Robust Applications Research Articles

Multi-Task Intelligent Monitoring of Construction Safety Based on Computer Vision

Effective safety management is vital for ensuring construction safety. Traditional safety inspections in construction heavily rely on manual labor, which is both time-consuming and labor-intensive. Extensive research has been conducted integrating computer-vision technologies to facilitate intelligent surveillance and improve safety measures. However, existing research predominantly focuses on singular tasks, while construction environments necessitate comprehensive analysis. This study introduces a multi-task computer vision technology approach for the enhanced monitoring of construction safety. The process begins with the collection and processing of multi-source video surveillance data. Subsequently, YOLOv8, a deep learning-based computer vision model, is adapted to meet specific task requirements by modifying the head component of the framework. This adaptation enables efficient detection and segmentation of construction elements, as well as the estimation of person and machine poses. Moreover, a tracking algorithm integrates these capabilities to continuously monitor detected elements, thereby facilitating the proactive identification of unsafe practices on construction sites. This paper also presents a novel Integrated Excavator Pose (IEP) dataset designed to address the common challenges associated with different single datasets, thereby ensuring accurate detection and robust application in practical scenarios.

Design and Implementation of a 3 Degrees of Freedom Robotic Arm Powered by Pneumatic Artificial Muscle

The development of a 3-DOF robotic arm powered by pneumatic artificial muscles is represented as an innovative approach in the field of robotics, with the advantages of lightweight and flexible design being combined with the power and control benefits of pneumatic actuation. The design process, challenges, and solutions that were encountered in the development of the robotic arm are outlined in this paper, with an emphasis on its potential applications in industrial and research settings being highlighted. Additionally, the robotic arm’s modular design is enabled to allow easy customization and scalability, making it possible for the arm to be tailored to a wide range of tasks, from precise laboratory work to more robust industrial applications.

The DairyPrint Model: A Decision-Support Model to Help Dairy Farmers and Other Stakeholders Towards Improved Sustainability

Dairy farmers face increasing pressure to reduce greenhouse gas (GHG) emissions [i.e., carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O)], but measuring on-farm GHG emissions directly is costly or impractical. Therefore, the dairy industry has relied upon mathematical models to estimate those emissions. However, current models tend to be not user-friendly, difficult to access or sometimes very research-focused, limiting their practical use. To address this, we introduce the DairyPrint model, a user-friendly tool designed to estimate GHG emissions from dairy farming. The model integrates herd dynamics, manure management, crop, and feed costs considerations, simplifying the estimation process while providing comprehensive insights. The herd module simulates monthly herd dynamics based on inputs as total cows, calving interval, and culling rate, outputting average annual demographics and estimating various animal related variables (i.e., dry matter intake, milk yield, manure excretion, and enteric CH4 emissions). These outputs feed into other modules, such as the manure module, which calculates emissions based on manure, weather data, and facility type. The manure module processes manure according to farm practices, and the crop module accounts for GHG emissions from manure, fertilizers, and limestone application, also estimating nutrient balances. The DairyPrint model was developed using the Shiny framework and the Golem package for robust production-grade shiny applications in the R programming language. We evaluated the model across 32 simulation scenarios by combining various factors and considering a standard free-stall system with 1000 dairy cows averaging 40 kg/day of milk production. These factors included 2 levels of NDF-ADF in the diet (28-22.8% and 24-19.5%), the presence or absence of 3-NOP dietary addition (yes or no) at an average dose of 70 mg/kg DM per cow daily, the type of bedding used (sawdust or sand), the frequency of manure pond emptying [once (only Fall) or twice a year (Fall and Spring)], and the utilization or non-utilization of a biodigester plus solid-liquid separator (Biod + SL). In our results across the 32 scenarios simulated, the average GHG emission was 0.811 kgCO2eq/kg of milk corrected for fat and protein contents (4% and 3.3%, respectively), ranging from 0.644 to 1.082. Notably, the scenario yielding the lowest GHG emission (i.e., 0.644 kgCO2eq/kg) involved a combination of factors, including a lower level of NDF-ADF in the diet in addition to incorporation of 3-NOP, utilization of sand as bedding, application of Biod + SL, and strategic manure pond emptying in both Fall and Spring. Conversely, the scenario that resulted in the highest GHG emission (i.e., 1.082 kgCO2eq/kg) involved a combination of higher level of NDF-ADF in the diet and excluded the incorporation of 3-NOP, utilization of sawdust as bedding, no application of Biod + SL, and manure pond emptying only in Fall. All these scenarios can be easily simulated in the DairyPrint model and results obtained immediately for user evaluation. Therefore, the DairyPrint model can help farmers move toward improved sustainability, providing a user-friendly and intuitive graphical user interface allowing the user to ask what-if questions.

