Minimum Distance Research Articles

Experimental study of streamer inception and transition characteristics for live working gap under switching impulse

The switching impulse discharge characteristics are the basis for determining the minimum approach distance in equipotential live working (EPLW) gap. The discharge mechanism of the EPLW gap is not fully deterministic currently. In this study, two actual EPLW gaps and two simplified gaps including sphere–plane gap and bundle conductor + rod-plane gap are established, and the discharge mechanism of these gaps is investigated. The result indicates that the discharge characteristics of sim-worker's back and feet are similar to the simplified electrode. For the sim-worker's back, the streamer inception time delay, inception voltage, injected charge, and transition time are 93.3 μs, 765 kV, 6.19 μC, and 44.7 μs, respectively. The current waveforms of the sim-worker's back and the sphere electrode (R = 0.3 m) exhibit similar characteristics, both containing single pulse, and streamer length nearly half the gap. For the sim-worker's feet, the corresponding values are 28.5 μs, 307 kV, 0.67 μC, and 81.6 μs, similar to the bundle conductor + rod electrode (R = 5 cm), with multiple pulse current waveforms and streamer length of 0.49 m, also similar to the bundle conductor + rod electrode (R = 5 cm). The result can contribute to lineman safety protection and simplified gap design for live working.

Investigation of the Effective Numerical Model for Seismic Response Analysis of Concrete-Faced Rockfill Dam on Deep Overburden

The construction of high rockfill dams on deep overburden in seismically active regions poses significant challenges. Currently, there are no standardized guidelines for defining the computational domain range in seismic analysis, necessitating the establishment of a universally applicable computational domain range that optimizes the balance between computational accuracy and efficiency. This has critical engineering implications for the seismic analysis of rockfill dams on deep overburden. This study employed the seismic wave input method to consider the dynamic interaction between the dam, overburden, and infinite domain. A systematic investigation was conducted on a concrete-faced rockfill dam (CFRD) constructed on deep overburden, considering the influences of overburden thickness, dam height, overburden properties, soil layer configuration, ground motion intensity, and the frequency content of the seismic waves. The acceleration response and seismic deformation of the dam were analyzed. Subsequently, the computational domain range corresponding to various levels of acceptable engineering precision was established. The results indicated that the lateral boundary length should extend a minimum distance equal to the sum of 3 times the overburden depth and 1.2 times the maximum dam height. Additionally, the depth below the overburden–bedrock interface should extend at least 1.2 times the maximum dam height. This study provides a crucial foundation for determining the optimal computational domain range in the seismic analysis of rockfill dams constructed on deep overburden.

Hybrid Sparse Array Design Based on Pseudo-Random Algorithm and Convex Optimization with Wide Beam Steering

In this paper, a hybrid optimization method utilizing a pseudo-random algorithm and convex optimization is proposed to avoid grating lobe and achieve lower side lobe level (SLL) of a planar sparse array when the minimum inter-element distance is one wavelength. The pseudo-random algorithm is utilized to distribute the positions of elements. The convex algorithm is utilized to optimize the excitations of elements. The results show that a planar sparse array with no grating lobe and peak side lobe level (PSLL) of −17 dB can be obtained with a minimum inter-element distance of one wavelength, which indicates the effectiveness of the hybrid optimization method. In addition, beam steering can be achieved within an 80∘ field of view range.

Optimal angles for independent femoral tunnel drillings to prevent damage to anatomic structures in single-bundle anterior cruciate ligament reconstruction.

