Miracle-dz/UniFAR-FTUs huggingface dataset:
Long-term Behavioural Rewriting of Maladaptive Drinking Memories via Reconsolidation-Update Mechanisms

Background Alcohol use disorders can be conceptualised as a learned pattern of maladaptive alcohol-consumption behaviours. The memories encoding these behaviours centrally contribute to long-term excessive alcohol consumption and are a key therapeutic target. The transient period of memory instability sparked during memory reconsolidation offers a therapeutic window to directly rewrite these memories using targeted behavioural interventions. However, clinically-relevant demonstrations of the efficacy of this approach are few. We examined key retrieval parameters for destabilising naturalistic drinking memories and the ability of subsequent counterconditioning to effect long-term reductions in drinking. Methods Hazardous/harmful beer-drinking volunteers (N=120) were factorially randomised to retrieve (RET) or not retrieve (No RET) alcohol reward memories with (PE) or without (No PE) alcohol reward prediction error. All participants subsequently underwent disgust-based counterconditioning of drinking cues. Acute responses to alcohol were assessed pre- and post-manipulation and drinking levels assessed up to 9 months. Results Greater long-term reductions in drinking were found when counterconditioning was conducted following retrieval (with and without PE), despite a lack of short-term group differences in motivational responding to acute alcohol. Large variability in acute levels of learning during counterconditioning were noted. ‘Responsiveness’ to counterconditioning predicted subsequent responses to acute alcohol in RET+PE only, consistent with reconsolidation-update mechanisms. Conclusions The longevity of behavioural interventions designed to reduce problematic drinking levels may be enhanced by leveraging reconsolidation-update mechanisms to rewrite maladaptive memory. However, inter-individual variability in levels of corrective learning is likely to determine the efficacy of reconsolidation-updating interventions and should be considered when designing and assessing interventions.
Spatial Predictions and Associated Uncertainty of Annual Soil Respiration at the Global Scale

Soil respiration (Rs), the soil‐to‐atmosphere CO2 flux produced by microbes and plant roots, is a critical but uncertain component of the global carbon cycle. Our current understanding of the variability and dynamics is limited by the coarse spatial resolution of existing estimates. We predicted annual Rs and associated uncertainty across the world at 1‐km resolution using a quantile regression forest algorithm trained with observations from the global Soil Respiration Database spanning from 1961 to 2011. This model yielded a global annual Rs estimate of 87.9 Pg C/year with an associated global uncertainty of 18.6 (mean absolute error) and 40.4 (root mean square error) Pg C/year. The estimated annual heterotrophic respiration (Rh), derived from empirical relationships with Rs, was 49.7 Pg C/year over the same period. Predicted Rs rates and associated uncertainty varied widely across vegetation types, with the greatest predicted rates of Rs in evergreen broadleaf forests (accounting for 20.9% of global Rs). The greatest prediction uncertainties were in northern latitudes and arid to semiarid ecosystems, suggesting that these areas should be targeted in future measurement campaigns. This study provides predictions of Rs (and associated prediction uncertainty) at unprecedentedly high spatial resolution across the globe that could help constrain local‐to‐global process‐based models. Furthermore, it provides insights into the large variability of Rs and Rh across vegetation classes and identifies regions and vegetation types with poor model performance that should be prioritized for future data collection.
Optimizing FPGA Logic Circuitry for Variable Voltage Supplies

