Miracle-dz/UniFAR-FTUs huggingface dataset:
The impact of two‐sided ultraviolet radiation and long‐term freezing on quality of date fruit at rutab stage

Pre‐packed rutab date fruit in polypropylene containers were exposed to long‐term freezing and two‐sided UV‐C radiation. During 42 days shelf life, the microbial load of long‐term frozen samples which were UV radiated, was 2.4–2.6 compared to 3.1 log CFU/g of control. This treatment combination also slowed down the rate of surface browning and flesh darkening as the basic quality attribute of rutab date for consumer perception. Against fruit Total soluble solids (TSS) that did not fallow a certain trend, fruit acidity increased by 3.7–5.9 times (from 8.1 mg/100 g). Total phenolic content (TPC) dropped gradually from the initial value of 1,247 mg GA/Kg FW and after 30 days, the loss in TPC for control samples was almost two times of those fruits that received both freezing and UV treatments. Totally, best quality of rutab date in terms of appearance, taste, and nutritional value were recorded in fruits that received both freezing and UV‐C radiation. PRACTICAL APPLICATIONS: For many consumers, rutab stage is considered as the most delicious and popular form of date fruit. The main properties of rutab date, that is, having high level of sugar and water content, makes it very vulnerable to microbial contamination. On the other side, the fruit soft texture makes it difficult to be washed or disinfected. The results of this work reveals that the conventional freezers can be used as an available and effective method of keeping rutab fruit quality for long term. Even though the antimicrobial effects of UV radiation have been reported frequently, but the used apparatus for applying UV radiation from both sides on the pre‐packed fruits can be easily redesigned to equip the commercial packing lines of date fruit.
Managing construction supply chains: the common sense approach

This paper will provide an introduction to the argument that there is considerable evidence of poor thinking within the construction industry. The failure to understand the circumstances that are facing industry players will prevent clients, contractors and suppliers from achieving their own objectives. The current problems are further compounded by the advice espoused by the government‐sponsored industry reports advocating generic approaches. In response to these problems the paper will provide practitioners with a theoretical framework for understanding: the structure of the industry and its constituent supply chains; the attributes of buyer and supplier power; the appropriateness of certain relationships according to the firm's power position within the construction supply chain; and, the circumstances where the recent industry initiatives and an integrated supply chain approach may be implemented with success.
Similarities between the tropical Atlantic seasonal cycle and ENSO: An energetics perspective

[1] The tropical Pacific and Atlantic Oceans have similar mean states - easterly winds and a zonally sloping thermocline which shoals in the east - but strikingly different sea surface temperature variability. Seasonal and interannual variations have comparable amplitudes, and correspond to different modes of oscillation in the Pacific. In the Atlantic, the seasonal cycle is dominant and has properties of both those modes. The Bjerknes feedback between the wind and SST, and its associated delayed, negative feedback constitute a free (interannual) mode of the Pacific, but in the Atlantic influence the seasonal cycle. This difference between the two oceans is attributable to the smaller dimensions of the Atlantic. An energetic analysis shows a circular relationship between available potential energy and the work done by the wind from April until September in the Atlantic. This energetics perspective suggests that a seasonally excited thermocline mode of coupled variability plays an active role in the seasonal cycle during these months, after which point seasonal forcing regains control.
Mining on Big Data Using Hadoop MapReduce Model

Customary parallel calculations for mining nonstop item create opportunity to adjust stack of similar data among hubs. The paper aims to review this process by analyzing the critical execution downside of the common parallel recurrent item-set mining calculations. Given a larger than average dataset, data apportioning strategies inside the current arrangements endure high correspondence and mining overhead evoked by repetitive exchanges transmitted among registering hubs. We tend to address this downside by building up a learning apportioning approach referred as Hadoop abuse using the map-reduce programming model. All objectives of Hadoop are to zest up the execution of parallel recurrent item-set mining on Hadoop bunches. Fusing the comparability metric and furthermore the locality-sensitive hashing procedure, Hadoop puts to a great degree comparative exchanges into an information segment to lift neighborhood while not making AN exorbitant assortment of excess exchanges. We tend to execute Hadoop on a 34-hub Hadoop bunch, driven by a decent change of datasets made by IBM quest market-basket manufactured data generator. Trial uncovers the fact that Hadoop contributes towards lessening system and processing masses by the uprightness of dispensing with excess exchanges on Hadoop hubs. Hadoop impressively outperforms and enhances the other models considerably.
The autonomous clean environmental (ACE) sampler: A trace‐metal clean seawater sampler suitable for open‐ocean time‐series applications

