Miracle-dz/UniFAR-FTUs huggingface dataset:
Essays on the Behavioral Economics of Motivated Memory

This thesis investigates whether individuals use their memory as a self-deceptive strategy to sustain motivated beliefs. It tests experimentally the existence and strength of memory manipulation in three economically relevant contexts: social interactions, individual performance and unethical decisions.Chapter one investigates whether people retrieve their memory self-servingly in social encounters. Do individuals forget the consequences of their actions on others? If so, does it depend on the nature (e.g. selfish or altruistic) of the action? Our results identify a causal effect of the responsibility of pro-social decisions on selective recalls. In contrast, there is no clear evidence of biased memory errors. Chapter two disentangles between two driving forces that have been proposed as explanations of memory failures for self-relevant information: self-enhancement and mood-congruency. We provide a controlled environment where the two theories predict different outcomes. Our results provide support for the existence and relative dominance of self-enhancing memory over mood-congruent memory and thereby underline the importance of motivational factors in the formation of optimistic beliefs about the self.Chapter three investigates the relative role of affect and strategic reasoning in motivated memory, with an application in the domain of unethical behavior. We study whether individuals manipulate the memory of past dishonest choices, and whether they use their memory as an instrument to justify future decisions. We find that hedonic considerations are not sufficient to trigger memory manipulation. When forgetting serves as a justification to not engage in future morally responsible behavior, however, individuals do motivate their memory.Together, these results show that memory errors in economic contexts can result from cognitive impairment but also from memory distortion motivated by the willingness to protect one's self-image and future choices.
Modeling of End-Space Convection Heat-Transfer for Internal and External Rotor PMSMs With Fractional-Slot Concentrated Windings

Modeling of convection heat-transfer at the end-windings for thermal networks using lumped-parameters has been widely discussed in the literature. Unfortunately, the resulting coefficients are highly influenced by the end-winding shape, the area in the vicinity of the end-windings, the cooling method of this region (if any), as well by the size and power of the machine. This makes it extremely difficult for the designer to choose a suitable coefficient for the thermal analysis during the design stage of an electric motor. A methodology to obtain the end-windings convection heat-transfer coefficients for fractional-slot concentrated winding permanent-magnet synchronous motors is proposed in this article. Machine designs with both internal and external rotors will be considered. The experimental tests required for the model characterization are described in detail. In this article, general expressions of the convection heat-transfer coefficients between the end-windings and the housing end-caps are proposed for both internal and external rotor designs. Differences observed with the results reported in the literature are also discussed.
Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis

More than 8.6 million people suffer from neurological disorders that affect their gait and balance. Physical therapists provide interventions to improve patient’s functional outcomes, yet balance and gait are often evaluated in a subjective and observational manner. The use of quantitative methods allows for assessment and tracking of patient progress during and after rehabilitation or for early diagnosis of movement disorders. This paper surveys the state-of-the-art in wearable sensor technology in gait, balance, and range of motion research. It serves as a point of reference for future research, describing current solutions and challenges in the field. A two-level taxonomy of rehabilitation assessment is introduced with evaluation metrics and common algorithms utilized in wearable sensor systems.
3-D Laser Scanning Landslide Deformation Monitoring and Data Processing Based on Computer Cluster

Landslide monitoring is an important technical means for monitoring and forecasting landslides. At present, three-dimensional laser scanning technology has been applied to deformation monitoring, such as landslide monitoring. The three-dimensional laser scanning technology can measure the three-dimensional coordinates of the object surface with high precision, high density and high speed, thus describing the details of the surface in detail. It has been successfully applied in static shape measurement. Computer cluster is widely used in various computationally intensive tasks because of its high cost performance and good scalability. Combined with the deformation information of deformation monitoring points, the deformation trend of landslide body is effectively monitored and predicted. In order to process the obtained data in a timely and rapid manner, the computer cluster performs the absolute orientation of the sequence point cloud collected in different periods to complete the coordinate system, and then acquires a set of points of different periods of point cloud data at the same position of the computer cluster. The data was compared and analyzed. In addition, the acquisition of ground-type 3D laser scanner data does not require the deployment of monitoring points, which can reflect the surface morphology of the landslide more quickly and accurately than the traditional monitoring means, and then obtain the surface deformation value.
Job Satisfaction and Related Factors among Intensive Care Nurses in Governmental Hospitals at Khartoum State - Sudan

