Competitive Technique Research Articles

Physico-chemical and biological characterization of urban municipal landfill leachate and treatment by ozone process

Thewaste production nationwide is increasing every year, on account of therapid urbanization and growing populations, also consumption modes. Algerian political authorities have chosen Technical Landfill Centres (TLC) as a competitive and safe technique of waste management.However, storing these wastes in a bad way poses several environmental challenges, especially in the Department of Saïda, the latter have significant groundwaters. The major problem registered on this Landfill is the leachate resulting from the degradation of buried wastes which were disposed off the outside of the leachate basinand present a source of pollution for the local groundwaters by heavy metals and pathogenic germs.The present paper investigates the leachate treatment ozone process produced by Dielectric Barrier Discharge (DBD) under high potential.The experimental resultsobtained allowed us to show the efficiency of the treatment process by ozone based on the micro pollutant analysis (COD, BOD5 , TOC, heavy metals) and microbial analysis, after ozonation treatment.The results show that 80% of micro pollutants are eliminated and 100% destruction of all bacteria which reveals the high efficiency of the process.

Ramadan fasting and exercise combination therapy: A novel approach for osteoporosis prevention in ovariectomized rats.

Osteoporosis is a chronic bone metabolic disease that affects millions of people worldwide, particularly the elderly and postmenopausal women. It is characterized by weakened bones, increasing the risk of fractures and leading to significant morbidity and mortality. The goal of the current study is to examine the reported osteo-preservative effects of exercise and/or fasting in the Ramadan fasting model (RFM) in ovariectomized (OVX) rats. The experimental intervention started 1month following the ovariectomy procedure and consisted of five 15-min exercise sessions per week at 18-25m/min and/or an approximately 13-h fast from sunrise to sunset (6:00 AM-19:00 PM). Serum bone metabolism biomarker levels were measured, and mineral concentrations in femoral ashed bones and digested serum were determined. Additionally, serum bone alkaline phosphatase (b-ALP), parathyroid hormone, osteocalcin, calcitonin, and vitamin D3 concentrations were measured using the competitive enzyme immunoassay technique. Calcium, magnesium, and phosphorus showed a notable decrease in mineral concentration among OVX rat femurs compared with the combination group (OVX + RFM + E) and control groups. In addition, homeostasis of serum concentrations of calcium, magnesium, and phosphorus was observed to increase in the OVX + RFM + E group rather than in the OVX group without intervention when compared with a control group. Furthermore, fasting and exercise, either alone or concurrently with ovariectomy, induced a non-significant elevation in osteocalcin, parathyroid hormone, and vitamin D3, whereas b-ALP and calcitonin increased significantly compared with those in control rats. The combination of the Ramadan fasting model and moderate intensity exercises among OVX rats manifested advantageous effects in bone biomarkers compared with OVX rats without intervention. This could be recommended as a lifestyle modification that is protective against osteoporosis, especially in the context of depleted estrogen hormone after menopause.

Deep hybrid approach with sequential feature extraction and classification for robust malware detection

Malware attacks have escalated significantly with an increase in the number of internet users and connected devices. With the increasingly different types of malware released by hackers, designing new and competitive techniques to detect advanced malware is essential. In the proposed research, we have developed a multi-level feature extraction technique using deep learning architectures and a classification model to classify malware families. The essential features from the malware images are extracted using the Gated Recurrent Unit in the first step, which are further fed to a Convolutional Neural Network model for extracting the final feature vector. The multi-level feature selection is followed by classification into various malware families using Cost-sensitive Boot Strapped Weighted Random Forest (CSBW-RF). The proposed approach gave promising results of 99.58 % accuracy in distinguishing the 25 different malware families on the Mallmg dataset. This hybrid model gave significantly better performance scores for classifying visually similar malware families. The generalizability of the proposed model is benchmarked with the popular Microsoft Big 2015 dataset and has achieved comparatively higher performance scores than many existing models. This benchmarking demonstrates the robustness and scalability of our approach. The use of cost-sensitive learning and bootstrapping techniques also contributed to the model’s ability to generalize well to new and unseen data. These enhancements ensure that our model can be effectively applied in diverse real-world scenarios, maintaining high performance across different environments and malware types. This research can contribute to detecting malware attacks and can be integrated in threat monitoring systems. The successful application of this hybrid model indicates its potential for deployment in real-world cybersecurity environments, providing a strong defense against evolving malware threats.

