Flexible Model Research Articles

Programmable MCMC with Soundly Composed Guide Programs

Probabilistic programming languages (PPLs) provide language support for expressing flexible probabilistic models and solving Bayesian inference problems. PPLs with programmable inference make it possible for users to obtain improved results by customizing inference engines using guide programs that are tailored to a corresponding model program. However, errors in guide programs can compromise the statistical soundness of the inference. This article introduces a novel coroutine-based framework for verifying the correctness of user-written guide programs for a broad class of Markov chain Monte Carlo (MCMC) inference algorithms. Our approach rests on a novel type system for describing communication protocols between a model program and a sequence of guides that each update only a subset of random variables. We prove that, by translating guide types to context-free processes with finite norms, it is possible to check structural type equality between models and guides in polynomial time. This connection gives rise to an efficient type-inference algorithm for probabilistic programs with flexible constructs such as general recursion and branching. We also contribute a coverage-checking algorithm that verifies the support of sequentially composed guide programs agrees with that of the model program, which is a key soundness condition for MCMC inference with multiple guides. Evaluations on diverse benchmarks show that our type-inference and coverage-checking algorithms efficiently infer types and detect sound and unsound guides for programs that existing static analyses cannot handle.

Exploring the potential of forest snow modeling at the tree and snowpack layer scale

Abstract. Boreal and sub-alpine forests host seasonal snow for multiple months per year; however, snow regimes in these environments are rapidly changing due to rising temperatures and forest disturbances. Accurate prediction of forest snow dynamics, relevant for ecohydrology, biogeochemistry, cryosphere, and climate sciences, requires process-based models. While snow schemes that track the microstructure of individual snow layers have been proposed for avalanche research, so far, tree-scale processes resolving canopy representations only exist in a few snow-hydrological models. A framework that enables layer- and microstructure-resolving forest snow simulations at the meter scale is lacking to date. To fill this research gap, this study introduces the forest snow modeling framework FSMCRO, which combines two detailed, state-of-the art model components: the canopy representation from the Flexible Snow Model (FSM2) and the snowpack representation of the Crocus ensemble model system (ESCROC). We apply FSMCRO to discontinuous forests at boreal and sub-alpine sites to showcase how tree-scale forest snow processes affect layer-scale snowpack properties. Simulations at contrasting locations reveal marked differences in stratigraphy throughout the winter. These arise due to different prevailing processes at under-canopy versus gap locations and due to variability in snow metamorphism dictated by a spatially variable snowpack energy balance. Ensemble simulations allow us to assess the robustness and uncertainties of simulated stratigraphy. Spatially explicit simulations unravel the dependencies of snowpack properties on canopy structure at a previously unfeasible level of detail. Our findings thus demonstrate how hyper-resolution forest snow simulations can complement observational approaches to improve our understanding of forest snow dynamics, highlighting the potential of such models as research tools in interdisciplinary studies.

Sudden cardiac death after myocardial infarction: individual participant data from pooled cohorts.

Risk stratification of sudden cardiac death after myocardial infarction and prevention by defibrillator rely on left ventricular ejection fraction (LVEF). Improved risk stratification across the whole LVEF range is required for decision-making on defibrillator implantation. The analysis pooled 20 data sets with 140 204 post-myocardial infarction patients containing information on demographics, medical history, clinical characteristics, biomarkers, electrocardiography, echocardiography, and cardiac magnetic resonance imaging. Separate analyses were performed in patients (i) carrying a primary prevention cardioverter-defibrillator with LVEF ≤ 35% [implantable cardioverter-defibrillator (ICD) patients], (ii) without cardioverter-defibrillator with LVEF ≤ 35% (non-ICD patients ≤ 35%), and (iii) without cardioverter-defibrillator with LVEF > 35% (non-ICD patients >35%). Primary outcome was sudden cardiac death or, in defibrillator carriers, appropriate defibrillator therapy. Using a competing risk framework and systematic internal-external cross-validation, a model using LVEF only, a multivariable flexible parametric survival model, and a multivariable random forest survival model were developed and externally validated. Predictive performance was assessed by random effect meta-analysis. There were 1326 primary outcomes in 7543 ICD patients, 1193 in 25 058 non-ICD patients ≤35%, and 1567 in 107 603 non-ICD patients >35% during mean follow-up of 30.0, 46.5, and 57.6 months, respectively. In these three subgroups, LVEF poorly predicted sudden cardiac death (c-statistics between 0.50 and 0.56). Considering additional parameters did not improve calibration and discrimination, and model generalizability was poor. More accurate risk stratification for sudden cardiac death and identification of low-risk individuals with severely reduced LVEF or of high-risk individuals with preserved LVEF was not feasible, neither using LVEF nor using other predictors.