Background and Objective: As advances have been made in medicine and technology, patient care has become more complex. And to provide appropriate health care, nurses needed specialized knowledge, skills and the health care delivery mechanisms to evolve and support the patients' needs for continuous monitoring and treatment. This study was conducted in governmental hospitals in Khartoum state to study job satisfaction and the factors influencing job satisfaction, to determine the overall level of job satisfaction and to relate socio-demographic characteristic of ICU nurses with overall level of job satisfaction. Method: A descriptive, cross-sectional hospital based study was done to all intensive care unit nurses. Questionnaire with likert type scale was used to collect necessary data; Likert scale was numerically translated to evaluate job satisfaction scores. Results: Results revealed high overall level of job satisfaction (mean 85.3 ± 13.1 on 5 points scale). There was significant association between overall level of job satisfaction and monthly salary (P = 0.025). Significant association also found between age group and chance for suggestion and recreation (P = 0.018). And significant association found between qualifications and workload (P = 0.025). Conclusion: Age group and different qualifications are related to nurse job satisfaction level. And intrinsic job characteristics are as important as extrinsic job conditions on ICU nurse job satisfaction.
Techno-Economic Optimization and Benchmarking of a Solar-Only Powered Combined Cycle with High-Temperature TES Upstream the Gas Turbine

This work presents a techno-economic parametric study of an innovative central receiver solar thermal power plant layout that applies the combined cycle (CC) as thermodynamic power cycle and a multi-tower solar field configuration together with open volumetric air receivers (OVARs). The topping gas turbine (GT) is powered by an air – air heat exchanger (two heat exchanger trains in the case of reheat). In order to provide dispatchability, a high-temperature thermocline TES system is placed upstream the gas turbine. The aim is threefold, (i) investigating whether the multi-tower concept has a techno-economic advantage with respect to conventional single-tower central receiver plants, (ii) indicating the techno-economic optimum power plant configuration, and (iii) benchmarking the techno-economic optimum of the CC plant against that of a conventional single-cycle Rankine steam plant with the same receiver and TES technology. It is concluded that the multi-tower configuration has a techno-economic advantage with respect to the conventional single-tower arrangement above a total nominal solar power level of about 150 MW. However, the benchmarking of the CC against a Rankine single-cycle power plant layout shows that the CC configuration has despite its higher solar-to-electric conversion efficiency a higher LCOE. The gain in electricity yield is not enough to outweigh the higher investment costs of the more complex CC plant layout.
Biodiversity–Ecosystem Functioning (BEF) approach to further understanding aquaculture–environment interactions with application to bivalve culture and benthic ecosystems

: Coastal benthic ecosystems may be impacted by numerous human activities, including aquaculture, which continues to expand rapidly. Indeed, today aquaculture worldwide provides more biomass for human consumption than do wild fisheries. This rapid development raises questions about the interactions the practice has with the surrounding environment. In order to design strategies of sustainable ecosystem exploitation and marine spatial planning, a better understanding of coastal ecosystem functioning is needed so that tools to quantify impacts of human activities, including aquaculture, may be developed. To achieve this goal, some possible directions proposed are integrated studies leading to new concepts, model development based on these concepts and comparisons of various ecosystems on a global scale. This review draws on existing literature to (i) briefly summarize the major ecological interactions between off ‐ bottom shellfish aquaculture and the environment, (ii) introduce research on the influence of benthic diversity on ecosystem functioning (BEF relationships) and (iii) propose a holistic approach to conduct aquaculture–environment studies using a BEF approach, highlighting the need for integrated studies that could offer insights and perspectives to guide future research efforts and improve the environmental management of aquaculture.
Identification and prevalence of ectoparasites on the fry of Asian sea bass (Lates calcalifer), white shrimp (Litopenaeus vannamei), and blue swimming crab (Portunus pelagicus)

The study aims to find out the types of ectoparasites infected in the fry of Asian Sea Bass (Lates calcalifer), White Shrimp (Litopenaeus vannamei), and Blue Swimming Crab (Portunus pelagicus), and to know their prevalence. The method used in this study is an examination of fish samples using the wet mount method. The results showed that the parasites found in the fry of Asian Sea Bass were Trichodina sp. , Caligus sp. , and Dactylogyrus sp. , with the prevalence, were 18.85%, 3.28%, and 1.64% respectively. While, the parasites of Vorticella sp. , Epistylis sp and Zoothamnium sp. infected on white shrimp with the prevalence respectively were 2.71%, 0.84%, and 0.17%. Furthermore, on the blue swimming crab was found parasite of Vorticella sp. with the prevalence was 16.47% and Epistylis sp. with the prevalence value was 12.54%.
Security and Energy Harvesting for MIMO-OFDM Networks

