Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward Intelligent Reconfiguration of RPL Networks using Supervised Learning

Designing scalable and energy-efficient routing protocols for IoT low power networks is a particularly challenging problem. The IETF ROLL Working Group has defined and standardized an IPv6 routing protocol for IoT low power networks called RPL (IPv6 Routing Protocol for Low-Power and Lossy Networks) [1]. This protocol builds and maintains dynamic routes among network devices based on various objective functions (OFs) that exploit different network metrics for parent node selection (e.g., ETX-based [2], Energy-based [3]), etc.). With such OFs, RPL organizes the network topology as a Destination Oriented Directed Acyclic Graph (DODAG). However, the performance of RPL may be affected by frequent network topology changes, which may be caused by different factors like node battery depletion, link quality degradation, etc. Indeed, in such situations, the OF functions do not guarantee optimal maintenance of the RPL tree. To address this issue, this paper describes how Supervised Learning can be leveraged to improve RPL performance and energy efficiency by mitigating RPL DODAG instability when the network conditions, used by the RPL’s OF functions, change frequently. We use an offline supervised learning to provide the optimal value of the transmission range (the maximal distance to which a node can send its data to another one) that mitigates the instability of the RPL network, and hence minimizes the energy consumption. The preliminary simulation results show that our proposal can improve network performance and increase network lifetime.
Room temperature bonding of GaN on diamond by using Mo/Au nano-adhesion layer

For solving the heat dissipation of high-power GaN devices, GaN was bonded on diamond at room temperature by using Mo/Au nano-adhesion layer. For a bonded GaN-on-diamond sample, the voidage is less than 3.0%, and the tensile strength reaches 6.8 MPa.
A Detection Aided Multi-Filter Target Tracking Algorithm

When the target is partially occluded or undergoes drastic appearance change, drift is prone to occur in traditional correlation filter-based trackers and ultimately leads to tracking failure. This paper proposes a robust tracking algorithm, which can be applied to practical engineering. The proposed algorithm divides all training samples into different training sets based on their similarities. An independent filter is trained for every training set. In each frame of the tracking process, the similarity between the candidate region and any training set’s training samples is calculated to select the most matched filter for target location. As a newly introduced training sample, a feature map within the current frame’s estimated target bounding box is assigned to the most similar training set to update the most matched filter. The updating of each filter is relatively independent. Each filter corresponds to one kind of target’s typical appearance, and thus, the proposed tracking algorithm can memorize a variety of typical target appearances that appeared in the past and adapt to target’s discontinuous appearance change. The detector improves tracking accuracy and assists occlusion judgment. After occlusion, the target may reappear with a different posture. As long as similar postures have been captured in the previous tracking process, the target can be retrieved accurately while not being confused with other objects of the target’s category. We evaluated our method on massive videos shot in actual engineering sites, and the result demonstrates that the proposed tracking algorithm can handle occlusion and target’s posture changes very well and significantly outperforms other tracking algorithms.
Predicting and Defining B2B Sales Success with Machine Learning

The objectives of this project are two-fold: 1) to use statistical modeling techniques to help a Fortune 500 paper and packaging company codify what drives sales success and 2) to develop a model that can predict sales success with a reasonable degree of accuracy. The desired long-run result is to enable the company to improve both top-line revenue and bottom-line profits by increasing sales close rates, shortening sales cycles, and decreasing the cost of sales. The research team generated several models to predict win propensities for individual sales opportunities, choosing the model with the greatest predictive power and ability to generate insights to use as the backbone for a client tool. To accomplish this, the team leveraged structured and unstructured data from the company's Salesforce.com customer relationship management system. The team experimented with several techniques including binomial logit and various decision tree methods, including boosting with gradient boost and random forest. Individual attributes of customers, opportunities, and internal documentation methods that have the greatest influence on sales success were identified. The best model predicted win propensity with an accuracy of 80%, with precision and recall of 86% and 77%, respectively, which proved to be an improvement over current sales forecast accuracy.
The MinMax Fuzzy C-Means

Fuzzy c-means (FCM) is one of the most popular fuzzy clustering methods and it is used in various applications in computer science. Most clustering methods including FCM, suffer from bad initialization problem. If initial cluster centers (membership degree initialization in FCM) are not selected appropriately, it may yield poor results. In this paper we propose a method called MinMax FCM to overcome this problem. A new objective function is designed in MinMax FCM to this aim. We use maximum variance of clusters as objective function. In this regard, high-variance clusters are penalized. We compare MinMax FCM with FCM in terms of sum of clusters’ variances, maximum variance of clusters, and execution time using a number of UCI datasets.
Dual-Input Single-Inductor Dual-Output Power Management Unit using Energy-Recycling/Backup and SAR-Based ZCS

