필립 풀먼(Philip Pullman)의 􋺷황금 나침반􋺸은 판타지와 디스토피아를 결합하여 상상력을 전체주의에 맞서는 힘으로 탐구한다. 소설의 중심에는 전체주의적 교회를 무너뜨리려는 라이라(Lyra)의 여정이 있으며, 이는 저항을 상징한다. 영혼의 화 신인 다이몬(daemons), 사랑과 의식을 상징하는 먼지 입자(dust) 등과 같은 환상적 장치들은 작품을 풍부하게 만들며, 억압에 맞서는 인간 정신의 반항을 은유적으로 묘사 한다. 풀먼의 소설은 청소년 디스토피아 소설의 보다 광범위한 주제와 공명하며, 급속 한 기술 및 생명공학 발전에 대한 불안감을 포착한다. 이 비평의 핵심은 풀먼의 허구 적 세계가 현대의 문제들을 어떻게 반영하고, 젊은 주인공들의 정의와 공정함을 향한 여정을 통해 상징적으로 해결하고 있는지를 조명하는 데 있다.

Gold nanoparticles (Au NPs) decorated carbon nanofibers (CNFs) have been prepared by an electrospinning approach and then carbonized. The prepared Au-CNFs were employed to modifying a screen printed electrode (SPE) for simultaneous determination of ascorbic acid (AA), dopamine (DA) and uric acid (UA). Au NPs are uniformly dispersed on carbon nanofibers were confirmed by the structure and morphological studies. The modified electrodes were tested in cyclic voltammetry (CV), differential pulse voltammetry (DPV) and chronoamperometry (CA) to characterize their electrochemical responses. Compared to bare SPE, the Au-CNFs/SPE had a better sensing response to AA, DA, and UA. The electrochemical oxidation signal of AA, DA and UA are well separated into three distinct peaks with peak potential separation of 280 mV, 159 mV and 439 mV between AA-DA, DA-UA and AA-UA respectively in CV studies and the corresponding peak potential separation in DPV studies are 290 mV, 166 mV and 456 mV. The Au-CNFs/SPE has a wide linear response of AA, DA and UA in DPV analysis over the range of 5–40 μM ( R2 = 0.9984), 2–16 μM ( R2 = 0.9962) and 2–16 μM ( R2 = 0.9983) with corresponding detection limits of 0.9 μM, 0.4 μM and 0.3 μM at S/N = 3, respectively. The developed modified SPE based sensor exhibits excellent reproducibility, stability, and repeatability. The excellent sensing response of Au-CNFs could reveal to a promising approach in electrochemical sensor.

Aluminum-based composites are in high demand in industrial fields due to their light weight, high electrical conductivity, and corrosion resistance. Due to its unique advantages for composite fabrication, powder metallurgy is a crucial player in meeting this demand. However, the size and weight fraction of the reinforcement significantly influence the components' quality and performance. Understanding the correlation of these variables is crucial for building high-quality components. This study, therefore, investigated the correlations among various parameters—namely, milling time, reinforcement ratio, and size—that affect the composite’s physical and mechanical properties. An artificial neural network model was developed and showed the ability to correlate the processing parameters with the density, hardness, and tensile strength of Al2024-B4C composites. The predicted index of relative importance suggests that the milling time has the most substantial effect on fabricated components. This practical insight can be directly applied in the fabrication of high-quality Al2024-B4C composites.

We investigate the evolution of initial fractal clusters at 3 kpc from the Galactic Center (GC) of the MilkyWay and show how red supergiant clusters (RSGCs)-like objects, which are considered to be the result of active star formation in the Scutum complex, can form by 16 Myr. We find that initial tidal filling and tidal over-filling fractals are shredded by the tidal force, but some substructures can survive as individual subclusters, especially when the initial virial ratio is ≤0.5. These surviving subclusters are weakly mass segregated and show a top-heavy mass function. This implies the possibility that a single substructured star cluster can evolve into multiple ‘star clusters’.

Forecasting port container throughput is crucial due to its impact on economic development. Socio-economic factors, which introduce uncertainty, are increasingly integrated into throughput forecasting. The complexity of common multivariate forecasting models significantly affects accuracy, yet few studies compare their performance on the same time series for throughput modeling. This study implements, evaluates, and compares the performance of eight multivariate forecasting models for port throughput within a proposed multiple-input single-output (MISO) system, chosen for their frequent use in container throughput research. It investigates two data preprocessing approaches: Random Forest Variable Importance Method (RF-VIM) and a Multi Lagged Value approach. The comparison uses six error metrics: normalized root mean squared error, mean absolute error, mean absolute percentage error, mean error, and root mean percentage error. Performances are discussed, and recommendations for adopting a suitable model are provided.