We consider a multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) network in which a source node, Alice, communicates with an energy-harvesting destination node, Bob, in the presence of a passive eavesdropper. To secure the wireless transmission, Alice generates a hybrid artificial noise (AN) in both frequency and time domains. Moreover, in order to collect more energy, Bob splits the received signal power of the cyclic prefix of each OFDM block. We then propose two non-convex optimization problems to balance both the need for security and the need for harvesting energy at Bob. While one considers maximizing the secrecy rate, the other approach aims at maximizing the harvested energy. Path-following algorithms of low computational complexity are developed and evaluated. Our numerical results show the gain of our proposed scheme and the effectiveness of our proposed algorithms.
Cluster-Based Chained Transfer Learning for Energy Forecasting With Big Data

Smart meter popularity has resulted in the ability to collect big energy data and has created opportunities for large-scale energy forecasting. Machine Learning (ML) techniques commonly used for forecasting, such as neural networks, involve computationally intensive training typically with data from a single building/group to predict future consumption for that same building/group. With hundreds of thousands of smart meters, it becomes impractical or even infeasible to individually train a model for each meter. Consequently, this paper proposes Cluster-Based Chained Transfer Learning (CBCTL), an approach for building neural networkbased models for many meters by taking advantage of already trained models through transfer learning. CBCTL first clusters the meters based on their load profiles. Next, Similarity-Based Chained Transfer Learning (SBCTL) is applied within each cluster; the first model within each cluster is trained in a traditional way and all other models transfer knowledge from existing models in a chain-like manner according to similarities between energy consumption profiles. A Recurrent Neural Network (RNN) was used as the base forecasting model, two initialization techniques were considered, and different similarity measures were explored. The experiments show that CBCTL and SBCTL achieve accuracy comparable to traditional ML training while taking only a fraction of time.
Optimization of the National Land Space Based on the Coordination of Urban-Agricultural-Ecological Functions in the Karst Areas of Southwest China

National land spatial planning is dominated by urban-agricultural-ecological functions and has become a Chinese national strategic issue. However, the three functional spaces have serious conflicts in the karst areas, causing inconsistencies in regional development and triggering poverty and a more serious situation for the ecological environment. In this study, we used the gray multi-objective dynamic programming model and the conversion of land use and its effects at small region extent model to simulate the developmental structures of future land use in the karst areas of Southwest China under a socioeconomic development scenario, an arable land protection scenario and an ecological security scenario. Finally, based on the coordination of the urban-agricultural-ecological functions, we used a functional space classification method to optimize the spatial structures of the national land space for 2035 year and to identify different functional areas. The results showed that the three scenarios with different objectives had differences in the quantities and spatial structures of land use but that the area of forestland was the largest and the area of water was the smallest in each scenario. The optimization of the national land space was divided into seven functional areas—urban space, agricultural space, ecological space, urban-agricultural space, urban-ecological space, agricultural-ecological space and urban-agricultural-ecological space. The ecological space was the largest and the urban-ecological space was the smallest among seven functional areas. The different types of functional spaces had significant differentiation characteristics in the layouts. The urban-agricultural space, urban-ecological space, agricultural-ecological space and urban-agricultural-ecological space can effectively alleviate the impacts of human activities and agricultural production activities in karst areas, promote the improvement of rocky desertification and improve the quality of the regional ecological environment. The results of this research can provide support for decisions about the balanced development of the national land space and the improvement of environmental quality in the karst areas.
UPΦ phages, a new group of filamentous phages found in several members of Enterobacteriales

Filamentous phages establish chronic infections in their bacterial hosts, and new phages are secreted by infected bacteria for multiple generations, typically without causing host death. Often, these viruses integrate in their host’s genome by co-opting the host’s XerCD recombinase system. In several cases, these viruses also encode genes that increase bacterial virulence in plants and animals. Here, we describe a new filamentous phage, UPϕ901, which we originally found integrated in a clinical isolate of uropathogenic Escherichia coli. UPϕ901 and closely related phages can be found in published genomes of over 200 other bacteria, including strains of Citrobacter koseri, Salmonella enterica, Yersinia enterocolitica, and Klebsiella pneumoniae. Its closest relatives are consistently found in urine or in the blood and feces of patients with urinary tract infections. More distant relatives can be found in isolates from other environments, including sewage, water, soil, and contaminated food. Each of these phages, which we collectively call “UPϕ viruses,” also harbors two novel genes of unknown function.
Uncovering the complete biodiversity structure in spatial networks: the example of riverine systems

