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The early prediction of ocular disease is certainly an obligatory concern in the domain of ophthalmic medicine. Although modern scientific discoveries have shown the potential to treat eye diseases by using artificial intelligence (AI) and machine learning, explainable AI remains a crucial challenge confronting this area of research. Although some traditional methods put in significant effort, they cannot accurately predict the proper ocular diseases. However, incorporating AI into diagnosing eye diseases in healthcare complicates the situation as the decision-making process of AI demonstrates complexity, which is a significant concern, especially in major sectors like ocular disease prediction. The lack of transparency in the AI models may hinder the confidence and trust of the doctors and the patients, as well as their perception of the AI and its abilities. Accordingly, explainable AI is significant in ensuring trust in the technology, enhancing clinical decision-making ability, and deploying ocular disease detection. This research proposed an efficient transfer learning model for eye disease prediction to transform smart vision potential in the healthcare sector and meet conventional approaches' challenges while integrating explainable artificial intelligence (XAI). The integration of XAI in the proposed model ensures the transparency of the decision-making process through the comprehensive provision of rationale. This proposed model provides promising results with 95.74% accuracy and explains the transformative potential of XAI in advancing ocular healthcare. This significant milestone underscores the effectiveness of the proposed model in accurately determining various types of ocular disease. It is clearly shown that the proposed model is performing better than the previously published methods. [ABSTRACT FROM AUTHOR]

For the first time, density functional theory (DFT) calculations have been employed for the measurement of the structural, mechanical, optical, and electrical properties of a bismuth-layered structure ferroelectrics (BLSFs) family member possessing an orthorhombic structure with Cmc21 space group. Based on the exchange–correlation approximation, our calculations show that Pb2Bi4Ti5O18 possesses an indirect band gap, while the materials X2Bi4Ti5O18 (X = Ba, Ca, and Sr) demonstrate direct band gap, where the estimated density functional fundamental band gap values lie between 1.84 to 2.33 eV, which are ideal for photovoltaic applications. The optical performance of these materials has been investigated by tuning the band gaps. The materials demonstrated outstanding optical characteristics, such as high absorption coefficients and low reflection. They exhibited impressive absorption coefficient (α = 105 cm−1) throughout a broad energy range, especially in the visible spectrum (105 cm−1 region). The findings show that the compounds demonstrate lower reflectivity in the visible and UV regions, making them suitable for single-junction photovoltaic cells and optoelectronic applications. The Voigt–Reuss–Hill averaging technique has been employed to derive elastic parameters like bulk modulus (B), Young's modulus, shear modulus (G), the Pugh ratio (B/G) and the Frantesvich ratio (G/B) at 0.1 GPa. The mechanical stability of the compounds was analyzed using the Born stability criteria. Pugh's ratio and Frantesvich's ratio show that all the compounds are ductile, making them ideal for flexible optical applications. [ABSTRACT FROM AUTHOR]

Background: Recent studies have associated total neoadjuvant therapy (TNT) with better treatment adherence, decreased toxicity, improved complete clinical response and anal sphincter preservation rates in patients with locally advanced rectal cancer (LARC). However, real-world experience with TNT in the management of LARC remains limited. Aim: This study aimed to evaluate the efficacy and safety outcomes of TNT for LARC in Western Australia. Methods: Patients with LARC (cT2-4 and/or cN1-2) who underwent induction chemotherapy followed by neoadjuvant chemoradiotherapy or neoadjuvant chemoradiotherapy followed by consolidation chemotherapy, followed by surgery were recruited from two hospitals in Western Australia. Efficacy outcomes assessed included clinical response (complete, partial, no response), and pathologic complete response (pCR) rate, R0 resection rate, and R1 resection rate were evaluated. Those patients who achieved clinical complete response following TNT were given the option of active surveillance. The safety and tolerability of TNT were assessed. Results: 32 patients with LARC were treated with TNT. In total, 17 patients (53%) received chemoradiotherapy followed by consolidation chemotherapy and 15 patients (47%) received induction chemotherapy followed by chemoradiotherapy. Nine (28%) of the patients with LARC treated with TNT had a complete clinical response, twenty-one (66%) patients had a partial clinical response, and two (6%) patients had no response to TNT. Of the 32 patients, 27 (84%) underwent surgery. There was a 100% R0 resection rate. The pCR rate was 15%. pCR, clinical response, and the R0 resection rate were similar between the two TNT regimens. TNT was well tolerated, with the majority of patients (88%) completing the chemotherapy course with grade 1 and 2 adverse effects. Conclusions: In conclusion, TNT emerges as a promising approach for the management of LARC. However, further research is warranted to refine the optimal TNT protocols, determine its long-term outcomes, and identify patient populations who would benefit the most from this innovative therapeutic strategy. [ABSTRACT FROM AUTHOR]

A finite element method is employed to examine the impact of a magnetic field on the development of plaque in an artery with stenotic bifurcation. Consistent with existing literature, blood flow is characterized as a Newtonian fluid that is stable, incompressible, biomagnetic, and laminar. Additionally, it is assumed that the arterial wall is linearly elastic throughout. The hemodynamic flow within a bifurcated artery, influenced by an asymmetric magnetic field, is described using the arbitrary Lagrangian–Eulerian (ALE) method. This technique incorporates the fluid–structure interaction coupling. The nonlinear system of partial differential equations is discretized using a stable P2P1 finite element pair. To solve the resulting nonlinear algebraic equation system, the Newton-Raphson method is employed. Magnetic fields are numerically modeled, and the resulting displacement, velocity magnitude, pressure, and wall shear stresses are analyzed across a range of Reynolds numbers (Re = 500, 1000, 1500, and 2000). The numerical analysis reveals that the presence of a magnetic field significantly impacts both the displacement magnitude and the flow velocity. In fact, introducing a magnetic field leads to reduced flow separation, an expanded recirculation area near the stenosis, as well as an increase in wall shear stress. [ABSTRACT FROM AUTHOR]

