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Crystallization of the title compound, fac-[ReBr(ppt-OMe)(CO)3] (ppt-OMe = C15H12N2OS), from CH2Cl2/n-pentane (1:5 v/v) at room temperature gave two polymorphs, which crystallize in monoclinic (P21/c; α form) and orthorhombic (Pna21; β form) space groups. The ReI complex molecules in either polymorph adopt a six-coordinate octahedral geometry with three facially-oriented carbonyl ligands, one bromido ligand, and two nitrogen atoms from one chelating ligand ppt-OMe. In the crystal, both polymorph α and β form di-periodic sheet-like architectures supported by multiple hydrogen bonds. In polymorph α, two types of hydrogen bonds (C—H...O) are found while, in polymorph β, four types of hydrogen bonds (C—H...O and C—H...Br) exist.

Failure to retrieve a distal filter-based embolic protection device (EPD) is a potential complication of carotid artery stenting. This may be caused by trapping of the proximal marker of the EPD within the stent tip marker. Maintaining an adequate distance between the two can prevent this. We examined the behavior of several stent-filter-based EPD combinations, focusing on their propensity to become trapped or disengage in vitro. Four physicians subjectively rated the force required to result in trapping using a 5-point scale. Moreover, the force required to disengage trapped devices was evaluated. The Casper stent-Spider FX EPD combination was difficult to disengage when entrapment occurred, which suggested that this phenomenon tended to occur with this combination. The stent tip marker of the closed-cell stents advanced as they shortened, which may be a unique feature of closed-cell stents. Although trapping is uncommon, it can cause serious complications. To prevent these complications, device characteristics should be well understood before they are used in patients.

Abstract Introduction The question remains to be elucidated: “Is treatment with antidepressants at doses approved in Japan effective for Japanese patients with MDD?” It is crucial to confirm this in order to provide appropriate treatments for Japanese patients with major depressive disorder (MDD). Therefore, we conducted a systematic review and random‐effects pairwise meta‐analysis including these nine double‐blind, randomized, placebo‐controlled trials. Methods We calculated the standardized mean difference (SMD) and risk ratio (RR) with a 95% confidence interval (95% CI). Results Pooled newer antidepressants outperformed placebo regarding improvement of depressive symptom scale scores [SMD (95% CI) = −0.20 (−0.27, −0.12), p

Sugary beverages are a significant contributor to sugar consumption, and their excessive consumption is associated with increased risks of elevated blood glucose levels and diabetes. Many individuals have a strong preference for sugary beverages and often find beverages with lower sugar content to be less satisfying. Attempts to switch to less sugary options are frequently short-lived, leading to a return to higher-sugar beverages. Recognizing that 75 – 95% of taste perception is influenced by scent, we investigated a scent-based approach to reduce sugar intake while preserving the perception of sweetness. This study introduces an olfactory interface in the form of a mug named “Aromug,” designed to emit a sweet scent in sync with the drinking action. Aromug incorporates motion sensing and scent presentation functions to enhance the perceived sweetness of a beverage, thereby encouraging a gradual reduction in sugar intake. Our experiments, involving 33 participants, demonstrated that the combined scents of sugar-free coffee and chocolate increased the perception of sweetness (p $= 1.641 \times 10^{-2}$ ). The study also found that the simultaneous presentation of scent and visual cues improved taste satisfaction and sweetness perception. Additionally, we observed variations in sweetness preference related to age and frequency of coffee consumption. It was particularly observed that people in their 20s and those who frequently drink coffee tend to perceive the taste of beverages as sweeter. This suggests a potential for Aromug to customize the scent experience based on individual preferences, offering a novel way to encourage healthier beverage choices.

