Does the Use of Nursing-Care Services Reduce the Information about Dementia Patients Provided by Their Caregivers?

Background: The rate of use of nursing-care services has been increasing dramatically in recent years with the upgrading of the public long-term care insurance system in Japan. We addressed how the increased use of the nursing-care services might affect the information on the patients provided by their caregivers. Methods: A questionnaire survey of 531 family caregivers caring for dementia patients at home was carried out to investigate how the use of these services might affect the information about the patients provided by the caregivers. The survey revealed that the use of the nursing-care services reduced the burden (quality, quantity, time of nursing care, and feeling) on the caregivers. Results: According to the observation provided by the caregivers, the patients’ behaviors and activities at home tended to decrease. These results indicated that the use of the nursing-care services resulted in a reduction in the opportunity for and the time spent on observation of the patients by the caregivers, making it more difficult for the caregiver to provide an appropriate assessment of the patient’s condition. Conclusions: We discussed the impact of the use of the nursing-care services on the Clinician’s Interview-Based Impression of Change plus (CIBIC-plus) rating. Due to the reduction in the time spent on nursing care and in the opportunity for observation of the patient’s activities of daily living by the caregiver resulting from the use of the nursing-care services, it is difficult to obtain an accurate picture of the patient’s clinical condition using the CIBIC-plus, probably leading to an inappropriate CIBIC-plus rating

Modification of triaxial deformation and change of spectrum in $^{25}_{\ \Lambda}MgcausedbyMg caused by \Lambda$ hyperon

The positive-parity states of $^{25}_{\ \Lambda}$Mg with a $\Lambda$ hyperon in $s$ orbit were studied with the antisymmetrized molecular dynamics for hypernuclei. We discuss two bands of $^{25}_{\ \Lambda}$Mg corresponding to the $K^\pi=0^+$ and $2^+$ bands of $^{24}$Mg. It is found that the energy of the $K^\pi = 2^+ \otimes \Lambda_s$ band is shifted up by about 200 keV compared to $^{24}$Mg. This is because the $\Lambda$ hyperon in $s$ orbit reduces the quadrupole deformation of the $K^\pi = 0^+ \otimes \Lambda_s$ band, while it does not change the deformation of the $K^\pi = 2^+ \otimes \Lambda_s$ band significantly.Comment: 19 pages, 3 figure

On Various Properties of Nodular Graphite Cast Steel

This paper describes at first the outline of the theory of high grade cast iron manufacturing that has been clarified by one of the authors through a fundamental study of the relation between cast iron and its oxygen contents. In place of the relation between the oxygen content of cast iron and its qualities, the relation between tensile strength in cast state and variation of structure was examined. A little difference in the degree of deoxidation of cast iron was indistinguishable by analysed oxygen content. To grade the qualities according to the variation of structure is the most accurate and simplest method. When nodular graphite hyper-eutectoid cast steel containing less than 1.7 per cent carbon is produced the theoretical foundation for determining carbon and silicon contents is to choose the reciprocal relation between the points on the line Eγ or in the lower areas under the line Eγ in the basic projection diagram of the Fe-C-Si system. According to this theory, an investigation of the various properties of nodular graphite hyper-eutectoid cast steel was carried out. The results obtained can be summarized as follows : (1) This cast steel does not call for so careful selection of materials, and is of higher value in mechanical properties than those of DCI, malleable iron and cast steel. (2) Since this metal has far better castability than cast steel and DCI, it is possible to manufacture castings of thin section and complicated shape. (3) Compared with gray cast iron and malleable cast iron, this cast steel is very useful for electric and magnetic material. (4) Wear resistivity of the pearlitic type of this metal is far superior to any of the ordinary cast iron, pearlitic cast iron and DCI. Wear resistivity of the ferrite type is the same as that of DCI, but it is far superior to the black heart malleable cast iron. (5) This cast steel is material that has excellent property for induction surface hardening, even in the case of ferrite type, and after heat.treatment it has the about 40～50 R_c in the surface hardness. The Jominy hardness curves of nodular graphite cast steel were measured. Compared with the pearlite type, cast steel of ferrite type has low quench-hardness bccause of its carbon concentration in austenite

