Miracle-dz/UniFAR-FTUs huggingface dataset:
Circadian Lying Behaviour Assessed in a Commercial Mixed Horned Dairy Goat Herd

In general, the lying behaviour of dairy goats follows a circadian progression. As lying times might have an effect on the health, performance and welfare of dairy goats, housing conditions and management practices should follow circadian behavioural patterns. In the present study, a mixed horned herd of Bunte Deutsche Edelziege was used. Goats were housed in a commercial dairy goat farm in south Germany. During two lactation periods, 20 goats in each period were fitted with an accelerometer to detect lying behaviour. To analyse the accelerometer data, a mixed linear model was used. The highest frequencies of lying bouts (FLBs) occurred in the afternoon, and the lowest ones occurred during milking. Generally, the least square means ± SE of the FLB counts per goat ranged between 1.72 ± 0.07 (20:00–21:59 P.M.) and 2.87 ± 0.07 (12:00–13:59 P.M.). The longest lying bouts (LBD) occurred at night, and lying bouts remained relatively short in the afternoon between milking times. The maximum LBD was 52.20 ± 0.01 min/bout (at night, 02:00–03:59 A.M.), and the shortest duration was 14.31 ± 0.01 min/bout (during morning milking, 08:00–09:59 A.M.). The interactions of time and month had a significant impact on FLB and LBD (p < 0.0001), and so did the interactions of horn status and time (p < 0.05). Further research is necessary to analyse the lying behaviour of dairy goats in more detail to improve management practices.
R-PCR: Recurrent Point Cloud Registration Using High-Order Markov Decision

Despite the fact that point cloud registration under noisy conditions has recently begun to be tackled by several non-correspondence algorithms, they neither struggle to fuse the global features nor abandon early state estimation during the iterative alignment. To solve the problem, we propose a novel method named R-PCR (recurrent point cloud registration). R-PCR employs a lightweight cross-concatenation module and large receptive network to improve global feature performance. More importantly, it treats the point registration procedure as a high-order Markov decision process and introduces a recurrent neural network for end-to-end optimization. The experiments on indoor and outdoor benchmarks show that R-PCR outperforms state-of-the-art counterparts. The mean average error of rotation and translation of the aligned point cloud pairs are, respectively, reduced by 75% and 66% on the indoor benchmark (ScanObjectNN), and simultaneously by 50% and 37.5% on the outdoor benchmark (AirLoc).
Design of a Cost-Effective Smart Mirror Using Raspberry Pi

As the technology becomes more and more advanced with time, automations are taking over our day to day lives. More and more innovations are being done in this space to enable people getting exposed to technology and in turn live a tech-supported lifestyle. This research paper proposes a model of a Smart Mirror which is a normal mirror but with which the user can interact. The proposed model is robust and full of features which can be directly used in a person’s day to day life. The proposed model consists of two subsystems namely software and hardware, they both work in unison to make a complete end to end device. The hardware sub system of the model includes a Raspberry Pi and a few other components. While the software sub system enables the functioning of the proposed system. The core of the smart mirror is the Magic Mirror module which stores all the APIs for the features which the model incorporates. These APIs are critical as they help in fetching real time information. The proposed model has features like Calendar, Music App, Weather Forecast, News etc, the model can be scaled to add as many features as one can in order to make it customized. The ability of the proposed model to use APIs and add more features makes it more robust and cost-effective in general.
Laser-Induced Au Catalyst Generation for Tailored ZnO Nanostructure Growth

ZnO nanostructures, semiconductors with attractive optical properties, are typically grown by thermal chemical vapor deposition for optimal growth control. Their growth is well investigated, but commonly results in the entire substrate being covered with identical ZnO nanostructures. At best a limited, binary growth control is achieved with masks or lithographic processes. We demonstrate nanosecond laser-induced Au catalyst generation on Si(100) wafers, resulting in controlled ZnO nanostructure growth. Scanning electron and atomic force microscopy measurements reveal the laser pulse’s influence on the substrate’s and catalyst’s properties, e.g., nanoparticle size and distribution. The laser-induced formation of a thin SiO2-layer on the catalysts plays a key role in the subsequent ZnO growth mechanism. By tuning the irradiation parameters, the width, density, and morphology of ZnO nanostructures, i.e., nanorods, nanowires, and nanobelts, were controlled. Our method allows for maskless ZnO nanostructure designs locally controlled on Si-wafers.
A Comparative Analysis on 1D & 2D ECG applying Data Compression Approaches

