Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of Dual-band Filter Based on Separate Electric and Magnetic Coupling Paths Technology

This paper proposes a dual-band dual-path BPF based on the separate electric and magnetic coupling paths (SEMCPs) technology. Each single-path consists of a half-wavelength UIR resonator and two quarter-wavelength SIR resonators. By adjusting the equivalent capacitive element and equivalent inductive element between the SIR resonators, the mixed coupling can be equivalently separated to a magnetic coupling path and an electric coupling path, then the TZs of the proposed filter can be controllable. This topology can not only realize the controllable filter TZs, but also has the advantages of high selectivity and miniaturization.
Detection of Bangladeshi-Produced Plant Disease Using a Transfer Learning Based on Deep Neural Model

Plant diseases pose a significant threat to agricultural productivity and food security in Bangladesh. In this research, we address the challenge of timely and accurate plant disease detection through the application of transfer learning with deep neural models. We curated a diverse dataset comprising 18 categories of plant leaf images, including Bell pepper Bacterial spot, Bell pepper Healthy, Peach Healthy, Potato Early Blight, Rice Leaf Blast, Rice Healthy, Rice Brown Spot, Potato Healthy, Peach Bacterial spot, Corn Blight, Potato Late blight, Corn Healthy, Tomato Bacterial spot, Strawberry Leaf Scorch, Tomato Early blight, Tomato Early blight, Strawberry Healthy, and Tomato Healthy. The dataset represents the most prevalent plant diseases observed in the Bangladeshi context. We employed three state-of-the-art deep learning algorithms, EfficientNetV2M, VGG-19, and NASNetLarge, to develop robust plant disease detection models. Through transfer learning, these pre-trained models were fine-tuned on our specialized dataset to adapt them for the task at hand. The performance evaluation revealed impressive results, with EfficientNetV2M achieving an accuracy rate of 99%, VGG-19 achieving 93%, and NASNetLarge attaining 83% accuracy. The high accuracy of EfficientNetV2M showcases its exceptional capability in accurately classifying plant diseases prevalent in Bangladesh. The success of these deep neural models in detecting various plant diseases signifies their potential in revolutionizing plant disease management and enhancing agricultural practices. Our research contributes valuable insights into the effective use of transfer learning for plant disease detection and emphasizes the significance of dataset curation for improved model performance. The developed models hold promise in providing timely and precise disease diagnosis to farmers and agricultural professionals, thereby facilitating prompt interventions and minimizing crop losses. Future research can explore the integration of these deep neural models into practical agricultural tools, enabling real-time disease detection and offering substantial benefits to the agricultural industry in Bangladesh.
MIA: A Transport-Layer Plugin for Immersive Applications in Millimeter Wave Access Networks

The highly directional nature of the millimeter wave (mmWave) beams pose several challenges in using that spectrum for meeting the communication needs of immersive applications. In particular, the mmWave beams are susceptible to misalignments and blockages caused by user movements. As a result, mmWave channels are vulnerable to large fluctuations in quality, which in turn, cause disproportionate degradation in end-to-end performance of Transmission Control Protocol (TCP) based applications. In this paper, we propose a reinforcement learning (RL) integrated transport-layer plugin, Millimeter wave based Immersive Agent (MIA), for immersive content delivery over the mmWave link. MIA uses the RL model to predict mmWave link bandwidth based on the real-time measurement. Then, MIA cooperates with TCP’s congestion control scheme to adapt the sending rate in accordance with the predictions of the mmWave bandwidth. To evaluate the effectiveness of the proposed MIA, we conduct experiments using a mmWave augmented immersive testbed and network simulations. The evaluation results show that MIA improves end-to-end immersive performance significantly on both throughput and latency.
All-Cause Mortality and Its Predictors in Haemato-Oncology Patients with Febrile Neutropenia

