Advanced Software Research Articles

CNN-GMM approach to identifying data distribution shifts in forgeries caused by noise: a step towards resolving the deepfake problem.

Recently, there have been notable advancements in video editing software. These advancements have allowed novices or those without access to advanced computer technology to generate videos that are visually indistinguishable to the human eye from real ones to the human observer. Therefore, the application of deepfake technology has the potential to expand the scope of identity theft, which poses a significant risk and a formidable challenge to global security. The development of an effective approach for detecting fake videos is necessary. Here, we introduce a novel methodology that employs a convolutional neural network (CNN) and Gaussian mixture model (GMM) to effectively differentiate between fake and real images or videos. The proposed methodology presents a novel CNN-GMM architecture in which the fully connected (FC) layer in the CNN is replaced with a customized Gaussian mixture model (GMM) fully connected layer. The GMM layer utilizes a weighted set of Gaussian probability density functions (PDFs) to represent the distribution of data frequencies in both real and fake images. This representation indicates there is a shift in the distribution of the manipulated images due to added noise. The CNN-GMM model demonstrates the ability to accurately identify variations resulting from different types of deepfakes within the probability distribution. It achieves a high level of classification accuracy, reaching up to 100% in training accuracy and up to 96% in validation accuracy. Notwithstanding the ratio of the genuine class to the counterfeit class being 16.6% to 83.4%, the CNN-GMM model exhibited high-performance metrics in terms of recall, accuracy, and F-score when classifying the least genuine class.

Machine Learning System Development in Information Systems Development Praxis

Advancements in hardware and software have propelled machine learning (ML) solutions to become vital components of numerous information systems. This calls for research on the integration and evaluation of ML development practices within software companies. To investigate these issues, we conducted expert interviews with software and ML professionals. We structured the interviews around information systems development (ISD) models, which serve as conceptual frameworks that guide stakeholders throughout software projects. Using practice theory, we analyzed how software professionals perceive ML development within the context of ISD models and identified themes that characterize the transformative impact of ML development on these conceptual models. Our findings show that developer-driven conceptual models, such as DevOps and MLOps, have been embraced as common frameworks for developers and management to understand and guide the ML development processes. We observed ongoing shifts in predefined developer roles, wherein developers are increasingly adopting ML techniques and tools in their professional work. Overall, our findings underscore that ML technologies are becoming increasingly prominent in software projects across industries, and that the incorporation of ML development in ISD models is an ongoing, largely practice-driven, process.

Characterization of domain formation in complex membranes.

A widely known property of lipid membranes is their tendency to undergo a separation into disordered (Ld) and ordered (Lo) domains. This impacts the local structure of the membrane relevant for the physical (e.g., enhanced electroporation) and biological (e.g., protein sorting) significance of these regions. The increase in computing power, advancements in simulation software, and more detailed information about the composition of biological membranes shifts the study of these domains into the focus of classical molecular dynamicssimulations. In this chapter, we present a versatile yet robust analysis pipeline that can be easily implemented and adapted for a wide range of lipid compositions. It employs Gaussian-based Hidden Markov Modelsto predict the hidden order states of individual lipids by describing their structure through the area per lipid and the average SCC order parameters per acyl chain. Regions of the membrane with a high correlation between ordered lipids are identified by employing the Getis-Ord local spatial autocorrelation statistic on a Voronoi tessellation of the lipids. As an example, the approach is applied to two distinct systems at a coarse-grained resolution, demonstrating either a strong tendency towards phase separation (1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC), 1,2-dilinoleoyl-sn-glycero-3-phosphocholine (DIPC), cholesterol) or a weak tendency toward phase separation (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC), 1-palmitoyl-2-docosahexaenoyl-sn-glycero-3-phosphocholine (PUPC), cholesterol). Explanations of the steps are complemented by coding examples written in Python, providing both a comprehensive understanding and practical guidance for a seamless integration of the workflow into individual projects.

Dosimetric comparison of hippocampal-sparing technologies in patients with low-grade glioma.

