Miracle-dz/UniFAR-FTUs huggingface dataset:
Dirofilariasis of Breast Mimicking Malignancy – A Rare Entity

Human dirofilarial infection usually presents as pulmonary coin lesion or as subcutaneous nodule. We report the case of a 60-year-old female presented clinically with swelling in the lower-outer quadrant of the right breast for 2 weeks. Ultrasound reported as atypical lesion suggest fine-needle aspiration cytology (FNAC) correlation and follow-up. Clinical examination – subcutaneous nodule of size 2 cm × 2 cm presents in the lower-outer quadrant. Clinical suspicious of carcinoma was considered and FNAC was done. Aspirated pus-like material and adult worm about 4 cm in length were attached to the needle. Smear was air-dried and stained with Giemsa. Smears were cellular and showed numerous neutrophils, eosinophils, and a few histiocytes in a necrotic background and also noted coiled adult dirofilarial worm. Cellblock was prepared and it showed the cut section of dirofilarial worm with cuticle, muscular layer, intestine, and reproductive tube. We report this case entity due to diagnostic confusion with breast malignancy clinically and radiologically.
The SCEEGNet: An Efficient Learning Method for Emotion Recognition Based on the Few Channels

Emotion recognition based on Electroencephalogram (EEG) is an essential task in the advanced stage of artificial intelligence. Two major limitations of the prior approaches are: (1) the features of brain laterality are not fully exploited, which results in inefficient learning; (2) too many channels are used to make the model hard to migrate and deploy. In the current study, we proposed a symmetric EEG channel convolutional neural network (SCEEGNet) using 8 EEG channels to solve the problems mentioned above. Specifically, we selected 4 pairs of symmetric EEG channels and reconstructed them into a Symmetric Channel Topology (SCT) which is a cross arrangement of left and right brain regions. After that, the SCEEGNet captured the features of the brain laterality via convolving dual EEG channels. The SCEEGNet was lightweight and had an end-to-end pipeline. The validity of the SCEEGNet was tested using the SEED dataset. The results showed that the SCEEGNet reached an average accuracy of 98.37% for classifying three emotions using only 8 EEG channels’ data, demonstrating the efficacy of our approach.
A Bayesian Optimized Spatial-Temporal LSTM for Traffic Flow Predition

Traffic state forecasting plays a vital role in the Intelligent Transportation System, a critical topic in transportation. Researchers have recently begun combining correlated information, such as spatial dependency, with this issue. A Bayesian optimized spatial-temporal attention long short-term memory neural network is proposed to improve the prediction performance. Considering the latent influence of spatial position and temporal period, a hybrid model based on long short-term memory utilizes convolutional modules and attention mechanisms to dig out the spatial and temporal features. Then, to reduce the influence of hyperparameters in the models, Bayesian optimization is utilized to search out the most suitable hyperparameters to make the forecasting performance the best. The RMSE and MAE of BO-STLSTM perform best in the real-world dataset comparison experiments at 25.77 and 18.98, respectively. Furthermore, the experiments also proved that the hybrid model has the best forecasting performance when the number of training epochs is 400.
TR-2006-014 High-Resolution Iris Image Reconstruction from Low-Resolution Imagery

We investigate the use of a novel multi-lens imaging system in the context of biometric identification, and more specifically, for iris recognition. Multi-lenslet cameras offer a number of significant advantages over standard single-lens camera systems, including thin form-factor and wide angle of view. By using appropriate lenslet spacing relative to the detector pixel pitch, the resulting ensemble of images implicitly contains subject information at higher spatial frequencies than those present in a single image. Additionally, a multi-lenslet approach enables the use of observational diversity, including phase, polarization, neutral density, and wavelength diversities. For example, post-processing multiple observations taken with differing neutral density filters yields an image having an extended dynamic range. Our research group has developed several multi-lens camera prototypes for the investigation of such diversities. In this paper, we present techniques for computing a high-resolution reconstructed image from an ensemble of low-resolution images containing sub-pixel level displacements. The quality of a reconstructed image is measured by computing the Hamming distance between the Daugman iris code of a conventional reference iris image, and the iris code of a corresponding reconstructed image. We present numerical results concerning the effect of noise and defocus blur in the reconstruction process using simulated data and report preliminary work on the reconstruction of actual iris data obtained with our camera prototypes.
Stress and Resilience of Nursing Students in Clinical Training During the COVID-19 Pandemic: Palestinian Perspective

