Limited Flexibility Research Articles

Bionic vine-stems structure for enhancing electrochromic cyclic stability and mechanical stability of multiband spectral regulation fiber

Wearable electrochromic (EC) devices can adjust visible-infrared spectra demonstrate promising potential for applications in display, decoration, personal thermal management, and adaptive thermal camouflage. However, existing flat layer-layer EC devices are hindered by poor air permeability, limited flexibility, and integration difficulties with fabrics, particularly in the realm of intelligent wearables. The creation of highly efficient fiber-shaped infrared (IR) modulation devices with exceptional structural and performance stability represents a significant challenge. We draw inspiration from the arboreous vine-stems structure observed in Chinese wistaria, which effectively minimizes the risk of damage to their vine and stems during harsh weather conditions. We have successfully designed a wearable high efficient multiband spectral regulation fiber (MSRF) with a bioinspired vine-stems structure using carbon fiber and polyaniline (PANI) as the active layer. The planar working electrode (Au) with PANI deposited on its surface was spirally wound around the counter electrode (carbon fiber) and separated by a gel electrolyte (PC/LiClO4), with polyolefin serving as the encapsulation layer. The distribution of electrical potential and lithium ions in the MSRF were analyzed using finite element analysis, demonstrating exceptional electrochromic stability without the length limit of the fiber. Moreover, the MSRF exhibited exceptional IR emissivity (△ε = 0.528) within the 8–14 μm range, fast response time (2.44 s) at low applied potentials (−0.8 V and 0.8 V), robust long-term cycling stability (at least 5000 cycles), and remarkable mechanical stability (at least 5000 bending cycles). The compatibility of the fiber with fabric and potential for patterning using MSRF with high length was also explored, providing new insights into the development of wearable adaptive thermal camouflage and spectral regulation fiber devices.

Flexible and transparent humidity sensors based on hyperbranched poly(ionic liquid)s for wearable sensing

The rapid development of wearable electronics is driving the exploration of flexible and transparent humidity sensors. However, the limited humidity response and mechanical flexibility of sensitive materials hinder the normal operation of the device. In this research, flexible humidity sensors with a fast response time (1 s) were prepared using transparent imidazolium-based hyperbranched poly(ionic liquid)s (HPIL). The sensor performance was improved by regulating the counter anions with different hydrophobicity, such as trifluoromethanesulfonimide ion (TFSI-). The HPIL-TFSI sensor featured an improved response under low humidity condition (6%∼33% RH) with good linearity, excellent bending humidity response, mechanical stability, and long-term stability over a wide humidity range from 6% to 98% RH. Moreover, accurate sensing was achieved for respiratory monitoring, non-contact sensing, and detecting moisture on patched skin. The HPIL-TFSI sensor paves the way for the production of high-performance wearable sensor platforms.

Region-Aware Portrait Retouching With Sparse Interactive Guidance

Portrait retouching aims to improve the aesthetic quality of input portrait photos and especially requires human-region priority. The deep learning-based methods largely elevate the retouching efficiency and provide promising retouched results. However, existing portrait retouching methods focus on automatic retouching, which treats all human-regions equally and ignores users' preferences for specific individuals, thus suffering from limited flexibility in interactive scenarios. In this work, we emphasize the importance of users' intents and explore the interactive portrait retouching task. Specifically, we propose a region-aware retouching framework with two branches: an automatic branch and an interactive branch. The automatic branch involves an encoding-decoding process, which searches region candidates and performs automatic region-aware retouching without user guidance. The interactive branch encodes sparse user guidance into a priority condition vector and modulates latent features with a region selection module to further emphasize the user-specified regions. Experimental results show that our interactive branch effectively captures users' intents and generalizes well to unseen scenes with sparse user guidance, while our automatic branch also outperforms the state-of-the-art retouching methods due to improved region-awareness.