Simple Summary: This study explored the effects of the different dietary combinations of soyhulls and the β-mannanase enzyme on production performance, economics, egg quality, blood biochemicals, nutrient digestibility, and gut health in laying hens during the late peak production phase. Golden brown hens were fed different diets for four weeks, and the combination of 3% soyhulls and 30 mg/kg β-mannanase showed potential benefits. It improved production performance and increased egg weight, albumen weight, and height while significantly lowering total cholesterol, LDL, and VLDL levels. This combination also improved gut morphology and enhanced nutrient digestibility. Overall, the inclusion of 3% soyhulls and 30 mg/kg β-mannanase in the diet may have positive effects on production performance, nutrient digestibility, and gut health and potentially lower serum cholesterol levels in laying hens while maintaining acceptable egg quality. This study investigated the effects of the β-mannanase enzyme and soyhulls on production performance, economics, egg quality, hematology and serum biochemistry, nutrient digestibility, gut morphology, digesta viscosity, and excreta consistency in laying hens during the late peak production phase (37 to 40 weeks of age). Golden brown hens (RIR × Fayoumi; n = 200) were fed a control diet (no soyhulls or enzymes) and diets containing four combinations, i.e., 3% soyhulls with 20 mg/kg β-mannanase (D1), 3% soyhulls with 30 mg/kg β-mannanase (D2), 9% soyhulls with 20 mg/kg β-mannanase (D3), and 9% soyhulls with 30 mg/kg β-mannanase (D4), for four weeks in four replicates of 10 birds each. Overall, a significantly higher (p < 0.05) feed intake, weight gain, feed conversion ratio, and water intake were calculated in the D2 group as compared to the control and remaining combinations of soyhulls and β-mannanase. No mortality was recorded during the entire experiment. Economically, the D1 and D2 groups showed the best results as compared to the D3 and D4 groups. Egg quality parameters like egg weight, shell weight and shell thickness, yolk weight, albumen weight and height, and the Haugh unit remained unchanged (p > 0.05). Similarly, the D2 group showed significantly lower total cholesterol, LDL, and VLDL levels and enhanced gut morphology with greater villus width, height, crypt depth, and surface area across intestinal segments. Crude protein (CP), crude fiber (CF), crude fat, and ash digestibility were higher (p < 0.05) in the D1 and D2 groups compared to the control. Digesta viscosity, excreta consistency, and other egg quality parameters remained unaffected. In conclusion, the dietary inclusion of a combination of 3% soyhulls and 30 mg/kg β-mannanase may have potential benefits for laying hens by improving some production performance and egg quality indicators and economics, lowering blood cholesterol, LDL, and VLDL levels, enhancing nutrient digestibility, and improving gut morphology without affecting egg quality. [ABSTRACT FROM AUTHOR]

Oil spillages on a sea's or an ocean's surface are a threat to marine and coastal ecosystems. They are mainly caused by ship accidents, illegal discharge of oil from ships during cleaning and oil seepage from natural reservoirs. Synthetic-Aperture Radar (SAR) has proved to be a useful tool for analyzing oil spills, because it operates in all-day, all-weather conditions. An oil spill can typically be seen as a dark stretch in SAR images and can often be detected through visual inspection. The major challenge is to differentiate oil spills from look-alikes, i.e., low-wind areas, algae blooms and grease ice, etc., that have a dark signature similar to that of an oil spill. It has been noted over time that oil spill events in Pakistan's territorial waters often remain undetected until the oil reaches the coastal regions or it is located by concerned authorities during patrolling. A formal remote sensing-based operational framework for oil spills detection in Pakistan's Exclusive Economic Zone (EEZ) in the Arabian Sea is urgently needed. In this paper, we report the use of an encoder–decoder-based convolutional neural network trained on an annotated dataset comprising selected oil spill events verified by the European Maritime Safety Agency (EMSA). The dataset encompasses multiple classes, viz., sea surface, oil spill, look-alikes, ships and land. We processed Sentinel-1 acquisitions over the EEZ from January 2017 to December 2023, and we thereby prepared a repository of SAR images for the aforementioned duration. This repository contained images that had been vetted by SAR experts, to trace and confirm oil spills. We tested the repository using the trained model, and, to our surprise, we detected 92 previously unreported oil spill events within those seven years. In 2020, our model detected 26 oil spills in the EEZ, which corresponds to the highest number of spills detected in a single year; whereas in 2023, our model detected 10 oil spill events. In terms of the total surface area covered by the spills, the worst year was 2021, with a cumulative 395 sq. km covered in oil or an oil-like substance. On the whole, these are alarming figures. [ABSTRACT FROM AUTHOR]

In this study, a multi-stage planning framework was constructed by using SWMM simulation modeling and NSGA-II and applied to optimize the layout of integrated grey–green infrastructure (IGGI) under land use change and climate change scenarios. The land use change scenarios were determined based on the master plan of the study area, with imperviousness of 50.7% and 62.0% for stage 1 and stage 2, respectively. Rainfall trends for stage 1 and stage 2 were determined using Earth-E3 from the CMIP6 model. The rainfall in stage 2 increased by 14.9% from stage 1. Based on these two change scenarios, the spatial configuration of IGGI layouts with different degrees of centralization of the layout (DCL) under the two phases was optimized, with the lowest life cycle cost (LCC) as the optimization objective. The results showed that the layout with DCL = 0 had better performance in terms of LCC. The LCC of the layout with DCL = 0 was only 66.9% of that of the layout with DCL = 90.9%. In terms of Tech-R, stage 2 had better performance than stage 1. Furthermore, the average technological resilience (Tech-R) index of stage 2 was 0.8–3.4% higher than that of stage 1. Based on the LCC and Tech-R indices of all of the layouts, TOPSIS was used to compare the performance of the layouts under the two stages, and it was determined that the layout with DCL = 0 had the best economic and performance benefits. The results of this study will be useful in exploring the spatial configuration of urban drainage systems under land use change and climate change for sustainable stormwater management. [ABSTRACT FROM AUTHOR]