Potential assessment of coordinated regulation of power load of emerging industrial users based on extreme scenarios of electric vehicle aggregators

AbstractWith the advancement of industrial low carbonization and electrification, emerging industrial production technologies have the characteristics of high‐power consumption and significant impact load. In the context of increasing global climate change, frequent extreme weather events have brought serious challenges to the balance of power and electricity in the power system, and have a significant impact on the production scheduling of industrial users, especially industrial users using electrified production technology. First, based on the representative high‐power industrial users of hydrogen reduction steel plants and internet datacentres (IDC), this paper establishes a flexible resource scheduling optimization model for single industrial users, and considers the collaborative relationship between multi‐user flexible resources to establish a collaborative scheduling optimization model for industrial users. Then, considering the charging and discharging characteristics of electric vehicles (EV) in extreme scenarios, the redundant capacity of EVs is aggregated by EV aggregators and sold to industrial users, and a collaborative scheduling optimization model of EV aggregators and industrial users is established. Finally, the effectiveness of the proposed model and algorithm is verified by simulation analysis. Compared with the traditional industrial user production optimization, the proposed model can tap the potential of multi‐agent scheduling operation in extreme scenarios.

CO-TSM: A Flexible Model for Secure Embedded Device Ownership and Management

The Consumer-Oriented Trusted Service Manager (CO-TSM) model has been recognised as a significant advancement in managing applications on Near Field Communication (NFC)-enabled mobile devices and multi-application smart cards. Traditional Trusted Service Manager (TSM) models, while useful, often result in market fragmentation and limit widespread adoption due to their centralised control mechanisms. The CO-TSM model addresses these issues by decentralising management and offering greater flexibility and scalability, making it more adaptable to the evolving needs of embedded systems, particularly in the context of the Internet of Things (IoT) and Radio Frequency Identification (RFID) technologies. This paper provides a comprehensive analysis of the CO-TSM model, highlighting its application in various technological domains such as smart cards, HCE-based NFC mobile phones, TEE-enabled smart home IoT devices, and RFID-based smart supply chains. By evaluating the CO-TSM model’s architecture, implementation challenges, and practical deployment scenarios, this paper demonstrates how CO-TSM can overcome the limitations of traditional TSM approaches. The case studies presented offer practical insights into the model’s adaptability and effectiveness in real-world scenarios. Through this examination, the paper aims to underscore the CO-TSM model’s role in enhancing scalability, flexibility, and user autonomy in secure embedded device management, while also identifying areas for future research and development.

Leakage Model-flexible Deep Learning-based Side-channel Analysis

Profiling side-channel analysis has gained widespread acceptance in both academic and industrial realms due to its robust capacity to unveil protected secrets, even in the presence of countermeasures. To harness this capability, an adversary must access a clone of the target device to acquire profiling measurements, labeling them with leakage models. The challenge of finding an effective leakage model, especially for a protected dataset with a low signal-to-noise ratio or weak correlation between actual leakages and labels, often necessitates an intuitive engineering approach, as otherwise, the attack will not perform well. In this paper, we introduce a deep learning approach with a flexible leakage model, referred to as the multi-bit model. Instead of trying to learn a pre-determined representation of the target intermediate data, we utilize the concept of the stochastic model to decompose the label into bits. Then, the deep learning model is used to classify each bit independently. This versatile multi-bit model can adjust to existing leakage models like the Hamming weight and Most Significant Bit while also possessing the flexibility to adapt to complex leakage scenarios. To further improve the attack efficiency, we extend the multi-bit model to profile all 16 subkey bytes simultaneously, which requires negligible computational effort. The experimental results show that the proposed methods can efficiently break all key bytes across four considered datasets while the conventional leakage models fail. Our work signifies a significant step forward in deep learning-based side-channel attacks, showcasing a high degree of flexibility and efficiency with the proposed leakage model.

