Software Systems Research Articles

Using palm oil residue for food nutrition and quality: from palm fatty acid distillate to vitamin E toward sustainability.

Increasing global palm oil production yields a valuable palm fatty acid distillate (PFAD) - a rich vitamin E (Vit-E) source and multifunctional ingredient in the food agro-industry - that can be utilized to achieve sustainability. This article reviews trends in the use and role of PFAD and its Vit-E in the food sector and proposes an integrated agro-industrial concept toward sustainability. Vit-E can be separated from PFAD with diverse and impactful pharmaceutical activities, including antioxidant, anti-inflammatory, anticancer and anti-ultraviolet effects. Based on in vivo experimental tests, PFAD and Vit-E supplementation can enhance the productivity and quality of livestock-based food products. PFAD is a plasticizer and antistatic packaging material in food packaging systems, and its derivatives can be used as food additives. Meanwhile, the Vit-E molecule in packaging can extend food shelf life by maintaining color stability, reducing lipid oxidation and rancidity, adding antimicrobial properties, and influencing changes in packaging properties such as water vapor, tensile strength, melting point and other physical properties. Toward sustainability, an integrated agro-industrial design has been proposed to implement clean production, increase the added value of palm oil industry residues, minimize environmental risks and increase profits to achieve long-term social welfare. In conclusion, PFAD residues and their Vit-E content have shown broad benefits in the food sector and prospects toward sustainability. © 2024 Society of Chemical Industry.

Unlocking data to inform public health policy and practice: A pilot study on utilising police data for linked population data research

ObjectiveThe UK Government increasingly emphasises a comprehensive, multi-agency approach to tackling crime. This pilot study explores the feasibility of integrating police data with routine health data to develop a holistic understanding of the predictors of domestic abuse (DA). ApproachThe study encompasses three work-packages a) coding the narrative data from Domestic Abuse, Stalking and Harassment (DASH) risk assessment report from Public Protection Notifications (PPNs), which are information-sharing documents recording safeguarding concerns and shared with partner agencies b) exploring to identify what works to harmonise different software systems within police data-sharing approach, c) constructing a case study by linking PPN DASH data with routine health and administrative records. These efforts illustrate the potential of national data-sharing initiatives. Results a) Text mining methods successfully identified and coded (with over 95% accuracy) 17 refereed/involved agencies from the PPN DASH data. b) Barriers to data sharing were attributed to a lack of clarity and consensus regarding appropriate information sharing, rather than technical obstacles. This barrier can be overcome by an unambiguous framework, endorsed at an elevated level, highlighting which data should be shared. c) The data-linkage study revealed that victims of DA had prior interactions with healthcare services before their initial PPN, and younger pregnant victims had higher risk of future healthcare emergency visits. Conclusion and ImplicationPolice and health data integration enhances evidence-based prevention and early identification of vulnerable individuals by both law enforcement and public health services.

Building a Historic Population Platform

ObjectivesWith the increasing digital availability of large population databases of historical census or vital event records, the tasks of storing, cleaning, processing, linking and analysing such data become more challenging. Suitable computing platforms and software systems are required to handle such databases, and facilitate the application of complex record linkage algorithms, for example to reconstruct populations that cover a full country over many decades. We present our efforts to achieve these goals on a database of over 20 million vital event records spanning over 120 years to create a “Historic Population Platform” (HiPP). ApproachWe created a graph database using the Neo4J software, where each birth, death and marriage certificate is represented as a node. We then generated actor nodes from these certificates which represent individuals (such as birth babies and their parents, or marriage brides and grooms). Data cleaning steps included the correction and imputation of invalid, corrupted, and missing age and date values using information from related certificates. ResultsOur initial graph database contains over 100 million nodes and nearly 200 million edges, while our data cleaning methods help to substantially increase the number of valid age and date values. ConclusionThe availability of large historical population databases provides exciting opportunities for social science and health research. However, existing methods have limitations in handling data quality and the sizes of such databases. We presented novel methods to deal with these challenges which we hope will be of use for other projects that aim to build a HiPP.