Radiotherapy (RT) plays an integral role in the management of low-grade gliomas (LGG). Late toxicity from RT can cause progressive neurocognitive dysfunction. Radiation-induced damage to the hippocampus (HCP) plays a considerable role in memory decline. Advancements in photon planning software have resulted in the development of multi-criteria optimization (MCO) and HyperArc technologies which may improve HCP sparing while maintaining planning target volume (PTV) target coverage. Three planning methods for hippocampal sparing (HS) were compared, volumetric modulated arc therapy (VMAT) without HS (VMAT_noHS), VMAT with HS (VMAT_HS), MCO with HS (MCO_HS), and HyperArc with HS (HyperArc_HS). Twenty-five patients were identified. The contralateral HCP was spared in 16 patients and bilateral HCP in 9 patients with superiorly located tumors. All 3 HS planning techniques showed significant reductions in dose to the spared HCP in contralateral cases but only VMAT_HS and MCO_HS achieved this in bilateral cases (P < .008). Only MCO_HS was superior to VMAT_HS in lowering the dose to both contralateral HCP and bilateral HCP in all measured metrics (P < .008). PTV and OAR (organ at risk) dose constraints were achieved for all plans. This retrospective dosimetric study demonstrated the feasibility of HS for low-grade glioma. All 3 HS planning techniques achieved significant dose reductions to the spared contralateral hippocampus, but only MCO_HS and VMAT_HS achieved this in bilateral cases. MCO was superior to other planning techniques for sparing both bilateral and contralateral hippocampi.

Open-Format Prescription Maps for Variable Rate Spraying in Orchard Farming

Highlights Straightforward procedure to code prescription maps independent of manufacturers. Methodology suitable for orchard crops and vineyards including pressures up to 10 bar and flowrates up to 100 L/min. Algorithm validated for both trellis-based continuous rows and traditional orchard rows of discrete trees. Method scalable to other agricultural operations, such as fertilizing or data registration for auditing purposes. Abstract. The implementation of crop protection solutions contributing to significant reductions in the use and risk of chemical pesticides has motivated the interest of site-specific systems, such as variable rate technologies based on user-set prescriptions. However, the practical implementation of variable rates has been dissuasive for many growers who are not familiar with digital solutions, as proprietary firmware is often difficult to grasp. This paper presents a method for formatting prescription maps that is independent of manufacturers, can be easily and freely implemented by industry and can be adopted by growers not familiar with advanced software tools. The methodology was successfully demonstrated for perennial specialty crops, in particular for trellised vineyards in vertical shoot position and for traditional olive groves. Results showed that the variations within user prescription maps were translated to the fields, but flow errors appeared due to the challenge of measuring flow and pressure in the highly pulsating actuation of pulse-width-modulated (PWM) valves. Such errors diminished with the use of low-volume rates and the optimal selection of the nozzles when adjusted to the real pressure measured in the field. The use cases validated in the field showed the consistency of the method for highly populated prescription maps of 1,221 points/ha accessible to advanced farmers, as well as for modest maps of 80 points/ha within reach of the majority of growers. Keywords: Automated map building, Local tangent plane coordinates, Orchard crop operations, Precision farming, Prescription maps, User-centered approach, Variable rate spraying.

Capital Budgeting Implementation by Public Sector Construction Firms in Tanzania’s Housebuilding Industry: An Exploratory Study

This exploratory study investigates the implementation of capital budgeting practices (CBP) by selected public sector construction firms (PSCF) in Tanzania. This investigation seeks to assess the extent, to which such practices are used for effective investment decision-making, with a particular focus on the country’s housebuilding sector. A case study research design was adopted to collect data from five Tanzanian PSCF, serving as cases through a mix of questionnaires, interviews and document reviews. The emergent data was analysed using thematic analysis. An appreciable level of CBP implementation was observed in all the cases. The internal rate of return (IRR), net present value (NPV) and payback period (PBP) capital budgeting techniques (CBT) were observed to be mostly used by these PSCF, while sensitivity analysis was widely deployed for risk mitigation. The influence of top management in decision- making, political influence and lack of advanced software were some of the implementation challenges identified. The findings contribute to the elucidation of CBP implementation praxis in the public sector organisation context in developing countries, as it relates to the housebuilding industry in Tanzania’s unexplored context. The study proves to be a valuable resource for public sector entities in developing countries intent for facilitating effective investment decision-making about infrastructure procurement and it contributes to improving knowledge of CBP implementation, consideration of risk mitigation factors and post-project implementation audits by public sector firms.