Improving organisation to improve care: ERN ReCONNET organisational reference model for systemic sclerosis patients' care pathway.

To optimise the organisation of care and encourage the adoption of good clinical practices, the RarERN Path© methodology was designed within ERN ReCONNET. The aim of our work was to report the application of RarERN Path© on systemic sclerosis within the ERN ReCONNET centres, providing a feasible and flexible organisational reference model for optimising the systemic sclerosis care pathway in different countries. RarERN Path© is a six-phase methodology which enables the creation of a reference organisational model co-designed on the basis of the expertise of different stakeholders. It foresees six phases, ranging from the map of existing patients' care pathways and patients' stories, to the consensus on a common organisational patient care pathways, and its key performance indicators definition. The agreed reference model highlights the importance of having an organisational flow for referrals that foresees how patients may access directly the specialised unit from the different referrals. Specific specialised visits were considered as mandatory to be organised and they included cardiologist, pneumologist, gastroenterologist, psychologist, nephrologist, dermatologist, wound care specialist/nurses and other healthcare professionals (such as nurses, social workers and nutritional counselling). Moreover, specific services related to therapy were highlighted as strongly recommended to be organised, mainly represented by infusion therapy and wound care, as well as occupation therapy and physiotherapy. The organisational model emerged from our investigation emphasises that the organisation of specific services for systemic sclerosis treatment should be organised as a solid support for implementing the existing recommendations on systemic sclerosis management in real life.

Modeling of in-plane floor flexibility in existing reinforced concrete buildings

The paper addresses the problem of determining the mechanical characteristics of an orthotropic slab that best represents the in-plane behavior of a complex reinforced concrete (RC) floor system. To represent the structural behavior of the real RC floor, a reference model composed of solid finite elements is developed. The mechanical parameters of the equivalent orthotropic slab are determined by different homogenization techniques. In particular, two numerical algorithms and an analytical procedure to derive the elastic parameters of the equivalent slab representing the in-plane behavior of a single floor cell are proposed. Then, to determine which homogenization technique best predicts the structural behavior, representative one-story and six-story structures are modeled using both the equivalent slabs obtained using the three proposed techniques and the reference floor model. A discussion of the suitability of modeling floor in-plane flexibility taking account of only the top RC slab, for the considered case studies, is also provided.

Prospects and Challenges to ICT Adoption in Teaching and Learning at Rural South African Universities: A Systematic Review

This study aimed to systematically review the prospects and challenges associated with adopting Information and Communication Technology (ICT) in teaching and learning at rural universities in South Africa. To achieve this aim, a systematic review methodology was employed. The systematic review findings revealed a multifaceted landscape wherein opportunities for enhancing education through ICT exist alongside significant challenges. Prospects include the potential for technology to bridge educational gaps, increase accessibility, and provide innovative learning experiences. However, challenges such as inadequate infrastructure, limited connectivity, and varying levels of digital literacy pose significant impediments. The study emphasizes the need for targeted interventions to address these challenges, including infrastructure development, capacity building, and localized content creation. Additionally, recommendations for flexible learning models and collaborative partnerships are outlined to facilitate effective ICT adoption in the unique context of rural South African universities. Continuous research and evaluation are advocated to inform future strategies and ensure sustainable improvements in rural education through ICT integration.

