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Structural displacement is an important metric for assessing structural conditions because it has a direct relationship with the structural stiffness. Many bridge displacement measurement techniques have been developed, but most methods require fixed reference points in the vicinity of the target structure that limits the field implementations. A promising alternative is to use reference-free measurement techniques that indirectly estimate the displacement by using measurements such as acceleration and strain. This paper proposes novel reference-free bridge displacement estimation by the fusion of single acceleration with pseudo-static displacement derived from co-located strain measurements. First, we propose a conversion of the strain at the center of a beam into displacement based on the geometric relationship between strain and deflection curves with reference-free calibration. Second, an adaptive Kalman filter is proposed to fuse the displacement generated by strain with acceleration by recursively estimating the noise covariance of displacement from strain measurements which is vulnerable to measurement condition. Both numerical and experimental validations are presented to demonstrate the efficiency and robustness of the proposed approach.

Fitness and sport have drawn significant attention in wearable and persuasive computing. Physical activities are worthwhile for health, well-being, improved fitness levels, lower mental pressure and tension levels. Nonetheless, during high-power and commanding workouts, there is a high likelihood that physical fitness is seriously influenced. Jarring motions and improper posture during workouts can lead to temporary or permanent disability. With the advent of technological advances, activity acknowledgment dependent on wearable sensors has pulled in countless studies. Still, a fully portable smart fitness suite is not industrialized, which is the central need of today’s time, especially in the Covid-19 pandemic. Considering the effectiveness of this issue, we proposed a fully portable smart fitness suite for the household to carry on their routine exercises without any physical gym trainer and gym environment. The proposed system considers two exercises, i.e., T-bar and bicep curl with the assistance of the virtual real-time android application, acting as a gym trainer overall. The proposed fitness suite is embedded with a gyroscope and EMG sensory modules for performing the above two exercises. It provided alerts on unhealthy, wrong posture movements over an android app and is guided to the best possible posture based on sensor values. The KNN classification model is used for prediction and guidance for the user while performing a particular exercise with the help of an android application-based virtual gym trainer through a text-to-speech module. The proposed system attained 89% accuracy, which is quite effective with portability and a virtually assisted gym trainer feature.

Temperature is one of the decisive environmental factors that is projected to increase by 1. 5°C over the next two decades due to climate change that may affect various agronomic characteristics, such as biomass production, phenology and physiology, and yield-contributing traits in oilseed crops. Oilseed crops such as soybean, sunflower, canola, peanut, cottonseed, coconut, palm oil, sesame, safflower, olive etc., are widely grown. Specific importance is the vulnerability of oil synthesis in these crops against the rise in climatic temperature, threatening the stability of yield and quality. The natural defense system in these crops cannot withstand the harmful impacts of heat stress, thus causing a considerable loss in seed and oil yield. Therefore, a proper understanding of underlying mechanisms of genotype-environment interactions that could affect oil synthesis pathways is a prime requirement in developing stable cultivars. Heat stress tolerance is a complex quantitative trait controlled by many genes and is challenging to study and characterize. However, heat tolerance studies to date have pointed to several sophisticated mechanisms to deal with the stress of high temperatures, including hormonal signaling pathways for sensing heat stimuli and acquiring tolerance to heat stress, maintaining membrane integrity, production of heat shock proteins (HSPs), removal of reactive oxygen species (ROS), assembly of antioxidants, accumulation of compatible solutes, modified gene expression to enable changes, intelligent agricultural technologies, and several other agronomic techniques for thriving and surviving. Manipulation of multiple genes responsible for thermo-tolerance and exploring their high expressions greatly impacts their potential application using CRISPR/Cas genome editing and OMICS technology. This review highlights the latest outcomes on the response and tolerance to heat stress at the cellular, organelle, and whole plant levels describing numerous approaches applied to enhance thermos-tolerance in oilseed crops. We are attempting to critically analyze the scattered existing approaches to temperature tolerance used in oilseeds as a whole, work toward extending studies into the field, and provide researchers and related parties with useful information to streamline their breeding programs so that they can seek new avenues and develop guidelines that will greatly enhance ongoing efforts to establish heat stress tolerance in oilseeds.

