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Objective: the primary correction of male epispadias consists of multiple surgical steps: isolation of the complex corpora-urethral plate, bladder neck plasty if incontinence is associated, tubularization of the urethral plate, correction of dorsal curvature, and skin coverage of the penile shaft. The present video demonstrates our center experience in managing incontinent male epispadias and addresses the above-mentioned technical details to achieve the best possible cosmetic and functional result. In particular, the bladder neck plasty through perineal approach, the correction of curvature by external rotation of the corpora and the penile shaft coverage with rotational skin flap Patient and surgical procedure: a 3-year-old boy was born with an isolated incontinent penopubic epispadias. The epispadias was repaired starting with the isolation of the corpora-urethral plate complex using disassembly technique and external rotation of both corpora to fix the dorsal chordee. In addition, the bladder neck was funneled to treat the incontinence. Dorsal skin coverage was achieved using penile skin rather than the redundant ventral prepuce. Result: the boy had an excellent outcome with no urethrocutaneous fistula or recurrence of dorsal chordee at one-year follow-up post-surgery. Conclusion: the described surgical steps allowed dorsal skin closure using the lateral rotation of ventral penile skin flap to improve cosmesis after epispadias repair and avoid the recurrence of chordee secondary to midline dorsal scarring

The increasing demand for sustainable and renewable energy solutions reflects the critical importance of advancing photovoltaic (PV) technology and its operational efficiency. In response, this study introduces a novel application of the k-Nearest Neighbor (k-NN) algorithm to assess the reliability and applicability of solar panel simulation data which aimed to classify current states of partial shading, open, and short circuit conditions, alongside regression-based analysis for predicting specific operating parameters. In this research, a published dataset that involved various PV module configurations under different environmental conditions was tested and evaluated. The k-NN technique was applied to both classify the operational status and predict performance metrics of the modules. The diagnosis model demonstrated an accuracy of 99.2 % and an F1 score of 99.2 %, indicating a high degree of reliability in identifying the operational states of PV modules. Concurrently, the regression model exhibited a Root Mean Square Error (RMSE) of 0.036 and an R2 value of unity that showcased its effectiveness in predicting the operational parameters based on the simulation data. The concluded results are further enriching the effectiveness of simulation-based data generation to be endorsed and implemented before jumping into real experimental applications, in addition to highlighting the potential applicability of k-NN and machine learning for PV cells productivity statistical analysis.

Moniezia expansa worms are a significant source of alkaline phosphatase (ALP) and acetylcholinesterase (AChE) enzymes. The current study presents a simple and reproducible ALP and AChE purification method from Moniezia expansa helminthes by precipitating the proteins with ammonium sulfate and chromatography on the Sephacryl S-300 column. The M. expansa ALP purified at 1070.8 U/mg, displaying 6.0 purification folds and 53.6% yield, while M. expansa AChE is at 5250 U/mg, displaying 2.0 purification folds and 43% yield. The M. expansa ALP isoenzyme displayed its optimum activity at pH 9.6, while the M. expansa AChE isoenzyme displayed its optimum activity at pH 8.0. The affinity of M. expansa ALP for several substrates revealed that p-nitrophenyl phosphate preferentially cleaved with a Km value of 4.4 mM. M. expansa AChE preferentially cleaved acetylthiocholine iodide with a Km value of 0.9 mM. M. expansa ALP is strongly stimulated with Co2+, Mn2+, Ni2+, and Mg2+and reduced with Zn2+, Cu2+, Ca2+, EDTA and DTT. On the other hand, M. expansa AChE is significantly induced with Co2+, Zn2+, and Ni2+and inhibited with Mg2+, Ca2+, EDTA, 1,10-phenanthroline and eserine. The antisera of the purified M. expansa ALP and AChE found effective for determining the two enzymes in different unknown sera from different animal species, including humans, sheep and fish. These results may provide a possible future application of such enzymes in producing ALP and AChE-coated ELISA plates for research purposes.