Development and validation of a nomogram model for predicting 28-day mortality in patients with sepsis

BackgroundThis study aimed to develop and validate a nomogram model for predicting 28-day mortality in patients with sepsis in the intensive care unit (ICU). MethodsWe retrospectively analyzed data from 331 patients with sepsis admitted to the ICU as a training set and collected a validation set of 120 patients. Both groups were followed for 28 days. Logistic regression analyses were performed to identify the potential prognostic factors for sepsis-related 28-day mortality. A nomogram model was generated to predict 28-day mortality in patients with sepsis in the ICU. Receiver operating characteristic (ROC) curve analysis, calibration curves, and decision curve analysis (DCA) were used to evaluate the model's prediction performance and clinical application. In addition, we used ROC curve analysis and DCA to compare this model with the sequential organ failure assessment (SOFA) and Acute Physiology and Chronic Health Evaluation (APACHE II) scores and further assessed the clinical value of our model. ResultsLogistic multivariate regression analysis revealed that mechanical ventilation, oxygenation index, and lactate and blood urea nitrogen (BUN) levels were independent predictors of 28-day mortality in patients with sepsis in the ICU. We developed a nomogram model based on these results to further predict 28-day mortality. The model demonstrated satisfactory calibration curves for both training and validation sets. Additionally, in the training set, the area under the ROC curve (AUC) for this model was 0.80. In the validation set, the AUC was 0.82. DCA showed that the high-risk thresholds ranged between 0 and 0.86 in the training set and between 0 and 0.75 in the validation set. We compared the ROC curve and DCA of this model with those of SOFA and APACHE II scores in both the training and validation sets. In the training set, the AUC of this model was significantly higher than those of the SOFA (P = 0.032) and APACHE II (P = 0.004) scores. Although the validation set showed a similar trend, the differences were not statistically significant for the SOFA (P = 0.273) and APACHE II (P = 0.320) scores. Additionally, the DCA showed comparable clinical utility in all three assessments. ConclusionThe present study used four common clinical variables, including mechanical ventilation, oxygenation index and lactate and BUN levels, to develop a nomogram model to predict 28-day mortality in patients with sepsis in the ICU. Our model demonstrated robust prediction performance and clinical application after validation and comparison.

Japan Trench event stratigraphy: First results from IODP giant piston coring in a deep-sea trench to advance subduction zone paleoseismology

