BAW critical experiment benchmark analysis using PRAGMA GPU-based Monte Carlo code

The relevance of the work is due to the growth of household waste flows and the need for accurate recognition of material categories in realistic scenes with uneven lighting, background noise and class imbalance. The practical effectiveness of such systems is determined not only by the choice of architecture, but primarily by the controlled quality of data and the reproducibility of experiments in a standardized environment. The article proposes a quality-oriented pipeline in which the quality control module is integrated directly into the training cycle. Filtering by sharpness, contrast, exposure balance and background clutter forms a cleaned subsample for further training of the pre-trained model. Managed cloud computing nodes based on Google Colab sessions with access to graphics accelerators, fixed library versions and artifact logging are used, which ensures the stability of the software environment and the comparability of series. The methodology is based on the MobileNetV3 Small architecture with ImageNet feature porting and replacing the classification head with a thirty-class problem statement. The Recyclable and Household Waste Classification Dataset with thirty categories, including paper, plastic, glass, and metal subclasses, as well as organic fractions, was used for experiments. The baseline evaluation on the raw sample yielded consistent results across metrics with an accuracy of 0.7703 and high areas under the ROC curves, indicating good resolution of probabilistic outputs and a reserve for stabilizing solutions in multi-class mode. Inclusion of filtering in the training cycle provided subject-specific improvements in classes prone to cross-validation errors due to gloss and weak texture. For paper_cups, an increase in accuracy of 13.13 percent, completeness of 10.69 percent, and integral F1 of 11.85 percent was recorded. Positive improvements were also obtained for steel_food_cans, clothing and magazines, where confusion with visually similar categories was reduced. The results obtained confirm the feasibility of shifting the emphasis from the complexity of architectures to managed data quality and the discipline of experimentation in a cloud environment. The proposed integration increases the robustness of classification and creates a basis for the reliable implementation of computer vision in the recycling infrastructure and supports circular economy practices.

This paper proposes an Information and Communication Technology (ICT) infrastructure management model tailored to the operational reality of municipal public administration and grounded in the Information Technology Infrastructure Library version 4 (ITIL 4) Infrastructure and Platform Management (IPM) practice. A research gap was identified through a structured literature search that found no studies specifically addressing the application of the ITIL 4 IPM practice in municipal environments. Using the Design Science Research method, the model was developed and documented in Business Process Model and Notation (BPMN), integrating item classification, prioritisation routines and procedures adapted to resource-constrained public settings. A pilot execution in a municipal health unit demonstrated its applicability, revealing gaps in asset data, contributing to a more structured infrastructure assessment and supporting decision-making processes related to monitoring, risk mitigation and planned replacement of equipment. The results provide initial evidence that the model offers structural support and supports more consistent infrastructure analysis within the pilot context, indicating its potential to support maturity assessment across successive assessment cycles.

Background: Herpes zoster (HZ) represents a substantial public health concern among aging populations, yet regional variability in clinical patterns and risk determinants remains insufficiently documented. In southeastern Romania, epidemiological data are limited, and the combined influence of demographic, behavioral, and metabolic factors on disease severity has not been systematically evaluated. Methods: We performed a retrospective observational study including 100 consecutive patients diagnosed with HZ between 2019 and 2023 in a dermatology department in southeastern Romania. Demographic characteristics, lifestyle behaviors, anthropometric status, clinical manifestations, and outcomes were extracted from medical records. Associations between categorical variables were assessed using Chi-square tests and Cramer’s V, while interaction patterns were explored through log-linear modeling. Heatmaps were generated in Python (version 3.10) using the Matplotlib library (version 3.7.1) to visualize distribution patterns and subgroup relationships. Results: The cohort showed a marked age dependence, with 77% of cases occurring in individuals ≥ 60 years, consistent with immunosenescence-driven reactivation. Women represented 59% of cases, and 84.7% of female patients were postmenopausal. Urban residents predominated (91%). Vesicular eruption (84%) and acute pain (79%) were the most frequent symptoms. Localized HZ was observed in 81% of cases, while ophthalmic involvement (11%) and disseminated forms (8%) were less common. Lifestyle factors significantly influenced clinical severity: smokers, alcohol consumers, and sedentary individuals exhibited higher proportions of postherpetic neuralgia (PHN) and ocular complications (p < 0.001). Overweight and obese patients demonstrated a higher burden of PHN, suggesting a role for metabolic inflammation, although BMI was not associated with incidence. No significant association between age category and complication type was detected, likely due to small subgroup sizes despite a clear descriptive trend toward increased severity with advanced age. Conclusions: These findings support a multifactorial model of HZ severity in southeastern Romania, shaped by age, lifestyle behaviors, hormonal status, and metabolic risk. While incidence patterns align with international data, the strong impact of modifiable factors on complication rates highlights the need for targeted prevention and individualized risk assessment. Results offer a regional perspective that may inform future multicenter investigations.