The valuation of ECG signal quality after compression is an important task to be performed for the compression process. Compression enables the signal achieving, accelerates and decreases the energy ingesting. In contrary, lossy compression approaches pursuance by both compression efficacy and quality of signal. The manuscript gives an overview of the objective algorithms for the valuation of both ECG signal quality. The paper gives an idea of the latest algorithms for the valuation of ECG signal quality after compression approaches and efficiency of the signal. In this domain there is the absence of extensive review. Electrocardiogram (ECG) data compression plays a significant role in localized digital storage or efficient communication channel utilization in telemedicine applications. Based on domain ECG data compression can be performed either one dimensional (1D) or two dimensional (2D) for utilization of inter and inter with intra beat correlation respectively. In this paper, a comparative study between 1D & 2D ECG data compression methods were taken out from existing literature to provide an update in this regard. ECG data compression techniques and algorithms in 1D & 2D domain have their own merits and limitations. Recently, numerous research and techniques in 1D ECG data compression have been developed including direct and transformed domain. Additionally, 2D ECG data compression research is reported based on period normalization and complexity sorting in recent times.
Finding Feature Similarities Between Geometric Trees

Geometric trees are graphs with no cycles in which the nodes have spatial co-ordinates and the edges are geometric curves. Many physical systems can be represented effectively using geometric trees, e.g. river beds, animal neurons, respiratory tracks of mammals etc. As these systems undergo structural metamorphosis, temporally or under the effect of some external stimulus, the underlying tree structures also change. Given two snapshots of structurally morphed trees, an algorithm for comparing them based on geometric and topological tree features is presented. Such comparison provides a wealth of information for interpreting the metamorphosis
Model selection via the lasso in conditional logistic regression

We propose a model selection procedure in the context of matched case-control studies and, more specifically, for the conditional logistic regression. The method is based on penalized conditional likelihood with an L1-type penalty of the regression coefficients, the so-called lasso. This penalty, which shrinks coefficients to improve the accuracy of prediction, is particularly adapted when the number of covariates is large (with respect to the number of events) or in case of collinearity between them. An attractive property of the lasso is that it performs parameter estimation and variable selection simultaneously. The implementation of the method is based on a simple modification of the R-package "penalized" for fitting generalized linear and Cox proportional hazard models with L1 or L2 penalties, developed by J. Goeman. The effectiveness of the algorithms allows the use of resampling methods for choosing the regularization term and for evaluating the stability of the selected model. Thus, K-fold likelihood cross-validation and bootstrap are applied, both taking into account the dependent nature of data. The methodology is illustrated with some examples.
Spray Underwater Glider Operations

AbstractOperational statistics for the Spray underwater glider are presented to demonstrate capabilities for sustained observations. An underwater glider is an autonomous device that profiles vertically by changing buoyancy and flies horizontally on wings. The focus has been on sustained observations of boundary currents to take advantage of the glider’s small size, which allows it to be deployed and recovered from small vessels close to land, and the fine horizontal resolution delivered by the glider, which is scientifically desirable in boundary regions. Since 2004, Spray underwater gliders have been deployed for over 28 000 days, traveling over 560 000 km, and delivering over 190 000 profiles. More than 10 gliders, on average, have been in the water since 2012. Statistics are given in the form of histograms for 297 completed glider missions of longer than 5 days. The statistics include mission duration, number of dives, distance over ground, and horizontal and vertical distance through water. A discuss.
Introduction of a Cloud Computing Architecture for the Condition Monitoring of a Reconfigurable Battery System for Electric Vehicles

The electrification of the transport sector is crucial to achieve the environmental goals. The application of Internet of Things (IoT) and Cloud Computing can be part of the solution for improving efficiency and fault detection, and increasing the driving range for electric vehicles. The growing data generation and new data analysis methods demand high computing power and data storage capabilities. By combining IoT devices with Cloud Computing, the condition of a vehicle can be monitored in real time from anywhere in the world. In this paper, a new cloud architecture for the analysis of stationary and mobile measurements is presented. Additionally, a web application is developed for a detailed visualization and analysis of historical and real time data.
Performance Analysis of Soft Information Relay Models

Traditional cooperative wireless networks suffer from error propagation to the destination in poor channel conditions. The incorrect decoding at the relay and low signalto-noise ratio (SNR) on the source-relay link causes significant degradation of the system. Thus, Soft Information Relaying (SIR) is presented as a robust technique to overcome this issue. This work studies the various decoder design techniques and associated soft noise distribution of the main SIR schemes and then introduces a noise amplification factor to reduce the effect of the relay link. It helps to model the distribution of log-likelihood ratio (LLR) distribution at the destination approximately Gaussian in parallel relay schemes. Further, the Bit Error Rate (BER) performance of each wireless relay scheme under single and multiple systems is evaluated. Also, the mutual information-based forwarding (MIF) SIR scheme outperforms all other traditional SIR schemes.
Eco-Friendly IoT Solutions for Smart Cities Development: An Overview