Introduction Stress reduces nursing students’ capacity to think critically, impedes decision-making, and eventually lowers academic performance and limits success. The circumstances surrounding the COVID-19 outbreak drove students into new virtual learning settings, adding stress to their academic responsibilities. Objectives The purpose of this study was to assess the perceived stress and resilience among nursing students in clinical training during the COVID-19 pandemic. Method A cross-sectional study was conducted on 315 students studying nursing. Data collection utilized the Perceived Stress Scale and the Connor–Davidson Resilience Scale. Results The analysis revealed that the stress score mean was 75.2 ± 6.6 and the resilience score mean was 26.0 ± 3.4. Also, there was a weak negative association between nursing students’ stress and their resilience (p < 0.05). Additionally, the results indicated statistically significant differences between stress and gender, study year level, and age (p < 0.05). Conclusion Stress was high and resilience was moderate during the COVID-19 pandemic among nursing students.
ITS: Improved Tabu Search Algorithm for Path Planning in UAV-Assisted Edge Computing Systems

Mobile Edge Computing (MEC) plays a crucial role in providing diverse computation and storage services to intelligent equipment. In recent years, the utilization of Unmanned Aerial Vehicles (UAVs) equipped with edge servers has emerged as a promising approach to enable ubiquitous edge computing services. This paradigm offers various benefits, such as reduced latency and flexible service provisioning. In the context of UAV-assisted edge computing, optimizing the location and trajectory of UAVs is vital, since the communication distance has significant impact on the communication rates between edge servers and devices. To address this optimization problem, this paper establishes a UAV-assisted edge computing system and focuses on investigating the path planning issue to expedite the offloading of computational tasks. In order to achieve this objective, we propose an improved tabu search algorithm that can efficiently optimize the number of UAVs, path planning. And to ensure reliable communication, we introduce reliability guarantees. Furthermore, we consider the energy limitations of UAVs to ensure practical feasibility. Through extensive simulations, we demonstrate that the proposed algorithm outperforms alternative approaches in terms of the specified objectives.
CPW fed Heart shaped Antenna for Ultra-Wide Band Applications

This paper demonstrates an ultra-wide band (UWB) antenna, based on the coplanar waveguide (CPW) feeding with overall dimension of $0.35\lambda_{L}\times 0.314\lambda_{L}\times 0.007\lambda _{L}$ (where $\lambda_{\mathbf{L}}$ is the wavelength corresponding to lowest cut of frequency). Heart shaped radiating patch and bended ground plane are used to get the wide bandwidth. Different antenna parameters like antenna reflection coefficient, antenna gain, radiation efficiency, antenna radiation patterns are analyzed. Results show 126.5% (2.62–11.62 GHz) impedance bandwidth, with maximum gain of 4.15dBi and maximum radiation efficiency of 84.3%. Approximately omnidirectional radiation pattern and less variation in antenna gain and radiation efficiency makes the proposed antenna a good candidate for diverse UWB (3.1-10.6GHz) applications.
A Deep Reinforcement Learning-Based Pointer Scoring Network for Edge Task Scheduling

In edge computing, service providers aim to provide users with low latency, high reliability, and more secure services by deploying services to edge servers close to users. However, scheduling service requests in edge computing scenarios is challenging because the edge service nodes typically have limited resources. This limitation makes it difficult to simultaneously optimize multiple objectives, including resource utilization, total running time, and average waiting time. Existing methods are insufficient to provide intelligent scheduling services that consider multiple optimization objectives, resulting in unsatisfactory scheduling results. To address this problem, we propose PSNet, a deep reinforcement learning-based Pointer Scoring Network, for edge task scheduling. PSNet integrates multiple pointer networks to optimize multiple objectives and obtain final scheduling results. Experiments carried out on three publicly available real-world datasets show that our method can effectively solve the multiobjective optimization problem on edge service requests, with an 11% improvement over several state-of-the-art methods.
Federated Learning Empowered Short-Term Load Forecasting Based on Bagging Learning with Self-Attention Mechanism