Abstract Version 4.00 of the library of computed spectra of the NASA Ames PAH IR Spectroscopic Database (PAHdb) includes an expanded set of polycyclic aromatic hydrocarbon (PAH) spectra, totaling 10,749, calculated using the harmonic approximation and a basis set containing polarization functions. The software tools offered through PAHdb have also been significantly expanded, including optimizations to more efficiently handle the large number of spectra now available. Major improvements have been made to the PAHdb website, which now also offers new libraries of laboratory and computed PAH cluster spectra. PAH boundary-edge codes are now being used throughout PAHdb to hold structural information and to search for specific PAH isomers. All enhancements and updates are described in expanded documentation. As a demonstration, the updated version 4.00 computational library and improved software tools are used to analyze the James Webb Space Telescope spectrum of the atomic photodissociation region of the Orion Bar and show a remarkable improvement in matching the 6.2 μ m emission band and the 10–15 μ m emission over earlier library versions.

This article presents updates to life x [Africa, SoftwareX (2022)], a C++ library for high-performance finite element simulations of multiphysics, multiscale, and multidomain problems. In this release, we introduce an additional intergrid transfer method for non-matching multiphysics coupling on the same domain, significantly optimize nearest-neighbor point searches and interface coupling utilities, extend the support for 2D and mixed-dimensional problems, and provide improved facilities for input/output and simulation serialization and restart. These advancements also propagate to the previously released modules of life x specifically designed for cardiac modeling and simulation, namely life x -fiber [Africa et al., BMC Bioinformatics (2023)], life x -ep [Africa et al., BMC Bioinformatics (2023)], and life x -cfd [Africa et al., Computer Physics Communications (2024)]. The changes introduced in this release aim at consolidating life x ’s position as a valuable and versatile tool for the simulation of multiphysics systems.

This study aims to evaluate the performance of Digilib services at Amikom University Purwokerto using the Information Technology Infrastructure Library (ITIL) version 3 framework in the Service Operation domain. The phenomenon underlying this study is the indication of a decline in the operational effectiveness of Digilib services as seen from the inconsistency of system performance, access speed, and limited service features. This study uses an evaluative quantitative approach by collecting data through a questionnaire compiled based on five main processes in the Service Operation domain, namely Event Management, Incident Management, Request Fulfillment, Problem Management, and Access Management. The research respondents were library staff directly involved in the management and operation of Digilib services. Data were analyzed using a process maturity model (Process Maturity Model) to assess service capabilities based on a scale of 0–5. The results of the analysis indicate that the overall maturity level of Digilib services is at Level 4 (Managed Process), with an average value of 4.07. This indicates that the operational process has been controlled and measured through certain performance indicators, although there are still opportunities for improvement towards Level 5 (Optimized), especially in the Request Fulfillment and Problem Management domains. These findings contribute to strengthening IT service governance in the higher education sector and provide strategic recommendations for improving automation, system integration, and data-driven service management. The implications of this research encourage the development of policies to improve the quality of digital services and serve as a reference for further research on ITIL implementation in academic settings.

Activation and transmutation simulations model the time evolution of a given nuclide inventory under various irradiation conditions. The results of such inventory simulations must be verified to give confidence in their predictions, with the reliability of these predictions dependent on the choice of nuclear data library. This work performs verification and validation of nuclear data libraries based on the fusion decay heat measurements performed at the Japanese FNS (Fusion Neutron Source) facility. Using the nuclear inventory code FISPACT-II, simulations have been performed with the latest official releases and test versions of TENDL, JEFF, and JENDL nuclear data libraries. The assessment compares the results between the nuclear data libraries themselves and benchmarks each against the experimental measurements, with example results presented for selected samples: barium, germanium, and chlorine. The high-fidelity simulations allow the contributions to the decay heat results from individual radionuclides to be studied. These results allow the quality of inventory predictions to be assessed for each library and indicates any inaccuracy/omission in the nuclear data, e.g. identification of missing production pathways and cross sections that need reviewing.