In their process of information seeking, human users tend to selectively ignore information that contradicts their pre-existing beliefs or opinions. These so-called “self-imposed filter bubbles” (SFBs) pose a significant challenge for argumentative conversational agents aiming to facilitate critical, unbiased opinion formation on controversial topics. With the ultimate goal of developing a system that helps users break their self-imposed filter bubbles (SFBs), this paper aims to investigate the role of co-speech gestures, specifically examining how these gestures significantly contribute to achieving this objective. This paper extends current research by examining methods to engage users in cooperative discussions with a virtual human-like agent, encouraging a deep reflection on arguments to disrupt SFBs. Specifically, we investigate the agent’s non-verbal behavior in the form of co-speech gestures. We analyze whether co-speech gestures, depending on the conveyed information, enhance motivation, and thus conversational user engagement, thereby encouraging users to consider information that could potentially disrupt their SFBs. The findings of a laboratory study with 56 participants highlight the importance of non-verbal agent behaviors, such as co-speech gestures, in improving users’ perceptions of the interaction and the conveyed content. This effect is particularly notable when the content aims to challenge the user’s SFB. Therefore, this research offers valuable insights into enhancing user engagement in the design of multimodal interactions with future cooperative argumentative virtual agents.

Abstract Background Repetitive transcranial magnetic stimulation (rTMS) is a widely used treatment for major depressive disorder (MDD), and its effectiveness in preventing relapse/recurrence of MDD has been explored. Although few small sample controlled studies exist, the protocols of maintenance rTMS therapy were heterogeneous and evidence of its effectiveness is not sufficient. Thus, this study aims to evaluate whether maintenance rTMS is effective in maintaining the treatment response in patients with MDD with a large sample size and feasible study design. Methods In this multicenter open-labelled parallel-group trial we plan to recruit 300 patients with MDD who have responded or remitted to acute rTMS therapy. Participants would be classified into two groups according to their preference; the maintenance rTMS and pharmacotherapy group, and the pharmacotherapy only group. The protocol of maintenance rTMS therapy is once a week for the first six months and once biweekly for the second six months. The primary outcome is the relapse/recurrence rates during 12 months following enrollment. Other measures of depressive symptoms and recurrence/relapse rates at different time points are the secondary outcomes. The primary analysis is the between-group comparison adjusted for background factors using a logistic regression model. We will perform the group comparison with inverse probability of treatment weighting as the sensitivity analysis to ensure the comparability of the two groups. Discussion We hypothesize that maintenance rTMS therapy could be an effective and safe treatment for preventing depressive relapse/recurrence. Considering the limitation of potential bias owing to the study design, we plan to use statistical approaches and external data to avoid overestimation of the efficacy. Trial registration Japan Registry of Clinical Trials, ID: jRCT1032220048 . Registered 1 May 2022.

Much garbage is produced daily in homes due to living activities, including cooking and eating. The garbage must be adequately managed for human well-being and environmental protection. Although the existing IoT-based smart garbage systems have gained high garbage classification accuracy, they still have a problem that they provide a small number of garbage categories, not enough for reasonable practices of household garbage separation. This study presents a new smart garbage bin system, SGBS, embedded with multiple sensors to solve the problem. We deployed temperature, humidity, and gas sensors to know the condition and identify the garbage content disposed of. Then, we introduce a new garbage content estimation method by training a machine learning model using daily collected fuse sensor readings combined with detailed household garbage contents annotations to perform garbage classification tasks. For evaluation, we deployed the designed SGBS in five households over one month. As a result, we confirmed that the leave-one-house cross-validation results showed an accuracy of 91% in 5 kitchen waste contents, also, 89% in 5 paper/softbox contents, and 85% in the 8 garbage categories for the classification tasks.

Summary: Biological invasion refers to the introduction, spread, and establishment of non-native species in a novel habitat. The ways in which invasive species successfully colonize new and different environments remain a fundamental topic of research in ecology and evolutionary biology. Here, we investigated the genomic and transcriptomic characteristics of the red swamp crayfish (Procambarus clarkii), a widespread invader in freshwater environments. Targeting a recently colonized population in Sapporo, Japan that appears to have acquired a high degree of cold tolerance, RNA-seq analysis revealed differentially expressed genes in response to cold exposure, and those involved in protease inhibitors and cuticle development were considered top candidates. We also found remarkable duplications for these gene families during evolution and their concerted expression patterns, suggesting functional amplification against low temperatures. Our study thus provides clues to the unique genetic characteristics of P. clarkii, possibly related to cold adaptation.