Unlike central processing units (CPUs), field-programmable gate arrays (FPGAs) have conventionally been powered with a fixed supply voltage (<inline-formula> <tex-math notation="LaTeX">$V_{\text {dd}}$ </tex-math></inline-formula>). However, recent efforts have shown that adopting dynamic voltage scaling reduces FPGA power consumption significantly. In this article, we analyze the delay sensitivity of different FPGA circuit elements to supply voltage changes and determine that conventional lookup table (LUT) designs greatly impact variable <inline-formula> <tex-math notation="LaTeX">${V_{\text {dd}}}$ </tex-math></inline-formula> operation. To build FPGAs with lower delay sensitivity to <inline-formula> <tex-math notation="LaTeX">${V_{\text {dd}}}$ </tex-math></inline-formula>, we propose several new LUT designs, including gate boosting the LUT, decoding the slowest two inputs of the LUT, and using separate voltage islands for the FPGA LUTs and routing. Our fastest proposed design (decode-driver island) reduces the area-delay product of the FPGA logic plus routing tile compared to a conventional design by 12% and 52% at <inline-formula> <tex-math notation="LaTeX">${V_{\text {dd}}}$ </tex-math></inline-formula> values of 0.8 V (the nominal voltage) and 0.6 V, respectively. Since our proposed FPGA tile designs are faster and have lower delay sensitivity to voltage, they offer better <inline-formula> <tex-math notation="LaTeX">${\text {Energy-Delay} {{^{\mathrm{ 2}}}}}$ </tex-math></inline-formula> product (<inline-formula> <tex-math notation="LaTeX">${\text {ED} {{^{\mathrm{ 2}}}}}$ </tex-math></inline-formula>) than that of the baseline at nominal <inline-formula> <tex-math notation="LaTeX">${V_{\text {dd}}}$ </tex-math></inline-formula> and below. Our decode-driver-island FPGA achieves a 26% <inline-formula> <tex-math notation="LaTeX">${\text {ED} {{^{\mathrm{ 2}}}}}$ </tex-math></inline-formula> reduction over the conventional design at the most efficient <inline-formula> <tex-math notation="LaTeX">${\text {ED} {{^{\mathrm{ 2}}}}}$ </tex-math></inline-formula> operating point.
Dealing with hypersexuality in geriatric care facilities : A literature review

The subject area of this literature review focuses on hypersexuality exhibited by patients in geriatric care facilities and how nurses can be prepared in the management of its occurrence. The aim of this study is to explore inappropriate sexual behavior in hope to promote awareness and give light to possible coping strategies that nurses and other healthcare professionals can apply in the management of care. The research questions are 1) What is hypersexuality? 1a) What are the triggering factors? 1b) What can be considered hypersexual behavior? 1c) How common is hypersexuality in nursing care facilities? 2) How can nurses be prepared in the care management of hypersexuality disorder? All questions have been answered despite the scarcity of scientific resources at the time the author is conducting his research. Scientific articles are obtained through Arcada’s academic databases Academic Search Elite, Cinahl, Pubmed and Sage journals. The main search terms used are: Hypersexuality AND dementia; “Inappropriate sexual behavior” AND elderly; Diagnosis AND “Inappropriate sexual behavior” AND dementia. The findings on this study suggest that including sexuality education into the regular nursing curriculum rather than as an elective study will introduce all nurses early towards hypersexuality. It will help improve their ability in managing its care interventions as well as in conducting proper patient education. The results also show that addressing inappropriate sexual behavior in a biopsychosocial approach gives better opportunities in devising a holistic care plan for nurses.
A flexible workflow for simulating transcranial electric stimulation in healthy and lesioned brains