Beamforming, an integral component of modern mobile networks, enables spatial selectivity and improves network quality. However, many beamforming techniques are iterative, introducing unwanted latency to the system. In recent times, there has been a growing interest in leveraging mobile users’ location information to expedite beamforming processes. This paper explores the concept of contextual beamforming, discussing its advantages, disadvantages, and implications. Notably, we demonstrate an impressive 53% improvement in the signal-to-interference-plus-noise ratio by implementing the adaptive beamforming maximum ratio transmission (MRT) algorithm compared to scenarios without beamforming. It further elucidates how MRT contributes to contextual beamforming. The importance of localization in implementing contextual beamforming is also examined. Additionally, the paper delves into the use of artificial intelligence (AI) schemes, including machine learning and deep learning, in implementing contextual beamforming techniques that leverage user location information. Based on the comprehensive review, the results suggest that the combination of MRT and zero-forcing techniques, alongside deep neural networks employing Bayesian optimization, represents the most promising approach for contextual beamforming. Furthermore, the study discusses the future potential of programmable switches, such as Tofino—an innovative switch developed by Barefoot Networks (now a part of Intel)—in enabling location-aware beamforming. This paper highlights the significance of contextual beamforming for improving wireless telecommunications performance. By capitalizing on location information and employing advanced AI techniques, the field can overcome challenges and unlock new possibilities for delivering reliable and efficient mobile networks.

Wild Caucasian squirrels are commonly seen in the mountain forests of the northern Iraqi and are sometimes kept as pets. However, no hematological or physiological reference values are set for these animals, even though they are often taken to private veterinary hospitals in the region. So, setting these values is necessary. A selection of hematological and physiological parameters is measured in 24 wild Caucasian squirrels (20 males and four females) in the mountain areas of Pshdar district, Sulaymaniyah, north of Iraq. The hematological parameters included the leukocytes, erythrocytes, hemoglobin, hematocrit, platelets, mean corpuscular volume, mean corpuscular hemoglobin, mean corpuscular hemoglobin concentration, and examination of blood smears stained with Giemsa. The vital signs included the heart rate, respiratory rate, oxygen saturation (SpO2), and rectal temperature. The physiological parameters were scored, and blood was taken from the animals without being sedated or anesthetized by immobilizing them using a specially designed jacket. Most values were as expected compared to other rodent species, and no significant differences were observed between the sexes. All squirrels are successfully rehabilitated and released after the experiments are conducted.

Iraqi Sulaymaniyah is a treasure land of wildlife, especially avian species. Most of this treasure is not discovered, and even the guidance map is not completed yet. The geographical area of Sulaymaniyah is suitable for many types of wildlife animals, including Chukar partridges, locally known as Khasa Kaw. The Chukar partridge is present in many countries in the Middle East region, but there is a subspecies in the northern of Iraq. The Sulaymaniyah nature is a natural habitat for this fantastic bird, seen in the valleys and around water springs throughout the region. Also, Chukar partridges are used as game birds and raised as pets or for fighting between the males. Unfortunately, there is no detailed scientific study on this bird, even though it is one of the most common and beloved birds. This article provides information on the history of the bird, its life cycle, behavior, taxonomy, distribution, sex, and age determination of Chukar partridges. It also mentions the most common diseases of chukar partridge and its natural predators.

