The renovation of historic buildings carries a potential risk concerning the preservation or restoration of their original or earlier form and appearance. This study examines the seven historic mosques in Tuzla, and the impact of the latest interventions on their original form and appearance. At least twelve mosques existed in (Donja) Tuzla at the end of the 16th century, of which seven are still extant. Rare records testify to their foundation, maintenance, or fire damage. On the other hand, no documents on their early renovations or alterations are available. It is, however, certain that repairs, renovations, and possibly alterations were typical in the past because of deteriorating construction materials and for other reasons. The oldest records of these mosques mostly date from the late 19th and early 20th centuries. The work is based on many years of observation of the mosques’ architecture together with changes that have subsequently occurred, as well as available references and information concerning their past. The study shows that the interventions that have been carried out have had various outcomes, including both successful restorations of previous forms and renovations that alter earlier known designs.

Morphine (MORPH) is natural alkaloid and mainly used as a pain reliever. Its monitoring in human body fluids is crucial for modern medicine. In this paper, we have developed an electrochemical sensor for submicromolar detection of MORPH. The sensor is based on modified carbon paste electrode (CPE) by investigating the FexW1-xO4 ratio in iron tungstate (FeWO4), as well as the ratio of this material in CPE. For the first time, the effect of the iron–tungsten ratio in terms of achieving the best possible electrochemical characteristics for the detection of an important molecule for humans was examined. Morphological and electrochemical characteristics of materials were studied. The best results were obtained using Fe1W3 and 7.5% of modifier in CPE. For MORPH detection, square wave voltammetry (SWV) was optimized. Under the optimized conditions, Fe1W3@CPE resulted in limit of detection (LOD) of the method of 0.58 µM and limit of quantification (LOQ) of 1.94 µM. The linear operating range between 5 and 85 µM of MORPH in the Britton–Robinson buffer solution (BRBS) at pH 8 as supporting electrolyte was obtained. The Fe1W3@CPE sensor resulted in good selectivity and excellent repeatability with relative standard deviation (RSD) and was applied in real-world samples of human urine. Application for direct MORPH detection, without tedious sample pretreatment procedures, suggests that developed electrochemical sensor has appeared to be a suitable competitor for efficient, precise, and accurate monitoring of the MORPH in biological fluids.

War could be traumatic. War trauma could often lead to severe and sustained health consequences on people’s physical and psychological health. War trauma is often prevalent in people who either participated in the war or lived near conflict zones, such as military professionals, refugees, and health workers. Advances in information and communication technologies, such as the speed, scale, and scope at which people worldwide could be exposed to the near-time happenings of the war, mean that an unprecedented number of people could face media-induced war trauma. Different from war experienced in person, which could be limited in scope and intensity, media-induced war trauma can be substantially more extensive and comprehensive—news reports on the war often cover all aspects and angles possible, possibly paired with disturbing, if not demoralizing, images, repeatedly 24/7. Although media-induced war trauma could have a profound influence on people’s mental health, particularly factoring in the compounding challenges caused by the pandemic, there is a dearth of research in the literature. To shed light on this issue, in this article, we aim to examine the implications of media-induced war trauma on people’s health and well-being. Furthermore, we discuss the duties and responsibilities of the media industry amid and beyond the current conflicts in Ukraine.

Purpose Educational interventions have already been shown to positively affect awareness of clinical trials (CTs) among medical students. We aimed to explore basic knowledge and attitudes about CTs among medical students in terms of educational interventions that should be reflected in their further involvement in performing CTs and their role in raising awareness about CTs. Methods This cross-sectional, self-report anonymous online survey involved undergraduate medical students of the Medical Faculty University of Sarajevo enrolled in classes held within the Department of Pharmacology and Toxicology in the academic year 2015–2016. To include all accessible subjects for better representation of the whole population, consecutive sampling was applied. Results Among 142 students who completed questionnaire, 50% of them expressed partial or full agreement with the questionnaire statement that they were satisfied with the available information on CTs. Only 38% said they would participate in a CT, 21% would not, while 41% were not sure. Positive correlations were detected for composite subscale scores of agreement with questionnaire statements conveying the student’s knowledge about ethical and legal aspects of CTs and their perception about reliability/integrity and impact of CTs on medical practice. Conclusion Students have knowledge of the basic design and ethical aspects of CTs. Positive attitudes toward the impact of CTs on medical practice were shown in students of higher years of study, where educational intervention of additional knowledge of CTs was inserted and those students expressed better knowledge of CTs. However, no significant impact was detected between knowledge and willingness to participate in CTs, irrespective of years of study, reflecting the third of students that would participate in CTs. Changes in medical curricula led to the change in students’ knowledge and attitudes regarding CTs as well as their involvement in CTs.

