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Purpose: The recently introduced Pipeline Vantage Embolization Device with Shield Technology is the fourth generation of Pipeline flow diverter devices. Due to the relatively high rate of intraprocedural technical complications, modifications were subsequently made to the device after a limited release of the device in 2020. This study aimed to evaluate the safety and efficacy of the modified version of this device., Methods: This was a multicentre retrospective series. The primary efficacy endpoint was aneurysm occlusion in the absence of retreatment. The primary safety endpoint was any neurological morbidity or death. Ruptured and unruptured aneurysms were included in the study., Results: A total of 52 procedures were performed for 60 target aneurysms. Treatment was performed on 5 patients with ruptured aneurysms. The technical success rate was 98%. The mean clinical follow-up time was 5.5 months. In patients presenting with unruptured aneurysms there were no deaths, 3 (6.4%) major complications and 7 (13%) minor complications. In the five patients presenting with subarachnoid haemorrhage there were 2 (40%) major complications with 1 (20%) of these resulting in death, and 1 (20%) minor complication. Of the patients 29 (56%) had undergone 6‑monthly postprocedural angiographic imaging with a mean time of 6.6 months demonstrating that 83% of patients had achieved adequate occlusion (RROC1/2) of the aneurysm., Conclusions: In this non-industry-sponsored study, the occlusion rates and safety outcomes were similar to those seen in previously published studies with flow diverter devices and earlier generation Pipeline devices. Modifications to the device appear to have improved ease of deployment., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany.)

Background: The Pipeline Vantage Embolization Device (PEDV) is the fourth-generation pipeline flow diverter for intracranial aneurysm treatment. There are no outcome studies for the second PEDV version. We aimed to evaluate safety and efficacy outcomes. Primary and secondary objectives were to determine outcomes for unruptured and ruptured cohorts, respectively., Methods: In this multicenter retrospective and prospective study, we analyzed outcome data from eight centers using core laboratory assessments. We determined 30-day and ≥3-month mortality and morbidity rates, and 6- and 18-month radiographic aneurysm occlusion rates for procedures performed during the period July 2021-March 2023., Results: We included 121 consecutive patients with 131 aneurysms. The adequate occlusion rate for the unruptured cohort at short-term and medium-term follow up, and also for the ruptured cohort at short-term follow up, was >90%. Two aneurysms (1.5%) underwent retreatment. When mortality attributed to a palliative case in the unruptured cohort, or subarachnoid hemorrhage in the ruptured cohort, was excluded then the overall major adverse event rate in respective cohorts was 7.5% and 23.5%, with 0% mortality rates for each. When all event causes were included on an intention-to-treat basis, the major adverse event rates in respective cohorts were 8.3% and 40.9%, with 0.9% and 22.7% mortality rates., Conclusions: For unruptured aneurysm treatment, the second PEDV version appears to have a superior efficacy and similar safety profile to previous-generation PEDs. These are acceptable outcomes in this pragmatic and non-industry-sponsored study. Analysis of ruptured aneurysm outcomes is limited by cohort size. Further prospective studies, particularly for ruptured aneurysms, are needed., Competing Interests: Competing interests: Microvention core laboratory for neurointerventional study: payment to TCB. Siemens Healthineers Speakers Bureau Educational lecture on brain tumours, not related to aneurysms: payment to TCB. Medtronic Speakers Bureau Educational lecture on an independent non-industry-sponsored study published in AJNR 2019 on Pipeline Flow Diverter device: payment to TCB. Bayer Speakers Bureau Educational lecture on Explainability in AI, not related to aneurysms: payment to TCB. Consulting and proctoring agreement with Medtronic, Stryker, Balt, and Cerenovus: payment to TP. Microvention, Balt, and Medtronic support to attend meetings: payment to TM., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ.)

