Search

In data-scarce regions, prudent planning and precise decision-making for sustainable development, especially in agriculture, remain challenging due to the lack of correct information. Remotely sensed satellite images provide a powerful source for assessing land use and land cover (LULC) classes and crop identification. Applying remote sensing (RS) in conjunction with the Geographical Information System (GIS) and modern tools/algorithms of artificial intelligence (AI) and deep learning has been proven effective for strategic planning and integrated resource management. The study was conducted in the canal command area of the Lower Chenab Canal system in Punjab, Pakistan. Crop features/classes were assessed using the Normalized Difference Vegetation Index (NDVI) algorithm. The Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m and Landsat 5 TM (thematic mapper) images were deployed for NDVI time-series analysis with an unsupervised classification technique to obtain LULC classes that helped to discern cropping pattern, crop rotation, and the area of specific crops, which were then used as key inputs for agricultural mechanization planning and resource management. The accuracy of the LULC map was 78%, as assessed by the error matrix approach. Limitations of high-resolution RS data availability and the accuracy of the results are the concerns observed in this study that could be managed by the availability of good quality local sources and advanced processing techniques, that would make it more useful and applicable for regional agriculture and environmental management.

More than 800 million people worldwide suffer from chronic kidney disease (CKD). It stands as one of the primary causes of global mortality, uniquely noted for an increase in death rates over the past twenty years among non-communicable diseases. Machine learning (ML) has promise for forecasting such illnesses, but its opaque nature, difficulty in explaining predictions, and difficulty in recognizing predicted mistakes limit its use in healthcare. Addressing these challenges, our research introduces an explainable ML model designed for the early detection of CKD. Utilizing a multilayer perceptron (MLP) framework, we enhance the model’s transparency by integrating Local Interpretable Model-agnostic Explanations (LIME), providing clear insights into the predictive processes. This not only demystifies the model’s decision-making but also empowers healthcare professionals to identify and rectify errors, understand the model’s limitations, and ascertain its reliability. By improving the model’s interpretability, we aim to foster trust and expand the utilization of ML in predicting CKD, ultimately contributing to better healthcare outcomes.

This study assessed the past changes in extreme precipitation and temperature events across the Himalayan Mountains of Pakistan. This cold and mountainous environmental region has witnessed a significant increase in climate-related disasters over the past few decades. Spatiotemporal changes in extreme temperature and precipitation events were analyzed using 24 indices developed by the Expert Team on Climate Change Detection and Indices (ETCCDI). For this study, in situ data of 16 national meteorological stations were obtained from the Pakistan Meteorological Department (PMD) for the past three decades (1991–2020). The significance of the trends was assessed using the modified Mann–Kendall (MMK) test, and the Theil–Sen (TS) slope estimator was used to estimate the slope of the trend. The results showed that there has been a consistent decline in the total precipitation amount across the Himalayan Mountains of Pakistan. The trend exhibited a decrease in the annual average precipitation at a rate of −6.56 mm/year. Simultaneously, there was an increasing trend in the annual average minimum and maximum temperatures at rates of 0.02 °C/year and 0.07 °C/year, respectively. The frequencies of consecutive wet days (CWDs) and maximum 5-day precipitation (RX5day) have decreased significantly, with decreasing rates of −0.40 days/year and −1.18 mm/year, respectively. The amount of precipitation during very wet days (R95p) and extremely wet days was decreased by −19.20 and −13.60 mm/decade, respectively. The warm spell duration (WSDI) and the frequency of warm days (TX90p) across the Himalayan Range both increased by 1.5 and 1.4 days/decade. The number of cold days (TX10p) and cold nights (TN10p) decreased by 2.9 and 3.4 days/decade. The average temperature of the hottest nights (TXn) and the diurnal temperature range (DTR) were increased by 0.10 and 0.30 °C/decade. The results indicated an increasing tendency of dry and warm weather in the Himalayan region of Pakistan, which could have adverse consequences for water resources, agriculture, and disaster management in the country. Therefore, it is essential to prioritize the implementation of localized adaptation techniques in order to enhance sustainable climate resilience and effectively address the emerging climate challenges faced by these mountainous regions.

This paper investigated the periodic and quasi-periodic orbits in the symmetric collinear four-body problem through a variational approach. We analyze the conditions under which homographic solutions minimize the action functional. We compute the minimal value of the action functional for these solutions and show that, for four equal masses organized in a linear configuration, these solutions are the minimizers of the action functional. Additionally, we employ numerical experiments using Poincaré sections to explore the existence and stability of periodic and quasi-periodic solutions within this dynamical system. Our results provide a deeper understanding of the variational principles in celestial mechanics and reveal complex dynamical behaviors, crucial for further studies in multi-body problems.

The use of advanced brittle composites in engineering systems has necessitated robotic rotary ultrasonic machining to attain high precision with minimal machining defects such as delamination, burrs, and cracks. Longitudinal–torsional coupled (LTC) vibrations are created by introducing helical slots to a horn’s profile to enhance the quality of ultrasonic machining. In this investigative research, modified ultrasonic horns were designed for a giant magnetostrictive transducer by generating helical slots in catenoidal and cubic polynomial profiles to attain a high amplitude ratio (TA/LA) and low stress concentrations. Novel ultrasonic horns with a giant magnetostrictive transducer were modelled to compute impedances and harmonic excitation responses. A structural dynamic analysis was conducted to investigate the effect of the location, width, depth and angle of helical slots on the Eigenfrequencies, torsional vibration amplitude, longitudinal vibration amplitude, stresses and amplitude ratio in novel LTC ultrasonic horns for different materials using the finite element method (FEM) based on the block Lanczos and full-solution methods. The newly designed horns achieved a higher amplitude ratio and lower stresses in comparison to the Bezier and industrial stepped LTC horns with the same length, end diameters and operating conditions. The novel cubic polynomial LTC ultrasonic horn was found superior to its catenoidal counterpart as a result of an 8.45% higher amplitude ratio. However, the catenoidal LTC ultrasonic horn exhibited 1.87% lower stress levels. The position of the helical slots was found to have the most significant influence on the vibration characteristics of LTC ultrasonic horns followed by the width, depth and angle. This high amplitude ratio will contribute to the improved vibration characteristics that will help realize good surface morphology when machining advanced materials.

Colon capsule endoscopy (CCE) has regained popularity for lower gastrointestinal investigations since the COVID-19 pandemic. While there have been systematic reviews and meta-analyses on colonic polyp detection using CCE, there is a lack of comprehensive evidence concerning colonic inflammation. Therefore, this systematic review and meta-analysis aimed to assess the diagnostic accuracy of CCE for colonic inflammation, predominantly ulcerative colitis (UC) and Crohn’s disease (CD). Methods: We systematically searched electronic databases (EMBASE, MEDLINE, PubMed Central, and Cochrane Library) for studies comparing the diagnostic accuracy between CCE and optical endoscopy as the standard reference. A bivariate random effect model was used for the meta-analysis. Results: From 3797 publications, 23 studies involving 1353 patients were included. Nine studies focused on UC, and ten focused on CD. For UC, CCE showed a pooled sensitivity of 92% (95% CI, 88–95%), a specificity of 71% (95% CI, 35–92%), and an AUC of 0.93 (95% CI, 0.89–0.97). For CD, the pooled sensitivity was 92% (95% CI, 89–95%), and the specificity was 88% (95% CI, 84–92%), with an AUC of 0.87 (95% CI, 0.76–0.98). Overall, for inflammatory bowel disease, the pooled sensitivity, specificity, and AUC were 90% (95% CI, 85–93%), 76% (95% CI, 56–90%), and 0.92 (95% CI, 0.94–0.97), respectively. Conclusions: Despite the challenges around standardised disease scoring and the lack of histological confirmation, CCE performs well in diagnosing inflammatory bowel disease. It demonstrates high sensitivity in both UC and Crohn’s terminal ileitis and colitis and high specificity in Crohn’s disease. Further studies are needed to evaluate the diagnostic accuracy of other colonic inflammatory conditions.