The proficiency of short-term load forecasting (STLF) is vital to the smart grid. Nevertheless, existing STLF methods are faced with two fundamental challenges. Firstly, there is a need to enhance the prediction accuracy. Secondly, the sensitivity of load data makes it challenging to maintain its privacy during the train process. To overcome these challenges, a new STLF model called Bagging-SA-HFL is proposed, incorporating bagging learning (Bagging), self-attention mechanism (SA), and horizontal federated learning (HFL). This model seeks to enhance accuracy through the use of Bagging and SA, and maintain privacy via HFL. A comparative analysis is performed between the proposed Bagging-SA-HFL model and other popular models including support vector regression (SVR), recurrent neural networks (RNN), long short-term memory (LSTM), gated recurrent unit (GRU) and Bagging using datasets from different regions in eastern China. The results demonstrate that the Bagging-SA-HFL method is highly accurate in its predictions while ensuring data privacy, which holds immense practical significance for STLF applications.
Beating G-EDF at its Own Game: New Results on G-EDF-like Schedulers ∗

The global earliest-deadline-ﬁrst (G-EDF) scheduling algorithm is useful for both hard real-time and soft real-time systems. Under G-EDF, each job is prioritized based on a priority point that is a ﬁxed time after its release, namely its deadline. By considering other such priority points, we deﬁne and examine a class of “G-EDF-like” scheduling algorithms and demonstrate that G-EDF is suboptimal in this class for hard real-time task systems. We also provide experimental evidence that soft real-time performance can be substantially improved by choosing priority points other than deadlines. We further provide a suﬃcient polynomial-time algorithm for determining priority points that will guarantee given response-time bounds, if possible.
FRI0063 Rapamycin induces remission in patients with refractory rheumatoid arthritis

Background Rheumatoid arthritis (RA) is a chronic inflammatory disease that results in destruction of joint cartilage and bone. However, many patients do not achieve satisfactory disease control by current therapy with high risk of adverse reactions. We have reported that absolute number of peripheral regulatory T (Treg) cells reduced in RA patients (EULAR Abstract). Moreover, rapamycin has been reported to inhibit differentiation of Th17 and promote growth of FoxP3 +Treg cells by inhibiting mTOR pathway [1]. Objectives To observe the therapeutic efficacy of rapamycin on the reduction of disease activity, increase in Tregs and decrease in Th17 to restore balance of Th17/Treg cells in RA patients with high disease activity (DAS28 ≥2.6). Methods Fifty RA patients who treated with two kinds of DMARDs for more than half a year did not achieve remission (DAS28 ≥2.6) were enrolled and were treated with rapamycin at a dose of 0.5 mg every 2 days for 24 weeks. The absolute number of CD4 +T cell subsets in peripheral blood from these patients were assessed by flow cytometry combined with internal standard beads before the treatment as baseline and at week 24 after treatment. Meantime, the DAS28, the dosage of corticosteroids and immunosuppressant were also recorded. Results Rapamycin treatment reduced the disease activity and induced remission (DAS28<2.6) in 44.9% of active RA patients. Their DAS28 was reduced from a median 2.9 (at week 0) to 1.9 (at week 24) (P<0.001) and the absolute number of peripheral Treg cells was increased from 27.14±15.11 cells/µl (at week 0) to 36.59±17.23 cells/µl (at week 24) (p=0.002). The ratios of Th17/Treg cells also had a significant decrease from 0.36±0.29 at baseline to 0.27±0.20 at week 24 (P<0.041). In contrast, the decrease in the absolute number of Th17 cells was not statistically significant (p=0.846). After the treatment, the proportion of patients taking glucocorticoids decreased from 66.0% to 64.0% and the mean dosage of prednisone decreased from 9.89 mg/d to 7.70 mg/d. And the usages of DMARDs were also reduced (P<0.001). Conclusions Rapamycin combined with low level of conventional therapy effectively reduced disease activity and induced remission among RA patients who received long-term conventional treatment without remission (DAS28 ≥2.6) by increasing the absolute number of Treg cells and restoring the balance of Th17 cells and Treg cells. As the research progresses, rapamycin is likely to become a promising therapeutic candidate. Reference [1] Shan J, Feng L, Sun G, et al. Interplay between mTOR and STAT5 signaling modulates the balance between regulatory and effective T cells. Immunobiology2015;220(4):510–517. Disclosure of Interest None declared
Assessing the benefits and drawbacks of cryolipolysis as a noninvasive body sculpting technique: risks, side effects, and future research – an editorial