This paper presents a dual-input single-inductor dual-output (DISIDO) power management unit (PMU) with energy recycling/backup and SAR-based zero current switching (ZCS). The proposed PMU is based on combining energy from two different low-power energy-harvesting sources, piezoelectric and thermoelectric, using time division multiplexing, along with a rechargeable battery to enhance system’s dependability and continuity. The implemented architecture undergoes four states of operation, namely, harvest, backup, recycle and battery charge, using a reconfigurable switch matrix. In addition, the proposed converter operates in the discontinuous conduction mode with constant on-time (COT) and dynamic off-time (DOT). Moreover, a SAR-based ZCS technique is introduced. The proposed DISIDO converter was designed using 0.13 μm CMOS technology.
Optimal Linear Exponential Quadratic Gaussian Estimation With Intermittent Observations

This letter is concerned with the problem of optimal linear exponential quadratic state estimation for discrete-time Gauss–Markov systems with intermittent observations. The optimal estimator and optimal cost are derived via an information state approach. The necessary and sufficient condition for the existence of the estimator is provided. For a special case when a scalar parameter in the exponential cost goes to zero, the derived estimator reduces to the corresponding Kalman filter. It is also interesting to note that the resulting estimator has the same form as that obtained from the <inline-formula> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> setting.
A Bio-inspired Method to Realize Fault-tolerance Online

A multi-layered array inspired by biological processes and structures to support the self-diagnosis and self-healing of circuits is proposed. A dedicated distributed dynamic maximum flow (DDMF) algorithm is carefully designed to run on the array with an acceptable cost of time and hardware resources which makes self-adaptive online recovery possible. The array consists of three layers logically. Cells of the Programmable Logic Layer can be configured to different circuit functions. The Immune Layer, which cooperates with cells to detect and locate faults, is inspired by the human body’s powerful immune system. Reconfiguration Manage Layer executes the dynamic routing algorithm to map the circuit function to spare cells automatically to realize online recovery. A fault-tolerance hardware platform is built based on the array. As the simulation and synthesis results show, the array performs well as a programmable platform and has the ability of distributed online self-diagnosis and self-healing.
ENGINEERING OF STREPTOMYCES TO ENHANCE ANTIBIOTIC PRODUCTION ENHANCING ANTIBIOTIC PRODUCTION THROUGH THE GENETIC ENGINEERING OF STREPTOMYCES SPECIES By PATRICIA P AK, BSc

Antibiotics have a prominent role in the human healthcare system. With natural sources, such as bacteria, acting as the main source of antibiotics, it is no wonder that the actinomycete bacterium Streptomyces coelicolor A3(2) is so prominent in this field of research. Although none of the four known antibiotics it produces have any clinical use, there is a wealth of genetic information and tools available for this model Streptomyces (producers of over two-thirds of the world's antibiotics ). In recent years, there has been a gradual decline in the discovery of new antimicrobial drugs despite the rising need to combat increasingly resistant strains of bacteria. As such, my work focused on investigating the various methods of antibiotic overproduction available through genetic manipulations. Phenotypic analysis, antibiotic assays, and RT-PCR demonstrated the effectiveness of the ermE* promoter from Saccharopolyspora erythraea, in conjunction with the tuJI ribosome-binding site, in the overexpression of the atrA gene in S. lividans. This provided support for the incorporation of both these regulatory elements in an effective heterologous overexpression vector for Streptomyces. Overexpression of regulator genes as a method of stimulating increased and/or novel antimicrobial compounds is a common endeavor. Here, we investigated the effectiveness of expressing S. coelicolor genes in the Streptomyces wild isolate, Cu#39. Additionally, as a tool for antibiotic research, we created a S. coelicolor strain constructed to direct its metabolic resources towards a designated metabolite. By eliminating select endogenous secondary metabolites, this strain holds the potential of serving as a host for increased yields of heterologous molecule production. In these three projects, I explored the use of Streptomyces as a reservoir for the identification of new antibiotics.
Segmentation of Whole Tumor Using Localized Active Contour and Trained Neural Network in Boundaries

One of the purposes from segmenting the brain tissues is to separate the damaged tissue in the patient's brain. In fact, brain tissue segmentation is one of the essential steps in the detection and treatment of brain abnormalities. This time-consuming task is usually performed by clinical experts who are not errorless. The proposed method in this paper is to automate the brain tumor segmentation with the aim of making the segmentation process more complete and closer to the clinical treatments. We propose a novel method that is a combination of neural networks and active contours to automatically segment the gliomas in MRI multi-modalities brain images. The proposed algorithm is trained locally by using a neural network at random points in tumor boundary patches, then, by combining the modality of the MRI images and the active contours, the complete tumor is segmented. The obtained results as well as the evaluation criteria such as DICE coefficient, show that the proposed model is highly competitive in comparison with the state of the art segmentation methods.
Gamification of eHealth Interventions to Increase User Engagement and Reduce Attrition

