Miracle-dz/UniFAR-FTUs huggingface dataset:
A Simple Nano Cerium Oxide Modified Graphite Electrode for Electrochemical Detection of Formaldehyde in Mushroom

In this present work, electrochemical detection and quantification of formalin (FAL) trace present in mushroom (Agaricus bisporus) was premeditated using a cerium oxide nanoparticle modified graphite paste electrode (CeO<sub>2</sub>@GP). Cerium oxide (CeO<sub>2</sub>) nanoparticles (nps) were synthesized through the sol-gel technique from cerium nitrate hexahydrate using poly (ethylene glycol) as a capping agent. The prepared CeO<sub>2</sub> nps were characterized using X-ray diffraction (XRD) and transmission electron microscopy (TEM) techniques which revealed the successful formation of the cubic phase of CeO<sub>2</sub> having crystallite size 4.84 nm. The prepared CeO<sub>2</sub> nps were used to modify the graphite paste electrode (bare GP). Cyclic voltammetry (CV) and differential pulse voltammetry (DPV) techniques were utilized to study comparative electroanalytical features of the fabricated electrodes. Under optimized experimental conditions, CeO<sub>2</sub>@GP exhibited a wide linear range from <inline-formula> <tex-math notation="LaTeX">$25~\mu \text{M}$ </tex-math></inline-formula>-1mM and a limit of detection of <inline-formula> <tex-math notation="LaTeX">$1~\mu \text{M}$ </tex-math></inline-formula>. Moreover, CeO<sub>2</sub>@GP featured high repeatability, reproducibility, and long-term stability. The electrode exhibited high selectivity for FAL in the presence of interferences like ethanol, methanol, formic acid, benzaldehyde, and acetone. CeO<sub>2</sub>@GP demonstrated exceptional aptitudes in electrochemical behavior when subjected to mushroom extract.
The Relation Between Precursor Gas Flows, Thickness Dependent Material Phases, and Opto-Electrical Properties of Doped a/nc-SiOX≥0:H Films

Doped layers are a determining factor for the performance of photovoltaic devices such as silicon heterojunction and thin film silicon solar cells. The material properties of doped hydrogenated amorphous/nanocrystalline silicon-oxide (a/nc-SiOX≥0:H) films processed by plasma-enhanced chemical vapor deposition generally exhibit a tradeoff between optical and electrical performance. The optoelectrical properties are the result of different material phases in these heterogeneous films, such as hydrogenated amorphous silicon and silicon-oxide tissue, nanocrystalline silicon grains, their corresponding fractions and extent of doping. In this article, all the precursor gas flows are varied to achieve a wide range of doped a/nc-SiOX≥0:H phases. A material phase diagram is introduced to clarify the complex interplay between processing conditions, dominant growth mechanisms, a/nc-SiOX≥0:H phases, and the resulting optoelectrical properties. In addition, it is discussed that material properties are strongly dependent on the thickness of the films, as the mix of different material phases is not uniform along the growth direction.
An Intelligent Multi-Sourced Sensing System to Study Driver’s Visual Behaviors

Understanding how changes in visual and attentional behaviors impact driving as we age is still a subject studied by the research community. However, little attention has been paid to using sensing and AI techniques to conduct such studies. We present a multi-sourced intelligent sensing system that infers the visual point of attention (VPoA) associated with five vehicle’s cockpit zones with an accuracy of 98%. The VPoA is inferred from the pitch, yaw, and roll angles of head movements captured with inertial sensors and a facial recognition application. The system also includes a tablet-based application that automatically collects data from the driving context, e.g., speed and location. It also enables an annotator to add observed drivers’ actions, e.g., interactions with a passenger. We conducted a naturalistic study with 15 younger adults and 15 older adults to demonstrate the system’s efficacy to identify visual behavior patterns similar to those identified in previous studies that have used traditional data collection methods. A new finding is that the younger group looks more frequently at the lap than the elderly group, independently if a passenger was present. The Lap was the VPoA associated with using the cellphone.
A Soft Switching Interleaved High Step-down Converter with Low Voltage Stress