Sensors for monitoring human body movements have gained much attention in the recent times especially in the health-care sector as these devices offer real-time monitoring of vital physiological signs, enabling health-care professionals to evaluate health conditions and provide remote feedback. In this work, we have fabricated carbon-nanotube (CNT)/ polydimethylsiloxane (PDMS) composite sensor through simple dispersion and freezing method for monitoring flexion movements in humans. Sensors with different CNT loadings, namely 0.1 wt %, 0.5 wt %, and 1 wt % were fabricated and analyzed to find the best performing sensor. Several characterizations like Raman, X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), thermogravimetric analysis (TGA), tensile strength measurements, and piezoresistive studies were carried out to study the features of the sensors. Among the fabricated sensors, the one with the loading concentration of 0.5 wt% is found to be most sensitive for flexion applications with higher gauge factor of 533 at 60% strain level, response time of ~ 140 ms and lower hysteresis loss. The feasibility of the sensor for monitoring flexion like finger bending, wrist bending, elbow bending, and knee bending is also analyzed making it ideal for use in sports for athletes, physicians, and trainers to investigate physical performance and well-being.

The most significant threat to the ecosystem is emerging pollutants, which are becoming worse each year and harming the planet severely and permanently. Many organic and inorganic contaminants are present and persistent due to various world events and population growth. As a result, there is a greater need for new technology and its application to address the problems caused by developing pollutants. Carbon composite nanomaterials have significant potential in the fight against numerous environmental contaminants due to their distinctive attributes. This review discusses the reports of customized carbon composite nanomaterials to meet the need for specific elimination of emerging contaminants. Physical and chemical features such as high surface area, conductivity (thermal and electrical), and vibroelectronic properties, size, shape, porosity, and composite nature are making these tailored materials of carbon-based nanomaterials an emerging and sustainable tool to remove persistent compounds like emerging contaminants in aqueous solution. Different composite materials are well discussed in this review, along with their adsorption efficiency of diverse emerging contaminants, including Bisphenol A, estradiol, metformin, etc. This review provides insight into the recent trends limited to 2017–2023. The limitations of carbon-based nanomaterials, such as regeneration and cost-effectiveness, have also been overcome in recent years by diverse modifications in the production process, which can be further improved to make these materials well suited for an extended group of emerging contaminants.

Herein, the present work focuses on the effective counter electrode for dye-sensitized solar cells. The bottom–up approach was adapted to synthesize Mn2O3 nanorods via the hydrothermal method and the reduced graphene oxide was merged with Mn2O3 to prepare a nanocomposite. The prepared nanocomposites were subjected to physio-chemical and morphological characterizations which revealed the crystalline nature of Mn2O3 nanorods. The purity level rGO was characterized using the Raman spectrum and the Fourier transform infrared spectroscopy employed to find the functional groups. The morphological micrographs were visualized using SEM and TEM and the high aspect ratio Mn2O3 nanorods were observed with 5–7 nm and supported by rGO sheets. The electrocatalytic nature and corrosion properties of the counter electrode towards the iodide electrolyte were studied using a symmetrical cell. The as-synthesized nanocomposites were introduced as counter electrodes for DSSC and produced 4.11% of photoconversion efficiency with lower charge transfer resistance. The fabricated DSSC devices were undergone for stability tests for indoor and outdoor atmospheres, the DSSC stability showed 93% and 80% respectively for 150 days.

이 연구에서는 Inception V3, SqueezeNet(local), VGG-16, Painters 및 DeepLoc의 다섯 가지 인공지능(AI) 모 델을 사용하여 차나무 잎의 병해를 분류하였다. 여덟 가지 이미지 카테고리를 사용하였는데, healthy, algal leaf spot, anthracnose, bird’s eye spot, brown blight, gray blight, red leaf spot, and white spot였다. 이 연구에서 사용한 소프트웨 어는 데이터 시각적 프로그래밍을 위한 파이썬 라이브러리로 작동하는 Orange3였다. 이는 데이터를 시각적으로 조작하여 분석하기 위한 워크플로를 생성하는 인터페이스를 통해 작동되었다. 각 AI 모델의 정확도로 최적의 AI 모 델을 선택하였다. 모든 모델은 Adam 최적화, ReLU 활성화 함수, 은닉 레이어에 100개의 뉴런, 신경망의 최대 반복 횟수가 200회, 그리고 0.0001 정규화를 사용하여 훈련되었다. Orange3 기능을 확장하기 위해 새로운 이미지 분석 Add-on을 설치하였다. 훈련 모델에서는 이미지 가져오기(import image), 이미지 임베딩(image embedding), 신경망 (neural network), 테스트 및 점수(test and score), 혼동 행렬(confusion matrix) 위젯이 사용되었으며, 예측에는 이미 지 가져오기(import image), 이미지 임베딩(image embedding), 예측(prediction) 및 이미지 뷰어(image viewer) 위젯 이 사용되었다. 다섯 AI 모델[Inception V3, SqueezeNet(로컬), VGG-16, Painters 및 DeepLoc]의 신경망 정밀도는 각 각 0.807, 0.901, 0.780, 0.800 및 0.771이었다. 결론적으로 SqueezeNet(local) 모델이 차나무 잎 이미지를 사용하여 차 병해 탐색을 위한 최적 AI 모델로 선택되었으며, 정확도와 혼동 행렬을 통해 뛰어난 성능을 보였다.