To determine the optimal angles for independent femoral tunnel drillings in single-bundle anterior cruciate ligament reconstruction (ACLR) in different races and genders with the aim of preventing damage to lateral femoral anatomic structures (LFAS), posterior cortex and medial femoral condyle. This study included 180 volunteers, including 90 Caucasian and 90 matched Chinese. Magnetic resonance imaging (MRI) was used to scan the knees to create three-dimensional bone models, the ACL femoral footprint centre and the LFAS. In each femur model, femoral tunnels were established using a set of 16 distinct angular combinations: 15°, 30°, 45°, and 60° in the axial plane, as well as 15°, 30°, 45°, and 60° in the coronal plane. The minimum distance from the tunnel exit to the LFAS was evaluated, and the tunnel length, posterior cortex damage and medial femoral condyle injury were assessed. Among the 180 patients with simulated ACL femoral tunnels, there was damage to the anatomical structure in parts of the model. According to the Cochran Q test results (P < 0.001), the percentage of safe tunnels varied significantly among the 16 different drilling angle combinations. The overall occurrence of the tunnel exit causing injury to LFAS were 8.3% and 8.1% in Chinese and Caucasian groups (P = 0.786). The means for tunnel length in Caucasians was 40.1 ± 7.9mm, respectively; for Chinese, the results was 38.8 ± 6.6mm (P < 0.001). Females had significantly shorter femoral tunnels than males in both Chinese and Caucasian (P < 0.001). The overall invasion rate of the posterior cortex and medial femoral condyle were 32.6% and 7.4% for Chinese; 31.0% and 7.4% for Caucasians, respectively. To reduce risks of injury to anatomical structures, such as the LFAS, posterior cortex and medial femoral condyle, specific angle combinations of 30°/45°, 45°/30°, 45°/45°, 45°/60°, 60°/30°, 60°/45° and 60°/60° should be used when creating the femoral tunnel in single-bundle ACLR. The selected drilling angles are critical for optimizing femoral tunnel placement.

Efficiency Limits of Energy Conversion by Light-Driven Redox Chains.

The conversion of absorbed sunlight to spatially separated electron-hole pairs is a crucial outcome of natural photosynthesis. Many organisms achieve near-unit quantum yields of charge separation (one electron-hole pair per incident photon) by dissipating as heat more than half of the light energy that is deposited in the primary donor. Might alternative choices have been made by Nature that would sacrifice quantum yield in favor of producing higher energy electron/hole pairs? Here, we use a multisite electron hopping model to address the kinetic and thermodynamic compromises that can be made in electron transfer chains, with the aim of understanding Nature's choices and opportunities in bioinspired energy-converting systems. We find that if the electron-transfer coordinates are even weakly coupled to a high-frequency vibrational mode, substantial energy dissipation is necessary to achieve the maximum possible energy storage in an electron-transfer chain. Since high-frequency vibronic coupling is common in physiological redox cofactors, we posit that biological reaction centers have recruited a strategy to convert light energy into redox potential with the near-optimum energy efficiency that is possible in an electron-transfer chain. Our simulations also find that charge separation in electron-transfer chains is subject to a minimum intercofactor separation distance, beneath which energy-dissipating charge recombination is unavoidable. We find that high quantum yield and low energy dissipation can thus be realized simultaneously for multistep electron transfer if recombination pathways are uncoupled from high-frequency vibrations and if the cofactors are held at small-to-intermediate distances apart (ca. 3 to 8 Å edge-to-edge). Our analysis informs the design of bioinspired light-harvesting structures that may exceed 60% energy efficiency, as opposed to the ∼30% efficiency achieved in natural photosynthesis.

Low-weight high-distance error-correcting fermionic encodings

We perform an extended numerical search for practical fermion-to-qubit encodings with error-correcting properties. Ideally, encodings should strike a balance between a number of the seemingly incompatible attributes, such as having a high minimum distance, low-weight fermionic logical operators, a small qubit to fermionic mode ratio and a simple qubit connectivity graph including ancilla qubits for the measurement of stabilizers. Our strategy consists of a three-step procedure in which we: First generate encodings with code distances up to d≤4 by a brute-force enumeration technique; subsequently, we use these encodings as starting points and apply Clifford deformations to them which allows us to identify higher-distance codes with d≤7; finally, we optimize the hardware connectivity graphs of resulting encodings in terms of the graph thickness and the number of connections per qubit. We report multiple promising high-distance encodings which significantly improve the weights of stabilizers and logical operators compared to previously reported alternatives. Published by the American Physical Society 2024

Design and Optimization of Non-Coplanar Orbits for Orbital Photovoltaic Panel Cleaning Robots

Aiming at the problem that it is difficult for an orbital photovoltaic panel cleaning robot to span a large distance between photovoltaic panels, a method of designing and optimizing a non-coplanar orbit based on Bezier curves is proposed. Firstly, the robot’s motion law is analyzed to obtain trajectory data for a single work cycle. Then, Bezier curves are utilized for trajectory design to ensure a smooth transition during the spanning motion phase. Thirdly, with the average value of the minimum distance between the Bezier curve and the point set data of the spanning motion phase as the optimization objective function, the nonlinear planning based on the SQP algorithm was adopted for the optimization of the upper and lower trajectories. Finally, the results of the case calculations indicate that the standard deviation of the optimized upper and lower trajectories was reduced by 35.63% and 40.57%, respectively. Additionally, the ADAMS simulation validation demonstrates that the trajectory errors of the four wheels decreased by a maximum of 8.79 mm, 23.78 mm, 10.11 mm, and 14.97 mm, respectively, thereby confirming the effectiveness of the trajectory optimization.

