Focal deficits in ischaemic stroke arise primarily from impaired perfusion downstream of a critical vascular occlusion. Though the consequent parenchymal lesion is traditionally used to predict clinical deficits, the underlying pattern of disrupted perfusion provides information upstream of the lesion, potentially yielding earlier predictive and localising signals. We previously developed a technique to compute perfusion maps from routine CT and CT angiography (CTA), an imaging modality widely deployed in clinical practice and available at large data scales. Analysing computed perfusion maps (derived from CT and CTA) from 1393 CTA-imaged patients with confirmed acute ischaemic stroke, here we use deep generative perfusion-deficit inference to localise the neural substrates of NIHSS sub-scores, explicitly disentangling the distinct topologies of disrupted perfusion and neural dependence. We show that our approach replicates known lesion-deficit relations without knowledge of the lesion itself and reveals novel neural dependents. The high achieved anatomical fidelity suggests acute CTA-derived computed perfusion maps may be of substantial clinical and scientific value in rich phenotyping of acute stroke. By relying only on an imaging modality well-established in the hyperacute setting, deep generative perfusion-deficit inference could power highly expressive models of functional anatomical relations in ischaemic stroke within the critical pre-interventional window.

No single component or manufacturing process should be analyzed and optimized in isolation. There will always be something that can be improved, simplified, or made cheaper if design and manufacturing are viewed from a slightly wider system perspective. During the late 1960s and throughout the 1970s, experiments with projectiles and ultra high-speed cutting were conducted by several research teams. Experiments have reported that there was no sudden reduction in tool wears at the very high cutting speeds and that the tool life was extremely short. Such findings meant that interest in high speed machining dropped-off after the mid-1980s, and it is only recently that interest has been revived. Even now the interest is focused on the higher production rates for aluminum and cast iron—not particularly the reduction of tool wear. All the evidence points to the fact that most materials—even soft aluminum alloys—begin to machine with a segmental or serrated chip at some particular threshold value of the cutting speed. It is demonstrated that such chip forms accelerate attrition wear especially in carbide tools. An ultimate objective is thus to increase speed and yet minimize the degree of segmentation and reduce the fatigue type loading on the tool edge.

The forces acting on the tool are an important aspect of machining. For those concerned with the manufacture of machine tools, knowledge of the forces is needed for estimation of power requirements and for the design of machine tool elements. The cutting forces vary with the tool angles, and accurate measurement of forces is helpful in optimizing tool design. Scientific analysis of metal cutting also requires knowledge of the forces. Many force measurement devices, known as dynamometers, have been developed that are capable of measuring tool forces with increasing accuracy. There are three force components: cutting force (Fc), feed force (Ft), and the third component, acting in the direction OZ. Fc is the largest of all the three and acts in the direction of the cutting velocity. Ft acts on the tool in the direction OX, parallel with the direction of feed. The very high normal stress levels account for the conditions of seizure on the rake face. The existing knowledge of stress and stress distribution at the tool-work interface is far too scanty to enable tools to be designed on the basis of the localized stresses encountered. Even for the simplest type of tooling only a few estimates are available, using a two-dimensional model. However, the present level of knowledge is useful in relation to analyses of tool wear and failure, the properties required of tool materials and the influence of tool geometry on performance.

Ischaemic stroke, a leading cause of death and disability, critically relies on neuroimaging for characterising the anatomical pattern of injury. Diffusion-weighted imaging (DWI) provides the highest expressivity in ischemic stroke but poses substantial challenges for automated lesion segmentation: susceptibility artefacts, morphological heterogeneity, age-related comorbidities, time-dependent signal dynamics, instrumental variability, and limited labelled data. Current U-Net-based models therefore underperform, a problem accentuated by inadequate evaluation metrics that focus on mean performance, neglecting anatomical, subpopulation, and acquisition-dependent variability. Here, we present a high-performance DWI lesion segmentation tool addressing these challenges through optimized vision transformer-based architectures, integration of 3563 annotated lesions from multi-site data, and algorithmic enhancements, achieving state-of-the-art results. We further propose a novel evaluative framework assessing model fidelity, equity (across demographics and lesion subtypes), anatomical precision, and robustness to instrumental variability, promoting clinical and research utility. This work advances stroke imaging by reconciling model expressivity with domain-specific challenges and redefining performance benchmarks to prioritize equity and generalizability, critical for personalized medicine and mechanistic research.