We present a practical vacuum pressure sensor based on the Schottky junction using graphene anchored on a vertically aligned zinc oxide nanorod (ZnO-NR). The constructed heterosystem of the Schottky junction showed characteristic rectifying behavior with a Schottky barrier height of 0.64 eV. The current–voltage (I–V) features of the Schottky junction were measured under various pressures between 1.0 × 103 and 1.0 × 10− 3 mbar. The maximum current of 38.17 mA for the Schottky junction was measured at – 4 V under 1.0 × 10− 3 mbar. The high current responses are larger than those of the previously reported vacuum pressure sensors based on ZnO nanobelt film, ZnO nanowires, and vertically aligned ZnO nanorod devices. The pressure-sensitive current increases with the vacuum pressure and reaches maximum sensitivity (78.76%) at 1.0 × 10− 3 mbar. The sensitivity and repeatability of the Schottky junction were studied by the current–time (I–T) behavior under variation of vacuum pressure. The sensing mechanism is debated from the surface charge transfer doping effect by oxygen chemisorption. The results suggest that this simple graphene/ZnO-NR Schottky junction device may have potential in the fabrication of vacuum pressure sensor with high sensitivity.

The present research focuses on the tribological behavior of the AA5083 alloy-based hybrid surface composite using aluminosilicate and multi-walled-carbon nanotube through friction stir processing for automotive applications. The friction stir processing parameters (tool rotation and traverse speed) are varied based on full factorial design to understand their influence on the tribological characteristics of the developed hybrid composite. The surface morphology and composition of the worn hybrid composite are examined using a field-emission scanning electron microscope and an energy-dispersive x-ray spectroscope. No synergistic interaction is observed between the wear rate and friction coefficient of the hybrid composite plate. Also, adhesive wear is the major wear mechanism in both base material and hybrid composite. The influence of friction stir process parameters on wear rate and the friction coefficient is analyzed using the hybrid polynomial and multi-quadratic radial basis function. The models are utilized to optimize the friction stir processing parameters for reducing the rate of wear and friction coefficient using multi-quadratic RBF algorithm optimization.

Hybrid nanocomposites of aluminium (NHAMMCs) made from AA5052 are fabricated via stir casting route by reinforcing 12 wt% Si3N4 and 0.5 wt% of graphene for usage in aeronautical and automotive applications due to the lower density and higher strength to weight proportion. The wear characteristics of the NHAMMCs are evaluated for different axial load, rotational speed, sliding distance and sliding time based on Box-Behnken design (BBD) of response surface methodology (RSM). Orowan strengthening mechanism is identified from optical image which improves the strength of the composite. Outcomes show that with higher axial load and rotational speed, there is substantial increase in wear loss whereas with increased sliding distance and sliding time there is no considerable increase in wear loss due to the lubricating nature of the reinforced graphene particles since it has higher surface area to volume ratio. Besides, artificial intelligence approach of neuro-fuzzy (ANFIS) model is developed to predict the output responses and the results are compared with the regression model predictions. Prediction from ANFIS outplays the regression model prediction.

The dyeing process is a very important unit operation in the leather and textile industries; it produces significant amounts of waste effluent containing dyes and poses a substantial threat to the environment. Therefore, degradation of the industrial dye-waste liquid is necessary before its release into the environment. The current is focusing on the reduction of pollutant loads in industrial wastewater through remediating azo and thiazine dyes (synthetic solutions of textile dye consortium). The current research work is focused on the degradation of dye consortium through photo-electro-Fenton (PEF) processes via using dimensionally stable anode (Ti) and graphite cathode. The ideal conditions, which included a pH of 3, 0.1 (g/L) of textile dye consortium, 0.03 (g/L) of iron, 0.2 (g/L) of H2O2, and a 0.3 mAcm-2 of current density, were achieved to the removal of dye consortium over 40 min. The highest dye removal rate was discovered to be 96%. The transition of azo linkages into N2 or NH3 was confirmed by Fourier transforms infra-red spectroscopic analysis. PEF process reduced the 92% of chemical oxygen demand (COD) of textile dye consortium solution, and it meets the kinetics study of the pseudo-first-order. The degradation of dye through the PEF process was evaluated by using the cyclic voltammetric method. The toxicity tests showed that with the treated dye solution, seedlings grew well.