Right to be forgotten in the Era of large language models: implications, challenges, and solutions

AbstractThe Right to be Forgotten (RTBF) was first established as the result of the ruling of Google Spain SL, Google Inc. v AEPD, Mario Costeja González, and was later included as the Right to Erasure under the General Data Protection Regulation (GDPR) of European Union to allow individuals the right to request personal data be deleted by organizations. Specifically for search engines, individuals can send requests to organizations to exclude their information from the query results. It was a significant emergent right as the result of the evolution of technology. With the recent development of Large Language Models (LLMs) and their use in chatbots, LLM-enabled software systems have become popular. But they are not excluded from the RTBF. Compared with the indexing approach used by search engines, LLMs store, and process information in a completely different way. This poses new challenges for compliance with the RTBF. In this paper, we explore these challenges and provide our insights on how to implement technical solutions for the RTBF, including the use of differential privacy, machine unlearning, model editing, and guardrails. With the rapid advancement of AI and the increasing need of regulating this powerful technology, learning from the case of RTBF can provide valuable lessons for technical practitioners, legal experts, organizations, and authorities.

Didactic video game prototype with recognization of geometric figures in augmented reality

The goal is to offer an educational and entertaining experience through pattern recognition and with the help of Artificial intelligence (AI) and Augmented reality (AR), classified in 2D and 3D that encourages the learning of geometric figures while playing. Technology has made students active participants who can manipulate and explore figures in a virtual environment. It encourages a deeper understanding, as they can see how the shapes interact with each other and with their environment (Toledo, 2017). However, there is still a world to investigate with the behavior demonstrated by 3rd grade primary school students. The pedagogical methodology of Local Theoretical Models (LTM) and the Prototyping methodology that is used to design and develop software systems or applications by building simplified and experimental versions. Currently you can get easy access to Software that helps create video games. Beginning to consolidate theoretical and cognitive knowledge that benefits stimulation in their learning and in attention, perception, interest, memory, thinking and reasoning with the help of AI and AR. The research helps 3rd grade primary school students understand the impact of technology to improve study and learning. The large number of applications implemented to strengthen the learning of basic level students has increased, converging between gaming technology and education, offering a new way to learn and practice mathematical skills in a fun and interactive way.

Innovative time and attendance system software selection for a private hospital: leveraging the entropy-TOPSIS method

Innovative time and attendance system software selection for a private hospital: leveraging the entropy-TOPSIS method

Conceptual Modflow Model Application with Boundary Condition Analysis

In this study, groundwater flow modeling of the aquifer in the Porsuk Basin, located in the Eskisehir province of Turkey, was conducted using the MODFLOW modeling method and the Groundwater Modeling System (GMS) software interface. The modeling was performed using the finite difference method under the assumption of steady flow, incorporating well data, aquifer boundaries, topographic elevations, riverbed data, and hydraulic parameters from the 1971 water year. This study provided insights into changes in groundwater over time and yielded a comprehensive water budget. By creating a three-dimensional solid model of the Porsuk Basin aquifer, general information about the region's hydrogeology was obtained. The results are expected to play a significant role in identifying potential drilling areas for groundwater extraction. Since the modeling utilizes objects from geographic information systems, it will enhance visualization of the regional structure for researchers, allowing them to observe the topographic features of the area in three dimensions. Additionally, it is anticipated that the groundwater conceptual model will inform drilling studies and serve as a foundation for research on groundwater transport and pollution.

