Partial Segment Research Articles

Method for real-time reconstruction of a transmission line based on the LiDAR point cloud data of a partial line segment

In a light detection and ranging (LiDAR) anti-external force damage monitoring system, the LiDAR installed on an electric power tower can only collect some point cloud data from the transmission line within a few tens of meters. Consequently, its anti-break system fails to calculate the distance between the transmission line and ground objects. In this study, a real-time reconstruction method for a transmission line based on a catenary mathematical model is proposed. Based on the three-dimensional (3D) point cloud data of the local power line, a 3D point cloud is mapped to a two-dimensional (2D) plane. In the 3D plane coordinate system, the catenary equation is fitted and calculated using the catenary model, and the 2D point cloud dataset of the transmission line is calculated according to this equation. Subsequently, the transmission line of the 2D plane is restored to the 3D space, and finally, the entire transmission line is restored to the 3D point cloud data. The field test results reveal that the proposed method can be used to effectively calculate and reconstruct the 3D transmission line model in real time and solve the spatial distance calculation problem of the anti-external force damage monitoring system.

Isolation and Genotypic Characterization of New Emerging Avian Reovirus Genetic Variants in Egypt

Avian reovirus (ARV) strains cause a variety of symptoms in chickens, including viral arthritis/tenosynovitis, a disease that has emerged as a significant cause of economic losses in commercial chicken flocks in recent years in various countries, including Egypt. Furthermore, ARV strains are frequently isolated from birds suffering from malabsorption. In the actual study, seventy-five samples were collected in 2021 and 2022 from broiler and vaccinated broiler breeder flocks at different farms in Giza Province, Egypt, with reovirus-like symptoms such as significant weight fluctuation and arthritis/malabsorption. ARV was screened using real-time PCR, and fifteen positive samples were detected (20%), which were then subjected to embryonated chicken egg (ECE) isolation and molecular characterization (11/15 sample) of a partial segment of the sigma (σ)C gene (S1-gene). Phylogenetically, nine strains were found to belong to genotypic cluster IV, with 82–89% identity with Israeli ARV 2018, and two strains belong to genotypic cluster V with a 78% nucleotide identity with Japan ARV 2021. No correlation between lesions and genotype was found. The strains under study had a low sequence identity (43–55%) when compared with various commercial vaccines belonging to genotypic cluster I (e.g., strain S1133). These findings imply that novel ARV genotypes representing clusters IV and V have recently been introduced to Egyptian poultry farms. A homologous vaccine is suggested; because this variation raises the possibility that commercial vaccines may not offer protection against circulating ARVs.

Efficient Approximate Multiplier Design Based on Hybrid Higher Radix Booth Encoding

Partial products of higher radix are approximated by partial product segmentation. The encoder skips few lower order bits to segment the partial products for approximation. For a particular type of approximation, one can have many options of choosing the higher radix for hybrid encoding. However, the selection of higher radix is the key to obtain an optimal approximate hybrid multiplier design. In this paper, we made an in-depth study on radix-64 and radix-256 encoding for 4-bit and 5-bit approximations. Proposed a two-term radix-64 encoding and three-term radix-256 encoding for 4-bit approximation to calculate partial-products with lowest maximum absolute error (MAE). Further, we have proposed a simple rule to select the higher radix for hybrid encoding to obtain an efficient approximate multiplier design. To demonstrate the usefulness of the selection rule, two hybrid encoding schemes named R4R64 and R4R256 are formulated by combining radix-4 separately with proposed two-term radix-64 and three-term radix-256 encoding schemes. Separate multiplier designs are derived using the proposed hybrid encoding. Comparison result shows that both the proposed approximate multipliers have identical output accuracy, but the multiplier design based on proposed hybrid R4R64 encoding is more hardware efficient than the other. Compared with the existing approximate multiplier based on hybrid R4R256 encoding for word-length 12, the proposed multiplier design based on R4R64 hybrid encoding involves nearly 15% less area-delay product (ADP) and a marginal 4% less power-delay-product (PDP), but calculates output with higher accuracy. The 2-D DCT structure using proposed R4R64 multiplier involves 13% lesser ADP and 14% less energy consumption when compared with the 2-D DCT structure based on existing hybrid R4R256 multiplier design and computes reconstructed images with nearly 10dB higher pick signal-to-noise ratio (PSNR).