The International Ocean Discovery Program (IODP) Expedition 386, Japan Trench Paleoseismology, represents the first utilization of giant piston coring (GPC) within scientific ocean research drilling. This allowed for a Mission Specific Platform (MSP) multi-site, multi-hole, shallow subsurface coring in an ultra-deep water subduction zone trench. The primary objective of the expedition was to investigate the concept of submarine paleoseismology in the Japan Trench, which involves studying long-term records of deposits in the deep sea that can provide insights into past earthquake events. In this paper, we compile and interpret initial shipboard data and results to (1) establish first-order event stratigraphic correlation of thick event beds (> 50 cm in thickness) between sites, (2) test previously published event-stratigraphic predictions of earthquake-related event deposits as proposed based on high-resolution hydro-acoustic subbottom profiler (SBP) data, and (3) derive SBP-scale event deposits age estimates to (4) discuss the advantages and limitations of giant piston coring for scientific drilling operations and the potential of new event stratigraphy results for advancing submarine paleoseismology.The findings of the study identified a total of 77 SBP-scale event beds across 15 sites along a trench-parallel transect spanning over 600 km. These event beds exhibit clear expressions in SBP data, with approximately 49 % matching precisely with SBP units previously identified by Kioka et al. (2019a). For the remaining 51 % of SBP-scale event beds, thin, acoustically-transparent bodies were observed between high-amplitude horizons, for which SBP-based seismic interpretation alone would not be definitive. Consequently, the study concluded that the SBP-scale event-stratigraphy observed in IODP 386 cores validates the event-bed mapping conducted by Kioka et al. (2019a) and improves SBP interpretation for event beds in the 0.5 to 1 m thickness range.The initial age constraints obtained from shipboard radiolarian biostratigraphy enable us to provide rough estimates of event ages by linearly interpolating between previously dated events occurring less than 2000 years ago and a datum around 11,000 years ago reported in four boreholes from trench basins in the Southern, Central, and Northern Japan Trench. Inter-site stratigraphic correlation reveals distinct SBP-scale event stratigraphies for the trench segments located to the north and south of the structurally complex “boundary area” at approximately 39.3–39.4°N, which is hypothesized to potentially act as a persistent rupture barrier for megathrust earthquakes. We observe more frequent but thinner event deposits in the Southern and Central Japan Trench, and fewer but thicker event beds in the Northern Japan Trench. This spatial variation may be related to the different seismogenic behavior of the various asperities along the Japan Trench megathrust and/or to differences in the response of slope sediments to earthquake shaking. However, here-presented investigations at the SBP-scale level are deemed too simplistic for robust application of the “submarine paleoseismology” approach. The extensive and high-quality dataset from IODP GPC, coupled with the encouraging initial correlation results presented here, leads us to hypothesize that further detailed studies can identify and characterize event deposition dynamics at the micro-facies level, refine sediment provenance, and constrain precise event ages necessary for evaluating synchronicity in paleoseismological interpretation. These studies will also enable robust exploration of along-strike correlations or variations, facilitating the extraction of paleo-earthquake signals from Japan Trench event stratigraphies.

Solvent Engineering of Thermo-Responsive Hydrogels Facilitates Strong and Large Contractile Actuations.

Thermo-responsive hydrogels can generate the actuation force through volumetric transitions in response to temperature changes. However, their weak mechanical properties and fragile actuation performance limit robust applications. Existing approaches to enhance these properties have typically depended on additional components, leading to an unavoidable interference to the actuation performance. In this work, robust thermo-responsive hydrogels are fabricated through solvent engineering. A particular solvent, N-methylformamide, interacts affinitively with the carbonyl group of N-isopropylacrylamide monomer, solubilizes the monomer with extremely high concentration, stabilizes chain propagation during polymerization, and greatly increases chain lengths and entanglements of the resulting polymer. The synthesized hydrogels are highly elastic, strong, and tough, displaying remarkable thermo-responsive contractile actuation. The simple synthetic process can broaden its applicability in designing robust functional hydrogel applications.

Interpenetrated Multinetwork Hybrid Aerogels by Layered Montmorillonite and One-Dimensional Hydroxyapatite Fibers for Heat and Fire Insulation.

It is of practical significance to develop aerogels with effective thermal insulation characteristics together with fireproof properties as well as high mechanical strength. Here, an interpenetrated multinetwork hybrid aerogel realizing thermal insulation, flame retardancy, and high compression modulus is demonstrated. Specifically, one-dimensional hydroxyapatite nanowires (HAP) played dual roles as the aerogel support skeleton to entangle with layered montmorillonite (MMT) each other to form a three-dimensional interpenetrated multinetwork structure and to optimize the thermal conductivity by adjusting the pore space in current HAP/MMT/PVA hybrid aerogels. Therefore, the interpenetrated multinetwork hybrid aerogels exhibit superior thermal insulation performance in room temperature (0.033 W m-1 K-1, 298 K, air conditions) and largely enhanced ultrahigh compression modulus (80 MPa). Moreover, the obtained hybrid aerogels also exhibit excellent flame retardancy and self-extinguishing smoke suppression properties (peak heat release rate and total smoke production as low as 92.44 kW m-2 and 0.1 m2, respectively), which is the outstanding interpenetrated multinetwork hybrid aerogel that has achieved synergistic improvement in heat and fire insulation and mechanical performance. Therefore, the interpenetrated multinetwork hybrid aerogels are promising candidates for efficient heat insulation, fire prevention, and mechanically robust applications.