Despite the prevalent application of 18% Ni maraging steel in critical sectors such as aerospace and automotive due to its unique characteristics, including high ductility, yield strength, and hardenability, its machining presents enormous challenges, categorizing it as a difficult-to-machine material. The cutting tool’s geometry is crucial in machining, significantly affecting chip formation, cutting forces, power consumption, and obtainable surface quality. In particular, wiper insert technology, characterized by its multi-radius design, offers an increased contact area compared to conventional inserts, potentially enhancing the quality of the machined surface. This study explores the effectiveness of wiper inserts in the face-milling of maraging steel 350, conducting a comparative analysis across three distinct machining setups. These setups vary by alternating the number of wiper and conventional inserts within the same cutter, thereby examining the influence of insert configuration on machining outcomes. The research employs a reliable and well-established statistical approach to evaluate how different variables, such as cutting speed and feed rate, affect surface quality, power consumption, and material removal rate (MRR). It also sheds light on the material removal mechanisms facilitated by each type of insert. The findings reveal that incorporating a higher number of wiper inserts significantly enhances the surface finish but concurrently increases power consumption. Thus, the study successfully identifies an optimal set of process parameters that attain a balance between achieving superior surface quality and maintaining energy efficiency in the machining of maraging steel 350. This balance is crucial for optimizing manufacturing processes while adhering to the stringent quality and sustainability standards required in aerospace and automotive manufacturing.

In this paper, we have relied on the dominant control system as an important tool in building the group of leaders because it allows leaders to contain less dense areas, avoid local areas and produce a more compact and diverse Pareto front. Nine standard nonlinear functions yielded this result. MaBAT/R2 appears to be more efficient than MOEAD, NSGAII, MPSOD, and SPEA2. MATLAB was used to generate all the results of the proposed method and other methods in the same field of work.

Considering the ongoing climate transformations, the appropriate and reliable phenotyping information of plant leaves is quite significant for early detection of disease, yield improvement. In real-life digital agricultural environment, the real-time prediction and identification of living plants leaves has immensely grown in recent years. Hence, cost-effective and automated and timely detection of plans species is vital for sustainable agriculture. This paper presents a novel, non-invasive method aiming to establish a feasible, and viable technique for the precise identification and observation of altering behaviour of plants species at cellular level for four consecutive days by integrating machine learning (ML) and THz with a swissto12 materials characterization kit (MCK) in the frequency range of 0.75 to 1.1 THz. For this purpose, measurements observations data of seven various living plants leaves were determined and incorporate three different ML algorithms such as random forest (RF), support vector machine, (SVM), and K-nearest neighbour (KNN). The results demonstrated that RF exhibited higher accuracy of 98.87% followed by KNN and SVM with an accuracy of 94.64% and 89.67%, respectively, for precise detection of different leaves by observing their morphological features. In addition, RF outperformed other classifiers for determination of water-stressed leaves and having an accuracy of 99.42%. It is envisioned that proposed study can be proven beneficial and vital in digital agriculture technology for the timely detection of plants species to significantly help in mitigate yield and economic losses and improve crops quality.

This paper explores the impact of various milling strategies, including up-milling, down-milling, and hybrid approaches, on the surface roughness of AISI P20 mold steel. The study is methodically divided into three stages to comprehensively understand the effects of these strategies. The first stage involves milling single slots with varying cutting parameters to establish baseline effects. The second stage examines the effects of consistent milling strategies (up-up and down-down) on surface quality. The third stage probes into hybrid strategies (up-down and down-up) to assess their effectiveness. Central to this investigation is not only the type of milling strategy but also how cutting speed and feed rate influence the resultant surface roughness. Our findings indicate that up-milling generally leads to a 22% increase in surface roughness compared to down-milling. This trend is visually verified by surface texture analyses. When comparing consistent strategies, up-up milling tends to produce rougher surfaces than down-down milling by approximately 25%, characterized by distinctive scratches and feed mark overlays. Remarkably, while the hybrid milling strategies do not exhibit significant differences in surface roughness, variations in cutting speed and feed rate play a crucial role. Specifically, at lower speeds, hybrid milling achieves smoother surfaces than the identical double milling mode, while at a cutting speed of 100 m/min, the double mode demonstrates a notable decrease in roughness. Additionally, this study introduces a color mapping simulation for machined pockets, validated by experimental results, to predict surface roughness based on the strategic history of milling, thereby offering valuable insights for optimizing milling processes.

