Miracle-dz/UniFAR-FTUs huggingface dataset:
Feeding Nigella sativa oil to broilers affects their performance, serum constituents and cecum microbiota

The objective of this study was to assess the effect of supplementation of Nigella sativa oil (NSO) as a growth promoter in broiler diets. A total of 300 unsexed one-day-old Arbor Acre broiler chicks were randomly divided into five treatment groups (60 chicks/group). Each group was divided into five replicates with 12 chicks per replicate. Group 1 was fed on the basal diet with no additives (control). Group 2 was fed the basal diet supplemented with oxytetracycline (OTC) at the level of 50 ppm. Groups 3, 4 and 5 were fed the basal diet supplemented with 500, 1000 and 1500 ppm NSO, respectively. Birds fed on 1500 ppm NSO diet showed the heaviest body weight at 14, 28 and 42 days of age. Feed conversion ratio was significantly improved at all ages with the inclusions of OTC and NSO generally superior to the control group. The relative weight of spleen was increased significantly by the addition of NSO at various levels. However, relative weights of thymus and bursa were not altered by the treatments. Total bacterial count, total yeast and moulds count, E. coli and salmonella spp. counts were reduced significantly, whereas the total lactic acid bacteria count was increased in OTC and NSO groups compared with control. Nigella sativa oil could be used in broiler chicken feeds as a natural alternative to antibiotic growth promoters to improve gut health and consequently growth performance.
Additive Digital Casting: From Lab to Industry

Concrete construction harms our environment, making it urgent to develop new methods for building with less materials. Structurally efficient shapes are, however, often expensive to produce, because they require non-standard formworks, thus, standard structures, which use more material than is often needed, remain cheaper. Digital fabrication has the potential to change this paradigm. One method is Digital Casting Systems (DCS), where the hydration of self-compacting concrete is controlled on the fly during production, shortening the required setting time and reducing hydrostatic pressure on the formwork to a minimum. This enables a productivity increase for standard concrete production. More importantly, though, it enables a rethinking of formworks, as the process requires only cheap thin formworks, thus, unlocking the possibility to produce optimised structural members with less bulk material and lower environmental cost. While DCS has already proven effective in building structural members, this process faces the challenge of moving into industry. This paper covers the next steps in doing so. First, we present the benchmark and expectations set by the industry. Second, we consider how we comply with these requirements and convert our fast-setting self-compacting mortar mix into a coarser one. Third, we present the next generation of our digital processing system, which moves closer to the industrial requirements in terms of size and the control system. Finally, two prototypes demonstrate how DSC: (a) increases standard bulk production by 50% and (b) can be cast into ultra-thin formworks. We discuss the results and the short-term industrial concerns for efficiency and robustness, which must be addressed for such a system to be fully implemented in industry.
Online Detection of Outliers and Structural Breaks using Sequential Monte Carlo Methods

Outliers and structural breaks occur quite frequently in time series data. Whereas outliers often contain valuable information about the process under study, they are known to have serious negative impact on statistical data analysis. Most obvious effect is model misspecification and biased parameter estimation which results in wrong conclusions and inaccurate predictions. Structural time series consist of underlying features such as level, slope, cycles or seasonal components. Structural breaks are permanent disruptions of one or more of these components and might be a signal of serious changes in the observed process. Detecting outliers and estimating the location of structural breaks has progressively become monumental both as a theoretical research problem and an essential part of applied data analysis. Among numerous applications include finance, industrial manufacturing, medical informatics, severe weather prediction. Given that these data arrive rather frequently and sequentially in time, fast reliable and accurate detection techniques are required. We propose a model from class of state-space models of the form yt = f(Xt, ψ, vt) and Xt = g(Xt−1, ψ, wt) where { Xt } t≥0 is a hidden Markov state process. The inference of { Xt } t≥0 depends on the observation process {yt}t≥1 and the parameter vector ψ, whose elements are usually unknown. The innovations vt and wt are conditionally Gaussian given the precision parameter λ and auxiliary state ω. We employ sequential Monte Carlo techniques to approximate the joint target distribution p(X0:t, ψ|y1:t). The posterior estimates for the auxiliary states ω will be used to identify outliers and structural breaks. The results prove that the algorithm is comparable to traditional and computationally expensive MCMC and superior to regular techniques such as Exponentially Weighted Moving Average (EWMA), Shewhart, and cumulative sum (CUSUM) control charts Acknowledgements I would like to thank my advisor Dr. Giovanni Petris for his patience, support, and encouragement throughout my time at University of Arkansas. His guidance has made this a thoughtful and rewarding journey. I would like to thank my dissertation committee for all their support and much needed insights. I also like to thank the department of Mathematical Sciences for granting me graduate assistantship, throughout my study, without which it would have been very difficult to realise my goal. I would like to thank most sincerely my wife Lydia, sons Henry and Alan for their unconditional love, patience and support throughout the course. Finally, I would like to thank all my family members and friends for their prayers and for expecting nothing less than completion from me.
Are students too anxious for statistics anxiety workshops?