Stroke is a leading cause of disability and death. Effective treatment decisions require early and informative vascular imaging. 4D perfusion imaging is ideal but rarely available within the first hour after stroke, whereas plain CT and CTA usually are. Hence, we propose a framework to extract a predicted perfusion map (PPM) derived from CT and CTA images. In all eighteen patients, we found significantly high spatial similarity (with average Spearman's correlation = 0.7893) between our predicted perfusion map (PPM) and the T-max map derived from 4D-CTP. Voxelwise correlations between the PPM and National Institutes of Health Stroke Scale (NIHSS) subscores for L/R hand motor, gaze, and language on a large cohort of 2,110 subjects reliably mapped symptoms to expected infarct locations. Therefore our PPM could serve as an alternative for 4D perfusion imaging, if the latter is unavailable, to investigate blood perfusion in the first hours after hospital admission.

The sensors and microprocessor radios of the Internet of Things require power and one possibility is a hybrid source consisting of energy harvesting coupled with a rechargeable battery. This article concerns a system with a low, but near constant, supply of harvested energy that is sufficient to slowly charge a battery, which buffers that energy and can then supply the higher power level necessary at data transmission. The cycle life of the battery is then significant and this article describes the cycling of a commercial lithium coin cell, Panasonic ML2020, for a small depth of discharge. Initial work was on a Bio-Logic battery tester and cells were successfully tested, without failure, for a few million cycles at a very shallow depth of discharge. Thereafter a battery tester was developed, based on the Particle Photon, that was much less expensive than a channel of a commercial battery tester. Four cells have been cycled, using this tester, for more than 50 million times with no deterioration in performance evident. Capacitors and an NiMH cell are being similarly tested.

Zinc (Zn) is a low-cost material that is widely used in plating and is under consideration as a reversible deposit for a range of energy storage applications. In recent years, researchers have demonstrated that the Zn morphology can be tuned by electrodepositing from an ionic liquid often leading to morphologies that improve cyclability. However, the underlying mechanisms that control deposition and morphology are not well understood. In this work, we evaluate the evolution of zinc morphology as a function of the deposition thickness using in situ atomic force microscopy (AFM), in situ ultra-small angle X-ray scattering (USAXS) and ex situ electron microscopy. Imaging reveals two dominant features: a hexagonal plate-like morphology associated with individual Zn crystals and larger domains in which the individual crystals appear co-aligned. Analysis of the key features observed by USAXS indicates that the growth of the domain size is non-linear with the charge passed and that at least some of this non-linearity can be attributed to increased coalescence of the individual plates as the deposit thickens. A more detailed analysis suggests that there is little change in the aspect ratio of the individual Zn crystals – this is consistent with a growth mechanism in which previously deposited plates grow in diameter as new plates nucleate on their surface and then coalesce into one crystal.

Importance: Cognitive impairment is the greatest single source of unmet need identified by stroke survivors. Knowledge of the factors that influence cognitive prognosis will lead to better preventive and rehabilitation strategies. Objective: To identify the factors that influence general cognitive function, memory and executive function in the first year after ischemic stroke. Design: Single centre longitudinal observational study. Setting: Hospital stroke service. Participants: A cohort of 179 patients identified within 7 days of first symptomatic ischaemic stroke were enrolled into a longitudinal cognitive study, STRATEGIC. General cognitive function, episodic memory and executive function were assessed in the first three months and again at one year after stroke. Lesion topography was defined by imaging (n=152) performed in the acute period. Cognitive evaluation was repeated at one year in 141 participants. Main Outcome Measures: Montreal Cognitive Assessment (MoCA) score at 1 year. Verbal free recall (Free and Cued Selective Reminding Test) and Digit Symbol Substitution Score provided secondary outcome measures of episodic memory and executive function respectively. Results: At 50+-19 days after stroke, diabetes mellitus and smoking were associated with MoCA score independent of other risk and demographic factors. Lesion vascular territory was independently associated with memory while white matter lesion burden was associated with executive function. In contrast to other risk factors, ischaemic heart disease was associated with change in cognitive scores and MoCA score at one year but not MoCA score at 3 months. IHD was the only factor significantly associated with change over time. This association was significant independent of other factors. Conclusions and Relevance: Associations between post-stroke cognition, and age, diabetes, smoking and white matter lesions, are likely to reflect the general effects of these factors on brain structure and function. These risk factors are not associated with change in cognitive function between 3 months and one year. In contrast, pre-existing ischemic heart disease was associated specifically with change in cognition over time. On average, patients with IHD showed decline in MoCA scores between 3 and 12 months while those free of IHD showed improvement. Intervention for IHD, alongside best-care stroke rehabilitation, merits investigation as a strategy to improve cognitive prognosis after stroke.