Context: Although sleep disordered breathing (SDB) is well-recognised in acromegaly, most studies have reported heterogeneous, often heavily treated, groups and few have performed detailed sleep phenotyping at presentation., Objective: To study SDB using the gold standard of polysomnography, in the largest group of newly-diagnosed, treatment-naïve patients with acromegaly., Setting and Patients: 40 patients [22 males, 18 females; mean age 54 years (range 23-78)], were studied to: (i) establish the prevalence and severity of SDB (ii) assess the reliability of commonly employed screening tools [Epworth Sleepiness Scale (ESS) and overnight oxygen desaturation index (DI)] to detect SDB (iii) determine the extent to which sleep architecture is disrupted., Results: Obstructive sleep apnoea (OSA), defined by the apnoea-hypopnoea index (AHI), was present in 79% of subjects (mild, n = 12; moderate, n = 5; severe, n = 14). However, in these individuals with OSA by AHI criteria, ESS (positive in 35% [n = 11]) and DI (positive in 71%: mild, n = 11; moderate, n = 6; severe, n = 5) markedly underestimated its prevalence/extent. Seventy-eight percent of patients exhibited increased arousal, with marked disruption of the sleep cycle, despite most (82%) having normal total time asleep. Fourteen patients spent longer in stage 1 sleep. Deeper sleep stages were severely attenuated in many subjects (reduced stage 2, n = 18; reduced slow wave sleep, n = 24; reduced rapid eye movement sleep, n = 32)., Conclusion: Our study provides strong support for clinical guidelines that recommend screening for sleep apnoea syndrome in patients with newly-diagnosed acromegaly. Importantly, however, it highlights shortcomings in commonly recommended screening tools (questionnaires, desaturation index) and demonstrates the added value of polysomnography to allow timely detection of obstructive sleep apnoea and associated sleep cycle disruption., (© 2023 The Authors. Clinical Endocrinology published by John Wiley & Sons Ltd.)

Process Mining is a technique looking into the analysis and mining of existing process flow. On the other hand, Machine Learning is a data science field and a sub-branch of Artificial Intelligence with the main purpose of replicating human behavior through algorithms. The separate application of Process Mining and Machine Learning for healthcare purposes has been widely explored with a various number of published works discussing their use. However, the simultaneous application of Process Mining and Machine Learning algorithms is still a growing field with ongoing studies on its application. This paper proposes a feasible framework where Process Mining and Machine Learning can be used in combination within the healthcare environment.

A previously independent 82-year-old woman presented with 5 months of worsening confusion, mobility and cognitive decline, with deficits in orientation, language and executive function. A cerebral dural arteriovenous fistula was identified and successfully embolised, after which her cognitive ability and independence dramatically improved. Although rare, a dural arteriovenous fistula may mimic a rapidly progressive dementia, but its early recognition and treatment can completely reverse the dementia., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: This review critically addresses the putative molecular targets of Diabetic Nephropathy (DN) and screens effective phytocompounds that can be therapeutically beneficial, and highlights their mechanistic modalities of action., Introduction: DN has become one of the most prevalent complications of clinical hyperglycemia, with individual-specific variations in the disease spectrum that leads to fatal consequences. Diverse etiologies involving oxidative and nitrosative stress, activation of polyol pathway, inflammasome formation, Extracellular Matrix (ECM) modifications, fibrosis, and change in dynamics of podocyte functional and mesangial cell proliferation adds up to the clinical complexity of DN. Current synthetic therapeutics lacks target-specific approach, and is associated with the development of inevitable residual toxicity and drug resistance. Phytocompounds provides a vast diversity of novel compounds that can become an alternative therapeutic approach to combat the DN., Methods: Relevant publications were searched and screened from research databases like GOOGLE SCHOLAR, PUBMED and SCISEARCH. Out of 4895 publications, the most relevant publications were selected and included in this article., Result: This study critically reviews over 60 most promising phytochemical and provides with their molecular targets, that can be of pharmacological significance in context to current treatment and concomitant research in DN., Conclusion: This review highlights those most promising phytocompounds that have the potential of becoming new safer naturally-sourced therapeutic candidates and demands further attention at clinical level., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Background:Moringa oleifera leaf is used for diabetes due to its pharmacologic effects. Patients with hyperglycemia experience beta cell destruction. However, no research on risk awareness has been done to ascertain its safety. The present study describes the antidiabetic effect of Moringa oleifera leaf, such as the protection of pancreatic beta cells and the induction of glycogen synthesis, before addressing the secondary effects of diabetes, such as hepatic and renal toxicity. Methods: Forty‐five Wistar rats weighed 160 ± 10 g were divided into nine groups. All animal operations complied with the National Institute of Health (NIH) guidelines for the care and use of laboratory animals as approved by the Animal Ethical Committee, University of Jos. Group I was normal control and Group II was diabetic animals induced with alloxan. Insulin and extract doses of 200, 400, and 800 mg/kg were given to diabetic Groups III‐VI. Normal animals in Groups VII–IX were given extract at doses of 200, 400, and 800 mg/kg for 28 days. Tissues were retrieved for biochemical and histological investigations using standard techniques. Results: There was decrease relative body weight of diabetic animals (95.50 ± 5.50) when compared to normal control (142.75 ± 20.08) with increased levels of urea (control 6.13 ± 0.523 and diabetes 29.23 ± 1.267) and creatinine (control 0.70 ± 0.057 and diabetes 2.13 ± 0.185). Histology of the liver and pancreas also points to organ damage due to hyperglycemia. However, oral administration of extract showed antidiabetic effect with protection of pancreatic beta cells and the induction of glycogen synthesis, no glycogen was deposited in the liver, addressing the secondary effects of diabetes, such as hepatic and renal toxicity. Further discovery revealed that extract elevated antioxidant enzyme expression. Conclusion: Leaf extract from Moringa oleifera reduces blood sugar and lessens the damage caused by hyperglycemia in the pancreas and liver. [ABSTRACT FROM AUTHOR]

