Anxiety Disorders in Pregnant Women: Causes and How to Overcome Them

The article explains the origins of anxiety disorders during pregnancy, and their treatment. Pregnant women's anxiety is normally manifests itself as emotions, perceptions, and behaviors, which is mediated by biological, social, and genetic influences. Probability analysis is done of this kind. Using a survey of 1500 rural residents as a random number between 100 and 1600 produces a 200% response rate. Those findings revealed that 60% of the participants had adequate reproductive age and 51% had mild to moderate anxiety, and 78.4% had high anxiety. The reasons leading to pregnant women's anxiety include their age, working status, lack of care from a boyfriend, having had previously given birth, and their wellbeing before becoming pregnant, all of the woman's family members, and maternal well-being. To resolve this is by sufficient relaxation, anti-depressants, a safe diet, and physical exercise, along with learning how to behave more positively, and by means of ourselves, or through prantal massage.

Material and Design Optimization of Micro-Sensors

Measurements of three fluidic parameters i.e. velocity / flow rate / wall shear stress, pressure and temperature are crucial in many industrial applications including aerospace, automobile, bio-medical and process control etc. The key requirement is to measure these parameters at very fine spatial resolutions, which ultimately depends upon the size of measuring device. Size minimization is possible only through Micro-Electro-Mechanical Systems (MEMS) or mirco-fabrication technology. Therefore, using a novel SOI CMOS fabrication process, this thesis develops a multi-sensing platform that measures these three fluidic parameters, simultaneously.The developed chip has dimensions of just 1.6 mm × 1.6 mm as compared with the minimum chip size of 3.8 mm × 3.8 mm reported previously, thus improving the flow spatial resolution by 82.2%. The developed multi-sensing chip includes a thermal flow sensor, a piezoresistive pressure sensor and a Resistive Temperature Detector (RTD) based temperature sensor. Before integrating these sensors in a single chip, their material and design optimization have been carried out. Ashby‟s materials selection methodology has been used to select the optimum materials for these sensors. Since no MEMS compatible materials database having micro-scale material properties was readily available, therefore first a MEMS materials database has been developed and integrated with a material selection software. The developed materials database along with the derived performance indices for these sensors has then been used to select the candidate materials. Additionally, performance of these candidate materials and the variety of promising designs has also been evaluated experimentally in a two step iterative process using SOI CMOS fabrication technology. In a first iteration, eight thermal flow sensors (i.e. having square / circular membranes and four membrane to heater length ratios), six piezoresistive pressure sensors (i.e. having square membranes, three piezoresistor materials and two piezoresistor layout designs) and twelve RTDs temperature sensors(i.e. having five different materials and six layouts) have been fabricated andexperimental characterized. The best sensor of each type in the first iteration has been further improved when integrated in the final optimized multi sensing chip. The results showed that a thermal flow sensor with a silicon oxide square membrane having a membrane to heater length ratio of 3.35 has the highest sensitivity to area ratio. Similarly, a piezoresistor pressure sensor with a square membrane having p-doped silicon piezoresistors planted at the center of each membrane edges gives maximum sensitivity. The RTDs made of p-doped silicon and having higher L/W ratio exhibited the highest sensitivity and linearity. The optimized thermal, pressure and RTD sensors are much more sensitive than the previously reported such type of sensors. .It is worth highlighting that in addition to being highly sensitive, optimized sensors reported in this research are also CMOS compatible, which make them attractive considering their low cost and ease of integration with other CMOS devices, sensors and circuits.