Celiac disease (CD) is a complicated autoimmune disease that is caused by gluten sensitivity. It was commonly believed that CD only affected white Europeans, but recent findings show that it is also prevailing in some other racial groups, like South Asians, Caucasians, Africans, and Arabs. Genetics plays a profound role in increasing the risk of developing CD. Genetic Variations in non-HLA genes such as LPP, ZMIZ1, CCR3, and many more influence the risk of CD in various populations. This study aimed to explore the association between LPP rs1464510 and ZMIZ1 rs1250552 and CD in the Punjabi Pakistani population. For this, a total of 70 human subjects were selected and divided into healthy controls and patients. Genotyping was performed using an in-house-developed tetra-amplification refractory mutation system polymerase chain reaction. Statistical analysis revealed a significant association between LPP rs1464510 (χ2 = 4.421, p = 0.035) and ZMIZ1 rs1250552 (χ2 = 3.867, p = 0.049) and CD. Multinomial regression analysis showed that LPP rs1464510 A allele reduces the risk of CD by ~52% (OR 0.48, CI: 0.24–0.96, 0.037), while C allele-carrying subjects are at ~2.6 fold increased risk of CD (OR 3.65, CI: 1.25–10.63, 0.017). Similarly, the ZMIZ1 rs1250552 AG genotype significantly reduces the risk of CD by 73% (OR 0.26, CI: 0.077–0.867, p = 0.028). In summary, Genetic Variations in the LPP and ZMIZ1 genes influence the risk of CD in Punjabi Pakistani subjects. LPP rs1464510 A allele and ZMIZ1 AG genotype play a protective role and reduce the risk of CD. [ABSTRACT FROM AUTHOR]

Skyrise greenery, including green roofs and vertical gardens, has emerged as an indispensable tool for sustainable urban planning with multiple ecological and economic benefits. A bibliometric analysis was used to provide a systematic review of the functions associated with skyrise greenery in urban landscapes. Key research tools, including the "Bibliometrix" R package and "CiteSpace" 6.2 R4, highlight the depth and breadth of the literature covering skyrise greenery. In 2000–2022, a total of 1474 original journal articles were retrieved. Over this period, there was an exponential increase in the number of publications, reflecting both enhanced knowledge and increasing concerns regarding climate change, the urban heat island, and urbanization. Of the total, ~58% of the articles originated from China, followed by the USA, Italy, Australia, and Canada. The research themes, such as urban heat islands, carbon sequestration, hydrology, and air quality, have been identified as the frontier in this fields. Furthermore, researchers from developed countries contributed the most publications to this domain, while developing countries, such as China, play an increasing role in the design and performance evaluation of vertical greenery. Key benefits identified in vertical green systems (e.g., green roofs and walls) include thermal regulation, sustainable water management, air-quality improvement, noise reduction, and biodiversity enhancement. In addition, several potential future research prospectives are highlighted. This review provides a comprehensive insight into exploring the pivotal role of skyrise greenery in shaping sustainable, resilient urban futures, coupled with sustainable urban planning. [ABSTRACT FROM AUTHOR]

Dibenzyltoluene (H0-DBT), a Liquid Organic Hydrogen Carrier (LOHC), presents an attractive solution for hydrogen storage due to its enhanced safety and ability to store hydrogen in a concentrated liquid form. The utilization of machine learning proves essential for accurately predicting hydrogen storage classes in H0-DBT across diverse experimental conditions. This study focuses on the classification of hydrogen storage data into three classes, low-class, medium-class and high-class, based on the hydrogen storage capacity values. We introduce Hydrogen Storage Prediction with the Support Vector Machine (HSP-SVM) model to predict the hydrogen storage classes accurately. The performance of the proposed HSP-SVM model was investigated using various techniques, which included 5-Fold Cross Validation (5-FCV), Resubstitution Validation (RV), and Holdout Validation (HV). The accuracy of the HV approach for the low, medium, and high class was 98.5%, 97%, and 98.5%, respectively. The overall accuracy of HV approach reached 97% with a miss clarification rate of 3%, whereas 5-FCV and RV possessed an overall accuracy of 93.9% with a miss clarification rate of 6.1%. The results reveal that the HV approach is optimal for predicting the hydrogen storage classes accurately. [ABSTRACT FROM AUTHOR]

Constructing underground reservoirs has emerged as a crucial strategy to address the shortage of fresh water in Rizhao, Shandong Province, China. However, the water quality, microbial community composition, and biogeochemical cycling of nutrients in underground reservoirs compared to raw water remain unknown. To unveil the characteristics of microbial community structures and their nitrogen cycling metabolic potential in coastal underground reservoirs, we utilized a functional gene array (GeoChip 5.0) in conjunction with high-throughput sequencing of 16S rRNA and 18S rRNA genes. Our findings indicate that the water quality in the underground reservoir exhibits a certain degree of eutrophication compared to raw water, with higher concentrations of TN, TP, NO3−N, NO2−-N, and Chl a, but lower concentrations of DO and NH4+-N. The alpha diversity of bacterial and microeukaryotic communities was significantly lower in the underground reservoir. The bacterial community presented a stronger correlation with environmental factors than the microeukaryotic community. Regarding the relative abundance of bacterial communities, Gammaproteobacteria dominated the bacterial community in raw water, while Gammaproteobacteria and Alphaproteobacteria dominated the bacterial community in underground reservoir water. Additionally, the relative abundance of Nitrospirae was noticeably higher in the underground reservoir water. Moreover, we found significantly higher sequence abundance of the archaea Thaumarchaeota in the underground reservoir. Furthermore, our analysis revealed that, except for the amoA functional gene, which significantly increased the metabolic potential of nitrification, the metabolic potential of other microbial nitrogen functional genes was significantly reduced. This reduction may contribute to the lower concentration of NH4+-N in the underground reservoir. This study provides a comprehensive understanding of the microbial community characteristics and their nitrogen cycling metabolic potential in underground reservoirs. It serves as a valuable reference for water source selection, the formulation of water quality assurance measures, and the construction and management of underground reservoirs for subsequent impounding. [ABSTRACT FROM AUTHOR]