OBP83b and OBP49a Involved in the Perception of Female-Derived Pheromones in Bactrocera dorsalis (Hendel).

In Bactrocera dorsalis, both males and females release chemical signals to attract mates. In our previous study, we identified ethyl laurate, ethyl myristate, and ethyl palmitate as potent female-derived pheromones that contribute to mate attraction. However, the mechanisms underlying the olfactory recognition remain unclear. In this study, we observed strong antennal and behavioral responses in male B. dorsalis to these female-derived pheromones, and further investigation revealed significant upregulation of OBP49a and OBP83b following exposure to these compounds. Through fluorescence competitive binding assays and RNA interference techniques, we demonstrated the crucial roles of OBP49a and OBP83b in detecting female-derived pheromones. Finally, molecular docking analysis identified key residues, including His134 in OBP83b and a lysine residue in OBP49a, which formed hydrogen bonds with female-derived pheromones, facilitating their binding. These findings not only advance our understanding of olfactory recognition of pheromones in B. dorsalis but also offer potential targets for developing olfaction-interfering techniques for pest control.

Establishment of Vietnamese human induced pluripotent stem cell-derived Orexin-A neurons

Narcolepsy type 1 is a neurological disorder that severely affects sleep control due to a decrease in orexin neurons. Although research on neurological diseases, including narcolepsy, is vital for advancing science and improving human life, progress is hindered by the challenge of obtaining human brain samples as well as the difficulty in culturing neuronal cell lines. To overcome this obstacle, we generated Orexin-A neurons from human induced pluripotent stem cells (hiPSCs) by utilising a differentiation medium supplemented with N-acetyl-D-mannosamine (ManNAc) and Bone morphogenetic protein 4 (BMP4). The cells obtained from our study were characterised using immunofluorescence staining, real-time PCR, and competitive ELISA technique. The results show that our research successfully generated a hiPSC line with pluripotent stem cell characteristics. Furthermore, Orexin-A neurons derived from hiPSCs showed positive results for NCAM1 and TUBB3 markers, an increase in HCRT gene expression, and the capacity to secrete Orexin-A. These promising findings suggest that our hiPSC-derived Orexin-A neurons could serve as a foundation for cell-based disease modelling and further investigations into the underlying mechanisms of related neurological disorders.

A novel competitive color-tone change fluorescence immunochromatographic assay for the ultrasensitive detection of pesticide and veterinary drug residues

One important challenge faced by competitive immunoassay techniques is the inability to visually distinguish weak color changes in the T-line during the detection of trace analyte concentrations. In this study, we introduced multi-color fluorescence into the immunoassay system and developed a novel competitive dual-channel color-tone change fluorescent immunochromatographic assay (CFICA) for the visual and quantitative detection of kanamycin (KAN) and carbendazim (CBZ). At the same time, we have developed matching, portable fluorescence-reading equipment, which can analyse the spectral data of the RGB channel individually and can effectively solve the problems of the traditional single-fluorescence reader – large reading error and poor detection sensitivity. After optimization, our CFICA, combined with the corresponding instrument, achieved low limits of detection (LOD) for KAN and CBZ, as low as 1.98 and 12.82 pg/mL, respectively. We believe that this CFICA detection system, with its high accuracy, low error, and multi-color detection capabilities, holds tremendous potential for visual and multi-channel competitive point-of-care testing.