Febrile neutropenia (FN) is one of the most important life-threatening complications in haemato-oncology. Our objective was to report all-cause mortality rates in patients ill with a hematological malignancy (HM) hospitalized with a first FN episode and to identify predictors for mortality. We conducted a historical retrospective cohort study of consecutive patients with an HM, >18 years of age, admitted between January 2012 and August 2018 for a first episode of FN. Data on all-cause mortality 12 months after admission for FN were obtained. The Kaplan–Meier curve was used to describe mortality during the follow-up period. Univariate and multivariable analyses identified predictors for 1,3 and 12-month mortality. One hundred and fifty-eight patients (mean age 69.5, 49.4% males) were included. Overall, 54 patients died (15.8%, 25.9%, and 34.1% died after 1, 3, and 12 months, respectively). Lower serum albumin, higher serum gamma-glutamyl transferase (GGT), lower estimated glomerular filtration rate (eGFR), older age, higher temperature, and lower absolute lymphocyte count at admission were independent predictors of all-cause mortality after 12 months. Further studies are needed to confirm our results and identify therapeutic strategies to improve survival.
Evaluation of the Seismic Performance of Single-Plate Metallic Slit Dampers Using Experimental and Numerical Data

Passive energy dissipation systems and devices are helpful in mitigating the danger of earthquake damage to structures. Metallic slit dampers (MSDs) are one of the most efficient and cost-effective solutions for decreasing seismic energy intake. The potential importance of MSDs in managing vibrations and limiting structural fatigue continues to grow as research advances and new materials and designs are introduced. This study evaluated the seismic performance of single-plate MSDs (SPMSDs) through a combination of numerical simulation and assessment of experimental results. ABAQUS software was used to create an assembly consisting of endplates, bolts, and SPMSDs. A real-world earthquake scenario was simulated using cyclic loads based on ASCE/SEI standards, and displacement-measuring devices such as strain gauges and LVDT were employed to record the behavior of the SPMSDs. The results of the experiment are used to assess the compliance of the SPMSDs and discuss their behavior as they undergo minimum and maximum displacements due to minimum and maximum applied forces. The energy dissipation capabilities of the dampers are presented by analyzing and comparing the area of their hysteresis loops, equivalent viscous damping, and their damping ratios. Actual failure modes are identified and shown to describe the limitations and potential vulnerability of the dampers. The relative error between the lowest and greatest recorded forces from experimental data and numerical simulation ranges from 4.4% to 5.7% for SPMSD 1 and from 1.6% to 2.1% for SPMSD 2, respectively. These deviation values represent a satisfactory level of precision, demonstrating that the numerical simulation accurately predicts the actual performance and behavior of the dampers when subjected to cyclic stress. The topology optimization performed in this study yielded an improved geometry of the SPMSD suited for a corresponding maximum considered earthquake (MCER) displacement of ±33 mm. This research also suggests practical implementations of the investigated and improved SPMSDs.
Explaining Entrepreneurial Success of SMEs Entrepreneurs: The Role of Entrepreneurial Characteristics

Small and medium sized enterprises have significant role in sustainable economic growth and country. This study aims to investigate the entrepreneurial characteristics predicting entrepreneurial success of small and medium sized enterprises. Following analytical research design, this study collected data using structured questionnaire from 263 entrepreneur managers. Result revealed that entrepreneurial motivation, entrepreneurial self-efficacy, and risk-taking propensity have significant role in predicting entrepreneurial success of SMEs but refuted for entrepreneurial innovativeness. Moderating role of gender was examined in the study. The result advocates that the study contributes to the entrepreneurial competency theory and contributes for policy makers for entrepreneurial growth for sustainable development goals.
Clinical implication of coronary artery bypass graft with surgical ventricular reconstruction in patients with left ventricular dysfunction in the current clinical practice