Development of crack and damage in shield tunnel lining under seismic loading: Refined 3D finite element modeling and analyses

This paper investigates the development of crack and damage in shield tunnel lining under seismic loading. Based on a recently constructed undersea-shield tunnel, i.e. the Shantou Bay Tunnel, a series of refined 3D Finite Element (FE) models are created and validated. In order to consider the stratum–structure interaction, a preliminary FE model is generated with a simplified lining tube. Thereafter, a refined lining model is created with the loading condition obtained by the aforementioned preliminary model. The refined lining model is employed for exploring the seismic response of the tunnel lining, considering the details of the lining components. Finally, two types of partial segment models are used for examining the cracking process during the seismic loading within the framework of quasi-static analysis. XFEM, as well as the CDP (Concrete Damage Plasticity) constitutive assumption, is adopted in the partial segment models. The simulation results show that: (i) the damage tends to occur at the crown and bottom of the tunnel lining; (ii) the segment assembling type has significant influence on the damage development; (iii) the direction of the bending-moment induced by the earthquake has little influence on the ultimate moment of the segment at failure, and the cracks tend to emerge at the middle of the segment first instead of the joint; (iv) some of the cracks in the standard segments is probable to initiate inside of the concrete instead of the outer rim of the segment; and (v) longitudinal cracks are dominant in the standard segment with the cracking area within the range of 1.4–1.6 m on both sides of the maximum bending-moment point of the segment.

Data-driven lithium-ion batteries capacity estimation based on deep transfer learning using partial segment of charging/discharging data

Accurate estimation of lithium-ion battery capacity is crucial for ensuring its safety and reliability. While data-driven modelling is a common approach for capacity estimation, obtaining cycling data during charging/discharging processes can be challenging. Collecting cycling data under various charging/discharging protocols is often unrealistic, and the collected data can be fragmented due to the random nature of working conditions in practice. To address these issues, we propose a deep transfer learning method that uses partial segments of charging/discharging data for battery capacity estimation. The proposed method utilizes capacity increment features of partial charging/discharging segments that is designed to satisfy practical scenarios. A deep transfer convolutional neural network (DTCNN) is trained with both source and target data, and a fine-tuning strategy is employed to effectively eliminate distribution discrepancies between different battery types or charging/discharging protocols, leading the improved estimation accuracy. Experimental results demonstrate that the proposed method accurately estimates the lithium-ion battery capacity, with values of RMSE, MAPE, and MD-MAPE of only 0.0220, 0.0247, and 0.0194, respectively, when using partial segments. These results highlight the promising prospects of the proposed method for lithium-ion battery capacity estimation.

A Flexible deep convolutional neural network coupled with progressive training framework for online capacity estimation of lithium-ion batteries

Machine learning-based methods have shown great application prospects on the capacity estimation of lithium-ion battery. However, most of extant approaches require complete charging and discharging profiles that rarely occurs in practical applications to extract health features matched by the cycling capacity. To address this limitation, this paper directly intercepted partial charging voltage segments from raw charging curves, evading complicated manual features extraction. A progressive training framework is proposed and integrated with the deep convolutional neural network to build the online capacity estimator. Three battery aging datasets involving 28 cells cyclic aging data are exploited for model training and validation. Thereinto, the source dataset and target dataset-1 are used to train the model parameters progressively, and the target dataset-2 is applied to validate model performance. Experimental results show that the deep convolutional neural network coupled with progressive training framework can significantly enhance the capacity estimation accuracy and reduce retraining time compared with the ordinary convolutional neural network. The comparison results of different machine learning algorithms demonstrate that the proposed method possesses high estimation precision with a maximum relative error of only 3.4% on the target dataset, which can be easily extended to different types of battery capacity estimation.

Egocentric Early Action Prediction via Adversarial Knowledge Distillation

Egocentric early action prediction aims to recognize actions from the first-person view by only observing a partial video segment, which is challenging due to the limited context information of the partial video. In this article, to tackle the egocentric early action prediction problem, we propose a novel multi-modal adversarial knowledge distillation framework. In particular, our approach involves a teacher network to learn the enhanced representation of the partial video by considering the future unobserved video segment, and a student network to mimic the teacher network to produce the powerful representation of the partial video and based on that predicting the action label. To promote the knowledge distillation between the teacher and the student network, we seamlessly integrate adversarial learning with latent and discriminative knowledge regularizations encouraging the learned representations of the partial video to be more informative and discriminative toward the action prediction. Finally, we devise a multi-modal fusion module toward comprehensively predicting the action label. Extensive experiments on two public egocentric datasets validate the superiority of our method over the state-of-the-art methods. We have released the codes and involved parameters to benefit other researchers. 1

First report of Bougainvillea spectabilis Willd bacterial leaf spot caused by Pantoea stewartii subspecies indologenes in China.