Some Investigations on the Cerium-treated Cast Iron

The effects of the addition of cerium on the macro- and microstructure of hypoeutectic and eutectic cast iron have been investigated and further, some experiments, were carried out to produce the high strength and rapid-malleable cast iron containing high silicon by the addition of cerium. The results obtained were as follows : (1) With the increase of the cerium content, the structure of cast iron changed from a grey iron to a white iron passing through an inverse chilled iron. When the composition of cast iron was fixed, the amount of cerium necessary to get white cast iron from a deoxidized melt was less than that from an oxidized melt. (2) It was verified that white cast ironization with the addition of cerium was not caused by its alloying such as in the case of manganese or chromium, in which they form special carbides, but by the supercooling of melt accompanied with strong deoxidation by cerium. (3) The mechanical properties of cerium-treated rapid malleable cast iron having the high silicon contents are higher than those of the commercial black heart malleable cast iron, and by controlling the amount of cerium, carbon and silicon in cast iron, the castings of a larger section than the maximum size which has been applied to ordinary malleable cast iron can be made

Refining Method for Refining Cast Iron by the Electrolysis of Slag

Original researches on the refining method of cast iron by the electrolysis of slag will be described in this paper. Cast iron is not so easily reduced as steel that strong deoxidizing reagents, such as alkali, alkali earth metals and Mg, are necessary to reduce cast iron melts satisfactorily. Charging positive voltage to slag and negative voltage to melt, the electrolysis was made of slag which covered the surface of cast iron melt ; then, the melt could be reduced, resulting in the nodular graphite structure. The electrolysis was performed for 10～30 minutes at 1,250℃～1,350℃. Slag such as fluorides, chlorides, carbonates, oxides of alkali, alkali earth metals or Mg, seem to be applicable to the refining method

On the Success Rate of Side-Channel Attacks on Masked Implementations: Information-Theoretical Bounds and Their Practical Usage

This study derives information-theoretical bounds of the success rate (SR) of side-channel attacks on masked implementations. We first develop a communication channel model representing side-channel attacks on masked implementations. We then derive two SR bounds based on the conditional probability distribution and mutual information of shares. The basic idea is to evaluate the upper-bound of the mutual information between the non-masked secret value and the side-channel trace by the conditional probability distribution of shares given its leakage, with a help of the Walsh–Hadamard transform. With the derived theorems, we also prove the security of masking schemes: the SR decreases exponentially with an increase in the number of masking shares, under a much more relaxed condition than the previous proof. To validate and utilize our theorems in practice, we propose a deep-learning-based profiling method for approximating the conditional probability distribution of shares to estimate the SR bound and the number of traces required for attacking a given device. We experimentally confirm that our bounds are much stronger than the conventional bounds on masked implementations, which validates the relevance of our theorems to practice

Perceived Information Revisited

In this study, we present new analytical metrics for evaluating the performance of side-channel attacks (SCAs) by revisiting the perceived information (PI), which is defined using cross-entropy (CE). PI represents the amount of information utilized by a probability distribution that determines a distinguishing rule in SCA. Our analysis partially solves an important open problem in the performance evaluation of deep-learning based SCAs (DL-SCAs) that the relationship between neural network (NN) model evaluation metrics (such as accuracy, loss, and recall) and guessing entropy (GE)/success rate (SR) is unclear. We first theoretically show that the conventional CE/PI is non-calibrated and insufficient for evaluating the SCA performance, as it contains uncertainty in terms of SR. More precisely, we show that an infinite number of probability distributions with different CE/PI can achieve an identical SR. With the above analysis result, we present a modification of CE/PI, named effective CE/PI (ECE/EPI), to eliminate the above uncertainty. The ECE/EPI can be easily calculated for a given probability distribution and dataset, which would be suitable for DL-SCA. Using the ECE/EPI, we can accurately evaluate the SR hrough the validation loss in the training phase, and can measure the generalization of the NN model in terms of SR in the attack phase. We then analyze and discuss the proposed metrics regarding their relationship to SR, conditions of successful attacks for a distinguishing rule with a probability distribution, a statistic/asymptotic aspect, and the order of key ranks in SCA. Finally, we validate the proposed metrics through experimental attacks on masked AES implementations using DL-SCA