Planar and ridged waveguide preparation on erbium pre-implanted fused silica by multi-energy helium ion implantation and femtosecond laser ablation

Ion implantation stands as a highly competitive technique for fabricating optical waveguide structures within photoelectric materials. In this work, both planar and ridge waveguides have been successfully realized on fused silica. The fabrication process begins with the implantation of erbium ions into fused silica, utilizing an energy of 400 keV and a fluence of 5×1015ions/cm2 to produce a fluorescence effect. Following this, helium ions are implanted at varying energies −450, 500, and 550 keV-with a consistent fluence of 3.2×1016ions/cm2 to create a planar waveguide structure. Subsequently, the ridge waveguide is meticulously prepared through the application of laser ablation, leveraging the pre-existing planar waveguide as a foundation. The guide mode of the planar waveguide is characterized at a wavelength of 632.8 nm using the prism coupling method. Additionally, the near-field light intensity distribution at the same wavelength is assessed via the end-face coupling technique and further analyzed using the finite-difference beam propagation method. To substantiate the practical utility of these waveguides, measurements of the propagation loss and fluorescence properties are conducted.

Medium-reach six-mode multiplexed transmission with Kramers–Kronig receiver enabled by mode permutation technique

We investigate the transmission distance of a 20-Gbaud 6-mode dual-polarization QPSK transmission system using the Kramers–Kronig(KK) receiver based on the mode permutation (MP) scheme. Compared to the system without the use of MP scheme, the maximum transmission distance is increased from 460 km to 910 km and to over 1000 km via the cyclic mode permutation and the mirror-flipped mode permutation, respectively, at the KP4 forward error correction (FEC) threshold of 2.2×10−4. The required carrier signal power budget of KK receiver is also reduced. The KK receiver can detect the signal of each mode after the transmission of 1000 km with potential advantages of low cost and low power. It is shown as a competitive detection technique in medium-reach metropolitan networks.

MixFormer: End-to-End Tracking With Iterative Mixed Attention.

Visual object tracking often employs a multi-stage pipeline of feature extraction, target information integration, and bounding box estimation. To simplify this pipeline and unify the process of feature extraction and target information integration, in this paper, we present a compact tracking framework, termed as MixFormer, built upon transformers. Our core design is to utilize the flexibility of attention operations, and we propose a Mixed Attention Module (MAM) for simultaneous feature extraction and target information integration. This synchronous modeling scheme allows us to extract target-specific discriminative features and perform extensive communication between target and search area. Based on MAM, we build our MixFormer trackers simply by stacking multiple MAMs and placing a localization head on top. Specifically, we instantiate two types of MixFormer trackers, a hierarchical tracker MixCvT, and a non-hierarchical simple tracker MixViT. For these two trackers, we investigate a series of pre-training methods and uncover the different behaviors between supervised pre-training and self-supervised pre-training in our MixFormer trackers. We also extend the masked autoencoder pre-training to our MixFormer trackers and design the new competitive TrackMAE pre-training technique. Finally, to handle multiple target templates during online tracking, we devise an asymmetric attention scheme in MAM to reduce computational cost, and propose an effective score prediction module to select high-quality templates. Our MixFormer trackers set a new state-of-the-art performance on seven tracking benchmarks, including LaSOT, TrackingNet, VOT2020, GOT-10 k, OTB100, TOTB and UAV123. In particular, our MixViT-L achieves AUC scores of 73.3% on LaSOT, 86.1% on TrackingNet and 82.8% on TOTB.

An effective weight initialization method for deep learning: Application to satellite image classification

The growing interest in satellite imagery has triggered the need for efficient mechanisms to extract valuable information from these vast data sources, providing deeper insights. Even though deep learning has shown significant progress in satellite image classification. Nevertheless, in the literature, only a few results can be found on weight initialization techniques. These techniques traditionally involve initializing the networks’ weights before training on extensive datasets, distinct from fine-tuning the weights of pre-trained networks. In this study, a novel weight initialization method is proposed in the context of satellite image classification. The proposed weight initialization method is mathematically detailed during the forward and backward passes of the convolutional neural network (CNN) model. Extensive experiments are carried out using six real-world datasets. Comparative analyses with existing weight initialization techniques made on various well-known CNN models reveal that the proposed weight initialization technique outperforms the previous competitive techniques in classification accuracy. The complete code of the proposed technique, along with the obtained results, is available at https://github.com/WadiiBoulila/Weight-Initialization.

