Natural fibre silk consists of two proteins i.e. sericin and fibroin, which synthesized at the middle and posterior silk gland of silkworm, respectively. Sericin is glue proteins, which envelop the core protein fibroin and constitute about 15-25% of cocoon weight. It is hydrophilic in nature and composed of 18 different kinds of amino acids among these the serine, glycine, aspartic acid and threonine are most dominant one. Sericin possesses tremendous biological properties like antioxidant, antibacterial, anti-tyrosinase, UV resistance, anti-cancerous which open a wide scope for its application in various fields. Further, moisturizing ability of sericin serve as valuable ingredients for cosmetic industries for developing hydrating skin creams and protection against ultraviolet radiation as well as it also utilized as therapeutic agent for wound healing. In the present review the medicinal properties and its implication has been discussed.

Weigh-In-Motion (WIM) data have been collected by state departments of transportation (DOT) in the U.S. and are anticipated to grow as state DOTs expand the number of WIM sites in order to better manage transportation infrastructure and enhance mobility. Traditional approaches for monitoring the vehicle weight measured in WIM systems include conducting statistical tests between two datasets obtained from two calibration visits. Depending on the frequency of visits, these traditional approaches are ineffective or resource-demanding for identifying calibration needs. Excessive vehicle-weight drifts exceeding 10% are usually indicative of poor performance by WIM systems. However, it has been difficult to consistently monitor such performance due to the sheer amount of data. In Georgia, the number of WIM sites have expanded from 12 to 29 in the past 3 years. This paper proposes a deep-learning-based temporal prediction approach for modeling sequential data and monitoring the time-history of the live loads imposed on roads and bridges. In total, 29 WIM sites in Georgia are analyzed to examine the effectiveness of a proposed temporal prediction approach for evaluating observed live loads. This study finds that the Jensen–Shannon divergence method is more effective than statistical difference tests, particularly when screening for live load anomalies. It is concluded that a LSTM neural network is able to capture temporal dynamics underlying the sequential load patterns observed in the WIM data and serves as an effective model for consistently monitoring the performance of WIM systems over time.

The nutrient composition of leaves directly affects the growth and development of tasar silkworm larvae and the economic characteristics of cocoons. In the present study, the role of nutrients such organic (Farm yard manures + green manures + biofertilizer) and inorganic(NPK) on fecundity was studied. Studies have been launched to find out the effect of soil nutrient on fecundity and retention of eggs. The results suggested that larval weight was higher (male 42.52 g and female 47.07g) inorganic treatments when compared to other treatments. However, higher shell weight (2.4gm) was detected in male cocoons of larvae feeded with host plants grown on organic supplemented soil treatments when compared with other exposure group. Organic treatments fecundity was 146, inorganic treatments 129, and control 113. The application of organic manure increases larval weight and fecundity, which may increase tasar silkworm output and productivity.

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Data collected using sensors plays an essential role in active bridge health monitoring. When analyzing a large number of bridges in the U.S., the National Bridge Inventory data as been widely used. Yet, the database does not provide information about live loads, one of the most indeterminate variables for monitoring bridges. Such asymmetric information can lead to an adverse selection problem in making maintenance, rehabilitation, and repair decisions. This study proposes a data-driven reliability analysis to assess probabilities of bridge failure by synthesizing NBI data and Weigh-In-Motion (WIM) data for a large number of bridges in Georgia. On the resistance side, tree ensemble methods are employed to support the hypothesis that the NBI operating load rating represents the distribution of bridge resistance capacities which change over time. On the loading side, the live load distribution is derived from field data collected using WIM sensors. Our results show that the proposed WIM data-enabled reliability analysis substantially enhances information symmetry and provides a reliability index that supports monitoring of bridge conditions, depending on live loads and load-carrying capacities.