The last three years showed global systems’ fragility in different disciplines, such as medicine, travel, and trade. Moreover, environmental changes are still happening in extreme weather phenomena, exacerbating COVID-19 in the world health sector. Therefore, the world needs to work hard in both traditional and modern technological ways to develop smart solutions for the development of smart cities to create open, interconnected networks for everyone. The focus of intense research worldwide is the development of standardized eco-friendly sensor networks enabled by IoT technology. This study presents a comprehensive overview of the challenges facing the world and humans in the current era and how modern technologies, including the Internet of Things (IoT), can make a real change in smart city development and save the global environment. These technological solutions and frameworks help to achieve innovative administration, high adaptability, better efficiency, and productivity. In particular, the paper spots light on top environmental challenges according to impact and likelihood classifications. Also, it discusses recent green technology solutions for smart city development in many applications, such as e-waste management, climate change adaptation, water crises, and natural disaster management.
Dynamic Programing-based Green Speed Advisory System Design for Mixed Platooning Connected Vehicles

Environmental concerns and related strategies, push hybrid and electric vehicles to have more market share due to their emission advantage which depends on electric carbon intensity. The traffic is getting mixed according to energy sources by increasing number of electric vehicles in use. On the other hand, smart city applications by the effective use of communication systems enable the development of green energy management systems. In this paper, a dynamic programing-based optimization system is developed to decrease Well-to-Wheels emissions of a platoon which consists of conventional and electric vehicles. The design variable of the optimizer is the speed profile and the objective function is the total emission. Both for conventional and electric vehicles, emission maps are developed for model-based optimization. For the defined use case validated results which were acquired by testing the vehicles, show that the optimizer can decrease the total emissions of the vehicles by 19.8%.
Phytochemicals Determination, and Antioxidant, Antimicrobial, Anti-Inflammatory and Anticancer Activities of Blackberry Fruits

A comprehensive characterization of the phytochemicals present in a blackberry fruit extract by HPLC-TOF-MS has been carried out. The main compounds in the extract were ursane-type terpenoids which, along with phenolic compounds, may be responsible for the bioactivity of the extract. In vitro antioxidant capacity was assessed through Folin–Ciocalteu (31.05 ± 4.9 mg GAE/g d.w.), FRAP (637.8 ± 3.2 μmol Fe2+/g d.w.), DPPH (IC50 97.1 ± 2.4 μg d.w./mL) and TEAC (576.6 ± 8.3 μmol TE/g d.w.) assays. Furthermore, the extract exerted remarkable effects on in vitro cellular antioxidant activity in HUVEC cells at a concentration of 5 mg/mL. Antimicrobial activity of the extract was also tested. Most sensible microorganisms were Gram-positive bacteria, such as E. faecalis, B. cereus and Gram-negative E. coli (MBC of 12.5 mg/mL). IC50 values against colon tumoral cells HT-29 (4.9 ± 0.2 mg/mL), T-84 (5.9 ± 0.3 mg/mL) and SW-837 (5.9 ± 0.2 mg/mL) were also obtained. Furthermore, blackberry extract demonstrated anti-inflammatory activity inhibiting the secretion of pro-inflammatory IL-8 cytokines in two cellular models (HT-29 and T-84) in a concentration-dependent manner. These results support that blackberry fruits are an interesting source of bioactive compounds that may be useful in the prevention and treatment of different diseases, mainly related to oxidative stress.
Topological Derivative for Nucleation of Non-Circular Voids

The hitherto existing literature concerning the topological derivative of shape functionals concerned perturbations of domains caused by introduction of circular or ball openings. In the present study the notion of the topologic- al derivative is generalized to the case of openings of arbitrary shape. The report is concerned with energy functionals related to the Neumann problem and the 2D elasticity system.
Academic identity at the intersection of global scientific communities and national science policies: societal impact in the UK and Netherlands

ABSTRACT This article investigates attitudes to societal impact of research as an entry point into understanding academic identities. Conceptually, we position academic identity at the intersection of global scientific fields and national science policies. We argue that the degree of alignment or misalignment between the two can create coherent academic identities, or on the contrary, tensions in academics’ identity. Empirically, we use the disciplines of philosophy and anthropology as proxies for scientific fields in the social sciences and humanities (SSH). The study is based on sixteen semi-structured interviews with mid-career philosophers and anthropologists in the United Kingdom and the Netherlands, and an analysis of how societal impact is positioned in the two national evaluation systems. We conclude that ‘coercive’ national impact policies (like the one in the UK) are less likely to be aligned with global disciplinary norms in the SSH and therefore create tensions in academic identity; these can undermine academics’ agency and be counterproductive in terms of reaching policy objectives. By contrast, ‘enabling’ national impact policies (like the one in the Netherlands) are conducive to more coherent academic identities that are better aligned with disciplinary notions of societal impact. By discussing academic identities in a comparative context, the study highlights the struggles of reconciling disciplinary and national notions of societal impact. To realise the potential societal impact of academic research, we recommend that impact is integrated into a wider ecosystem of interactions where policy-driven notions are aligned with disciplinary norms and values.
The Direct Anti-Virulence but Not Bactericidal Activity of Human Neutrophil Elastase against Moraxella catarrhalis