Visual Interpretation of High-Resolution Aerial Imagery: A Tool for Land Managers

Abstract Remotely sensed imagery from various collection platforms (e.g., satellites, crewed and uncrewed aircraft) are used by biologists and other conservation personnel to support management activities ranging from monitoring invasive species to assessing land cover and vegetation characteristics. Although remote sensing–based vegetation indices and models have been developed and used for some management applications, straightforward visual interpretation of imagery by on-the-ground personnel may be a pragmatic approach for obtaining time-sensitive and spatially relevant information to support and guide local management activities. Our primary objective was to qualitatively assess our ability to identify patches of target invasive plant species based on simple visual interpretation of high-resolution aerial imagery. We also sought to compare the high-resolution imagery to widely available imagery (e.g., National Agriculture Imagery Program) to determine the efficacy of each for assessing vegetation communities and land-cover features in support of management activities. To accomplish these objectives, we obtained high-resolution imagery and visually scanned and assessed the imagery by using standard geographic information system software. We were able to differentiate patches of crownvetch Securigera varia (L.) Lassen and wild parsnip Pastinaca sativa L., but not spotted knapweed Centaurea stoebe L. or leafy spurge Euphorbia esula L. The relative success in identifying these species had a relationship to plant characteristics (e.g., flower color and morphology, height), time of year (phenology), patch size and density, and potentially site characteristics such density of the underlying vegetation (e.g., grasses), substrate color characteristics (i.e., color contrast with flowers), and physical disturbance. Our straightforward, qualitative assessment suggests that visual interpretation of high-resolution imagery, but not some lower-resolution imagery, may be an efficient and effective tool for supporting local invasive species management through activities such as monitoring known patches, identifying undetected infestations, assessing management actions, guiding field work, or prioritizing on-the-ground monitoring activities.

SP7.7 - A 6-month retrospective re-audit of “Code Red” trauma requiring surgical intervention in a regional major trauma centre

Abstract Aim Major trauma (MT) is a significant cause of morbidity and mortality in the United Kingdom, with an estimated incidence of 20,000 cases annually in England. A certain proportion of patients presenting following MT require immediate operative intervention. This study is a re-audit of the process for managing these patients in order to assess the impact of introduction of a Damage Control Surgery (DCS) pathway. Methods Data were reviewed for all patients presenting as Code Red MT and requiring immediate operative intervention from May to November 2023. This accounts for all patients since the implementation of the DCS pathway. Patient details were taken from a MT database, Electronic Care Records were used to determine aetiologies of trauma and outcomes, and Theatre Management System software was used to determine timings. Results 9 patients were identified, with a median age of 30.33. 89% of patients underwent preoperative CT imaging, with an average time to CT of 15 minutes (range 5 to 57 minutes). Median time to theatre was 88 minutes (range 39 to 325 minutes). Specialties involved included General Surgery, Vascular, Cardiothoracics and Trauma and Orthopaedics, with multispecialty involvement in some cases. Overall mortality was 22%. Seven cases were the result of road traffic collisions. Conclusions MT remains a significant cause of mortality in our cohort, but implementation of a DCS pathway has led to significant improvements in both time to CT and time to theatre compared with our initial audit cycle (32 minutes/144.5 minutes).

Expert System Software Framework for Nutritional Analysis and Diet-Related Disease

Nigeria faces a serious health crisis with diet-related diseases, which calls for creative ways to improve nutritional results and public knowledge. In addressing the challenges, this study introduces an Expert System Software Framework for Nutritional Analysis and Diet-Related Diseases, leveraging advanced technologies to address healthcare and nutrition challenges. Developed through rigorous research, the framework offers dynamic capabilities, personalized recommendations, and a user-friendly interface. Key features include customized algorithms, blockchain authentication for security, dynamic scheduling, and robust administrative tools. It fills critical gaps by providing insights into nutritional contents and health impacts of foods, supporting informed decision-making and proactive management of diet-related disorders. The performance evaluation demonstrates a high accuracy rate of 91.7%, ensuring reliability in dietary recommendations. Future directions include integrating wearable technology and AI to provide real-time health data for personalized dietary recommendations. This framework sets a new standard in nutritional analysis and disease management, contributing to advancements in healthcare and nutrition. Its recommendations for sustained engagement, collaboration with medical professionals, and global expansion aim to improve user outcomes and quality of life beyond the Nigerian setting.