The therapeutic strategy for the patients with chronic myocardial infarction and severe left ventricular dysfunction remains controversial. Yang and colleagues compared clinical outcomes between coronary artery bypass grafting (CABG) and surgical ventricular reconstruction (SVR) and isolated CABG without SVR. They showed that CABG + SVR was associated with a lower cumulative incidence of cardiovascular events than isolated CABG (1). Several concerns have been raised.
Implementation of a Metamodel-Based Optimization for the Design of a High Power Density Wound Field Traction Motor

Traction motors onboard electric vehicles (EVs) are faced with ever-increasing power density requirements and cost reduction challenges. Although permanent magnet (PM) traction motors have been favored for their potential of higher power densities, the price and supply volatility of PM materials are the major drawbacks. Instead of a single load point, the performance of a traction motor needs to be optimized over various combinations of torque and speed to represent real-world vehicular driving scenarios, which tends to be a time-consuming task with direct optimizations. In this article, as a PM-free solution, a wound field synchronous motor (WFSM) is designed and optimized using a metamodel-based approach to maximize its efficiency over a custom set of load points, while utilizing a fully per-unitized geometry template. The workflow has proven to be timesaving and the optimal design is prototyped and tested.
Development of a Risk Prediction Model for endometrial carcinoma among postmenopausal women in the Western province of Sri Lanka.

Introduction Globally, endometrial carcinoma is the most common reproductive tract cancer among women. Risk prediction model is a simple, low-cost tool to identify women with increased risk of developing endometrial carcinoma. Objectives: The aim of the study was to develop a model to predict the risk of endometrial carcinoma among postmenopausal women in Sri Lanka. Methods A case control study was conducted. The cases and the controls were defined as postmenopausal women who had and had not been diagnosed as endometrial carcinoma based on histological confirmation respectively. Variable selection was done considering the objectivity and feasibility of the measurements in addition to the statistical criteria. A scoring system [0-9] was designed based on weighted score of each risk predictor. Predictive validity of the model was tested by calibration and discrimination. Receiver Operator Characteristic (ROC) curve was used to determine the cut-off value. Results The developed model consisted of six predictors; Age >55 years, never conceived, age at menarche ≤11 years, ever experienced postmenopausal bleeding, having family history of any type of cancer among first degree relative, generalized obesity. Discrimination of the model was measured by the area under the ROC curve (0.92, 95% Confidence Interval: 0.88-0.95). Calibration with goodness of fit by Hosmer and Lemeshow test (p=0.72) was satisfactory. The tool demonstrated a good predictive ability with sensitivity of 79.5% (CI: 68.9%-87.3%) and specificity of 90.7% (CI: 86.8%-93.5%) at the cut-off point of 4.5. Conclusions Model demonstrated good discrimination and well calibration. It can be used in screening of high-risk women for developing endometrial carcinoma.
The metric compactification of $L_{p}$ represented by random measures

We present a complete characterization of the metric compactification of $L_{p}$ for all $1 \leq p < \infty$. Each element of the metric compactification of $L_{p}$ is represented by a random measure on a certain Polish space. By way of illustration, we revisit the $L_{p}$-mean ergodic theorem for $1 < p < \infty$, and Alspach's example of an isometry on a weakly compact convex subset of $L_{1}$ with no fixed points.
Possum resource selection in a fragmented landscape, Cass, New Zealand

