Comparison of data hiding using LSB and DCT for image

Steganography is the art and science of hiding information by embedding data into cover media. There are two main domains namely spatial and frequency domain. In this project, a comparison is made between hiding in spatial domain using LSB technique and hiding in frequency domain using DCT technique. Various BMP cover images with 256x256 and 512x512 resolutions for LSB and DCT techniques are used in the experimentations. For DCT technique, various BMP image is converted to JPEG image. Then data is hidden into the JPEG cover image using DCT technique. After that, the JPEG stego image is converted back to BMP stego image. These BMP stego images from LSB and DCT are compared using PSNR. Results from these experiments show that 75% of the stego images hidden using LSB has shown higher PSNR values than stego images hidden using DCT. This means that the stego image hidden using LSB has shown a much closer similarity to the cover image than stego image hidden using DCT, thus much harder to detect hidden data in the stego image by LSB

Time and space multi-manned assembly line balancing problem using genetic algorithm

Purpose: Time and Space assembly line balancing problem (TSALBP) is the problem of balancing the line taking the area required by the task and to store the tools into consideration. This area is important to be considered to minimize unplanned traveling distance by the workers and consequently unplanned time waste. Although TSALBP is a realistic problem that express the real-life situation, and it became more practical to consider multi-manned assembly line to get better space utilization, few literatures addressed the problem of time and space in simple assembly line and only one in multi-manned assembly line. In this paper the problem of balancing bi-objective time and space multi-manned assembly line is proposed Design/methodology/approach: Hybrid genetic algorithm under time and space constraints besides assembly line conventional constraints is used to model this problem. The initial population is generated based on conventional assembly line heuristic added to random generations. The objective of this model is to minimize number of workers and number of stations. Findings: The results showed the effectiveness of the proposed model in solving multi-manned time and space assembly line problem. The proposed method gets better results in solving real-life Nissan problem compared to the literature. It is also found that there is a relationship between the variability of task time, maximum task time and cycle time on the solution of the problem. In some problem features it is more appropriate to solve the problem as simple assembly line than multi-manned assembly line. Originality/value: It is the first article to solve the problem of balancing multi-manned assembly line under time and area constraint using genetic algorithm. A relationship between the problem features and the solution is found according to it, the solution method (one sided or multi-manned) is definedPeer Reviewe

Medical errors : Healthcare professionals’ perspective at a tertiary hospital in Kuwait

Medical errors are of economic importance and can contribute to serious adverse events for patients. Medical errors refer to preventable events resulting from healthcare interactions, whether these events harm the patient or not. In Kuwait, there is a paucity literature detailing the causes, forms, and risks of medical errors in their state-funded healthcare facilities. This study aimed to explore medical errors, their causes and preventive strategies in a Kuwait tertiary hospital based on the perceptions and experience of a cross-section of healthcare professionals using a questionnaire with 27 open (n = 10) and closed (n = 17) questions. The recruited healthcare professionals in this study included pharmacists, nurses, physicians, dentists, radiographers, hospital administrators, surgeons, nutritionists, and physiotherapists. The collected data were analysed quantitatively using descriptive statistics. A total of 203 participants filled and completed the survey questionnaire. The frequency of medical errors in Kuwait was found to be high at 60.3% ranging from incidences of prolonged hospital stays (32.9%), adverse events and life-threatening complications (32.3%), and fatalities (20.9%). The common medical errors result from incomplete instructions, incorrect dosage, and incorrect route of administration, diagnosis errors, and labelling errors. The perceived causes of these medical errors include high workload, lack of support systems, stress, medical negligence, inadequate training, miscommunication, poor collaboration, and non-adherence to safety guidelines among the healthcare professionals.Peer reviewe

Data-Driven Chance-Constrained Design of Voltage Droop Control for Distribution Networks

This paper addresses the design of local control methods for voltage control in distribution networks with high levels of distributed energy resources (DERs). The designed control methods modulate the active and reactive power output of DERs proportional to the deviation of the local measured voltage magnitudes from a reference voltage, which is referred to as droop control; thus, the design focuses on determining the droop characteristics that satisfy network-wide voltage magnitude constraints. The uncertainty and variability of DERs renders the design of optimal droop controls very challenging; hence, this paper proposes chance constraints to limit the risk from intermittent DERs by designing droop control coefficients that guarantee the satisfaction of network operational constraints with a specific probability. In addition, the proposed approach relies entirely on historical data rather than assuming knowledge of the probability distributions that characterize the uncertainty of DERs. The efficacy of the proposed method is demonstrated on a 37-bus distribution feeder

Interpreting Primal-Dual Algorithms for Constrained Multiagent Reinforcement Learning

Constrained multiagent reinforcement learning (C-MARL) is gaining importance as MARL algorithms find new applications in real-world systems ranging from energy systems to drone swarms. Most C-MARL algorithms use a primal-dual approach to enforce constraints through a penalty function added to the reward. In this paper, we study the structural effects of this penalty term on the MARL problem. First, we show that the standard practice of using the constraint function as the penalty leads to a weak notion of safety. However, by making simple modifications to the penalty term, we can enforce meaningful probabilistic (chance and conditional value at risk) constraints. Second, we quantify the effect of the penalty term on the value function, uncovering an improved value estimation procedure. We use these insights to propose a constrained multiagent advantage actor critic (C-MAA2C) algorithm. Simulations in a simple constrained multiagent environment affirm that our reinterpretation of the primal-dual method in terms of probabilistic constraints is effective, and that our proposed value estimate accelerates convergence to a safe joint policy.Comment: 19 pages, 8 figures. Presented at L4DC 202