Building envelopes and indoor environments exhibit thermal inertia, forming a virtual energy storage system in conjunction with the building air conditioner (AC) system. This system represents a current demand response resource for building electricity use. Thus, this study centers on the CatBoost algorithm within machine learning (ML) technology, utilizing the LASSO regression model for feature selection and applying the Optuna framework for hyperparameter optimization (HPO) to develop a cost-optimal control method for minimizing building AC loads. This method addresses the challenges associated with traditional load forecasting and control methods, which are often impacted by environmental temperature, building parameters, and user behavior uncertainties. These methods struggle to accurately capture the complex dynamics and nonlinear relationships of AC operations, making it difficult to devise AC operation and virtual energy storage scheduling strategies effectively. The proposed method was applied and validated using a case study of an office building in Nanjing, China. The prediction results showed coefficient of variation in root mean square error (CV-RMSE) values of 6.4% and 2.2%. Compared with the original operating conditions, the indoor temperature remained within a comfortable range, the AC load was reduced by 5.25%, and the operating energy costs were reduced by 24.94%. These results demonstrate that the proposed method offers improved computational efficiency, enhanced model performance, and economic benefits. [ABSTRACT FROM AUTHOR]

In recent years, research has noted the increasing prevalence of mental health problems among university students. The current mental health needs of the university population, along with the multitude of stressors they face, have increased the importance of examining their psychological well-being and determining the personal resources that effectively promote it. In this context, the present research aims to analyze the roles of psychological capital (PsyCap) and coping strategies as personal resources that are significantly related to the psychological well-being (PWB) of university students. Specifically, the mediating roles of various coping strategies (both adaptive and maladaptive) in the relationship between PsyCap and PWB are explored. The study involves 391 university students from Spain. The results show partial mediation effects of adaptive coping strategies (cognitive restructuring and social support) on the relationship between PsyCap and PWB. Likewise, PsyCap is shown to be a direct positive predictor of adaptive coping strategies and PWB, as well as a direct negative predictor of maladaptive coping strategies (self-criticism). Therefore, it is concluded that PsyCap and some adaptive coping strategies serve as valuable personal resources that predict PWB in university students. PsyCap is also associated with a lower tendency to engage in maladaptive coping strategies, such as self-criticism. Similarly, the use of cognitive restructuring and/or social support is related to high levels of PWB among university students. [ABSTRACT FROM AUTHOR]

