Miracle-dz/UniFAR-FTUs huggingface dataset:
Mathematical Modeling to Estimate the Diffusion of Traffic-Related Air Pollutants in Almaty City

this article discusses the distribution of emissions of pollutants from traffic congestion at the intersections in Almaty city transport network. A mathematical model has been developed by using a recurrent operator method for modeling and calculating the spread of harmful impurities in the urban environment. To solve partial differential equations of the second order, integral-differential operators are introduced in the form of a special series with constant coefficients determined from the recurrence relations associated with the differential equation in question. The particular solutions obtained in this case are relatively simpler, since they were based on a single recurrence relation, which leads to simpler differentiation formulas. Based on the approach, the general solutions were proposed in terms of arbitrary functions and are not related to the solution with the definition of the roots of the characteristic equation of the function. The obtained results were illustrated graphically and given in tables.
Digital Health for Patients Undergoing Cardiac Surgery: A Systematic Review

Digital health interventions have shown promise in improving patient outcomes and experiences in various healthcare settings. However, their effectiveness in the context of cardiac surgery remains uncertain. This systematic review aims to evaluate the existing evidence on the use of digital health interventions for patients undergoing cardiac surgery. A comprehensive search of PubMed MEDLINE, Elsevier EMBASE, Elsevier Scopus databases, and ClinicalTrials.gov was conducted to identify relevant studies published up to the present. Studies that examined the effects of digital health interventions, including mobile applications and web-based interventions, on perioperative care and patient outcomes in cardiac surgery were included. The data were extracted and synthesized to provide a comprehensive overview of the findings. The search yielded 15 studies composed of 4041 patients, analyzing the feasibility and implementation of mobile or internet applications for patients undergoing cardiac surgery. The studies included the use of mobile applications (ManageMySurgery, SeamlessMD, mHeart, Telediaglog, ExSed, Soulage Tavie, Heart Health application, and Mayo Clinic Health Connection) and web-based interventions (Heartnet and Active Heart). The findings indicated that these digital health interventions were associated with improved patient engagement, satisfaction, and reduced healthcare utilization. Patients reported finding the interventions helpful in their recovery process, and there was evidence of enhanced symptom monitoring and timely intervention. The completion rates of modules varied depending on the phase of care, with higher engagement observed during the acute phase. Interest in using digital health applications was expressed by patients, regardless of age, gender, or complexity of the cardiac defect. The results demonstrated that web-based interventions resulted in improvements in mental health, quality of life, and eHealth literacy. This systematic review highlights the potential benefits of digital health interventions in the context of cardiac surgery. Further research, including randomized controlled trials, is needed to establish the effectiveness, feasibility, and generalizability of digital health interventions in cardiac surgery.
L’IQE : un indicateur de biodiversité multi-usages pour les sites aménagés ou à aménager

There are currently no standardized assessment tools to define the ecological quality of industrial sites and landfills sites. The Natural Heritage Service of the National Museum of Natural History has developed an Ecological Quality Index (Indicateur de Qualite Ecologique, IQE). This is based on a six-day inventory at the scale of species and ecosystems i) on the diversity of natural habitats and birds, ii) on wildlife and natural heritage value of a site and iii) an index of ecological functionality. Inventories conducted on 29 sites over a four-year period, were compared with published literature and expert knowledge. This approach allowed the modification or validation of each parameter, including the scoring system of these two indices. A shorter version of this index, based on a one-day inventory, the Ecological Potentiality Index (Indicateur de Potentialite Ecologique, IPE) has also been developed, using a similar structure
Introducing the TRUMPET project: TRUstworthy Multi-site Privacy Enhancing Technologies

This paper is an overview of the EU-funded project TRUMPET (https://trumpetproject.eu/), and gives an outline of its scope and main technical aspects and objectives. In recent years, Federated Learning has emerged as a revolutionary privacy-enhancing technology. However, further research has cast a shadow of doubt on its strength for privacy protection. The goal of TRUMPET is to research and develop novel privacy enhancement methods for Federated Learning, and to deliver a highly scalable Federated AI service platform for researchers, that will enable AI-powered studies of siloed, multi-site, cross-domain, cross-border European datasets with privacy guarantees that follow the requirements of GDPR. The generic TRUMPET platform will be piloted, demonstrated and validated in the specific use case of European cancer hospitals, allowing researchers and policymakers to extract AI-driven insights from previously inaccessible cross-border, cross-organization cancer data, while ensuring the patients' privacy.
The Sight for Hearing: An IoT-Based System to Assist Drivers with Hearing Disability