Approximately one in five people in the United States are affected by mental illness, with anxiety disorders being the most common. Barriers to treatment include limited access to trained professionals and high financial cost. eHealth applications are one alternative to treatment outside of a traditional clinical setting. Patients can readily access eHealth interventions on their own time via devices such as computers, tablets, and smartphones. Despite the scalability and accessibility of eHealth applications, their benefits are overshadowed by high attrition rates. MindTrails (MT), an existing eHealth platform, uses Cognitive Bias Modification (CBM) to treat anxiety through online interventions designed to change negative thinking patterns. The MindTrails program has the potential to treat a large population of anxious individuals. The objective of this work is to identify, analyze, and implement strategies to increase user engagement with MindTrails by exploring the integration of gamification/engagement strategies into the program. Our design for increasing engagement focuses on the Doherty Web Strategies, incorporating interactive, personal, supportive, and social elements. Using this new design, users will be able to set personalized goals that are clear, actionable, and reasonably challenging. To meet our objective, we developed high fidelity wireframes and prototypes, with the intent of utilizing user studies to evaluate the efficacy in MindTrails. Results of user tests are hypothesized to show the effectiveness of a personalized gamification feature in increasing user engagement while simultaneously reducing attrition. The improved design will be included in the next launch of the MindTrails program and demonstrates progress toward increasing the effectiveness of CBM treatment in eHealth applications.
Burying and feeding activity of adult Holothuria scabra (Echinodermata: Holothuroidea) in a controlled environment

This study investigated the relationship between temperature and burying and feeding behaviour of adult Holothuria scabra (sandfish) within a diel cycle. Animals were kept in aquaria in a constant light regime (14 h light/10 h darkness) and temperature was reduced 1°C each day from 24°C to 17°C. A scoring system was used to categorise the animals’ burial state and behaviour (e.g. burying, feeding, resting) at two-hourly intervals. Sandfish showed a distinct diel burying and feeding cycle, with most animals exposed and feed ing between 13:00 h and 20:00 h, and most buried and inactive between 1:00 h and 9:00 h. Periods of being buried increased with decreasing temperature from 6.7 h per day at 24°C, to 14.5 h per day at 17°C. Feeding activity decreased from 9.8 h a day at 24°C, to 0.8 h per day at 17°C. Temperature was significantly posi tively correlated with both feeding and burying behaviour. Based on the results of this study, the most suitable time to conduct population surveys of H. scabra in the southern hemisphere would be during summer (December to February) from midday to late afternoon.
Accuracy, Robustness, and Efficiency of the Linear Boundary Condition for the Black-Scholes Equations

We briefly review and investigate the performance of various boundary conditions such as Dirichlet, Neumann, linear, and partial differential equation boundary conditions for the numerical solutions of the Black-Scholes partial differential equation. We use a finite difference method to numerically solve the equation. To show the efficiency of the given boundary condition, several numerical examples are presented. In numerical test, we investigate the effect of the domain sizes and compare the effect of various boundary conditions with pointwise error and root mean square error. Numerical results show that linear boundary condition is accurate and efficient among the other boundary conditions.
Working Papers – Series 2005 SWP 2005 / 17 LEVIATHAN RESISTS : THE ENDOGENOUS RELATIONSHIP BETWEEN PRIVATISATION AND FIRM PERFORMANCE

Using a unique panel data set of privatised cement firms in Turkey, we test the hypothesis that privatisation and firm performance are determined simultaneously in a political economy context. By focusing on the shortand medium-run joint relationship between privatisation and firm performance, we find that: i) private sector increases the output in the medium-run, but by changing the scale property of production from increasing returns-to-scale to decreasing returnsto-scale, ii) the only factor that increases the labour productivity in the short run is a decrease in labour stock, iii) the negative impact of labour growth on productivity is less severe during the privatised period, and iv) the likelihood of the privatisation of firms decreases if the number of workers employed in firms is high, if they exhibit favourable short-run performance, if the voter preference is less fractionalised and the government representation is weak in the provinces that they are located, and if they are based in socio-economically less developed regions.
Adoption of Smart Grid Framework Which Tracks Power Consumption of Household Appliances in Zimbabwe

