Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection Techniques for mMTC

Massive machine-type communications (mMTC) is one of the key application scenarios of ﬁfth generation (5G) and beyond cellular networks. Bringing the unique technical challenge of supporting a huge number of MTC devices (MTCD) in cellular networks, how to efﬁciently estimate the channel, detect the active users and data in this scenario is an open research topic. In this regard, this paper aims to present an overview of different techniques to address the problem of channel estimation, activity and data detection speciﬁcally for the mMTC scenario. In order to highlight potential solutions and to propose new research directions, we discuss the performance of the state-of-the-art techniques in the literature using a uniﬁed evaluation framework.
Data-Driven Models for Canopy Temperature-Based Irrigation Scheduling

Normalized crop canopy temperature, termed crop water stress index (CWSI), was proposed over 40 years ago as an irrigation management tool but has experienced limited adoption in production agriculture. Development of generalized crop-specific upper and lower reference temperatures is critical for implementation of CWSI-based irrigation scheduling. The objective of this study was to develop and evaluate data-driven models for predicting the reference canopy temperatures needed to compute CWSI for sugarbeet and wine grape. Reference canopy temperatures for sugarbeet and wine grape were predicted using machine learning and regression models developed from measured canopy temperatures of sugarbeet, grown in Idaho and Wyoming, and wine grape, grown in Idaho and Oregon, over five years under full and severe deficit irrigation. Lower reference temperatures (TLL) were estimated using neural network models with Nash-Sutcliffe model efficiencies exceeding 0.88 and root mean square error less than 1.1°C. The relationship between TLL minus ambient air temperature and vapor pressure deficit was represented with a linear model that maximized the regression coefficient rather than minimized the sum of squared error. The linear models were used to estimate upper reference temperatures that were nearly double the values reported in previous studies. A daily CWSI, calculated as the average of 15 min CWSI values between 13:00 and 16:00 MDT for sugarbeet and between 13:00 and 15:00 local time for wine grape, were well correlated with irrigation events and amounts. There was a significant (p < 0.001) linear relationship between the daily CWSI and midday leaf water potential of Malbec and Syrah wine grapes, with an R2 of 0.53. The data-driven models developed in this study to estimate reference temperatures enable automated calculation of the CWSI for effective assessment of crop water stress. However, measurements taken under conditions of wet canopy or low solar radiation should be disregarded as they can result in irrational values of the CWSI.
The Verification of Ecological Citizen Science Data: Current Approaches and Future Possibilities

Citizen science schemes (projects) enable ecological data collection over very large spatial and temporal scales, producing datasets of high value for both pure and applied research. However, the accuracy of citizen science data is often questioned, owing to issues surrounding data quality and verification, the process by which records are checked after submission for correctness. Verification is a critical process for ensuring data quality and for increasing trust in such datasets, but verification approaches vary considerably among schemes. Here, we systematically review approaches to verification across ecological citizen science schemes, which feature in published research, aiming to identify the options available for verification, and to examine factors that influence the approaches used (Baker et al. 2021). We reviewed 259 schemes and were able to locate verification information for 142 of those. Expert verification was most widely used, especially among longer-running schemes. Community consensus was the second most common verification approach, used by schemes such as Snapshot Serengeti (Swanson et al. 2016) and MammalWeb (Hsing et al. 2018). It was more common among schemes with a larger number of participants and where photos or video had to be submitted with each record. Automated verification was not widely used among the schemes reviewed. Schemes that used automation, such as eBird (Kelling et al. 2011) and Project FeederWatch (Bonter and Cooper 2012) did so in conjunction with other methods such as expert verification. Expert verification has been the default approach for schemes in the past, but as the volume of data collected through citizen science schemes grows and the potential of automated approaches develops, many schemes might be able to implement approaches that verify data more efficiently. We present an idealised system for data verification, identifying schemes where this hierachical system could be applied and the requirements for implementation. We propose a hierarchical approach in which the bulk of records are verified by automation or community consensus, and any flagged records can then undergo additional levels of verification by experts.
Customer Loyalty Analysis on Online Food Delivery Services

Indonesia's e-commerce growth has increased by 17% in the last ten years. One of the most popular e-commerce businesses in Indonesia is online food delivery services. Promotion is one of the reasons that makes online food delivery service users increase every year. This research aims in alayzing the effects of e-service quality (e-servqual), food quality, and sales promotion toward perceived value and customer satisfaction, as well as their impacts on customer loyalty. A questionnaire was distributed to 200 respondents who had used online food delivery services in DKI Jakarta. The Structural Equation Model (SEM) with the PLS-SEM was used to test the validity, reliability, and hypothesis. The results of this study indicate that customers satisfaction impacted customer loyalty, which is the benchmark for customers in using online food delivery services. Not only that, company’s services, quality, and promotion impact customer satisfaction.
The relation between joint physical custody, interparental conflict, and children's mental health