Increasing the detectability of long-period and nulling pulsars in next-generation pulsar surveys

Abstract Recent discoveries of multiple long-period pulsars (periods ${\sim}10\,$ s or larger) are starting to challenge the conventional notion that coherent radio emission cannot be produced by objects that are below the many theorised death lines. Many of the past pulsar surveys and software have been prone to selection effects that restricted their sensitivities towards long-period and sporadically emitting objects. Pulsar surveys using new-generation low-frequency facilities are starting to employ longer dwell times, which makes them significantly more sensitive in detecting long-period or nulling pulsars. There have also been software advancements to aid more sensitive searches towards long-period objects. Furthermore, recent discoveries suggest that nulling may be a key aspect of the long-period pulsar population. We simulate both long-period and nulling pulsar signals, using the Southern-sky MWA Rapid Two-meter (SMART) survey data as reference and explore the detection efficacy of popular search methods such as the fast Fourier transform (FFT), fast-folding algorithm (FFA) and single pulse search (SPS). For FFT-based search and SPS, we make use of the PRESTO implementation, and for FFA we use RIPTIDE. We find RIPTIDE’s FFA to be more sensitive; however, it is also the slowest algorithm. PRESTO’s FFT, although faster than others, also shows some unexpected inaccuracies in detection properties. SPS is highly sensitive to long-period and nulling signals, but only for pulses with high intrinsic signal-to-noise ratios. We use these findings to inform current and future pulsar surveys that aim to uncover a large population of long-period or nulling objects and comment on how to make optimal use of these methods in unison.

AN INNOVATIVE METHODOLOGY TO DESIGN GYROID HEAT EXCHANGERS FOR METAL ADDITIVE MANUFACTURING

This study demonstrates an innovative approach to the automatic design of compact gyroid heat exchangers using the advanced engineering software nTopology, which is based on implicit modeling technology. The aim is to provide a modern enhancement to traditional 'Shell and Tube' type heat exchangers. Utilizing functions in implicit modeling and parametric design features, complex internal gyroid structures can be used as an alternative heat transfer interface in 'Shell and Tube' type heat exchangers. The most striking aspect of the methodology is its ability to fully automate the design process. By consolidating specific parameters into a single function block and entering scalar values, a fast and flexible workflow is activated, automatically generating the final geometry. Gyroid structures with high thermal performance and fluid dynamics are automatically adapted for various volumes and geometries. These structures can improve the overall efficiency of heat exchangers and offer significant advantages, especially in specialized application areas such as aerospace and space industries. In the design and sizing process of the gyroid heat exchanger, if manufacturing is planned, it is necessary to pay attention to the design principles for metal additive manufacturing. In conclusion, this study demonstrates that the advanced engineering software known as nTopology can create a synergistic effect in the rapid and easy design of gyroid heat exchangers and in establishing the automation of the design process.

Lab Automation

Abstract: This lab automation project aims to revolutionize scientific workflows by implementing a comprehensive system that integrates cutting-edge technologies to streamline processes and enhance accuracy in laboratory settings. Leveraging robotics, sensor networks, and advanced software, our solution automates repetitive tasks, minimizes human error, and accelerates experimentation cycles. The system's modular design allows seamless integration into existing laboratory infrastructure, ensuring adaptability across diverse research domains. Through real-time data monitoring and analysis, our automation solution not only increases efficiency but also facilitates data-driven decision-making. This project represents a significant step towards the future of laboratory operations, fostering scientific advancements by optimizing resource utilization and promoting reproducibility in experimental outcomes

B2B Customer Engagement Customer Behaviour Forecast Application

The paper presents the "Casper Customer Behavior Forecast Application" a creative project focused on the creation of an advanced decision support software system. The purpose of this system is to closely observe, fully analyze, and precisely predict the behaviors of Casper Business-to-Business (B2B) clients. Our research study implements an innovative methodology by focusing on the enhancement of client interactions through the utilization of RESTful services. Our objective is to reimagine the future of customer relationship management by analyzing and forecasting client behavior in the business-to-business (B2B) setting. The project involves a comprehensive methodology that incorporates extensive study, complex software design, and careful data analysis. In more detail, it involves extensive monitoring of consecutive RESTful interactions carried out by business-to-business consumers across a period of time. By leveraging the capabilities of modern machine learning and deep learning algorithms, our objective is to develop prediction models that establish novel benchmarks within the industry. The methodology employed in this study includes the development of labeled behavioral datasets and the utilization of a supervised machine learning framework. The evaluation of model performance will be performed systematically using a range of metrics, such as F-Score and Accuracy, in order to establish the model's robustness and reliability in making accurate predictions. The expected outcomes of this project have the potential to bring about significant changes. Primarily, the integration of machine learning and deep learning algorithms will provide our company with an important amount of knowledge. Our organization will obtain an innovative software solution that possesses the capability to precisely forecast the future actions of B2B customers. This results in enhanced inventory management and a significant reduction in client waiting periods, resulting in increased levels of customer satisfaction. Additionally, this study has the potential to make significant additions to the global academic body of knowledge in the domains of machine learning and deep learning. In simple terms, the project titled "Casper Customer Behavior Forecast Application" embodies an innovative and academic effort aimed at enhancing customer engagement and predicting customer behavior within the context of business-to-business (B2B) interactions. This research project carries considerable importance, both in its potential to reinvent the field of customer relationship management and its ability to contribute to the global academic conversation on machine learning and deep learning.