Cancer data analysis using competitive ensemble machine learning techniques

Cancer data analysis using competitive ensemble machine learning techniques

Quantum Field Theory for Multipolar Composite Bosons with Mass Defect and Relativistic Corrections

Atomic high-precision measurements have become a competitive and essential technique for tests of fundamental physics, the Standard Model, and our theory of gravity. It is therefore self-evident that such measurements call for a consistent relativistic description of atoms that eventually originates from quantum field theories like quantum electrodynamics. Most quantum metrological approaches even postulate effective field-theoretical treatments to describe a precision enhancement through techniques like squeezing. However, a consistent derivation of interacting atomic quantum gases from an elementary quantum field theory that includes both the internal structure as well as the center of mass of atoms, has not yet been addressed. We present such a subspace effective field theory for interacting, spin carrying, and possibly charged ensembles of atoms composed of nucleus and electron that form composite bosons called cobosons, where the interaction with light is included in a multipolar description. Relativistic corrections to the energy of a single coboson, light-matter interaction, and the scattering potential between cobosons arise in a consistent and natural manner. In particular, we obtain a relativistic coupling between the coboson’s center-of-mass motion and internal structure encoded by the mass defect. We use these results to derive modified bound-state energies, including the motion of ions, modified scattering potentials, a relativistic extension of the Gross-Pitaevskii equation, and the mass defect applicable to atomic clocks or quantum clock interferometry. Published by the American Physical Society 2024

Harnessing potential of avian eggshell membrane derived collagen hydrolysate for bone tissue regeneration.

Natural bone grafts are the highly preferred materials for restoring the lost bone, while being constrained of donor availability and risk of disease transmission. As a result, tissue engineering is emerging as an efficacious and competitive technique for bone repair. Bone tissue engineering (TE) scaffolds to support bone regeneration and devoid of aforesaid limitations are being vastly explored and among these the avian eggshell membrane has drawn attention for TE owing to its low immunogenicity, similarity with the extracellular matrix, and easy availability. In this study, the development of bone ingrowth support system from avian eggshell membrane derived collagen hydrolysates (Col-h) is reported. The hydrolysate, cross-linked with glutaraldehyde, was developed into hydrogels with poly-(vinyl alcohol) (PVA) by freeze-thawing and further characterized with ATR-FTIR, XRD, FESEM. The biodegradability, swelling, mechanical, anti-microbial, and biocompatibility evaluation were performed further for the suitability in bone regeneration. The presence of amide I, amide III, and -OH functional groups at 1639cm- 1,1264cm- 1, and 3308cm- 1 respectively and broad peak between 16°-21° (2θ) in XRD data reinstated the composition and form. The maximum ratio of Col-h/PVA that produced well defined hydrogels was 50:50. Though all the hydrogel matrices alluded towards their competitive attributes and applicability towards restorative bone repair, the hydrogel with 40:60 ratios showed better mechanical strength and cell proliferation than its counterparts. The prominent E. coli growth inhibition by the hydrogel matrices was also observed, along with excellent biocompatibility with MG-63 osteoblasts. The findings indicate strongly the promising application of avian eggshell-derived Col-h in supporting bone regeneration.