This work presents the valorization of Millettia laurentii carpentry sawdust waste into sustainable natural dye for ecological dyeing and functional finishing of wool textiles. A simple aqueous extraction method was used to isolate natural dye, then the extracted bio-dye was subjected to different qualitative and quantitative analyses. Wool yarns were dyed in conjunction with different metal salts (aluminum sulfate, ferrous sulfate, copper sulfate, and zinc sulfate) and their binary combinations (Al/Fe, Al/Cu, Al/Zn, Fe/Cu, Fe/Zn, and Cu/Zn) as mordants. Practically, 11 different eco-friendly shades were developed of varying hues and tones employing 75% (o.w.f.) of M. laurentii sawdust. The antibacterial action against E. coli and S. aureus, free radical scavenging power, and UV protection properties of dyed wool yarns were recorded. The introduction of different metal salts and their combinations enhanced the color properties with a semi-durable antimicrobial and antioxidant finish. Statistical analysis of the color parameters of binary mixed metal combinations confirms the synergetic contribution of individual metal ions in each combination. The amount of individual mordant adsorbed (weight percentage) from a particular combination was further assessed by EDX analysis of mordanted dyed wool yarns. Overall, the results suggest that copper sulfate and ferrous sulfate pre-treatment were the best combinations (Fe/Cu) for wool yarns in conjunction with 75.0% (o.w.f.) M. laurentii sawdust, considering the color fastness, antimicrobial, and antioxidant properties. The results provide ample scope for the upcycling of timber industry waste into a natural dye for the eco-friendly coloration of wool textiles. [ABSTRACT FROM AUTHOR]

Background: Coccoloba uvifera L. (Family: Polygonaceae) known as sea grape is natively distributed in middle and south America. The aqueous leaf extract showed inhibitory activities against α-glucosidase and α-amylase in previous reports. Moreover, the hydroalcoholic leaves extract ameliorated hyperglycemia in the oral glucose tolerance test. Despite these promising results, the extracts used in these studies were not standardized, nor was their mechanism of action elucidated. The current study aims to standardize the ethanolic C. uvifera leaves extract (CU) using markers, and assess its ameliorative activity against diabetes and its hepatoprotective activity against diabetic complications. Results: Standardized leaves' ethanolic extract contained 0.09 ± 0.00057 and 0.23 ± 0.0011 mg/g gallic acid and rutin, respectively, as estimated by HPLC. Administration of CU (100, 200 and 400 mg/kg) for 6 weeks ameliorated DM manifestations in STZ-induced diabetic rats in a dose-dependent manner. The ethanolic extract reduced fasting blood glucose, increased serum insulin and reduced elevated liver enzymes. CU counteracted oxidative stress, promoted glucose metabolizing enzymes and reduced gluconeogenesis enzymes. The underlying mechanism involved increased expression of IR, IRS-1, IRS-2 and GLUT2 in liver tissue through activation of PI3K/AKT signaling. The histopathological study demonstrated reduced inflammation and hepatocyte degeneration. Conclusion: CU could be used as a promising antidiabetic drug with hepatoprotective activity in diabetes hepatic complications. The standardized CU ethanolic extract should be further assessed clinically alone or in combination with other antidiabetic remedies. [ABSTRACT FROM AUTHOR]

The current study proceeded to reduce the environmental hazards spreading worldwide due to synthetic dyes. To overcome these problems, eco-friendly natural dyes are introduced as alternative sources of synthetic dyes. The present study was focused on exploring the bio-colorant of the aqueous and acidic extract of the bark of Melia azedarach L. for the dyeing of both silk and cotton samples. The results of the extraction medium specified that the aqueous extract gave maximum colorant solubility and upon fabric dyeing produced higher color strength in contrast to the acidic medium. The optimization experimentation data showed that excellent color strength of silk fabric was found at 45 min dyeing time duration, in 35:1 mL dye extract, and using 2% salt (NaCl) as an exhausting agent, whereas cotton fabric showed the maximum K/S value at 60 min dyeing time, in a 45:1 mL liquor ratio, and with the use of 2% salt. Bio-mordants produce different shades on both fabrics. Bio-mordanting experiments on silk revealed that pre-mordanting with 2% turmeric and 3% pomegranate, and post-mordanting using 3% turmeric and 2% pomegranate produced a darker shade. In the case of cotton, the pre-mordanted samples with 2% turmeric and 3% pomegranate and the post-mordanted samples with 4% turmeric and 4% pomegranate gave the highest color strengths. All the mordanted samples gave excellent fastness ratings. Overall, it has been found that Bakain bark proved to be an excellent source of tannin. The result of this study showed that it could be a cost-effective and eco-friendly dye source for textile progress. [ABSTRACT FROM AUTHOR]