The current study evaluates the potential of merged satellite precipitation datasets (MSPDs) against rain gauges (RGs) and satellite precipitation datasets (SPDs) in monitoring meteorological drought over Pakistan during 2000–2015. MSPDs evaluated in the current study include Regional Weighted Average Least Square (RWALS), Weighted Average Least Square (WALS), Dynamic Clustered Bayesian model Averaging (DCBA), and Dynamic Bayesian Model Averaging (DBMA) algorithms, while the set of SPDs is Global Precipitation Measurement (GPM)-based Integrated Multi-Satellite Retrievals for GPM (IMERG-V06), Tropical Rainfall Measurement Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA 3B42 V7), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and ERA-Interim (re-analyses dataset). Several standardized precipitation indices (SPIs), including SPI-1, SPI-3, and SPI-12, are used to evaluate the performances of RGs, SPDs, and MSPDs across Pakistan as well as on a regional scale. The Mann–Kendall (MK) test is used to assess the trend of meteorological drought across different climate regions of Pakistan using these SPI indices. Results revealed higher performance of MSPDs than SPDs when compared against RGs for SPI estimates. The seasonal evaluation of SPIs from RGs, MSPDs, and SPDs in a representative drought year (2008) revealed mildly to moderate wetness in monsoon season while mild to moderate drought in winter season across Pakistan. However, the drought severity ranges from mild to severe drought in different years across different climate regions. MAPD (mean absolute percentage difference) shows high accuracy (MAPD <10%) for RWALS-MSPD, good accuracy (10% < MAPD <20%) for WALS-MSPD and DCBA-MSPD, while good to reasonable accuracy (20% < MAPD < 50%) for DCBA in different climate regions. Furthermore, MSPDs show a consistent drought trend as compared with RGs, while SPDs show poor performance. Overall, this study demonstrated significantly improved performance of MSPDs in monitoring the meteorological drought.

Emergency vehicle detection plays a critical role in ensuring timely responses and reducing accidents in modern urban environments. However, traditional methods that rely solely on visual cues face challenges, particularly in adverse conditions. The objective of this research is to enhance emergency vehicle detection by leveraging the synergies between acoustic and visual information. By incorporating advanced deep learning techniques for both acoustic and visual data, our aim is to significantly improve the accuracy and response times. To achieve this goal, we developed an attention-based temporal spectrum network (ATSN) with an attention mechanism specifically designed for ambulance siren sound detection. In parallel, we enhanced visual detection tasks by implementing a Multi-Level Spatial Fusion YOLO (MLSF-YOLO) architecture. To combine the acoustic and visual information effectively, we employed a stacking ensemble learning technique, creating a robust framework for emergency vehicle detection. This approach capitalizes on the strengths of both modalities, allowing for a comprehensive analysis that surpasses existing methods. Through our research, we achieved remarkable results, including a misdetection rate of only 3.81% and an accuracy of 96.19% when applied to visual data containing emergency vehicles. These findings represent significant progress in real-world applications, demonstrating the effectiveness of our approach in improving emergency vehicle detection systems.

A new image encryption scheme is presented based on the chaotic system and the swapping operations of the pixels both at the decimal and DNA levels. By randomly choosing two arrays of the given input image for a number of times, randomly chosen pixels of these two arrays are swapped with each other. Same operation is performed on the two randomly chosen columns to get the scrambled image. Next, an XOR operation is performed between the scrambled image and the key stream of random data given by the chaotic system. Further, both the image data and the streams of random numbers are DNA-encoded. Again, the DNA-encoded pixels data are scrambled the way, scrambling was performed on the decimal data but with the different key streams of random numbers. To realize the effects of diffusion at the DNA level, the DNA-encoded scrambled pixels data and the DNA-encoded key stream are XORed with each other. Finally, the DNA-encoded data is translated back into its decimal equivalent. SHA-256 hash codes for the given input image have been used in the proposed cipher in order to achieve the plaintext sensitivity. The simulation and the performance analysis portray the good security effects, defiance to the varied threats and the bright prospects for the real world application of the proposed cipher.

Heavy metal stress affects crop growth and yields as wheat ( Triticum aestivum L.) growth and development are negatively affected under heavy metal stress. The study examined the effect of cobalt chloride (CoCl 2 ) stress on wheat growth and development. To alleviate this problem, a pot experiment was done to analyze the role of sulfur-rich thiourea (STU) in accelerating the defense system of wheat plants against cobalt toxicity. The experimental treatments were, i) Heavy metal stress (a) control and (b) Cobalt stress (300 µM), ii) STU foliar applications; (a) control and (b) 500 µM single dose was applied after seven days of stress, and iii) Wheat varieties (a) FSD-2008 and (b) Zincol-2016. The results revealed that cobalt stress decreased chlorophyll a by 10%, chlorophyll b by 16%, and carotenoids by 5% while foliar application of STU increased these photosynthetic pigments by 16%, 15%, and 15% respectively under stress conditions as in contrast to control. In addition, cobalt stress enhances hydrogen peroxide production by 11% and malondialdehyde (MDA) by 10%. In comparison, STU applications at 500 µM reduced the production of these reactive oxygen species by 5% and by 20% by up-regulating the activities of antioxidants. Results have revealed that the activities of SOD improved by 29%, POD by 25%, and CAT by 28% under Cobalt stress. Furthermore, the foliar application of STU significantly increased the accumulation of osmoprotectants as TSS was increased by 23% and proline was increased by 24% under cobalt stress. Among wheat varieties, FSD-2008 showed better adaptation under Cobalt stress by showing enhanced photosynthetic pigments and antioxidant activities compared to Zincol-2016. In conclusion, the foliar-applied STU can alleviate the negative impacts of Cobalt stress by improving plant physiological attributes and upregulating the antioxidant defense system in wheat. Graphical Abstract