Abstract Aim Transcranial magnetic stimulation (TMS) is a noninvasive brain stimulation technique that shows potential for treating psychiatric disorders. Although several studies have sought to investigate new TMS modalities for the treatment of various psychiatric disorders, no study has yet examined publication trends in research on TMS modalities for psychiatric disorders. This study investigated publication trends in TMS research for 13 psychiatric disorders, including addiction, dementia, major depressive disorder (MDD), and obsessive‐compulsive disorder (OCD), and schizophrenia, as well as 9 TMS modalities, including bilateral stimulation, deep TMS, high‐frequency stimulation, low‐frequency stimulation, and theta burst stimulation. Methods Articles published in PubMed from 1985 to 2019 were searched to determine the number of published articles for each year in each category using the “Results by year” tool from the PubMed database. Results Over the past 30 years, an increasing number of articles were published regarding TMS research for the treatment of MDD, addiction, and dementia, which were among those most commonly investigated psychiatric disorders, whereas the number of articles addressing schizophrenia and OCD treated via TMS remained steady since 2015. Regarding TMS modalities, previous high‐frequency stimulation, low‐frequency stimulation, and bilateral stimulation were the most common topics, with research regarding deep TMS and theta burst stimulation having increased since 2000 and 2005, respectively. Conclusion TMS applications are rapidly developing and becoming increasingly ubiquitous in various psychiatric disorders. Determining publication trends in TMS research can be a useful method for monitoring TMS research interests and applications of new TMS modalities for psychiatric disorders.

Background: Transthoracic impedance values have not been widely used to measure extravascular pulmonary water content due to accuracy and complexity concerns. Our aim was to develop a foundational model for a novel system aiming to non-invasively estimate the intrathoracic condition of heart failure patients. Methods: We employed multi-frequency bioelectrical impedance analysis to simultaneously measure multiple frequencies, collecting electrical, physical, and hematological data from 63 hospitalized heart failure patients and 82 healthy volunteers. Measurements were taken upon admission and after treatment, and longitudinal analysis was conducted. Results: Using a light gradient boosting machine, and a decision tree-based machine learning method, we developed an intrathoracic estimation model based on electrical measurements and clinical findings. Out of the 286 features collected, the model utilized 16 features. Notably, the developed model demonstrated high accuracy in discriminating patients with pleural effusion, achieving an area under the receiver characteristic curves (AUC) of 0.905 (95% CI: 0.870–0.940, p < 0.0001) in the cross-validation test. The accuracy significantly outperformed the conventional frequency-based method with an AUC of 0.740 (95% CI: 0.688–0.792, and p < 0.0001). Conclusions: Our findings indicate the potential of machine learning and transthoracic impedance measurements for estimating pleural effusion. By incorporating noninvasive and easily obtainable clinical and laboratory findings, this approach offers an effective means of assessing intrathoracic conditions.

Information about a subjective user opinion towards an argument is crucial for argumentative systems in order to present appropriate content and adapt their behaviour to the individual user. However, requesting explicit feedback regarding the discussed arguments is often impractical and can hinder the interaction. To address this issue, we investigate the automatic recognition of user opinions towards arguments that are presented by means of a virtual avatar from social signals. We focus on two different user opinion categories (convincing and interesting) and two different types of social signals (facial expressions and eye movement). The recognition is addressed as a supervised learning problem and realized using the argument search evaluation data discussed in previous work. The overall performance is compared to a human annotation on a subset of the collected data. The results show that the machine learning performance is similar to human performance in both recognition tasks.