This article discusses cryolipolysis, a noninvasive body sculpting method that freezes and removes fat cells. It explores the historical context of fat reduction treatments and the development of noninvasive techniques as alternatives to invasive liposuction. Cryolipolysis gained popularity after U.S. Food and Drug Administration (FDA) approval and has become one of the most frequently utilized noninvasive cosmetic procedures globally. The abstract delves into case reports and complications associated with cryolipolysis, such as persistent lumps, frostbite lesions, and skin necrosis, highlighting potential risks and limitations. It also addresses common side effects like erythema, numbness, and bruising, as well as more severe adverse events such as dysesthesia and paradoxical adipose hyperplasia. Safety considerations and relative contraindications are discussed, emphasizing the importance of consulting qualified medical professionals who adhere to pretreatment and posttreatment instructions and use FDA-approved technology. Patience and a healthy lifestyle are stressed for optimal outcomes. In conclusion, cryolipolysis offers a safe and convenient fat removal option with long-lasting benefits. However, it may not be suitable for significant fat loss or certain medical conditions. Consulting medical experts and considering personal preferences are essential in making an informed decision about cryolipolysis as a body sculpting option.
A Short Introduction to Climate Change

Preface 1. The spirit of enquiry 2. Global warming 3. Weather is not climate 4. The thermostat 5. Droughts and flooding rains 6. Snow and ice 7. The ocean 8. From ice-house to greenhouse 9. The last two thousand years 10. Carbon dioxide and methane 11. Denial 12. Bet your grandchildrens' lives on it too?
Landslide-prone areas in Makale Selatan with the analytical hierarchy process method

Landslides are one of the most common natural disasters in Indonesia. The Tana Toraja area has not been spared landslides, especially in the Makale Selatan district. Makale Selatan is one of the supply areas for agricultural products in South Sulawesi. Therefore it is necessary to map landslide-prone areas to support landslide disaster mitigation. This study aims to map landslide-prone regions and determine the factors most influencing landslide events in Makale Selatan. This study uses an Analytical Hierarchy Process (AHP) method with a weighting factor. The parameters, namely; slope, rainfall, land use, soil texture, soil type, and rock formations. The results showed that the incidence of landslides in Makale Selatan was strongly influenced by the slope, with the highest weighting factor with a weight of 0.410 (Score of 41%). Rainfall and land use with the same weight of 0.170 (Score of 17%), then the parameters of soil type and soil texture also with the exact weight is 0.096 (score of 9.6%), and the rock formation parameters with a weight of 0.058 (score 5.8%). The level of landslide-prone areas in the study area namely; 12.46% for the not prone area, 51.72% for slightly prone, prone areas 25.72%, and very prone was 10.11% for the total study area. The not-prone village is Tiromanda, and the most prone village is Lembang Randan Batu. The slope parameter is the parameter that most influences landslides in Makale Selatan. Land use for agriculture must be mechanically conserved to reduce the slope angle and prevent landslides.
Scratching increases epidermal neuronal branching and alters psychophysical testing responses in atopic dermatitis and brachioradial pruritus

