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It is well documented that the self-employed experience higher levels of happiness than waged employees even when their incomes are lower. Given the UK government’s asymmetric treatment of waged workers and the self-employed, we use a unique Covid-19 period data set which covers the months leading up to the March lockdown and the months just after to assess three aspects of the Covid-19 crisis on the self-employed: hours of work reductions, the associated income reductions and the effects of both on subjective well-being. Our findings show the large and disproportionate reductions in hours and income for the self-employed directly contributed to a deterioration in their levels of subjective well-being compared to waged workers. It appears that their resilience was broken when faced with the reality of dealing with rare events, particularly when the UK welfare support response was asymmetric and favouring waged employees.

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This paper considers the network protection technique of shared backup path protection (SBPP) in comparison with 1 + 1 path protection for elastic optical networks. We develop integer linear programming (ILP) models to minimize both the required spare capacity and the maximum number of link frequency slots (FSs) used. We consider transponder tunability that corresponds to the condition of whether or not the same set of FSs is required to be used for both the working and protection lightpaths. We also apply the bandwidth squeezed restoration technique to obtain the maximum restoration levels for the affected service flows, subject to a limited FS capacity on each fiber link. Our studies show that the proposed SBPP technique requires much lower spare capacity compared to the traditional 1 + 1 path protection approach. The flexibility of allowing the working and protection lightpaths to use different sets of FSs (i.e., with full transponder tunability) has the advantage of reducing both the number of FSs needed and the spare capacity redundancy required.

T his paper analyzes interactions between a firm that seeks to discriminate between normal users and hackers that try to penetrate and compromise the firm's information assets. We develop an analytical model in which a variety of factors are balanced to best manage the detection component within information security management. The approach not only considers conventional factors such as detection rate and false-positive rate, but also factors associated with hacker behavior that occur in response to improvements in the detection system made by the firm. Detection can be improved by increasing the system's discrimination ability (i.e., the ability to distinguish between attacks and normal usage) through the application of maintenance effort. The discrimination ability deteriorates over time due to changes in the environment. Also, there is the possibility of sudden shocks that can sharply degrade the discrimination ability. The firm's cost increases as hackers become more knowledgeable by disseminating security knowledge within the hacker population. The problem is solved to reveal the presence of a steady-state solution in which the level of system discrimination ability and maintenance effort are held constant. We find an interesting result where, under certain conditions, hackers do not benefit from disseminating security knowledge among one another. In other situations, we find that hackers benefit because the firm must lower its detection rate in the presence of knowledge dissemination. Other insights into managing detection systems are provided. For example, the presence of security shocks can increase or decrease the optimal discrimination level as compared to the optimal level without shocks.

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