Harnessing the characteristic of compound parabolic concentrators for directional concentration of emitted thermal radiation and solar shielding in building-integrated radiative cooling

Radiative sky cooling (RC) is a promising solution for meeting the growing cooling demands by passively dissipating waste heat into frigid outer space. However, current RC systems suffer from low cooling power density and limited installation flexibility, impeding their effective application as building cooling strategies. To overcome these challenges, a novel concentrated RC system coupled with a compound parabolic concentrator (CPC) is proposed and experimentally studied. The objective is to investigate the effectiveness of the CPC in enhancing the cooling capability of the RC system and the feasibility of achieving all-day RC in unfavorable working conditions when integrated with building roofs. During nighttime experiments in the humid Nottingham region, the CPC-RC system exhibited an average emitter temperature that was 5.83 °C lower than the ambient temperature, representing a 30 % and 13.6 % improvement in RC performance compared to the flat-RC system and trapezoidal-concentrated RC system, respectively. The Photopia optical software simulation indicates that when the modules are tilted to the north, the CPC functions as a solar shield, effectively limiting solar radiation reaching the emitter surface, which is advantageous for conducting daytime RC experiments. In the daytime experiment, the emitter temperature of the CPC-RC module in anti-sunward group was still 1.59 °C lower than the ambient temperature and 5.49 °C lower than that of flat-RC module. At night, the CPC-RC module of the three placement groups all showed the highest RC effect in the same group. The average emitter temperature of the CPC-RC module in the horizontal placement group is 0.6 °C lower than that of the flat RC module. This novel CPC-RC scheme presents a new energy-saving strategy for buildings and showcases its potential for achieving 24-hour RC when integrated into anti-sunward roofs.

Analysis of implementation processes in a hybrid effectiveness-implementation trial of interpersonal psychotherapy (IPT) for major depressive disorder in prisons: Training, supervision, and recommendations.

There are 10 million admissions to U.S. prisons and jails each year. More than half of those admitted have mental health problems. The goal of this article is to inform: (1) implementation of evidence-based mental health treatments in prisons and jails, an important effort that needs more evidence to guide it; (2) psychotherapy and interpersonal psychotherapy (IPT) training efforts, especially in low-resource settings. A randomized hybrid effectiveness-implementation trial of group IPT for major depressive disorder (MDD) in state prisons found that IPT increased rates of MDD remission and lowered posttraumatic stress disorder symptoms relative to prison treatment as usual. The trial used prison counselors, only some of whom had prior psychotherapy training/experience, to deliver IPT. IPT treatment adherence was high (96%), but trial training and supervision were too costly to be scalable outside the trial. The current article reports results from a planned qualitative analysis of 460 structured implementation and supervision documents in that trial to describe training and supervision processes and lessons learned, inform training recommendations, and facilitate future work to optimize training and supervision for under-resourced settings. Themes identified in implementation and supervision process notes reflected: work on psychotherapy basics (reflective listening, focusing on emotions, open-ended questions, specific experiences), IPT case conceptualization (forming a conceptualization, what is and is not therapeutic work, structure and limit setting, structure vs. flexibility), IPT techniques (enhancing social support, role plays, communication analysis), psychotherapy processes (alliance repair, managing group processes), and managing difficult situations (avoidance, specific clients, challenging work settings). Counselors were receptive to feedback; some relied on study supervisors for support in managing stressful prison working conditions. Findings can be used to make future training and supervision more efficient. Based on our results, we recommend that initial and refresher training focus on IPT case conceptualization, steps for addressing each IPT problem area, and reflective listening. We also recommend supervision through at least counselors' first two rounds of groups. More low-cost, scalable training methods are needed to get mental health treatment to individuals who need it most, who are often served in challenging, low-resource settings such as prisons. This is a mental health access and equity issue. The trial was registered at clinicaltrials.gov (NCT01685294).

A Wearable Fluorescence Imaging Device for Intraoperative Identification of Human Brain Tumors.