Background Chronic scratching imposes a major stress on the skin and can lead to itch intensity worsening, and consequently, patients may enter an itch–scratch cycle. This repetitive mechanical stress can result in lichenification, worsening of epidermal barrier function, and enhanced cutaneous inflammation. Furthermore, a reduction of intraepidermal nerve fibers was previously described in lichenification. Aim The aim of this study was to investigate the influence of chronic scratching on the epidermal neuroanatomy and on sensory changes, in particular the prevalence of hyperknesis and alloknesis in patients after mechanical, chemical, and electrical stimuli. Methods Analyses were performed on pruritic lichenified (chronically scratched), pruritic non-lichenified (not chronically scratched), and non-pruritic non-lesional (unaffected) skin areas of patients with inflammatory pruritus, i.e., atopic dermatitis (n = 35), and neuropathic pruritus, i.e., brachioradial pruritus (n = 34) vs. healthy matched controls (n = 64). Our fine-grained spatial skin characterization enabled specifically studying the differential effects of chronic scratching in inflammatory and neuropathic itch. Results Analysis of intraepidermal nerve fiber density showed rarefaction of fibers in all three skin areas of patients compared with healthy controls in both diagnoses. Even more, the two pruritic areas had significantly less nerve fibers than the unaffected skin, whereas electrically induced itch was massively increased. Epidermal branching of the remaining nerve fibers in lichenified/chronically scratched skin was increased, particularly in patients with brachioradial pruritus, which may contribute to the pronounced local neuronal sensitivity. Hyperknesis and alloknesis were found to increase independently of lichenification. Conclusion Our results indicate that chronic scratching may not affect intraepidermal nerve fiber density but leads to a stronger branching pattern of intraepidermal nerve fibers, which may contribute to local hypersensitivity. The increased sensitivity in the pruritic areas suggests mechanisms of peripheral sensitization, whereas the increased sensation of electrically and chemically induced itch in unaffected skin indicates central sensitization for itch.
Development of strategic plan for palm sugar agro-industry using SWOT analysis and business model canvas: Case study in Lombo Village, Sidrap District

Agro-industry is the fundamental part of a development of the agricultural sector. One form of promising agro-industry to be developed in Indonesia is the development of products from palm sugar commodities. The purpose of this research is to determine how to develop a business strategy and increase the potential of the palm sugar industri in Lombo Village, Sidrap District. This study aims to: 1. determine external and internal factors; 2. formulate and analyze internal and external factors, which include strengths, weaknesses, opportunities, and threats, from the development of palm sugar agribusiness. 3. Determine strategic priorities based on existing potentials, capabilities, and constraints. 4. Define a business model canvas.The method used in this research is descriptive qualitative. This study used an accidental sampling method with a total sample size of 40 palm sugar farmers. Data analysis was performed using SWOT matrix analysis and the Business Model Canvas (BMC). The conclusion from the internal matrix analysis is that the Strategic Factor (IFAS) 5.295 and the External Strategic Factor (EFAS) 4.89. This illustrates that the palm sugar industry in Lombo Village, Sidrap District has strengths that can be developed further. The advice to palm sugar farmers in this area is to maintain product taste, as well as carrying out various innovations in diversifying palm sugar products and making product packaging attractive so that it will increase added value.
The effect of preheated whey protein concentrate addition on high protein biscuit

WPC (Whey Protein Concentrate) is a product that has a high biological value (BV) and nutritional composition with a protein content ranging from 34–80%. WPC can be applied in the development of high protein food products such as biscuits. However, the excessive use of WPC might result in a hard texture that will have an impact on decreasing the palatability of the product. Preheated treatment can be used to modify WPC so that it loses its functional properties as a structure builder. WPC will be denatured and cause the formation of whey protein aggregates. The denaturation that occurs makes WPC tend to lose its reactivity and become more stable. The aim of this study was to determine the effect of preheated WPC in a high protein biscuit. There were 7 treatments; control biscuit (without WPC), biscuits with the addition of non-preheated (NPH) and preheated (PH) WPC with 11%, 13%, and 15% protein content. The results showed that the addition of preheated WPC could produce better physical and sensory characteristics when compared to biscuits using non-preheated WPC. Biscuits PH 11% can produce the best characteristics with a hardness value of 1,171.543 g; crumb structure with small pores; porosity 18.944%; and a DF value of 2.482. This is also supported by the results of the triangle test, where the panellists could not distinguish the colour, taste, and texture of the biscuit when it was compared with the control.