Background: Vancomycin-resistant Enterococcus (VRE) is a pathogen that can cause nosocomial infection leading to increased morbidity and mortality. Healthcare provider interactions have been reported to cause nosocomial infection acquisition. Here we aim to understand patient-staff interactions by mapping their interactions together with the movement of VRE colonised patients in an acute inpatient setting in a tertiary hospital for one month to create a dynamic visualisation map. Methods: Staff-patient interactions were obtained through documentation in the electronic health records (EHR). Hospital-onset VRE Hospital-Onset acquisition is defined as a positive screen/culture on or after the third day of admission. The cohort was categorised by their VRE statuses such as Hospital-onset (HO), Community-onset (CO) defined occurring before the 3rd day of admission, Infectious period (XO) and Negative (NO). XO is estimated to be the two days before a positive test HO acquisition. NO patients in this cohort eventually turn positive during their admission. A network was generated where patients and staff were represented as nodes, with edges weighted by number of interactions with the specific staff. Node colours were assigned based on VRE status. An interactive patient-staff interaction network map was developed using Python 3.12.8, using PyVis library version 0.3.2 for network visualization and Dash version 2.18.2 for web-based interactivity. Results: There are a total of 207 patients who tested positive for VRE in SGH in July 2024. 54 (26.1%) were HO. A snapshot of the map filtered for 10/07/24 to 11/07/24 can be seen in Figure 1. 14 (11.8%) were HO, 58 (48.7%) were CO, 44 (37.0%) were NO and 3 (2.5%) were XO. As seen in figure 1, there are 7 (5.9%) are in isolated singular clusters, 6 (5.0%) are in pair clusters and 3 (2.5%) are in a triple cluster. In total, the largest cluster consists of 103 (86.6%) patients. This cluster consists of HO,NO,CO and XO patients. This intermingling highlights potential routes for cross-transmission of VRE. Conclusion: The network map reveals notable intermingling of CO, HO, XO and NO contacts within the dominant cluster and suggests potential routes of transmission. This underscores the need for better understanding of transmission dynamics to allow enhancement of existing infection prevention policies to prevent this spread.

Residual networks have shown great success and become indispensable in recent deep neural network models. In this work, we aim to re-investigate the training process of residual networks from a novel perspective of loafing, and further propose a new training scheme as well as three improved strategies for boosting residual networks beyond their performance limits. Previous research has suggested that residual networks can be considered as ensembles of shallow networks, which implies that the final performance of a residual network is influenced by a group of subnetworks. Furthermore, we identify a previously overlooked problem, where subnetworks within a residual network are prone to exert less effort when working as part of a group compared to working alone. We define this problem as network loafing. Since network loafing may inevitably cause the sub-par performance of the residual network, we propose a novel training scheme called stimulative training, which randomly samples a residual subnetwork and calculates the KL divergence loss between the sampled subnetwork and the given residual network for extra supervision. In order to unleash the potential of stimulative training, we further propose three simple-yet-effective strategies, including a novel KL- loss that only aligns the network logits direction, random smaller inputs for subnetworks, and inter-stage sampling rules. Comprehensive experiments and analysis verify the effectiveness of stimulative training as well as its three improved strategies. For example, the proposed method can boost the performance of ResNet50 on ImageNet to 80.5% Top1 accuracy without using any extra data, model, trick, or changing the structure. With only uniform augment, the performance can be further improved to 81.0% Top1 accuracy, better than the best training recipes provided by Timm library and PyTorch official version. We also verify its superiority on various typical models, datasets, and tasks and give some theoretical analysis. As such, we advocate utilizing the proposed method as a general and next-generation technology to train residual networks.

The rapid development of information technology drives improvements in service quality, including help desk services that act as a central point of customer support. The research aims to enhance the achievement of Service Level Agreement (SLA) response and resolution targets in a digital port and logistics company that has yet to meet its SLA response goal of 95%, with an average fulfillment of only 64%. The research adopts a descriptive qualitative method using a case study approach. Data were collected through SLA reports from December 2023 to April 2024 and interviews with the helpdesk team. The research object is the company's helpdesk system. The analysis is conducted using the Information Technology Infrastructure Library version 4 (ITIL v4) framework through Service Value Chain (SVC) practices and supported by an Agile approach. The results show that implementing an ITIL v4-based chatbot combined with Agile practices can significantly improve SLA response due to the chatbot's availability and instant response capabilities. Additionally, SLA resolution is also improved as the chatbot supports initial triage and accelerates ticket routing to the appropriate teams. The average gap after implementation indicates a 26% improvement in SLA response and a -1% gap in SLA resolution (exceeding the target). The research highlights the strong potential of integrating ITIL v4 into chatbot design and management, even though academic studies exploring the relationship between ITIL v4 and chatbot functions remain limited.