Antibiotic resistant bacteria are a growing threat to global health security. Whilst the emergence of antimicrobial resistance (AMR) is a natural phenomenon, it is also driven by antibiotic exposure in health care, agriculture, and the environment. Antibiotic pressure and inappropriate use of antibiotics are important factors which drive resistance. Apart from their use to treat bacterial infections in humans, antibiotics also play an important role in animal husbandry. With limited antibiotic options, alternate strategies are required to overcome AMR. Passive immunization through oral, nasal and topical administration of egg yolk-derived IgY antibodies from immunized chickens were recently shown to be effective for treating bacterial infections in animals and humans. Immunization of chickens with specific antigens offers the possibility of creating specific antibodies targeting a wide range of antibiotic-resistant bacteria. In this review, we describe the growing global problem of antimicrobial resistance and highlight the promising potential of the use of egg yolk IgY antibodies for the treatment of bacterial infections, particularly those listed in the World Health Organization priority list.

Maraging steel, characterized by its superior strength-to-weight ratio, wear resistance, and pressure tolerance, is a material of choice in critical applications, including aerospace and automotive components. However, the machining of this material presents significant challenges due to its inherent properties. This study comprehensively examines the impacts of face milling variables on maraging steel’s surface quality, cutting temperature, energy consumption, and material removal rate (MRR). An experimental analysis was conducted, and the gathered data were utilized for training and testing five machine learning (ML) models: support vector machine (SVM), K-nearest neighbor (KNN), artificial neural network (ANN), random forest, and XGBoost. Each model aimed to predict the outcomes of different machining parameters efficiently. XGBoost emerged as the most effective, delivering an impressive 98% prediction accuracy across small datasets. The study extended into applying a genetic algorithm (GA) for optimizing XGBoost’s hyperparameters, further enhancing the model’s predictive accuracy. The GA was instrumental in multi-objective optimization, considering various responses, including surface roughness and energy consumption. The optimization process evaluated different weighting methods, including equal weights and weights derived from the analytic hierarchy process (AHP) based on expert insights. The findings indicate that the refined XGBoost model, augmented by GA-optimized hyperparameters, provides highly accurate predictions for machining parameters. This outcome holds significant implications for industries engaged in the machining of maraging steel, offering a pathway to optimized operational efficiency, reduced costs, and enhanced product quality amid the material’s machining challenges.

Conventional mechanical machining of composite is a challenging task, and thus, electric discharge machining (EDM) was used for the processing of the developed material. The processing of developed composite using different electrodes on EDM generates different surface characteristics. In the current work, the effect of tool material on the surface characteristics, along with other input parameters, is investigated as per the experimental design. The experimental design followed is an RSM-based Box–Behnken design, and the input parameters in the current research are tool material, current, voltage, pulse-off time, and pulse-on time. Three levels of each parameter are selected, and 46 experiments are conducted. The surface roughness (Ra) is investigated for each experimental setting. The machine learning approach is used for the prediction of surface integrity by different techniques, namely Xgboost, random forest, and decision tree. Out of all the techniques, the Xgboost technique shows maximum accuracy as compared to other techniques. The analysis of variance of the predicted solutions is investigated. The empirical model is developed using RSM and is further solved with the help of a teaching learning-based algorithm (TLBO). The SR value predicted after RSM and integrated approach of RSM-ML-TLBO are 2.51 and 2.47 µm corresponding to Ton: 45 µs; Toff: 73 µs; SV:8V; I: 10A; tool: brass and Ton: 47 µs; Toff: 76 µs; SV:8V; I: 10A; tool: brass, respectively. The surface integrity at the optimized setting reveals the presence of microcracks, globules, deposited lumps, and sub-surface formation due to different amounts of discharge energy.