Spiking neural networks and neuromorphic hardware platforms that simulate neuronal dynamics are getting wide attention and are being applied to many relevant problems using Machine Learning. Despite a well-established mathematical foundation for neural dynamics, there exists numerous software and hardware solutions and stacks whose variability makes it difficult to reproduce findings. Here, we establish a common reference frame for computations in digital neuromorphic systems, titled Neuromorphic Intermediate Representation (NIR). NIR defines a set of computational and composable model primitives as hybrid systems combining continuous-time dynamics and discrete events. By abstracting away assumptions around discretization and hardware constraints, NIR faithfully captures the computational model, while bridging differences between the evaluated implementation and the underlying mathematical formalism. NIR supports an unprecedented number of neuromorphic systems, which we demonstrate by reproducing three spiking neural network models of different complexity across 7 neuromorphic simulators and 4 digital hardware platforms. NIR decouples the development of neuromorphic hardware and software, enabling interoperability between platforms and improving accessibility to multiple neuromorphic technologies. We believe that NIR is a key next step in brain-inspired hardware-software co-evolution, enabling research towards the implementation of energy efficient computational principles of nervous systems. NIR is available at neuroir.org Neuromorphic software and hardware solutions vary widely, challenging interoperability and reproducibility. Here, authors establish a representation for neuromorphic computations in continuous time and demonstrate support across 11 platforms. [ABSTRACT FROM AUTHOR]

This study addresses the magnetohydrodynamic flow of a squeezed ternary nanofluid between two horizontal parallel Riga plates. The importance of this problem lies in understanding the complex interactions between magnetic fields, nanofluid dynamics, and heat transfer, which are crucial for optimizing thermal management systems. This study utilizes a numerical approach, specifically a collocation method implemented in MATLAB, to solve the governing equations with high precision. Key results acquired indicate that the magnetic field and Riga plate actuator significantly enhance fluid velocity, whereas the variation in thermal conductivity, radiation, and viscous dissipation increases the temperature distribution. Quantitative analysis illustrates the impact of all these factors on skin friction and Nusselt number. Sensitivity analysis using the response surface methodology exhibits the conditions for optimized heat transfer. The novelty of this work lies in its comprehensive analysis of the magnetohydrodynamic flow in the presence of a microcantilever sensor, which provides deep understanding of optimization of heat transfer rates. This research offers a detailed examination of the combined effects of various physical phenomena and also validates them through graphical comparisons with existing studies. [ABSTRACT FROM AUTHOR]

The article introduces a newer hybrid method (HAIS-GA) of optimizing and choosing the ideal machining parameters for cryogenic processing. It depends on two late methodologies genetic algorithm (GA) and artificial immune system (AIS), which are connected to numerous troublesome combinatorial streamlining issues with specific qualities and shortcomings. These developmental calculations are proposed to find the best arrangement of process factors for the clashing prerequisites in multi objective capacities. Hybrid model optimization also comes with challenges, such as selecting the right combination of techniques, tuning parameters, potential increases in complexity, and the need for expertise in multiple optimization methods. The key reason for this hybrid approach (HAIS-GA) is the improvement in the results that is achieved due to the characteristics of GA and AIS. Three test functions are employed to compare the outcomes in terms of these functions' ability to achieve the lowest value. Cryogenic processing is used to validate the optimised values that were obtained. The attained results showcase that HAIS-GA approach, in conclusion exhibits a more favourable minimal objective function within a reasonable duration. Due to the nature of Unrestricting to local optima, and it being self-adaptive HAIS-GA provides better result compared to GA and AIS. Based on the least value of the objective function and time for each method. [ABSTRACT FROM AUTHOR]