The underlying causes of differential migration: assumptions, hypotheses, and predictions.

Mechanisms governing the migratory decisions of birds have long fascinated ecologists and sparked considerable debate. Identifying factors responsible for variation in migration distance, also known as differential migration, has been a popular approach to understanding the mechanisms underlying migratory behaviour more generally. However, research progress has been slowed by the continued testing of overlapping, non-mechanistic, and circular predictions among a small set of historically entrenched hypotheses. We highlight the body size hypothesis and suggest that the predictions commonly tested have impeded progress because body size relationships with migration distance are predictions made by several distinct hypotheses with contrasting mechanisms. The cost of migration itself has not been adequately accounted for in most hypotheses, and we propose two flight efficiency hypotheses with time- and energy-minimizing mechanisms that allow individuals to mitigate the risks inherent to longer migrations. We also advance two conceptual versions of the social dominance hypothesis based on two distinct underlying mechanisms related to distance minimization and food maximization that will help clarify the role of competition in driving migratory decisions. Overall, we describe and refine 12 mechanistic hypotheses proposed to explain differential migration (along with several other special-case hypotheses), seven of which have underlying mechanisms related to food limitation as past research has identified this to be an important driver of differential migration. We also thoroughly reviewed 145 publications to assess the amount of support for 10 critical assumptions underlying alternative hypotheses for differential migration in birds. Our review reveals that surprisingly few studies explicitly evaluate assumptions within a differential migration context. Generating and testing strong predictions and critical assumptions underlying mechanisms of alternative hypotheses will improve our ability to differentiate among these explanations of differential migration. Additionally, future intraspecific progress will be greatest if investigators continue to focus on mechanisms underlying variation in migration distance within rather than among demographic classes, as previous research has found differing mechanisms to be responsible for differential migration among demographic classes. Interspecifically, a thorough comparative analysis that seeks to explain variation in migration distance among species would broaden both our understanding of the mechanisms regulating current differential migration patterns and those that led to the evolution of migration more generally. Collectively, we provide a framework that, together with advances in animal-borne tracking and other technology, can be used to advance our understanding of the causes of differential migration distance, and migratory decisions more generally.

Application of Odd Chen-Log-Logistic Distribution to Covid-19 Data

This article created the Odd Chen-Log-Logistic distribution from Odd Chen-G family distributions. We derive various statistical features. The parameter estimation theory focuses on selecting the best estimators. We estimate distribution parameters using maximum likelihood, moment, least squares, weighted least, L-moment, maximum product spacing, and minimal distance methods. We will examine Kolmogorov-Smirnov simulation studies that compare estimator efficiency. Finally, we analyze a genuine COVID-19 data set to demonstrate the flexibility of our model and its accuracy compared to other distributions.

Model-predictive fault-tolerant control of safety-critical processes based on dynamic safe set

Industrial systems and chemical plants heavily rely on automation and control systems for seamless operations. However, the susceptibility of these systems to various faults poses threats to processes, leading to economic losses and safety risks. Here, a robust fault-tolerant control (FTC) strategy is developed that can take proactive measures during faults involving in-time activation of a backup controller, to ensure that the system remains within safe operational limits. It is based on the Dynamic Safe Set (DSS) which is the set of initial process states that meet safety constraints at all times, and the dynamic safety margin (DSM) which is the minimum distance from the DSS boundary. For just-in-time corrective action, a critical fault function is introduced, defined as the time required by the system to cross the DSS boundary under the nominal controller only. This critical fault function is calculated offline and is integrated with a real-time fault size estimation to formulate the controller reconfiguration logic to keep system within DSS. A linear functional observer is used to estimate fault size, combined with a predictive scheme, to enhance robustness during the transient period of fault estimation. This configuration avoids unnecessary control actions while ensuring timely intervention. The proposed FTC strategy is tested on an exothermic Continuous Stirred Tank Reactor (CSTR) case study. The results demonstrate the strategy's effectiveness in handling process faults, ensuring both stability and safety constraints are met. Thus, this paper contributes to the advancement of FTC ensuring the resilience of industrial systems in the face of unforeseen challenges.