Malignant glioma (MG) is the most common type of primary malignant brain tumors. Surgical resection of MG remains the cornerstone of therapy and the extent of resection correlates with patient survival. A limiting factor for resection, however, is the difficulty in differentiating the tumor from normal tissue during surgery. Fluorescence imaging is an emerging technique for real-time intraoperative visualization of MGs and their boundaries. However, most clinical grade neurosurgical operative microscopes with fluorescence imaging ability are hampered by low adoption rates due to high cost, limited portability, limited operation flexibility, and lack of skilled professionals with technical knowledge. To overcome the limitations, we innovatively integrated miniaturized light sources, flippable filters, and a recording camera to the surgical eye loupes to generate a wearable fluorescence eye loupe (FLoupe) device for intraoperative imaging of fluorescent MGs. Two FLoupe prototypes were constructed for imaging of Fluorescein and 5-aminolevulinic acid (5-ALA), respectively. The wearable FLoupe devices were tested on tumor-simulating phantoms and patients with MGs. Comparable results were observed against the standard neurosurgical operative microscope (PENTERO® 900) with fluorescence kits. The affordable and wearable FLoupe devices enable visualization of both color and fluorescence images with the same quality as the large and expensive stationary operative microscopes. The wearable FLoupe device allows for a greater range of movement, less obstruction, and faster/easier operation. Thus, it reduces surgery time and is more easily adapted to the surgical environment than unwieldy neurosurgical operative microscopes. Clinical and Translational Impact Statement-The affordable and wearable fluorescence imaging device developed in this study enables neurosurgeons to observe brain tumors with the same clarity and greater flexibility compared to bulky and costly operative microscopes.

Does a Single Session of Cupping Therapy Increase Range of Motion in Individuals With Limited Hamstring Flexibility? A Critically Appraised Topic

Context: Improving hamstring tightness is a common therapeutic goal to maintain performance and reduce injury risk. How effective is a single session of therapeutic cupping on improving hamstring flexibility? Methods: PubMed was searched in the Summer of 2023 using the search terms: cupping and range of motion (ROM). Included studies were intervention studies that evaluated cupping and flexibility within the lower extremity during a single treatment session. Hip flexion ROM was measured with the passive or active straight leg test. Means, SDs, 95% CI, and effect sizes were calculated. Results: Three articles were screened. Warren et al. reported a statistically significant increase in ROM from pre- to posttest of 4.42° using a digital inclinometer during the passive straight leg test. However, this is below a reported minimal detectable difference for the passive straight leg test (10°). Schafer et al. and Williams et al. reported no significant cupping effect over time between groups. Conclusions: There is consistent evidence that a single session of cupping therapy does not increase lower-extremity ROM in patients with limited hamstring flexibility.

Hybrid bioinspired approach for secret sharing algorithm and ownership transfer optimization in cloud based models

It is widely accepted that secret sharing models like Shamir cryptosystem allows cloud designers to deploy highly secure multichannel data transfer mechanisms. But these systems are majorly static, and do not incorporate context-aware share-selection techniques. Moreover, SaaS based cloud deployments require ownership transfers for efficient real-time operations. These ownership transfer mechanisms are backed by selection encryption models assisting in permission-aware data sharing. Scholars have put forward pre-emptive and bioinspired models to accomplish these tasks, but nearly all of them are still sophisticated or demonstrate limited flexibility in real-time scenarios. The proposed structure first gathers cloud logs for various temporal requests and groups them based on IP & resource constraints with the goal of getting around these problems. The modified rules are initially tested on dummy ownership requests, and are later on real-time ownership-transfer scenarios for validation of their efficiency levels based on accuracy of misconfiguration detection, delay needed for processing the requests, consistency of blocking invalid ownership requests, and request throughput levels. Based on this evaluation, The accuracy might be increased by 3.5% using the suggested model, reducing delay by 2.9%, while having 6.4% higher consistency, and 8.3% higher throughput than existing secret sharing & ownership transfer methods under similar deployment scenarios.

Coatable strain sensors for nonplanar surfaces.