Simulating transcranial electric stimulation is actively researched as knowledge about the distribution of the electrical field is decisive for understanding the variability in the elicited stimulation effect. Several software pipelines comprehensively solve this task in an automated manner for standard use-cases. However, simulations for non-standard applications such as uncommon electrode shapes or the creation of head models from non-optimized T1-weighted imaging data and the inclusion of irregular structures are more difficult to accomplish. We address these limitations and suggest a comprehensive workflow to simulate transcranial electric stimulation based on open-source tools. The workflow covers the head model creation from MRI data, the electrode modeling, the modeling of anisotropic conductivity behavior of the white matter, the numerical simulation and visualization. Skin, skull, air cavities, cerebrospinal fluid, white matter, and gray matter are segmented semi-automatically from T1-weighted MR images. Electrodes of arbitrary number and shape can be modeled. The meshing of the head model is implemented in a way to preserve feature edges of the electrodes and is free of topological restrictions of the considered structures of the head model. White matter anisotropy can be computed from diffusion-tensor imaging data. Our solver application was verified analytically and by contrasting tDCS simulation results with another simulation pipeline (SimNIBS 3.0). An agreement in both cases underlines the validity of our workflow. Our suggested solutions facilitate investigations of irregular structures in patients (e.g. lesions, implants) or of new electrode types. For a coupled use of the described workflow, we provide documentation and disclose the full source code of the developed tools.
Autonomous UAV Trajectory for Localizing Ground Objects: A Reinforcement Learning Approach

Disaster management, search and rescue missions, and health monitoring are examples of critical applications that require object localization with high precision and sometimes in a timely manner. In the absence of the global positioning system (GPS), the radio received signal strength index (RSSI) can be used for localization purposes due to its simplicity and cost-effectiveness. However, due to the low accuracy of RSSI, unmanned aerial vehicles (UAVs) or drones may be used as an efficient solution for improved localization accuracy due to their agility and higher probability of line-of-sight (LoS). Hence, in this context, we propose a novel framework based on reinforcement learning (RL) to enable a UAV (agent) to autonomously find its trajectory that results in improving the localization accuracy of multiple objects in shortest time and path length, fewer signal-strength measurements (waypoints), and/or lower UAV energy consumption. In particular, we first control the agent through initial scan trajectory on the whole region to 1) know the number of nodes and estimate their initial locations, and 2) train the agent online during operation. Then, the agent forms its trajectory by using RL to choose the next waypoints in order to minimize the average location errors of all objects. Our framework includes detailed UAV to ground channel characteristics with an empirical path loss and log-normal shadowing model, and also with an elaborate energy consumption model. We investigate and compare the localization precision of our approach with existing methods from the literature by varying the UAV's trajectory length, energy, number of waypoints, and time. Furthermore, we study the impact of the UAV's velocity, altitude, hovering time, communication range, number of maximum RSSI measurements, and number of objects. The results show the superiority of our method over the state-of-art and demonstrates its fast reduction of the localization error.
Empirical wavelet transform-based fog removal via dark channel prior

Haze and fog removing from videos and images has got massive concentration in the field of video and image processing because videos and images are severely affected by fog in tracking and surveillance system, object detection. Different defogging techniques proposed so far are based on polarisation, colour-line model, anisotropic diffusion, dark channel prior (DCP) etc. However, these methods are unable to produce output image with desirable quality in the presence of dense fog and sky region. In this study, the authors have proposed a novel fog removal technique where DCP is applied on the low-frequency component of empirical wavelet transformation coefficients of the foggy input image. They apply unsharp masking on wavelet coefficients of the embedded wavelet transformed image for improving the sharpness of the output image. Later contrast limited adaptive histogram equalisation technique is used as a post-processing task to the inverse transformed image for producing the sharp and high contrast output. Finally, the colour and intensity of the contrast-enhanced image are uplifted through S-channel and V-channel gain adjustment. The proposed method provides significant improvement to the overall quality of the output image compared to contemporary techniques. The quantitative and qualitative measurements confirm the claims.
Effects of Public Private Partnerships on Education Service Delivery in Uganda