This paper proposes a zero-voltage transition (ZVT) PWM high step-down converter. The proposed converter consists of a high step-down interleaved buck converter (IBC) and an auxiliary circuit with only one switch. The auxiliary circuit provides zero voltage switching (ZVS) condition for the IBC switches, while zero current switching condition is achieved for the auxiliary switch and diodes. Due to utilizing a high step-down IBC, the voltage gain of the converter is much less than the conventional IBC. The proposed converter is analyzed, and its operating principles and design procedure are presented. In order to verify the analysis, the simulation results of a 120W prototype converter operating at 100kHz are reported.
Structural Health Fuzzy Classification of Bridge based on Subjective and Objective Inspections

Bridge structural health monitoring (BSHM) and management system is an aid for systematized logistics and management for bridge assessment and recondition. BSHM is inherently formed by visual, advanced, and non-destructive technical inspections. Optimal criterion per parameter is one of the crucial actions in classifying the current condition of bridges. Inconsistency and variation of fundamental parameters makes the process challenging. But there are dominant efforts in determining the preeminent parameters that can only be done after diverse and recursive experiments. On the other side, most prevalent structure condition classification methods are morphologically and linguistically rated, showing impreciseness and uncertainties in evaluations. This paper proposed a new fuzzy system assumed or based on both the objective assessment and subjective approach, along with the optimum value of parameters based on reconstructed data. The best possible fixed and variable parameters of the system's model will be designated as the input for the fuzzy model with membership functions utilizing the concept of the statistical distributions and cognitive limitations. Fundamental arithmetic rules of the fuzzy expert system made the the condition rating of the fuzziness of the system. fuzzy inference systems being one of the established structures for noise tolerance (uncertain and unprecise). The proposed system can be a leading- edge technique for current structural health classification for bridges.
Fair Subcarrier Allocation for Securing OFDMA in IoT Against Full-Duplex Hybrid Attacker

Secure communication with low computational resources is a critical issue in the Internet-of-Things (IoT) implementations. It is more challenging in the presence of hybrid adversary enabled with full-duplex (FD) capability to perform eavesdropping and jamming simultaneously. In this work we aim to address this issue through optimal subcarrier allocation towards combating the FD hybrid attacker. We begin with secrecy performance analysis in a multi-user IoT system considering statistical channel state information only of all attacker links. Novel analytical expression for the exact intercept probability is derived and a closed-form approximation is also provided. We further propose an optimisation framework for fair subcarrier allocation with a novel objective of minimising maximum intercept probability among multiple users. Considering the proposed optimisation framework as a non-convex combinatorial, we propose a low-complexity sub-optimal solution by leveraging the integer linear program (ILP) structure of the problem. To reduce the complexity further, the original problem is mapped to the assignment model and solved by exploiting its special structure with graph theory tools providing an optimal solution in polynomial time. Comprehensive investigations, conducted to verify the analysis and quantify the secrecy performance, demonstrate that proposed optimal solutions yield significant enhancement in secrecy performance over relevant schemes.
Charge Conduction in Polymer Dielectrics: Theoretical Perspectives and First-Principle Approaches

This review highlights the updated picture of charge transport in polymer dielectrics and offers suggestions for future research. It begins with the backgrounds of the various theoretical charge transport models and the underlying concepts of the physical quantities. The techniques for deriving the microscopic physical quantities, e.g., the Marcus parameters and the spectral density of bath, from first-principles methods are outlined for tutorial purposes. Our recent multi-scale simulation studies are presented to demonstrate how and why the macroscopic charge transfer behavior can be predicted from these microscopic quantities, without the use of phenomenological/ empirical models nor ad hoc parameters. Some new insights gained through the multiscale modeling technique are summarized, with a focus on the relation between the chemical structure of polymers and the carrier transport properties. Finally, what is further required for computer-aided (rational) dielectric polymer design is discussed.
Wi-Fi Localization Enabling Self-Calibration