Opportunities and challenges of diffusion models for generative AI

ABSTRACT Diffusion models, a powerful and universal generative artificial intelligence technology, have achieved tremendous success and opened up new possibilities in diverse applications. In these applications, diffusion models provide flexible high-dimensional data modeling, and act as a sampler for generating new samples under active control towards task-desired properties. Despite the significant empirical success, theoretical underpinnings of diffusion models are very limited, potentially slowing down principled methodological innovations for further harnessing and improving diffusion models. In this paper, we review emerging applications of diffusion models to highlight their sample generation capabilities under various control goals. At the same time, we dive into the unique working flow of diffusion models through the lens of stochastic processes. We identify theoretical challenges in analyzing diffusion models, owing to their complicated training procedure and interaction with the underlying data distribution. To address these challenges, we overview several promising advances, demonstrating diffusion models as an efficient distribution learner and a sampler. Furthermore, we introduce a new avenue in high-dimensional structured optimization through diffusion models, where searching for solutions is reformulated as a conditional sampling problem and solved by diffusion models. Lastly, we discuss future directions about diffusion models. The purpose of this paper is to provide a well-rounded exposure for stimulating forward-looking theories and methods of diffusion models.

Enhancing business resilience: Innovation and adaptation during and after the global pandemic

Purpose: In response to the unprecedented challenges posed by the global pandemic, this study rigorously examines the strategic interplay between innovation and adaptation as catalysts for business resilience. Method: The research investigates the influence of two independent variables – innovation in business and adaptive strategies – on the dependent variable of business resilience, establishing a robust conceptual framework based on a thorough literature review. Employing a mixed-methods approach, incorporating case studies, surveys, and interviews, the study meticulously explores the multifaceted dimensions of business responses during the pandemic. Results: A detailed analysis of innovation encompasses technological, process, and product innovations, while simultaneously scrutinizing adaptive strategies such as flexible business models, employee empowerment, and customer-centric approaches. The research reveals that businesses embracing process and technological innovation alongside radical shifts in their business models achieved higher levels of resilience. Qualitative narratives assess the performance metrics and success stories of these resilient businesses. The examination extends to the post-pandemic landscape, evaluating the sustainability of innovation and adaptation in a stable environment. Emphasis is placed on the enduring relevance of these strategies and their integration into organizational culture, thereby shaping long-term business resilience. Limitations: The study identifies and addresses challenges and barriers hindering innovation and adaptation, including economic constraints, technological barriers, and organizational resistance. Contribution: This study offers a nuanced and insightful exploration of the intricate dynamics between innovation, adaptation, and business resilience during and after the pandemic. The findings not only contribute to the academic discourse but also provide actionable insights for businesses seeking to fortify their resilience amidst ongoing global uncertainties.

Agile Governance Public Information Management at the Communication, Informatics, and Statistics Service of Riau Province

This study aims to analyze the implementation of agile governance in public information management at the Riau Province Communication, Informatics and Statistics Service (Diskominfotik). Agile governance is a governance approach that emphasizes flexibility, responsiveness, and rapid collaboration in providing public services. The research method used is qualitative descriptive with an explanatory approach. Data collection was carried out through observation, interviews, and document studies analyzed using Nvivo 14 software. The results of the study indicate that the implementation of agile governance at Diskominfotik Riau is still faced with a number of obstacles, including limited knowledge and capacity of human resources (HR) in understanding the concept and a bureaucratic structure that does not fully support flexibility. In addition, performance evaluations that are not yet based on quick wins slow down the adaptation process. Nevertheless, Diskominfotik Riau has utilized various digital platforms to provide access to public information, although improvements need to be made in terms of responsiveness and timeliness of service. Recommendations from this study include increasing training for HR, implementing a more flexible governance model, and periodic evaluations based on short-term results. The implementation of agile governance is expected to improve the quality of public services and strengthen public trust in government transparency.

A Multifrequency Data Fusion Deep Learning Model for Carbon Price Prediction

ABSTRACTIn response to the global need for effective management of carbon emissions and alignment with sustainable development goals, predicting carbon trading prices accurately is critical. This study introduces a multifrequency data fusion carbon price prediction model (MFF‐CPPM), addressing the nonlinear characteristics of carbon trading prices and inconsistent feature factor frequencies. The MFF‐CPPM consists of a feature‐extraction frontend, a multifrequency data fusion transformer, and a fusion regression layer, offering a novel methodological approach in forecasting studies. The model's validity was tested in Guangdong, China's largest carbon trading pilot market. The results demonstrated that the MFF‐CPPM outperformed baseline models in terms of carbon price‐prediction accuracy and trend forecasting. Additional trials conducted in Hubei and Beijing confirmed the model's robustness and generalization capabilities, providing valuable evidence of its effectiveness and reliability across varying market contexts. This study presents a novel predictive model for carbon trading prices, with a unique capability to harness data at differing frequencies. The MFF‐CPPM not only enhances forecasting accuracy but also offers an innovative approach to effectively incorporate multifrequency information. This advancement paves the way for flexible forecasting models in any scenario where data arrive at differing frequencies.