Uncovering biodiversity as an inherent feature of ecosystems and understanding its effects on ecosystem processes is one of the most central goals of ecology. Studying organisms’ occurrence and biodiversity patterns in natural ecosystems has spurred the discovery of foundational ecological rules, such as the species–area relationship, and is of general scientific interest. Recent global changes add relevance and urgency to understanding the occurrence and diversity of organisms, and their respective roles in ecosystem processes. While information on ecosystem properties and abiotic environmental conditions are now available at unprecedented, highly‐resolved spatial and temporal scales, the most fundamental variable – biodiversity itself – is still often studied in a local perspective, and generally not available at a wide taxonomic breadth, high temporal scale and spatial coverage. This is limiting the capacity and impact of ecology as a field of science. In this forum article, we propose that complete biodiversity assessments should be inclusive across taxonomic and functional groups, across space, and across time to better understand emergent properties, such as ecosystem functioning. We use riverine ecosystems as a case example because they are among the most biodiverse ecosystems worldwide, but are also highly threatened, such that an in‐depth understanding of these systems is critically needed. Furthermore, their inherent spatial structure requires a multiscale perspective and consideration of spatial autocorrelation structures commonly ignored in biodiversity–ecosystem functioning studies. We show how recent methodological advances in environmental DNA (eDNA) provide novel opportunities to uncover broad biodiversity and link it to ecosystem processes, with the potential to revolutionize ecology and biodiversity sciences. We then outline a roadmap for using this technique to assess biodiversity in a complete and inclusive manner. Our proposed approach will help to get an understanding of biodiversity and associated ecosystem processes at spatial scales relevant for landscape ecology and environmental managers.
Recurrent Semantic Preserving Generation for Action Prediction

In this paper, we propose a recurrent semantic preserving generation (RSPG) method for action prediction. Unlike most existing methods which don’t make full use of information from partially observed sequences, we develop a generation architecture to complement the sequence of skeletons for predicting the action, which can exploit more potential information of the movement tendency. Our method learns to capture the tendency of observed sequences and complement the subsequent action with adversarial learning under some constrains, which preserves the consistency between the generation sequence and the observed sequence. By generating the subsequent action, our method can predict the action with the most probability. Moreover, the redundant generation introduces the noise and disturbs the prediction. The insufficient generation cannot exploit the potential information for improving the effect of predicting the action. Our RSPG controls the generation step in a recurrent manner for maximizing the discriminative information of actions, which can adapt to the variable length of different actions. We evaluate our method on four popular action datasets: NTU, UCF101, BIT, and UT-Interaction, and experimental results show that our method achieves very competitive performance with the state-of-the-art.
Effective mining of crime patterns from growing volumes of data using improved FP-growth algorithm

The spate of crimes in Tanzania, as in many other countries, has been on the increase in the last few years. The successes recorded by criminals have been attributed by improper mechanisms for crime detection, prevention and control. Proactive measures are needed to preempt further crimes. Frequent pattern mining stand to aid in finding emerging patterns, series, and trends in the crime data. This will eventually help Tanzania Police Force and other law enforcement agencies to understand crime trends and predict or forecast future occurrences and thus improve preventive measures against crimes. FP-Growth is the most effective and most widely used algorithm for frequent pattern mining and association rules generation. Unfortunately, studies have shown two main weaknesses associated with this algorithm when used in the growing volumes of crime data. Inability of the algorithm to scale-up well with the growing volumes data is the first weakness. Second weakness of FP-Growth is associated with the nature of crime data. Crime data consists of items (different crimes) that vary greatly in terms of frequencies of occurrence. Some of crimes (e.g. robbery) happen so frequently and thus frequently appear in the dataset while other crimes (e.g. killing of people with albinism, in the case of Tanzania) happens seasonally and therefore rarely found in the dataset. Classical FP-Growth algorithm extract frequent patterns by using single user-defined minimum support. This is the main source of the algorithm’s challenge, especially when used in crime datasets. To tackle the challenges, this study have proposed a multiple minimum support FP-Growth algorithm that scans the dataset and automatically assigns minimum support values to each crime item basing on how frequently it has appeared in the dataset. The proposed solution is based on the Shannon Entropy in which an algorithm for obtaining multiple item support values was developed. In connection, the study developed a working prototype that was based on the proposed approach to help in recording crime data and mine crime patterns from data. The proposed approach was evaluated against classical FP-Growth as well as CFPGrowth algorithm on the varying sizes crime data. Evaluation results showed that the proposed approach in this research is more efficient in mining frequent crime patterns, and more effective in terms of run-time and memory use. Basing on the results found, the study recommends for further experimentation of the proposed approach on streaming data and on distributed environments, among other recommendations as stipulated in the dissertation.