Objective: This study investigated the relationship between joint physical custody and children’s mental health, and tested whether interparental conflict moderated the association. Background: Joint physical custody is an emerging post-separation care arrangement that is expected to counter the negative effects of family dissolution on children’s overall well-being. There is, however, substantial disagreement about the impact that joint physical custody may have on children’s mental health when interparental conflict is high. Method: The statistical analysis was based on data from the Family Models in Germany (FAMOD) study, which was conducted in 2019. The analytical sample consisted of 1,087 post-separation families practicing either sole physical custody or joint physical custody. Linear regression models were estimated to determine the relationship between physical custody arrangements, interparental conflict, and children’s mental health problems. Results: Living in a joint physical custody arrangement was positively related to children's mental health, whereas high levels of interparental conflict were negatively related to children's mental health. However, when levels of interparental conflict were high children in joint physical custody arrangements displayed levels of mental health problems that were quite similar to those of children in sole physical custody arrangements. Conclusion: The findings emphasize that joint physical custody is not a "one-size-fits-all model" that is suitable for all post-separation families, and that it is only beneficial for children's mental health when interparental conflict is low.
What Major Product Characteristics of Dry Bulk Cargo Terminal Create Competitive Positioning of Georgian Port industry, the Case Study of Poti and Batumi Ports

This study aims to identify factors which give the Georgian port industry (particularly, Poti and Batumi ports) a competitive advantagein comparison with such competitive ports as Novorossiysk, Odessa, Bandar Abbas and the Russian railway system that acts as themain rival for the Georgian transportation transit route. The product and value comparison study is based on a multi-level approachwhich allows transferring and using the quantitative and qualitative information gained during the project work for a numericalcalculation/projection model. Research identified key bottlenecks of Georgian Ports and its functional benefits within the core andphysical port infrastructure. Recommended directions in the value creation process is given.Keywords: competitive advantage, differentiation, Georgian port industry, product structure, value creationJEL: L9
A Systematic Review on The Relationship Between Organizational Sustainability, HR Analytics, And Organizational Culture

HR analytics is a novel approach that has attracted the attention of researchers and practitioners in the recent time and is being applied in organizations as well to a great extent. HR analytics allows the human resource managers to take decision based upon human capital and it influences the outcomes in business. HR analytics is considered as a tool which aims to improve the insights in the organizations in terms of sustainable growth and future advancements or innovation. HR analytics plays a vital role in sustainability and culture of the organization. It is well known that organizational culture is influenced by organizational sustainability. This research attempts to review systematically the numerous studies conducted by the researchers of the recent time on HR analytics, organizational culture and organizational sustainability and their relationships as identified by these researchers.
Evaluating multi-core graph algorithm frameworks

Multi-core and GPU-based systems offer unprecedented computational power. They are, however, challenging to utilize effectively, especially when processing irregular data such as graphs. Graphs are of great interest, as they are now used to model geographic, social-and neural networks. Several interesting programming frameworks for graph processing have therefore been developed these past few years. In this work, we highlight the strengths and weaknesses of the Galois, GraphBLAST, Gunrock and Ligra graph frameworks through benchmarking their single source shortest path (SSSP) implementations using the SuiteSparse Matrix Collection. Tests were done on an Nvidia DGX2 system, except for Ligra, which only provides a multi-core framework. D-IrGL, built on Galois, also provided a multi-GPU option for SSSP. We also look at program size, documentation and overall ease of use. High performance generally comes at the price of high complexity. D-IrGL shows its strength on the very largest graphs, where it achieved the best run-time, while Gunrock processed most other large sets the fastest. However, GraphBLAST, with a relatively low-complexity interface, achieves the greatest median throughput across all our test cases. This despite that its SSSP implementation size is only 1/10th of Gunrock, which for our tests has the highest peak throughput and the fastest run-time in most cases. Ligra had less computational resources available, and consequently performed worse in most cases, but it is also a very compact and easy to use framework. Futher analyses and some suggestions for future work are also included.
Modeling and Estimation of Synchronous Machine Parameters from Digitized Sudden Short-Circuit Armature

This paper presents a novel approach for optimal estimation of the non-saturated parameters of synchronous machines from the digitized samples of the sudden short-circuit armature current. The proposed technique uses the least error squares (LES) parameter estimation algorithm to identify these parameters. The proposed technique splits the armature current into three components, namely; the steady state armature current, transient current, and the subtransient
The Role of RNA Editing Within Calcium-dependent Activator for Secretion 1 in Large Dense Core Vesicle Fusion