The objective of this paper is to propose a new system to assist drivers with hearing disability, deaf or unfocused persons by recognizing and transforming audible signals, such as emergency vehicle sirens or honks into alerts displayed in the dashboard. Such conversion from audio to alert messages attracts the attention of unfocused drivers to hear the honking of other cars, and enhances the safety and the quality of life for deaf or hard-of-hearing drivers. We develop an IoT based system to identify, denoise and translate any significant voice signal around the driver's car into alert messages. The signal acquired by sensors is processed to identify the source and display the associated message through the use of several machine learning models with majority voting. Our experiments results show that the proposed solution is able to achieve a 95% accuracy when trained and validated against a real dataset of 600 files.
Performance Characterisation of Microstrip Antenna when Utilised for RF Power Harvesting in Manufacturing Environments

This paper investigates the impact of Electromagnetic Interference (EMI) and Radio Frequency Interference (RFI) on the power output that can be harvested in a typical man-ufacturing environment where equipment capable of generating EMI and RFI is utilized [1]. This study has significant implications for the development of RF energy harvesting systems in manufacturing environments where EMI and RFI are commonly present [1]. The results demonstrate the feasibility of using RF energy harvesting to generate power for sensors and other low-power devices in such environments [2]. The findings contribute to the understanding of the potential sources and impact of EMI and RFI on power harvesting in manufacturing environments [3]. Specifically, a rectifying microstrip antenna designed for Radio Frequency (RF) power harvesting at 2.4 GHz with 4% bandwidth is characterised according to EN 300 328 standards [2]. Although this frequency band may not be the most energy-rich, it is widely available in most manufacturing environments. To support this study, relevant literature is reviewed, including studies on RF energy harvesting, EMI, and RFI in manufacturing environments [1–5]. Additionally, the study draws on relevant standards and guidelines, such as the EN 300 328 standard for RF power harvesting [2], and the CE mark requirement for electromagnetic compatibility [5]. To assess the performance and efficiency of the antenna, simulated and actual test results are compared [2]. The obtained results show that the effects of EMI and RFI on the antenna's usable bandwidth are minimal, with a loss of approximately 0.7%. The findings conclude that the negative impact of EMI and RFI on power harvesting in a non-Conformité Européene (CE) manufacturing environment is negligible. The potential power that is harvestable still makes energy harvesting in a manufacturing environment a viable option for alternative power source generation. The consensus is that more work will need to be done to identify a more energy-rich frequency or other means that will allow more energy to be captured.
The relationship between entrepreneurial orientation, organisational orientation and innovation performance of manufacturing small and medium enterprises in KwaZulu-Natal province

This study was conducted to investigate the relationship between entrepreneurial orientation, organisational orientation, and innovation performance of manufacturing small and medium enterprises (SMEs) in KwaZulu Natal province. To instil innovation culture, an organisational architecture of SMEs should accommodate both organisational and entrepreneurial factors in order to create a synergy that is likely to achieve innovation objectives of SMEs. The empirical investigation was based on a quantitative study and used a cross-sectional survey design to collect data from owner-managers of 308 small and medium firms in the manufacturing sector. The provincial SMEs database from the Kwa-Zulu Natal Department of Economic Development and Tourism was used and it contained an estimate population of 1255 SMEs. This study found that there was a positive and significant correlation between entrepreneurial orientation and organisational orientation dimensions. It further established that organisational and entrepreneurial dimensions were correlated with innovation performance dimensions in the SMEs. An aptly entrepreneurial orientation is proven to be grounded in a related organisational orientation. This implies that an organisational strategy, its culture, structure, systems and the management style in SMEs strengthen the entrepreneurial strategy leading to improvements in the standard of the product, the process, the market position and the business model of SMEs. In the same context, other organisational factors such as available rewards, SMEs’ age, size and ownership provided another dimension and an insight into the innovation performance of SMEs. Based on the findings, the researcher suggests two models: the proximity model of the correlation between entrepreneurial orientation and organisational orientation and the new model of innovation performance for SMEs. The managerial implication is that the success of an entrepreneurial strategy of an SME is rooted into organisational orientation dimensions: culture, structure, strategy, systems and management style of owner-managers. However, an organisational orientation is proven to be driven by a mindset which, if entrepreneurial, leads to an entrepreneurial orientation and consequently achieve an innovation performance of SMEs. With such variables, the study recommends new approaches in line with the suggested models in support of manufacturing SMEs and the manufacturing sector in terms of managerial decision-making about firms’ innovation performance and competitiveness at organisational and sectorial levels.
Atmospheric and Surface Contributions to Planetary Albedo and their Relationship to the Total Meridional Energy Transport

