Miracle-dz/UniFAR-FTUs huggingface dataset:
The ventral capsule and ventral striatum—Stereotactic targets for the management of treatment-resistant depression. A systematic literature review

Background Deep brain stimulation (DBS) is still an experimental treatment modality for psychiatric disorders including treatment-resistant depression (TRD). There is preliminary evidence that stimulation of brain reward circuit structures including the ventral striatum (VS) may exert an antidepressant effect. The main nucleus of the reward circuit is the nucleus accumbens (NAc). The NAc is a major structure of VS that plays a critical role in reward-seeking behavior, motivation, and addiction. Aims This study aimed to review the current studies including randomized clinical trials, open-label trials, and case reports of NAc/VS and VC DBS for TRD in humans. Method The literature was reviewed using a medical database—Medical Literature, Analysis, and Retrieval System Online (MEDLINE) on NAc/VS or VC DBS in TRD. The identified studies were assessed based on the patient's characteristics, clinical outcomes, and adverse events related to DBS as well as the stereotactic technique used to guide the implantation of DBS electrodes. The inclusion and exclusion criteria of DBS for TRD were presented and discussed. Results The searched literature revealed one case report, three open-label studies (OLS), one multicenter open-label study (mOLS), and two randomized clinical trials (RCTs). There were three additional studies reporting the clinical outcomes in the long term in TRD patients included in the two mentioned RCTs. The total number of patients with TRD treated by NAc/VS or VC is estimated to be 85 individuals worldwide. The response rate to DBS defined as a 50% reduction of postoperative Montgomery-Asberg Depression Rating Scale (MADRS) scores was achieved in 39.8% of the operated patients (range, 23–53%). The remission defined as MADRS scores of < 10 was found in 17.8% after DBS (range, 0–40%). The mean follow-up was 19.7 months (range 3.7–24 months). Conclusion The current results of NAc/VS and VC DBS are still limited by a relatively small number of patients treated worldwide. Nevertheless, the results suggest that NAc/VS and VC can be regarded as promising and efficacious targets for DBS, taking into account the response and remission rates among TRD patients with no other treatment option. The adverse events of NAc/VS and VC DBS are reversible due to the adjustment of stimulation parameters. The most common adverse events were hypomanic/manic states, suicidal thoughts/attempts, and suicides. Patients with TRD after NAc/VS and VC DBS should be strictly followed to prevent or diminish these stimulation-induced adverse events.
Three Methods for Mitigating Airwaves in Shallow Water Marine CSEM Data

Over the past several years, Marine Controlled-Source EM (MCSEM) techniques have been successfully applied in deep water (water depth >1 km) for oil/gas exploration. In contrast, application of this technology in shallow water, although available, is challenged due to ‘airwaves’ that mask the signal from the target reservoir at depth. Based upon ‘lateral wave’ theory, we propose three airwave-mitigation approaches to reduce the effects of these arrivals on MCSEM data. By comparing the detectability of a target in deep water versus shallow water for models including bathymetry, we show that the airwave effects in a shallow water environment can be reduced leading to a greater reservoir detectability.
Incorporating expert knowledge in the estimate of farmers’ opportunity cost of supplying environmental services in rural Cameroon

This paper applies a Bayesian approach to incorporate non-data information in estimating the opportunity cost for farmers in rural Cameroon to engage in biodiversity conservation and carbon sequestration efforts. Findings from our field survey reveal that only a small percentage of farmers are willing to participate in environmental protection programmes without compensation. A multidimensional preferences analysis indicates that this behavior may be attributed to a disconnection between environmental values and socioeconomic values. Bayesian analysis of the Tobit model, examining Willingness to Accept (WTA) compensation for agroforestry participation, highlights that factors such as aging, higher educational attainment, and higher socioeconomic status are highly likely to promote pro-environmental behaviors. The estimated opportunity cost of supplying environmental services is 10,775 CFA francs with a standard deviation of 333.6 CFA francs per farmer. These results differ qualitatively from the existing literature, underscoring the relative significance of considering expert knowledge in the interpretation of environmental policies.
Multi-level marketing system with industrial food processing in Africa: smallholder tomato farmers in Tanzania