The fundamental role of the micronutrient Fe in controlling phytoplankton growth in large parts of Earth's oceans and a lack of information on seasonal transitions in remote regions motivated us to create an autonomous water sampler capable of collecting uncontaminated open‐ocean seawater samples with monthly resolution over a full annual cycle. Phytoplankton are at the base of the food chain, which take up carbon dioxide through photosynthesis. Assessing Fe availability is essential to understanding both ocean productivity and our climate. This need is particularly important in the Southern Ocean, where Fe limitation is wide spread and access is difficult especially in winter. To address this need, we have developed an autonomous system capable of observing iron concentrations over a full seasonal cycle, at the subnanomolar concentrations that are important in the open ocean. The automated clean environmental sampler has been developed initially for 1‐yr deployments on oceanographic moorings. Twelve samples per unit can be programmed to collect 65 mL of seawater through a noncontaminating, primarily Teflon sample path. The system is vaned to maintain its intake tubes in upstream water and has been tested to 100 m depth. The system was deployed during a GEOTRACES section (SR3 line, GS01) in 2018 alongside an industry standard, trace metal clean rosette, and no significant difference was found in Fe concentrations in the 50 pmol L–1 range, proving its capability at collecting uncontaminated seawater samples in the open ocean at temperatures above 0°C.
A Comparison Between Moodle, Facebook, and Paper-based Assessment Tools: Students' Perception of Preference and Effect on Performance

Literature abounds with research on the benefits of technology and its effect on students’ performance and satisfaction. The use of Learning Management Systems (LMS) such as blackboard, eCollege, Moodle and others are being used throughout the educational sector. Some even incorporate such technology to measure teaching effectiveness and student performance. Furthermore, social websites, like Facebook.com, have become so popular that 85 to 99% of college students use it in one form or another (Hargittai, 2007; Jones & Fox, 2009; Matney & Borland, 2009). This research explores the students’ perspective on how the effectiveness of tools such as Moodle and Facebook.com as well as paper based learning (traditional learning) on their performance. The paper also looks at different factors, such as gender, major type and age to try to shed light on the preferred tool among students to use for education. When it comes to students’ tool preference based on age and gender, we found no significant difference but when looking at students’ majors and time spent in university, we found that Moodle was rated the top, followed by paper-based learning, then Facebook. When viewing students’ perception on effect of these tools on performance, we found no statistical significant difference by age or time spent in the university but did find a significant difference among different genders; where male students showed greater satisfaction than did female students, and statistical significant different among majors.
Application of a non‐linear k‐ε model in prediction of convective heat transfer through ribbed passages

A numerical investigation has been undertaken to study fluid flow and heat transfer through artificially rib‐roughened channels. Such flows are of particular interest in internal cooling of advanced gas turbine blades. The main objective is to test the suitability of recently developed variants of the cubic non‐linear k‐e model for the prediction of cooling flows through ribbed passages. The numerical approach used in this study is the finite‐volume method together with the SIMPLE algorithm. For the modelling of turbulence, the Launder and Sharma low‐Re k‐e model and a new version of the non‐linear low‐Re two equation model that have been recently shown to produce reliable thermal predictions in impinging jet flows and also flows through pipe expansions, have been employed. Both models have been used with the form of the length‐scale correction term to the dissipation rate originally proposed by Yap and also more recently developed differential version, NYap. The numerical results over a range of flow parameters have been compared with the reported experimental data. The mean flow predictions show that both linear and non‐linear k‐e models with NYap can successfully reproduce the distribution of the measured streamwise velocity component, including the length and width of the separation bubble, formed downstream of each rib. As far as heat transfer predictions are concerned, the recent variant of the non‐linear k‐e leads to marked improvements in comparison to the original version of Craft et al. Further improvements in the thermal prediction result through the introduction of a differential form of the turbulent length scale correction term to the dissipation rate equation. The version of the non‐linear k‐e that has been shown in earlier studies to improve thermal predictions in pipe expansions and impinging jets; it is thus found to also produce reasonable heat transfer predictions in ribbed passages.
Comparison of α-Helix and β-Sheet Structure Adaptation to a Quantum Dot Geometry: Toward the Identification of an Optimal Motif for a Protein Nanoparticle Cover

While quantum dots (QDs) are useful as fluorescent labels, their application in biosciences is limited due to the stability and hydrophobicity of their surface. In this study, we tested two types of proteins for use as a cover for spherical QDs, composed of cadmium selenide. Pumilio homology domain (Puf), which is mostly α-helical, and leucine-rich repeat (LRR) domain, which is rich in β-sheets, were selected to determine if there is a preference for one of these secondary structure types for nanoparticle covers. The protein sequences were optimized to improve their interaction with the surface of QDs. The solubilization of the apoproteins and their assembly with nanoparticles required the application of a detergent, which was removed in subsequent steps. Finally, only the Puf-based cover was successful enough as a QD hydrophilic cover. We showed that a single polypeptide dimer of Puf, PufPuf, can form a cover. We characterized the size and fluorescent properties of the obtained QD:protein assemblies. We showed that the secondary structure of the Puf proteins was not destroyed upon contact with the QDs. We demonstrated that these assemblies do not promote the formation of reactive oxygen species during illumination of the nanoparticles. The data represent advances in the effort to obtain a stable biocompatible cover for QDs.
Data, information, and knowledge-driven manipulation between strategical planning and technical implementation for wireless sensor network construction