Various Flow in a Slender Tube: Mach Number and Domain Shape Influenced by Boundary Obstacles Via Small Disturbance Equations

With the exponential advancements in computer hardware and software in recent years, computational fluid dynamics (CFD) has emerged as a highly efficient alternative to traditional fluid mechanics studies. Its prowess in accurately predicting and analyzing flow mechanics has led to widespread adoption across diverse engineering disciplines. This research delves into a numerical analysis of flow conditions utilizing the small disturbance equation (SDE). The study bridges the gap between mathematical formulations and computational language, exemplified through Python code, by harnessing the successive over-relaxation (SOR) method, Neumann boundary conditions (BC), and the ghost point technique. The research underscores its predictions' precision in subsonic and supersonic scenarios by meticulously evaluating the resultant errors. This accuracy is further corroborated by examining overarching flow patterns and Mach number distributions. However, the program's outputs indicate a discernible lack of precision in the transonic case, with errors surpassing acceptable thresholds. This discrepancy is hypothesized to stem from potential mischaracterizations of points at the inflow and outflow boundaries. This study, thus, offers invaluable insights while also highlighting areas necessitating further refinement.

Transforming Emergency Medical Services with Electric Ambulance Technology

Abstract: The focus of this project is to design and implement cutting-edge software features within the Electric Ambulance. These features are tailored to streamline the identification and availability of medical appliances and medications on board. The goal is to optimize the response time and ensure that paramedics have immediate access to the necessary tools for providing effective patient care. Complementing the software enhancements is the integration of a Laboratory System, driven by Lab-on-Chip (LOC) technology. This system is poised to revolutionize pre-hospital care by enabling on-the-spot health examinations, facilitating accurate first aid, and initiating prompt treatment. The marriage of advanced software and laboratory capabilities sets the stage for a new era in emergency medical services.

Using Medical Data and Clustering Techniques for a Smart Healthcare System

With the rapid advancement of information technology, both hardware and software, smart healthcare has become increasingly achievable. The integration of medical data and machine-learning technology is the key to realizing this potential. The quality of medical data influences the results of a smart healthcare system to a great extent. This study aimed to design a smart healthcare system based on clustering techniques and medical data (SHCM) to analyze potential risks and trends in patients in a given time frame. Evidence-based medicine was also employed to explore the results generated by the proposed SHCM system. Thus, similar and different discoveries examined by applying evidence-based medicine could be investigated and integrated into the SHCM to provide personalized smart medical services. In addition, the presented SHCM system analyzes the relationship between health conditions and patients in terms of the clustering results. The findings of this study show the similarities and differences in the clusters obtained between indigenous patients and non-indigenous patients in terms of diseases, time, and numbers. Therefore, the analyzed potential health risks could be further employed in hospital management, such as personalized health education control, personal healthcare, improvement in the utilization of medical resources, and the evaluation of medical expenses.

Integrating analytical statistical models, sequential pattern mining, and fuzzy set theory for advanced mobile app reliability assessment

The study presents a new method for evaluating the reliability of mobile applications using the Corcoran model. This model includes several aspects of reliability, including performance, reliability, availability, scalability, security, usability, and testability. The Corcoran model can be applied to evaluate mobile applications by analysing key reliability metrics. Using the model significantly improves the reliability assessment of applications compared to traditional methods, which are primarily focused on desktop and server configurations. The aim of the study is to offer a more optimised approach to evaluating the reliability of mobile applications. The paper examines the problems faced by mobile app developers. This study represents a new application of the Corcoran model in evaluating the reliability of mobile applications. This model is characterised by an emphasis on the use of quantitative statistics and the ability to provide an accurate estimate of the probability of failure without any inaccuracies, which distinguishes this model from other software reliability models. The paper suggests using a combination of analytical statistical models, data extraction methods such as sequential pattern analysis, and fuzzy set theory to implement the Corcoran model. The application of the methodology is demonstrated by studying software error reports and conducting a comprehensive statistical analysis of them. To improve the results of future research, the paper suggests making more extensive use of the Corcoran model in various mobile applications and environments. It is recommended to change the model to take into account the constantly changing characteristics of mobile applications and their increasing complexity. In addition, it is advisable to conduct additional research to improve the data mining methods used in the model and explore the possibility of integrating artificial intelligence for more advanced software reliability analysis. Applying the Corcoran model to the mobile app development process to evaluate reliability can significantly improve the quality of applications, leading to increased customer satisfaction and trust in mobile apps. This model can serve as a guide for developers and companies to evaluate and improve their applications, driving innovation and continuous improvement in the competitive mobile app sector.