Self-supervised Learning Approach for Anomaly Detection in Rotating Machinery

Early fault detection in rotating machinery needs careful expert analysis of vibration data for monitoring a component state. Online methods that automatically set a threshold and raise an alarm when the vibration signature is anomalous are meant to efficiently manage key assets in a preventive maintenance plan. In recent years a focus has raised on data driven methods in parallel with the increasing attention towards machine learning and, particularly, deep learning models. In this regard, for rotating equipment components, an important aspect relates to labelled data scarcity for supervised training. On the other hand, the advent of the Internet of Things allows to gather data from multiple assets with relevant information on the asset state itself. Self-supervised learning methods in deep learning application are currently tackling this problem. Investigating Self-learning approaches to integrate domain knowledge and learn relevant features from unlabeled data is therefore important for condition monitoring applications. In this paper a methodology is proposed based on cycle consistency representation learning for training an embedder network on univariate unlabeled data. In order to learn a distance metric in the embedding space the original data are transformed to generate sequences of augmented inputs to enforce learnable pattern similarity in the augmented pairs. A differentiable cycle-consistency loss is chosen to maximize the numbers of augmented pairs in the learned embedding space that have minimum features distance. The pretext task in the described self-supervised setting aims to train a feature extractor for discriminating dissimilar samples in the embedding space by a distance metric and to provide a useful representation for down-stream tasks. The paper analyzes the performance of the approach for anomaly detection in rotating machinery. The methodology is tested on vibration data provided by the Center for Intelligent Maintenance Systems (IMS), University of Cincinnati, considering different accelerated life test campaigns. The data were collected to monitor the fault development in bearings and the model shows how the learned embedding space discriminates effectively anomalous samples from normal ones in the degradation stages of the bearings.

Automatic Orbital Pair Selection for Multilevel Local Coupled-Cluster Based on Orbital Maps.

We present an automatic, orbital-map based orbital-pair selection scheme for multilevel local coupled-cluster approaches that exploits the locality of chemical reactions by focusing on the part of the molecule directly involved in the reaction. The previously introduced pair-selected multilevel extension to domain-based local pair natural orbital coupled-cluster with singles, doubles, and semicanonical perturbative triples [DLPNO-CCSD(T0)] partitions the orbital pairs according to relative changes in pair correlation energies [Bensberg, M.; Neugebauer, J. Orbital pair selection for relative energies in the domain-based local pair natural orbital coupled-cluster method. J. Chem. Phys. 2022, 157, 064102. 10.1063/5.0100010]. To this end, maps between localized orbitals are required which in turn require maps between the atoms of structures along reaction paths. So far, these atom maps have been manually determined, which can be a (human) time-consuming procedure. Here, we present an automatic atom mapping algorithm based on the principle of minimum chemical distance that incorporates orientation dependence through dihedral angles. A similar strategy is then introduced to obtain orbital maps, which proves advantageous over the previously used direct orbital selection. Along with a modified orbital pair prescreening, this results in an improved variant of the pair-selected multilevel DLPNO-CCSD(T0) method. The performance of this approach is demonstrated for various reaction types showing a significant efficiency gain and accurate results due to beneficial, systematic error cancellation. The presented method operates in a black-box manner due to its fully automatized algorithms with only the need to specify a single target-accuracy parameter. Additionally, we demonstrate that basis set extrapolation techniques can be applied. In this context, the approach shows deficiencies for the use of large basis sets, especially with diffuse basis functions, which can be traced back to the semicanonical triples correction.

Hybrid secure routing and monitoring mechanisms in IoT-based wireless sensor networks using Egret-Harris optimization