To provide guidance to and integrate directly with the information technology side implementation to achieve coherent data, information, and knowledge coordination and robust value-oriented adaptability for maximization of business profitability, we propose to leverage the ideology of service economics to achieve bidirectional computational bridging between business planning and technical implementation of ubiquitous services/XaaS in the application program interface economy for promoting the macro-service market, especially in global value chains. Enlightened by value-driven design, we propose a systemic formalization from value calculation to design quality measurement which binds the technical modification and variation on design artifacts with business value strategy through a framework of managed quality properties in a wireless sensor network–based service system creation process. In the process of building a service system, we use data, information, and knowledge flow to abstract various data, information, and knowledge manipulation and usage scenarios. We propose to improve system reliability and robustness by managing data and information reuse, redundancy as well as structure.
Introducing Quantum Cryptography Based Secured Encryption and Decryption Algorithms

With the expansion in electronic communication, the significance of cryptography is apparently increasing every year. Encryption is a cryptography technique to send unintelligible information and decryption to restore the information. These techniques usually undergo difficulty with eavesdropping of plaintext and ciphertext. The conventional cryptography is vulnerable to attacks using high computational resources. Necessarily, an elegant concept of Quantum cryptography based on laws of quantum mechanics is introduced to offer secure and private communication. This paper gives a method for encryption and decryption using Toffoli quantum gate named as VBA Quantum Encryption and Decryption Algorithms. The incorporation of gate renders security which acts as a cumbersome to eavesdropping attack. Besides, a way to detect known plaintext, ciphertext only and chosen plaintext attack through public discussion is explained.
Deep-Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks

This paper investigates a deep reinforcement learning (DRL)-based MAC protocol for heterogeneous wireless networking, referred to as a Deep-reinforcement Learning Multiple Access (DLMA). Specifically, we consider the scenario of a number of networks operating different MAC protocols trying to access the time slots of a common wireless medium. A key challenge in our problem formulation is that we assume our DLMA network does not know the operating principles of the MACs of the other networks—i.e., DLMA does not know how the other MACs make decisions on when to transmit and when not to. The goal of DLMA is to be able to learn an optimal channel access strategy to achieve a certain pre-specified global objective. Possible objectives include maximizing the sum throughput and maximizing <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>-fairness among all networks. The underpinning learning process of DLMA is based on DRL. With proper definitions of the state space, action space, and rewards in DRL, we show that DLMA can easily maximize the sum throughput by judiciously selecting certain time slots to transmit. Maximizing general <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>-fairness, however, is beyond the means of the conventional reinforcement learning (RL) framework. We put forth a new multi-dimensional RL framework that enables DLMA to maximize general <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>-fairness. Our extensive simulation results show that DLMA can maximize sum throughput or achieve proportional fairness (two special classes of <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>-fairness) when coexisting with TDMA and ALOHA MAC protocols without knowing they are TDMA or ALOHA. Importantly, we show the merit of incorporating the use of neural networks into the RL framework (i.e., why DRL and not just traditional RL): specifically, the use of DRL allows DLMA (i) to learn the optimal strategy with much faster speed and (ii) to be more robust in that it can still learn a near-optimal strategy even when the parameters in the RL framework are not optimally set.
Conductive MXene Nanocomposite Organohydrogel for Flexible, Healable, Low‐Temperature Tolerant Strain Sensors

Conductive hydrogels are attracting tremendous interest in the field of flexible and wearable soft strain sensors because of their great potential in electronic skins, and personalized healthcare monitoring. However, conventional conductive hydrogels using pure water as the dispersion medium will inevitably freeze at subzero temperatures, resulting in the diminishment of their conductivity and mechanical properties; meanwhile, even at room temperature, such hydrogels suffer from the inevitable loss of water due to evaporation, which leads to a poor shelf‐life. Herein, an antifreezing, self‐healing, and conductive MXene nanocomposite organohydrogel (MNOH) is developed by immersing MXene nanocomposite hydrogel (MNH) in ethylene glycol (EG) solution to replace a portion of the water molecules. The MNH is prepared from the incorporation of the conductive MXene nanosheet networks into hydrogel polymer networks. The as‐prepared MNOH exhibits an outstanding antifreezing property (−40 °C), long‐lasting moisture retention (8 d), excellent self‐healing capability, and superior mechanical properties. Furthermore, this MNOH can be assembled as a wearable strain sensor to detect human biologic activities with a relatively broad strain range (up to 350% strain) and a high gauge factor of 44.85 under extremely low temperatures. This work paves the way for potential applications in electronic skins, human−machine interactions, and personalized healthcare monitoring.
Brain tissue analysis using texture features based on optical coherence tomography images