Rapidly fabricating flexible and stretchable sensors on nonplanar surfaces is crucial for wearable device applications. We employed a novel fabrication method, incorporating molds and gels into electroless plating, to enable direct printing of sensors on a wide array of surfaces, from those with up to 100 μm profile heights to hydrogels with a Young's modulus of 100 kPa. This coatable strain (CS) sensor offers several potential advantages. Firstly, it is designed to circumvent the typical limitations of limited flexibility, plastic deformation, and low repeatability found in viscoelastic polymers by being directly coated onto the surface without requiring a substrate. Secondly, it potentially increases the effective contact area and signal-to-noise ratio by eliminating voids between the sensor and the surface. Finally, the CS sensor can obtain any desired patterning at room temperature in a matter of minutes, significantly reducing energy and time consumption. In this study, we demonstrated the versatility of the CS sensor by applying it to a range of substrates, showcasing its adaptability to diverse materials, surface roughness levels, and Young's modulus values. Our primary focus was on plant growth monitoring, a challenging application that showcased the sensor's efficacy on surfaces like needles, hairy leaves, and fruits. These applications, traditionally difficult for conventional polymer-based sensors, serve to illustrate the CS sensor's potential in a range of complex environmental contexts. The successful deployment of the CS sensor in these settings suggests its broader applicability in various scientific and technological fields, potentially contributing to significant developments in the area of wearable devices and beyond.

RTMN 2.0—An Extension of Robot Task Modeling and Notation (RTMN) Focused on Human–Robot Collaboration

This paper describes RTMN 2.0, an extension of the modeling language RTMN. RTMN combines process modeling and robot execution. Intuitive robot programming allows those without programming expertise to plan and control robots through easily understandable predefined modeling notations. These notations achieve no-code programming and serve as templates for users to create their processes via drag-and-drop functions with graphical representations. The design of the graphical user interface is based on a user survey and gaps identified in the literature We validate our survey through the most influential technology acceptance models, with two major factors: the perceived ease of use and perceived usefulness. While RTMN focuses on the ease of use and flexibility of robot programming by providing an intuitive modeling language, RTMN 2.0 concentrates on human–robot collaboration (HRC), which represents the current trend of the industry shift from “mass-production” to “mass-customization”. The biggest contribution that RTMN 2.0 makes is the creation of synergy between HRC modes (based on ISO standards) and HRC task types in the literature. They are modeled as five different HRC task notations: Coexistence Fence, Sequential Cooperation SMS, Teaching HG, Parallel Cooperation SSM, and Collaboration PFL. Both collaboration and safety criteria are defined for each notation. While traditional isolated robot systems in “mass-production” environments provide high payload capabilities and repeatability, they suffer from limited flexibility and dexterity in order to be adapted to the variability of customized products. Therefore, human–robot collaboration is a suitable arrangement to leverage the unique capabilities of both humans and robots for increased efficiency and quality in the new “mass-customization” industrial environments. HRC has made a great impact on the robotic industry: it leads to increased efficiency, reduced costs, and improved productivity, which can be adopted to make up for the skill gap of a shortage of workers in the manufacturing industry. The extension of RTMN 2.0 includes the following notations: HRC tasks, requirements, Key Performance Indicators (KPIs), condition checks and decision making, join/split, and data association. With these additional elements, RTMN 2.0 meets the full range of criteria for agile manufacturing—light-out manufacturing is a manufacturing philosophy that does not rely on human labor.

Experimental and techno-economic analysis of solar-assisted heat pump drying of biomass