Abstract This paper presents a block-chain enabled inkjet-printed ultrahigh frequency radiofrequency identification (UHF RFID) system for the supply chain management, traceability and authentication of hard to tag bottled consumer products containing fluids such as water, oil, juice, and wine. In this context, we propose a novel low-cost, compact inkjet-printed UHF RFID tag antenna design for liquid bottles, with 2.5 m read range improvement over existing designs along with robust performance on different liquid bottle products. The tag antenna is based on a nested slot-based configuration that achieves good impedance matching around high permittivity surfaces. The tag was designed and optimized using the characteristic mode analysis. Moreover, the proposed RFID tag was commercially tested for tagging and billing of liquid bottle products in a conveyer belt and smart refrigerator for automatic billing applications. With the help of block-chain based product tracking and a mobile application, we demonstrate a real-time, secure and smart supply chain process in which items can be monitored using the proposed RFID technology. We believe the standalone system presented in this paper can be deployed to create smart contracts that benefit both the suppliers and consumers through the development of trust. Furthermore, the proposed system will paves the way towards authentic and contact-less delivery of food, drinks and medicine in recent Corona virus pandemic.

Safe, passive immunization methods are required against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and its variants. Immunization of chickens with antigen is known to induce specific IgY antibodies concentrated in the egg yolk and has a good safety profile, high yield of IgY per egg, can be topically applied, not requiring parenteral delivery. Our data provide the first evidence of the prophylactic efficacy of Immunoglobulin Y antibodies against SARS-CoV-2 in mice. Lohmann hens were injected with recombinant SARS-CoV-2 RBD protein; IgY-Abs were extracted from the eggs and characterized using SDS-PAGE. Antiviral activity was evaluated using plaque reduction neutralization tests. In additional experiments, IgY-RBD efficacy was examined in mice sensitized to SARS-CoV-2 infection by transduction with Ad5-hACE2 (mild disease) or by using mouse-adapted virus (severe disease). In both cases, prophylactic intranasal administration of IgY-Abs reduced SARS-CoV-2 replication, and reduced morbidity, inflammatory cell infiltration, hemorrhage, and edema in the lungs and increased survival compared to control groups that received non-specific IgY-Abs. These results indicate that further evaluation of IgY-RBD antibodies in humans is warranted.

AISI 1045 can be machined well in all machining operations, namely drilling, milling, turning, broaching and grinding. It has many applications, such as crankshafts, rollers, spindles, shafts, and gears. Wiper geometry has a great influence on cutting forces (Fr, Ff, Fc and R), temperature, material removal rate (MRR) and surface roughness (Ra). Wiper inserts are used to achieve good surface quality and avoid the need to buy a grinding machine. In this paper, an optimization-based investigation into previously reported results for Taguchi’s based L27 orthogonal array experimentations was conducted to further examine effect of the edge geometry on the turning performance of AISI 1045 steel in dry conditions. Three input parameters used in current research include the cutting speed (Vc), feed (f) and depth of cut (ap), while performance measures in this research were Ra, Fr, Ff, Fc, R, temperature (temp) and MRR. The Vise Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) method was used to normalize and convert all the performance measures to a single response known as the VIKOR-based performance index (Vi). The machine learning (ML) approach was used for the prediction and optimization of the input variables. A correlation plot is developed between the input variable and Vi using the ML approach. The optimized setting suggested by Vi-ML is Vc: 160 m/min; ap: 1 mm and f: 0.135 mm/rev, and the corresponding value of Vi was 0.2883, while the predicted values of Ra, Fr, Ff, Fc, R, temp and MRR were 2.111 µm, 43.85 N, 159.33 N, 288.13 N, 332,16 N, 554.4 °C and 21,600 mm3/min, respectively.