Abstract This study presents indoor and outdoor levels of airborne fine particles (PM2.5), particle bound polycyclic aromatic compounds (PACs) including parent-, alkylated-, nitro-, and oxy-PAHs. Week-long simultaneous measurements were conducted inside and outside 15 occupied homes in southern Sweden during wintertime. The homes were single-family houses or apartments located in urban, semi-urban, and rural areas. The PM2.5 and PACs levels were low compared to studies worldwide. There was great variation in concentrations between sites, which likely is due to proximity to road and traffic intensity. The lower concentrations of nitro and oxy-PAHs compared to parent PAHs in this study, compared to other studies, could possibly be due to lower atmospheric photochemical formation outdoors because the cold climate. This assumption could not be confirmed and need to be further tested. The results point to that particle PAC levels found inside arise primarily from outdoor. This correlation was not as clear for PM2.5. The results of a comparison between residences before and after energy renovation did not indicate an improvement in indoor air regarding PACs. To understand exposure and assess risks it is important to measure wide range of PACs both in gas and particle phase.

The shallowest intracrustal layer (extending to 8 ± 2 km depth) beneath the Mars InSight Lander site exhibits low seismic wave velocity, which is likely related to a combination of high porosity and other lithological factors. The SsPp phase, an SV‐ to P‐wave reflection on the receiver side, is naturally suited for constraining the seismic structure of this top crustal layer since its prominent signal makes it observable with a single station without the need for stacking. We have analyzed six broadband and low‐frequency seismic events recorded on Mars and made the first coherent detection of the SsPp phase on the red planet. The timing and amplitude of SsPp confirm the existence of the ∼8 km interface in the crust and the large wave speed (or impedance) contrast across it. With our new constraints from the SsPp phase, we determined that the average P‐wave speed in the top crustal layer is between 2.5 and 3.2 km/s, which is a more precise and robust estimate than the previous range of 2.0–3.5 km/s obtained by receiver function analysis. The low velocity of Layer 1 likely results from the presence of relatively low‐density lithified sedimentary rocks and/or aqueously altered igneous rocks that also have a significant amount of porosity, possibly as much as 22%–30% by volume (assuming an aspect ratio of 0.1 for the pore space). These porosities and average P‐wave speeds are compatible with our current understanding of the upper crustal stratigraphy beneath the InSight Lander site.

Background Engagement is key to interventions that achieve successful behavior change and improvements in health. There is limited literature on the application of predictive machine learning (ML) models to data from commercially available weight loss programs to predict disengagement. Such data could help participants achieve their goals. Objective This study aimed to use explainable ML to predict the risk of member disengagement week by week over 12 weeks on a commercially available web-based weight loss program. Methods Data were available from 59,686 adults who participated in the weight loss program between October 2014 and September 2019. Data included year of birth, sex, height, weight, motivation to join the program, use statistics (eg, weight entries, entries into the food diary, views of the menu, and program content), program type, and weight loss. Random forest, extreme gradient boosting, and logistic regression with L1 regularization models were developed and validated using a 10-fold cross-validation approach. In addition, temporal validation was performed on a test cohort of 16,947 members who participated in the program between April 2018 and September 2019, and the remaining data were used for model development. Shapley values were used to identify globally relevant features and explain individual predictions. Results The average age of the participants was 49.60 (SD 12.54) years, the average starting BMI was 32.43 (SD 6.19), and 81.46% (39,594/48,604) of the participants were female. The class distributions (active and inactive members) changed from 39,369 and 9235 in week 2 to 31,602 and 17,002 in week 12, respectively. With 10-fold-cross-validation, extreme gradient boosting models had the best predictive performance, which ranged from 0.85 (95% CI 0.84-0.85) to 0.93 (95% CI 0.93-0.93) for area under the receiver operating characteristic curve and from 0.57 (95% CI 0.56-0.58) to 0.95 (95% CI 0.95-0.96) for area under the precision-recall curve (across 12 weeks of the program). They also presented a good calibration. Results obtained with temporal validation ranged from 0.51 to 0.95 for area under a precision-recall curve and 0.84 to 0.93 for area under the receiver operating characteristic curve across the 12 weeks. There was a considerable improvement in area under a precision-recall curve of 20% in week 3 of the program. On the basis of the computed Shapley values, the most important features for predicting disengagement in the following week were those related to the total activity on the platform and entering a weight in the previous weeks. Conclusions This study showed the potential of applying ML predictive algorithms to help predict and understand participants’ disengagement with a web-based weight loss program. Given the association between engagement and health outcomes, these findings can prove valuable in providing better support to individuals to enhance their engagement and potentially achieve greater weight loss.