Variable-frequency phase-shifting algorithm with least-squares iteration for hybrid errors reduction under structured-light illumination

In this paper, a novel variable-frequency phase-shifting algorithm is proposed to reduce hybrid errors under structured-light illumination, which combines a least-squares iteration with regularization processing. Theoretical formulas and related calculation techniques for the variable-frequency fringe model are respectively derived and proposed considering phase-shifting errors, temporal intensity fluctuations and Gamma distortion. From the perspective of theoretical model, it is successfully verified and implemented to establish a minimal and sufficient system of equations using a least number of fringe patterns. To tackle the challenge of solving numerous unknown parameters in the derived model caused by these sources, a grouped step-by-step strategy is adopted during the least-squares iteration process. Error point determination and compensation tricks are further developed to reduce the phase jump problem caused by underdetermined solution. A Gamma prediction technique based on bi-frequency single-frame fringe patterns is also put forward to enhance the accuracy of phase retrieval results. Corresponding simulation and experimental results have demonstrated that the proposed algorithm with attractive potentiality can achieve a significant enhancement in accuracy using the least number of phase-shifting fringe patterns (taking bi-frequency two-step as an example in this study) compared to other existing competitive techniques.

Mid-infrared optical frequency comb spectroscopy using an all-silica antiresonant hollow-core fiber.

We present the first mid-infrared optical frequency comb spectrometer employing an absorption cell based on self-fabricated, all-silica antiresonant hollow-core fiber (ARHCF). The spectrometer is capable of measuring sub-mL sample volumes with 26 m interaction length and noise equivalent absorption sensitivity of 8.3 × 10-8 cm-1 Hz-1/2 per spectral element in the range of 2900 cm-1 to 3100 cm-1. Compared to a commercially available multipass cell, the ARHCF offers a similar interaction length in a 1000 times lower gas sample volume and a 2.8 dB lower transmission loss, resulting in better absorption sensitivity. The broad transmission windows of ARHCFs, in combination with a tunable optical frequency comb, make them ideal for multispecies detection, while the prospect of measuring samples in small volumes makes them a competitive technique to photoacoustic spectroscopy along with the robustness and prospect of coiling the ARHCFs open doors for miniaturization and out-of-laboratory applications.

Sero-Detection of HEV in Cancer Patients and Healthcare Workers, Sudan

Background: Sudan, as a developing country, suffers from most of the risk factors related to the transmission of the hepatitis E virus (HEV). Objective: This study aimed to determine the sero-frequency of HEV in cancer patients and healthcare workers and its associated risk factors. Methods: A descriptive cross-sectional design was followed, recruited populations were 120 cancer patients and 120 healthcare workers. Sociodemographic data and risk factors of the study subject were collected using a questionnaire and patient records. HEV-specific anti-HEV IgM and IgG antibodies screened by a competitive ELISA technique. A Chi-square test was determined for the association between HEV IgG sero-positivity andsubject information. Results: The percentage of cancer patients positive for anti-HEV IgG and IgM antibodies was 50% (60/120) and 6.7% (8/120) while for the healthcare workers group was 25.8% (31/120) and 4.2% (5/120) respectively. The most documented risk factors for HEV among cancer patients were consumption of raw meat 94.2% (113/120), contact with animals 88.3% (106/120), and history of blood transfusion 81.7% (98/120) while raw meat consumption was commonly found in 94.2% (113/120) of healthcare workers group. A significant association was observed between IgG sero-positive healthcare workers and consumption of raw meat (p-value = 0.005). Conclusion: The HEV sero-positivity was found at a high rate in both study groups, which requires further studies on its prevalence, epidemiology, and prevention.

THE THEORY OF FUNCTIONAL SYSTEMS AS A UNIVERSAL CONCEPT FOR ORGANIZING SPORTS TRAINING

The training process constructing fundamentals and the validity of its progress degree are considered from the perspec-tive of the functional systems theory by P. K. Anokhin. The sports characteristics analysis from the perspective of the functional systems theory is provided. It is noted that during sports training the structural and content components of the main competitive exercise technique as a form of the functional systems theory manifestation. And their connection with the energy and vegetative support of activity are not fully taken into account. A new approach and algorithm for organizing sports training is proposed.