This study presents the results of an investigation to determine the resources utilised by possums in a spatially heterogeneous landscape in the Canterbury high country. The study area comprised a mosaic of forest, scrub, shrubland, grassland and swamp at the University of Canterbury field station at Cass, inland Canterbmy. The vegetation communities at Cass were miginally divided into seven different categories based on species composition. These areas are referred to as the swamp, grassland, shrubland, scrubl, scrub 2, native forest, and exotic forest. Communities were classified using two multivariate techniques; TWINSPAN and Detrended conespondence analysis. A vegetation map of Cass was produced using ground survey and aetial maps, and displayed the extent and coverage of vegetation communities. These vegetation communities reflect the influence of burning and farming. The study area comprises c. 195 ha. The overall possum density was low, with approximately one possum per hectare. This result may be due to resources that possums need for survival, being deficient in the area. Possum movements were investigated by radio-collaring six female possums and six male possums at Cass. These possums were radio-tracked six times during a period of ten months, with each radio-tracking session undertaken for a period of three consecutive nights. The data collected were used to dedve individual home ranges using Minimum Convex Polygon and Kemel home range estimates. There was no statistically significant difference between female and male home ranges at Cass. No seasonal difference in possum home range was detected, although other possum home range studies have found seasonal differences. There were too few den site location areas to enable accurate infmmation conceming seasonal variation of den sites. Possums had relatively large home ranges, which probably result from a low possum density in the areas, as well as spatial heterogeneity of key resources. Nine possums showed some degree of home range overlap, but there did not appear to be any difference between females and males with respect to this. Sixty percent of the possums studied exhibited bimodal home ranging behaviour. Bimodal behaviour almost certainly results from the spatial heterogeneity of key resources, with possums having to travel long distances in order to use all the resources.
A Comparative Analysis of Unsupervised K-Means, PSO and Self-Organizing PSO for Image Clustering

This paper presents a comparative analysis of three algorithms namely K-means, Particle swarm Optimization (PSO) and Self-Organizing PSO (SOPSO) for image clustering problems. The traditional K-means algorithm found to be trapped in local minima. However, PSO and SOPSO overcome the problem of local minima and provide better results. In this work gbest model is used in PSO and both West and gbest models are used in SOPSO based on self-Organizing rules. It is shown that PSO and SOPSO produce better results compared to K-means with respect to the quantization error, inter- and intra-cluster distances.
Short-Term Load Forecasting in Power System Using Recurrent Neural Network

As energy demand increases rapidly, short-term load forecasting is becoming progressively vital in power system dispatch and demand response. This study proposes a short-term load forecasting approach for the power system in Vietnam. In this regard, a gated recurrent unit-based deep learning model is applied to use the historical load sequences to forecast the single-step and multi-step ahead values of the load consumption. The hourly load consumption dataset is provided by Ho Chi Minh City Power Corporation (EVNHCMC). Simulation results prove the effectiveness of the developed prediction algorithm for short-term load forecasting, especially for multi-step forecasting.
Latent Trajectories of Subjective Well-Being: An Application of Latent Growth Curve and Latent Class Growth Modeling

This study proposed a three-stage measurement model utilizing the Latent Growth Curve Modeling and Latent Class Growth Analysis. The measurement model was illustrated using repeated data collected through a four-week prospective study tracking the subjective well-being of volunteer college students (n=154). Firstly, several unconditional growth models were estimated to define the model providing a better representation of individual growth trajectories. Secondly, several conditional growth models were formulated to test the usefulness of covariate variables hypothesized to explain observed variance in growth factors. Finally, latent class models were estimated to explore different latent trajectory classes further. Results showed that students' subjective well-being changed over time, and the rate of this change, as well as its covariates, were not constant for the entire sample. This study clearly illustrates how a longitudinal measurement approach can enhance the scope of findings and the depth of inferences when repeated measurements are available.
Caracterização dos fatores predisponentes para a hipertensão arterial presentes na população de Diamantina, Minas Gerais

A transverse study carried out in the years of 1995 and 2000. The aim was to describe a specific population in Diamantina, according to some predisposing high blood pressure factors. The variables studied were: high blood pressure, gender, age, race, smoking and drinking habits. The frequency of high blood pressure in 1995 was of 44,77% and 33,66% in 2000, being higher among men, in black people and increasing as they grow older. High blood pressure was found to be highest in the smoking and/or drinking habits group in 1995. However, in 2000, the highest blood pressure was found among the non-smoking and non-drinking group.
Five-Level Inverter Fed Sensorless PMSM Drive Using Modified MPTC with Novel Reduced Search Space Algorithm