The meridional distribution of incident solar radiation and planetary albedo both contribute to the equator-to-pole gradient in absorbed solar radiation (ASR) in the observed climate system. While the former component is determined by the Earth-Sun geometry and composes 60% of the equator-to-pole gradient in ASR, the latter component makes a significant (40%) contribution to the ASR gradient and is potentially a function of climate state due to its dependence on both atmospheric and surface albedo. In turn, the equator-to-pole gradient in planetary albedo is found to be primarily (86% 89%) dictated by atmospheric albedo with meridional gradients in surface albedo playing a much smaller role in forcing the climate system on the equator-to-pole scale. Simulations of the pre-industrial climate system using the CMIP3 coupled models show large differences in the equator-to-pole gradient in planetary albedo which are mainly due to differences in the simulated cloud distribution, with surface processes playing a much smaller role. The inter-model spread in total meridional heat transport is also primarily due to differences in the simulated cloud distribution. Further model simulations demonstrate that the surface albedo changes associated with moving from the present climate to an ice free climate have a small effect on the equator-to-pole gradient of ASR as compared to the uncertainty in simulated cloud distributions, and hence a small effect on the meridional heat transport.
Top-Performing Unifying Architecture for Network Intrusion Detection in SDN Using Fully Convolutional Network

Internet's omnipresent adoption over the course of many years leading up to the current era along with Wireless Sensor Networks' (WSN) limitations has only increased the security threats to these high-speed networks. As a result, it is critical to deploy Intrusion Detection Systems (IDS) to monitor malicious activities in the network framework. Software Defined Network (SDN) has served its purpose to a certain extent by enabling centralized control and management of WSN and providing ways to implement IDSs more efficiently. However, like any emerging technology, SDN also has its share of vulnerabilities as there exists a gap for contemporary architectures, which can exploit global network assets and balance network resource consumption while providing programmability in terms of security of the network. To tackle this problem, a model has been developed that uses a fitting feature selection method to cut down on the count of features extracted and minimize the data volume relayed over the routes. Conclusions drawn from the scheme's trials demonstrate the proposed IDS technology's potency and effectiveness. The openly accessible University of Nevada - Reno Intrusion Detection Dataset (UNR-IDD) was used to evaluate the intrusion detection algorithm involving the Fully Convolutional Network (FCN) architecture and its effect using a statistical technique in detecting different types of attacks.
A comprehensive characterization of longevity and culling reasons in Canadian Holstein cattle based on various systematic factors

Abstract The decision of premature culling cows directly impacts the profitability of dairy farms. A comprehensive characterization of the primary causes of culling reasons would greatly improve both management and selection objectives in dairy cattle breeding programs. Therefore, this study aimed to analyze the temporal frequencies of 34 culling reasons in Canadian Holstein cows. After data editing and quality control, records from 3,096,872 cows culled from 9,683 herds spread across Canada were used for the analyses covering the periods from 1996 to 2020. Reproductive issues were the main culling reason accounting for 23.02%, followed by milk production (20.82%), health (20.39%), conformation problems (13.69%), economic factors (13.10%), accidents (5.67%), age-related causes (1.67%), and workability (1.63%). Nearly fifty-eight percent of cows were culled after 47 months of age. The observed frequencies of culling due to economic factors were lower than expected from 1996 to 2014 and higher than expected between 2015 and 2020. Reproduction issues had the highest culling frequencies during fall (24.54%), winter (24.02%), and spring (22.51%), while health issues were the most frequent (22.51%) culling reason in the summer season. Health issues (25.50%) and milk production (27.71%) were the most frequent culling reasons in the provinces of Quebec and Ontario, respectively. Reproductive issues showed the highest frequency across climates based on the Köppen climate classification, except for Csb (Dry-summer subtropical or Mediterranean climate) and Bsk (Middle latitude steppe climate), which correspond to small regions in Canada, where production was the most frequent culling reason (29.42% and 21.56%, respectively). Reproductive and milk performance issues were the two main culling reasons in most ecozones, except in Boreal Shield and Atlantic Marine, where health issues had the highest frequencies (25.12 and 23.75%, respectively). These results will contribute to improving management practices and selective decisions to reduce involuntary culling of Holstein cows.
Predicting Out-of-Hospital Vital Sign Measurements through Deep Learning

People may want to monitor their health conditions while they conduct their daily activities. For this, they need solutions that perform continuous health monitoring. Today, most existing solutions in the market (e.g., Apple watch, SpO2 ring) only report current vital sign measurements; they do not predict future values of those vital signs. We developed two predictive models that give people (patients or non-patients) predictions regarding the measurements of two critical vital signs, namely, heart rate and Oxygen saturation level. Our experimental results indicate that both models have a good accuracy.
Morphological and Molecular Study of the Fish Parasitic Crustaceans Cymothoa indica and Mothocya collettei (Isopoda: Cymothoidae), with New Distribution Records