Bougainvillea spectabilis Willd. is an important ornamental flowering plant belonging to the family Nyctaginaceae. It is widely used in landscape designs in tropical and subtropical regions. In December 2020, severe disease-causing leaf spots were discovered on the leaves of B. spectabilis in the Modern Agricultural Park (110°19' E, 21°26' N) Zhanjiang City, Guangdong Province, China. Field surveys revealed that the disease was widespread, with an incidence of 60-80%. Early symptoms on the leaves appeared as tiny leaf spots that later developed into concentric circles surrounded by a yellowish halo (Fig. 1). Diseased leaves with typical symptoms were collected for pathogen isolation. The leading edges of the lesions were excised, sanitized in 75% ethanol for 30 s and in 3% sodium hypochlorite for 3 min, and rinsed three times with sterile distilled water (SDW). The diseased tissue was crushed in 1 mL SDW, soaked for 15 min, and then spread onto nutrient agar medium on a petri dish. Circular, bright yellow colonies with smooth margins were observed after 24 h of incubation at 28 °C. The isolate (SJM1) was a gram-negative bacillus with positive results for catalase, indole synthesis, maltose, and arbutin and negative results for sorbitol, lactose, salicin, and starch hydrolysis. The SJM1 genomic DNA was extracted using the TIANamp Bacterial DNA Kit, and partial 16S rDNA gene segments were amplified using the bacterial generic primers 27F and 1492R. The collated 16S rDNA gene sequences were submitted to the NCBI GenBank (MZ723935). BLAST analysis of the sequences revealed 99.38% identity with Pantoea stewartii (MG517424.1). Amplification using subspecies-specific primers galE (#562/564; Gehring et al. 2014), glmS (#356/341; Wensing et al. 2010), and pstC + pstS (#338/339; Wensing et al. 2010) revealed that the genes showed 99-100% identity with P. stewartii subsp. indologenes (galE = 100%, MZ754494.1; glmS = 99.79%, MZ75496.1; and pstC + pstS = 99.89%, MZ754495.1). Phylogenetic trees were constructed using the neighbor-joining method (MEGA X), with both the 16S rDNA sequence (Fig. 2 2A) and the concatenated 16S rDNA, galE, pstC + pstS, and glmS sequences (Fig.2 2B). The SJM1 isolate belonged to the same clade as P. stewartii subsp. indologenes and was 99% homologous to P. stewartii subsp. indologenes strain ZJ-FGZX1 (Fig. 2 2B; Ren et al. 2020). Pathogenicity tests were performed through prick wound inoculation. Sterile needles were used to create fresh wounds on healthy young leaves of one-year-old B. spectabilis plants. Wounds were inoculated with 20 μl bacterial suspension (1 × 108 CFU/ml) or SDW. Four leaves per plant and three plants per treatment were evaluated. The plants were incubated at 28 °C temperature and 80-90% relative humidity. After 4-7 days of inoculation, all plants inoculated with the bacterial suspension had spot symptoms with a halo, similar to those observed in the field. However, leaves inoculated with SDW alone did not show any symptoms. Furthermore, the colony morphology and 16S rDNA sequences of the strains isolated from the inoculated leaves were identical to those of the original isolates. These results verified Koch's postulates. Based on biochemical identification and sequencing analysis, the pathogen causing B. spectabilis leaf spot was identified as P. stewartii subsp. indologenes. Previous reports have shown that P. stewartii subsp. indologenes can cause diseases in Dracaena sanderiana, Cenchrus americanus, and Allium cepa (Zhang et al. 2020, Ashajyothi et al. 2021, Stumpf et al. 2018). To our knowledge, this is the first report of P. stewartii subsp. indologenes causing B. spectabilis leaf spot disease in China.