Pharmacogenomics (PGx)-guided drug treatment is one of the cornerstones of personalized medicine. However, the genes involved in drug response are highly complex and known to carry many (rare) variants. Current technologies (short-read sequencing and SNP panels) are limited in their ability to resolve these genes and characterize all variants. Moreover, these technologies cannot always phase variants to their allele of origin. Recent advance in long-read sequencing technologies have shown promise in resolving these problems. Here we present a long-read sequencing panel-based approach for PGx using PacBio HiFi sequencing. A capture based approach was developed using a custom panel of clinically-relevant pharmacogenes including up- and downstream regions. A total of 27 samples were sequenced and panel accuracy was determined using benchmarking variant calls for 3 Genome in a Bottle samples and GeT-RM star(*)-allele calls for 21 samples.. The coverage was uniform for all samples with an average of 94% of bases covered at >30×. When compared to benchmarking results, accuracy was high with an average F1 score of 0.89 for INDELs and 0.98 for SNPs. Phasing was good with an average of 68% the target region phased (compared to ~20% for short-reads) and an average phased haploblock size of 6.6kbp. Using Aldy 4, we compared our variant calls to GeT-RM data for 8 genes (CYP2B6, CYP2C19, CYP2C9, CYP2D6, CYP3A4, CYP3A5, SLCO1B1, TPMT), and observed highly accurate star(*)-allele calling with 98.2% concordance (165/168 calls), with only one discordance in CYP2C9 leading to a different predicted phenotype. We have shown that our long-read panel-based approach results in high accuracy and target phasing for SNVs as well as for clinical star(*)-alleles.

This paper investigates recursive feasibility, recursive robust stability and near-optimality properties of policy iteration (PI). For this purpose, we consider deterministic nonlinear discrete-time systems whose inputs are generated by PI for undiscounted cost functions. We first assume that PI is recursively feasible, in the sense that the optimization problems solved at each iteration admit a solution. In this case, we provide novel conditions to establish recursive robust stability properties for a general attractor, meaning that the policies generated at each iteration ensure a robust \KL-stability property with respect to a general state measure. We then derive novel explicit bounds on the mismatch between the (suboptimal) value function returned by PI at each iteration and the optimal one. Afterwards, motivated by a counter-example that shows that PI may fail to be recursively feasible, we modify PI so that recursive feasibility is guaranteed a priori under mild conditions. This modified algorithm, called PI+, is shown to preserve the recursive robust stability when the attractor is compact. Additionally, PI+ enjoys the same near-optimality properties as its PI counterpart under the same assumptions. Therefore, PI+ is an attractive tool for generating near-optimal stabilizing control of deterministic discrete-time nonlinear systems.

As peer-to-peer energy trading and local energy market are gaining momentum, a follow-up challenge is scaling up to include multi-community, multi-region power schedule and trading. This study introduces community-to-community power trading and schedules considering various generating units, including coal, gas, wind, and solar, as well as import and export prices from community transactions. These generating sources are used to fulfil the demand requirements of each community over a time horizon, as well as absorbing or trading the margin among the inter-connected communities, while fulfilling certain distribution network constraints. For a practical case, the uncertainties in wind power generations are considered. An optimality condition decomposition technique is used to decompose the overall problem into a community-based local problem. Thus, individual community solves their optimisation local problem in parallel for operational efficiency of real-time multi-commodity power schedule and trading. The initial results indicate that each community acts in its best interest to minimise its costs when there is a change in the variable. When emission costs are applied as a constraint to their generation, a reduction in power generation is observed augmented by an increase of up to 30.8% of power transferred in the inter-community transaction.

Tofacitinib is an oral Janus kinase (JAK) inhibitor and is registered for the treatment of ulcerative colitis (UC). The effectiveness of tofacitinib has been evaluated up to 12 months of treatment.

Abstract C24H16CoBr2N4O4, monoclinic, P21/c (no. 14), a = 11.1134(4) Å, b = 16.1731(6) Å, c = 13.7057(5) Å, β = 105.363(4)°, V = 2375.41(16) Å3, Z = 4, R gt(F) = 0.0588, wR ref(F 2) = 0.1101, T = 170(2) K.

Previous research in human-robot interaction has explored using robots to increase objective and hedonic aspects of well-being and quality of life, but there is no literature on how robots might be used to support eudaimonic aspects of well-being (such as meaning in life). A sense of meaning has been shown to positively affect health and longevity. We frame our study around the Japanese concept of ikigai, which is widely used with Japanese older adults to enhance their everyday lives, and is closely related to the concept of eudaimonic well-being (EWB) known in Western countries. Using a mixed-methods and exploratory approach, including interviews with 17 older adults and the collection of 100 survey responses, we explored how older adults in the US experience a sense of meaning, and if and how a social robot could be used to help foster this sense. We find that meaning for older adults is often obtained by helping others, through family connections, and/or through activities of daily life, and that sources of meaning often differ based on the older adults’ living situation. Assessing how meaning compares to happiness and social connection, we highlight general similarities and differences, and also find that living situation influences older adults’ sources of happiness, desire for social connection, and barriers to well-being., in addition to companionship and happiness having a weaker correlation with meaning for those who live alone than for those who live with others. Additionally, we evaluated initial perceptions of a social robot (QT) meant to enhance ikigai and overall well-being. Finding mostly positive perceptions, though those who live alone also reported being less willing to adopt a social robot into their homes. Using both data collected on older adults’ meaning and the potential use of QT to support meaning, we make several design recommendations with regards to using robots to enhance ikigai, such as by prompting daily reflecting, enhancing family bonds, and suggesting new experiences and volunteer opportunities.