Channel state information (CSI) based Wi-Fi localization can achieve admirable decimeter-level accuracy; however, such systems require labor-intensive site survey to calibrate the AP position and the antenna array direction, which hinders practical large-scale deployment. In this article, we reveal an interesting finding that the calibration efforts for deploying the CSI localization system can be significantly reduced by simply replacing the ordinary linear antenna layout of the AP with the non-linear layout. In particular, we first present an autonomous self-calibrating method to significantly facilitate site survey for deploying CSI localization systems. Then we propose a systematical evaluation mechanism to show the fundamental reason why linear antenna layout usually leads to serious errors and why non-linear antenna layout is better off. Finally, we build a testbed with COTS devices and conduct comprehensive experiments. Results show that triangular antenna layout can achieve 80% angle of arrival (AoA) measurement error within 9° for any direction in contrast to 16° based on linear antenna layout. Moreover, we can realize promising localization accuracy as previous works even without labor-intensive site survey, where 80% localization error is within $0.60m$ .
False Data Injection Attacks Against State-of-Charge Estimation of Battery Energy Storage Systems in Smart Distribution Networks

Advancement of battery energy storage systems (BESSs) has made BESSs typical cyber-physical systems (CPSs), which exposes BESSs, especially for the state of charge (SoC) estimation function, to severe cyber attacks. This article investigates the vulnerability of SoC estimation of BESSs in smart distribution networks (SDNs) to false data injection attacks (FDIAs) to provide a basis to study the attack mechanisms against BESSs and a theoretical guide to develop countermeasures. The mechanism of FDIAs against SoC estimation is investigated by theoretically analyzing the SoC estimation errors caused by compromised measurements, and a static FDIA model targeting one snapshot of SoC estimation is formulated. Considering temporal correlation among measurements, a detection method using the innovation test is proposed for static FDIAs, where the innovations are derived statistically. Considering the error accumulation effect, a novel sequential FDIA is proposed, which consists of a sequence of static FDIAs with small magnitudes. They can bypass most of the existing bad data detection algorithms, including the innovation test, with significant attack effects. An online approach is proposed for the practical construction of sequential FDIAs, which is formulated as a linear programming problem. Case studies based on modified IEEE 13 bus test feeder demonstrate the vulnerability of SoC estimation to FDIAs.
Data-Aided Iterative Algorithms for Linearizing IM/DD Optical Transmission Systems

DSP-enhanced intensity-modulation direct-detection (IM/DD) systems can support up to 56 Gb/s over 100 km signal transmissions at C-band. To achieve higher data rates and longer transmission distances, we propose data-aided iterative algorithm (DIA) and decision-directed DIA (DD-DIA) to digitally mitigate signal-signal beating interference (SSBI) without requiring any modifications to physical layer structures. DIA utilizes pilot symbols with uniformly spaced insertions to relax the modified Gerchberg-Saxton (G-S) algorithms that suffer from the local optimum problem. To further improve the symbol error rate (SER) performance and convergence speed, DD-DIA introduces a decision process to generate pseudo-pilots. We numerically compare DIA with other algorithms and find that DIA can recover signals subject to large fiber dispersions corresponding to which conventional IA and Volterra filter (VF) fail, while DD-DIA significantly accelerates the convergence speed and improves the reconstruction performance compared with DIA, it can support 100-Gb/s PAM4 over 400-km IM/DD transmissions within just 50 iterations. Two orders of magnitude reductions in SER is observed for 100 Gb/s PAM4 signal transmission over 100-km SSMFs. Compared with conventional IA, the proposed techniques have higher convergence speeds, better global optimum features and large tolerances to physical model errors. In particular, DD-DIA results in larger optical signal-to-noise ratio (OSNR)/ received optical power (ROP) improvement, higher transmission capacities and less computational complexity. DD-DIA is a promising algorithm for efficiently reconstructing 2-dimensional optical field for conventional IM/DD optical transmission systems.
A Study on Compact Microstrip Antenna Design for Advanced Wireless Applications