There are few challenges greater than the puzzle of how to move perishable goods from smallholders’ fields to final consumers, particularly where transportation barriers abound. Industrial processing can relax the perishability constraint and improve smallholders’ options. In Iringa, Tanzania, multiple tomato processing establishments and traditional marketing agents are available for farmers to use. Information about the channels is expected to be vital for producers to choose optimally. In this study, we collected field data from 286 smallholder farmers and analyzed their market channel choice using the random utility model implemented with multinomial logit regression. Revenue performance was further evaluated. Our results confirm that access to market information and extension services were associated with higher farm revenue. Women farmers had more concerns than men about lacking information. Market participation differed by size of the business. Farmers with higher harvested quantity were more likely to choose industrial processing. Producers with small farms preferred full marketing service and mid-size farms chose wholesale markets. Agribusiness management scholars may follow similar research design with future attention paid to elicitation of producers’ information about the marketing channels.
Multifarious Plant Growth-Promoting Rhizobacterium Enterobacter sp. CM94-Mediated Systemic Tolerance and Growth Promotion of Chickpea (Cicer arietinum L.) under Salinity Stress.

BACKGROUND Chickpea is one of the most important leguminous crops and its productivity is significantly affected by salinity stress. The use of ecofriendly, salt-tolerant, plant growth-promoting rhizobacteria (PGPR) as a bioinoculant can be very effective in mitigating salinity stress in crop plants. In the present study, we explored, characterized, and evaluated a potential PGPR isolate for improving chickpea growth under salt stress. METHODS A potential PGPR was isolated from rhizospheric soils of chickpea plants grown in the salt-affected area of eastern Uttar Pradesh, India. The isolate was screened for salt tolerance and characterized for its metabolic potential and different plant growth-promoting attributes. Further, the potential of the isolate to promote chickpea growth under different salt concentrations was determined by a greenhouse experiment. RESULTS A rhizobacteria isolate, CM94, which could tolerate a NaCl concentration of up to 8% was selected for this study. Based on the BIOLOG carbon source utilization, isolate CM94 was metabolically versatile and able to produce multiple plant growth-promoting attributes, such as indole acetic acid, 1-aminocyclopropane-1-carboxylic acid (ACC) deaminase, siderophore, hydrogen cyanide (HCN), and ammonia as well as solubilized phosphate. A polyphasic approach involving the analysis of fatty acid methyl ester (FAME) and 16S rRNA gene sequencing confirmed the identity of the isolate as Enterobacter sp. The results of greenhouse experiments revealed that isolate CM94 inoculation significantly enhanced the shoot length, root length, and fresh and dry weight of chickpea plants, under variable salinity stress. In addition, inoculation improved the chlorophyll, proline, sugar, and protein content in the tissues of the plant, while lowering lipid peroxidation. Furthermore, isolate CM94 reduced oxidative stress by enhancing the enzymatic activities of superoxide dismutase, catalase, and peroxidase compared to in the respective uninoculated plants. CONCLUSIONS Overall, the results suggested that using Enterobacter sp. CM94 could significantly mitigate salinity stress and enhance chickpea growth under saline conditions. Such studies will be helpful in identifying efficient microorganisms to alleviate salinity stress, which in turn will help, to devise ecofriendly microbial technologies.
Optimal Number of Image Keypoints for Real Time Visual Odometry

A visual odometer can estimate robot motion by tracking a set of invariant keypoints over a sequence of camera frames, at a computational cost that is roughly linear in the number of extracted keypoints. The leading literature suggests to extract all keypoints with a response value—e.g., a Laplacian or Hessian determinant—above a given threshold. We find that the number of image keypoints that pass a given threshold is highly variable between images, which is impractical for real time systems where motion needs to be estimated in constant time. Here we propose to extract a constant number of keypoints that have the highest response for their respective frame. To find the optimal number of extracted keypoints, we define a range of thresholds on odometer performance, and study how the number of image pairs for which a visual odometer passes the thresholds depends on the number of extracted keypoints. We find that the shapes of the resulting graphs are relatively invariant to the chosen threshold values and that for all threshold values the region where good odometer performance is balanced with computational efficiency is relatively small. We conclude that if odometer performance has to be weighed against computational cost, there is relatively little room for trade, and that a robust optimum can quickly and easily be found.
Contextualization of a Radical Language Detection System Through Moral Values and Emotions

The popularity of current communication technologies has boosted the spread of polarization and radical ideologies, which can be exploited by terrorist organizations. Building upon previous research, this work focuses on the task of automatic radicalization detection in texts using natural language processing and machine learning techniques. In this way, we investigate the effectiveness of integrating moral values through the Moral Foundations Theory (MFT). Moral values play a crucial role in identifying ideological inclinations and can have a significant impact on the radicalization detection task. Our approach distinguishes itself in the feature extraction stage, leveraging moral values, emotions, and similarity-based features that utilize word embeddings. Additionally, we thoroughly evaluate the proposed representations with three distinct datasets that model radicalization and use the SHAP method to gain relevant insight into the models’ reasoning.
Opinions and Attitudes of Education Faculty Students towards Research Methods in Education Course