Cymothoidae are parasitic isopods that infest fishes inhabiting marine, brackish, and fresh-water environments. The systematics, distribution, and host specificity of cymothoids is often unknown. In this study, morphological observations and molecular analysis using the COI and 16S rRNA genes were conducted on a juvenile individual of Cymothoa indica infesting the wide-banded hardyhead silverside, Atherinomorus lacunosus, and an ovigerous female of Mothocya collettei infesting the hound needlefish, Tylosurus crocodilus, both collected from Okinawa Island, Japan. This is the first time that these two species of cymothoid have been collected in the Ryukyu Islands, and the juvenile C. indica in particular represents a new northern range limit for its species. That specimen was identified using molecular analysis and showed almost identical morphology to previously reported juveniles, but it differed in the numbers of robust setae on pereopods 6 and 7. The ovigerous female of M. collettei showed a high morphological similarity to M. collettei but had some morphological traits consistent with Mothocya affinis. DNA analysis indicated that these two nominal species of Mothocya are very closely related, and morphological and molecular studies using larger populations of both are needed.
Simulation and Projection of Monso on Rainfall and Rain Patterns over Eastern China under Global Warming by RegCM3

Abstract The authors investigate possible changes of monsoon rainfall and associated seasonal (June-July-August) anomaly patterns over eastern China in the late 21st century under the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emission Scenarios (SRES) A2 emission scenario as simulated by a high-resolution regional climate model (RegCM3) nested in a general circulation model (FvGCM/CCM3). Two sets of multi-decadal simulations are performed at 20-km grid spacing for present day and future climate conditions. Results show that the RegCM3 reproduces the mean rain-fall distribution; however the evolution of the monsoon rain belt from South China to North China is not well simulated. Concerning the rain pattern classifications, RegCM3 overestimates the occurrence of Pattern 1 (excessive rainfall in northern China) and underestimates that of Pattern 2 (increased rainfall over the Huai River basin). Under future climate conditions, RegCM3 projects less occurrence of Pattern 1, more of Pattern 2, and little change of Pattern 3 (rainfall increase along the Yangtze River). These results indicate that there might be increased rainfall over the Huai-Yellow River area and reduced rainfall over North China in the future, while rain-fall over the lower reaches of the Yangtze River basin is not modified significantly. Uncertainties exist in the present study are also discussed.
Smartphone- and Paper-Based Delivery of Balance Intervention for Older Adults Are Equally Effective, Enjoyable, and of High Fidelity: A Randomized Controlled Trial

Home-based rehabilitation programs for older adults have demonstrated effectiveness, desirability, and reduced burden. However, the feasibility and effectiveness of balance-intervention training delivered through traditional paper-versus novel smartphone-based methods is unknown. Therefore, the purpose of this study was to evaluate if a home-based balance-intervention program could equally improve balance performance when delivered via smartphone or paper among adults over the age of 65. A total of 31 older adults were randomized into either a paper or phone group and completed a 4-week asynchronous self-guided balance intervention across 12 sessions for approximately 30 min per session. Baseline, 4-week, and 8-week walking and standing balance evaluations were performed, with exercise duration and adherence recorded. Additional self-reported measures were collected regarding the enjoyment, usability, difficulty, and length of the exercise program. Twenty-nine participants completed the balance program and three assessments, with no group differences found for any outcome measure. Older adults demonstrated an approximately 0.06 m/s faster gait velocity and modified balance strategies during walking and standing conditions following the intervention protocol. Participants further self-reported similar enjoyment, difficulty, and exercise effectiveness. Results of this study demonstrated the potential to safely deliver home-based interventions as well as the feasibility and effectiveness of delivering balance intervention through a smartphone-based application.
Explaining Binary Obfuscation

Binary obfuscation is a very broad set of techniques widely employed in the context of code protection from piracy. However, it is also used for malicious goals, e.g. virus writers often employ obfuscation in order to evade signature-based antivirus detection. Thus, the ability to detect if an executable has been obfuscated is of paramount importance, as it allows to thwart the execution of potentially malicious code. The task of detection, however, is not easy, since many different obfuscating transformations exist and the alteration of an original code is not always easily detectable. In this paper, we want to shed light on the blurry task of obfuscation detection. We will look at this task through the brand new lenses of explainable artificial intelligence (XAI), in order to finally sharpen the obscure landscape of obfuscated software. Thanks to XAI we will be able to identify the relevant features altered by the transformating obfuscation as well as the invariant ones, that can be used for obfuscation-resistant malware signatures. We show our findings thanks to an evaluation with a dataset of obfuscated and non-obfuscated binaries, explaining the important features that lead to the detection of obfuscating transformations.