Drying enhances the attributes of biomass, such as storability and heating value. In regions with low solar radiation, solar drying has demonstrated limited economic viability. Hybrid systems, combining solar drying with a stable heat source such as a heat pump, present an opportunity to improve drying performance and achieve cost savings, especially with time-variable electricity pricing. To quantify this potential, we constructed an experimental drying system in Finland, incorporating an air-source heat pump and solar thermal collectors for drying woody biomass. Experimental work, consisting of 316 h of operation in varied conditions, evaluated the performance of the hybrid drying system. Subsequently, we developed data-driven models to estimate drying rates and power consumption in both hybrid and solar operation modes. Finally, these models were integrated into a techno-economic optimization model to assess the economic feasibility of the concept through hourly simulations. Hybrid drying experiments resulted in a significantly improved average drying rate (33.0 kg/h ± 5.4 kg/h) compared to solar drying (9.0 kg/h ± 3.2 kg/h) with a 45% increase in specific electricity consumption. The experimental work also revealed limited operating flexibility in colder temperatures due to an exponentially increasing preheating time (up to 4.2 h at 0 °C). With techno-economic modelling, the simulations yielded a simple payback time of 7.5 years for a commercial-scale drying system without investment subsidies. Applying an optimized operating strategy, system operation shifted on average from hybrid mode (96–33%) to solar mode (3–36%) and inactive state (1–31%) with an increased electricity price level, highlighting the need for advanced operation planning. Commercial deployment should prioritize use cases with high value increase through drying or obtaining compensation from avoided biogenic CO2 emissions through drying, as these two parameters were shown to have the greatest impact on the investment payback time.

“Sometimes it’s Like Putting the Track in Front of the Rushing Train”: Having to Be ‘On Call’ for Work Limits the Temporal Flexibility of Crowdworkers

Research suggests that the temporal flexibility advertised to crowdworkers by crowdsourcing platforms is limited by both client-imposed constraints (e.g., strict completion times) and crowdworkers’ tooling practices (e.g., multitasking). In this paper, we explore an additional contributor to workers’ limited temporal flexibility: the design of crowdsourcing platforms, namely requiring crowdworkers to be ‘on call’ for work. We conducted two studies to investigate the impact of having to be ‘on call’ on workers’ schedule control and job control. We find that being ‘on call’ impacted: (1) participants’ ability to schedule their time and stick to planned work hours, and (2) the pace at which participants worked and took breaks. The results of the two studies suggest that the ‘on-demand’ nature of crowdsourcing platforms can limit workers’ temporal flexibility by reducing schedule control and job control. We conclude the paper by discussing the implications of the results for: (a) crowdworkers, (b) crowdsourcing platforms, and (c) the wider platform economy.

Solid Oxide Membrane-assisted Controllable Electrolytic Fabrication of Ti5Si3/TiC Composites in Molten Salt

Background: The titanium silicide Ti5Si3 possesses many desirable properties, such as a high melting point, excellent high-temperature oxidation resistance, low density, and relatively high hardness, and it is considered a promising structural intermetallic compound. However, like most ceramic materials, originating from low symmetry (D88) in its crystal structure, Ti5Si3 has poor fracture toughness and limited flexibility at room temperature, and at high temperatures, its creep resistance also drops sharply, which hinders its application. To overcome these shortcomings, it is suggested that TiC is a practical addition to Ti5Si3 to overcome the brittleness. Compared with monolithic Ti5Si3, Ti5Si3/TiC composites have a higher fracture toughness. Ti5Si3/TiC composites can be prepared by many ways, which commonly require high energy cost, complex processes and provide low efficiency. Therefore, the search for environmentally friendly strategies for the production of Ti5Si3/TiC is still ongoing. Objective: This article proves that we can successfully prepare Ti5Si3/TiC composites from CaTiO3/SiO2/C precursor by using SOM technology and explores the reaction mechanism of electrochemical process. Methods: In the process of electroreduction of CaTiO3/SiO2/C particles into Ti5Si3/TiC composites, we mainly used SOM technology at 1273 K and 4.0 V in molten CaCl2 and under an argon atmosphere. Results: The results show that the Ti5Si3/TiC composites can also be successfully electrosynthesized from CaTiO3/SiO2/C precursors by using SOM-based anode systems at 1273 K and 4.0 V in molten CaCl2. Conclusion: This work demonstrates that Ti5Si3/TiC composites have been successfully electrosynthesized from CaTiO3/SiO2/C precursors using SOM-based anode systems at 1273 K and 4.0 V in molten CaCl2. The Ti5Si3/TiC has a smooth surface and micro/nano-porous structure. The formation routes for Ti5Si3 and TiC are independent. In summary, the SOM-assisted controllable electroreduction process has the potential to provide a novel one-step route from CaTiO3/ SiO2/C precursors to Ti5Si3/TiC composites in molten salts.