The evolution of delta and riverbank erosion within the river basin can significantly impact the environment, ecosystems, and lives of those residing along rivers. The Vietnamese Mekong Delta (VMD), counted among the world's largest deltas, has undergone significant morphological alterations via natural processes and human activities. This research aims to examine these morphological alterations and their impacts on local economic and social conditions in the VMD. This study utilized satellite data from 1988 to 2020, coupled with population density and land use/land cover (LULC) maps from 2002, 2008, and 2015. The findings reveal that the VMD experienced widespread erosion over the past three decades, covering an area of 66.8 km2 and affecting 48% of the riverbank length (682 km). In contrast to riverbanks, islets showed an accretion trend with an additional area of 13.3 km2, resulting in a decrease in river width over the years. Riverbank and islet erosion has had a profound impact on the LULC, population, and economy of the provinces along the VMD. From 2002 to 2020, eight different land use types were affected, with agricultural land being the most severely eroded, constituting over 86% of the total lost land area (3235.47 ha). The consequences of land loss due to erosion affected 31,273 people and resulted in substantial economic damages estimated at VND 19,409.90 billion (USD 799.50 million) across nine provinces along the VMD. Notably, even though built-up land represented a relatively small portion of the affected area (6.58%), it accounted for the majority of the economic damage at 70.6% (USD 564.45 million). This study underscores the crucial role of satellite imagery and GIS in monitoring long-term morphological changes and assessing their primary impacts. Such analysis is essential for formulating effective plans and strategies for the sustainable management of river environments. [ABSTRACT FROM AUTHOR]

This review presents a comprehensive evaluation for the manufacture of organic molecules via efficient microfluidic synthesis. Microfluidic systems provide considerably higher control over the growth, nucleation, and reaction conditions compared with traditional large-scale synthetic methods. Microfluidic synthesis has become a crucial technique for the quick, affordable, and efficient manufacture of organic and organometallic compounds with complicated characteristics and functions. Therefore, a unique, straightforward flow synthetic methodology can be developed to conduct organic syntheses and improve their efficiency. [ABSTRACT FROM AUTHOR]

Drought is a complex natural disaster with significant implications for agriculture, water resources, and socioeconomic development. Accurate and timely assessment of meteorological drought is crucial for effective management and mitigation strategies. Climate change has led to a rise in climatic anomalies, such as droughts, floods, heatwaves, and cold snaps, which have severe impacts on human well-being and societal patterns. Droughts, which are prolonged periods of limited or absent rainfall, pose significant challenges for sectors like agriculture, energy, and enterprises, especially in economically reliant countries with inadequate water management infrastructure. Drought indicators are essential in meteorology, agriculture, and hydrology for monitoring drought conditions. Accurate drought assessment relies on quantitative index-based comprehensive drought indices, such as India's Aridity Anomaly Index (AAI), Deciles Index, Percent of Normal Index, Reconnaissance Drought Index (RDI), and the Palmer Drought Severity Index (PDSI). Drought management involves analyzing risk components and using analytical tools for decision making. A decision support system includes institutional, methodological, public, and operational components. Long-term actions include demand reduction through economic incentives, while short-term actions include increasing water supply through wastewater reutilization, inter-basin water conveyance, reservoir construction, and agricultural ponds. Impact minimization is achieved through educational initiatives, reallocating water resources, early warning systems, and insurance programs. Challenges include developing technologies to integrate data sources and create unified indicators, and geospatial decision-support systems facilitate hazard mapping and strategic drought management plans. [ABSTRACT FROM AUTHOR]

Some dams globally have negatively affected downstream structures. Constructing subsidiary weirs may solve this problem. This novel study focuses on investigating the parameters of seepage beneath the original structure and the proposed subsidiary weir. Conformal mapping and finite element methods are used for the analysis. The proposed subsidiary weir consists of a sloping central apron, flat aprons on both the downstream and upstream ends, and upstream and downstream sheet piles of varying depths. The existing structure also has sheet piles of different depths at its upstream and downstream ends, with an impervious layer situated at a specific depth below both the structures. The study derives equations for the simulation of the upwards pressure on both the structures, seepage rate, and exit gradient along the downstream bed and the filter at an intermediate location. Our own developed software for the analysis and a commercial software for numerical methods named Finite Element Heat Transfer (FEHT)-version-1are used to calculate these parameters. The accuracy of the analytical and numerical methods is verified by comparing the results with experimental data, which demonstrate a good level of agreement. This study also simulates the impacts of various factors, such as sheet pile configurations, the depth of the stratum beneath the structure, the ratio of effective heads, and the length of the intermediate filter. [ABSTRACT FROM AUTHOR]

Microbes hold immense potential, based on the fact that they are widely acknowledged for their role in mitigating the detrimental impacts of chemical fertilizers and pesticides, which were extensively employed during the Green Revolution era. The consequence of this extensive use has been the degradation of agricultural land, soil health and fertility deterioration, and a decline in crop quality. Despite the existence of environmentally friendly and sustainable alternatives, microbial bioinoculants encounter numerous challenges in real-world agricultural settings. These challenges include harsh environmental conditions like unfavorable soil pH, temperature extremes, and nutrient imbalances, as well as stiff competition with native microbial species and host plant specificity. Moreover, obstacles spanning from large-scale production to commercialization persist. Therefore, substantial efforts are underway to identify superior solutions that can foster a sustainable and eco-conscious agricultural system. In this context, attention has shifted towards the utilization of cell-free microbial exudates as opposed to traditional microbial inoculants. Microbial exudates refer to the diverse array of cellular metabolites secreted by microbial cells. These metabolites enclose a wide range of chemical compounds, including sugars, organic acids, amino acids, peptides, siderophores, volatiles, and more. The composition and function of these compounds in exudates can vary considerably, depending on the specific microbial strains and prevailing environmental conditions. Remarkably, they possess the capability to modulate and influence various plant physiological processes, thereby inducing tolerance to both biotic and abiotic stresses. Furthermore, these exudates facilitate plant growth and aid in the remediation of environmental pollutants such as chemicals and heavy metals in agroecosystems. Much like live microbes, when applied, these exudates actively participate in the phyllosphere and rhizosphere, engaging in continuous interactions with plants and plant-associated microbes. Consequently, they play a pivotal role in reshaping the microbiome. The biostimulant properties exhibited by these exudates position them as promising biological components for fostering cleaner and more sustainable agricultural systems. [ABSTRACT FROM AUTHOR]