Work-integrated learning in the humanities, arts and social sciences: where to from here?

ABSTRACT Amidst increasing pressures on Humanities, Arts, and Social Sciences (HASS) disciplines regarding graduate career readiness, this study examines HASS student participation in work-integrated learning (WIL) and its impacts on employability and short-term employment outcomes, using data from the Australian Graduate Outcomes Survey from 2020 to 2023. The research examined the extent of participation across WIL types (work-based, non-workplace and global) and its influence on graduates’ perceived employability and employment outcomes (full-time employment and perceived overqualification), while investigating demographic differences using descriptive analysis and logistic regression models. Results indicated rising WIL participation yet identified disparities by activity type and among student groups. Work-based WIL (e.g. internships) was the most effective type for enhancing employability and facilitating transitions into professional roles and work aligned to degree study. Non-workplace WIL (e.g. projects), while beneficial for skill development, showed limited impact on immediate employment outcomes and there was relatively low participation in global WIL (e.g. industry study tours) across all discipline groups. The study illuminates the need for more inclusive WIL programmes and flexible models to accommodate diverse student needs. Recommendations for HASS faculties include integrating career development learning, optimising industry engagement and providing targeted support for underrepresented groups. By aligning WIL experiences with emerging labour market opportunities, HASS faculties can prepare graduates for successful careers, addressing both individual aspirations and broader economic needs. Future research regarding WIL in HASS could focus on postgraduate participation and industry perspectives on scaling programmes to ensure initiatives effectively bridge the gap between academic learning and professional practice.

PST-Diff: Achieving High-Consistency Stain Transfer by Diffusion Models With Pathological and Structural Constraints.

Histopathological examinations heavily rely on hematoxylin and eosin (HE) and immunohistochemistry (IHC) staining. IHC staining can offer more accurate diagnostic details but it brings significant financial and time costs. Furthermore, either re-staining HE-stained slides or using adjacent slides for IHC may compromise the accuracy of pathological diagnosis due to information loss. To address these challenges, we develop PST-Diff, a method for generating virtual IHC images from HE images based on diffusion models, which allows pathologists to simultaneously view multiple staining results from the same tissue slide. To maintain the pathological consistency of the stain transfer, we propose the asymmetric attention mechanism (AAM) and latent transfer (LT) module in PST-Diff. Specifically, the AAM can retain more local pathological information of the source domain images, while ensuring the model's flexibility in generating virtual stained images that highly confirm to the target domain. Subsequently, the LT module transfers the implicit representations across different domains, effectively alleviating the bias introduced by direct connection and further enhancing the pathological consistency of PST-Diff. Furthermore, to maintain the structural consistency of the stain transfer, the conditional frequency guidance (CFG) module is proposed to precisely control image generation and preserve structural details according to the frequency recovery process. To conclude, the pathological and structural consistency constraints provide PST-Diff with effectiveness and superior generalization in generating stable and functionally pathological IHC images with the best evaluation score. In general, PST-Diff offers prospective application in clinical virtual staining and pathological image analysis.

Dynamic choice HIV prevention with cabotegravir long-acting injectable in rural Uganda and Kenya: a randomised trial extension