The quality and level of groundwater tables have rapidly declined because of intensive pumping in Punjab (Pakistan). For sustainable groundwater supplies, there is a need for better management practices. So, the identification of potential groundwater recharge zones is crucial for developing effective management systems. The current research is based on integrating seven contributing factors, including geology, soil map, land cover/land use, lineament density, drainage density, slope, and rainfall to categorize the area into various groundwater recharge potential zones using remote sensing, geographic information system (GIS), and analytical hierarchical process (AHP) for Punjab, Pakistan. The weights (for various thematic layers) and rating values (for sub-classes) in the overlay analysis were assigned for thematic layers and then modified and normalized using the AHP. The result indicates that about 17.88% of the area falls under the category of very high groundwater potential zones (GWPZs). It was found that only 12.27% of the area falls under the category of very low GWPZs. The results showed that spatial technologies like remote sensing and geographic information system (GIS), when combined with AHP technique, provide a robust platform for studying GWPZs. This will help the public and government sectors to understand the potential zone for sustainable groundwater management.

Exogenous application of sodium nitroprusside (SNP) has previously been reported to trigger plant tolerance against a variety of environmental stresses. The present study was planned to investigate the possible role/s of exogenously applied SNP (50 or 100 μM) in alleviating cadmium (Cd)-induced effects on physio-biochemical processes, yield attributes, and grain quality traits of three fragrant rice cultivars, viz., Meixiangzhan-2 (MXZ), Guixiangzhan (GXZ), and Xiangyaxiangzhan (XYXZ) under 50 mg Cd kg−1 of soil. The results revealed that foliar spray of SNP (50 or 100 μM) on Cd-stressed rice plants reduced oxidative stress (lower hydrogen peroxide (H2O2), malondialdehyde (MDA), and electrolyte leakage (EL)) and improved the photosynthetic apparatus through higher chlorophyll contents, gas exchange attributes, and intact chloroplast configurations, and reduced Cd concentration in the leaves and grains of aromatic rice cultivars. The reduced levels of cellular ROS, MDA, and EL were related to the endogenous NO-mediated improvement in the activity of anti-oxidative enzymes and those involved during the ascorbate–glutathione cycle. However, among the different SNP levels, the foliar spraying of 50 μM of SNP was recorded to be the best treatment for fragrant rice growth, which increased grain yield by 42.06%, 46.03%, and 31.21%, and the quality trait of 2-acetyl-1-pyrroline (2-AP) content by 43.12%, 55.84%, and 35.72% in MXZ, GXZ, and XYXZ respectively, suggesting that GXZ is more responsive to SNP than MXZ and XYXZ fragrant rice cultivars. Collectively, our results deduced that cultivating the GXZ fragrant rice cultivar along with foliar application of 50 μM of SNP could sustain the grain yield and quality features of aromatic rice cultivation in heavy metal (especially Cd)-polluted soils.

The close interaction between neurons and astrocytes has been extensively studied. However, the specific behavior of these cells after ischemia-reperfusion injury and hypothermia remains poorly characterized. A growing body of evidence suggests that mitochondria function and putative transference between neurons and astrocytes may play a fundamental role in adaptive and homeostatic responses after systemic insults such as cardiac arrest, which highlights the importance of a better understanding of how neurons and astrocytes behave individually in these settings. Brain injury is one of the most important challenges in post-cardiac arrest syndrome, and therapeutic hypothermia remains the single, gold standard treatment for neuroprotection after cardiac arrest. In our study, we modeled ischemia-reperfusion injury by using in vitro enhanced oxygen-glucose deprivation and reperfusion (eOGD-R) and subsequent hypothermia (HPT) (31.5 °C) to cell lines of neurons (HT-22) and astrocytes (C8-D1A) with/without hypothermia. Using cell lysis (LDH; lactate dehydrogenase) as a measure of membrane integrity and cell viability, we found that neurons were more susceptible to eOGD-R when compared with astrocytes. However, they benefited significantly from HPT, while the HPT effect after eOGD-R on astrocytes was negligible. Similarly, eOGD-R caused a more significant reduction in adenosine triphosphate (ATP) in neurons than astrocytes, and the ATP-enhancing effects from HPT were more prominent in neurons than astrocytes. In both neurons and astrocytes, measurement of reactive oxygen species (ROS) revealed higher ROS output following eOGD-R, with a non-significant trend of differential reduction observed in neurons. HPT after eOGD-R effectively downregulated ROS in both cells; however, the effect was significantly more effective in neurons. Lipid peroxidation was higher after eOGD-R in neurons, while in astrocytes, the increase was not statistically significant. Interestingly, HPT had similar effects on the reduction in lipoperoxidation after eOGD-R with both types of cells. While glutathione (GSH) levels were downregulated after eOGD-R in both cells, HPT enhanced GSH in astrocytes, but worsened GSH in neurons. In conclusion, neuron and astrocyte cultures respond differently to eOGD-R and eOGD-R + HTP treatments. Neurons showed higher sensitivity to ischemia-reperfusion insults than astrocytes; however, they benefited more from HPT therapy. These data suggest that given the differential effects from HPT in neurons and astrocytes, future therapeutic developments could potentially enhance HPT outcomes by means of neuronal and astrocytic targeted therapies.

With the advent of “next-generation” sequencing and the continuous reduction in sequencing costs, an increasing amount of genomic data has emerged, such as whole-genome, whole-exome, and targeted sequencing data. These applications are popular not only in mega sequencing projects, such as the 1000 Genomes Project and UK BioBank, but also among individual researchers. Evolutionary genetic analyses, such as the dN/dS ratio and Tajima’s D, are demanded more and more for whole-genome-level population data. These analyses are often carried out under a uniform custom bin size across the genome. However, these analyses require subdivision of a genomic region into functional units, such as protein-coding regions, introns, and untranslated regions, and computing these genetic measures for large-scale data remains challenging. In a recent investigation, we successfully devised a method to address this issue. This method requires a multi-sample VCF file containing population data, a reference genome, target regions in the BED file, and a list of samples to be included in the analysis. Given that the targeted regions are extracted in a new VCF file, targeted population genetic analysis can be performed. We conducted Tajima’s D analysis using this approach on intact and pseudogenes, as well as non-coding regions.

We propose a new definition of the γ-convex stochastic processes (CSP) using center and radius (CR) order with the notion of interval valued functions (C.RI.V). By utilizing this definition and Mean-Square Fractional Integrals, we generalize fractional Hermite–Hadamard–Mercer-type inclusions for generalized C.RI.V versions of convex, tgs-convex, P-convex, exponential-type convex, Godunova–Levin convex, s-convex, Godunova–Levin s-convex, h-convex, n-polynomial convex, and fractional n-polynomial (CSP). Also, our work uses interesting examples of C.RI.V(CSP) with Python-programmed graphs to validate our findings using an extension of Mercer’s inclusions with applications related to entropy and information theory.

The use of the blow fly Calliphora vicina as a potential pollination species to augment the current reliance on honeybees (Apis mellifera) in Australian horticulture requires knowledge of how best to mass-rear this fly species. Calliphora vicina lays eggs onto carrion soon after death, and the resultant larvae that hatch are necrophagous and feed on the decomposing tissues of the dead animal. Newly hatched larvae of this fly were provided with plant-based meals (soya bean and canola) and compared with larvae provided with livestock-derived meatmeal to determine if plant-based meal could be used to mass-rear this blow fly species. Both soya bean and canola meal media did not support larval survival through to adult emergence. The addition of only 10% whole egg powder to the plant-based meals enabled survival to eclosion of 39% and 13% on soya bean and canola-based media, respectively, compared with 76% on livestock-based meatmeal with 10% whole egg powder. Larvae fed a diet of livestock-based meatmeal with 10% whole dried egg powder had the fastest development to the pupal stage, the highest pupation rate, the heaviest pupae, and the highest subsequent adult eclosion. This study concluded that the use of plant-based meals as a diet for the mass-rearing of the blow fly C. vicina was not a viable option.