ABSTRACT Security is the primary concern when creating security protocols for wireless sensor networks (WSN), which motivates many academics to find security solutions that effectively provide a few benefits, such as low consumption of electricity, flexible communication, and low cost. A few restrictions still remain, including the inability to exactly select the expected cluster, the sensor nodes’ constrained functionality, and their poor efficiency. Thus, Egret-Harris optimization for hybrid secure routing and monitoring mechanisms in IoT-based WSNs (EHO optimized routing protocol in WSN) was introduced in this research. The created EHO algorithm combines the search, fitness function, and hunting phase features from Harris hawks and egrets to determine the best solution among all practical solutions while ensuring safe data transfer from the cluster heads (CHs) to the Base station (BS). Specifically, the EHO-enabled clustering is applied to the suggested model to efficiently choose the ideal group of CHs. Additionally, the EHO algorithm assists in choosing the best possible routes with minimal distance and less delay for facilitating energy-efficient transmission. With 100 nodes analyzed, the suggested EHO-WSN approach without any attacks achieved 22 alive nodes, a delay of 0.10 ms, a normalized energy of 0.346J, and a throughput of 0.64 bps, respectively. Additionally, in the presence of a Sybil attack, the suggested EHO-WSN technique achieves 14, 0.214 J, 0.010 ms, and 0.512 bps for an analysis involving 100 nodes. Compared to previous methods, the suggested EHO-WSN model without attack achieves a delay of 0.07 ms, a throughput of 0.30 bps, an energy of 0.374 J, and 37 alive nodes for 200 node evaluation. For 200 nodes under examination, the EHO-WSN technique yields superior results of attaining 12 alive nodes, a delay of 0.072 ms, a throughput of 0.181 bps, and a normalized energy of 0.330 J even in the presence of the Sybil attack and exceeded other traditional techniques.

Provision of land use and forest density maps in semi-arid areas of Iran using Sentinel-2 satellite images and vegetation indices

Zagros forests of Iran have several environmental, ecological, and socioeconomic values which provide the unique habitats for many endemic species. However, these ecosystems have severely been destroyed by many anthropogenic and natural factors in recent years. Knowledge of area, distribution, and density of these forests and current natural and human-made land uses inside these ecosystems is necessary for protective management of Zagros forests. This research was performed to obtain the land use and forest density maps in Zagros vegetative area of Fars province in southwestern Iran using Sentinel-2A, vegetation indices, Google Earth, and field data. First the boundary of Zagros forests in Fars province was digitized on Google Earth images. Then, Sentinel-2A satellite images covering the forest region in Fars province were obtained from Copernicus website. A pilot area (16000 ha) in the province was considered to investigate the precision of classification methods. Then, Sentinel-2A satellite image of pilot region was classified by some supervised classification methods (ML: maximum likelihood, MD: minimum distance, MaD: mahalanobis distance, SAM: spectral angle mapper, NN: neural network, SVM: support vector machine, and RF: random forest). In addition, the efficiency of different vegetation indices (NDVI, TNDVI, SAVI, and RVI) was evaluated for classifying the forest density in the pilot region. Furthermore, pilot area was considered to test the precision of Google Earth satellite images in this study. For this purpose, 270 field samples were taken as square plots in nine land uses. Selection of the plots inside each land use was randomly. After initial analysis, all satellite images covering the forest area in Fars province were classified by the most accurate algorithm (SVM) to obtain the land use map. On the other hand, the most accurate vegetation index (SAVI) was used to classify the forest density in the study area. Validation of final maps was performed using several random plots on Google Earth images. The initial results of this research indicated that Google Earth images have an overall accuracy (OA) of 96% compared to ground truth in Fars province. Results of land use map showed that Zagros forest has covered an area of 794651.79 ha in Fars province. The findings of this research demonstrated that SVM algorithm (OA: 92.94%, and k: 0.85) has efficiently classified the land uses in Fars province. In addition, SAVI (r2=0.719, p<0.001) has highly been correlated to the forest density in Zagros vegetative area of Fars province. Therefore, land use mapping using SVM algorithm and Sentinel-2A images, and forest density mapping using SAVI is highly recommended in Zagros forests of Fars province in time series which is essential for protective management of these forests in spatio-temporal scale.

Structural modulation of methyl 4-(2,11-diisopropylbenzopentaphen-5-yl based chromophore with B12N12 nano cage for enhancing the NLO Properties: A DFT study

Currently, key electronic and nonlinear optics (NLO) properties of DPOB chromophore were explored through doping with B12N12 nanocage. Therefore, two complexes I and II were designed by doping nanocage on methoxy and carbonyl group of DPOB at minimum distance. The DFT/TD-DFT approaches at B3LYP-GD3/6-311G(d,p) functional were utilized to accomplished the NLO properties. A band gap: 3.109 and 2.707 eV in complexes I and II, respectively with higher value of softness and least hardness were investigated in doped material than their pure forms (DPOB and B12N12). The hyperpolarizability of studied compounds increases as: B12N12 < DPOB<Complex I<Complex II. Among all investigated compounds, complex II showed the maximum values of dipole moment (15.826 D), linear polarizability (7.04 × 102a.u.) and non-linear polarizability (βtot = 4.13 × 103 and γtot = 6.70 × 105a.u.). These findings illustrated that doping of inorganic nanocages over organic surfaces is an efficient strategy to improve the NLO properties.