HIV infections are ongoing globally despite efficacious biomedical prevention options. We sought to determine whether an HIV prevention package providing choice of daily pills or long-acting injectable cabotegravir and opportunities to change prevention options could increase biomedical prevention coverage and reduce new HIV infections. This study was an extension of three randomised trials that used SEARCH dynamic choice HIV prevention to recruit adults (aged ≥15 years) at risk for HIV from antenatal, outpatient, and community settings in rural Uganda and Kenya. In this 48-week open-label extension, participants maintained their original (1:1) randomisation group; the option to choose cabotegravir long-acting injectable was added for intervention participants. Inclusion criteria for the extension were previous enrolment in a SEARCH dynamic choice HIV prevention trial, negative HIV rapid test, and residence in study region. The intervention provided person-centred choice of oral pre-exposure prophylaxis (PrEP) or post-exposure HIV prophylaxis (PEP) or cabotegravir long-acting injectable, with the option to switch according to participant preference. The control provided standard-of-care access to oral PrEP and PEP, but not cabotegravir long-acting injectable. Biomedical prevention coverage (proportion of follow-up covered by oral PrEP, PEP, or cabotegravir long-acting injectable; primary outcome) and HIV incidence (secondary outcome) were compared between groups using targeted minimum loss-based estimation. The trial (NCT05549726) is closed to recruitment. Of 1534 participants initially randomly assigned (from April 15, 2021 to Sept 29, 2022), 984 (487 in the intervention group and 497 in the standard-of-care group) reconsented to the extension (from Jan 2 to March 3, 2023). The mean proportion of follow-up covered by biomedical HIV prevention was 69·7% (95% CI 64·9-74·5) in the intervention group versus 13·3% (10·2-16·3) in the standard-of-care group, corresponding to an absolute difference of 56·4 percentage points (95% CI 50·8-62·1; p<0·0001). The intervention significantly improved coverage across prespecified subgroups (sex and age groups). During the study, 274 (56%) of 485 intervention participants used cabotegravir long-acting injectable, 255 (53%) used oral PrEP, and ten (2%) used PEP. Among cabotegravir long-acting injectable initiators, 118 (43%) of 274 were not previously using oral PrEP or PEP. There were seven incident HIV infections in 390 person-years of follow-up in the standard-of-care group and no infections in 400 person-years of follow-up in the intervention group (incidence rate difference per 100 person-years 1·8, 95% CI 0·4-3·2; p=0·014). Offering people the choice of HIV biomedical prevention options including cabotegravir long-acting injectable in a flexible model can increase prevention coverage and reduce incident HIV infections. HIV programmes should support dynamic choice HIV prevention programmes that include effective oral and injectable long-acting products. National Institutes of Health.

A fast search method of buckling load for telescopic boom structure

The telescopic boom structures are extensively utilized in engineering applications involving large mobile cranes, aerial work platforms of significant height, and similar mechanical devices. These are predominantly fabricated using solid-webbed box types, latticed truss designs, or a combination thereof. Under load, they exhibit pronounced geometric nonlinear effects, with the relationship between load and displacement demonstrating marked nonlinear characteristics. This paper focuses on the geometric nonlinear modeling of slender multi flexible beam structures. The overall structural system is divided into several substructures, and the concept of multi flexible system modeling is borrowed to establish a follow-up connected body foundation on each substructure. By decomposing the node displacement and rotation in the substructure, the large displacement and large rotation of the node are decomposed into rigid body motion of the connected base and small displacement and small rotation relative to the connected base, effectively representing the large displacement and large rotation of the substructure as rigid body motion of the connected base. A modeling method for the geometric nonlinear analysis of slender and flexible multibody beam structures is proposed. By decomposing the nodal displacement and rotation within the substructures, the large displacement and rotation of the nodes are broken down into the rigid body motion of the body-fixed coordinate and small displacement and rotation relative to the body-fixed coordinate. This approach establishes the application conditions for calculating the structure’s virtual power of deformation using traditional beam elements with linear strain. A method for solving the instability load of slender and flexible multibody structures is proposed. This method integrates the characteristics of the system’s nonlinear equilibrium equations with respect to load, by deriving the system equations with respect to a load increment control parameter. This paper converts nonlinear equilibrium equations into first-order ordinary differential equation (ODE). The method proposed in this paper provides a more efficient solution for the buckling load of the telescopic boom. It can be used to systematically and quickly calculate the structure of the telescopic boom.