In many real-world scenarios, being able to infer specific software versions or variations of cryptographic libraries is critical to mounting targeted exploits. For this, traditional version-detection approaches often rely on direct inspection of programs. However, modern computing platforms frequently employ protection for code, e.g., using execute-only memory (XOM) or trusted execution environments (TEE) to safeguard sensitive code from disclosure and reverse engineering. This paper demonstrates how side-channel measurements via CPU port contention reveal distinctive execution signatures, even when code is inaccessible for inspection. Our proof-of-concept implementation PortPrint identifies cryptographic functions, reveals library versions, and even uncovers whether a WolfSSL build is vulnerable to CVE-2024-1544 or if Spectre mitigations are active in Xen. We verify that PortPrint works despite state-of-the-art code protection mechanisms, such as memory protection keys, hypervisor-based XOM, Intel SGX, Intel TDX, and AMD SEV. We also report a negative result for leaking code protected with these techniques using Meltdown and Foreshadow, providing valuable insights into the limitations of these attacks. Our results show that hardware-based isolation is insufficient to conceal instruction streams.

Third-party libraries are increasingly used in software development. With new versions released, many conflicts due to API breaking may be introduced. Therefore, semantic versioning with a set of rules and requirements is proposed to inform users about incompatibilities and other changes in a new release. Simply speaking, major, minor, and patch version numbers should be updated when breaking, non-breaking, and internal changes are made, respectively. However, many third-party libraries do not follow semantic versioning principles, bringing many efforts to adapt new versions of libraries. Hence developers are often unwilling to update dependencies. In this paper, we first retrospectively investigate the three types of existing techniques that can be used in automatic versioning, a task for more reliable versions: 1) rule-based, considering keywords only, 2) machine learning-based, concerning many aspects of features, and 3) source code analysis for semantic versioning compliance. Having found the limitation of these approaches, we propose a new and simple approach AutoVer, which can capture the intent of developers through commit messages. Specifically, we treat the tokens and phrases in the commit messages as features and train AutoVer using XGBoost, a well-known machine learning method that performs well in many classification tasks. The evaluation results show that AutoVer outperforms our investigated approaches in terms of many metrics. Specifically, the major, minor and major type F1-Scores of AutoVer are 0.889, 0.992 and 0.998, respectively. We also comprehensively investigate many settings that affect the performance of AutoVer, including choice of machine learning model, number of features and keywords in commit messages as well as provide practitioners and researchers with some implications for future studies, e.g., writing clear commit messages for better understanding the intent of making changes, and combining semver-compliance checking and automatic versioning.

Libraries assist in accelerating the development of software applications by providing reusable functionalities. Libraries and applications that declare these libraries as dependencies become their clients. However, as libraries evolve, maintaining the dependencies in client projects can be challenging if the new version contains breaking changes. Yet, limited research focuses on analyzing the impact of breaking changes on client projects when updating dependencies in the wild. Hence, we conduct an empirical analysis using Java projects built using Maven to investigate the impact of breaking changes introduced between two library versions. Our dataset included 18,415 Maven artifacts, declaring 142,355 direct dependencies, out of which 71.60% were not up-to-date. We automatically updated these dependencies and discovered that 11.58% of the dependency updates resulted in breaking changes that affected the client, and almost half of them were introduced during a non-major update. We analyzed the changes in the libraries that contributed towards these breaking changes, and our results indicate that changes in transitive dependencies were a significant factor in introducing breaking changes. We further investigated if it was common for clients to use functionalities of transitive dependencies directly without declaring them. This showed that over half of the clients use transitive functionality. Therefore, we analyzed actions suggested to resolve these breaking changes introduced by transitive dependencies under the discussions on open-source platforms, and the frequently suggested action was to exclude the transitive dependency from the project configuration.