Digital farming has revolutionized agriculture by integrating technologies like IoT, AI, big data analytics, and remote sensing. This paper explores the impact of digital farming on agricultural engineering practices, highlighting the changes it has brought to the agri-food landscape. By using real-time data collection, analysis, and predictive modeling, agricultural engineers can make informed decisions, enabling precise and sustainable resource management. Precision agriculture technologies can reduce fertilizer and pesticide use by up to 30%, increase yields by 10–20%, and conserve water by up to 50%. Digital farming practices have also increased efficiency and productivity, with autonomous farm machinery and smart irrigation systems. Autonomous tractors operate without human intervention, freeing up farmers to focus on other tasks. Smart irrigation systems automatically adjust watering schedules based on real-time weather and soil moisture data, ensuring optimal watering for crops. The objective of this study is to demonstrate the capacity of digital farming to bring about significant changes in agricultural engineering techniques, in contrast to conventional approaches. It will examine the effects of digital farming on resource allocation, environmental sustainability, and the global food supply, thereby highlighting its potential for transformation. The research aims to inspire stakeholders in the agricultural sector to embrace digital farming as a transformative force, shaping the future of agricultural engineering practices for a more efficient, resilient, and prosperous agriculture sector. [ABSTRACT FROM AUTHOR]

In the food and medical packaging industries, clean packaging is crucial to both customer satisfaction and hygiene. An operational Quality Assurance Department (QAD) is necessary for detecting contaminated packages. Manual examination becomes tedious and may lead to instances of contamination being missed along the production line. To address this issue, a system for contamination detection is proposed using an enhanced deep convolutional neural network (CNN) in a human–robot collaboration framework. The proposed system utilizes a CNN to identify and classify the presence of contaminants on product surfaces. A dataset is generated, and augmentation methods are applied to the dataset for nine classes such as coffee, spot, chocolate, tomato paste, jam, cream, conditioner, shaving cream, and toothpaste contaminants. The experiment was conducted using a mechatronic platform with a camera for contamination detection and a time-of-flight sensor for safe machine–environment interaction. The results of the experiment indicate that the reported system can accurately identify contamination with 99.74% mean average precision (mAP). [ABSTRACT FROM AUTHOR]

Anthrax, a severe zoonotic disease, is infrequently reported in anthrax-endemic regions of Pakistan. Despite clinical reports indicating its presence, particularly cutaneous anthrax, there is insufficient laboratory evidence regarding disease occurrence and environmental persistence. The present study aimed to confirm Bacillus anthracis presence, accountable for animal mortality and human infection, while exploring environmental transmission factors. Between March 2019 and July 2021, a total of 19 outbreaks were documented. Of these, 11 affected sheep/goats in Zhob district and 8 affected cattle/sheep in Bajour Agency. Clinical signs suggestive of Bacillus anthracis outbreak were observed in 11 animals. Blood and swab samples were collected for confirmation. The study followed a One Health approach, analyzing animal, environmental (soil/plant), and human samples. Of the 19 outbreaks, 11 were confirmed positive for anthrax based on growth characteristics, colony morphology, and PCR. Soil and plant root samples from the outbreak areas were collected and analyzed microscopically and molecularly. Cutaneous anthrax was observed in six humans, and swab samples were taken from the lesions. Human serum samples (n = 156) were tested for IgG antibodies against PA toxin and quantitative analysis of anthrax toxin receptor 1 (ANTXR1). Bacillus anthracis was detected in 65 out of 570 (11.40%) soil samples and 19 out of 190 (10%) plant root samples from the outbreak areas. Four out of six human samples from cutaneous anthrax lesions tested positive for Bacillus anthracis. Human anthrax seroprevalence was found to be 11% and 9% in two districts, with the highest rates among butchers and meat consumers. The highest ANTXR1 levels were observed in butchers, followed by meat consumers, farm employees, meat vendors, veterinarians, and farm owners. These findings highlight the persistence of anthrax in the region and emphasize the potential public health risks. [ABSTRACT FROM AUTHOR]

Several primary mechanisms are less utilized in engineering and recent technologies due to unsustainable heating. The impact of viscous dissipation and Joule heating is very important to examine current density and heat rate across a magnetized cylinder. The key objective of this examination was to insulate excessive heat around the cylinder. The present effort investigated the impact of viscous dissipations, Joule heating, and magnetohydrodynamics (MHD) on the transitory motion of convective-heat transport and magnetic flux features of dissipative flows throughout a magnetized and warmed cylinder at suitable places. The suggested turbulent dynamical structure of mathematics is offered for an associated method of partial differentiation equations impacted by boundary values. The complex equations are translated via non-dimensional shapes by using relevant non-dimensional numbers. The non-dimensional representation has been improved to make it easier to conduct uniform computational calculations. The computational answers for these linked dimensionalized formulations have been achieved using the Prandtl coefficient Pr, Joule heating parameter ζ , Eckert number E c , the magneto-force number ξ , the buoyancy parameter λ , and multiple additional predefined factors. The important contribution of this work is based on non-fluctuating solutions that are utilized to examine the oscillating behavior of shearing stress, rate of fluctuating heat transport, and rate of fluctuating magnetic flux in the presence of viscous dissipation and Joule heating at prominent angles. It is shown that the velocity of a fluid increases as the buoyancy parameter increases. The maximum frequency of heat transmission is illustrated for each Eckert variable. [ABSTRACT FROM AUTHOR]