A study on compact antennas for fifth generation wireless communication, which operates at K and Ka band frequencies are discussed in this paper. The antenna miniaturization is achieved by making simple rectangular slots on the patch. The overall size of the antenna is $6.3\ \text{mm} \times 6.0\ \text{mm}$. The proposed patch size is $4.6\text{mm}\times 3.6\ \text{mm}$, which means a notable miniaturization ratio has been achieved. The discussed antennas are designed on RogersRT5880 substrate considering a thickness of 0.787mm. The maximum gain of 7.43 dB at 24.1GHz is achieved for antenna I. The presented antennas are designed and simulated using High Frequency Structure Simulator (HFSS).
Online Assessment of Conservation Voltage Reduction Effects With Micro-perturbation

Online assessment of conservation voltage reduction (CVR) has great effects on the performance of CVR-based applications, especially for real-time CVR-based controls. The challenges of online CVR assessment mainly come from time-varying load composition and inevitable noise. Besides, the dynamic process resulting from motors further complicates this problem. This article proposes a micro-perturbation based load-to-voltage (LTV) process identification method to online assess the CVR effects. In particular, we construct a well-designed pseudo-random-binary-sequence voltage perturbation to excite the CVR process continually and fully. With the experimental data, the CVR factor and transfer function of LTV are estimated by the cross-correlation method, which can reflect both steady-state and dynamic CVR effects without being affected by noise. The proposed method is verified on a specific hardware-in-the-loop testbed under various cases. In addition, through the energy-saving and power smoothing applications, the impact of CVR assessment on performance of CVR-based applications is also analyzed, which further verify the validity and economic benefits of the proposed assessment method.
The Wiley Encyclopedia of Health Psychology

What drives a compelling story? What pushes people to tune in each week to watch a story unfold on their televisions? Shonda Rhimes, an award‐winning writer, producer, and creator of multiple widely successful television shows such as Grey’s Anatomy and Scandal, revealed her secret for creating a narrative that millions of people around the world want to follow each week: writing about loneliness. Her answer also tapped into the idea that humans have a “need to belong”—a fundamental need or drive to seek out and maintain social relationships. It has been suggested that this desire to connect with others is as essential to our existence as other basic needs such as food, water, and shelter, providing us with advantages for survival (Baumeister & Leary, 1995). If being social is such a crucial aspect of being human, then we would expect that not having social relationships would be associated with disadvantages and detrimental outcomes for our health and survival. Indeed, decades of research support the notion that lacking social connections has consequences for physical and mental health, including a significantly higher risk for all‐cause mortality. In this chapter, we first briefly discuss measurement issues relevant to social isolation and then highlight health outcomes associated with social isolation. We follow this by a discussion of mechanisms that have been proposed to explain the relationship between social isolation and health, as well as consideration of interventions to reduce social isolation.
Resistance Response of Tomato Cultivars and Rootstocks Carrying the Mi-1.2 Gene to Isolates of Meloidogyne arenaria, M. incognita, and M. javanica at Two Different Growing Periods

The reaction of four tomato cultivars and four rootstocks, carrying the Mi-1.2 gene, to different isolates of Meloidogyne arenaria (4 isolates), M. incognita (2 isolates), and M. javanica (2 isolates) were determined in pot experiments conducted in a non-temperature controlled greenhouse. Tomato cultivars and rootstocks were screened by PCR-based co-dominant SCAR marker Mi23 for the zygosity states of the Mi-1 locus. All resistant tomato cultivars and the rootstock ‘Comfort’ were classified as heterozygous resistant (Mimi), whereas the rootstocks ‘Arazi’, ‘Beaufort’, and ‘Kingkong’ were homozygous resistant (MiMi) at the Mi-1 locus. The response of resistant genotypes to isolates of Meloidogyne was assessed 8 and 16 weeks after inoculation with 3,000 nematode eggs for short and long growing periods, respectively. Nematode reproduction on resistant cultivars was similar or higher than on resistant rootstocks for all tested Meloidogyne isolates in the short period experiment (p ˂ 0.05), but not in the long period experiment. In both experiments, all tomato cultivars and rootstocks responded as highly resistant (reproduction index ˂ 10%) to all tested Meloidogyne isolates except for M. incognita A-11. Of the resistant genotypes inoculated with M. incognita A-11, in both experiments, the cultivars ‘Crisol’ and ‘Adamset’ were moderately resistant, whereas the rootstocks ‘Beaufort’ and ‘Comfort’ were highly resistant. ‘Alsancak’, ‘Esin’, ‘Arazi’, and ‘Kingkong’ showed reduced resistance in the long growing period and responded as moderately resistant to M. incognita A-11. These findings showed that the resistance response of tomato cultivars and rootstocks to Meloidogyne isolates varies depending on the isolates of Meloidogyne or the length of the growing period. Additional key words: management, PCR, reproduction, root-knot nematodes, Solanum lycopersicum L.
Estimation of Batteries Voltages and Resistances in Modular Multilevel Converter With Half-Bridge Modules Using Modified PSO Algorithm