Numerous technologies exist for promoting a healthier lifestyle. These technologies collectively referred to as “Behavior Change Support Systems”. However, the majority of existing apps use quantitative data representation. Since it is difficult to understand the meaning behind quantitative data, this approach has been suggested to lower users’ motivation and fail to promote behavior change. Therefore, an interpretation of quantitative data needs to be provided as a supplement. However, different descriptions of the same data may lead to different outcomes. In this paper, we explore the impact of different communication styles for interpretations of quantitative data on behavior change by developing and evaluating Walkeeper – a web-based app that provides interpretations of the users’ daily step counts using different levels of elaborateness and indirectness with the aim of promoting walking. Through the quantitative analysis and results of a user study, we contribute new knowledge on designing such interpretations for quantitative data.

Separation of isotopes of heavier gases than hydrogen or helium is essential for biomedical applications, but current methods are very energy and time consuming. Here the authors report cryogenic separation of oxygen and methane isotopes through adsorption in nanoporous materials, based on a collective nuclear quantum effect.

Some respondents make careless responses due to the “satisficing,” which is an attempt to complete a questionnaire as quickly and easily as possible. To obtain results that reflect a fact, detecting satisficing and excluding the responses with satisficing from the analysis targets are required. One of the devised methods detects satisficing by adding questions that check violations of instructions and inconsistencies. However, this approach may cause respondents to lose their motivation and prompt them to satisficing. Additionally, a deep learning model that automatically answers these questions was reported. This threatens the reliability of the conventional method. To detect careless responses without inserting such screening questions, machine learning (ML) detection using data obtained from answer results was attempted in a previous study, with a detection rate of 55.6%, which is not sufficient from the viewpoint of practicality. Therefore, we hypothesized that a supervised ML model with a higher detection rate could be constructed by using on-screen answering behavior as features. However, (1) no existing questionnaire system can record on-screen answering behavior and (2) even if the answering behavior can be recorded, it is unclear which answering behavior features are associated with satisficing. We developed an answering behavior recording plug-in for LimeSurvey, an online questionnaire system used all over the world, and collected a large amount of data (from 5,692 people) in Japan. Then, a variety of features were examined and generated from answering behavior, and we constructed ML models to detect careless responses. We call this detection method the ML-ABS (ML-based answering behavior scale). Evaluation by cross-validation demonstrated that the detection rate for careless responses was 85.9%, which is much higher than the previous ML method. Among the various features we proposed, we found that reselecting the Likert scale and scrolling particularly contributed to the detection of careless responses.

Abstract Aim Clinical trials and meta‐analyses have demonstrated the efficacy of high‐frequency repetitive transcranial magnetic stimulation (rTMS) over the left prefrontal cortex in treatment‐resistant depression. The aim of this study was to prospectively evaluate the effectiveness of the conventional 37.5‐minute vs 18.75‐minute rTMS protocol over the left prefrontal cortex in patients with treatment‐resistant depressive episode. Methods Thirty patients with treatment‐resistant depression or bipolar disorder depressive episode were randomized 1:1 to either 37.5‐minute or 18.75‐minute rTMS protocol groups. rTMS treatment was applied at 120% resting motor threshold with 10 Hz over the left prefrontal cortex. Treatment sessions were delivered for a total of 3000 pulses/d, 5 days a week, for 4‐6 weeks. Patients received a 75 trains with “4 sec on and 26 sec off” for 37.5 minutes or a 75 trains with “4 sec on and 11 sec off” for 18.75 minutes. Severity of depression was rated with the Quick Inventory of Depressive Symptomatology (QIDS) and Patient Health Questionnaire (PHQ‐9). Remission was defined as a total score of 5 or less on the QIDS. The primary outcome measure was to compare the remission rate between the both groups. Results Thirteen of 30 patients (43.3%) showed remission at week 6. There were no significant differences in the remission rate between the conventional 37.5‐ and 18.75‐minute protocol groups (46.7% and 40.0%, respectively). No seizures or treatment‐emergent mania/hypomania were occurred. Conclusion These findings suggest that, compared with the conventional one, rTMS with 18.75‐minute protocol might be equally effective and clinically beneficial in saving the treatment session length. Further well‐designed studies are needed.