Introduction Bombings, accounting for approximately 50% of global terrorist incidents, frequently cause high-morbidity thoracic trauma, including blast lung injury. This retrospective radiological review characterizes injury patterns in bomb blast victims to guide mass casualty response and improve patient outcomes. Methods This retrospective observational review, conducted at Aga Khan University Hospital (January 2004–October 2024), included 130 patients with bomb blast injuries. Demographics, injury mechanisms, and imaging findings were categorized by blast type and summarized using frequencies, percentages, medians, and interquartile ranges. Results Among 130 victims (94.6% males; median (interquartile range) age, 32 (26.0–43.5) years), initial chest X-ray was performed in 85.4% of cases, detecting foreign bodies (22.8%), emphysema (10.4%), and atelectasis (10.4%). Computed tomography was performed in 28.5% of the patients on the second imaging assessment; however, foreign bodies and atelectasis persisted at 14.4%–15.9% on follow-up. Primary blast injuries predominated (68.4%–78.8%), followed by secondary (15.0%–23.3%), tertiary (0%–4.7%), and quaternary (1.8%–4.4%) injuries; additionally, 48.5% of patients did not undergo a third study. Conclusions Primary blast injuries predominate, with frequent foreign bodies, emphysema, and atelectasis. Initial chest X-ray facilitates rapid assessment, while computed tomography is reserved for complex cases. Tailored imaging protocols may enhance timely care and outcomes in resource-limited settings.

Environmental microbes living in communities engage in complex interspecies interactions that are challenging to decipher. Nevertheless, the interactions provide the basis for shaping community structure and functioning, which is crucial for ecosystem service. In addition, microbial interactions facilitate specific adaptation and ecological evolution processes particularly essential for microbial communities dwelling in resource-limiting habitats, such as the deep oceans. Recent technological and knowledge advancements provide an opportunity for the study of interactions within complex microbial communities, such as those inhabiting deep-sea waters and sediments. The microbial interaction studies provide insights into developing new strategies for biotechnical applications. For example, cooperative microbial interactions drive the degradation of complex organic matter such as chitins and celluloses. Such microbiologically-driven biogeochemical processes stimulate creative designs in many applied sciences. Understanding the interaction processes and mechanisms provides the basis for the development of synthetic communities and consequently the achievement of specific community functions. Microbial community engineering has many application potentials, including the production of novel antibiotics, biofuels, and other valuable chemicals and biomaterials. It can also be developed into biotechniques for waste processing and environmental contaminant bioremediation. This review summarizes our current understanding of the microbial interaction mechanisms and emerging techniques for inferring interactions in deep-sea microbial communities, aiding in future biotechnological and therapeutic applications.

AbstractObjective: Cardiac catheterization using the distal radial artery (DRA) access, at the level of the anatomical snuff box post-radial artery bifurcation, may be linked to a lower rate of arterial occlusion and better hemostasis. In this meta-analysis, we compare DRA versus proximal radial artery (PRA) access in cardiac catheterization or angiography. Methods: A detailed literature search was performed on PubMed, Cochrane, Embase, and Clinicaltrials.gov from inception till June 2024. Risk ratios (RRs) and mean differences (MDs) were pooled for categorical and continuous outcomes, respectively. Random effects meta-analysis was undertaken on RevMan. Results: Our meta-analyses include 21 randomized controlled trials with 9,539 patients (DRA 4,761, PRA 4,778). DRA significantly reduced 24-h radial artery occlusion rates (RR 0.30, 95% CI: 0.23 to 0.40, p ≤ 0.00001) and time to hemostasis (minutes) (MD −44.46, 95% CI: −50.64 to −38.92, p < 0.00001), whereas PRA was significantly superior in terms of the puncture success rate (RR 0.96, 95% CI: 0.93 to 0.99, p < 0.01), the crossover rate (RR 2.89, 95% CI: 2.02 to 4.15, p < 0.00001), and puncture attempts (MD 0.69, 95% CI: 0.37 to 1.00, p = 0.00001). Conclusion: DRA was associated with a lower risk of occlusion and lower time to hemostasis, but required a greater number of puncture attempts and had lower success rate. Further research is required to elucidate the most optimal approach.