Neutrophil elastase (NE) contributes to innate antibacterial defense at both the intracellular (phagocytosis) and extracellular (degranulation, NETosis) levels. Moraxella catarrhalis, a human respiratory pathogen, can exist in an inflammatory milieu which contains NE. No data are available on the action of NE against M. catarrhalis or on the counteraction of NE-dependent host defenses by this pathogen. Using time-kill assays we found that bacteria are able to survive and replicate in the presence of NE. Transmission electron microscopy and flow cytometry studies with NE-treated bacteria revealed that while NE admittedly destabilizes the outer membrane leaflet, it does not cause cytoplasmic membrane rupture, suggesting that the enzyme does not target components that are essential for cell integrity. Using LC-MS/MS spectroscopy we determined that NE cleaved at least three virulent surface proteins in outer membrane vesicles (OMVs) of M. catarrhalis, including OMP CD, McaP, and TbpA. The cleavage of OMP CD contributes to the significant decrease in resistance to serum complement in the complement-resistant strain Mc6. The cleavage of McaP did not cause any sensitization to erythromycin nor did NE disturb its drug action. Identifying NE as a novel but subtle anti-virulence agent together with its extracellularly not-efficient bactericidal activity against M. catarrhalis may facilitate the pathogen’s existence in the airways under inflammation.
A Framework for the Adoption of Emerging Technologies to Reduce Under-Five Mortality in Zimbabwe

Under-five mortality remains a global health concern as many countries have failed to achieve the United Nations Millennium Development Goal 4 (MDG 4). Children under five (under-fives) continue to perish to preventable deaths globally. Zimbabwe is amongst the Sub-Saharan African countries that failed to achieve the MDG 4 on under-five mortality. Regardless of evidence from other regions that emerging technologies help eliminate preventable deaths among under-fives, Zimbabwe's adoption of such technologies in public health facilities remains nascent. The country has introduced some digital health technologies in public facilities, but they are not specific to paediatric care. Likewise, research on digital health in Zimbabwe has paid little attention to paediatric care. Therefore, this study proposes a framework for adopting and utilizing emerging technologies to reduce under-five mortality in Zimbabwe's public health facilities. The pragmatism philosophy guided the study. It employed a sequential exploratory mixed-methods design to explore factors affecting the adoption of emerging technologies in Zimbabwe's public health facilities, and the perceived role of emerging technologies, with the aim of designing a technology adoption framework. Future studies could focus on integrating the existing digital health systems in Zimbabwe to harness the data generated to enhance paediatric care through utilizing such data in paediatric care information systems.
Effect of Starch Type and Pre-Treatment on the Properties of Gelatin–Starch Foams Produced by Mechanical Foaming

Incorporating biopolymers in packaging foams can contribute to a more circular packaging system, utilizing renewable and compostable materials. Gelatin, with its favorable physicochemical properties, allows for producing gelatin foams via mechanical foaming, a well-established and low-investment process. To improve foam properties, starch can be added to the gelatin formulation. However, the variability in the properties of starch powders can impact the polymer blend and, consequently, the properties of the dry foam. This study aimed to investigate the impact of different starch powders from different botanical origins (tapioca and corn) and treatments (native or pregelatinized) on the properties of gelatin–starch foams produced by mechanical foaming. The study successfully produced foams with densities of approximately 45–50 kg/m3 and compression properties comparable to EPS (expanded polystyrene) foams. The starch type and pre-treatment significantly influenced the properties of the foam. Pregelatinized starches exhibited slightly higher densities due to lower foamability caused by higher viscosity. Using starch exhibiting total loss of birefringence led to denser foams with greater compression properties than those with starch with a certain degree of crystallinity remaining. Therefore, selecting the appropriate starch type is crucial when developing starch-based materials to ensure optimal material and processing properties align with application requirements.
Multistep Model Predictive Control of Induction Motors for Reducing Switching Frequency

A multistep model predictive current control strategy for reducing the switching frequency of inverters is proposed to address the challenges of low energy efficiency of small power induction motors. Based on the discrete mathematical model of the motor drive system, the strategy extends the current trajectory corresponding to each allowable switching sequence employing iterative prediction with quadratic interpolation, and uses average switching frequency within the predicted range as a cost function. In order to acquire the ideal switching vector in real-time, the cost function conducts online rolling optimization. Compared to model predictive direct current control, this method can reduce the average switching frequency of the two-level inverter while enhancing current harmonic performance. The simulation results verify the efficiency of the proposed method.