Hiragana is one of the primary alphabets used in Japanese. Hiragana is a phonetic symbol; each letter represents one syllable. Hiragana letters are formed from curved lines and strokes. However, detecting Hiragana letters causes many errors because people still rely on their vision to detect the letters, especially people familiar with them for the first time. It will be difficult and not very clear to read the letters. Therefore, a Convolutional Neural Network (CNN) method is used to detect handwritten Hiragana letters and help people who first get to know Hiragana letters when the letters are too complicated for human eyes to detect. This research uses the YOLOv8 model as a handwritten Hiragana letter detection algorithm. The Hiragana letters to be detected are basic letters with 46 characters. This research uses the YOLOv8 model run on Google Collaboratory with the Ultralytics library version 8.0.20 using the Python programming language. The dataset is collected from the internet and annotated using the Roboflow framework and dataset 4600 Hiragana letters. From the test results, the best model is YOLOv8l using SGD optimizer and learning rate 0.01 with a precision value of 98.5%, recall value of 95.7%, f1-score value of 97.1%, and mAP value of 95.5%. In the future, we aim to expand the number of datasets and employ a broader range of hyperparameter values to optimize the classification precision and accuracy of the Hiragana Letter Detection system.

Hospital Information System (HIS) is an important component in supporting effective and efficient health services. Evaluation of the maturity of information systems is needed to ensure that the implementation of SIRS is in accordance with the standards and needs of the organization. This study aims to evaluate the maturity level of information systems at Amanah Hospital using the Information Technology Infrastructure Library (ITIL) version 3 framework, specifically in the Service Operation domain. The research method uses a quantitative approach with data collection through questionnaires distributed to respondents involved in managing information systems. The evaluation was carried out on five subdomains, namely Event Management, Incident Management, Request Fulfillment, Problem Management, and Access Management. The results showed that the maturity level of the Amanah Hospital information system was at level 3.81 (Managed), with the highest value in the Incident Management and Access Management subdomains (4.00), while the lowest value in Event Management and Request Fulfillment (3.66). Based on these results, several improvements are recommended including the development of an integrated monitoring system, strengthening operational procedures, improving HR competencies, and implementing more structured periodic evaluations to achieve an optimal level of maturity.

This research aims to interpret and show how libraries and librarians are represented in the music video Bertahan/Pergi-Raisa. The research method is qualitative using Ferdinand de Saussure’s semiotics analysis to reveal the relationship between signifier and signified which constructs the audience's understanding of libraries and librarians. Signifier and signified regarding libraries and librarians are dissected through the images/video and dialogue between characters. In the Bertahan/Pergi’s clip, the type of library shown is the latest library version, with a typical cafe interior design. However, the library is still described as a place with no visitors and has a dark atmosphere. The library is also represented as a place to look for references, as well as a meeting space between writers and book readers. Apart from that, this clip also represents librarians as people who love reading, respecting toward social interaction, and are able to think critically, moreover they are also kind and intelligent.

The mass spectral library of the Scientific Working Group for the Analysis of Seized Drugs (SWGDRUG) is the most comprehensive free reference database of its kind in the world. It provides reliable mass spectra for identification of seized drugs, their metabolites, and related forensic compounds when using gas chromatography/mass spectrometry (GC/MS). The SWGDRUG library (version 3.13) contains spectra for 3598 compounds. All spectra are evaluated by the Mass Spectrometry Data Center (MSDC) at the National Institute of Standards and Technology (NIST). Over the past few years, new evaluation methods aided by improved software have been developed. First, all chemical information, such as chemical structure and name, is confirmed. Second, the product ions in each spectrum are verified to match the compound structure using the NIST MS Interpreter software tool. Subsequently, the mass spectra are compared to the same or similar compounds across six different mass spectral reference libraries using three distinct library search methods. Additionally, the NIST Artificial Intelligence Retention Indices (AIRI) software is used to help confirm the corresponding compounds of spectra, especially for those without molecular ions. Low-quality and incorrect spectra are rejected for inclusion in the library.

The Geometric Algebra Fulcrum Library (GA-FuL) version 1.0 is introduced in this paper as a comprehensive computational library for geometric algebra (GA) and Clifford algebra (CA), in addition to other classical algebras. As a sophisticated software system, GA-FuL is useful for practical applications requiring numerical or symbolic prototyping, optimized code generation, and geometric visualization. A comprehensive overview of the GA-FuL design is provided, including its core design intentions, data-driven programming characteristics, and extensible layered design. The library is capable of representing and manipulating sparse multivectors of any dimension, scalar kind, or metric signature, including conformal and projective geometric algebras. Several practical and illustrative use cases of the library are provided to highlight its potential for mathematical, scientific, and engineering applications. The metaprogramming code optimization capabilities of GA-FuL are found to be unique among other software systems. This allows for the automated production of highly efficient code, based on powerful geometric modeling formulations provided by geometric algebra.

Portability and scalability evaluation of large-scale statistical modeling and prediction software through HPC-ready containers