Blockchain-IoT-Driven Nursing Workforce Planning for Effective Long-Term Care Management in Nursing Homes.

Due to the global ageing population, the increasing demand for long-term care services for the elderly has directed considerable attention towards the renovation of nursing homes. Although nursing homes play an essential role within residential elderly care, professional shortages have created serious pressure on the elderly service sector. Effective workforce planning is vital for improving the efficacy and workload balance of existing nursing staff in today's complex and volatile long-term care service market. Currently, there is lack of an integrated solution to monitor care services and determine the optimal nursing staffing strategy in nursing homes. This study addresses the above challenge through the formulation of nursing staffing optimisation under the blockchain-internet of things (BIoT) environment. Embedding a blockchain into IoT establishes the long-term care platform for the elderly and care workers, thereby decentralising long-term care information in the nursing home network to achieve effective care service monitoring. Moreover, such information is further utilised to optimise nursing staffing by using a genetic algorithm. A case study of a Hong Kong nursing home was conducted to illustrate the effectiveness of the proposed system. We found that the total monthly staffing cost after using the proposed model was significantly lower than the existing practice with a change of -13.48%, which considers the use of heterogeneous workforce and temporary staff. Besides, the care monitoring and staffing flexibility are further enhanced, in which the concept of skill substitution is integrated in nursing staffing optimisation.

Generating probabilistic Boolean networks from a prescribed transition probability matrix.

Probabilistic Boolean networks (PBNs) have received much attention in modeling genetic regulatory networks. A PBN can be regarded as a Markov chain process and is characterised by a transition probability matrix. In this study, the authors propose efficient algorithms for constructing a PBN when its transition probability matrix is given. The complexities of the algorithms are also analysed. This is an interesting inverse problem in network inference using steady-state data. The problem is important as most microarray data sets are assumed to be obtained from sampling the steady-state.

Distribution and enumeration of attractors in probabilistic Boolean networks.

Many mathematical models for gene regulatory networks have been proposed. In this study, the authors study attractors in probabilistic Boolean networks (PBNs). They study the expected number of singleton attractors in a PBN and show that it is (2 - (1/2)(L-1))(n), where n is the number of nodes in a PBN and L is the number of Boolean functions assigned to each node. In the case of L=2, this number is simplified into 1.5(n). It is an interesting result because it is known that the expected number of singleton attractors in a Boolean network (BN) is 1. Then, we present algorithms for identifying singleton and small attractors and perform both theoretical and computational analyses on their average case time complexities. For example, the average case time complexities for identifying singleton attractors of a PBN with L=2 and L=3 are O(1.601(n)) and O(1.763(n)), respectively. The results of computational experiments suggest that these algorithms are much more efficient than the naive algorithm that examines all possible 2(n) states.

Optimal control policy for probabilistic Boolean networks with hard constraints.

It is well known that the control/intervention of some genes in a genetic regulatory network is useful for avoiding undesirable states associated with some diseases like cancer. For this purpose, both optimal finite-horizon control and infinite-horizon control policies have been proposed. Boolean networks (BNs) and its extension probabilistic Boolean networks (PBNs) as useful and effective tools for modelling gene regulatory systems have received much attention in the biophysics community. The control problem for these models has been studied widely. The optimal control problem in a PBN can be formulated as a probabilistic dynamic programming problem. In the previous studies, the optimal control problems did not take into account the hard constraints, i.e. to include an upper bound for the number of controls that can be applied to the captured PBN. This is important as more treatments may bring more side effects and the patients may not bear too many treatments. A formulation for the optimal finite-horizon control problem with hard constraints introduced by the authors. This model is state independent and the objective function is only dependent on the distance between the desirable states and the terminal states. An approximation method is also given to reduce the computational cost in solving the problem. Experimental results are given to demonstrate the efficiency of our proposed formulations and methods.

Soluble sunscreens fully protect E. coli from disinfection by electrohydraulic discharges.

We show that the ultraviolet radiation emitted, rather than the thermal/pressure shocks or the chemical species that are generated in these events, is the lethal agent that inactivates Escherichia coli colonies exposed to electrohydraulic discharges, EHD. Disinfection is completely suppressed in the presence of <100 mg L(-1) of 2,2'-dihydroxy-4,4'-dimethoxybenzophenone-5,5'-disulfonic acid, BP9, a commercial water-soluble sunscreen. Viable bacteria decay by logit kinetics with the number of EHD. The fact that the slopes of the logit plots depend inversely on BP9 concentration, and vanish above approximately 30 mg BP9 L(-1), is direct evidence that E. coli is exclusively sterilized by UV radiation in these experiments. Present LD50 photon doses are about 10(3) times larger than those required from low-power germicidal lamps, indicating that high-intensity radiation is able to further promote into inactive channels the lower excited state(s) of nucleic acids responsible for DNA damage. The present study confirms the existence of a significant trade-off between photon efficiency and radiative power in bacterial disinfection by UV light.

Escherichia coli disinfection by electrohydraulic discharges.

We study the survival of single-strain Escherichia coli colonies in aqueous media exposed to 5.5 kV, 90 kA electrohydraulic discharges (EHD). The probability of survival (Pn) of a 4 x 10(7) cfu mL(-1) E. coli population after n consecutive EHDs follows a logit distribution: In(Pn/ 100 - Pn) = 1.329 - 1.579 ln n with r2 = 0.993 that corresponds to lethal doses of LD50 = 2.2 and LD90 = 10.5 EHDs. Considering that the reactor is thoroughly mixed during each discharge and that LD50 = 0.9 values are nearly independent of E. coli concentrations in the range of 2 x 10(3) < or = E coli/cfu mL(-1) < or = 3 x 10(6), we ascribe the nonexponential Pn decay of single-strain E. coli colonies to a shielding phenomenon where inactive cells protect the successively smaller numbers of viable cells in the EHD. The qualitatively similar concentration dependence observed for survival under 254 nm of radiation, in contrast with the lower resistance of denser colonies to 20 kHz power ultrasound and the delayed onset of extracellular beta-D-galactosidase activity in bacterial populations already decimated by EHDs, support the view that UV radiation is the dominant disinfection agent generated by electrohydraulic discharges.