, outperforming CNN by 52% and Inception-V3 by 70%. Moreover, Precision, Recall, and F1 Measure are utilized to ascertain accuracy levels. The investigation also enhances our comprehension of the distinct roles played by various genes in abiotic stress tolerance among diverse wheat varieties. The outcomes of the research hold the potential to transform agricultural practices by introducing a more effective, data-driven approach to plant breeding management.