Deep investigation of muography in discovering geological structures in mineral exploration: a case study of Zaozigou gold mine

Summary Muography is a promising and rapidly developing physical prospecting technique based on the attenuation of muon flux. The feasibility and potential of applying muography to mining were presented in a small number of previous case studies. In this work, we aimed to address three challenges that limit the applicability and efficiency of muography in mineral exploration: (1) application to low-density-contrast ore body exploration, (2) analysis of altitudinal impacts on measurements, and (3) precise and efficient reconstruction. We conducted the first case of applying muography to the exploration for low-density-contrast ore bodies. Six muon imaging systems were placed underground to collect surviving muons for roughly half a year. We analyzed the altitudinal impact on the data measurements and proposed a simplified method to eliminate it. We also developed the seed algorithm, a novel three-dimensional reconstruction algorithm, that can reconstruct anomalies located far away from the detectors and avoid their elongation along the observed directions. Benefiting from the seed algorithm, a low-density-contrast orebody and a limonitic siliceous slate structure were reconstructed, demonstrating the sensitivity of this technique in density distinction; discoveries of several mined-out areas are important for accident avoidance; and reconstruction of the stope and scarps served as strong circumstantial evidence of the reliability of the result. The success of this experiment shows the great value of muography in the economic, research and safety aspects of mineral exploration and inspection. Moreover, the overcoming of challenges is helpful for the development of muography, making it an effective and competitive technique in this field.

A hybrid MGO-JAYA based clustered routing for FANETs

In recent years, Flying Ad-hoc Networks (FANETs) have gained significant attention among researchers due to the widespread applications and increasing popularity of Unmanned Aerial Vehicles (UAVs). As technology advances and more research is undertaken, FANETs are expected to become a vital aspect of modern times, allowing for more effective and creative applications in different domains. However, FANETs also face several challenges, including high mobility, dynamic topology, energy constraints, and communication reliability. Addressing these challenges is essential to unlock the full potential of FANETs and to ensure reliable and timely delivery of data. In this paper, we propose HMGOC, a novel clustered routing model for FANETs, utilizing a hybrid approach that combines the Mountain Gazelle Optimizer (MGO) and Jaya Algorithms. The dynamic flying behavior of UAVs demands an adaptive and efficient clustering strategy to maintain network stability and ensure robust and reliable communication among UAVs. In this context, MGO, one of the most recent swarm-based optimization methods, is enhanced and employed for FANET clustering process. Also, we design a routing mechanism based on conditional Bayes' theorem which adapts to changing network conditions, reduces packet losses, and ensures timely data delivery. HMGOC offers several advantages over other competitive techniques, including improved load balancing, minimized energy consumption and latency, and enhanced network throughput and lifespan. The simulation results demonstrate that the HMGOC technique beats the existing methods in terms of enhanced cluster stability and lifetime, increased packet deliverability, energy efficiency, reduced latency, and minimized overhead.

Anti-Fatigue Tandem Organic Photovoltaics for Indoor Illumination.

The ability of achieving high efficiency makes tandem organic photovoltaics (PVs) a competitive technique in potential indoor applications. Except high efficiency, reliable indoor energy supply also calls for outstanding stability. However, unavoidable unstable voltage supply from the circuit control system for indoor light sources like light emitting diodes (LED) and incandescent lamps would cause carrier density fluctuation and device fatigue driven by periodic light/dark switching. In this work, the strobing-induced fatigue within the bulk heterojunction (BHJ)/interconnecting layer (ICL) interface is first revealed and overcome. Based on reliable and effective interfacial doping between conjugated acceptor and metal oxide, the interfacial capacitance that determines the strobing-induced fatigue, has been significantly restrained. The imbalance carrier migration and fierce inter-layer accommodating during the burn-in stage caused by light strobing are substantially diminished. Benefit from this method, the stability of tandem devices is highly enhanced under strobing indoor illumination, and a champion efficiency (35.02%) is obtained. The method provides guidance for further material design for interconnecting layers in organic photovoltaics.