Enterprises must become ‘sensing, smart and sustainable (S3)’ to face global challenges related to local, national and global market dynamics. Therefore, reconceptualisation and redesign in these enterprises must accommodate emergent technologies, new practices and strategies. In this sense, enterprises have used new product development as a strategy for remaining competitive in the marketplace; thus, they can provide a new generation of products offering solutions to contemporary social problems and responding to changing consumer demands. These new-generation products are mostly technology-based and consider sustainable objectives. In this context, concepts such as sensing, smart and sustainable products (S3 products) have emerged to satisfy different social requirements. Therefore, this work focuses on providing a reference framework that presents a systematic process for the development of S3 products. This reference framework is based on the integrated product, process and manufacturing system development reference model. The main objective of this work is to fill the gap vis-à-vis the current lack of design roadmaps that permit the development of this new generation of products in S3 enterprises. The development of a reconfigurable micro-machine tool is presented as that of an S3 product.

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Abstract A design methodology is proposed for electronic systems powered by energy harvesting. The methodology first considers the operating environment. It then evaluates the supply-side (the attributes of the harvester), the demand-side (the engineering application or load which receives and uses the converted power), and the power conditioning needed between supply and demand. A test case is presented in which the vibrations of an electromagnetic device are harvested, converted, and used to power a wireless sensor node. Such a node is being used for the condition based monitoring of manufacturing equipment.

We demonstrate the feasibility of using novel, small energy harvesters to power atmospheric sensors and radios simply attached to a single conductor of existing overhead power distribution lines. We demonstrate the ability to harvest the required power for operating multiple atmospheric and power-system sensors, together with short-range radios that could broadcast atmospheric sensor data to the cellphones of people nearby. Occasional long-range broadcasts of the data could also be made of both atmospheric and power-line conditions.

Next-generation manufacturing enterprises need to be sensing, smart and sustainable to be competitive: sensing' refers to context awareness at internal and external levels; smart' refers to knowledge-based organisations that adapt to changes and sustainable' refers to the ability to operate without damaging the environment, community or economy. Although technologies do exist to support the development of such enterprises, there is a need for methodologies that help in the entire enterprise engineering problem. This paper proposes a methodology, based on the principles of enterprise architecture, to design a sensing, smart and sustainable manufacturing enterprise. The methodology aims at adopting the best practices used in enterprise engineering while dealing with the relevant gaps. An analysis of the characteristics of the Sensing, Smart and Sustainable Manufacturing Enterprise' (S-3-ME) is presented to understand the advantages of using the proposed methodology. The methodology comprises the instantiation of five viewpoints presented in the Reference Model of Open Distributed Processing (ISO/IEC 10746 RM-ODP) and redefined by the Smart and Sensing Enterprise Reference Model ((SE)-E-2-RM). The viewpoint instantiation is described and then exemplified with a case study. As a result of the instantiation, the enterprise is defined through an enterprise model.

As industrial and commercial demand for thin, small-footprint energy storage devices increases (e.g., for wearable electronics, wireless sensor networks, etc.), it is essential to develop theoretical descriptions for those devices in order to make effective design choices. Of particular interest are porous interdigitated electrodes, which can function as supercapacitors but which are nontrivial to analyze due to their unique geometry. In this paper, we develop a purely mathematical model to determine the dependence of a cell's capacitance and/or resistance on electrode height, width, and spacing, and electrolyte layer height. We then extend this model to incorporate effects from the porous nature of the electrodes, and show that in many cases of interest, the system can be described using a simple equivalent circuit. (C) 2017 The Electrochemical Society. All rights reserved.