Statistics is a widely taught subject in Higher Education but for many students, anxiety about statistics interferes with the learning process. Statistics anxiety workshops to help students understand and reduce statistics anxiety were developed by the authors and in 2020/21 delivered collaboratively and remotely with specific cohorts of students at three institutions. Prior to the workshops, all students within the targeted cohorts were asked to complete a survey which included measures of statistics anxiety, and asked if they were interested in attending the voluntary workshop. This enabled a comparison of the characteristics of groups who were interested or not. The workshops successfully attracted the targeted students, since those attending had higher overall statistics anxiety, software and maths anxiety, and anxiety around learning statistics. However, students with higher help seeking anxiety were less likely to attend.
Analysis and optimal configuration of distributed opportunistic scheduling techniques in wireless networks

The phenomenon of fading in wireless communications has traditionally been considered as a source of unreliability that needs to be mitigated. In contrast, Opportunistic Scheduling (OS) techniques exploit quick channel quality oscillations in fading links, during the assignment of transmission opportunities, to improve the performance of wireless networks. While centralized mechanisms rely on a central entity with global knowledge, Distributed Opportunistic Scheduling (DOS) techniques have recently been proposed to work in distributed networks, i.e., where either such a central entity is not available, or the communication overhead to feed timely information to this central entity is prohibitive. With DOS, each station contends for the channel with a certain access probability. If a contention is successful, the station measures the channel conditions and transmits if the channel quality is above a certain threshold. Otherwise, the station does not use the transmission opportunity, allowing all stations to recontend. Given the fact that different stations, in different time instances, experience different channel conditions, it is likely that the channel is used by a link with better conditions, improving overall performance. In this thesis we first propose ADOS, an adaptive mechanism that drives the system to an optimal allocation of resources in terms of proportional fairness. We show that this mechanism outperforms previous approaches, particularly in scenarios with non-saturated stations (that do not always have data to transmit). The distributed nature of DOS makes it particularly vulnerable to selfish users that seek to maximize their own performance at the expense of those that cooperate for the common welfare. We thus design a punishing mechanism, namely DOC, that (i) drives the system to the optimal point of operation when all stations follow the protocol, and (ii) removes any potential gain by deviating from it (and thus, the incentive to misbehave). Finally, we propose a novel allocation criterion, namely the EF criterion, to balance between the most energy-eficient configuration (where all resources are given to the most energy e cient devices) and the throughput-optimal allocation (where all devices evenly share the resources regardless of their power consumption). Due to the lack of models that accurately predict the power consumption behavior of wireless devices, we perform a thorough experimental study to devise a power consumption model that completes existing literature. Finally, we apply these findings to design an EF-optimal strategy in DOS networks. --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Effects of Fused Silica Addition on Thermal Expansion, Density, and Hardness of Alumix-231 Based Composites

Fused silica is a ceramic with promising applications as a filler in composites due to its near-zero thermal expansion. Substitution of heavy cast iron with Al-based light alloys is of utmost importance for the automotive industry. However, the high thermal expansion of Al alloys is an obstacle to their use in some applications. As such, ceramic fillers are natural candidates for tuning thermal expansion of Al-based matrices, due to their inherently moderate or low thermal expansion. Alumix-231 is a new promising alloy, and fused silica has never been used before to lower its thermal expansion. Composites with the addition of 5 to 20 vol.% of fused silica were developed through powder metallurgy, and the best results in terms of reduction of thermal expansion were reached after liquid phase sintering at 565 °C. Coefficients of thermal expansion as low as 13.70 and 12.73 × 10−6 °C−1 (between 25 and 400 °C) were reached for the addition of 15 and 20 vol.% of fused silica, a reduction of 29.9% and 34.8%, respectively, in comparison to neat Alumix-231. In addition, the density and hardness of these composites were not significantly affected, since they suffered only a small decrease, no higher than 6% and 5%, respectively. As such, the obtained results showed that Alumix-231/fused silica composites are promising materials for automotive applications.
3D localization and mapping using automotive radar