A dual graph neural networks model using sequence embedding as graph nodes for vulnerability detection

Context:Detecting critical to ensure software system security. The traditional static vulnerability detection methods are limited by staff expertise and perform poorly with today’s increasingly complex software systems. Researchers have successfully applied the techniques used in NLP to vulnerability detection as deep learning has developed. The existing deep learning-based vulnerability detection models can be divided into sequence-based and graph-based categories. Sequence-based embedding models cannot use structured information embedded in the code, and graph-based embedding models lack effective node representations. Objective:To solve these problems, we propose a deep learning-based method, DGVD (Double Graph Neural Network for Vulnerability Detection). Methods:We use the sequential neural network approach to extract local semantic features of the code as nodes embedded in the control flow graph. First, we propose a dual graph neural network module (DualGNN) that consists of GCN and GAT. The altered module utilizes two different graph neural networks to obtain the global structural information of the control flow and the relationship between the nodes and fuses the two. Second, we propose a convolution-based feature enhancement module (TC-FE) that uses different convolution kernels of different sizes to capture information at different scales so that subsequent readout layers can better aggregate node information. Results:Experiments demonstrate that DGVD outperforms existing models, obtaining 64.23% vulnerability detection accuracy on CodeXGLUE’s real benchmark dataset. Conclusion:The proposed DGVD achieves better performance than the state-of-the-art DGVD has a more effective source code feature extraction capability on real-world datasets.

Development and Application of Unmanned Aerial High-Resolution Convex Grating Dispersion Hyperspectral Imager.

This study presents the design and development of a high-resolution convex grating dispersion hyperspectral imaging system tailored for unmanned aerial vehicle (UAV) remote sensing applications. The system operates within a spectral range of 400 to 1000 nm, encompassing over 150 channels, and achieves an average spectral resolution of less than 4 nm. It features a field of view of 30°, a focal length of 20 mm, a compact volume of only 200 mm × 167 mm × 78 mm, and a total weight of less than 1.5 kg. Based on the design specifications, the system was meticulously adjusted, calibrated, and tested. Additionally, custom software for the hyperspectral system was independently developed to facilitate functions such as control parameter adjustments, real-time display, and data preprocessing of the hyperspectral camera. Subsequently, the prototype was integrated onto a drone for remote sensing observations of Spartina alterniflora at Yangkou Beach in Shouguang City, Shandong Province. Various algorithms were employed for data classification and comparison, with support vector machine (SVM) and neural network algorithms demonstrating superior classification accuracy. The experimental results indicate that the UAV-based hyperspectral imaging system exhibits high imaging quality, minimal distortion, excellent resolution, an expansive camera field of view, a broad detection range, high experimental efficiency, and remarkable capabilities for remote sensing detection.

Automatic motion estimation with applications to hiPSC-CMs

Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are an effective tool for studying cardiac function and disease, and hold promise for screening drug effects on human tissue. Understanding alterations in motion patterns within these cells is crucial for comprehending how the administration of a drug or the onset of a disease can impact the rhythm of the human heart. However, quantifying motion accurately and efficiently from optical measurements using microscopy is currently time consuming. In this work, we present a unified framework for performing motion analysis on a sequence of microscopically obtained images of tissues consisting of hiPSC-CMs. We provide validation of our developed software using a synthetic test case and show how it can be used to extract displacements and velocities in hiPSC-CM microtissues. Finally, we show how to apply the framework to quantify the effect of an inotropic compound. The described software system is distributed as a python package that is easy to install, well tested and can be integrated into any python workflow.