Structural Reform of Police Organization at Village Level Government in Bandung Regency, West Java Province

The research aimed to enhance the Police's performance in delivering exemplary community service and harmonize the National Police's structure with the Village/District Level Regional Government. The study employed qualitative description with SWOT and ASOCA Analysis. In Bandung Regency, there's a notable security vulnerability. The data indicates: 1) Potential disturbances exceed 70%, 2) Disorder threshold surpasses 60%, and 3) Actual disruptions are above 30%. Addressing this and to achieve structural synchronization between the National Police and the Regional Government, a strategy is devised titled "Structural Reform of the Police Organization at the Village Government Level." This aims to optimize police performance, resonating with the National Police's 2005-2025 Grand Strategy. SWOT analysis reveals 1) Strength in Leadership, 2) Weakness in Organizational Structure, 3) Opportunities within the Legal Framework, and 4) Threats from misalignment with the Village's Regional Government. ASOCA Analysis emphasizes several factors. Internally, it points out the Police Leadership's ability and members' loyalty. It stresses the importance of harnessing police resources and an Organizational Performance Improvement Program. There's a highlight on the intellectual prowess of police members and their potential to act creatively. Externally, the community's aspiration for better police service is noted. The Bandung Regency's distinct culture and traditions play a role. Challenges persist, like the misalignment of the organizational structure with geographical realities and limited flexibility in local Police management. Community participation and educational levels remain concerns. Lastly, the Village Police follows a "Line and Staff Organization" model. This aligns with the higher-level organizational structure, emphasizing task decentralization and role clarity, ensuring a robust hierarchy and efficient task delegation.

Stochastic MILP Model for Merging EV Charging Stations with Active Distribution System Expansion Planning by considering Uncertainties

Radial Power Distribution Networks (PDNs) often suffer from limited reliability, flexibility, and efficiency, leading to service interruptions. Planning for radial PDNs is essential to enhance redundancy resilience, reduce disruptions, and improve overall efficiency. However, traditional PDN planning methods have become obsolete due to the proliferation of Distributed Generation (DG) resources and energy storage systems. Additionally, the rise of Electric Vehicles (EVs) demands sophisticated charging infrastructure planning. This article presents a Mixed-Integer Linear Programming (MILP) model for joint expansion planning of PDN and Electric Vehicle Charging Stations (EVCSs). The model takes into account the construction or reinforcement of substations and circuits, along with the integration of EVs, the installation of DGs, and the placement of capacitor banks, all regarded as traditional conventional expansion options alternatives. To address uncertainties associated with DG generation, conventional loads, and EV demand, our model identifies optimal installation and asset locations. We formulate this as a stochastic scenario-based program with chance constraints for Power Distribution Network Expansion Planning (PDNEP), minimizing investment, operational, and energy loss cost costs over a planning horizon. Through two deterministic and stochastic approaches, encompassing six case studies on an 18-node test system, we evaluate the effectiveness of our model. Results are further validated on a 54-node system, confirming the model’s robustness. Notably, the numerical findings underscore the substantial cost reduction achieved by including EVCSs in the stochastic expansion planning approach, demonstrating its cost-effectiveness. In case study I, where all EVs charge at home during peak hours, it’s the worst case for the PDN. The 54-node system, more complex, demands longer computational time. In the 18-node system, costs improve from 9.97% (case study II) to 3.96% (case study VI) versus the worst-case (case I). In the 54-node system, improvements range from 10.47% (case study II) to 1.40% (case study VI). As a result, In comparative analyses against deterministic and stochastic approaches, our model consistently outperforms in diverse test case studies. The proposed model’s adaptability to address uncertainties underscores its suitability for solving the PDNEP problem in PND.