This thesis considers the problem of localization and mapping for a vehicle using front-looking radar. Using a reference vehicle equipped with a DGPS-system, measurements of the static environment are accumulated into an Occupancy Grid Map with a resolution of 0.2 meters. Since the radar has the ability to produce unambiguous measurements in both azimuth and elevation, we create the map in 3D. The method recursively updates the occupancy map of the static environment using an inverse sensor model. A vehicle driving through the same area can then be located by utilizing the information stored in the map. By fusing the inertial measurement from the vehicle with the radar measurements, together with the offline 3D map, the vehicle states are estimated in all six degrees of freedom (position and orientation). We employ two different methods for localization, one using a modified Rao-Blackwellized particle filter, and one based on registration utilizing tricubic interpolation and gradient search. Both proposed solutions are able to accurately estimate the ego-position of the car, within 0.3 meters of ground truth in most estimates, in both simulation and on real data. Cumulative errors, caused by only using inertial measurements, are successfully removed by both implementations. Radar’s ability to distinguish dynamic from static targets, together with weather robustness, enables 3D radar to play a vital part for automotive positioning systems.
Knowledge-based configuration design

Unlike other phases of product development, conceptual design lacks computer-based support. The process is qualitative and requires expertise and creativity. In an attempt to support or automate this process, researchers are applying techniques from the field of AI. We discuss the application of knowledge-based systems (KBSs) to conceptual design. We proposes an approach for automating the generation of design concepts. With this approach, conceptual design is regarded as a configuration problem. New design solutions are generated automatically by adapting and configuring existing subsolutions. The technique has been applied to the area of robot gripper design.
Close spacing of settling chamber screens

Screen separations as little as 0.02 chamber diameters do not adversely affect suppression of mean-flow non-uniformity provided the overall pressure drop coefficient, K, does not exceed about 3.5. At larger K a velocity overshoot develops near the wall boundary layers, which decreases only slowly with wider separation. A simple approximate extension can be made to the theory of Taylor and Batchelor for any number of closely-spaced screens. This shows good agreement with measurements and with low-drag screens predicts perfect attenuation when the overall pressure-drop coefficient is 3.2 or 7.6. Some inferences regarding the effect of close screens on turbulence suppression are also drawn.
Spatial Sigma-Delta Modulation in a Massive MIMO Cellular System

In cellular networks, frequency channels are used to separate the users. However frequency spectrum is an ever-decreasing resource as wireless units and applications increase. Thus, other means to separate the users are of interest. By using hundreds of antennas and beamforming in the system, the users can be efficiently multiplexed in the spatial domain, while using the same time-frequency resource. The large amount of antennas however requires extensive data processing and transport which is difficult and expensive to handle. Thus, an investigation of how to reduce the data bandwidth required in such a system is of interest. This master thesis project evaluates the use of the spatial dimension in a  modulator as part of the analog-to-digital conversion. It is put into the context of beamforming and evaluated. A simulator developed in MATLAB is utilized to show the properties and qualities of different modulator structures in a beamforming application. In particular, performance at low converter speeds is evaluated. The results show that by using the spatial dimension in a  modulator it is possible to reduce the bandwidth requirement, through reduction of sampling rate while still retaining the signal quality. However, this is applicable only in one direction at a time, and there are multiple implementation issues regarding the proposed design. The findings suggest that with the spatial dimension in a  modulator there are possibilities to reduce the bandwidth requirement in antenna array systems but that there are considerable challenges to overcome.
The Impact of Self-Employment on the Health of Migrant Workers: Evidence from China Migrants Dynamic Survey

Rural-to-urban migrant workers are at high risk of health inequalities in cities. Since labor is a central social determinant of health, this paper provided evidence on the health consequences of self-employment among mobile populations in developing countries. The cross-sectional data from the 2017 data of the China Migrants Dynamic Survey (CMDS) and the IV-Oprobit model are used to examine the effects of self-employment on health. The results showed that: (1) Self-employment was positively related to health; (2) among the self-employed, the health effects of opportunity self-employed are larger than those of necessity self-employed; (3) in the subsample analysis, the health effect of self-employment was greater for male and Han nationality migrant workers; (4) self-employment promotes health primarily through reducing manual labor, increasing flexibility time, job stability, financial rewards, and social integration directly or indirectly. Thus, focusing on improving the social security system, granting entrepreneurial subsidies, and optimizing the business environment mean boosting the positive effect of self-employment on economic development.
Mechanistic Investigation of the Formation of Nickel Nanocrystallites Embedded in Amorphous Silicon Nitride Nanocomposites