Toward Optimal Deep-Learning Based Side-Channel Attacks: Probability Concentration Inequality Loss and Its Usage

In this paper, we present solutions to some open problems for constructing efficient deep learning-based side-channel attacks (DL-SCAs) through a theoretical analysis. There are two major open problems in DL-SCAs: (i) the effect of the difference in secret key values used for profiling and attack phases is unclear, and (ii) the optimality of the negative log-likelihood (NLL) loss function used in the conventional learning method is unknown. These two problems have hindered the accurate performance evaluation and optimization of DL-SCAs. To address the problem (i), we clarified the strict conditions under which the use of different correct keys in profiling and attack phases affects the performance of DL-SCA. For the problem (ii), we then analyzed the relationship between the NLL loss and direct performance metrics of DL-SCAs (i.e., success rate (SR)/guessing entropy (GE)) and proved that the minimum NLL loss is sufficient but not necessary to achieve the optimal distinguisher of DL-SCA. This explains why DL-SCA succeeds even when the NLL loss is large and motivated us to design a new loss function. Based on the above analysis result, we also propose a new loss function called the probability concentration inequality (PCI) loss function. We derive the PCI loss as an upper bound of GE and a lower bound of the SR using a probability concentration inequality. Minimizing the PCI loss during training can directly optimize the GE and SR of the subsequent attack phase. In this paper, we describe the characteristics of PCI loss and NLL loss and introduce a new learning method that takes full advantage of the characteristics. We also analytically investigate the difference between the PCI loss and ranking loss reported in a previous work for a similar purpose and explain the advantage of PCI loss over the ranking loss. Finally, we validate the analysis and demonstrate the effectiveness of the proposed DL-SCA using the PCI loss through experimental attacks on public datasets

Formal Analysis of Non-profiled Deep-learning Based Side-channel Attacks

This paper formally analyzes two major non-profiled deep-learning-based side-channel attacks (DL-SCAs): differential deep-learning analysis (DDLA) by Timon and collision DL-SCA by Staib and Moradi. These DL-SCAs leverage supervised learning in non-profiled scenarios. Although some intuitive descriptions of these DL-SCAs exist, their formal analyses have been rarely conducted yet, which makes it unclear why and when the attacks succeed and how the attack can be improved. In this paper, we provide the first information-theoretical analysis of DDLA. We reveal its relevance to the mutual information analysis (MIA), and then present three theorems stating some limitations and impossibility results of DDLA. Subsequently, we provide the first probability-theoretical analysis on collision DL-SCA. After presenting its formalization with a proposal of our distinguisher for collision DL-SCA, we prove its optimality. Namely, we prove that the collision DL-SCA using our distinguisher theoretically maximizes the success rate if the neural network (NN) training is completely successful (namely, the NN completely imitates the true conditional probability distribution). Accordingly, we propose an improvement of the collision DL-SCA based on a dedicated NN architecture and a full-key recovery methodology using multiple neural distinguishers. Finally, we experimentally evaluate non-profiled (DL-)SCAs using a newly created dataset using publicly available first-order masked AES implementation. The existing public dataset of side-channel traces is insufficient to evaluate collision DL-SCAs due to a lack of substantive side-channel traces for different key values. Our dataset enables a comprehensive evaluation of collision (DL-)SCAs, which clarifies the current situation of non-profiled (DL-)SCAs