The risk premium in times of financial crisis: an assessment from ICAPM on the MENA region

The purpose of our research is to study the impact of financial crisis on Risk premium evolution for a set of emerging countries of MENA (Middle East and North Africa), giving special attention to the appreciation of the local risk premium in addition to the currency risk premium through the study of the dynamics of the financial integration under two assumptions: perfect integration and partial segmentation. At the methodological level, we test a conditional version of the international model of the financial assets ICAPM of De initially proposed by Adler and Dumas 1983 [Adler, M., & Dumas, B. (1983). International portfolio choice and corporation finance: A synthesis. The Journal of Finance, 38(3), 925-984.]. Our analysis is based on the conditional model of regime change of Bekaert and Harvey (1995) [Bekaert, G., & Harvey, C. R. (1995). Time‐varying world market integration. The Journal of Finance, 50(2), 403-444.], we follow the econometric modeling using the Kalman filter and the Markov regime-switching model with variable transition probabilities.

Evolution of Coulomb failure stress in Sulaiman Lobe, Pakistan, and its implications for seismic hazard assessment

In this research work, we present the evolution of Coulomb failure stress (CFS) in the Sulaiman Lobe and its implications for seismic hazard assessment. The Chaman transform fault, ~1,000 km long, is the major active fault that marks the western boundary between Pakistan and Afghanistan on the Indian Plate. To date, few studies have been conducted to unveil the interactions among earthquakes and the implications of these interactions for seismic hazard assessment in the region. We thoroughly investigated the published and online catalog to construct a sequence of major earthquakes that occurred in this region during the past. The final earthquake sequence was composed of 15 earthquakes of <italic>M</italic>_w ≥ 6.0, beginning with the 1888 earthquake. We used the stress-triggering theory to numerically simulate the evolution of CFS caused by these earthquakes. The numerical results revealed that 8 out of 15 earthquakes were triggered by the preceding earthquakes. The earthquakes in 1908, 1910, 1935, 1966, and 1997 were rather independent earthquakes in this sequence. Although the epicenters of the 1975a and 1975b earthquakes were in the stress shadow zone, the partial rupture segments of both these earthquakes were in high-CFS regions. The CFS induced by the 1935 earthquake was notable, as it later triggered the 2008 doublet. Moreover, our results revealed that the northern segment of the Chaman Fault, the southern segment of the Ghazaband Fault, and the northwestern segment of the Urghargai Fault demonstrated a high change in CFS that could trigger seismicity in these regions. The necessary arrangements must therefore be made to mitigate any possible seismic hazards in the region.

A METHOD OF PIECE-QUADRATIC INTERPOLATION OF A TABLED FUNCTION WITH A CONTINUOUS FIRST DERIVATIVE BASED ON LINEAR COMBINATION OF CENTRAL-SYMMETRIC PARABOLAS

In engineering practice, one often has to deal with reference and experimental data, in particular, with tabularly specified functional dependencies. Such functions characterize the properties of materials, as well as physical and technical objects. The dielectric properties of materials are represented by functional dependences of the electric polarization of a substance or electric displacement on the strength of the electric field. Such functions are called polarization curves of dielectrics. Their significant nonlinearity is manifested in ferroelectrics. The magnetic properties of materials are represented by functional dependences of the magnetization of a substance or magnetic induction on the strength of the magnetic field. Such functions are called magnetization curves. Their essential nonlinearity is manifested in ferro- and ferrimagnets. The properties of technical objects are largely determined by the properties of materials, designs, geometric dimensions of parts, elements and assemblies. Information about the properties of standardized materials and products from them can be contained in reference books in graphical or tabular form in cases of non-linearity of the corresponding functional dependencies. In the course of technical calculations, they need to be smoothly interpolated. A method of piecewise-quadratic interpolation of a tabularly given function with a continuous first derivative is proposed based on the linear combination of modified (centrally symmetric) parabolas, which does not require the use of logical operations in calculations to determine a partial segment of the grid of argument values. A method for weighted minimization of the oscillatory effect for non-uniform grids of argument values is proposed. Computational experiments with the reference main magnetization curve of electrical steel show the high numerical stability of the proposed method with respect to the effect of oscillation and the technical flexibility in its application for solving practical problems of processing tabularly specified functional dependencies on essentially non-uniform grids of argument values, including those with a small number of tabular points.