The aim of this study was to design a smart grid framework which tracks power consumption of household appliances. The study explored technology being used with current traditional power grid to transmit and distribute electricity from power generation sources until power reaches the final consumer. The challenges and problems of the current traditional grid were unearthed in this study to bring best solution to mitigate the current power shortage problems. Smart grid model which supports real-time electricity power usage monitoring and controlling capability is proposed as an alternative measure to control unnecessary power usage for domestic and commercial purposes. Real-time power usage monitoring, distributed power generation, varying energy demand, remote detection and faults identification, and bi-directional communication are cross-cutting technology and drivers of the implementation of smart grid. The researchers recommended energy authorities to engage with power customers to craft a policy that govern remote monitoring and controlling of electrical appliances’ power consumption to ensure complete adoption of smart grid.
The Effects of Corrective Feedback Strategies on English Majors’ Writing

The present study, through sample analysis and questionnaires, investigates the effects of different types of corrective feedback in 250 essays of the first-year English major students and the students’ responses to different feedback strategies. The study shows that 1) the students can improve their writing with the help of the feedback and that different types of errors in the English majors’ writing require different feedback strategies; 2) indirect feedback (coded feedback and uncoded feedback) can be mainly used, supplemented by direct correction; and 3) the English majors highly appreciate the teacher’s feedback; their preference is coded feedback, followed by direct correction and uncoded feedback This study sheds light on how different feedback strategies can be used in teaching L2 English at the university level.
Are fair trade organisations necessarily social enterprises

Purpose – Fair trade organisations (FTOs) have been taken quite early as examples of Social Enterprises (SE) and have contributed to the shaping of the SE concept. The purpose of this paper is to examine more deeply the link between FTOs and SE, both at a conceptual and at an empirical level.Design/methodology/approach – First, different theoretical frameworks of SE are introduced and confront FT to each of these frameworks. The second step is an empirical study of FTOs across four European countries to illustrate and deepen the links between FT and SE, focusing on the goals and the governance structures of FTOs.Findings – All the FTOs combine in some way economic, social and sometimes also political goals. FTOs are thus coherent to the “hybrid goals” nature of SEs. FTOs' governance is also quite specific and often innovative in terms of organisational architecture and stakeholders' involvement. Some FTOs are closer to the European – participatory – approach while others are closer to US – individual – ap.
Effect of temperature on the aggregation of Skeletonema costatum (Bacillariophyceae) and the implication for carbon flux in coastal waters

Skeletonema costatum (Greville) Cleve was isolated from a mucilaginous algal bloom in the Northern Adriatic Sea during April 1993. In nitrate-limited continuous culture the formation of aggregations of cells was positively correlated with temperature. Raising the temperature from 10 to 20°C caused cells to aggregate without a change in culture biomass. Reducing the temperature to 10°C caused washout of the aggregates, again with no change in biomass. In batch culture there was a positive relationship between aggregate concentration and temperature at 10, 15, 20 and 25°C. Aggregates and the cell surface of S. costatum adsorbed Alcian blue, a stain for acid and sulphated polysaccharides. Disruption of the aggregates on addition of 0 2 M Na2EDTA indicated that cross-linking between polysaccharides by divalent cations (probably Ca2+ in seawater) bound the aggregates together. Aggregates may form in situ under conditions of temperature increase and high phytoplankton biomass. Surface water temperature increased by 0.2"C d-' in the second half of July 1992 in the Northern Adriatic, coinciding with a chlorophyll a increase of 10 to 50 pg I-'. A temperature difference of 1.2OC was observed at 2 stations 4 m apart in May 1992 on either side of a plume front associated with the River PO; transfer of phytoplankton in eddies from cold to warm waters may lead to the formation of aggregates. Aggregate &sruption may occur with the sinking of aggregates from surface waters to the relatively cool waters below the thermocline. The temperature decrease through the thermocline vaned between 2.7 and 4.1°C in May 1992. The relationship between aggregation and temperature may be an important factor in determining the flux rate of fixed carbon and nutrients from the photic zone to deeper waters and the sea bed. Aggregated cultures had signlf~cantly higher sinking rates than unaggregated cultures.
Power injection of renewable energy sources using modified model predictive control

This paper presents a simple model predictive control (MPC) approach to control the power injection system (PIS) for renewable energy applications. A DC voltage source and a single-phase inverter that is connected to the grid by an LCL filter form the PIS. Grid voltage is considered a disturbance for the system. For eliminating this disturbance, a modified model is proposed. It is usual to control output current to inject a desired power to grid. But due to the presence of the LCL filter, we face a third-order system and other states should be bounded during operation. In this work, we ensure the stability of other state variables and, consequently, system stability, by defining a proper cost function. In this regard, reference signals are calculated for all state variables. For getting the benefit of the switching nature of the inverter, we use a finite control set model predictive control (FCS-MPC). Proposed predictive control is implemented in a digital scheme and, thereby, the discrete model of the system is extracted. The proposed controller does not require any other control loop or modulation method. Simulation results show the effective performance of the proposed control scheme.