A thermochemical conversion method known as hydrothermal carbonization (HTC) is appealing, because it may convert wet biomass directly into energy and chemicals without the need for pre-drying. The hydrochar solid product’s capacity to prepare precursors of activated carbon has attracted attention. HTC has been utilized to solve practical issues and produce desired carbonaceous products on a variety of generated wastes, including municipal solid waste, algae, and sludge in addition to the typically lignocellulose biomass used as sustainable feedstock. This study aims to assess the in-depth description of hydrothermal carbonization, highlighting the most recent findings with regard to the technological mechanisms and practical advantages. The process parameters, which include temperature, water content, pH, and retention time, determine the characteristics of the final products. The right setting of parameters is crucial, since it significantly affects the characteristics of hydrothermal products and opens up a range of opportunities for their use in multiple sectors. Findings reveal that the type of precursor, retention time, and temperature at which the reaction is processed were discovered to be the main determinants of the HTC process. Lower solid products are produced at higher temperatures; the carbon concentration rises, while the hydrogen and oxygen content declines. Current knowledge gaps, fresh views, and associated recommendations were offered to fully use the HTC technique's enormous potential and to provide hydrochar with additional useful applications in the future.

Graphene oxide (GO) and ultrafine slag (UFS) have been applied to reinforce cement mortar cubes (CMC) in this research. The consequences of GO and UFS on the mechanical attributes of the CMC were explored through experimental investigations. Established on the results, at the 28 days of hydration, the CMC compressive and flexural strength with 0.03% of GO and 10% UFS were 89.8 N/mm2 and 9.1 N/mm2, respectively. Furthermore, the structural changes of CMC with GO and UFS were qualitatively analysed with instrumental techniques such as scanning electron microscope (SEM), X-ray fluorescence (XRF), thermogravimetric analysis (TGA), Fourier transform infrared spectroscopy (FT-IR), FT Raman spectroscopy, atomic force microscopy (AFM), and 27Al, 29Si-Nuclear magnetic resonance spectroscopy (NMR). SEM results reported that GO and UFS formed an aggregated nanostructure that improved the microstructural properties of the CMC. TGA analysis revealed the quantum of calcium hydrate and bound water accomplished by supplementing GO bound to the UFS aggregates. FT-IR analysis of the CMC samples confirmed the ‘O-’comprising functional groups of GO which expedited the formation of complexes between calcium carbonate ( CaCO3) and UFS. 0.03% GO was the optimum dosage that enhanced the compressive and flexural attributes when combined with 10% UFS in CMC.

증산은 적정 관수 관리에 중요한 역할을 하므로 수분 스트레스에 취약한 토마토와 같은 작물의 관개 수요에 대한 지식이 필요하다. 관수량을 결정하는 한 가지 방법은 증산량을 측정하는 것인데, 이는 환경이나 생육 수준의 영향을 받는다. 본 연구는 분단위 데이터를 통해 수학적 모델과 딥러닝 모델을 활용하여 토마토의 증발량을 추정하 고 적합한 모델을 찾는 것을 목표로 한다. 라이시미터 데이터는 1분 간격으로 배지무게 변화를 측정함으로써 증산 량을 직접 측정했다. 피어슨 상관관계는 관찰된 환경 변수가 작물 증산과 유의미한 상관관계가 있음을 보여주었다. 온실온도와 태양복사는 증산량과 양의 상관관계를 보인 반면, 상대습도는 음의 상관관계를 보였다. 다중 선형 회귀 (MLR), 다항 회귀 모델, 인공 신경망(ANN), Long short-term memory(LSTM), Gated Recurrent Unit(GRU) 모델을 구 축하고 정확도를 비교했다. 모든 모델은 테스트 데이터 세트에서 0.770－0.948 범위의 R2 값과 0.495mm/min－ 1.038mm/min의 RMSE로 증산을 잠재적으로 추정하였다. 딥러닝 모델은 수학적 모델보다 성능이 뛰어났다. GRU 는 0.948의 R2 및 0.495mm/min의 RMSE로 테스트 데이터에서 최고의 성능을 보여주었다. LSTM과 ANN은 R2 값이 각각 0.946과 0.944, RMSE가 각각 0.504m/min과 0.511로 그 뒤를 이었다. GRU 모델은 단기 예측에서 우수한 성능 을 보였고 LSTM은 장기 예측에서 우수한 성능을 보였지만 대규모 데이터 셋을 사용한 추가 검증이 필요하다. FAO56 Penman-Monteith(PM) 방정식과 비교하여 PM은 MLR 및 다항식 모델 2차 및 3차보다 RMSE가 0.598mm/min으로 낮지만 분단위 증산의 변동성을 포착하는 데 있어 모든 모델 중에서 가장 성능이 낮다. 따라서 본 연구 결과는 온실 내 토마토 증산을 단기적으로 추정하기 위해 GRU 및 LSTM 모델을 권장한다.