Multi-level inverters (MLI's) have gained vast interest in the field of drives to enhance the performance of the drive by minimizing the torque ripples. Developing the vector control algorithms for the MLI fed drives is one of the challenging tasks. In the era of vector control methods, the model predictive control has proven its standards in improving the performance of the drive over other methods, such as, field-oriented control, direct torque control, etc. Among the model predictive control methods, the modified model predictive torque control (MMPTC) method provides better torque and speed characteristics. However, the MMPTC method for multi-level inverter requires more computational time and it increases the burden on the controller. Therefore, a novel reduced search space algorithm is proposed based on the geometrical congruity between the two level and multi-level space vector diagrams to reduce the computational complexity of multi-level inverter switching. Further, dependency of speed sensor information in the controller makes the system vulnerable and speed sensorless drive make the system stronger. In this article, the five-level inverter fed MMPTC based sensorless PMSM drive with novel reduced search space algorithm is proposed and the performance characteristics of proposed method is compared with two-level and three-level inverters. The hardware prototype setup is developed in the laboratory and the simulation results are validated with the experimental results over a wide speed range.
Possible Warehouse Management Model According to Turkish Customs Regime Codes

Mankind has been in motion since its existence. The known history, dating back to 10,000 years ago, includes an adventure that started with hunter-gatherer and later became an agricultural society and settled-life after rehabilitating plants, and finally, today's lifestyle was formed with industrial and technological revolutions. In almost every step of this adventure, it is seen that human beings need to store the products they need in order to continue their life. After the hunter-gatherer living human beings catch their prey and gather the food they need, after the settled human beings harvest their products, and the human beings who buy the products they need today, they think about the future and feel the need for storage by nature. Just as individuals have storage needs at the micro level, businesses at the macro level want to keep various products such as raw materials, spare parts and intermediate products related to their business activities in their warehouses when needed, and they store certain product groups for this purpose. When we look at it today, it is seen that warehousing activities, which are a part of logistics processes as much as in-house warehouse activities, have an important place. Considering the product inventory costs, businesses want to buy the goods or products needed in the production process "on-time" by outsourcing the warehousing service, rather than keeping the goods or products they need within their own structure. At this point, it is seen that warehousing and warehousing activities as a part of the supply chain have gained more importance than ever, especially with the effect of technological developments in international trade. Warehouses and customs warehouses, which were seen as a cost element in both domestic and international logistics activities, respectively, today appear as economically effective structures that reduce shipment and customer order time cycles and most importantly provide value-added services. In short, we can see this pillar of logistics activities, which we encounter as a warehouse in domestic logistics activities and a customs warehouse in international logistics, as "the gear without moving that enables the machine to work all parts of a machine-like non-moving gear". In this study, after defining the warehouse, which is an important link of the supply chain, and focusing on its importance, the concept of warehouse used in international logistics will be explained and the effect of customs regime codes on the storage of goods in the customs warehouse will be explained
Development of a Wearable Human Pulse Measuring Device using Piezoelectric Sensor

Pulse diagnosis, an ancient technique in traditional Chinese medicine (TCM), is an effective non-invasive method for diagnosing human diseases. Traditionally, the practitioners use their fingertips to sense the pulse of the blood vessels on the patient’s wrist. However, this method heavily and subjectively depends on the doctor’s experience. Additionally, it’s impossible to store the patient’s pulse records for posterior review or analysis. To solve such restrictions, a wearable device that could precisely measure the wrist-based pulse signal and save it as digital data was proposed. In the proposed device, a piezoelectric sensor, which is a highly sensitive, lightweight, stable sensor, was used to record variations of the pulse. Since wrist pulse signals collected by the device contain various types of noises which make it difficult to assess the shape of the pulse wave, a slight low-pass filter was applied to remove high-frequency noises. The processed data was then compared with simultaneous electrocardiogram (ECG) data for the sake of evaluation. The preliminary results showed that recorded data from the proposed device is consistent with the equivalent ECG data.