811 research outputs found
Awardees data
No full textThis dataset contains past laureates from the physics field and is used as a benchmark dataset for the study
CCDC 1058510: Experimental Crystal Structure Determination
No full textRelated Article: Ahmad Raheel, Imtiaz-ud-Din, Sohaila Andleeb, Simko Ramadan, Muhammad Nawaz Tahir|2017|Appl.Organomet.Chem.|31|e3632|doi:10.1002/aoc.363
sj-xlsx-1-smo-10.1177_20503121221148613 – Supplemental material for 25-Hydroxyvitamin-D deficiency in chronic kidney disease stages III, IV, and V in South Asian population: a retrospective cohort
No full textSupplemental material, sj-xlsx-1-smo-10.1177_20503121221148613 for 25-Hydroxyvitamin-D deficiency in chronic kidney disease stages III, IV, and V in South Asian population: a retrospective cohort by Muhammad Raheel Abdul Razzaque, Sameer Saleem Tebha, Alaa Tukruna, Aabiya Arif, Lucas Marian Kogut, Nasir Ali Afsar, Dania Shabbir and Zain Ali Zaidi in SAGE Open Medicine</p
Road Safety Risk Evaluation Using GIS-Based Data Envelopment Analysis—Artificial Neural Networks Approach
No full textIdentification of the most significant factors for evaluating road risk level is an important question in road safety research, predominantly for decision-making processes. However, model selection for this specific purpose is the most relevant focus in current research. In this paper, we proposed a new methodological approach for road safety risk evaluation, which is a two-stage framework consisting of data envelopment analysis (DEA) in combination with artificial neural networks (ANNs). In the first phase, the risk level of the road segments under study was calculated by applying DEA, and high-risk segments were identified. Then, the ANNs technique was adopted in the second phase, which appears to be a valuable analytical tool for risk prediction. The practical application of DEA-ANN approach within the Geographical Information System (GIS) environment will be an efficient approach for road safety risk analysis
A Low-Profile Phase Correcting Solution to Improve Directivity of Horn Antenna
No full textIn this paper, we present a phase-corrected horn antenna with improved directivity. An increase in the horn aperture size results in a significantly non-uniform phase distribution contributing to the poor radiation characteristics. A low-profile phase correction surface (PCS) is therefore designed to address this problem. Significant improvement has been achieved in the horn antenna performance by placing the proposed PCS right at the mouth of the horn. The near-field transformation method is applied to demonstrate an improvement of 10 dBi in the peak directivity at the operating frequency of 11 GHz. This feature can be extended to manipulate the far-field pattern of horn and even for beam steering applications
Modelling and identification of characteristic kinematic features preceding freezing of gait with convolutional neural networks and layer-wise relevance propagation
No full textBACKGROUND: Although deep neural networks (DNNs) are showing state of the art performance in clinical gait analysis, they are considered to be black-box algorithms. In other words, there is a lack of direct understanding of a DNN's ability to identify relevant features, hindering clinical acceptance. Interpretability methods have been developed to ameliorate this concern by providing a way to explain DNN predictions. METHODS: This paper proposes the use of an interpretability method to explain DNN decisions for classifying the movement that precedes freezing of gait (FOG), one of the most debilitating symptoms of Parkinson's disease (PD). The proposed two-stage pipeline consists of (1) a convolutional neural network (CNN) to model the reduction of movement present before a FOG episode, and (2) layer-wise relevance propagation (LRP) to visualize the underlying features that the CNN perceives as important to model the pathology. The CNN was trained with the sagittal plane kinematics from a motion capture dataset of fourteen PD patients with FOG. The robustness of the model predictions and learned features was further assessed on fourteen PD patients without FOG and fourteen age-matched healthy controls. RESULTS: The CNN proved highly accurate in modelling the movement that precedes FOG, with 86.8% of the strides being correctly identified. However, the CNN model was unable to model the movement for one of the seven patients that froze during the protocol. The LRP interpretability case study shows that (1) the kinematic features perceived as most relevant by the CNN are the reduced peak knee flexion and the fixed ankle dorsiflexion during the swing phase, (2) very little relevance for FOG is observed in the PD patients without FOG and the healthy control subjects, and (3) the poor predictive performance of one subject is attributed to the patient's unique and severely flexed gait signature. CONCLUSIONS: The proposed pipeline can aid clinicians in explaining DNN decisions in clinical gait analysis and aid machine learning practitioners in assessing the generalization of their models by ensuring that the predictions are based on meaningful kinematic features.status: Publishe
Morpho-Physiological and Enzymatic Responses of Zinnia (Zinnia elegans L.) to Different Metal Hoarded Wastewaters
No full textWastewater, as an irrigation source, offers various advantages, significantly enhancing soil fertility, crop development, soil health, and preventing soil alkalinization. The introduction of non-conventional water resources (treated greywater, and treated wastewater) in floriculture plays a crucial role in water conservation. This study examined the impact of treated greywater (wastewater generated from domestic activities such as bathing, showering, laundry, and dishwashing) and treated wastewater (water from toilets, showers, kitchen sinks, and industrial processes) on photosynthetic characteristics, stress-related metabolites, and antioxidative enzymes in zinnia plants. A six-month pot experiment was conducted, with three water sources (canal water, treated greywater, and treated wastewater) and two zinnia varieties (Peter pan and Dreamland). During the flowering stages, when the zinnia flower petals were fully opened. Several parameters were measured, including pigments, photosynthetic attributes, total soluble protein, hydrogen peroxide and glycinebetaine. Moreover, antioxidant enzymatic activities like peroxidase (POX), catalase (CAT), superoxide dismutase (SOD), and ascorbate peroxidase (APX) were also investigated in zinnia plants to assess their resilience against abiotic stressors caused by high levels of heavy metals and excessive nutrients present in wastewaters. Results indicated that treated greywater (TGW) significantly improved vegetative parameters, such as plant height, leaf number, leaf area, and stem diameter. Additionally, flowering attributes, including the number and diameter of flowers, as well as fresh and dry plant biomass, increased in Peter pan variety under treated greywater irrigation. Chlorophyll and carotenoid contents were notably reduced in plants irrigated with treated greywater and treated wastewater. Photosynthetic attributes, such as stomatal conductance, transpiration rate, and photosynthetic rate, significantly improved in zinnia plants under treated greywater irrigation. Stress-related metabolites and antioxidant enzymatic activities also showed substantial enhancements under treated greywater irrigation. Principal Component Analysis (PCA) confirmed the positive effect of treated greywater on flower quality, plant biomass, and physiological processes. Utilizing treated greywater and wastewater for Zinnia cultivation is a promising approach. Ensuring the ongoing monitoring and management of harmful substances in wastewater is vital, involving regular testing, quality control, and other actions. This practice contributes to environmental conservation, sustainable agriculture, efficient water resource management, reduced energy use, and enhanced soil health, making it a cost-effective and eco-friendly option for floriculture
Eyeing the Generals
No full textPakistan is watching the battle of two Sharifs—Prime Minister Muhammad Nawaz Sharif versus powerful army chief General Raheel Sharif. A political crisis is fueling tensions between the country’s civil and military institutions
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