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The application of rinse cycle softener after the household laundry process has become more common in recent times. This study aims to understand the effect of repeated rinse cycle softener treatment on the mechanical and frictional properties of the cotton fabric.

Cotton-woven fabric is treated with commercial rinse cycle softener repeatedly for 15 times. After treatment, the fabric was evaluated for the changes in mechanical properties using the Kawabata evaluation system.

The results of this study revealed that the softener treatment reduces the tensile properties (41.25%) and increases the overall extensibility of the fabric up to 20.89%. The shear (34.57%) and bending rigidity of the treated fabric are reduced considerably than the untreated fabric (58.02%). The increment in the fabric softness and fluffiness was confirmed with the increment in the compression and the difference between the initial and final thickness at maximum pressure. Statistical significance (p < 0.05) is noted only in the case of bending and surface friction properties (dynamic friction).

The usage of rinse cycle softeners in the household laundry has a significant influence on the comfort characteristics of the cotton-woven fabric. Repeated usage of rinse cycle softener increased the fabric softness and fluffiness of the fabric and also reduced the tensile properties significantly.

The purpose of this paper is to investigate the volatility clustering in the return of land markets through both theoretical and empirical approaches.

Using extensive monthly panel data at the provincial level from 1986 to 2013, the authors identify the existence of time-correlated and time-varying returns in Canadian land markets.

Consistent with the proposed theory, volatility clustering in land markets tends to be observed in more populated areas.

The result has significant implications for portfolio management, economic theory and government policy by revealing the systematic pattern of volatility clustering in land markets.

This paper aims to propose a supply model of periodic review with joint replenishment for multi-products grouped by several variables with random and time dependence demand.

The products are grouped by multivariate cluster analysis. The stochastic inventory model describes the random demand of each product, considering the temporal dependency through a generalized autoregressive moving average model. Stochastic programming for the total cost of inventory is obtained considering the expected value of the demand per unit of time.

The total costs for the products grouped with the proposed model are 6% lower than for the individual inventory policy. The expected shortage units decrease significantly in the proposed grouped model with temporary dependence. In addition, the proposal with temporary dependency has lower costs than when the independent and identically distributed demand is considered.

The proposed policy is exemplified with real-world data from a Chilean hospital, where the products (drugs) are segmented by grouping variables, forming clusters of drugs with homogeneous behavior within the groups and heterogeneous behavior between groups.

This work reports on a developing method time measurement system for measuring manufacturing and assembly processes automatically. This automatic system enables the production engineers and management to detect, process, and display concise and accurate information about the operations in real time.

This system is based on Internet of things technology and RFID-antenna. This methodology consists of seven main steps and one final optimization step. Mainly, the operator is equipped by RFID reader, and the work station tools and devices are provided by RFID tags. Responding the RFID tags to the reader will refer to the certain operations, the difference time between start and end of the operations will be collected immediately and calculated by the microprocessor of the system.

This automatic system is promising, considering the accurate time measurements and recommendations that obtained from the case study which includes measuring manual assembly operations to be followed in order to overcome the limitations which are not only technical but also managerial, legal and organizational.

The acquired data about timing and duration of individual operations are anonymized to guarantee the compliance with respect to the privacy laws (GDPR and Italian work's laws).

This work presents a unique system to measure the time instead of traditional methods in the factories environment and satisfies the requirements to study the recommendations in order to overcome the challenges.

The electric power system is a complex system, whose operating condition may not remain at a constant value. The various contingencies like outage of lines, transformers, generators and sudden increase of load demand or failure of equipments are more common. This causes overloads and system parameters to exceed the limits thus resulting in an insecure system. The purpose of this paper is to enhance the power system security by alleviating overloads on the transmission lines.

Fuzzy logic system (FLS) with particle swarm optimization based optimal power flow approach is used for overload alleviation on the transmission lines. FLS is modeled to find the changes in inertia weight by which new weights are determined and their values are applied to particle swarm optimization (PSO) algorithm for velocity and position updation.

The proposed method is tested and examined on the standard IEEE-30 bus system under base case and increased load conditions at different contingency. This method gives better results in terms of optimum fuel cost and fast convergence under base case and could alleviate the line overloads at different contingency with optimum generation cost, when compared to adaptive particle swarm optimization (APSO) and PSO.

FLS is modeled in MATLAB environment. The effectiveness of the proposed method is tested and examined on the standard IEEE-30 bus system and their results are compared with APSO and PSO under MATPOWER environment. The results show that the proposed algorithm is capable of improving the transmission security with optimum generation cost.

This study aims to characterize the behavior of blended concrete, including metakaolin (MK) and quarry dust (QD), as supplementary cementing materials. The study focuses on evaluating the effects of these materials on the fresh and hardened properties of concrete.

MK, a pozzolanic material, and QD, a fine aggregate by-product, are potentially sustainable alternatives for enhancing concrete performance and reducing environmental impact. The addition of different percentages of MK enhances the pozzolanic reaction, resulting in improved strength development. Furthermore, the optimum dosage of MK, mixed with QD, and mechanical properties like compressive, flexural and split tensile strength of concrete were evaluated to investigate the synergetic effect of MK and quarry dust for M20-grade concrete.

The results reveal the influence of metakaolin and QD on the overall performance of blended concrete. Cost analysis showed that the optimum mix can reduce the 7%–8% overall cost of the materials for M20-grade concrete. Energy analysis showed that the optimum mix can reduce 7%–8% energy consumption.

The effective utilization is determined with the help of the analytical hierarchy process method to find an optimal solution among the selected criteria. According to the AHP analysis, the optimum content of MK and quarry dust is 12% and 16%, respectively, performing best among all other trial mixes.