Low-Impact Development (LID) represents a cogent strategy designed to conserve or reestablish antecedent hydrological states through an array of innovative mechanisms and methodologies. Since the dawn of the millennium, LID-centric research has demonstrated a persistent upward trajectory, mainly focusing on its capacity to mitigate climate change repercussions, particularly runoff and peak flows. However, a standardized rubric and toolkit for LID evaluation remain elusive. While numerous studies have documented the hydrological and water quality benefits of LID, the impacts of climate change on its effectiveness remain uncertain due to varying spatial and temporal climate patterns. This comprehensive review examined 1355 peer-reviewed articles in English, comprising both research articles and reviews, indexed in the Web of Science up until 2022. Findings from the bibliometric analysis revealed significant contributions and emergent trends in the field. Notably, there is an increasing emphasis on performance evaluation and efficiency of LID systems, and on understanding their impact on hydrology and water quality. However, this review identified the lack of a standardized LID evaluation framework and the uncertainty in LID effectiveness due to varying climate patterns. Furthermore, this study highlighted the urgent need for optimization of current hydrological models, advancement of LID optimization, modeling, monitoring, and performance, and stakeholder awareness about LID functionality. This review also underscored the potential future research trajectories, including the need to quantify LID's effectiveness in urban flooding and water quality management and refining LID simulation models. Cumulatively, this review consolidates contemporaneous and prospective research breakthroughs in urban LID, serving as an indispensable compendium for academics and practitioners in the discipline. [ABSTRACT FROM AUTHOR]

This bibliometric review elucidates the emerging intersection of Internet of Things (IoT) technologies and Green Stormwater Infrastructure (GSI), demonstrating the potential to reshape urban stormwater management. The study analyzes a steadily increasing corpus of literature since 2013, pointing out considerable international collaboration. Prominent contributions originate from the United States, Canada, Italy, China, and Australia, underscoring the global acknowledgement of the potential of IoT-enhanced GSI. Diverse GSI applications such as green roofs, smart rain barrels, bioretention systems, and stormwater detention ponds have demonstrated enhanced efficiency and real-time control with IoT integration. However, existing literature reveals several challenges, notably the requirement of advanced monitoring, the development of predictive optimization strategies, and extensive scalability. Comprehensive cost–benefit analyses are also critical for the widespread acceptance of IoT-integrated GSI. Current research addresses these challenges by exploring innovative strategies such as microbial-fuel-cell-powered soil moisture sensors and large-scale RTC bioretention systems. Emphasis is also on the need for security measures against potential digital threats. Future research needs to focus on real-time data-based monitoring plans, model validation, continuous optimization, and supportive policy frameworks. As the world confronts urban development, climate change, and aging infrastructure, IoT and GSI synergism presents a promising solution for effective stormwater management and enhancement of cultural ecosystem services. Continued exploration in this promising domain is crucial to pave the way for smarter, greener urban environments. [ABSTRACT FROM AUTHOR]

Extended-spectrum β-lactamases (ESBL) give rise to resistance against penicillin and cephalosporin antibiotics in multiple bacterial species. The present study was conducted to map genetic determinants and related attributes of ESBL-producing bacteria in three wild aquatic bird species and chickens at the "Trimmu Barrage" in district Jhang, Punjab province, Pakistan. To study the prevalence of ESBL-producing bacteria, a total of 280 representative samples were collected from wild bird species; cattle egrets (Bubulcus ibis), little egrets (Egretta garzetta) and common teals (Anas crecca) as well as from indigenous chickens (Gallus gallus domesticus) originating from a local wet market. The isolates were confirmed as ESBL producers using a double disc synergy test (DDST) and bacterial species were identified using API-20E and 20NE strips. A polymerase chain reaction (PCR) was used to detect ESBL genetic determinants and for genus identification via 16S rRNA gene amplification. A phenotypic antimicrobial susceptibility test was performed for ESBL-producing isolates against 12 clinically relevant antibiotics using the Kirby–Bauer disk diffusion susceptibility test. A phylogenetic tree was constructed for the sequence data obtained in this study and comparative sequence data obtained from GenBank. The overall prevalence of ESBL-producing bacteria was 34.64% (97/280). The highest percentage (44.28%; 31/70) of ESBL-producing bacteria was recovered from chickens (Gallus gallus domesticus), followed by little egrets (Egretta garzetta) (41.43%; 29/70), common teal (Anas crecca) (28.57%; 20/70) and cattle egrets (Bubulcus ibis) (24.28%; 17/70). Five different ESBL-producing bacteria were identified biochemically and confirmed via 16S rRNA gene sequencing, which included Escherichia coli (72; 74.23%), Enterobacter cloacae (11; 11.34%), Klebsiella pneumoniae (8; 8.25%), Salmonella enterica (4; 4.12%) and Pseudomonas aeruginosa (2; 2.06%). Based on PCR, the frequency of obtained ESBL genes in 97 isolates was blaCTX-M (51.55%), blaTEM (20.62%), blaOXA (6.18%) and blaSHV (2.06%). In addition, gene combinations blaCTX-M + blaTEM, blaTEM + blaOXA and blaCTX-M + blaSHV were also detected in 16.49%, 2.06% and 1.03% of isolates, respectively. The ESBL gene variation was significant (p = 0.02) in different bacterial species while non-significant in relation to different bird species (p = 0.85). Phylogenetic analysis of amino acid sequence data confirmed the existence of CTX-M-15 and TEM betalactamases. The average susceptibility of the antibiotics panel used was lowest for both Klebsiella pneumoniae (62.5% ± 24.42) and Salmonella enterica (62.5% ± 31.08) as compared to Enterobacter cloacae (65.90% ± 21.62), Pseudomonas aeruginosa (70.83% ± 33.42) and Escherichia coli (73.83% ± 26.19). This study provides insight into the role of aquatic wild birds as reservoirs of ESBL-producing bacteria at Trimmu Barrage, Punjab, Pakistan. Hence, active bio-surveillance and environment preservation actions are necessitated to curb antimicrobial resistance. [ABSTRACT FROM AUTHOR]