In many countries globally including Uganda, governments have partnered with the private actors to deliver education services traditionally delivered by the private sector. This paper explored whether secondary schools under Public Private Partnership (PPP) arrangement are more efficient in respect to pass rate and enrollment. The study adopting a comparative research design compared secondary schools under Public Private Partnership (PPP) arrangement and those schools which are not under PPP arrangement. The analysis was based on sample of 95 secondary schools randomly selected from Mukono district. The results show that Non-PPP schools were more efficient than secondary schools under PPP arrangement in respect to pass rates. However, in respect to enrollment, secondary school schools under PPP were found to be more efficient. This implies that PPP arrangement impacts school enrollment by increasing the number of supported students to join the private school but this does not translate into school performance. Thus, although government efforts to partner with private secondary schools increases student enrollment, it has implications on school efficiency in respect to pass rate. The study recommends that PPP arrangement between government and private sector schools need to be revised to reflect the realities of the schools with respect to enrollment and performance outcomes.
Smart Grid Monitoring Systems based on Advanced Encryption Standard and Wireless Local Area Network

China’s power grid is developing in the direction of informatization and increasingly large-scale at present. In view of the problems of optical cable interference and poor data security in the existing power grid detection system, a grid detection system based on wireless local area network (WLAN) and improved advanced encryption standard (AES) is proposed. Firstly, wireless local area network technology is introduced on the basis of the original power grid detection system to realize wireless transmission of power data. Then the newly-proposed system improves the S-BOX in the traditional AES encryption algorithm to improve the security of the algorithm. The improved AES encryption algorithm is applied to the grid detection system to realize the secure transmission of power data. Finally, the practical value of the grid detection system is verified by simulation.
An Efficient Feature Extraction Method for the Detection of Material Rings in Rotary Kilns

Rotary kilns are important industrial plants to process material. In this article, we address the problem of detecting material rings formed in alumina rotary kilns, which leads to high waste and substantial economic loss. This necessitates the use of artificial intelligence to help kiln operators detect material rings promptly. We describe a recently developed system that extracts useful features and detects the presence of material rings efficiently and accurately in real time. Our major contribution is a novel feature extraction method based on geometric transformation and peak localization exploiting the domain knowledge. We also contribute a large data set covering many thousand labeled frames for the evaluation of ring detection. On this data set, we demonstrate that our novel feature extraction method outperforms other alternatives in the literature. Our method is also demonstrated as favorable over deep learning approaches. Our system produces an overall accuracy of nearly 96%, which is acceptable for deployment.
Study on the viability of egg shell as a lost circulation material in synthetic based drilling fluid

Lost circulation is one of the most severe issues in drilling operation which can cause fracture formation or be induced during drilling activities. These scenarios make it imperative to design the drilling fluid in a minimal invasion of the fluid that occur in the formation along with assist in strengthening the wellbore condition. To solve this problem, Lost Circulation Materials (LCM) are required which is expected to bridge and seal the fractures. As the contemporary worldwide development towards environment friendly, technical ability and cost-effectiveness, hence the viability of Egg Shells as LCM are being investigated since the daily disposal waste is rich in Calcium Carbonate (CaCO3) compound. In addition, the effect of egg shell particle size distribution on the drilling fluid performance was also study. The performance of egg shells was compared with the industry standard-sized CaCO3 in term of its rheology properties, fluid loss, fluid invasion, and lubrication ability. The rheology properties result shows a stable reading while for High Pressure High Temperature (HPHT) filter press test, the drilling fluids performance showed the same mud cake thickness of 1/32 but slightly different in fluid loss. The drilling fluids also has a minimal invasion on Sand Bed Test (SBT) with lubrications coefficient of 0.0753. From this study, it is found that egg shells can function effectively as LCM additives, the same as the standard CaCO3 used in the industry.
Control of Karman Vortex Street By Using Plasma Actuators