Energy harvesting devices have demonstrated their ability to provide power autonomy to wireless sensor networks. However, the adoption of such powering solutions by the industry is challenging due to their reliance on very specific environmental conditions such as vibration at a specific frequency, direct sunlight, or a local temperature difference. Dynamic thermoelectric harvesting has been shown to expand the applicability of thermoelectric generators by creating a local spatial temperature gradient from a temporal temperature fluctuation. Here, a simple method for prototyping or short-run production of such devices is introduced. It is based on the design and 3-D printing of an insulating container, insertion of a phase change material in encapsulated form, and use of commercial thermoelectric generators. The simplicity of this dry assembly method is demonstrated. Two prototype devices with double-wall insulation structures are fabricated, using a stainless-steel and a plastic phase change material encapsulation and a commercial TEG. Performance tests under a temperature cycle between ±25 °C show energy output of 43.6 and 32.1 J from total device masses of 69 and 50 g, respectively. Tests under multiple temperature cycles demonstrate the reliability and performance repeatability of such devices. The proposed method addresses the complication of requiring a wet stage during the final assembly of dynamic thermoelectric harvesters. It allows design and customization to particular size, energy, and insulation geometry requirements. This is important because it makes dynamic harvesting prototyping widely available and easy to reproduce, test, and integrate into systems with various energy requirements and size restrictions.

Piezoelectric energy harvesting is an attractive alternative to battery powering for wireless sensor networks. However, in order for it to be a viable long term solution the fatigue life needs to be assessed. Many vibration harvesting devices employ bimorph piezoelectric bending beams as transduction elements to convert mechanical to electrical energy. This paper introduces two degradation studies performed under symmetrical and asymmetrical sinusoidal loading. It is shown that besides a loss in output power, the most dramatic effect of degradation is a shift in resonance frequency which is highly detrimental to resonant harvester designs. In addition, micro-cracking was shown to occur predominantly in piezoelectric layers under tensile stress. This opens the opportunity for increased life time through compressive operation or pre-loading of piezoceramic layers.

This work provides a systematized process for the Sensing, Smart and Sustainable Product Development (S3-Product) applied to develop Cyber-Physical Production Systems (CPPSs). This reference framework is based on the Integrated Product, Process, and Manufacturing System Development Reference Model (IPPMD). The IPPMD will permit the integration between the different engineering domains that comprise the S3-Product (mechanical, electrical/electronic and software engineering). Therefore, the framework proposed provides a set of stages, activities, and tools in order to guide designers in the S3-Product realization process. The main objective of this work is to fill the lack of the design roadmaps that permit the realization of this new generation of products. A development of a S3-Microfactory as CPPS is presented as case study.

Memory impairment is common and a cause of unmet need after stroke. One third of patients recover spontaneously over the first year. Others develop later decline. The mechanism of recovery and the contribution of comorbid neurodegenerative pathology are not well understood. Data from older adults and Mild Cognitive Impairment suggest preserved memory depends on reorganisation of function between the fornix and other temporal lobe white matter tracts. The STRATEGIC study investigates memory over the first year post-stroke. The current analysis uses baseline data to investigate the contributions of initial infarct and white matter status to initial memory impairment. Patients (n = 21; 5 lacunar, 12 MCA, 4 PCA; 11 left hemisphere, 10 right) performed a cognitive battery and had diffusion-weighted MRI at 30-95 days after stroke onset (median = 64 days). Healthy controls (n = 33) provided the same measures. We reconstructed the fornix using HARDI-based deterministic tractography. Lesions were drawn manually on FLAIR images and direct injury of the fornix was excluded. We tested free recall using a delayed verbal memory test and recognition memory using a face recognition test with trialwise confidence ratings. Recall was markedly lower in patients than controls (50% vs 71%, p < 0.001) but did not correlate with age, lesion volume or fornix integrity in patients. Recognition memory was unimpaired in patients. For confidently remembered items, performance correlated with fornix mean diffusivity in both controls (r = -0.344, p < 0.05) and patients (r = -0.544, p < 0.05). Controlling for age eliminated the correlation in controls but in patients there was a correlation independent of age and lesion volume (r = -0.531, p < 0.05). These findings demonstrate that preserved recognition memory after stroke depends on the status of the fornix and is independent of infarct volume. Compromise of fornix structure from pre-existing neurodegeneration may be a predictor of poor cognitive outcome after stroke. Verbal recall in patients was independent of fornix integrity. Investigation of other temporal lobe pathways may reveal reorganisation underpinning partially preserved recall in some patients.