Recent Developments in Sustainable Bio‐Based Wax Nanoemulsions as Edible Coatings for Food Commodities: A Comprehensive Review

ABSTRACTWaxes are a diverse class of organic compounds that are characterised by their hydrophobic nature. They are typically derived from long‐chain fatty acids and alcohols. Waxes serve various purposes in nature and industry due to their unique physical and chemical properties. Waxes find applications in diverse industries, including pharmaceuticals, food and textiles, for purposes such as coating, lubrication and insulation. The rising emphasis on sustainable and eco‐friendly approaches in the food sector has generated increasing attention towards the advancement of edible coatings for food products. Within this array of coatings, sustainable waxes have surfaced as a prospective approach to improve storage stability, safety and quality of diverse food items. Bio‐based wax nanoemulsions serve as effective nanocarriers in edible food coatings, enhancing their antimicrobial efficacy and preserving food quality. The incorporation of bio‐based nanoemulsion waxes as edible coatings signifies a substantial advancement towards establishing more environmentally sustainable and ecologically responsible systems of food packaging and preservation. The review examines recent advancements in the field of sustainable nanoemulsion waxes as edible coatings, focusing on their applications, formulation strategies and impact on food commodities. The review also discusses the influence of sustainable nanoemulsion wax coatings on the physicochemical properties of coated foods, including moisture barrier, gas permeability and mechanical strength.

The In-Flight Realtime Trigger and Localization Software of GECAM

Realtime trigger and localization of bursts are the key functions of GECAM, an all-sky gamma-ray monitor launched on 2020 December 10. We developed a multifunctional trigger and localization software operating in the CPU of the GECAM Electronic Box. This onboard software has the following features: high trigger efficiency for real celestial bursts with a suppression of false triggers caused by charged particle bursts and background fluctuation, dedicated localization algorithm optimized for both short and long bursts, and low time latency of the trigger information which is downlinked through the Global Short Message Communication service of the global BeiDou navigation system. This paper provides a detailed description of the design and development of the trigger and localization software system for GECAM. It covers the general design, workflow, the main functions, and the algorithms used in the system. The paper also includes on-ground trigger tests using simulated gamma-ray bursts generated by a dedicated X-ray tube, as well as an overview of the performance for real celestial bursts during its in-orbit operation.

Fine-grained vulnerability detection for medical sensor systems

The Internet of Things (IoT) has revolutionized the healthcare system by connecting medical sensors to the internet, while also posing challenges to the security of medical sensor networks (MSN). Given the extreme sensitivity of medical data, any vulnerability may result in data breaches and misuse, impacting patient safety and privacy. Therefore, safeguarding MSN security is critical. As medical sensor devices rely on smart healthcare software systems for data management and communication, precisely detecting system code vulnerabilities is essential to ensuring network security. Effective software vulnerability detection targets two key objectives: (i) achieving high accuracy and (ii) directly identifying vulnerable code lines for developers to fix. To address these challenges, we introduce Vulcoder, a novel vulnerability-oriented, encoder-driven model based on the Bidirectional Encoder Representations from Transformers (BERT) architecture. We propose a one-to-one mapping function to capture code semantics through abstract syntax trees (AST). Combined with multi-head attention, Vulcoder achieves precise function- and line-level detection of software vulnerabilities in MSN. This accelerates the vulnerability remediation process, thereby strengthening network security. Experimental results on various datasets demonstrate that Vulcoder outperforms previous models in identifying vulnerabilities within MSN. Specifically, it achieves a 1%–419% improvement in function-level prediction F1 scores and a 12.5%–380% increase in line-level localization precision. Therefore, Vulcoder helps enhance security defenses and safeguard patient privacy in MSN, facilitating the development of smart healthcare.

PERANCANGAN PENILAIAN KINERJA PERUSAHAAN MENGGUNAKAN BALANCED SCORECARD BERBASIS DIGITAL PADA PERUSAHAAN TEKSTIL DI CIREBON