In the past two decades, there have been three coronavirus outbreaks that have caused significant economic and health crises. Biologists predict that more coronaviruses may emerge in the near future. Therefore, it is crucial to develop preventive vaccines that can effectively combat multiple coronaviruses. In this study, we employed computational approaches to analyze genetically related coronaviruses, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its variants, focusing on the spike glycoprotein as a potential vaccine candidate. By predicting common epitopes, we identified the top epitopes and combined them to create a multi-epitope candidate vaccine. The overall quality of the candidate vaccine was validated through in silico analyses, confirming its antigenicity, immunogenicity, and stability. In silico docking and simulation studies suggested a stable interaction between the multi-epitope candidate vaccine and human toll-like receptor 2 (TLR2). In silico codon optimization and cloning were used to further explore the successful expression of the designed candidate vaccine in a prokaryotic expression system. Based on computational analysis, the designed candidate vaccine was found to be stable and non-allergenic in the human body. The efficiency of the multi-epitope vaccine in triggering effective cellular and humoral immune responses was assessed through immune stimulation, demonstrating that the designed candidate vaccine can elicit specific immune responses against multiple coronaviruses. Therefore, it holds promise as a potential candidate vaccine against existing and future coronaviruses. [ABSTRACT FROM AUTHOR]

Polyamines (PAs) contribute to diverse plant processes, environmental interaction, and stress responses. In citrus, the mechanism underlying the biosynthesis of polyamines is poorly understood. The present study aims to identify the biosynthesis of PA gene family members in satsuma mandarin (Citrus unshiu) and investigate their response against various stresses. The identified biosynthesis of PA genes in C. unshiu showed clustering in six groups, i.e., SPMS, SPDS, ACL5, ADC, ODC, and SAMDC. Syntenic analysis revealed that segmental duplication was prevalent among the biosynthesis of PA genes compared to tandem duplication. Thus, it might be the main reason for diversity in the gene family in C. unshiu. Almost all biosynthesis of PA gene family members in C. unshiu showed syntenic blocks in the genome of Arabidopsis, Citrus sinensis, Poncirus trifoliata, and Citrus reticulata. Analysis of Cis-regulatory elements (CREs) indicated the occurrence of hormones, light, defense, and environmental stress responses as well as the development and other plant mechanisms-related elements in the upstream sequence of the biosynthesis of PA genes. Expression profiling revealed that the biosynthesis of PA gene expression modulates in different organs during various developmental stages and stress in C. unshiu. This information will provide a deep understanding of genomic information and its expression in multiple tissues to better understand its potential application in functional genomics. [ABSTRACT FROM AUTHOR]

The study examines the applicability of six metaheuristic regression techniques—M5 model tree (M5RT), multivariate adaptive regression spline (MARS), principal component regression (PCR), random forest (RF), partial least square regression (PLSR) and Gaussian process regression (GPR)—for predicting short-term significant wave heights from one hour to one day ahead. Hourly data from two stations, Townsville and Brisbane Buoys, Queensland, Australia, and historical values were used as model inputs for the predictions. The methods were assessed based on root mean square error, mean absolute error, determination coefficient and new graphical inspection methods (e.g., Taylor and violin charts). On the basis of root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) statistics, it was observed that GPR provided the best accuracy in predicting short-term single-time-step and multi-time-step significant wave heights. On the basis of mean RMSE, GPR improved the accuracy of M5RT, MARS, PCR, RF and PLSR by 16.63, 8.03, 10.34, 3.25 and 7.78% (first station) and by 14.04, 8.35, 13.34, 3.87 and 8.30% (second station) for the test stage. [ABSTRACT FROM AUTHOR]

Demand response (DR) strategies are recieving much attention recently for their applications in the residential sector. Electric vehicles (EVs), which are considered to be a fairly new consumer load in the power sector, have opened up new opportunities by providing the active utilization of EVs as a storage unit. Considering their storage capacities, they can be used in vehicle-to-grid (V2G) or vehicle-to-community (V2C) options instead of taking power in peak times from the grid itself. This paper suggests a community-based home energy management system for microgrids to achieve flatter power demand and peak demand shaving using particle swarm optimization (PSO) and user-defined constraints. A dynamic clustered load scheduling scheme is proposed, including a method for managing peak shaving using rules specifically designed for PV systems that are grid-connected alongside battery energy storage systems and electric vehicles. The technique being proposed involves determining the limits of feed-in and demand dynamically, using estimated load demands and profiles of PV power for the following day. Additionally, an optimal rule-based management technique is presented for the peak shaving of utility grid power that sets the charge/discharge schedules of the battery and EV one day ahead. Utilizing the PSO algorithm, the optimal inputs for implementing the rule-based peak shaving management strategy are calculated, resulting in an average improvement of about 7% in percentage peak shaving (PPS) when tested using MATLAB for numerous case studies. [ABSTRACT FROM AUTHOR]

The emergence of SARS-CoV-2 variants has posed a challenge to disease control efforts worldwide. This study explored the genomic diversity and phylogenetic relationship of SARS-CoV-2 variants reported in Pakistan. Our objective was to understand the transmission dynamics of different lineages within the country. We retrieved and analyzed spike protein sequences from Pakistan and compared them with reference sequences reported worldwide. Our analysis revealed the clustering of Pakistan-origin isolates in nine different clades representing different regions worldwide, suggesting the transmission of multiple lineages within the country. We found 96 PANGO lineages of SARS-CoV-2 in Pakistan, and 64 of these corresponded to 4 WHO-designated variants: Alpha, Beta, Delta, and Omicron. The most dominant variants in Pakistan were Alpha (B.1.1.7), Beta (B.1.351), Delta (B.1.617.2, AY.108), and Omicron (BA.2.75, BA.5.2), and the N-terminal domain and receptor binding regions were the most hypervariable regions of the spike gene. Compared to the reference strain, characteristic substitutions were found in dominant variants. Our findings emphasize the importance of continuously monitoring and assessing nucleotide and residue substitutions over time to understand virus evolutionary trends better and devise effective disease control interventions. [ABSTRACT FROM AUTHOR]