Evaluation of technical approaches for real-time data transfer from electronic health record systems

Background and ObjectiveReal-time data (RTD) are data that are delivered immediately after creation. The key feature of RTD is low delivery latency. Information systems in health care are extremely time-sensitive and their building block is the electronic health record (EHR). Real-time data from EHRs play an important role to support decision-making, analytics and coordination of care. This is well mentioned in the literature, but the process has not yet been described, providing reference implementations and testing. Real-time data delivery can technically be achieved using several methods. The objective of this work is to evaluate the performance of different transfer methods of RTD from EHRs by measuring delivery latency. MethodsIn our work we used four approaches to transfer RTD from EHRs: REST hooks, WebSocket notifications, reverse proxy and database triggers. We deployed a Fast Health Interoperability Resources (FHIR) server as it is one of the most widely used EHR standard. For the reference implementations we used Python and Golang. Delivery latency was selected as performance metric, derived by subtracting the timestamp of the EHR resource creation from the timestamp of the EHR resource receipt in millisecond. The data was analyzed using descriptive statistics, cumulative distribution function (CDF), Kruskal-Wallis and post-hoc tests. ResultsThe database trigger approach had the best mean delivery latency 13.52±5.56 ms, followed by the reverse proxy 14.43±4.58 ms, REST hooks 19.26±5.76 ms and WebSocket 27.32±9.44 ms. The reverse proxy showed a tighter range of the values and lower variability. There were significant differences in the latencies between all pairs of approaches, except for reverse proxy and database trigger. ConclusionReal-time data transfer is vital for the development of robust and innovative healthcare applications. Properties of current EHR systems as a data source predefine the approaches for transfer. In our work for the first time the performance of RTD transfer from the EHRs with reference implementations is measured and evaluated. We found that database triggers achieve lowest delivery latency. Reverse proxy performed slightly slower, but offered more stability, followed by REST hooks and WebSocket notifications.

Nonlinear properties of uni-directional cellulose nanofiber-reinforced poly (lactic acid) composite filaments: a multiscale computational study

In the quest to elevate the mechanical properties of cellulose nanofiber-reinforced poly (lactic acid) composite materials for robust industrial applications, we conducted multiscale nonlinear finite element analysis rooted in homogenization theory. Our study delved into the intricate web of mechanical mechanisms and their pivotal influencers. This paper zeroed in on three paramount factors: the distribution of fibers, the fiber–matrix interface, and the aspect ratio of the fibers. For fiber distribution, we assumed fiber orientation control in fused filament fabrication and clarified the limit of material improvement by setting one-dimensional orientation as the upper limit. Our analysis of the fiber–matrix interface proposed a comprehensive three-scale structure, encompassing microstructures featuring intricate interfacial phases around the fibers, sub-microstructures with myriad dispersed fibers, and macrostructures subjected to external loads. The profound impact of the fiber–matrix interface on material properties was unveiled through a meticulous two-step homogenization process. Furthermore, we examined the substantial influence of the fiber aspect ratio on material properties. By unraveling the complex interplay among these critical factors, this research provided new design guidelines for fiber morphology to the performance of cellulose composites.

Clinical applicability of Linac-integrated CBCT based NIPAM 3D dosimetry: a dual-institutional investigation

Objective. To develop and benchmark a novel 3D dose verification technique consisting of polymer gel dosimetry (PGD) with cone-beam-CT (CBCT) readout through a two-institution study. The technique has potential for wide and robust applicability through reliance on CBCT readout. Approach. Three treatment plans (3-field, TG119-C-shape spine, 4-target SRS) were created by two independent institutions (Institutions A and B). A Varian Truebeam linear accelerator was used to deliver the plans to NIPAM polymer gel dosimeters produced at both institutions using an identical approach. For readout, a slow CBCT scan mode was used to acquire pre- and post-irradiation images of the gel (1 mm slice thickness). Independent gel analysis tools were used to process the PGD images (A: VistaAce software, B: in-house MATLAB code). Comparing planned and measured doses, the analysis involved a combination of 1D line profiles, 2D contour plots, and 3D global gamma maps (criteria ranging between 2%1 mm and 5%2 mm, with a 10% dose threshold). Main results. For all gamma criteria tested, the 3D gamma pass rates were all above 90% for 3-field and 88% for the SRS plan. For the C-shape spine plan, we benchmarked our 2% 2 mm result against previously published work using film analysis (93.4%). For 2%2 mm, 99.4% (Institution A data), and 89.7% (Institution B data) were obtained based on VistaAce software analysis, 83.7% (Institution A data), and 82.9% (Institution B data) based on MATLAB. Significance. The benchmark data demonstrate that when two institutions follow the same rigorous procedures gamma passing rates up to 99%, for 2%2 mm criteria can be achieved for substantively different treatment plans. The use of different software and calibration techniques may have contributed to the variation in the 3D gamma results. By sharing the data across institutions, we observe the gamma passing rate is more consistent within each pipeline, indicating the need for standardized analysis methods.