The Impact of Competitive Strategies on Performance of Banking Sector; The Mediating Role of Corporate Social Responsibility and Operational Excellence

This study examined the mediating role of corporate social responsibility practices and the nexus between the operational excellence of competitive strategies, and performance, for Islamic banks operating in Pakistan. Particularly, two competitive strategies, namely, the cost-sensitive strategy and the differentiation strategy were considered. This study employed a structured questionnaire to collect the data from 512 respondents (361 from fully-fledged Islamic banks, FFIBs, and 151 from Islamic window banks, IWBs). A partial least square–structural equation modeling approach was employed to test the hierarchal component model. As a robustness check, the Gaussian copula approach was used to address the endogeneity issue, and the Partial least square prediction-oriented segmentation approach was adopted to explore the unobserved heterogeneity. The findings showed evidence of complementary partial mediation between competitive strategies and bank performance, channeled through operational excellence. However, corporate social responsibility (CSR) practices partially mediated the relationship between cost-sensitive strategy and bank performance. This study is worthwhile for managers, policy makers and regulatory authorities concerned with religiously conscious corporations.

Revisiting English written VP-ellipsis and VP-substitution: a dependency-based analysis

Abstract The present study quantitatively investigates the structural features of English written VP-ellipsis (VPE) and VP-substitution (VPS) under the theoretical framework of dependency grammar. Results show that: (a) Both VPE and VPS are human language mechanisms that follow the least effort principle; (b) Dependency distance of segmental and discontinuous linguistic units under the influence of VPE and VPS conform to the right-truncated Zipf-Alekseev distribution; (c) Dependency-based indices are effective in distinguishing similar structures of VPE and VPS, especially the Do-Support in ellipsis and Do + X in substitution. The study not only explores the regularity of partial segments of human language under the mechanisms of VPE and VPS, but also offers quantitative evidence for distinguishing confusing structures in VPE and VPS, which may aid in raising the retrieving precision of similar linguistic structures in corpora.

A viral metagenomic protocol for nanopore sequencing of group A rotavirus

AimDevelopment of an unbiased methodology using Oxford Nanopore Technology (ONT) sequencing to obtain whole-genome sequences (WGS) of Rotavirus A (RVA) from clinical samples. Methods157 RVA qRT-PCR positive faecal samples were enriched by virus-like particle (VLP) purification and host nuclease digestion to enhance the detection of viral nucleic acids and cDNA generated as per the NetoVIR protocol. ONT sequencing was then performed using the ONT Native Barcoding kit (SQK-LSK-109) on the GridION platform. Data was basecalled, demultiplexed and assembled into near complete RVA genomes. The accuracy and quality of the obtained sequences was assessed by comparing to Sanger sequencing and RVA reference genomes. ResultsThe developed protocol generated 146 near-complete RVA WGS out of the 157 RVA-positive clinical samples. The quality of the assembled genomes was assessed by comparison against publicly-available sequences with results showing 98.76 % ± 0.03 % similarity and > 90 % genome coverage. A concordance assessment was performed comparing the identity of partial RVA VP7 and VP4 segments obtained by Sanger sequencing (n = 51) against corresponding nanopore sequences which demonstrated an overall identity of 100.0 % ± 0.02 %. ConclusionsThe nanopore protocol generated both high quality and accurate RVA WGS extracted from faecal samples. This protocol can be extended to other viral agents in other sample types.

A Real-Time Tunable ECG Noise-Aware System for IoT-Enabled Devices

The Internet of Things (IoT)-enabled electrocardiogram (ECG) monitoring system is an evolution in healthcare, and it is used to monitor heart conditions during daily living conditions. However, the ECG signals from the wearable device are often contaminated with noise, such as power line interference, baseline wander, and muscle artefact. The presence of these noises increases the communication cost of IoT-enabled devices by transmitting unusable noisy signals as well as affecting the clinical decision-making at the application level. A real-time ECG noise detection system at the IoT-enabled gateway can reduce this communication cost and improve the overall quality of the transmitted signal. In this article, we propose a tunable ECG noise localization system to detect noisy ECG segments with a selected percentage of noise-free ECG levels. The proposed system will hold the following: 1) deployable ECG noise detector in the low-computational devices, such as smart phone; 2) reduce the data dropping rate at the network layer; 3) reduce the transmission cost; and 4) improve the reliability of the automated analysis of ECG at the IoT-enabled gateway. We evaluated the performance of the proposed ECG noise detector using publicly available and real-time ECG datasets from wearable ECG sensors. We also measured the data drop rate and compared it with the state-of-the-art algorithms. The impact of reduced data dropping rate also justifies using two commonly used R-peak detection algorithms. The proposed system reduces the data dropping rate by 21.09% on average over public and real-time datasets. Similarly, it increases the number of R-Peak detection by 15.33% on average compared with the existing binary noise classification system by saving partial noisy ECG segments. Thus, the proposed noise detection system improves the reliability of the automated ECG analysis. In summary, the study results support the necessity of a noise detection system for wearable ECG monitoring in daily living conditions to improve the data acquisition efficiency. In addition, a noise localizer can be used for dropping a noisy portion of the signal or filtering the only noisy segment of the ECG signal without blindly imposing a filter on both noise-free and noisy ECG signals.