Safety of vertical osteotomies in segmental Le Fort I procedures: a one-year radiological follow-up study

The aim of this study was to evaluate the frequency of dental and periodontal injuries, as well as radiological bone healing, at the vertical osteotomies in patients treated with segmental Le Fort I (LFI) osteotomy, as observed on cone beam computed tomography (CBCT) scans. This retrospective study analysed 105 patients who underwent segmental LFI osteotomy, in whom vertical osteotomies between the lateral incisor and canine were performed using a bur and extended in depth with an osteotome. CBCT scans were obtained preoperatively and at the 1-week and 1-year follow-ups. Measurements at the 1-week follow-up included interdental distances, root injuries, and periodontal detachment, while assessment at the 1-year follow-up included endodontic treatment and healing of the vertical osteotomies. The results showed no damage to the 420 roots at risk adjacent to the osteotomies; however, the osteotomy extended into the periodontal ligament in 38 roots. The mean preoperative minimum distance between roots differed significantly between osteotomy sites with intact periodontal ligament and those with periodontal ligament detachment (P < 0.001). One tooth was endodontically treated at the 1-year follow-up. Incomplete healing of the vertical osteotomy occurred more frequently in female patients than in male patients (P = 0.012). The study findings suggest that segmental LFI osteotomy is safe when performed with a bur and osteotome, provided a minimum distance of 2.5 mm between roots is maintained.

Minimum Fascia-Tumour Distance for Differentiating Deep Lobe From Superficial Lobe Benign Parotid Tumours: A Retrospective Study and Meta-Analysis.

This study was designed to evaluate the diagnostic efficacy of the minimum fascia-tumour distance (MFTD) in distinguishing deep-lobe benign parotid tumours from superficial-lobe tumours through both an original study and a meta-analysis. In this study, we performed a retrospective analysis of data from 91 patients who had been diagnosed with benign parotid tumours. The MFTD values were sourced from preoperative ultrasound examinations. The locations of these tumours were confirmed through surgical findings. We assessed the diagnostic accuracy of MFTD by utilising receiver operating characteristic (ROC) curves. Additionally, we conducted a systematic review of the pertinent literature and performed a diagnostic meta-analysis to ascertain the overall diagnostic efficacy of MFTD in identifying benign parotid tumours. Patients with tumours in the deep lobe had a significantly greater MFTD than patients with tumours in the superficial lobe. Using a cutoff value of 3.50 mm for MFTD, we found an AUC of 0.93, a sensitivity of 81.8%, and a specificity of 98.8%. Our meta-analysis included seven studies covering a total of 1689 tumours. The pooled values for sensitivity, specificity, and diagnostic odds ratio (OR) of MFTD were 81.0%, 89.0%, and 32.2, respectively. The AUC of the summarised ROC curve of MFTD was 0.90. The MFTD demonstrated reliable diagnostic accuracy in identifying deep-lobe benign parotid tumours and may be incorporated into standard evaluations before parotidectomy.

Achieving wheat seedling freezing injury assessment during the seedling stage using Unmanned Ground Vehicle (UGV) and hyperspectral imaging technology

Freezing injury may cause irreversible damage to wheat (Triticum aestivum L) tissues and can significantly reduce yield and quality. Therefore, quick and non-destructively estimating the degree of frost damage for formulating anti-freezing protection strategies and preventing frost damage is very crucial. In this study, we obtained hyperspectral images of wheat leaves for accurate identification of frost damage. A remote-controlled Unmanned Ground Vehicle (UGV) equipped with an imaging spectral camera was used to capture the hyperspectral images of frost-damaged wheat leaves. We compared the efficiency of two methods (the one without removal of weeds, and the other is to remove the corresponding area of weeds from the hyperspectral image by Deeplab V3+) for estimation of wheat freezing damage degree by using four different algorithms; Support Vector Machine Classification (SVM), Mahalanobis Distance Classification (MaD), Minimum Distance Classification (MiD), and Maximum Likelihood Classification (ML). We found that, Deeplab V3+ can efficiently identify the weeds from hyperspectral images, as the overall accuracy (OA) values of different algorithms were high in images with weeds removal as compared to the values in weeds containing images. Further, applying ML model after weeds removal have high OA (93.26 %) as compared to the other models. Thus, using Deeplab V3+ and ML can be a potential approach to identify the freezing injury in wheat for sustainable agricultural productivity.