Abstract Aim Whether patients with adult bipolar disorder (BD) who have been clinically stabilized with lithium or lamotrigine should continue this medication is not established fully. This systematic review and meta‐analysis evaluated the efficacy and safety of lithium and lamotrigine for maintenance treatment in clinically stable patients with adult BD. Methods This meta‐analysis included only double‐blind, randomized, placebo‐controlled trials with an enrichment design that selected patients who responded acutely to lithium or lamotrigine. Reports prior to November 15, 2018, were retrieved from the PubMed/Cochrane Library/Embase. The primary outcome was the relapse rate due to any mood episode at the study endpoint. Other outcomes were relapse rates due to a manic/hypomanic/mixed episode or depression at the study endpoint, discontinuation rate, death, and death by suicide. Risk ratios (RRs) (95% confidence intervals) were calculated. When the random‐effects model showed significant differences between groups, the number‐needed‐to‐treat (NNT) was estimated. Results The search retrieved two studies regarding lithium (N = 218) and four evaluating lamotrigine (N = 706). Both drugs were superior to placebo for reducing the relapse rate due to any mood episode [lithium: RR = 0.52 (0.41‐0.66), P

Abstract Objective It is unknown whether there are differences in efficacy and safety between quetiapine extended‐release, 300 mg/d (QUEXR300), and olanzapine, 5‐20 mg/d (OLA), for Japanese patients with bipolar depression. Methods We conducted a Bayesian analysis of data from phase 3 studies in Japan of QUEXR300 and OLA. Outcomes were remission rate (primary), response rate, improvement on the Montgomery‐Åsberg Depression Rating Scale and 17‐item Hamilton Depression Rating Scale scores, discontinuation rate, and incidence of individual adverse events. We calculated the standardized mean difference (SMD) and the risk ratio (RR) and 95% credible interval (95% CrI) for continuous and dichotomous data, respectively. Results There were no significant differences between QUEXR300 and OLA for any of the efficacy outcomes. QUEXR300 was associated with a higher incidence of somnolence than OLA (RR = 5.517; 95% CrI = 1.563, 19.787), while OLA was associated with greater increase body weight (SMD = −0.488; 95% CrI = −0.881, −0.089) and blood prolactin levels (SMD = −0.642; 95% CrI = −1.073, −0.213) than QUEXR300, and a greater decrease in high‐density lipoprotein cholesterol levels (SMD = −0.408; 95% CrI = −0.785, −0.030) than QUEXR300. Conclusion Although the two drugs’ efficacy did not differ, OLA increased the risk of metabolic syndrome and QUEXR300 the risk of somnolence. A large scale, long‐term, head‐to‐head comparison study of QUEXR300 vs OLA for Japanese patients with bipolar depression is needed to confirm the results of the current study.

There has been a subsequent increase in the number of elderly people living alone, with contribution from advancement in medicine and technology. However, hospitals and nursing homes are crowded, expensive, and uncomfortable, while personal caretakers are expensive and few in number. Home monitoring technologies are therefore on the rise. In this study, we propose an anonymous elderly monitoring system to track potential risks in everyday activities such as sleep, medication, shower, and food intake using a smartphone application. We design and implement an activity visualization and notification strategy method to identify risks easily and quickly. For evaluation, we added risky situations in an activity dataset from a real-life experiment with the elderly and conducted a user study using the proposed method and two other methods varying in visualization and notification techniques. With our proposed method, 75.2% of the risks were successfully identified, while 68.5% and 65.8% were identified with other methods. The average time taken to respond to notification was 176.46 min with the proposed method, compared to 201.42 and 176.9 min with other methods. Moreover, the interface analyzing and reporting time was also lower (28 s) in the proposed method compared to 38 and 54 s in other methods.