The objective of the present study was to investigate the impact of MgO 0.5 g/kg loaded in different organic waste materials on the properties of the modified biochars obtained. The waste materials included tea waste, wood waste, water chestnut peel, and pomegranate peel, which were used to create tea waste MgO-modified biochar (TWMgO-MBC), wood waste MgO-modified biochar (WSMgO-MBC), water chestnut peel MgO-modified biochar (WCMgO-MBC), and pomegranate peel MgO-modified biochar (PPMgO-MBC). All the MgO-modified biochars were prepared at 600 °C for 2 h and applied at 0.5 and 1% doses for the immobilization of Ni, Cu, Pb, and Cr in stone crushing and mining-polluted soil and the reduction in their uptake by pearl millet (Pennisetum glaucum) plant. The greatest fresh and dry biomasses were observed at 45.04% and 31.29%, respectively, with the application of TWMgO-MBC 1% in stone-crushing-polluted soil. The highest degree of immobilization of Ni (76.67%) was observed for the WSMgO-MBC 1% treatment, Cu (73.45%) for WCMgO-MBC 1%, Pb (76.78%) for WSMgO-MBC 1%, and Cr (70.55%) for WCMgO-MBC 1%, in comparison with the control. The maximum uptake of Ni, Cu, Pb, and Cr in the shoot of pearl millet was reduced by 78.43% with WSMgO-MBC 1%, 75.06% with WSMgO-MBC 1%, 90.81% with WCMgO-MBC 1%, and 85.71% with WSMgO-MBC 1% as compared with the control. The greatest reduction in Ni, Cu, Pb, and Cr in the root of pearl millet was observed at 77.81% with WSMgO-MBC 1%, 68.09% with WCMgO-MBC 1%, 84.03% with WCMgO-MBC 1%, and 88.73% with WCMgO-MBC 1%, in comparison with the control. The present study demonstrated that the TWMgO-MBC 1% treatment was highly effective for improving plant growth, while the WSMgO-MBC 1%, and WCMgO-MBC 1% treatments were found to be highly effective for immobilizing heavy metals in polluted soils, thus facilitating safe crop cultivation. Future studies should concentrate on the long-term application of MgO-modified biochars for the remediation of multimetal-polluted soils.

Underwater wireless sensor networks are gaining popularity since supporting a broad range of applications, both military and civilian. Wireless acoustics is the most widespread technology adopted in underwater networks, the realization of which must face several challenges induced by channel propagation like signal attenuation, multipath and latency. In order to address such issues, the attention of researchers has recently focused on the concept of cooperative communication and networking, borrowed from terrestrial systems and to be conveniently recast in the underwater scenario. In this paper, we present a comprehensive literature review about cooperative underwater wireless sensor networks, investigating how nodes cooperation can be exploited at the different levels of the network protocol stack. Specifically, we review the diversity techniques employable at the physical layer, error and medium access control link layer protocols, and routing strategies defined at the network layer. We also provide numerical results and performance comparisons among the most widespread approaches. Finally, we present the current and future trends in cooperative underwater networks, considering the use of machine learning algorithms to efficiently manage the different aspects of nodes cooperation.

X-rays can induce morphological as well as functional changes in cells. Platelets are anuclear cellular fragments originating from megakaryocytes and are the major regulators in hemostasis and thrombosis. Platelet products are irradiated to avoid medical complications associated with platelet transfusion. So far, gamma, UV, and laser radiation have been used for this purpose. However, scientists are divided about the effects of radiation on platelet quality. The present study was designed to explore the possible effects of X-rays in washed human platelets and understand the molecular mechanism behind them. In the present study, we exposed washed human platelets to 10 or 30 Gy X-rays at 0.25 Gy/min. Flow cytometry, aggregometry, and western blot were performed to investigate the effect of X-rays on platelet degranulation, integrin activation, platelet aggregation, and apoptosis. It was found that X-rays immediately induced granular secretions with no effect on GP IIb/IIIa activation. Not surprisingly, due to granule secretions in irradiated platelets, platelet aggregation was significantly reduced. In contrast to granular secretions and platelet aggregation, X-rays induced mitochondrial transmembrane potential depolarization in a time-dependent manner to induce apoptosis and activated protein kinase C (PKC) signaling. This study revealed and explained the molecular mechanism activated by X-rays in washed human platelets. Here we also introduced Gö 6983, a PKC inhibitor, as an agent that counteracts X-ray-induced changes and maintains the integrity of platelets.

An explicit computational scheme is proposed for solving fractal time-dependent partial differential equations (PDEs). The scheme is a three-stage scheme constructed using the fractal Taylor series. The fractal time order of the scheme is three. The scheme also ensures stability. The approach is utilized to model the time-varying boundary layer flow of a non-Newtonian fluid over both stationary and oscillating surfaces, taking into account the influence of heat generation that depends on both space and temperature. The continuity equation of the considered incompressible fluid is discretized by first-order backward difference formulas, whereas the dimensionless Navier–Stokes equation, energy, and equation for nanoparticle volume fraction are discretized by the proposed scheme in fractal time. The effect of different parameters involved in the velocity, temperature, and nanoparticle volume fraction are displayed graphically. The velocity profile rises as the parameter I grows. We primarily apply this computational approach to analyze a non-Newtonian fluid’s fractal time-dependent boundary layer flow over flat and oscillatory sheets. Considering spatial and temperature-dependent heat generation is a crucial factor that introduces additional complexity to the analysis. The continuity equation for the incompressible fluid is discretized using first-order backward difference formulas. On the other hand, the dimensionless Navier–Stokes equation, energy equation, and the equation governing nanoparticle volume fraction are discretized using the proposed fractal time-dependent scheme.

The present study emphasizes the importance of documenting ethnomedicinal plants and herbal practices of the local rural communities of Tehsil Hajira (Pakistan). The aim was to document, explore and quantify the traditional ethnomedicinal knowledge. Ethnobotanical data were collected using semi-structured questionnaires and analyzed using various quantitative indices. The results showed that 144 medicinal plant species from 70 families and 128 genera play an important role in herbal preparations. The most common type of preparation was powder (19.0%), followed by paste (16.7%), aqueous extract (15.7%), decoction (14.7%) and juice (11.0%). Fragaria nubicola (0.94) and Viola canescens (0.93) had the highest relative frequency of mention (RFC), while Berberis lycium (1.22) and Fragaria nubicola (1.18) had the highest use value (UV). Geranium wallichianum (85.5), Ligustrum lucidum (83) and Indigofera heterantha (71.5) were the most important species in the study area with the highest relative importance (RI) value. The diseases treated were categorized into 17 classes, with diseases of the digestive system and liver having the highest Informant Consensus Factor (ICF) value, followed by diseases of the oropharynx and musculoskeletal system. Important plants mentioned for the treatment of various diseases of the gastrointestinal tract are Zanthoxylum alatum, Berberis lycium, Mentha longifolia, Punica granatum, Rubus ellipticus and Viola canescens. New applications of rarely documented plants from this area are: Oxalis corniculata paste of the whole plant to treat vitiligo, Carthamus tinctorius flowers to treat chicken pox, Dioscorea deltoidea tuber powder to treat productive cough, Inula cappa root decoction to treat miscarriage, Habenaria digitata tuber juice for the treatment of fever, Viola canescens leaves and flowers for the treatment of sore throat and Achillea millefolium root and leaf juice for the treatment of pneumonia. These plants may contain interesting biochemical compounds and should be subjected to further pharmacological studies to develop new drugs. Traditional medicinal knowledge in the area under study is mainly limited to the elderly, traditional healers and midwives. Therefore, resource conservation strategies and future pharmacological studies are strongly recommended.