Reuse of polyvinyl chloride residues—in powder and fiber forms—as potential components for green mortar pavers

Plastic wastes are considered a significant environmental pollution load because of their mostly non-biodegradable nature. Consequently, the need to lessen the environmental impact calls for employing eco-friendly strategies such as reusing materials in construction. The objective of this study is to analyze the mechanical behavior and environmental impacts of mortar elements, with a specific focus on pavers, by incorporating waste polyvinyl chloride (WPVC) as aggregate and partially replacing cement. Two distinct forms of this waste were considered in the investigation: (i) as it is generated in industrial processes, mainly in fiber form, WPVCf, and in the form of powder obtained by sieving the former, WPVCp. The ratio of conventional mortar materials replaced by WPVC and the final water-to-binder ratio changed based on waste type and mix workability. The experimental work involved preparing 84 pavers, each measuring 100 mm × 150 mm × 60 mm. For each type of paver, 12 samples were prepared, ensuring a minimum of 4 sets of test data for each curing period (7, 14, and 28 days). Flexural strength tests, conducted in accordance with the Colombian standard NTC 2017, utilized a standard flexural test machine with a 150 kN capacity. A specially designed steel tool applied a point load at the center of each paving block, maintaining a minimum distance of 10 millimeters from the edges. The findings demonstrated that pavers containing fiber plastic waste exhibited the requisite strength for paved walkways, even in the short term, when the weight ratio of WPVCf to sand (WPVCf/S) was 24 %. These results derive an eco-friendly solution for reusing PVC wastes currently deposited directly in landfills. Moreover, to analyze the environmental impacts of this solution, a Life Cycle Assessment (LCA) analysis was carried out in this research. The LCA results pointed out that dosing with a WPVCf/S ratio of 24 % emerges as the most ecologically responsible, manifesting substantial reductions across impact categories, such as a significant 23 % decrease in climate change, a 23 % reduction in Photochemical ozone formation, and a 22 % reduction in renewable energy consumption. This underscores the potential of WPVCf incorporation as a consequential step toward eco-friendly construction materials. Furthermore, this solution not only translates to diminished paving block costs by requiring less fine aggregate and cement but also contributes to reduced transportation expenses owing to the decreased weight of the pavers.

Structural response of precast reinforced concrete structure with emulative moment connections under internal column loss scenarios

Depending on the design, the connections of precast concrete (PC) structures can be susceptible to severe damage under large deformation. Consequently, PC structures may behave differently from cast-in-situ reinforced concrete (RC) structures under column loss scenarios. This study focuses on a class of PC structures that utilize splice sleeves (SSs) to assemble the precast components with integrated beam-column connections (IBCCs). This PC design can alleviate rebar congestion issues and minimize the need for post-cast concrete in the connection regions during assembly. The IBCCs are typically designed to mimic the behavior of corresponding RC connections under service loading conditions. However, the extent of structural emulation under column loss scenarios is not immediately clear. In this paper, the collapse behavior of PC structures with IBCCs under an internal column removal scenario is thoroughly assessed using experimental investigations and numerical analyses. Three PC subassemblages and one RC subassemblage are tested experimentally to provide experimental data for the calibration and validation of finite element (FE) models. The collapse behavior of full-span PC and RC subassemblages are next compared numerically under the internal column loss scenario, with an accompanying investigation on the influence of horizontal SSs. It is found that the PC subassemblages exhibit emulative collapse behavior as the RC subassemblage under internal column loss scenarios, provided that the horizontal SSs are located beyond the beam plastic region under the internal column loss scenario. To achieve the latter, a minimum distance of 0.15 L (L is clear beam span before column removal) is recommended for the positioning of horizontal SSs under the internal column loss scenario. Finally, it is shown that the fracture load and corresponding displacement can be enhanced with the incorporation of high ductility rebars.