Objective: Low-profile visualized intraluminal support (LVIS®) is a braided stent utilized for stent-assisted coil embolization of intracranial aneurysms. In this report, we review the technical notes of coil embolization for unruptured intracranial aneurysm using LVIS® stents. Case description: We reported six consecutive cases of unruptured saccular aneurysm of the internal carotid artery all of whom underwent stent-assisted coil embolization with the LVIS® stent. After the first three cases, dry lab training using a complicated parent artery model allowed us to determine how to effectively place an LVIS® stent and to shorten operative time significantly. All patients were followed up for at least 1 year after the procedure. Thus far, none of the patients have experienced recurrence or complications. Conclusions: Our experience with patients undergoing stent-assisted coil embolization using LVIS® stents before and after dry lab training reveal that several simple technical tips, including careful measurement of the parent artery diameter, choice of an appropriate stent size, and evasion of pushing or pulling the stent beyond what is necessary, are important for easy and stable deployment of LVIS® stents.

Self-assembled growth of blue-green-yellow-red InGaN quantum dots (QDs) on GaN templates using plasma-assisted molecular beam epitaxy were investigated. We concluded that growth conditions, including small N2 flow and high growth temperature are beneficial to the formation of InGaN QDs and improve the crystal quality. The lower In/Ga flux ratio and lower growth temperature are favorable for the formation of QDs of long emission wavelength. Moreover, the nitrogen modulation epitaxy method can extend the wavelength of QDs from green to red. As a result, visible light emissions from 460 nm to 622 nm have been achieved. Furthermore, a 505 nm green light-emitting diode (LED) based on InGaN/GaN MQDs was prepared. The LED has a low external quantum efficiency of 0.14% and shows an efficiency droop with increasing injection current. However, electroluminescence spectra exhibited a strong wavelength stability, with a negligible shift of less than 1.0 nm as injection current density increased from 8 A/cm2 to 160 A/cm2, owing to the screening of polarization-related electric field in QDs.

Information on congestion of buses, which are one of the major public transportation modes, can be very useful in light of the current COVID-19 pandemic. Because it is unrealistic to manually monitor the number of riders on all buses in operation, a system that can automatically monitor congestion is necessary. The main goal of this paper’s work is to automatically estimate the congestion level on a bus route with acceptable performance. For practical operation, it is necessary to design a system that does not infringe on the privacy of passengers and ensures the safety of passengers and the installation sites. In this paper, we propose a congestion estimation system that protects passengers’ privacy and reduces the installation cost by using Bluetooth low-energy (BLE) signals as sensing data. The proposed system consists of (1) a sensing mechanism that acquires BLE signals emitted from passengers’ mobile terminals in the bus and (2) a mechanism that estimates the degree of congestion in the bus from the data obtained by the sensing mechanism. To evaluate the effectiveness of the proposed system, we conducted a data collection experiment on an actual bus route in cooperation with Nara Kotsu Co., Ltd. The results showed that the proposed system could estimate the number of passengers with a mean absolute error of 2.49 passengers (error rate of 38.8%).

To further implement decentralized renewable energy resources, blockchain based peer-to-peer (P2P) energy trading is gaining attention and its architecture has been proposed with virtual demonstrations. In this paper, to further socially implement this concept, a blockchain based peer to peer energy trading system which could coordinate with energy control hardware was constructed, and a demonstration experiment was conducted. Previous work focused on virtually matching energy supply and demand via blockchain P2P energy markets, and our work pushes this forward by demonstrating the possibility of actual energy flow control. In this demonstration, Plug-in Hybrid Electrical Vehicles(PHEVs) and Home Energy Management Systems(HEMS) actually used in daily life were controlled in coordination with the blockchain system. In construction, the need of a multi-tagged continuous market was found and proposed. In the demonstration experiment, the proposed blockchain market and hardware control interface was proven capable of securing and stably transmitting energy within the P2P energy system. Also, by the implementation of multi-tagged energy markets, the number of transactions required to secure the required amount of electricity was reduced.