In advancing the transition of the energy sector toward heightened sustainability and environmental friendliness, biopolymers have emerged as key elements in the construction of triboelectric nanogenerators (TENGs) due to their renewable sources and excellent biodegradability. The development of these TENG devices is of significant importance to the next generation of renewable and sustainable energy technologies based on carbon-neutral materials. This paper introduces the working principles, material sources, and wide-ranging applications of biopolymer-based triboelectric nanogenerators (BP-TENGs). It focuses on the various categories of biopolymers, ranging from natural sources to microbial and chemical synthesis, showcasing their significant potential in enhancing TENG performance and expanding their application scope, while emphasizing their notable advantages in biocompatibility and environmental sustainability. To gain deeper insights into future trends, we discuss the practical applications of BP-TENG in different fields, categorizing them into energy harvesting, healthcare, and environmental monitoring. Finally, the paper reveals the shortcomings, challenges, and possible solutions of BP-TENG, aiming to promote the advancement and application of biopolymer-based TENG technology. We hope this review will inspire the further development of BP-TENG towards more efficient energy conversion and broader applications.

The authors wish to make the following corrections to this paper [...]

The construction industry strongly relies on concrete and clay bricks for various applications. The escalating demand for these materials, driven by rapid population growth, has led to resource depletion and increased construction and demolition waste (CDW). Recycling CDW into construction materials, particularly in the form of recycled concrete aggregates (RCAs) and recycled brick aggregates (RBAs), has emerged as a promising solution. This study deals with the structural performance of concrete incorporating RCAs and RBAs. The experimental program encompasses material characterization, concrete mix design, and several tests to assess density, compressive strength, bond behavior, and flexural properties. The results indicate that the replacement of fine natural aggregate (NA) with fine RCAs or RBAs has a negligible impact on density, while the partial replacement of coarse NAs with RAs yields modest reductions in compressive strength. Notably, the bond strength between steel rebar and concrete is influenced by the type and content of RA, with specimens containing RCAs exhibiting a higher bond strength than those with RBAs. Empirical models used to predict bond strength generally align with experimental results, with conservative predictions by some models, such as ACI 318, and overestimation by others, such as models proposed by AS-3600 and CEB-FIB. The flexural tests of beams highlight the variation in stiffness and load-bearing capacity with the proportion of NAs replaced by RAs. While beams with 50% NA replacement demonstrate comparable performance to control beams, those with 100% RA replacement exhibit lower cracking and yielding stiffness. Cracking patterns in beams with RAs differ from control beams, with RA-containing beams showing more cracks and an altered crack distribution. The findings underscore the feasibility of using recycled aggregates in construction, with partial NA replacement offering a balance between sustainable material usage and desired structural properties.

Groundwater represents a pivotal asset in conserving natural water reservoirs for potable consumption, irrigation, and diverse industrial uses. Nevertheless, human activities intertwined with industry and agriculture contribute significantly to groundwater contamination, highlighting the critical necessity of appraising water quality for safe drinking and effective irrigation. This research primarily focused on employing the Water Quality Index (WQI) to gauge water’s appropriateness for these purposes. However, the generation of an accurate WQI can prove time-intensive owing to potential errors in sub-index calculations. In response to this challenge, an artificial intelligence (AI) forecasting model was devised, aiming to streamline the process while mitigating errors. The study collected 422 data samples from Mirpurkash, a city nestled in the province of Sindh, for a comprehensive exploration of the region’s WQI attributes. Furthermore, the study probed into unraveling the interdependencies amidst variables in the physiochemical analysis of water. Diverse machine learning classifiers were employed for WQI prediction, with findings revealing that Random Forest and Gradient Boosting lead with 95% and 96% accuracy, followed closely by SVM at 92%. KNN exhibits an accuracy rate of 84%, and Decision Trees achieve 77%. Traditional water quality assessment methods are time-consuming and error-prone; a transformative approach using artificial intelligence and machine learning addresses these limitations. In addition to WQI prediction, the study conducted an uncertainty analysis of the models using the R-factor, providing insights into the reliability and consistency of predictions. This dual approach, combining accurate WQI prediction with uncertainty assessment, contributes to a more comprehensive understanding of water quality in Mirpurkash and enhances the reliability of decision-making processes related to groundwater utilization.

Array-designed triboelectric nanogenerators (AD-TENGs) have firmly established themselves as state-of-the-art technologies for adeptly converting mechanical interactions into electrical signals. Central to the AD-TENG’s prowess is its inherent modularity and the multifaceted, grid-like design that pave the way to robust and adaptable detection platforms for wearables and real-time health monitoring systems. In this review, we aim to elucidate the quintessential role of array design in AD-TENGs for healthcare detection, emphasizing its ability to heighten sensitivity, spatial resolution, and dynamic monitoring while ensuring redundancy and simultaneous multi-detection. We begin from the fundamental aspects, such as working principles and design basis, then venture into methodologies for optimizing AD-TENGs that ensure the capture of intricate physiological changes, from nuanced muscle movements to sensitive electronic skin. After this, our exploration extends to the possible cutting-edge electronic systems that are built with specific advantages in filtering noise, magnifying signal-to-noise ratios, and interpreting complex real-time datasets on the basis of AD-TENGs. Culminating our discourse, we highlight the challenges and prospective pathways in the evolution of array-designed AD-TENGs, stressing the necessity to refine their sensitivity, adaptability, and reliability to perfectly align with the exacting demands of contemporary healthcare diagnostics.

Length of stay (LoS) prediction is deemed important for a medical institution’s operational and logistical efficiency. Sound estimates of a patient’s stay increase clinical preparedness and reduce aberrations. Various statistical methods and techniques are used to quantify and predict the LoS of a patient based on pre-operative clinical features. This study evaluates and compares the results of Bayesian (simple Bayesian regression and hierarchical Bayesian regression) models and machine learning (ML) regression models against multiple evaluation metrics for the problem of LoS prediction of cardiac patients admitted to Tabba Heart Institute, Karachi, Pakistan (THI) between 2015 and 2020. In addition, the study also presents the use of hierarchical Bayesian regression to account for data variability and skewness without homogenizing the data (by removing outliers). LoS estimates from the hierarchical Bayesian regression model resulted in a root mean squared error (RMSE) and mean absolute error (MAE) of 1.49 and 1.16, respectively. Simple Bayesian regression (without hierarchy) achieved an RMSE and MAE of 3.36 and 2.05, respectively. The average RMSE and MAE of ML models remained at 3.36 and 1.98, respectively.

Limited efficiency, lower durability, moisture absorbance, and pest/fungal/bacterial interaction/growth are the major issues relating to porous nonwovens used for acoustic and thermal insulation in buildings. This research investigated porous nonwoven textiles composed of recycled cotton waste (CW) fibers, with a specific emphasis on the above-mentioned problems using the treatment of silicon coating and formation of nanofibers via facile-solution processing. The findings revealed that the use of an economic and eco-friendly superhydrophobic (contact angle higher than 150°) modification of porous nonwovens with silicon nanofibers significantly enhanced their intrinsic characteristics. Notable improvements in their compactness/density and a substantial change in micro porosity were observed after a nanofiber network was formed on the nonwoven material. This optimized sample exhibited a superior performance in terms of stiffness, surpassing the untreated samples by 25–60%. Additionally, an significant enhancement in tear strength was observed, surpassing the untreated samples with an impressive margin of 70–90%. Moreover, the nanofibrous network of silicon fibers on cotton waste (CW) showed significant augmentation in heat resistance ranging from 7% to 24% and remarkable sound absorption capabilities. In terms of sound absorption, the samples exhibited a performance comparable to the commercial standard material and outperformed the untreated samples by 20% to 35%. Enhancing the micro-roughness of fabric via silicon nanofibers induced an efficient resistance to water absorption and led to the development of inherent self-cleaning characteristics. The antibacterial capabilities observed in the optimized sample were due to its superhydrophobic nature. These characteristics suggest that the proposed nano fiber-treated nonwoven fabric is ideal for multifunctional applications, having features like enhanced moisture resistance, pest resistance, thermal insulation, and sound absorption which are essential for wall covers in housing.