Horizontal-axis wind turbines are the most popular wind machines in operation today. These turbines employ aerodynamic blades that may be oriented either upward or downward. HAWTs are the most common non-conventional source of energy generation. These turbine blades fail mostly due to fatigue, as a large centrifugal force acts on them at high rotational speeds. This study aims to increase a turbine's service life by improving the turbine blades' fatigue life. Predicting the fatigue life and the design of the turbine blade considers the maximum wind speed range. SolidWorks, a CAD program, is used to create a wind turbine blade utilizing NACA profile S814. The wind turbine blade's fatigue life is calculated using Morrow's equation. A turbine blade will eventually wear out due to several forces operating on it. Ansys software is used to analyze these stresses using the finite element method. The fatigue study of wind turbine blades is described in this research paper. To increase a turbine blade's fatigue life, this research study focuses on design optimization. Based on the foregoing characteristics, an improved turbine blade design with a longer fatigue life than the original one is intended in this study. The primary fatigue parameters are the length of a chord twist angle and blade length. The experimental data computed with the aid of a fatigue testing machine are also used to validate the numerical results, and it is found that they are very similar to one another. By creating the most effective turbine blades with the longest fatigue life, this research study can be developed further. The most effective turbine blades with the longest fatigue life can be designed to further this research investigation. [ABSTRACT FROM AUTHOR]

Gamma-aminobutyric acid (GABA) is a non-protein amino acid known for its role in the nervous system of animals. However, research has also revealed its presence and function in plants recently. In plants, GABA is a signal molecule involved in multiple physiological processes, including stress response, growth, and development. This review aims to present a thorough summary of the current knowledge regarding the role of GABA in plants. We begin by discussing the biosynthesis and transport of GABA in plants, followed by a detailed examination of its signaling mechanisms. Additionally, we explore GABA's potential roles in various plant physiological processes, such as abiotic stress response, and its potential application in horticultural plants. Finally, we highlight current challenges and future directions for research in this area. Overall, this review offers a comprehensive understanding of the significance of GABA in plants and its potential implications for plant physiology and crop improvement. [ABSTRACT FROM AUTHOR]

This study investigates the ability of a new hybrid neuro-fuzzy model by combining the neuro-fuzzy (ANFIS) approach with the marine predators' algorithm (MPA) in predicting short-term (from 1 h ahead to 1 day ahead) significant wave heights. Data from two stations, Cairns and Palm Beach buoy, were used in assessing the considered methods. The ANFIS-MPA was compared with two other hybrid methods, ANFIS with genetic algorithm (ANFIS-GA) and ANFIS with particle swarm optimization (ANFIS-PSO), in predicting significant wave height for multiple lead times ranging from 1 h to 1 day. The multivariate adaptive regression spline was investigated in deciding the best input for prediction models. The ANFIS-MPA model generally offered better accuracy than the other hybrid models in predicting significant wave height in both stations. It improved the accuracy of ANFIS-PSO and ANFIS-GA by 8.3% and 11.2% in root mean square errors in predicting a 1 h lead time in the test period. [ABSTRACT FROM AUTHOR]

In years with unfavorable weather, winter frost during the blossoming season can play a significant role in reducing fruit yield and impacting the profitability of cultivation. The mango Naomi cultivar Mangifera indica L. has a low canopy that is severely affected by the effects of frost stress. As a result of the canopy being exposed to physiological problems, vegetative development is significantly inhibited. The current investigation aimed to study the influence of spraying nitric oxide and fogging spray systems on Naomi mango trees grafted on 'Succary' rootstock under frost stress conditions. The treatments were as follows: nitric oxide (NO) 50 and 100 μM, fogging spray system, and control. In comparison to the control, the use of nitric oxide and a fogging system significantly improved the leaf area, photosynthesis pigments of the leaf, the membrane stability index, yield, and physical and chemical characteristics of the Naomi mango cultivar. For instance, the application of 50 μM NO, 100 μM NO, and the fogging spray system resulted in an increase in yield by 41.32, 106.12, and 121.43% during the 2020 season, and by 39.37, 101.30, and 124.68% during the 2021 season compared to the control, respectively. The fogging spray system and highest level of NO decreased electrolyte leakage, proline content, total phenolic content, catalase (CAT), peroxidases (POX), and polyphenol oxidase (PPO) enzyme activities in leaves. Furthermore, the number of damaged leaves per shoot was significantly reduced after the application of fogging spray systems and nitric oxide in comparison to the control. Regarding vegetative growth, our results indicated that the fogging spray system and spraying nitric oxide at 100 μM enhanced the leaf surface area compared to the control and other treatments. A similar trend was noticed regarding yield and fruit quality, whereas the best values were obtained when the fogging spray system using nitric oxide was sprayed at a concentration of 100 μM. The application of fogging spray systems and nitric oxide can improve the production and fruit quality of Naomi mango trees by reducing the effects of adverse frost stress conditions. [ABSTRACT FROM AUTHOR]

WRKY proteins are an important group of transcription factors (TFs) gene family and were identified primarily in plants. WRKY TFs play vital roles in modulating gene expression when plants face detrimental effects due to the environment. In the current study, we focused on using the mandarin citrus (Citrus reticulata) genome to understand the impact of the WRKY gene family on the extraction of alleles mining in mandarins. The mining of the C. reticulata genome identified 46 CrWRKY genes that were classified into three main groups (G1, G2, and G3) further with five subclasses (IIa, IIb, IIc, Iid, and IIe) in the G2 group, and all were presented on 29 scaffolds representing numerous segmental duplications of 100% events established. Multiple sequence analysis predicted the presence of the "WRKYGQK" domain and metal-chelating zinc-finger motif C2H2 in 45 genes, while the "WRKYGQK" domain was replaced with "WRKYGKK" only in CrWRKY20. The comparative relationship of CrWRKY with other plant species using dual synteny analysis revealed that the divergence between C. reticulata and C. grandis occurred after the evolutionary divergence of C. clementine, C. sinensis, C. medica, and C. ichangensis. The possible functions of the CrWRKY genes in mitigating environmental effects were predicted using cis-regulatory elements analysis and in silico RNAseq analysis, for the development of plants. These results provide a robust platform and absence of knowledge for the functional identification from key genes of CrWRKY genes in the mandarin for the possible use to improve key desirable agronomic and consumer-driven fruit quality traits in mandarins and related species. [ABSTRACT FROM AUTHOR]