Brain tissue differentiation is highly demanded in neurosurgeries, i.e. tumor resection. Exact navigation during the surgery is essential in order to guarantee best life quality afterwards. So far, no suitable method has been found that perfectly covers this demands. With optical coherence tomography (OCT), fast three dimensional images can be obtained in vivo and contactless with a resolution of 1-15 μm. With these specifications OCT is a promising tool to support neurosurgeries. Here, we investigate ex vivo samples of meningioma, healthy white and healthy gray matter in a preliminary study towards in vivo brain tumor removal assistance. Raw OCT images already display structural variations for different tissue types, especially meningioma. But, in order to achieve neurosurgical guidance directly during resection, an automated differentiation approach is desired. For this reason, we employ different texture feature based algorithms, perform a Principal Component Analysis afterwards and then train a Support Vector Machine classifier. In the future we will try different combinations of texture features and perform in vivo measurements in order to validate our findings.
Bandwidth Enhancement and Generation of CP of Yagi-Uda-Shape Feed on a Rectangular DRA for 5G Applications

A wideband circularly polarized rectangular dielectric resonator antenna (DRA) fed by a single feeding mechanism has been studied theoretically and experimentally. The purpose of the study is to determine how adding a parasitic strip next to the flat surface metallic feed would affect various far- and near-field antenna characteristics. Initially, the basic antenna design, i.e., the T-shape feed known as antenna A, produced a 4.81% impedance matching bandwidth (|S11| −10 dB). Due to the narrow and undesirable results of the initial antenna design, antenna-A was updated to the antenna-B design, i.e., Yagi-Uda. The antenna-B produced a decent result (7.89% S11) as compared to antenna-A but still needed the bandwidth widened, for this, a parasitic patch was introduced next to the Yagi-Uda antenna on the rectangular DRA at an optimized location to further improve the results. This arrangement produced circular polarization (CP) waves spanning a broad bandwidth of 28.21% (3.59–3.44 GHz) and a broad impedance |S11| bandwidth of around 29.74% (3.71–3.62 GHz). These findings show that, in addition to producing CP, parasite patches also cause the return loss to rise by a factor of almost three times when compared to results obtained with the Yagi-Uda-shape feed alone. Computer simulation technology was used for the simulation (CST-2017). The planned antenna geometry prototype was fabricated and measured. Performance indicators show that the suggested antenna is a good fit for 5G applications. The simulated outcomes and measurements match up reasonably.
Developing a Time-Based Evaluation Method for Functional Exercises of Emergency Medical Operations

Public health service is one of the most important sectors in terms of saving lives. During a disaster, hospitals and medical groups implement extension tasks from their daily activities. Enhancing coordination across organizations contributes to the removal of communication barriers. Functional exercises are simulated trainings for emergency responders that aim to enhance coordination capabilities. The application of time elements in exercise evaluation methods is a significant area of potential research. We develop methods to quantitatively analyze time spent on completing unit tasks in functional exercises. This study focuses on analyzing observed time data in two functional exercises of the Disaster Medical Operation Center in Kitakyushu, which were repeated in October and November 2015. We employed a censored regression method to analyze the time spent on both complete and incomplete unit tasks together. Differences in processing time for 39 tasks, which were repeated in the two exercises, are visually inspected. Benefits of time study in the evaluation of exercises are presented.
A Review on Nanotechnology: Analytical Techniques Use and Applications

The combination of nanotechnology with molecular biology, information technology and instrumentation, is opening the door to a new industrial age. The aim of this review article is to summarize the current knowledge of nanotechnology in synthesizing, identifying and characterization of nanomaterials using analytical techniques. Physical and chemical approach synthesis of nanomaterials befalls challenges in the development of analytical techniques used to characterize them. The major techniques include: Transmission Electron Microscopy, Scanning Electron Microscopy, Atomic Force Microscopy, Dynamic Light Scattering, X ray Photoelectron Spectroscopy, X-ray Diffraction, Single Phase Inductively Coupled Plasma Mass Spectroscopy, X ray Fluorescence Spectroscopy, Auger Electron Spectroscopy, X ray Absorption Fine Structure, Capillary Electrophoretic Separations, Magnetic nano particles coupled HPLC and Dynamic light scattering. Nanomaterials have been characterized for the extensive potential applications in optics, electronics, magnetics, and catalysts; chemical sensing, biomedicine, micro reactor, and they have been applied in food, biological, environmental and pharmaceuticals uses. In spite of the extended use of nano particles in diverse consumer products, there is a great concern over the unexpected impact or effects on humans due to exposure.