Research is the whole of the systematic efforts of individuals by making new discoveries and inventions to find solutions to the problems they face or to improve the current situation. After the implementation of teacher training activities by higher education institutions, courses such as "Research Techniques", "Research Methods in Education" and "Research Methods" are included in the curriculum of higher education institutions to provide students with research competencies. It is important for prospective teachers to have positive attitudes towards this course for them to acquire the knowledge and skills in question effectively. In this direction, in this study, it was tried to determine the attitudes and opinions of teacher candidates towards the Research Course in Education. In this research, in which the attitudes and opinions of Bayburt University Faculty of Education students towards the RME course are tried to be determined, mixed method and explanatory sequential design was used. In the study, a single survey model was used to determine the attitudes of students towards the RME course, a relational survey model was used to examine the sub-dimensions of attitudes according to the departments and classes of the students, and focus group interview was used to determine their views about the course.The Kruskal Wallis H test was used to determine whether the attitudes of the groups showed an independent evaluation from each other. In the research, an attitude scale developed by the researcher was used as a data collection tool to determine the attitudes of the students towards the RME course, and a focus group interview form was used to determine their opinions. The quantitative sample of the study consists of 190 university students selected through purposive sampling, and the qualitative sample consists of 10 candidate teachers selected from this group with the same method. When the research findings were examined, it was seen that the attitudes of the novice teachers towards the RME course differed significantly in favor of men according to the self-confidence variable in the sub-dimensions of the attitude scale. It is thought that including the RME course, which is among the elective courses in Education faculties, among the compulsory courses, extending the one-semester and 3-credit RME course in terms of credit.
Sparsity-promoting migration accelerated by message passing

Seismic imaging via linearized inversion requires multiple iterations to minimize the least-squares misfit as a function of the medium perturbation. Unfortunately, the cost for these iterations are prohibitive because each iteration requires many wave-equation simulations, which without direct solvers require an expensive separate solve for each source. To overcome this problem, we use dimensionality-reduction to decrease the size of seismic imaging problem by turning the large number of sequential shots into a much small number of simultaneous shots. In our approach, we take advantage of sparsifying transforms to remove source crosstalk resulting from randomly weighting and stacking sequential shots into a few super shots. We also take advantage of the fact that the convergence of large-scale sparsity-promoting solvers can be improved significantly by borrowing ideas from message passing, which are designed to break correlation built up between the linear system and the model iterate. In this way, we arrive at a formulation where we run the sparsity-promoting solver for a relatively large number of iterations. Aside from leading to a significant speed up, our approach had the advantage of greatly reducing the memory imprint and IO requirements. We demonstrate this feature by solving a sparsity-promoting imaging problem with operators of reverse-time migration, which is computationally infeasible without the dimensionality reduction.
EFFECTIVENESS OF A LINE APPLICATION TOGETHER WITH TELEPHONE-BASED CONSULTATION AND EDUCATION PROGRAM ON THE DIETARY KNOWLEDGE AND BEHAVIOR AMONG CAREGIVERS OF END-STAGE RENAL DISEASE PATIENTS ON HEMODIALYSIS

This study aimed to evaluate the effectiveness of a Line application-based consultation and education program combined with tele-counseling on improving nutritional knowledge and dietary behavior among 68 Caregivers of Caregivers of ESRD patients on hemodialysis in Chiang Mai Province, Thailand. Participants were purposively sampled and assigned to either a control group (n=34) or an intervention group (n=34) using a matched-pair technique. The intervention group underwent an 8-week educational program through the Line application and tele-counseling, while the control group received no intervention. Results demonstrated a significant improvement in both dietary knowledge and behavior scores in the intervention group compared to the control group (p < 0.05). Specifically, the post-test average scores for dietary knowledge in the experimental group increased from 14.76 (SD = 2.72) to 17.97 (SD = 1.59), while the control group's average scores were 14.85 (SD = 2.87). For dietary behavior, the post-test average scores in the experimental group improved from 1.21 (SD = 0.32) to 1.90 (SD = 0.48), compared to 1.29 (SD = 0.29) in the control group. The findings suggest that using the Line application, a popular communication platform in Thailand, in conjunction with tele-counseling can effectively enhance the nutritional knowledge and dietary behavior of Caregivers of ESRD patients. Healthcare professionals should consider implementing such interventions to improve the overall nutritional status and well-being of Caregivers of ESRD patients on hemodialysis.
PERFORMANCE ANALYSIS AND EVALUATION OF IMAGE CLASSIFICATION MODELS USING MACHINE LEARNING