ACE configurator for ELISpot: optimizing combinatorial design of pooled ELISpot assays with an epitope similarity model.

The enzyme-linked immunosorbent spot (ELISpot) assay is a powerful in vitro immunoassay that enables cost-effective quantification of antigen-specific T-cell reactivity. It is used widely in the context of cancer and infectious diseases to validate the immunogenicity of predicted epitopes. While technological advances have kept pace with the demand for increased throughput, efforts to increase scale are bottlenecked by current assay design and deconvolution methods, which have remained largely unchanged. Current methods for designing pooled ELISpot experiments offer limited flexibility of assay parameters, lack support for high-throughput scenarios and do not consider peptide identity during pool assignment. We introduce the ACE Configurator for ELISpot (ACE) to address these gaps. ACE generates optimized peptide-pool assignments from highly customizable user inputs and handles the deconvolution of positive peptides using assay readouts. In this study, we present a novel sequence-aware pooling strategy, powered by a fine-tuned ESM-2 model that groups immunologically similar peptides, reducing the number of false positives and subsequent confirmatory assays compared to existing combinatorial approaches. To validate ACE's performance on real-world datasets, we conducted a comprehensive benchmark study, contextualizing design choices with their impact on prediction quality. Our results demonstrate ACE's capacity to further increase precision of identified immunogenic peptides, directly optimizing experimental efficiency. ACE is freely available as an executable with a graphical user interface and command-line interfaces at https://github.com/pirl-unc/ace.

Optofluidic-based cell multi-axis controllable rotation and 3D surface imaging

The controlled rotation of individual cells plays a crucial role in enabling three-dimensional multi-angle observation of cellular structure, function, and dynamic processes. Reported cell rotation techniques often struggle to strike a balance between high precision and simple control, and they exhibit limited control flexibility, typically achieving only uniaxial cell rotation. In this study, we propose a cell rotation technique in three dimensions based on optofluidics, which utilizes optical tweezers to immobilize the cell and exploits the asymmetry of the surrounding flow to drive cell rotation. By adjusting the focal position of the optical tweezers, cells can be positioned within various flow profiles, enabling control of the rotation speed, rotation direction, and rotation axis of cells. This approach simplifies the manipulation procedure, achieving desirable control precision and greater rotation flexibility. Using our approach, multi-angle surface imaging projections of target cells can be rapidly obtained, followed by capturing the cell contour data from the images. By combining the cell contour data with corresponding angular position information, we have reconstructed the 3D surface of the target cell. We have employed this technique in experiments for the analysis of red blood cell morphology. Based on the constructed 3D surface images of diverse-shaped red blood cells, we quantified structural parameters including cell surface area, volume, sphericity, and surface roughness, which demonstrates the potential application of this cell rotation technique for cellular morphological analysis.

Macro welfare effects of flexible labor contracts

We develop a Dynamic-Stochastic-General-Equilibrium model to study the macro welfare effects of flexible labor contracts. Capturing the empirical properties observed in the data, we incorporate two labor sectors in our DSGE framework: a fixed sector and a flexible sector. The fixed sector offers contracts that exhibit rigidities in working-hours and wages while the flexible sector offers flexible contracts in both dimensions. We calibrate the model utilizing Eurozone data and show that in the benchmark currency union economy, welfare would monotonically increase if fixed sector’s wage rigidity goes up, while welfare would monotonically decline in wage rigidity if the benchmark economy were to switch to inflation targeting. We also show that it is the working-hour flexibility of flexible labor contracts that reinforces the desirability of wage stickiness in the fixed sector. Finally, we obtain strikingly different optimal flexible sector sizes for economies in currency union and inflation targeting monetary policy regimes: while the optimal flexible sector size for a currency union economy turns out to be 0%, the optimal flexible sector size for an economy with inflation targeting is 100%. These important results prevail because large fluctuations in consumption associated with flexible labor arrangements are too costly for households in a currency union with limited monetary policy flexibility — outweighing the production flexibility gains from flexible labor contracts. In this respect, our paper formalizes the welfare concerns related to the growing flexible labor sector sizes in Eurozone and urge policy makers to pay special attention to regulations that induce more flexible labor arrangements in this region.