Lysophosphatidic acid (LPA) serves as a fundamental constituent of phospholipids. While prior studies have shown detrimental effects of LPA in a range of pathological conditions, including brain ischemia, no studies have explored the impact of LPA in the context of cardiac arrest (CA). The aim of this study is to evaluate the effects of the intravenous administration of an LPA species containing oleic acid, LPA (18:1) on the neurological function of rats (male, Sprague Dawley) following 8 min of asphyxial CA. Baseline characteristics, including body weight, surgical procedure time, and vital signs before cardiac arrest, were similar between LPA (18:1)-treated (n = 10) and vehicle-treated (n = 10) groups. There was no statistically significant difference in 24 h survival between the two groups. However, LPA (18:1)-treated rats exhibited significantly improved neurological function at 24 h examination (LPA (18:1), 85.4% ± 3.1 vs. vehicle, 74.0% ± 3.3, p = 0.045). This difference was most apparent in the retention of coordination ability in the LPA (18:1) group (LPA (18:1), 71.9% ± 7.4 vs. vehicle, 25.0% ± 9.1, p < 0.001). Overall, LPA (18:1) administration in post-cardiac arrest rats significantly improved neurological function, especially coordination ability at 24 h after cardiac arrest. LPA (18:1) has the potential to serve as a novel therapeutic in cardiac arrest.

The approach of federated learning (FL) addresses significant challenges, including access rights, privacy, security, and the availability of diverse data. However, edge devices produce and collect data in a non-independent and identically distributed (non-IID) manner. Therefore, it is possible that the number of data samples may vary among the edge devices. This study elucidates an approach for implementing FL to achieve a balance between training accuracy and imbalanced data. This approach entails the implementation of data augmentation in data distribution by utilizing class estimation and by balancing on the client side during local training. Secondly, simple linear regression is utilized for model training at the client side to manage the optimal computation cost to achieve a reduction in computation cost. To validate the proposed approach, the technique was applied to a stock market dataset comprising stocks (AAL, ADBE, ASDK, and BSX) to predict the day-to-day values of stocks. The proposed approach has demonstrated favorable results, exhibiting a strong fit of 0.95 and above with a low error rate. The R-squared values, predominantly ranging from 0.97 to 0.98, indicate the model’s effectiveness in capturing variations in stock prices. Strong fits are observed within 75 to 80 iterations for stocks displaying consistently high R-squared values, signifying accuracy. On the 100th iteration, the declining MSE, MAE, and RMSE (AAL at 122.03, 4.89, 11.04, respectively; ADBE at 457.35, 17.79, and 21.38, respectively; ASDK at 182.78, 5.81, 13.51, respectively; and BSX at 34.50, 4.87, 5.87, respectively) values corroborated the positive results of the proposed approach with minimal data loss.

This contribution addresses a fractal numerical scheme that can be employed for handling fractal time-dependent parabolic equations. The numerical scheme presented in this contribution can be used to discretize integer order and fractal derivatives in a given differential equation. Therefore, the scheme and results can be used for both cases. The proposed finite difference scheme is based on two stages. Fractal time derivatives are discretized by employing the proposed approach. For the scalar convection–diffusion equation, we derive the stability condition of the proposed fractal scheme. Using a nonlinear chemical reaction, the approach is also used to solve the Quantum Calculus model of a Williamson nanofluid’s unsteady Darcy–Forchheimer flow over flat and oscillatory sheets. The findings indicate a negative correlation between the velocity profile and the porosity parameter and inertia coefficient, with an increase in these factors resulting in a drop in the velocity profile. Additionally, the fractal scheme under consideration is being compared to the fractal Crank–Nicolson method, revealing that the proposed scheme exhibits a superior convergence speed compared to the fractal Crank–Nicolson method. Several problems involving the motion of non-Newtonian nanofluids through magnetic fields and porous media can be investigated with the help of the proposed numerical scheme. This research has implications for developing more efficient heat transfer and energy conversion devices based on nanofluids.

The Internet of Things (IoT) is an innovative technology that presents effective and attractive solutions to revolutionize various domains. Numerous solutions based on the IoT have been designed to automate industries, manufacturing units, and production houses to mitigate human involvement in hazardous operations. Owing to the large number of publications in the IoT paradigm, in particular those focusing on industrial IoT (IIoT), a comprehensive survey is significantly important to provide insights into recent developments. This survey presents the workings of the IoT-based smart industry and its major components and proposes the state-of-the-art network infrastructure, including structured layers of IIoT architecture, IIoT network topologies, protocols, and devices. Furthermore, the relationship between IoT-based industries and key technologies is analyzed, including big data storage, cloud computing, and data analytics. A detailed discussion of IIoT-based application domains, smartphone application solutions, and sensor- and device-based IIoT applications developed for the management of the smart industry is also presented. Consequently, IIoT-based security attacks and their relevant countermeasures are highlighted. By analyzing the essential components, their security risks, and available solutions, future research directions regarding the implementation of IIoT are outlined. Finally, a comprehensive discussion of open research challenges and issues related to the smart industry is also presented.

For decades, understanding the dynamics of infectious diseases and halting their spread has been a major focus of mathematical modelling and epidemiology. The stochastic SIRS (susceptible–infectious–recovered–susceptible) reaction–diffusion model is a complicated but crucial computational scheme due to the combination of partial immunity and an incidence rate. Considering the randomness of individual interactions and the spread of illnesses via space, this model is a powerful instrument for studying the spread and evolution of infectious diseases in populations with different immunity levels. A stochastic explicit finite difference scheme is proposed for solving stochastic partial differential equations. The scheme is comprised of predictor–corrector stages. The stability and consistency in the mean square sense are also provided. The scheme is applied to diffusive epidemic models with incidence rates and partial immunity. The proposed scheme with space’s second-order central difference formula solves deterministic and stochastic models. The effect of transmission rate and coefficient of partial immunity on susceptible, infected, and recovered people are also deliberated. The deterministic model is also solved by the existing Euler and non-standard finite difference methods, and it is found that the proposed scheme forms better than the existing non-standard finite difference method. Providing insights into disease dynamics, control tactics, and the influence of immunity, the computational framework for the stochastic SIRS reaction–diffusion model with partial immunity and an incidence rate has broad applications in epidemiology. Public health and disease control ultimately benefit from its application to the study and management of infectious illnesses in various settings.

This ethnobiological study addresses the complicated relationship between the Dard ethnic group and their natural environment in the Gurez region of the Kashmir Valley. The study documents their traditional knowledge of the use of plant species for medicinal and culinary purposes. A total of 87 plant species from 41 different families were cataloged, with the Asteraceae family (15 species) and the Lamiaceae family (12 species) being the most commonly used. These plants were found to be used to treat 20 different ailments, with menstrual cramps being the most common (12 species). The fidelity values for these plants ranged from 11.10 to 71.42, demonstrating their importance in traditional medicine. In addition, 17 plant species were found to be useful for gastronomic purposes, with Juglans regia being the most valuable (use value of 0.73). The study also evaluated the conservation status of these plants and found that seven of them are considered critically endangered, ten endangered, and four endangered according to the IUCN classification. This study offers insights into the Dard people’s deep connection to their natural environment and has significant implications for policy formulation, cultural conservation, and sustainable use of endemic species, as well as potential applications in pharmaceutical research for therapeutic compounds.