Hanjani Group is a company related to the textile industry. In the performance evaluation process, currently there is no system that can enter all sales data at each branch of the company. The proposed solution is creating a website using the balanced scorecard method that allows the evaluation process to be presented with practical nominal results in performance assessment when viewed from four perspectives, there are finance, customers, internal business processes, and growth. This website provides a better view that managers will use to conduct statistical processes and compare systems from various branches of the company because it allows for integration or connection between disciplines. Elements of system analysis and design are explained using context diagrams, flowcharts, DFDs, and ERDs. The steps taken by designing a software system using PHP (metatext preprocessor) as the translation language and MySQL as the database manager. After testing by the company owner, this website can load performance evaluation reports at various branches of the company in Hanjani Group. Key words: Performance Assessment, Balanced Scorecard, Hanjani Group

Intelligent Packaging System: A Promising Approach to Reduce Food Waste

Intelligent Packaging System: A Promising Approach to Reduce Food Waste

An enhanced control solutions for efficient urban waste management using deep learning algorithms

This study focuses on developing an efficient urban waste management system using deep learning algorithms and Internet of Things (IoT) technology. The goal is to improve waste management in Ikot Ekpene municipality by enabling quick disposal responses to prevent environmental pollution. The researchers employed an Object-Oriented Analysis and Design (OOAD) methodology to develop a software system that integrates mobile GIS techniques and IoT sensors to monitor and manage waste. The system was trained using a dataset of 510 images of six classes of waste-related scenarios, with an 84% training set and 16% validation set. The results showed that the garbage bin half-full class had the highest F1 score (above 80%) at a confidence score of 0.4, indicating accurate detection. The average F1 score for all classes was 0.45 at a confidence score of 0.238. The system’s API was designed using Python, supporting both web-based and Android-based applications. The integration of IoT sensors and mobile GIS techniques enables real-time monitoring and efficient waste management. This study demonstrates the potential of deep learning algorithms and IoT technology in improving waste management services, contributing to a cleaner and healthier environment. The system’s effectiveness in detecting waste levels and triggering timely disposal responses can help prevent environmental pollution and enhance the well-being of citizens.

Future climate suitability of Hemileia vastatrix in arabica coffee under CMIP6 scenarios.

Coffee (Coffea arabica L.) is one of the most important commodities today, with a high economic value worldwide. Coffee leaf rust (Hemileia vastatrix Berk. et Br.) has been showing a high impact on Brazilian coffee trees among the various diseases that attack coffee. The climate has a great influence on the development of diseases, especially when fungi are the causal agents. This study aimed to carry out the zoning of climate favorability for coffee leaf rust in the traditional and main coffee-producing regions of Brazil. The study was conducted in 13 locations in the states of Paraná, São Paulo, Rio de Janeiro, Espírito Santo, Minas Gerais, Goiás and Bahia. Air temperature and daily precipitation data for the current scenario were collected using the WorldClim version 2.1 platform for the last climatological normal and future climate change data. The ideal climate conditions for coffee leaf rust consist of a mean air temperature ranging from 21 to 25 °C and precipitation >30 mm per month. The Coupled Model Intercomparison Projects scenarios associated with the Intergovernmental Panel on Climate Change reports consisted of the Shared Socio-Economic Pathways SSP-1 2.6, SSP-2 4.5, SSP-3 7.0 and SSP-5 8.5, the latter being considered one of the most catastrophic. All steps to carry out the suitability zoning were performed in a tool using the QGIS geographic information system software. Zoning for coffee leaf rust had three classes: favorable, relatively favorable and unfavorable. Currently, the largest coffee-producing region in Brazil has 49.1% of its analyzed area classified as favorable, 39.2% as relatively favorable and 11.7% as unfavorable. In the current scenario, Patrocínio and Três Pontas are locations with high coffee production in which the favorable class is predominant. The state of Minas Gerais has an annual mean of 55.3% of its entire territory apt for the disease, with the highest occurrence between September and March. Climate change has a negative impact on the development of coffee leaf rust, mainly in the long term, as in the period of 2081-2100, in which the SSP-5 8.5 scenario led to a decrease in the favorable and unfavorable areas and an increase in the relatively favorable areas of 9.8%, 18.6% and 71.5% for the Brazilian territory, respectively. © 2024 Society of Chemical Industry.