InGaN quantum dots (QDs) are promising candidates for GaN-based all-visible optoelectronic devices such as micro light-emitting diode and laser. In this study, self-assembled InGaN/GaN multi-quantum dots (MQDs) have been grown by plasma-assisted molecular beam epitaxy on c-plane GaN-on-sapphire template. A high density of over 3.8 × 1010 cm−2 is achieved and InGaN QDs exhibit a relatively uniform size distribution and good dispersity. Strong localization effect in as-grown InGaN QDs has been evidenced by temperature-dependent photoluminescence (PL). The variation of peak energy is as small as 35 meV with increasing temperature from 10 K to 300 K, implying excellent temperature stability of emission wavelength for InGaN MQDs. Moreover, the radiative and nonradiative recombination times were calculated by time-resolved PL (TRPL) measurements, and the temperature dependence of PL decay times reveal that radiative recombination dominates the recombination process due to the low dislocation density of QDs structure.

The emergence of various types of commercial cameras (compact, high resolution, high angle of view, high speed, and high dynamic range, etc.) has contributed significantly to the understanding of human activities. By taking advantage of the characteristic of a high angle of view, this paper demonstrates a system that recognizes micro-behaviors and a small group discussion with a single 360 degree camera towards quantified meeting analysis. We propose a method that recognizes speaking and nodding, which have often been overlooked in existing research, from a video stream of face images and a random forest classifier. The proposed approach was evaluated on our three datasets. In order to create the first and the second datasets, we asked participants to meet physically: 16 sets of five minutes data from 21 unique participants and seven sets of 10 min meeting data from 12 unique participants. The experimental results showed that our approach could detect speaking and nodding with a macro average f1-score of 67.9% in a 10-fold random split cross-validation and a macro average f1-score of 62.5% in a leave-one-participant-out cross-validation. By considering the increased demand for an online meeting due to the COVID-19 pandemic, we also record faces on a screen that are captured by web cameras as the third dataset and discussed the potential and challenges of applying our ideas to virtual video conferences.

Changing behavior related to improper lifestyle habits has attracted attention as a solution to prevent lifestyle diseases, such as diabetes, heart disease, arteriosclerosis, and stroke. To drive health behavior changes, wearable devices are needed, and they must not only provide accurate sensing and visualization functions but also effective intervention functions. In this paper, we propose a health support system, WaistonBelt X, that consists of a belt-type wearable device with sensing and intervention functions and a smartphone application. WaistonBelt X can automatically measure a waistline with a magnetometer that detects the movements of a blade installed in the buckle, and monitor the basic activities of daily living with inertial sensors. Furthermore, WaistonBelt X intervenes with the user to correct lifestyle habits by using a built-in vibrator. Through evaluation experiments, we confirmed that our proposed device achieves measurement of the circumference on the belt position (mean absolute error of 0.93 cm) and basic activity recognition (F1 score of 0.95) with high accuracy. In addition, we confirmed that the intervention via belt vibration effectively improves the sitting posture of the user.

With the spread of smart devices, people may obtain a variety of information on their surrounding environment thanks to sensing technologies. To design more context-aware systems, psychological user context (e.g., emotional status) is a substantial factor for providing useful information in an appropriate timing. As a typical use case that has a high demand for context awareness but is not tackled widely yet, we focus on the tourism domain. In this study, we aim to estimate the emotional status and satisfaction level of tourists during sightseeing by using unconscious and natural tourist actions. As tourist actions, behavioral cues (eye and head/body movement) and audiovisual data (facial/vocal expressions) were collected during sightseeing using an eye-gaze tracker, physical-activity sensors, and a smartphone. Then, we derived high-level features, e.g., head tilt and footsteps, from behavioral cues. We also used existing databases of emotionally rich interactions to train emotion-recognition models and apply them in a cross-corpus fashion to generate emotional-state prediction for the audiovisual data. Finally, the features from several modalities are fused to estimate the emotion of tourists during sightseeing. To evaluate our system, we conducted experiments with 22 tourists in two different touristic areas located in Germany and Japan. As a result, we confirmed the feasibility of estimating both the emotional status and satisfaction level of tourists. In addition, we found that effective features used for emotion and satisfaction estimation are different among tourists with different cultural backgrounds.