Internet security is a major concern these days due to the increasing demand for information technology (IT)-based platforms and cloud computing. With its expansion, the Internet has been facing various types of attacks. Viruses, denial of service (DoS) attacks, distributed DoS (DDoS) attacks, code injection attacks, and spoofing are the most common types of attacks in the modern era. Due to the expansion of IT, the volume and severity of network attacks have been increasing lately. DoS and DDoS are the most frequently reported network traffic attacks. Traditional solutions such as intrusion detection systems and firewalls cannot detect complex DDoS and DoS attacks. With the integration of artificial intelligence-based machine learning and deep learning methods, several novel approaches have been presented for DoS and DDoS detection. In particular, deep learning models have played a crucial role in detecting DDoS attacks due to their exceptional performance. This study adopts deep learning models including recurrent neural network (RNN), long short-term memory (LSTM), and gradient recurrent unit (GRU) to detect DDoS attacks on the most recent dataset, CICDDoS2019, and a comparative analysis is conducted with the CICIDS2017 dataset. The comparative analysis contributes to the development of a competent and accurate method for detecting DDoS attacks with reduced execution time and complexity. The experimental results demonstrate that models perform equally well on the CICDDoS2019 dataset with an accuracy score of 0.99, but there is a difference in execution time, with GRU showing less execution time than those of RNN and LSTM.

Given the rising consumption of plastic products, it is becoming imperative to prioritize the recycling of plastic items as a solution to reducing plastic waste and environmental pollution. In this context, this research focuses on assessing the impact of incorporating rice husk and wood flour into recycled high-density polyethylene (rec-HDPE) to analyze its mechanical properties, flammability, and thermal stability. The combined rec-HDPE content of wood flour and rice husk varied between 0% and 20%. The rec-HDPE content of maleic anhydride grafted polyethylene (MAPE) was fixed at 3%. Mechanical characteristics such as flexural, tensile, and impact strengths were assessed. Cone calorimetry (CC) tests, limited oxygen index (LOI) tests, and horizontal and vertical burning tests were performed to determine the flammability or fire retardancy of these composites. On the other hand, to characterize the thermal characteristics of these composites, thermogravimetric analysis (TGA) was used. To further characterize the fluctuation in these characteristics, scanning electron microscopy (SEM) and infrared spectroscopy (FTIR) studies were carried out. The mechanical characteristics were found to be increased in response to adding rice husk or wood flour. An 8% increase in tensile strength and a 20% increase in elastic modulus enhancement were recorded for a 20% rice husk-added composite. SEM revealed the reason for the variation in tensile properties, based on the extent of agglomeration and the extent of uniform distribution of fillers in rec-HDPE. Following these lines, the 20% rice husk-added composite also showed a maximum increase of around 6% in its flexural strength and a maximum increase of 50% in its flexural modulus. A decrease in impact strength was recorded for rice husk and wood flour-reinforced composites, compared with unreinforced rec-HDPE. Hybrid composites displayed a lack of mechanical strength due to changes in their nature. FTIR tests were performed for a much more elaborate analysis to confirm these results. Twenty percent of rice husk-added rec-HDPE displayed the best thermal properties that were tested, based on TGA and derivative thermogravimetric (DTG) analysis. This 20% composite also displayed the best fire-retardancy characteristics according to UL 94 tests, cone calorimetry tests, and limited oxygen index tests, due to the barrier created by the silica protective layer. These tests demonstrated that the incorporation of both fillers—rice husk and wood flour—effectively enhanced the thermal, mechanical, and fire-retardant attributes of recycled HDPE.

Solar energy is a green and sustainable source of energy which can be converted to electrical power through photovoltaic (PV) panels. In an electrical network system, electrical energy is supplied to end consumers through utility networks. Smart Grids allow consumers to generate electrical energy locally and surplus energy at the user’s end can be supplied to the national grid. Bidirectional energy meters keep the record of the imported and exported units to the grid and vice versa. Photovoltaic panels cannot generate power at night, so users have to import power from electric grid. The utility charges the consumer monthly as per net balance of the energy meter depending on the energy trade balance. This IoT-based meter allows real-time monitoring of power ratings via the Android application.

Sustainable urban water management is essential to handle water scarcity, leakage, and inefficient distribution. This paper covers water management in urban areas, including an introduction, an overview of water management practices, the characteristics and functioning of water distribution systems, monitoring and control systems for efficient distribution, smart systems for optimization, strategies for water conservation and waste management, per capita water demand analysis, and desalination plant overviews. The article proposes a blockchain-based water management architecture with IoT sensors for accurate reporting. The framework uses blockchain technology to authenticate and share real-time data between sensors and the water distribution dashboard. It also has a modular API for water leakage detection and flow control to decrease water waste and enhance distribution. The suggested approach might enhance water management; however, its execution is complex. Maintaining the framework’s efficacy is advised. The research provides insights into water management and proposes a technology solution employing blockchain and IoT sensors for trustworthy data reporting and effective water distribution to promote sustainable urban water management.

Rice is one of the major food crops across the globe, and its quality and safety highly influence human health. It is the basis of many different products, including rice flour, rice bread, noodles, rice vinegar, and others. Therefore, the rice supply chain has garnered increasing attention due to the high demand for food safety. Furthermore, malpractices in the rice supply chain can impact farmers by generating low revenues despite their great efforts in rice cultivation. In addition, they would cause governments to suffer significant economic losses due to the high cost of importing rice crops from other countries during the off-season. These issues derive from the lack of reliability, trust, transparency, traceability, and security in the rice supply chain. In this research, we propose a secure, trusted, reliable, and transparent framework based on a Blockchain for rice crop supply chain’s traceability from farm to fork. A new crypto token, the Rice Coin (RC), is introduced to keep a record of all transactions between the stakeholders of the rice supply chain. Moreover, the proposed framework includes an economic model and a crypto wallet and introduces an Initial Coin Offering (ICO) for the RC. Based on smart contracts, a transaction processing system was developed for the transparency and traceability of rice crops, including the conversion of the RC to fiat currency. Furthermore, the InterPlanetary File System (IPFS) is proposed in this research to store encrypted data of companies, retailers, and farmers, so to increase data security, transparency, and availability. In the end, the experimental results showed a better performance of the proposed framework compared to already available supply chain solutions in terms of transaction verification time, transaction average gas cost, and new block latency.

Clinical decision-making in chronic disorder prognosis is often hampered by high variance, leading to uncertainty and negative outcomes, especially in cases such as chronic kidney disease (CKD). Machine learning (ML) techniques have emerged as valuable tools for reducing randomness and enhancing clinical decision-making. However, conventional methods for CKD detection often lack accuracy due to their reliance on limited sets of biological attributes. This research proposes a novel ML model for predicting CKD, incorporating various preprocessing steps, feature selection, a hyperparameter optimization technique, and ML algorithms. To address challenges in medical datasets, we employ iterative imputation for missing values and a novel sequential approach for data scaling, combining robust scaling, z-standardization, and min-max scaling. Feature selection is performed using the Boruta algorithm, and the model is developed using ML algorithms. The proposed model was validated on the UCI CKD dataset, achieving outstanding performance with 100% accuracy. Our approach, combining innovative preprocessing steps, the Boruta feature selection, and the k-nearest neighbors algorithm, along with a hyperparameter optimization using grid-search cross-validation (CV), demonstrates its effectiveness in enhancing the early detection of CKD. This research highlights the potential of ML techniques in improving clinical support systems and reducing the impact of uncertainty in chronic disorder prognosis.

To maximize crop production, reference evapotranspiration (ET0) measurement is crucial for managing water resources and planning crop water needs. The FAO-PM56 method is recommended globally for estimating ET0 and evaluating alternative methods due to its extensive theoretical foundation. Numerous meteorological parameters, needed for ET0 estimation, are difficult to obtain in developing countries. Therefore, alternative ways to estimate ET0 using fewer climatic data are of critical importance. To estimate ET0 with alternative methods, difference climatic parameters of temperatures, relative humidity (maximum and minimum), sunshine hours, and wind speed for a period of 20 years from 1996 to 2015 were used in the study. The data were recorded by 11 meteorological observatories situated in various climatic regions of Pakistan. The significance of the climatic parameters used was evaluated using sensitivity analysis. The machine learning techniques of single decision tree (SDT), tree boost (TB) and decision tree forest (DTF) were used to perform sensitivity analysis. The outcomes indicated that DTF-based models estimated ET0 with higher accuracy and fewer climatic variables as compared to other ML techniques used in the study. The DTF technique, with Model 15 as input, outperformed other techniques for the most part of the performance metrics (i.e., NSE = 0.93, R2 = 0.96 and RMSE = 0.48 mm/month). The results indicated that the DTF with fewer climatic variables of mean relative humidity, wind speed and minimum temperature could estimate ET0 accurately and outperformed other ML techniques. Additionally, a non-linear ensemble (NLE) of ML techniques was further used to estimate ET0 using the best input combination (i.e., Model 15). It was seen that the applied non-linear ensemble (NLE) approach enhanced modelling accuracy as compared to a stand-alone application of ML techniques (R2 Multan = 0.97, R2 Skardu = 0.99, R2 ISB = 0.98, R2 Bahawalpur = 0.98 etc.). The study results affirmed the use of an ensemble model for ET0 estimation and suggest applying it in other parts of the world to validate model performance.