Isotopologues of potassium 2,2,2-trifluoroethoxide for applications in positron emission tomography and beyond

The 2,2,2-trifluoroethoxy group increasingly features in drugs and potential tracers for biomedical imaging with positron emission tomography (PET). Herein, we describe a rapid and transition metal-free conversion of fluoroform with paraformaldehyde into highly reactive potassium 2,2,2-trifluoroethoxide (CF3CH2OK) and demonstrate robust applications of this synthon in one-pot, two-stage 2,2,2-trifluoroethoxylations of both aromatic and aliphatic precursors. Moreover, we show that these transformations translate easily to fluoroform that has been labeled with either carbon-11 (t1/2 = 20.4 min) or fluorine-18 (t1/2 = 109.8 min), so allowing the appendage of complex molecules with a no-carrier-added 11C- or 18F- 2,2,2-trifluoroethoxy group. This provides scope to create candidate PET tracers with radioactive and metabolically stable 2,2,2-trifluoroethoxy moieties. We also exemplify syntheses of isotopologues of potassium 2,2,2-trifluoroethoxide and show their utility for stable isotopic labeling which can be of further benefit for drug discovery and development.

Raman Sideband Cooling of Molecules in an Optical Tweezer Array to the 3D Motional Ground State

Ultracold polar molecules are promising for quantum information processing and searches for physics beyond the standard model. Laser cooling to ultracold temperatures is an established technique for trapped diatomic and triatomic molecules. Further cooling of the molecules to near the motional ground state is crucial for reducing various dephasings in quantum and precision applications. In this work, we demonstrate Raman sideband cooling (RSC) of CaF molecules in optical tweezers to near their motional ground state, with average motional occupation quantum numbers of n¯x=0.16(12), n¯y=0.17(17) (radial directions), and n¯z=0.22(16) (axial direction), and a 3-D motional-ground-state probability of 54±18% of the molecules that survive the RSC. This process paves the way to increase molecular coherence times in optical tweezers for robust quantum computation and simulation applications. Published by the American Physical Society 2024

Model Validation and Real-Time Process Control of a Continuous Flow Ohmic Heater

Ohmic heating is a highly efficient method for rapid fluid heating, with applications in fields such as food processing, pharmaceuticals, and chemical engineering. Prior to its industrial application, thorough analysis and modeling are crucial to ensure safe and efficient operations. Therefore, this research focuses on the development and validation of a transfer function-based model for a continuous flow ohmic heater (CFOH). Validation metrics include root mean square error (RMSE) and mean absolute percentage error (MAPE). The developed model achieves an RMSE of ±1.48 and a MAPE of ±2.58% compared to experimental results, demonstrating its accuracy. Furthermore, the research presents the implementation of robust real-time applications of advanced process controllers, including PID, MPC, and AMPC. These controllers were first simulated using the developed model and subsequently deployed in the pilot plant ohmic heater system to achieve precise temperature control and optimised input voltage. The reliability of this procedure was affirmed through a comparison between simulated results and empirical data obtained from the CFOH pilot plant. The study concludes by suggesting potential applications in fault diagnosis, educational training, system identification, and controller design.

Bryophyllum Pinnatum leaf extract mediated MoS2/ZnO nanocomposite for robust photocatalysis applications