A thermal flexible rotor dynamic modelling for rapid prediction of thermo-elastic coupling vibration characteristics in non-uniform temperature fields

The flexible rotors within aero-engines operate in complex thermal environments, where temperature influences both the vibration frequency and amplitude. This study establishes a simple thermal flexible rotor dynamics model to rapidly and precisely predict thermo-elastic coupling vibration characteristics within a non-uniform temperature field. The thermal potential energy of the thermal rotor element is derived for any temperature field, and the motion equation is obtained using the Euler-Lagrange equation. Specifically, the generalized vector of an arbitrary point and cross-sectional non-uniform thermal stress of the thermal rotor element are considered in the thermal potential energy. The model's frequency error is less than 1% under identical boundary conditions. Numerical findings indicate that thermal stress, temperature-dependent material properties, and the coupling effect collectively reduce the natural frequency (NF), with thermal stress having a more pronounced impact under axial constraint. Additionally, thermal stress and material decrease the amplitude across a broad range of rotation speeds, contrasting with thermal bending. This model will play a key role in the iterative calculation of thermo-elastic coupling vibration control due to its accuracy and simplicity.

Creation and evaluation of a three-dimensional-printed synthetic vas deferens for microsurgical training

ABSTRACT Introduction: Individuals choose to undergo vasectomy reversal for a variety of reasons, including remarriage or due to the death of a child. To be proficient in this procedure, the surgeons need to be high volume and the trainees require a safe environment to develop their microsurgical skills. To overcome this challenge, we used three-dimensional (3D) printing to create a synthetic model of the vas deferens with properties similar to the human vas deferens. We distributed this model to experienced microsurgeons for evaluation. Methods: The vas deferens model was created using thermoplastic polyurethane filament. The filament was then infused with a foaming agent to allow for temperature-dependent tuning of the material’s stiffness. The model’s outer diameter was 1 mm and the inner lumen was 0.5 mm. Fellowship-trained male reproductive urologists were recruited from the Society for the Study of Male Reproduction website. They used our model and judged it on several factors by completing a 13-question survey. Results: We received completed evaluations from five microsurgeons. Eighty percent of the surgeons were able to complete a full anastomosis on the model using 9-0 and 10-0 sutures. The majority of the completed anastomoses were performed using the one-layer technique. The average responses for the model’s usefulness as a practice tool, a training tool, and overall assessment ranged from 72 to 79 out of 100. Comments for the improvement included the need for a more flexible and softer model. Conclusions: We created a 3D-printed synthetic vas deferens that serves as a valuable training and practice tool.

Analysing the Performance of Large-Scale Rooftop Solar PV System Using Helioscope, PVsyst and PV*SOL Photovoltaic Simulation Software: As a Renewable Energy Strategy

In Malaysia, government initiatives and policymakers play significant role in creating effective strategies aiming at enhancing the usage of residential, industrial and commercial solar PV systems. However, the most significant barrier to the widespread adoption of photovoltaic (PV) systems is their high installation costs; as a result, numerous studies on PV system modification are now being conducted. Several simulation tools are available for the efficient and extensive integration of solar energy. To predict energy output using climate data and assess performance ration in accordance, such software can be optimized in terms of parameters like PV module number, inverter, tilt angle, and module design structure. The accuracy of these figures fluctuates, though, because climate factors affect how productive photovoltaic systems are. In this current paper, the simulation and design of a 6.9 MW solar power plant in University Tun Hussein Onn Malaysia, have been investigated using three popular software PVsyst v5.06, Helioscope and PV*SOL. This study's main objective is to assess the potential of solar renewable energy sources to produce electrical energy under the Supply Agreement of Renewable Energy (SARE) program using flexible solar system design modelling tools, PVsyst, Helioscope, and PV *SOL. The comparison results showed that Helioscope and PVsyst are considered to be the most dependable software. PVsyst appraised the definite energy production cost with lowermost relative error of 0.12 % and Helioscope estimated performance ratio with lowest relative error of 4.74%. In terms of energy losses, environmental element contributing to the most energy losses where PVsyst recorded 11.1% photovoltaic (PV) loss caused by irradiation and temperature changes. Whereby Helioscope clearly shows that environmental power loss of 8.4% represents the largest portion caused by irradiation and temperature changes. PV*SOL recorded an energy loss of 7.3% due to shading, temperature and reflection on module interface.