Herein, we report the mechanistic investigation of the formation of nickel (Ni) nanocrystallites during the formation of amorphous silicon nitride at a temperature as low as 400 °C, using perhydropolysilazane (PHPS) as a preformed precursor and further coordinated by nickel chloride (NiCl2); thus, forming the non-noble transition metal (TM) as a potential catalyst and the support in an one-step process. It was demonstrated that NiCl2 catalyzed dehydrocoupling reactions between Si-H and N-H bonds in PHPS to afford ternary silylamino groups, which resulted in the formation of a nanocomposite precursor via complex formation: Ni(II) cation of NiCl2 coordinated the ternary silylamino ligands formed in situ. By monitoring intrinsic chemical reactions during the precursor pyrolysis under inert gas atmosphere, it was revealed that the Ni-N bond formed by a nucleophilic attack of the N atom on the Ni(II) cation center, followed by Ni nucleation below 300 °C, which was promoted by the decomposition of Ni nitride species. The latter was facilitated under the hydrogen-containing atmosphere generated by the NiCl2-catalyzed dehydrocoupling reaction. The increase of the temperature to 400 °C led to the formation of a covalently-bonded amorphous Si3N4 matrix surrounding Ni nanocrystallites.
Developing An Automated COVID Risk Counselor: Situation Type and
 Perceived Risk

The COVID-19 pandemic has required individuals to understand and respond to evolving threats during their daily lives. Our goal is to develop an interactive risk counselor to help people evaluate risk-related implications of participating in daily situations and consider behaviors to mitigate risk. Automated tools have already been developed to help determine whether users may have COVID and should seek care, or to help users evaluate daily risk and provide advice and guidelines. Our tool will use a conversational interface to educate users about risk in a wider range of scenarios and provide recommendations that simplify risk perception. As an initial step, we conducted a survey to elicit people’s concerns about COVID risk in daily situations. Participants were asked to identify situations and for each situation, they were asked about dimensions relevant to risk, similar to the questions we envision the agent asking. Participants responded to our survey in a variety of ways. Some unexpectedly responded with comments, expressing opinions or observations about the pandemic circumstances. Others responded as we expected, describing a variety of objectively risky situations (e.g., indoor activities with little social distancing), and many reported moderate/extreme levels of concern about these situations. The descriptions, whether comments or situations, seemed to cluster into categories. We report several analyses of the survey data that help characterize differences between these two general kinds of responses. We next plan to confirm our initial observations that the data set enables reliable classification of user comments and situation descriptions. The classifiers would enable the agent to respond to a diverse user goals, including recognizing types of situations with known risk attributes related to the pandemic, so the agent can ask questions to help users evaluate situational risk. User responses will in turn allow the agent to educate the users about sources of risk mitigating behaviors.
Online Scheduling of Distributed Machine Learning Jobs for Incentivizing Sharing in Multi-Tenant Systems

To save cost, companies usually train machine learning (ML) models on a shared multi-tenant system. In this cooperative environment, one of the fundamental challenges is how to distribute resources fairly among tenants such that each tenant is satisfied. A satisfactory allocation policy needs to meet the following properties. First, the performance of each tenant in the shared cluster is at least the same as that in its exclusive cluster partition. Second, no tenant can get more benefits by lying about its demands. Third, tenants cannot use the idle resources of others for free. Moreover, the resource allocation for ML workloads should avoid costly migration overhead. To this end, we propose a three-layer scheduling framework Astraea: i) a batch scheduling framework groups unprocessed ML jobs into multiple batches; ii) a round-by-round algorithm enables tenants to reserve their share of resources and schedule ML jobs in a non-preemptive manner; iii) one-round algorithm based on the primal-dual approach and posted pricing framework, which encourages tenants to report truthful demands and computes a schedule with maximal revenue for each ML job. Besides, our algorithm also allows tenants to trade unused resources on a paid basis. Astraea is proven to achieve performance guarantee, polynomial time complexity and some desirable properties of resource sharing, including batch sharing incentive, strategy-proofness, Pareto efficiency and gain-as-you-contribute fairness. Extensive trace-driven simulations show that our algorithm advances in both fairness and cluster efficiency by at least 20% compared to three state-of-the-art baselines.