One of the primary problems of the twenty-first century is the removal or degradation of organic dyes and antibiotics from water systems since they both contribute significantly to water pollution. To address this issue, molybdenum disulfide and zinc oxide nanocomposite (MoS2/ZnO) were produced by simple, eco-friendly and low cost process using Bryophyllum pinnatum (leaf) phytoextract (BPP) as a reducing as well as capping agent. The prepared BPP-mediated MoS2/ZnO nanocomposite (BP-MoS2/ZnO NC) samples were characterized through various techniques including powder XRD, Rietveld refinement, UV–Vis spectroscopy, FESEM, DLS and FT-IR spectroscopy. The BP-MoS2/ZnO samples were further tested for their use in the sunlight-powered photocatalytic breakdown of methylene blue (MB) and their efficiency to break MB molecules was contrasted with the BPP-mediated ZnO (BP-ZnO), Chemical ZnO (Ch-ZnO) and pure MoS2. The photocatalytic investigation reveals a remarkable enhancement in the photocatalytic activity of pristine ZnO when sensitized with MoS2. These results underscore the synergistic effect achieved by combining ZnO and MoS2. Notably, we found that the optimal catalytic performance is achieved at a Mo atomic percentage of 2 % relative to Zn, emphasizing the importance of precise control for maximizing efficacy. Under sun exposure, 0.5 g/L of BP-MoS2/ZnO resulted in 89.35 % photodegradation of a 20 mg/L MB solution with a rate constant of 0.07537 min-1, which was around 10.42 %, 28.51 %, and 51.86 % greater than BP-ZnO, Ch-ZnO, and pure MoS2, respectively. Further, the efficacy of the BP-MoS2/ZnO NC as solar-driven photocatalysts for removal of pharmaceutical drugs (Amoxicillin & Metronidazole) and textile dyes (Methyl orange) pollutants in sunlight was also investigated. These results show that Bryophyllum extract-mediated MoS2/ZnO is a promising material for solar-based photocatalytic applications.

Enhancing corrosion detection in pulsed eddy current testing systems through autoencoder-based unsupervised learning

Pulsed Eddy Current Testing (PECT) stands out as an advanced method in Non-Destructive Testing due to its extensive spectrum characteristics in comparison to traditional ECT techniques, making it exceptionally suitable for identifying corrosion. Nonetheless, the analysis of PECT signals for corrosion detection poses a challenge due to the transient nature of these signals and the impact of sensor lift-off effects. As a result, conventional methods are facing hurdles in dealing with corrosion signals of poor quality. In this study, the challenge is addressed by employing unsupervised learning methods utilizing an autoencoder neural network. This autoencoder integrates Long Short-Term Memory and 1D convolutional layers, acquiring the underlying features of normal PECT signals from non-corrosive regions. Significantly, the model is trained exclusively on this normal data, thereby obviating the necessity for pre-existing corrosion information. Through learning the inherent structure of normal signals, the model can detect anomalies in unseen data, potentially indicating corrosion. The unsupervised framework presents several advantages, such as reducing reliance on prior corrosion knowledge, mitigating inherent noise, and addressing sensor lift-off effects. Experimental results were conducted to compare with traditional methods like the lift-off of intersection and lift-off compensation methods. This approach resulted in a significant improvement in SNR, ranging from 100 % to 200 %, thus facilitating more robust NDT applications employing smart PECT sensors empowered by unsupervised learning techniques.

Electric field induced mechanical flapping motors enabling soft robotic and wearable applications

Here we present a new type of soft motors, termed Electric Field Induced Mechanical Flapping (EFIMF) motors, which are robust and present versatile potential applications. The EFIMF motors harness an actuation mechanism that combines two processes: 1) using the electric field to induce charges, and 2) coupling the electromechanical forces to achieve fast and robust actuation. Our EFIMF motor can remain functional even with its electrode punctured by a metal needle. The EFIMF motors also present the fault-tolerant capability, which helps them to survive the dielectric breakdown. We demonstrate a robust crawling soft robot, which is powered by one EFIMF motor and capable of surviving continuous hammering. We also show a wearable haptic glove powered by five EFIMF motors, generating rich notification signals with a wide frequency range and different mechanical feedback intensities. The robustness of the soft EFIMF motors enhances their lifetime and performances, and represents an important step towards realistic soft mobile machines and wearable human machine interfaces.

The History of Classification Systems for Periprosthetic Femoral Fractures: A Literature Review.