This paper offers a novel dual-mode double stator wound rotor synchronous machine for variable speed applications. The proposed motor integrates the benefits of both the traditional wound rotor synchronous machine (WRSM) and brushless wound rotor synchronous machine (BL-WRSM). A constant torque can be attained in the maximum torque per ampere region by operating the proposed machine as a traditional WRSM in Mode I, and a constant power can be attained in the field-weakening region by operating it as a BL-WRSM in Mode II. Moreover, due to the dual-stator structure, the proposed machine exhibits improved performance in terms of high torque density as compared to the existing single stator BL-WRSM. By using a special stator winding arrangement to achieve the sub-harmonic component of the stator magnetomotive force, the brushless operation of the proposed machine is achieved. The additional sub-harmonic component induces a voltage in the harmonic winding placed on the rotor, which is then rectified and provided a DC current to field winding for brushless excitation. In order to validate the effectiveness of the proposed machine, a two-dimensional finite element analysis (FEA) is carried out.

We propose to use a particular case of Kaniadakis’ logarithm for the exploratory analysis of compositional data following the Aitchison approach. The affine information geometry derived from Kaniadakis’ logarithm provides a consistent setup for the geometric analysis of compositional data. Moreover, the affine setup suggests a rationale for choosing a specific divergence, which we name the Kaniadakis divergence.

Heart disease is a significant global health issue, contributing to high morbidity and mortality rates. Early and accurate heart disease prediction is crucial for effectively preventing and managing the condition. However, this remains a challenging task to achieve. This study proposes a machine learning model that leverages various preprocessing steps, hyperparameter optimization techniques, and ensemble learning algorithms to predict heart disease. To evaluate the performance of our model, we merged three datasets from Kaggle that have similar features, creating a comprehensive dataset for analysis. By employing the extra tree classifier, normalizing the data, utilizing grid search cross-validation (CV) for hyperparameter optimization, and splitting the dataset with an 80:20 ratio for training and testing, our proposed approach achieved an impressive accuracy of 98.15%. These findings demonstrated the potential of our model for accurately predicting the presence or absence of heart disease. Such accurate predictions could significantly aid in early prevention, detection, and treatment, ultimately reducing the mortality and morbidity associated with heart disease.

Background: Cardiac arrest (CA) can lead to neuronal degeneration and death through various pathways, including oxidative, inflammatory, and metabolic stress. However, current neuroprotective drug therapies will typically target only one of these pathways, and most single drug attempts to correct the multiple dysregulated metabolic pathways elicited following cardiac arrest have failed to demonstrate clear benefit. Many scientists have opined on the need for novel, multidimensional approaches to the multiple metabolic disturbances after cardiac arrest. In the current study, we have developed a therapeutic cocktail that includes ten drugs capable of targeting multiple pathways of ischemia–reperfusion injury after CA. We then evaluated its effectiveness in improving neurologically favorable survival through a randomized, blind, and placebo-controlled study in rats subjected to 12 min of asphyxial CA, a severe injury model. Results: 14 rats were given the cocktail and 14 received the vehicle after resuscitation. At 72 h post-resuscitation, the survival rate was 78.6% among cocktail-treated rats, which was significantly higher than the 28.6% survival rate among vehicle-treated rats (log-rank test; p = 0.006). Moreover, in cocktail-treated rats, neurological deficit scores were also improved. These survival and neurological function data suggest that our multi-drug cocktail may be a potential post-CA therapy that deserves clinical translation. Conclusions: Our findings demonstrate that, with its ability to target multiple damaging pathways, a multi-drug therapeutic cocktail offers promise both as a conceptual advance and as a specific multi-drug formulation capable of combatting neuronal degeneration and death following cardiac arrest. Clinical implementation of this therapy may improve neurologically favorable survival rates and neurological deficits in patients suffering from cardiac arrest.

This study aimed to assess the impact of workforce agility on private hospital nursing staff’s safety behavior with the mediating role of mindful organizing. This study was cross-sectional. A self-administered questionnaire was used to collect data from 369 nursing staff. The structural equation modeling (SEM) technique was used to check the internal consistency, convergent validity, discriminant validity, and hypotheses testing. For mediation analysis, the bootstrapping technique was used. Our findings suggested that workforce agility is the possible predictor of mindful organizing, as all of these dimensions have a positive impact on mindful organizing. Reference to safety performance sub-dimensions, proactivity, adaptability, and resilience had a positive significant impact on (a) safety compliance, and proactivity had a positive impact on (b) safety participation. Further, mindful organizing was also found to be positively associated with safety performance. Evidence for mediation between workforce agility and safety performance was also observed. Proactivity, adaptability, and resilience can enhance safety performance for the nursing staff. Workforce agility can also help the organization to attain mindful organizing, which will help them to achieve operational excellence, whereas in the past, high-reliability organizations were mainly found practicing mindful organizing. This study demonstrated the key impact of workforce agility and mindful organizing on safety behaviors directly and indirectly.

To boost productivity, commercial strategies, and social advancement, neural network techniques are gaining popularity among engineering and technical research groups. This work proposes a numerical scheme to solve linear and non-linear ordinary differential equations (ODEs). The scheme’s primary benefit included its third-order accuracy in two stages, whereas most examples in the literature do not provide third-order accuracy in two stages. The scheme was explicit and correct to the third order. The stability region and consistency analysis of the scheme for linear ODE are provided in this paper. Moreover, a mathematical model of heat and mass transfer for the non-Newtonian Casson nanofluid flow is given under the effects of the induced magnetic field, which was explored quantitatively using the method of Levenberg–Marquardt back propagation artificial neural networks. The governing equations were reduced to ODEs using suitable similarity transformations and later solved by the proposed scheme with a third-order accuracy. Additionally, a neural network approach for input and output/predicted values is given. In addition, inputs for velocity, temperature, and concentration profiles were mapped to the outputs using a neural network. The results are displayed in different types of graphs. Absolute error, regression studies, mean square error, and error histogram analyses are presented to validate the suggested neural networks’ performance. The neural network technique is currently used on three of these four targets. Two hundred points were utilized, with 140 samples used for training, 30 samples used for validation, and 30 samples used for testing. These findings demonstrate the efficacy of artificial neural networks in forecasting and optimizing complex systems.

Utilizing zeolitic imidazolate frameworks (ZIFs) poses a significant challenge that demands a facile synthesis method to produce uniform and nanometer-scale materials with high surface areas while achieving high yields. Herein, we demonstrate a facile and cost-effective strategy to systematically produce ZIF8 nanocrystals. Typically, ZIF8 nanocrystal synthesis involves a wet chemical route. As the reaction time decreased (150, 120, and 90 min), the size of the ZIF8 crystals decreased with uniform morphology, and productivity reached as high as 89%. The composition of the product was confirmed through XRD, FE-SEM, TEM, EDS, and Raman spectroscopy. The ZIF8 synthesized with different reaction time was finally employed for catalyzing the electrochemical hydrogen evaluation reaction (HER). The optimized ZIF8-3 obtained at 90 min of reaction time exhibited a superior catalytic action on the HER in alkaline medium, along with a remarkably long-term stability for 24 h compared with the other ZIF8 nanocrystals obtained at different reaction times. Specifically, the optimized ZIF8-3 sample revealed an HER overpotential of 172 mV and a Tafel slope of 104.15 mV·dec−1. This finding, thus, demonstrates ZIF8 as a promising electrocatalyst for the production of high-value-added green and sustainable hydrogen energy.