Utilisation of a mitochondrial intergenic region for species differentiation of fruit flies (Diptera: Tephritidae) in South Africa

BackgroundFruit flies (Diptera: Tephritidae) comprise species of agricultural and economic importance. Five such fruit fly species are known to affect commercial fruit production and export in South Africa: Ceratitis capitata, Ceratitis cosyra, Ceratitis rosa, Ceratitis quilicii, and Bactrocera dorsalis. Management practices for these pests include monitoring, application of pest control products, post-harvest disinfestation measures and inspection of consignments both prior to shipment and at ports of entry. In activities relating to monitoring and inspection, accurate identification of these pests to species level is required. While morphological keys for adult stages of these fruit fly species have been well developed, morphological keys for earlier life stages remain problematic. In instances where closely related species cannot be reliably distinguished morphologically, there is a need for molecular tools to assist in identifying these five fruit fly species during surveillance practices, where sequencing-based approaches would be beneficial.ResultsTwo complete mitochondrial genomes were assembled for each fruit fly species investigated using high throughput sequencing data generated in this study. A single primer set was designed to amplify a region between tRNAile and tRNAmet. The amplicon consists of a partial segment of tRNAile, intergenic region I (tRNAile - tRNAgln), the complete sequence of tRNAgln, intergenic region II (tRNAgln - tRNAmet), and a partial segment of tRNAmet. PCR amplicons were generated for 20 specimens of each species, five of which were colony adult males, five colony larvae, and 10 wild, trap-collected specimens. Upon analysis of the amplicon, intergenic region I was identified as the most informative region, allowing for unambiguous identification of the five fruit fly species. The similarity in intergenic region II was too high between C. rosa and C. quilicii for accurate differentiation of these species.ConclusionThe identity of all five fruit flies investigated in this study can be determined through sequence analysis of the mitochondrial intergenic regions. Within the target amplicon, intergenic region I (tRNAile - tRNAgln) shows interspecific variation sufficient for species differentiation based on multiple sequence alignment. The variation in the length of intergenic region I is proposed as a potential tool for accurately identifying these five fruit flies in South Africa.

State‐of‐health estimation for lithium‐ion batteries based on partial charging segment and stacking model fusion

AbstractState‐of‐health (SOH) estimation is essential for evaluating the aging process of lithium‐ion batteries, which can effectively guarantee the steady application of the battery system. Most existing prediction approaches apply a single model or a single feature to achieve SOH estimation based on the entire charging curve. In this paper, a multifeature‐based stacked ensemble learning framework is proposed for SOH prediction using partial charging curves. Firstly, combined with the range of state‐of‐charge (SOC) commonly used in the actual operation of vehicles, the charging segment is effectively intercepted through the mapping correlation between the SOC and the terminal voltage. Then, five relevant features characterizing the battery health status are extracted from multiple data, such as temperature, voltage, and incremental capacity profiles. Finally, a two‐level stacking ensemble framework is developed to fuse several individual estimation methods for higher SOH accuracy. To validate the performance of the proposed method, the Oxford University data set and the NASA data set are deployed for comparison experiments, and the results reveal the superior precision and robustness of the developed model in SOH estimation.