BACKGROUND: The present study aimed to evaluate cardiometabolic risks [weight gain, blood lipid levels (total cholesterol and triglycerides), blood glucose levels, hemoglobin A1c (HbA1c) levels, and corrected QT interval (QTc) prolongation] associated with the use of blonanserin and perospirone versus other antipsychotics in the management of patients with schizophrenia. METHOD: We conducted a systematic review and meta-analysis of patient data from randomized controlled trials comparing blonanserin or perospirone with other antipsychotics. RESULTS: In total, 4 blonanserin studies (n = 1080) were identified [vs. risperidone (2 studies, n = 508); vs. haloperidol (2 studies, n = 572)]. Blonanserin produced less weight gain compared with risperidone (weighted mean difference = -0.86, 95% confidence intervals = -1.36 to -0.36, p = 0.0008; 2 studies, 480 patients). However, no significant differences were observed in blood lipid, glucose, and HbA1c levels or QTc prolongation between blonanserin and risperidone or haloperidol. For perospirone studies, 5 studies [562 adult patients with schizophrenia randomized to perospirone (n = 256), olanzapine (n = 20), quetiapine (n = 28), risperidone (n = 53), aripiprazole (n = 49), haloperidol (n = 75), or mosapramine (n = 81)] were identified. Perospirone did not differ from other antipsychotics with regard to weight gain and total cholesterol levels. CONCLUSIONS: Our results suggest that blonanserin is associated with a lower of weight gain compared with other antipsychotics. Because the number of studies was small, additional controlled clinical trials with larger number of patients are indicated.

Novel soluble polyimides having long-chain alkyl groups on their side chain were synthesized via polymer reaction with the polyimides having phenolic OH groups and 3,4,5-tris(dodecyloxy)benzoic acid (12GA) using N,N′-dicyclohexylcarbodiimide (DCC) as a dehydration reagent. The polyimides having phenolic OH groups were synthesized from the tetracarboxylic dianhydrides such as 5-(2,5-dioxotetrahydrofuryl)-3-methyl-3-cyclohexene-1,2-dicarboxylic anhydride (cyclohexene-DA), 4,4′-hexafluoroisopropylidendi(phthalic anhydride) (6FDA), and 3,3′,4,4′-diphenylsulfone tetracarboxylic dianhydride (DSDA) and aromatic diamines such as 4,4′-diamino-3,3′-dihydroxybiphenyl (HAB). The polymer reactions were carried out in NMP and the progresses of polymer reactions were quantitatively monitored by 1H NMR measurements (conversion; 12.2–98.7%). The obtained polyimides bearing long-chain alkyl groups have enough molecular weights, good film-forming ability, good solubility for various organic solvents, and enough thermal stability. The water contact angles of the polyimide films were investigated, and it is noted that the introduction of long-chain alkyl groups increases the hydrophobicity of polyimide surface. These polyimides are expected to be applicable as the functional materials for microelectronics such as the alignment layers of LCDs.

Crystallization of the title compound, fac-[ReBr(ppt-OMe)(CO)3] (ppt-OMe = C15H12N2OS), from CH2Cl2/n-pentane (1:5 v/v) at room temperature gave two polymorphs, which crystallize in monoclinic (P21/c; α form) and ortho­rhom­bic (Pna21; β form) space groups. The ReI complex mol­ecules in either polymorph adopt a six-coordinate octa­hedral geometry with three facially-oriented carbonyl ligands, one bromido ligand, and two nitro­gen atoms from one chelating ligand ppt-OMe. In the crystal, both polymorph α and β form di-periodic sheet-like architectures supported by multiple hydrogen bonds. In polymorph α, two types of hydrogen bonds (C—H⋯O) are found while, in polymorph β, four types of hydrogen bonds (C—H⋯O and C—H⋯Br) exist. [ABSTRACT FROM AUTHOR]

The article offers an examination of augmented reality (AR)-assisted disaster preparedness guidelines designed to improve public awareness and engagement. Topics include the use of AR to enhance disaster preparedness by incorporating object recognition for environmental hazard identification, a comparison of AR-based guidelines with traditional paper-based ones in terms of usability and task performance, and the findings from testing these interfaces across different age groups.