The main aim of this contribution is to construct a numerical scheme for solving stochastic time-dependent partial differential equations (PDEs). This has the advantage of solving problems with positive solutions. The scheme provides conditions for obtaining positive solutions, which the existing Euler–Maruyama method cannot do. In addition, it is more accurate than the existing stochastic non-standard finite difference (NSFD) method. Theoretically, the suggested scheme is more accurate than the current NSFD method, and its stability and consistency analysis are also shown. The scheme is applied to the linear scalar stochastic time-dependent parabolic equation and the nonlinear auto-catalytic Brusselator model. The deficiency of the NSFD in terms of accuracy is also shown by providing different graphs. Many observable occurrences in the physical world can be traced back to certain chemical concentrations. Examining and understanding the inter-diffusion between chemical concentrations is important, especially when they coincide. The Brusselator model is the gold standard for describing the relationship between chemical concentrations and other variables in chemical systems. A computational code for the proposed model scheme may be made available to readers upon request for convenience.

Numerous investigations on plant ethnomedicinal applications have been conducted; however, knowledge about the medicinal use of wild animals is still limited. This present study is the second on the medicinal and cultural meaning of avian and mammalian species used by the population in the surrounding area of the Ayubia National Park, KPK, Pakistan. Interviews and meetings were compiled from the participants (N = 182) of the study area. The relative frequency of citation, fidelity level, relative popularity level, and rank order priority indices were applied to analyze the information. Overall, 137 species of wild avian and mammalian species were documented. Of these, 18 avian and 14 mammalian species were utilized to treat different diseases. The present research showed noteworthy ethno-ornithological and ethno-mammalogical knowledge of local people and their connection with fauna, which might be useful in the sustainable utilization of the biological diversity of the Ayubia National Park, Khyber Pakhtunkhwa. Furthermore, in vivo and/or in vitro examination of the pharmacological activities of species with the highest fidelity level (FL%) as well as frequency of mention (FM) might be important for investigations on faunal-based new drugs.

Chrysin (5,7-dihydroxyflavone) has many pharmacological properties including anti-inflammatory actions. The objective of this study was to evaluate the anti-arthritic activity of chrysin and to compare its effect with the non-steroidal anti-inflammatory agent, piroxicam, against complete Freund’s adjuvant (CFA)-induced arthritis in a pre-clinical model in rats. Rheumatoid arthritis was induced by injecting CFA intra-dermally in the sub-plantar region of the left hind paw of rats. Chrysin (50 and 100 mg/kg) and piroxicam (10 mg/kg) were given to rats with established arthritis. The model of arthritis was characterized using an index of arthritis, with hematological, biological, molecular, and histopathological parameters. Treatment with chrysin significantly reduced the arthritis score, inflammatory cells, erythrocyte sedimentation rate, and rheumatoid factor. Chrysin also reduced the mRNA levels of tumor necrosis factor, nuclear factor kappa-B, and toll-like recepter-2 and increased anti-inflammatory cytokines interleukin-4 and -10, as well as the hemoglobin levels. Using histopathology and microscopy, chrysin reduced the severity of arthritis in joints, infiltration of inflammatory cells, subcutaneous inflammation, cartilage erosion, bone erosion, and pannus formation. Chrysin showed comparable effects to piroxicam, which is used for the treatment of rheumatoid arthritis. The results showed that chrysin possesses anti-inflammatory and immunomodulatory effects that make it a potential drug for the treatment of arthritis.

Current research deals with thermoplastic polyamide (PA6)-based composites reinforced with basalt and Kevlar fabrics. Hybrid composites were developed by altering the stacking sequence of basalt and two kinds of Kevlar fabrics. Pure-basalt- and pure-Kevlar-based samples were also developed for comparison purposes. The developed samples were evaluated with respect to mechanical and thermomechanical properties. Mechanical tests, e.g., tensile, flexural, and impact strength, were conducted along with thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC) to ascertain the load-bearing and high-temperature stability of the hybrid composite samples vis-à-vis pure-basalt- and Kevlar-based samples. Scanning electron microscopy (SEM) was carried out to study the nature of fracture and failure of the composite samples. The pure-basalt-based PA6 thermoplastic composites exhibited the best mechanical performance. Hybridization with basalt proved to be beneficial for improving the mechanical performance of the composites using Kevlar fabrics. However, a proper stacking sequence and density of Kevlar fabric has to be selected. The thermogravimetric analysis showed minimal weight loss for basalt-based composites. Furthermore, the thermal stability of the composites using Kevlar fabric was improved by hybridization with basalt fabric. The thermomechanical characteristics of hybrid composites may be altered by changing the stacking order of the reinforcements. Differential scanning calorimetry further established that the hybrid composites with alternate layers of basalt and Kevlar can improve the heat flow rate and enable survivability at extreme temperatures. Such novel hybrid composites can be used for high-load-bearing and high-temperature applications, e.g., defense, aerospace, automotives, and energy applications.

Weeds are one of the most harmful agricultural pests that have a significant impact on crops. Weeds are responsible for higher production costs due to crop waste and have a significant impact on the global agricultural economy. The importance of this problem has promoted the research community in exploring the use of technology to support farmers in the early detection of weeds. Artificial intelligence (AI) driven image analysis for weed detection and, in particular, machine learning (ML) and deep learning (DL) using images from crop fields have been widely used in the literature for detecting various types of weeds that grow alongside crops. In this paper, we present a systematic literature review (SLR) on current state-of-the-art DL techniques for weed detection. Our SLR identified a rapid growth in research related to weed detection using DL since 2015 and filtered 52 application papers and 8 survey papers for further analysis. The pooled results from these papers yielded 34 unique weed types detection, 16 image processing techniques, and 11 DL algorithms with 19 different variants of CNNs. Moreover, we include a literature survey on popular vanilla ML techniques (e.g., SVM, random forest) that have been widely used prior to the dominance of DL. Our study presents a detailed thematic analysis of ML/DL algorithms used for detecting the weed/crop and provides a unique contribution to the analysis and assessment of the performance of these ML/DL techniques. Our study also details the use of crops associated with weeds, such as sugar beet, which was one of the most commonly used crops in most papers for detecting various types of weeds. It also discusses the modality where RGB was most frequently used. Crop images were frequently captured using robots, drones, and cell phones. It also discusses algorithm accuracy, such as how SVM outperformed all machine learning algorithms in many cases, with the highest accuracy of 99 percent, and how CNN with its variants also performed well with the highest accuracy of 99 percent, with only VGGNet providing the lowest accuracy of 84 percent. Finally, the study will serve as a starting point for researchers who wish to undertake further research in this area.

This study addresses heat and mass transfer of electrical magnetohydrodynamics (MHD) Williamson fluid flow over the moving sheet. The mathematical model for the considered flow phenomenon is expressed in a set of partial differential equations. Later, linear and nonlinear ordinary differential equations (ODEs) are obtained. The finite element method tackles a reduced system of ODEs with boundary conditions. Galerkin weighted residuals and constructs of weak formulations constitute the basis of this method. An iterative procedure is considered for handling nonlinear terms in a given system of ODEs. Some results acquired using the finite element method are compared with those reported in previous research via the Matlab solver bvp4c in order to validate the obtained solutions of ODEs. It is seen that the velocity profile is decayed by enhancing the Wiesenberg number. The finite element method also converges to an accurate solution by increasing the number of elements, whereas Matlab solver bvp4c produces accurate results on small grid points. Our intention is for this paper to serve as a guide for academics in the future who will be tasked with addressing pressing issues in the field of industrial and engineering enclosures.