Conversational artificial intelligence intervention to support patients with atrial fibrillation: process evaluation of a randomised controlled trial

Abstract Background/Introduction Atrial fibrillation (AF) is a multifaceted disease with patients needing ongoing support and care. European Society of Cardiology AF guidelines recommend the use of digital tools to support AF self-management and the recent popularity of conversational artificial intelligence (AI) suggests its potential in engaging consumers. However, use of these interventions in AF and associated patient perspectives remain underexplored. Purpose Evaluate the acceptability, engagement, barriers, and enablers to the use of a conversational AI intervention designed to support patients with AF in the community. Methods Mixed methods study involving the process evaluation of a 6-month 2-arm randomised controlled trial (RCT) and post-intervention semi-structured interviews. The trial evaluated an intervention comprising seven outreaches with automated telephone calls (conversational AI technology and voice recognition), text messages, emails; and an education website. The 1st outreach was at 24 to 48 hours after discharge from hospital services. Outreaches provided education and behaviour support, assessed AF symptoms and other aspects of self-management, with participants’ responses triggering different system responses, including the identification of red flags requiring escalation. User satisfaction, completeness rate, and other engagement metrics were tracked. Purposive sampling techniques were used to invite patients from the intervention arm to participate in interviews. Interviews were recorded, transcribed verbatim, and analysed in NVivo using thematic analysis. Results 82 participants were allocated to receive the intervention (81 completed, 1 withdrew), with 69% males and a mean age of 64 (SD 11) years. 30 participants completed semi-structured interviews [21 male; mean age 66 (SD 12) years]. On average, across the 7 outreaches, 88% of patients replied "yes" to the question "Did you find the information in this call helpful"? The average outreach completion rate was 61%, starting at 76% for the first call. Participant interviews revealed that the digital health intervention was perceived to address gaps in AF post-discharge care, providing important information that both increased patients’ knowledge of AF and assisted them with modifying lifestyle and self-management. However, participants noted that the limited dialogue flexibility in phone calls, limited personalisation of content and contact (mode and frequency) were barriers to engagement. Participants also reported the need for ongoing adaptation of the intervention to account for the episodic nature of their AF symptoms. Conclusion To date, this is the first mixed methods study exploring user perspectives on a conversational AI intervention to support patients with AF. The capabilities of this technology to address the current gaps in AF models of care remains promising, however, requires further optimisation, such as additional dialogue flexibility and personalisation.

Self-Test Library Generation for In-Field Test of Path Delay Faults

New semiconductor technologies for advanced applications are more prone to defects and imperfections related, among several different causes, to the manufacturing process, aging and cross-talks. These phenomena negatively affect the circuit’s timing and can be effectively modeled by means of the path delay fault (PDF) model. While path delay testing is currently supported by commercial Automatic Test Pattern Generation tools for scan designs, functional testing covering PDFs is not widely adopted, mainly because of the high cost for test generation. On the other side, functional test is already widely adopted for in-field test of stuck-at faults, which is often performed resorting to the execution of suitable test programs (Self Test Libraries, or STLs). This approach is attractive, since it can be performed at-speed with limited time constraints and high flexibility, making it a suitable in-field test solutions. Previous work assessed the feasibility and validity of functional approaches based on test programs targeting PDFs. In this work, we present the first systematic method for the development of very high fault coverage test programs for PDFs, which largely outperform test programs written for other fault models. Moreover, the proposed method allows the identification of functionally untestable faults. The effectiveness of the proposed approach was proven on an open-source RISC-V processor core, where 100% coverage of the functionally testable longest paths was achieved, compared with an initial coverage of 0.52% achieved with test programs targeting stuck-at faults. Results demonstrate that shorter paths are also effectively covered.