• Book : 211()
• Pub. Date : 2025
• Page : pp.111029
• Keyword :

Abstract

The need for the replacement of cadmium, indium, and tellurium in their compounds for sensor applications is a novel study. The copper zinc tin sulfide (Cu2ZnSnS4) thin films were synthesized from Cu (99.99%), Sn (99.99%), and Zn (99.99%) using the thermal evaporation method. The same volumetric parameters were maintained throughout the synthesis process. The films were further irradiated using an isotope of cesium-137 (Cs-137) from a gamma source at different doses (0-0.6 kGy) and dose rates of 0.1007 Gy/h at room temperature. Both the pristine (0 kGy) and irradiated (0.1, 0.3, and 0.6 kGy) films were characterized with a Raman spectroscope, a field emission scanning electron microscope (FESEM) with the JEOL JSM-7600F model, energy dispersive X-rays (EDX), an ultraviolet-visible-near infrared (UV-Vis-NIR) spectroscope, and four-point probe techniques. The Raman results confirmed that all the films for both pristine and irradiated films have a main and secondary phases. The EDX results showed that the pristine and 0.1 kGy films were Cu-rich films, while the 0.3 kGy and 0.6 kGy films turned out to be Zn-rich films with an increase in gamma radiation dose. The optical properties of all the films showed also that the band gap decreased from 1.6 to 1.48±0.03 eV for the pristine and irradiated films, while the electrical resistivity results decreased as the gamma radiation dose increased. However, as the structural, optical, and electrical properties of the Cu2ZnSnS4 thin films responded linearly with the increasing gamma radiation dose, this suggests the usefulness and possibility of designing a new solid-state sensor for dosimetry applications to replace cadmium telluride (CdTe) and copper indium gallium sulfide (CIGS) thin films.

• Book : 11(2)
• Pub. Date : 2025
• Page : pp.022002
• Keyword :

• Book : 12(suppl1)
• Pub. Date : 2025
• Page :
• Keyword :

• Book : 12(suppl1)
• Pub. Date : 2025
• Page :
• Keyword :

This research paper monitors the patient’s health using sensor data, cloud, and big data Hadoop tools and used to predict heart attack and related results were discussed in detail. The integration of big data, and wearable sensors in pervasive computing has significantly enhanced healthcare services. This proposal focuses on developing an advanced healthcare monitoring system tailored for tracking the activities of elderly individuals. The wearable sensors are placed on humans at a right angle, left arm, right arm, and chest to collect the data. The large data are split into smaller segments using the map and reduce process of big data Hadoop tools. The intensity-modulated radiation therapy (IMRT) approach is used for the mapping phase and deep convolutional neural network (DCNN), deep belief network (DBN), and long short-term memory (LSTM) and proposed deep learning heart rate prediction (DLHRP) algorithms are used for the combiner/reduce phase. The reduction process combines similar segments of data to predict identical classes to predict the severity of human conditions. The proposed IMRT-DLHRP system has improved performance of 96.34% accuracy compared with 84.25%, 89.47%, and 91.58% compared to DCNN, DBN, and LSTM respectively, therefore proposed framework has significant improvement over existing approaches.

• Book : 37(1)
• Pub. Date : 2025
• Page : pp.300
• Keyword :

• Book : 258(2)
• Pub. Date : 2025
• Page : pp.124647
• Keyword :

• Book : 50()
• Pub. Date : 2025
• Page : pp.100893
• Keyword :

• Book : 6()
• Pub. Date : 2025
• Page : pp.100391
• Keyword :

• Book : 381(2)
• Pub. Date : 2025
• Page : pp.133338
• Keyword :

Abstract

There is an urgent need to adapt crop breeding strategies to boost resilience in the face of a growing food demand and a changing climate. Achieving this requires an understanding of how weather and climate variability impacts crop growth and development. Using the United Kingdom (UK) as an example, we evaluate changes in the UK agroclimate and analyse how these have influenced domestic wheat production. Here we quantify spatial and temporal variability and changes in weather and climate across growing seasons over the last four decades (1981-2020). Drawing on variety trial data, we then use statistical modelling to explore the interaction between genotype and agroclimate variation.

We show that changes in the UK agroclimate present both risks, and opportunities for wheat growers, depending on location. From 1981-2020, in Wales, the West Midlands, large parts of the North West, and Northern Ireland, there was an overall increase in frost risk in early spring of 0.15 additional frost days per year, whilst in the east early frost risk decreased by up to 0.29 d per year. Meanwhile, over the period 1987-2020, surface incoming shortwave radiation during grainfill increased in the east by up to 13% but decreased in Western areas by up to 15%. We show significant inter-varietal differences in yield responses to growing degree days, heavy rainfall, and the occurrence of late frost. This highlights the importance of evaluating variety-climate interactions in variety trial analyses, and in climate-optimised selection of crops and varieties by growers. This work provides guidance for future research on how climate change is affecting the UK agroclimate and resulting impacts on winter cereal production.

• Book : 2(1)
• Pub. Date : 2025
• Page : pp.015002
• Keyword :