A mathematical model for unsteady electroand aerodynamic processes in the presence of a plasma actuator has been elaborated through physical modeling of the dielectric barrier discharge. A specialized computational fluid dynamics package has been developed accordingly in order to calculate steady and unsteady laminar and turbulent flows. For the numerical simulation of the dielectric barrier discharge, in particular, two equations have been added to the Navier-Stokes equations and solved. They describe the distribution of the applied voltage and the charged particles density. The impact of the plasma actuator on air has been accounted for through the Lorentz force, included as a source term in the momentum balance equation. The system of governing equations for the considered hydrodynamics and electrodynamics has been written in an arbitrary curvilinear coordinate system in dimensionless form and integrated in the framework of a finite volume method. A TVD scheme with a third-order ISNAS flow limiter has been chosen for the convective terms approximation. The obtained block-matrix system of linear algebraic equations has been solved by the generalized minimal residual (GMRES) method with ILU(k) preconditioning. Using this approach, the occurrence of a propulsion force, emerging as a result of the action of plasma actuators on a cylinder in quiescent air, has been investigated. The possibility to mitigate the cylinder drag coefficient with the help of the plasma actuators, due to the ensuing suppression of the Karman vortex street, has been demonstrated.
Detection of Accounting Frauds Using the Rule-Based Expert Systems within the Scope of Forensic Accounting

The change of production methods, the industrial revolutions, technological developments, and digital transformation have affected almost all functions in the enterprises. Accounting and auditing areas are also quite affected by this transformation. Another important result of technology and digitalization is the rapid increase in errors, frauds, and irregularities. Enterprises are looking for new solutions and investigations against irregularities and frauds. Audits for errors, frauds, or irregularities are among the interests of forensic accounting. Many methods are used to identify errors and frauds in the forensic accounting. However, it is inevitable that digital technologies should be utilized in forensic accounting applications as a result of the rapid spread of automation and computer programs in enterprises within the framework of digitalized business activities. Hence, enterprises will be able to get more effective results through computer programs and artificial intelligence in terms of fraud audit in forensic accounting. Expert system applications use artificial intelligence to enable computer programs to behave just like people. One of the most widely used, most easily applicable, and most understandable types of expert system is rule-based expert system. The aim of this study is to determine the accounting fraud that may occur in enterprises within the framework of forensic accounting through rule-based expert systems. For this purpose, various applications have been implemented in a large-scale production enterprise through the use of rule-based expert systems for the determination of accounting fraud. Benford’s Law, risk levels, and various other criteria were used in the creation of expert systems. According to the results obtained from the study, it has been seen that by means of rule-based expert system applications, enterprises can better detect existing frauds and prevent further irregularities in the future. The study is important and it is expected that the study will contribute to the literature because it is shown in the study that the rule-based expert systems, applied in many fields under the title of social sciences, can also be applied in the field of forensic accounting and auditing.
Spen links RNA-mediated endogenous retrovirus silencing and X chromosome inactivation

Dosage compensation between the sexes has emerged independently multiple times during evolution, often harnessing long noncoding RNAs (lncRNAs) to alter gene expression on the sex chromosomes. In eutherian mammals, X chromosome inactivation (XCI) in females proceeds via the lncRNA Xist, which coats one of the two X chromosomes and recruits repressive proteins to epigenetically silence gene expression in cis1,2. How Xist evolved new functional RNA domains to recruit ancient, pleiotropic protein partners is of great interest. Here we show that Spen, an Xist-binding repressor protein essential for XCI3-7, binds to ancient retroviral RNA, performing a surveillance role to recruit chromatin silencing machinery to these parasitic loci. Spen inactivation leads to de-repression of a subset of endogenous retroviral (ERV) elements in embryonic stem cells, with gain of chromatin accessibility, active histone modifications, and ERV RNA transcription. Spen binds directly to ERV RNAs that show structural similarity to the A-repeat of Xist, a region critical for Xist-mediated gene silencing8-9. ERV RNA and Xist A-repeat bind the RRM3 domain of Spen in a competitive manner. Insertion of an ERV into an A-repeat deficient Xist rescues binding of Xist RNA to Spen and results in local gene silencing in cis. These results suggest that insertion of an ERV element into proto-Xist may have been a critical evolutionary event, which allowed Xist to coopt transposable element RNA-protein interactions to repurpose powerful antiviral chromatin silencing machinery for sex chromosome dosage compensation.