This work presents an image classification process using machine and deep learning. The machine learning has feature extraction and classification modules. It can extract certain features of images but unable to select differentiating features from the training set of data. Deep learning can find naturally the relevant features for the adopted applications. The convolutional neural network (CNN) is one of the common deep learning approaches. CNN has an input layer, hidden layers, and an output layer. An image is constructed as a matrix of pixels where the pixel values are given to the input layer supported with weights and biases. The hidden layers are convolutional, pooling and/or fully connected layers. The output layer is a fully connected layer to classify the image to which class it belongs to. Moreover, a set of hyper-parameters are analyzed and investigated. The parameters play an important role in the performance of the image classification process. A set of experiments are operated to see the effect of every hyper parameter. The parameters include; but not limited to; the number of hidden layers, the number of epochs, filter size, number of filters, batch size, learning rate, optimization method, and others. Moreover, a useful supervised machine learning approach is adopted to classify the images. The number of selected features has a vital role on the performance of the support vector machine (SVM). Both the CNN and SVM are operated and tested using two big datasets. The first dataset; CIFAR-10; has ten classes and 60,000 images where the second one; MNIST; has ten classes and 70,000 images. The performance of both deep learning and SVM approaches are compared. Some measurable criteria are considered such as accuracy, learning time, prediction time, and others. The classification accuracy using CNN outperforms that accuracy value for the SVM. The performance of CNN using the MNIST dataset is better than the CNN using the CIFAR-10 dataset. This means that the dataset size, nature, and characterization play an important role in the performance of machine and deep learning approaches. The learning time and prediction time for CNN approach are greater than those corresponding values of SVM. The obtained results in this work are better than some of the related efforts published in the literatures by others using the same machine learning approaches and the same datasets.
A Machine-Learning-Based Access Point Selection Strategy for Automated Guided Vehicles in Smart Factories

Automated Guided Vehicles (AGVs) are becoming popular at many manufacturing facilities. To ensure mobility and flexibility, AGVs are often controlled by wireless communication, eliminating the constraints of physical cables. These AGVs require multiple Access Points (APs) to ensure uninterrupted coverage across the site. As AGVs move, they need to switch between these APs seamlessly. A primary challenge is that the communication downtime during this link-switching process must be minimal for effective AGV monitoring and control. Current AP selection strategies based on observed Received Signal Strength Indicator (RSSI) often fail in manufacturing environments due to RSSI’s inherent instability. This paper introduces a new AP selection technique for AGVs navigating these sites. Our approach harnesses the distinct movement patterns of AGVs and uses machine learning techniques to learn location-, trajectory-, and orientation-specific RSSI from the APs. Real-world factory data from our unique dataset revealed that our method extends the potential communication duration per route by 1.34 times compared to the prevalent signal strength-based switching methods commonly implemented in current drivers provided by chipset vendors or open-source Wi-Fi drivers. These results indicate that the automatic evaluation and tuning of the wireless environment using the proposed method is beneficial in reducing the time and effort required to investigate the detailed propagation paths needed to adapt AGV to existing APs.
Association between Acute Respiratory Distress Syndrome Due to COVID-19 and Long-Term Sleep and Circadian Sleep–Wake Disorders

Current studies agree on the impact of sleep and circadian rest–activity rhythm alterations in acute respiratory distress syndrome (ARDS) survivors. However, research on the duration of this impact is scarce. In this study, we evaluate the impact of ARDS on the sleep and circadian rest–activity rhythm of COVID-19 survivors twelve months after hospital discharge. This is a prospective study including COVID-19 survivors with and without ARDS during hospitalization. Data was collected four and twelve months after hospital discharge. The interventions included one-week wrist actigraphy and a home sleep apnea test (HSAT), and evaluations were conducted according to the Pittsburgh sleep quality index (PSQI), Epworth sleepiness scale (ESS), and insomnia severity index (ISI). Fifty-two patients were evaluated (ARDS = 31 and non-ARDS = 21); they had a median age of 49.0 [39.0;57.2] years and 53.8% were male. After twelve months, 91.3% presented poor sleep quality, 58.7% presented insomnia, 50% presented daytime somnolence, and 37% presented comorbid insomnia and obstructive sleep apnea (COMISA). No significant improvement was observed in relation to sleep or the circadian rest–activity rhythm between four and twelve months. A tendency of poor sleep quality, insomnia, daytime somnolence, and COMISA was observed. Finally, there was no significant impact on the circadian rest–activity rhythm between four and twelve months or between the groups.