A nested square combination of ε-negative high EMR symmetric metamaterial structure for triple band wireless applications

In this article, a novel metamaterial is presented that is constructed with split ring resonators (SRR) arranged in a nested square configuration. The proposed metamaterial unit cell is constructed over a 1.5 mm thick FR-4 substrate with electrical dimensions of 0.11 λ × 0.11 λ, where λ is the wavelength calculated at 2.8 GHz. The resonating patch contains five interconnected nested squares, including two square split ring resonators (SSRRs). The unit cell exhibits three distinct frequencies at 2.8 GHz, 5.8 GHz, and 11.2 GHz, with corresponding magnitudes of −45 dB, −50 dB, and −30 dB, respectively. Negative permittivity (ε-Negative) is observed in frequency ranges of 2.3–3.20 GHz, 4.4–6.9 GHz, and 10.8–11.30 GHz, with near-zero refractive index. The simulated outcome acquired in CST microwave studio is subsequently confirmed using analogous circuit modeling in Advanced System software (ADS). Moreover, the prototype of the proposed metamaterial is developed that shows similar resonances obtained in the simulation. The effective medium ratio (EMR) of the unit cell is calculated and a high EMR of 8.3 expresses the compactness of the metamaterial. Due to its structural compactness, this new metamaterial with ε-negative, near zero refractive index, and triple band resonance properties holds significant potential for wireless applications such as radar systems, satellite communications, and wireless sensor networks.

NephroCAGE-German-Canadian Consortium on AI for Improved Kidney Transplantation Outcome: Protocol for an Algorithm Development and Validation Study.

Recent advances in hardware and software enabled the use of artificial intelligence (AI) algorithms for analysis of complex data in a wide range of daily-life use cases. We aim to explore the benefits of applying AI to a specific use case in transplant nephrology: risk prediction for severe posttransplant events. For the first time, we combine multinational real-world transplant data, which require specific legal and technical protection measures. The German-Canadian NephroCAGE consortium aims to develop and evaluate specific processes, software tools, and methods to (1) combine transplant data of more than 8000 cases over the past decades from leading transplant centers in Germany and Canada, (2) implement specific measures to protect sensitive transplant data, and (3) use multinational data as a foundation for developing high-quality prognostic AI models. To protect sensitive transplant data addressing the first and second objectives, we aim to implement a decentralized NephroCAGE federated learning infrastructure upon a private blockchain. Our NephroCAGE federated learning infrastructure enables a switch of paradigms: instead of pooling sensitive data into a central database for analysis, it enables the transfer of clinical prediction models (CPMs) to clinical sites for local data analyses. Thus, sensitive transplant data reside protected in their original sites while the comparable small algorithms are exchanged instead. For our third objective, we will compare the performance of selected AI algorithms, for example, random forest and extreme gradient boosting, as foundation for CPMs to predict severe short- and long-term posttransplant risks, for example, graft failure or mortality. The CPMs will be trained on donor and recipient data from retrospective cohorts of kidney transplant patients. We have received initial funding for NephroCAGE in February 2021. All clinical partners have applied for and received ethics approval as of 2022. The process of exploration of clinical transplant database for variable extraction has started at all the centers in 2022. In total, 8120 patient records have been retrieved as of August 2023. The development and validation of CPMs is ongoing as of 2023. For the first time, we will (1) combine kidney transplant data from nephrology centers in Germany and Canada, (2) implement federated learning as a foundation to use such real-world transplant data as a basis for the training of CPMs in a privacy-preserving way, and (3) develop a learning software system to investigate population specifics, for example, to understand population heterogeneity, treatment specificities, and individual impact on selected posttransplant outcomes. DERR1-10.2196/48892.