Calcium-dependent activator protein for secretion 1 (CAPS1) is involved in synaptic vesicle and large dense core vesicle fusion by bridging vesicles and the plasma membrane prior to vesicle fusion and cargo release. CAPS1 is subject to a post-transcriptional modification called RNA editing. RNA editing recodes a single amino acid in the region of the protein that interacts with the vesicle. This recoding event within CAPS1 has cellular consequences in neurons where edited CAPS1 overexpression increases basal secretion from neural synaptic vesicles. Our goal was to investigate how RNA editing within CAPS1 affects large dense core vesicle secretion in an insulinoma cell type that secretes insulin. We modified CAPS1 editing frequency within insulin-secreting cells, then measured non-stimulated and glucose stimulated secretion. We found cells with increased non-edited CAPS1 had less basal secretion compared to normal insulin-secreting cells, but edited CAPS1 did not affect secretion. Our study provides insight into how even slight modifications of CAPS1 RNA editing frequency could have profound effects on cellular secretion, which may suggest that CAPS1 editing is paramount to regulating cellular processes like neurotransmission and glucose homeostasis.
Randomized Phase II Clinical Trials using Fisher’s Exact Test

A phase II trial is to evaluate an experimental therapy using an early efficacy outcome, such as tumor shrinkage, before proceeding to a large-scale phase III trial. Traditionally, a typical phase II trial has been conducted using a single-arm design recruiting patients only to the experimental therapy to be compared with a historical control. Due to a small sample size and heterogeneity of patient population, the characteristics of the patients in a new phase II trial is often different from that of the selected historical control, so that the single-arm phase II trial results in false positive or false negative conclusions. A randomized phase II trial can resolve such problems by randomizing patients between an experimental arm and a control arm. In this paper, we propose randomized phase II trial designs based on 2-stage Fisher’s exact tests allowing for both superiority and futility early stopping options, so that we can save number of patients when experimental therapy is definitely efficacious as well as when it is futile. We propose a weighted expected sample size as a new criterion to define optimal twostage designs.
Guaranteed Diversity and Optimality in Cost Function Network Based Computational Protein Design Methods

Proteins are the main active molecules of life. Although natural proteins play many roles, as enzymes or antibodies for example, there is a need to go beyond the repertoire of natural proteins to produce engineered proteins that precisely meet application requirements, in terms of function, stability, activity or other protein capacities. Computational Protein Design aims at designing new proteins from first principles, using full-atom molecular models. However, the size and complexity of proteins require approximations to make them amenable to energetic optimization queries. These approximations make the design process less reliable, and a provable optimal solution may fail. In practice, expensive libraries of solutions are therefore generated and tested. In this paper, we explore the idea of generating libraries of provably diverse low-energy solutions by extending cost function network algorithms with dedicated automaton-based diversity constraints on a large set of realistic full protein redesign problems. We observe that it is possible to generate provably diverse libraries in reasonable time and that the produced libraries do enhance the Native Sequence Recovery, a traditional measure of design methods reliability.
Modelling spatial and inter-annual variations of nitrous oxide emissions from UK cropland and grasslands using DailyDayCent

Agricultural soils are the primary source of nitrous oxide emissions due to management practices including fertiliser application. While fertiliser rates are one of the main drivers of nitrous oxide emissions, emissions are also dependent on other variables such as climate and soil properties. To understand the spatial and inter-annual variations in emission rate, simulations of N2O emissions were made from 2000 to 2010 for UK grass and croplands. In addition, the sensitivity of these emissions to soil and climate inputs was also tested. Emissions of between 0.3 to 3.5 kg N ha yr and 0.7–7 kg N ha yr were simulated across UK croplands and grasslands, respectively. While inter-annual variations can be attributed to climate influences, the primary driver of spatial variations in emissions was soil clay content. However, when the sensitivity of nitrous oxide emissions to soil clay content alone was tested, it was not always the best predictor of emissions, when soil texture is altered outside of the normal range used as inputs to the model from different databases.
Highly Enhanced Inductance Sensing Performance of Dual-Quartz Crystal Converter

This paper presents ways of inductance sensitivity improvement in a quartz crystal converter for low inductance measurement. To improve the converter’s sensitivity, two quartz crystals that were connected in parallel and additional capacitance connected to the two quartz crystals in the oscillator’s circuit are used. The new approach uses a converter with special switchable oscillator and multiplexer switches to compensate for the crystal’s natural temperature-frequency characteristics and any other influences, such as parasitic capacitances and parasitic inductances, which reduce them to a minimum. The experimental results demonstrate improved sensitivity and well-compensated dynamic temperature influence on the converter’s output frequency. The fundamental quartz crystal frequency-temperature characteristics in the temperature range between 0–40 °C are simultaneously compensated. Furthermore, the converter enables the measurement of the influence of its own hysteresis at different values of inductances at the selected sensitivity by parallel capacitances connected either to the single- or dual-quartz crystal unit. The results show that the converter converting inductances in the range between 85–100 μH to a frequency range between 1–150 kHz only has ±0.05 ppm frequency instability (during the temperature change between 0–40 °C), which gives the converter a resolution of 1 pH. As a result, the converter can be applied where low inductance measurement, nondestructive testing, impedance change measurement, and magnetic material properties measurement are important.
Molecular Diagnosis of Chlamydia pneumoniae in Coronary Heart disease Patients in Diyala City