Interoception plays an important role in emotion processing. However, the neurobiological substrates of the relationship between visceral responses and emotional experiences remain unclear. In the present study, we measured interoceptive sensitivity using the heartbeat discrimination task and investigated the effects of individual differences in interoceptive sensitivity on changes in pulse rate and insula activity in response to subjective emotional intensity. We found a positive correlation between heart rate and valence level when listening to music only in the high interoceptive sensitivity group. The valence level was also positively correlated with music-elicited anterior insula activity. Furthermore, a region of interest analysis of insula subregions revealed significant activity in the left dorsal dysgranular insula for individuals with high interoceptive sensitivity relative to individuals with low interoceptive sensitivity while listening to the high-valence music pieces. Our results suggest that individuals with high interoceptive sensitivity use their physiological responses to assess their emotional level when listening to music. In addition, insula activity may reflect the use of interoceptive signals to estimate emotions. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) algorithms hold the potential to revolutionize radiology. However, a significant portion of the published literature lacks transparency and reproducibility, which hampers sustained progress toward clinical translation. Although several reporting guidelines have been proposed, identifying practical means to address these issues remains challenging. Here, we show the potential of cloud-based infrastructure for implementing and sharing transparent and reproducible AI-based radiology pipelines. We demonstrate end-to-end reproducibility from retrieving cloud-hosted data, through data pre-processing, deep learning inference, and post-processing, to the analysis and reporting of the final results. We successfully implement two distinct use cases, starting from recent literature on AI-based biomarkers for cancer imaging. Using cloud-hosted data and computing, we confirm the findings of these studies and extend the validation to previously unseen data for one of the use cases. Furthermore, we provide the community with transparent and easy-to-extend examples of pipelines impactful for the broader oncology field. Our approach demonstrates the potential of cloud resources for implementing, sharing, and using reproducible and transparent AI pipelines, which can accelerate the translation into clinical solutions. A significant portion of the scientific literature on AI for radiology lacks transparency and reproducibility, which hampers sustained progress toward clinical translation. Here, the authors offer a blueprint for transparent AI pipelines on cloud platforms, focusing on lung cancer prediction and biomarker discovery. [ABSTRACT FROM AUTHOR]

This paper deals with the power supply queuing model M/M/∞. In this paper, we analyze total cost of power supply queuing model with infinite servers. We develop a total cost function and suject it to optimization, which consequently gives us non-linear equations. These non-linear equations are further solved by using Newton-Raphson method with the help of R-software. We also present analysis of sensitivity, tables and graphs of the model to exhibit the comprehensive interpretation of the same. [ABSTRACT FROM AUTHOR]

The proliferation of battery-operated end-user electronic devices due to technological advancements, especially in medical image processing applications, demands low power consumption, high-speed operation, and efficient coding. The design of these devices is centered on the Application-Specific Integrated Circuits (ASIC), General Purpose Processors (GPP), and Field Programmable Gate Array (FPGA) frameworks. The need for low-power functional blocks arises from the growing demand for high-performance computational units that are part of high-speed processors operating at high clock frequencies. The operational speed of the processor is determined by the computational unit, which is the workhorse of high-speed processors. A novel approach to integrating Very Large-Scale Integration (VLSI) ASIC design and the concepts of low-power VLSI compatible with medical image compression was embraced in this research. The focus of this study was the design, development, and implementation of a Power Delay Product (PDP) optimized computational unit targeted for medical image compression using ASIC design flow. This stimulates the research community's quest to develop an ideal architecture, emphasizing on minimizing power consumption and enhancing device performance for medical image processing applications. The study uses area, delay, power, PDP, and Peak Signal-to-Noise Ratio (PSNR) as performance metrics. The research work takes inspiration from this and aims to enhance the efficiency of the computational unit through minor design modifications that significantly impact performance. This research proposes to explore the trade-off of high-performance adder and multiplier designs to design an ASIC-based computational unit using low-power techniques to enhance the efficiency in power and delay. The computational unit utilized for the digital image compression process was synthesized and implemented using gpdk 45 nm standard libraries with the Genus tool of Cadence. A reduced PDP of 46.87% was observed when the image compression was performed on a medical image, along with an improved PSNR of 5.89% for the reconstructed image. [ABSTRACT FROM AUTHOR]

The changing social, cultural and physical characteristics and uses of public spaces by migrants are of longstanding interest to social scientists. Often embedded in uses of public spaces are splinters, resonances and connections to home and migration. This paper examines the religious spaces that Sri Lankan migrants engage with in Sydney and Melbourne, Australia. To untangle the complexities associated with these spaces, we integrate a framework of belonging that encompasses rituals, relationships and restrictions. A multilayered dataset, which includes interviews with the Sri Lankan diaspora, (auto)ethnography, field observations and photography, revealed that within the Sri Lankan diaspora, individuals often used religious spaces to maintain rituals and identities. The data uncovered that some participants in the diaspora used religious spaces to gather and socialise with other diaspora members while others had dynamic relationships with these spaces – that is, the meanings attributed to religious spaces were at times fraught with tensions and hostilities towards religious practice and feelings of welcome. In this paper, we offer a snapshot of a growing diaspora in Australia and their negotiations to belong (or not). [ABSTRACT FROM AUTHOR]