Periprosthetic femoral fractures (PPFFs) following total hip arthroplasty (THA) present a significant clinical challenge due to their increasing incidence with an aging population and evolving surgical practices. Historically, classifications were primarily based on anatomical fracture location, the stability of the implant, and bone quality surrounding the implant. We critically analyzed 25 classification systems, highlighting the emergence and adaptations of key systems such as the Vancouver classification system (VCS) and the Unified classification system (UCS), which are lauded for their simplicity and effectiveness yet require further refinement. VCS, developed in 1995, categorizes fractures based on the site, implant stability, and bone quality, and remains widely used due to its robust applicability across different clinical settings. Introduced in 2014, UCS expands the VCS to encompass all periprosthetic fractures with additional fracture types, aiming for a universal application. Despite their widespread adoption, these systems exhibit shortcomings, including the incomplete inclusion of all PPFF types and the imprecise assessment of implant stability and surrounding bone loss. These gaps can result in misclassification and suboptimal treatment outcomes. This paper suggests the necessity for ongoing improvements in classification systems to include emerging fracture types and refined diagnostic criteria, ensuring that they remain relevant to contemporary orthopedic practices and continue to facilitate the precise tailoring of treatment to patient-specific circumstances. This comprehensive historical review serves as a foundation for future innovations in classification systems, ultimately aiming to standardize PPFF treatment and improve patient prognosis.

Design of Web Applications in ASP.NET

Abstract: In the digital era, the development of robust web applications is crucial for providing efficient solutions across various domains. This paper presents the design and development of two distinct web applications using ASP.NET Core: a resort booking application and an expense tracker application. The resort booking application utilizes ASP.NET Core Model-ViewController (MVC) to manage resort operations, while the expense tracker leverages ASP.NET Web API with Angular for comprehensive financial management of the user. The applications are designed to be scalable, secure, and user-friendly, addressing common performance bottlenecks and ensuring optimal performance

The representation, quantification, and nature of genetic information

Current genetics studies often refer to notions from information science. The purpose of this paper is to summarize and structure the different notions of information used in biology, as a step towards developing a taxonomy of information. Within this framework we propose an extension of Floridi’s conceptual model of information. We also make use of the concept of specified information and show that functional information and many other notions of information are either special cases of, or are closely related to, specified information. Since functionality of the proteins that genes code serves as an external and independent specification, this makes it possible to define genetic information in a way that includes semantic aspects. In particular, we discuss how to understand the qualitative aspects of genetic information, how to measure its quantitative aspects, and how variants of Shannon’s information measure can be applied to molecular sequence data of protein families. While a mathematical framework may not be able to incorporate all that is included within biological information, some aspects of it allow for statistical modelling. This is especially true if we restrict our focus on the discipline of genetics. The concept of genetic information is still disputed because it attributes semantic traits to what seems to be regular biochemical entities. Some researchers maintain that the use of information in biology is just metaphorical and may even be misleading. We argue that the foundation of the metaphorical view is relatively weak given the current findings in bioinformatics and show that the present understanding of genetics fits well into the context of the modern philosophy of information. The paper concludes that informational concepts have robust scientific applications at the level of genes.

Transgenic Targeting of Fcrls Creates a Highly Efficient Constitutively Active Microglia Cre Line with Differentiated Specificity.

Microglia carry out important functions as the resident macrophages of the brain. To study their role in health and disease, the research community needs tools to genetically modify them with maximum completeness in a manner that distinguishes them from closely related cell types, such as monocytes. While currently available tamoxifen-inducible CreERT2 lines can achieve the differentiation from other cells, the field needs improved and publicly available constitutively active Cre lines, especially ones with favorable efficiency and specificity profiles for studies where high recombination efficiency is imperative and where tamoxifen administration is contraindicated. Here, we leverage the microglia-specific Fcrls gene to generate mice expressing Cre. Using genomic methods, we show correct positioning of the transgene and intact microglia homeostasis in Fcrls-2A-Cre mice. Crossing Fcrls-2A-Cre mice to four different reporters, we demonstrate highly efficient recombination in microglia across differentially sensitive loxP alleles in different genomic contexts, indicating robust applicability of the line. Further, we show that microglia recombine a loxP reporter during early embryonic development, supporting the use of the line for developmental studies. Finally, using immunofluorescence and flow cytometry, we reveal that most border-associated macrophages are also targeted whereas only few liver and spleen macrophages and virtually no white blood cell subsets exhibit Cre activity, distinguishing this line from another publicly available Cre line, Cx3cr1-CreM Fcrls-2A-Cre mice are immediately available (JAX #036591) and serve as a valuable addition to the community's microglia toolbox by providing highly efficient constitutive Cre activity with excellent specificity, particularly for studies where tamoxifen administration is undesirable.