Chlamydia pneumoniae has been highly associated with & implicated in the procoagulatory & inflammatory component of atherosclerosis. The present study has been designed to detect C. pneumoniae DNA and C. pneumoniae IgG and IgM antibodies among Iraqi patients with coronary heart disease and the association between the bacterium and risk factors. The study involved 71 patients diagnosed with (angina, myocardial infarction and atherosclerosis) and 30 healthy controls. Out from 71 patients, 28.2% were smoking, 26.8% were diabetic and 45.1% were hypertensive. Detection of antiC. pneumoniae IgM and IgG antibodies was done by Enzyme-Linked Immunosorbent assay. C. pneumoniae IgG antibodies were detected in 57.7% of patients and 14.6% of healthy controls, while seropositivity of C.pneumoniae IgM antibodies were detected in 9.9% patients. DNA were extracted from blood samples and PCR was used for detection of the C. pneumoniae genes. Thirty DNA samples of the coronary heart disease patients were amplified by PCR using three genes 16SrRNA with 194bp and outer membrane protein (OMP) and were included as internal with 239bp and outer with 499bp both specific gene for C. pneumoniae. The results of all genes revealed that 10 patients (33.3%) were positive , while it was negative for all control group.
Removal of Cu(II) from Aqueous Solutions Using Shell and Seed of Kelenkeng Fruits ( Euphoria Longan Lour )

The ability of shell and seed of Kelengkeng fruit(Euphoria longan Lour)to remove Cu(II) ion from aqueous solution were investigated. The experiment were carried out by column method. Several parameter such as the solution of pH, initial concentrations, particle size and flow rate were studied. The optimum condition for adsorption of Cu(II) were at pH 3, concentration 400 mg/L, biosorben dose 0,5 g, and particle size250 µm with adsorption capacity 3,734 mg/g for seed and 7,513 mg/ for shell, respectively
Experimental Analysis of Pneumatic Powered Air Engine

In present scenario CNC turning operation plays a vital role in metal based manufacturing industries. Therefore selection of optimum Compressed-air engine as a pneumatic actuator that converts one form of energy into another. The Air Driven Engine is an eco-friendly engine which operates with compressed air. This Engine uses the expansion of compressed air to drive the pistons of the engine. An Air Driven Engine is a pneumatic actuator that creates useful work by expanding compressed air. There is no mixing of fuel with air as there is no combustion. An Air Driven Engine makes use of Compressed Air Technology for its operation The Compressed Air Technology is quite simple. If we compress normal air into a cylinder the air would hold some energy within it. This energy can be utilized for useful purposes. When this compressed air expands, the energy is released to do work. So this energy in compressed air can also be utilized to d isplace a p iston. Compressed air propulsion may also be incorporated in hybrid systems, e.g., battery electric propulsion and fuel tanks to recharge the batteries. This kind of system is called hybrid-pneumatic electric propulsion. Additionally, regenerative braking can also be used in conjunction with this system. KEYWO RDS : AIR, CO MPRESSED, ENGINE, ENERGY, PRO PULSION, PNEUMATIC
Improving the Recognition Performance of Lip Reading Using the Concatenated Three Sequence Keyframe Image Technique

This paper proposes a lip reading method based on convolutional neural networks applied to Concatenated Three Sequence Keyframe Image (C3-SKI), consisting of (a) the StartLip Image (SLI), (b) the Middle-Lip Image (MLI), and (c) the End-Lip Image (ELI) which is the end of the pronunciation of that syllable. The lip area’s image dimensions were reduced to 32×32 pixels per image frame and three keyframes concatenate together were used to represent one syllable with a dimension of 96×32 pixels for visual speech recognition. Every three concatenated keyframes representing any syllable are selected based on the relative maximum and relative minimum related to the open lip’s width and height. The evaluation results of the model’s effectiveness, showed accuracy, validation accuracy, loss, and validation loss values at 95.06%, 86.03%, 4.61%, and 9.04% respectively, for the THDigits dataset. The C3-SKI technique was also applied to the AVDigits dataset, showing 85.62% accuracy. In conclusion, the C3-SKI technique could be applied to perform lip reading recognition. Keywords-concatenated frame images; convolutional neural network; keyframe reduction; keyframe sequence; lip reading