Partial nasal bone reconstruction with acrylic bone cement: experimental study

BackgroundThe aim of this study was to evaluate the effectiveness of acrylic bone cement in partial nasal bone reconstruction.MethodsThis study was conducted using nine New Zealand rabbits. The left nasal bones of the rabbits were included in the experimental group, and the right nasal bones were evaluated as the control group. The partial bone segments on the bilateral nasal bones were marked and removed symmetrically. A synthetic graft material made of acrylic bone cement was placed in experimental group, and the partial bone segment removed from the right side was placed in control group as an autograft. All rabbits were sacrificed at the end of the 28th day. Samples were taken from the grafts and from the surrounding soft tissues for histopathological examination. Acute inflammation, chronic inflammation, vascularization, fibrosis, foreign body reaction, bone proliferation, and the presence of empty lacunae were evaluated under a light microscope for both groups.ResultsSurrounding soft tissue on synthetic and autograft were the same in terms of chronic inflammation. There was no statistically significant difference for vascularization, fibrosis, and foreign body reaction. Synthetic graft and autograft were the same in terms of chronic inflammation, fibrosis, and bone proliferation. There was no statistically significant difference for vascularization, foreign body reaction, and presence of empty lacunae (p > 0.05).ConclusionThis study showed no significant differences between the use of acrylic bone and the use of an autograft for partial nasal bone reconstruction in terms of graft or tissue healing. Acrylic bone cement may therefore serve as a good alternative for nasal bone reconstruction.

ANALYSIS OF THE APPLICATION OF DIGITAL SOLUTIONS IN LOGISTICS AND THEIR IMPACT ON THE BUSINESS OF LOGISTICS OPERATORS IN SOUTHEAST EUROPE

In the last few years, new digital solutions have appeared that expand the horizons of logistics operators. These solutions improved the existing business, but also enabled them to deploy new services. While technology has made cargo delivery more efficient in the eyes of customers, it has also improved driver and cargo safety. These benefits create value for the supply chain, but can be difficult to achieve. The analysis of the impact and application of digital solutions in logistics on the operations of logistics operators in Southeast Europe gives the value of research into supply chains of both complete and partial segments in the domain of storage, transportation, distribution and production itself. They can accelerate the digitization of transport and logistics and the potential for growth in a multi-sector environment. Assumptions for the elimination of costs, as a result of the inadequate response of the administration (customs, border police, barriers, etc.) in Southeast Europe can be mitigated through e-CMR - a key investment in the new digital world. The world of transport and logistics is advancing rapidly. Digital development in particular is a game changer with new opportunities and challenges in the logistics sector, especially in terms of data security. Digitization is redefining the way logistics participants collaborate. It can be expected to increase efficiency and reduce costs within the supply chain, but significant performance requires much more than just networking between participants in the trade of goods and services in Southeast Europe. The aim of this work is to conduct research on the impact of digital business in logistics on the business and efficiency of logistics operators in Southeastern Europe. The data that was used in the data analysis is primary, which was researched by the author, through a questionnaire sent to the employees of logistics operators in the area of Southeastern Europe. Accordingly, a hypothesis was set up in the paper, which was examined, and reads: “There is a positive impact of improving business processes by applying digital solutions on the operations of logistics operators in Southeast Europe”. According to the results of the analysis and the coefficients presented in the previous table, it can be clearly seen that the hypothesis has been proven. Namely, according to the results, it is clearly seen that there is a positive and significant impact of the digital solution in logistics on the operations of logistics operators, and that the same is positive and statistically significant in terms of significance.

A state of health estimation method of lithium-ion batteries based on DT-IC-V health features extracted from partial charging segment

ABSTRACT To ensure a safe and reliable operation, accurate estimation of the state of health (SOH) of lithium-ion batteries is necessary. In order to improve the accuracy and practicability of the SOH estimation, this paper proposes a SOH estimation method based on differential temperature-incremental capacity-voltage (DT-IC-V) health features (HFs). A new DT-related health feature extraction method is proposed by analyzing the potential relationship between the temperature difference profile and SOH. A set of DT-IC-V HFs are designed in a relatively small charging segment to reduce the difficulty of obtaining data in practice. And a battery SOH estimation model based on deep belief network (DBN) and extreme learning machine (ELM) is designed. The number of nodes in each hidden layer of the DBN-ELM model is determined by the Sparrow Search Algorithm (SSA). The proposed method is validated on different types of batteries. The results show that the method can accurately estimate the SOH, with the mean absolute percent error remaining within 0.43% and 1.35% in the Oxford and NASA datasets, respectively.