In this article, I discuss how children and grandchildren of North Korean war refugees who were displaced during the Korean War construct identity and belonging in relation to their North Korean heritage. Drawing from the concept of postmemory, I examine how their northern heritage is experienced, constructed, mediated, and even solidified across generations who did not directly experience the Korean War. Unlike existing literature that predominantly focuses on the traumatic aspects of postmemory, I found that one's construction of postmemory also encompasses positive family memories. These affirming memories exist alongside traumatic ones, countering the overdetermined paradigm of trauma across memory studies. Thus, I propose alternative ways of remembering that capture a nuanced understanding of how the second and third generations construct positive postmemories alongside the traumatic memories of their ancestors. [ABSTRACT FROM AUTHOR]

Horses revolutionized human history with fast mobility1. However, the timeline between their domestication and their widespread integration as a means of transport remains contentious2–4. Here we assemble a collection of 475 ancient horse genomes to assess the period when these animals were first reshaped by human agency in Eurasia. We find that reproductive control of the modern domestic lineage emerged around 2200 bce, through close-kin mating and shortened generation times. Reproductive control emerged following a severe domestication bottleneck starting no earlier than approximately 2700 bce, and coincided with a sudden expansion across Eurasia that ultimately resulted in the replacement of nearly every local horse lineage. This expansion marked the rise of widespread horse-based mobility in human history, which refutes the commonly held narrative of large horse herds accompanying the massive migration of steppe peoples across Europe around 3000 bce and earlier3,5. Finally, we detect significantly shortened generation times at Botai around 3500 bce, a settlement from central Asia associated with corrals and a subsistence economy centred on horses6,7. This supports local horse husbandry before the rise of modern domestic bloodlines.Analyses of 475 ancient horse genomes show modern horses emerged around 2200 bce, coinciding with sudden expansion across Eurasia, refuting the narrative of large horse herds accompanying earlier migrations of steppe peoples across Europe. [ABSTRACT FROM AUTHOR]

This paper analyzes, designs and implements a reconfigurable phase-shifted full-bridge (PSFB) converter. It adopts the topology of the traditional PSFB converter and incorporates clamping circuits to solve some fundamental problems of conventional topology. In addition, auxiliary switches are employed for output reconfiguration, which allows expanding the output voltage range without compromising the system efficiency. Single pole double throw (SPDT) mechanical switches are used to realize series and parallel connections. In this paper, the characterization of the PSFB converter with clamping circuit and its design considerations are discussed. A 10 kW prototype with a power density of 0.485 W/cm3, 900 V input voltage and 400/800 V nominal output voltage was manufactured. The experimental results validated the analysis and confirmed the high conversion efficiency for a wide load range; an efficiency of 96.69% was obtained for the full load condition. [ABSTRACT FROM AUTHOR]

Nickel cobalt selenide presents a typical Faraday charge storage mechanism, but its electrochemical performance is limited due to its poor reaction kinetics. As a common defect type, vacancy defects can eliminate the limitations of intrinsic chemical and structural characteristics, thereby improving the reaction kinetics of the electrode. Herein, we report a Va-NiCo2Sex nanomaterial with adjustable Se vacancy concentrations achieved by NaBH4 induction, realizing an enhancement of the performance of the supercapacitor. This adjustment of vacancy concentrations can not only essentially regulate the adsorption site and ion valence state and enhance the adsorption capacity and redox activity of ions, but also reveal the significant improvement of vacancy modification on the surface reaction kinetics of electrode materials through density functional theory (DFT) calculation. The electrochemical results show that the V2-NiCo2Sex electrode material with optimal vacancy concentrations has the highest specific capacity of 310.8 mA h g−1 at 1 A g−1. The assembled hybrid supercapacitor V2-NiCo2Sex//AC-HSC can provide a high energy density of 76.5 W h kg−1 at a power density of 800.1 W kg−1. This work provides an effective strategy for exploring the structure–function relationship between vacancy concentrations and electrochemical performance. [ABSTRACT FROM AUTHOR]