This research is focused on estimation of the batteries voltages and resistances in a modular multilevel converter (MMC) with half-bridge topology using particle swarm optimization (PSO). This estimation can be helpful to reduce the cost of MMCs by removing the voltage sensors required for the modules. In this paper, the concept of PSO is introduced, modified, and applied for estimation of the batteries voltages and resistances in the MMC modules. Half-bridge modules are considered in this investigation for the MMC. Different settings of PSO have been considered and the simulation results show that such estimation can be effectively realized by the modified PSO algorithm.
Multi-objective Optimization of a Permanent Magnet Synchronous Motor for Gearless Elevator

Permanent magnet synchronous machine (PMSM) benefits from high efficiency, and high torque/power densities caused by PMs. These merits attract a great deal of attention in various applications like low-speed-direct drives. For instance, surface-mounted PMSM has been proposed for gearless elevator systems. In this paper, a surface-mounted PMSM using rare-earth magnets is designed and optimized by Taguchi optimization method considering two objectives including the maximum back-EMF and the minimum cogging torque points of view. As a multi-objective design is more difficult than a single objective one, the optimization may encounter with conflict. As a solution, a decision-making algorithm (DMA) using technique for order of preference by similarity to ideal solution (TOPSIS) is employed. The effectiveness of this method is evaluated by electromagnetic performance investigation via finite element method (FEM). It is shown that the cogging torque of the proposed optimum design of surface mounted PMSM is minimized at 0.61Nm, whereas, its maximum rms value of back-emf equals to 219.33V.
An Efficient Practical Non-Blocking PageRank Algorithm for Large Scale Graphs

PageRank algorithm is a benchmark for many graph analytics and is the underlying kernel for link predictions, recommendation systems. It is an iterative algorithm that updates ranks of pages until the value converges. Implementation of PageRank algorithm on a shared memory architecture while taking advantage of fine-grained parallelism using large-scale graphs is a challenging task. In this paper, We present parallel algorithms for computing the PageRank suitable to the shared memory systems. Initially, we present parallel implementations of page-rank algorithms using barrier and lock variants. Later, we propose new approaches which are lock-free and are barrier-less synchronization to overcome the issues of lock based methods.A detailed experimental analysis of our approach is carried out using real-world web graphs from SNAP and Synthetic Graphs from RMAT on an Intel(R) Xeon E5-2660 v4 processor architecture with 56 threads using the POSIX thread library.
A New Method for Peer Matching and Negotiation of Prosumers in Peer-to-Peer Energy Markets

This article presents a scalable mechanism for peer-to-peer (P2P) energy trading among prosumers in a smart grid. In the proposed mechanism, prosumers engage in a non-mediated negotiation with their peers to reach an agreement on the price and quantity of energy to be exchanged. Instead of concurrent bilateral negotiation between all peers with high overheads, an iterative peer matching process is employed to match peers for bilateral negotiation. The proposed negotiation algorithm enables prosumers to come to an agreement, given that they have no prior knowledge about the preference structure of their trading partners. A greediness factor is introduced to model the selfish behavior of prosumers in the negotiation process and to investigate its impact on the negotiation outcome. In order to recover the costs related to power losses, a transaction fee is applied to each transaction that enables the grid operator to recover incurred losses due to P2P trades. The case studies demonstrate that the proposed mechanism discourages greedy behavior of prosumers in the negotiation process as it does not increase their economic surplus. Also, it has an appropriate performance from the computation overheads and scalability perspectives.