Recording Progress in Junior Athletes' Proprioceptive Training using Modern Sports Technology

This study investigates the efficacy of modern sports technology in monitoring and enhancing proprioceptive training among junior athletes. The research focuses on recording and analyzing the progress of young athletes engaged in proprioceptive exercises using state-of-the-art technological tools. Through a comprehensive examination of these advancements, this study aims to explore the impact of such technology on improving proprioception in junior athletes. The methods involve implementing specific training protocols while utilizing advanced tracking devices and analytical software to monitor and record the athletes' progress. The findings aim to provide insights into the effectiveness of modern sports technology in optimizing proprioceptive training among young athletes, thereby contributing to the enhancement of their overall athletic performance and injury prevention strategies.

Stochastic Excitation-Incorporated Seismic Fragility Assessment Using Monte Carlo Simulations

Structures are susceptible to damage from natural disasters like tornadoes, floods, and seismic events. A resilient structure can withstand such damage and ensure cost-effectiveness. Therefore, accurately assessing the probability of structural failure is imperative for structural designers. This study employs an innovative fragility methodology to assess seismic damage in structures. In comparison to conventional fragility approaches, this method is cost-effective, universally applicable across various seismic sources, and highly precise. Specifically, this method involves the generation of artificial seismic records using the point-source stochastic seismological model and Monte Carlo simulation method. These synthetic seismic records offer distinct advantages when compared to the seismic data collected from real earthquakes at recording stations, such as the ability to adjust the epicentral distance of the synthetic waves as needed. Therefore, this approach enables a comprehensive analysis of structural responses under seismic waves characterized by different attributes. For example, the influence of different seismological parameters such as wave magnitude and epicentral distance are well studied and compared. The advanced finite element software, OpenSees, serves as the computational platform for examining how structures react to diverse seismic waves. The research findings suggest that, compared to traditional methods, utilizing OpenSees can significantly enhance computational efficiency. Furthermore, the methods employed introduce a novel technique for factoring in wave magnitudes and distances during the calculation of structural seismological responses/fragility. This innovation holds significance for the development of next-generation structural resilience designs.

Ultra-high-resolution reflected-light imaging for dendrochronology

Dendroclimatic reconstructions play a key role in contextualizing recent climate change by improving our understanding of past climate variability. The Blue Intensity (BI) measurement technique is gaining prominence as a more accessible alternative to X-ray densitometry for producing climatically highly-sensitive tree-ring predictors. Nevertheless, accurately representing low-frequency trends and high-frequency extremes using scanner-based BI remains a challenge due to color biases and resolution limitations. Herein we introduce several methodological advances in sample surfacing, imaging, and image processing which yield measurement series analogous to BI from ultra-high-resolution (UHR; ∼74 700 dpi) images. Such series capture changes in tree-ring anatomical density by representing wood anatomical structure using binary (i.e., black-white) segmentation of sample images. We refer to this novel technique as Binary Surface Intensity (BSI). By utilizing a UHR system and entirely eliminating color and light intensity as variables, the most substantial drawbacks of scanner BI (i.e., discoloration and resolution biases) are bypassed, resulting in more accurate representations of low-frequency climatic trends and high-frequency extremes. Comparisons of several chronologies developed with the BSI and BI techniques, including a multiparameter dataset from Björklund et al. (2019), showed that BSI datasets outperform BI in terms of common signal (r-bar), but also contain strong climatic signals that can exceed those obtained from BI and X-ray density, and even match density datasets based on quantitative wood anatomy. However, measurement software advancements are still required to unlock the full potential of tree-ring parameters produced using the BSI technique. Ongoing development of this new technique will not only aid the attainment of long unbiased chronologies by overcoming color biases and resolution limitations, but also holds promise for unlocking UHR analyses of surface anatomical (sQWA) parameter datasets from reflected-light images. These advances will lead to more accurate tree-ring-based paleoclimatic reconstructions and could also serve a wider range of dendrochronological applications.

Porosity Measurements and Analysis of Sintered and Thermochemical Heat Treated P/M Compacts

The objective of this paper was to examine the porosity in fluidized-bed carburizing on sintered alloys obtained by the powder metallurgy route using an image software analysis, and to compare the results with those obtained using the conventional porosity measuring technique. A material's porosity is a measurement of its vacancy percentage. The volume of empty space divided by the material's bulk volume, given as a percentage, determines the overall porosity of the material. The advancement of digital imaging and software has resulted in a novel and appropriate technique for figuring out the porosity of materials used in powder metallurgy.