In the present research, AISI P20 mold steel was processed using the milling process. The machining parameters considered in the present work were speed, depth of cut (DoC), and feed (F). The experiments were designed according to an L27 orthogonal array; therefore, a total of 27 experiments were conducted with different settings of machining parameters. The response parameters investigated in the present work were material removal rate (MRR), surface roughness (Ra, Rt, and Rz), power consumption (PC), and temperature (Temp). The machine learning (ML) approach was implemented for the prediction of response parameters, and the corresponding error percentage was investigated between experimental values and predicted values (using the ML approach). The technique for order of preference by similarity to ideal solution (TOPSIS) approach was used to normalize all response parameters and convert them into a single performance index (Pi). An analysis of variance (ANOVA) was conducted using the design of experiments, and the optimized setting of machining parameters was investigated. The optimized settings suggested by the integrated ML–TOPSIS approach were as follows: speed, 150 m/min; DoC, 1 mm; F, 0.06 mm/tooth. The confirmation results using these parameters suggested a close agreement and confirmed the suitability of the proposed approach in the parametric evaluation of a milling machine while processing P20 mold steel. It was found that the maximum percentage error between the predicted and experimental values using the proposed approach was 3.43%.

This paper investigated the influence of cutting parameters, including feed rate, cutting speed, tool nose radius, and wet or dry cutting conditions, on the resultant force, cutting edge/workpiece temperature, and surface roughness when turning grey cast iron. Results showed that increasing the feed rate increased the resultant force, cutting temperature, and surface roughness. At the same time, increasing the cutting speed and nose radius increased the cutting temperature, which in turn reduced the resultant force. For practical applications, basic mathematical calculations based on the sole effect of each parameter on the output of the experiments were used to estimate the extent of percentage increase in cutting temperature due to increasing feed rate, cutting speed, and nose radius. Similarly, the same approach was used to estimate the effect of increasing feed rate, cutting speed, and nose radius on average surface roughness. Results showed that increasing the feed rate increases the cutting temperature by 5 to 11% depending on the nose radius and cutting speed. On the other hand, increasing the cutting speed was found to have limited effect on cutting temperature with small nose radius whereas this effect increases with increasing the nose radius reaching about 11%. Increasing the nose radius also increases the cutting temperature, depending mainly on cutting speed, reaching a maximum of 21% at higher cutting speeds. Results also showed that increasing the feed rate increased the average surface roughness considerably to about 120% at high cutting speeds and a large nose radius. On the other hand, increasing the cutting speed and nose radius reduced the surface roughness (i.e., improved surface quality) by a maximum of 29 and 23%, respectively. In order to study the combined effects of the cutting parameters on the three responses, namely, the resultant cutting force, cutting temperature, and surface roughness, full factorial design and ANOVA were used, where it was found to be in good agreement with mathematical calculations. Additionally, the desirability function optimization tool was used to minimize the measured responses whilst maximizing the material removal rate.

The present research used the stir-casting method to develop an Al-based composite. The developed composite exhibited challenges while being processed on conventional machining. Thus, a non-traditional machining process was opted to process the composite. The machining variables selected for the current research were the pulse off time (Toff), pulse on time (Ton), servo voltage (SV), current (I), and tool electrode. Three tool electrodes (SS-304, copper, and brass) were used to process the developed composite (Al/SiC/Gr). The experimental plan was designed using response surface methodology (RSM). The output responses recorded for the analysis were the material removal rate (MRR) and tool wear rate (TWR). The obtained data was optimized using complex proportional assessment (COPRAS) and machine learning methods. The optimized settings predicted by the RSM–COPRAS method were Ton: 60 µs; Toff: 60 µs; SV: 7 V; I: 12 A; and tool: brass. The maximum MRR and TWR at the suggested settings were 1.11 g/s and 0.0114 g/s, respectively. A morphological investigation of the machined surface and tool surface was conducted with scanning electron microscopy. The morphological examination of the surface (machined) presented the presence of cracks, lumps, etc.

In this article, a printed series fed tri-slot coplanar Vivaldi antenna is studied and investigated for RADAR application. The antenna consists of three exponentially tapered slots excited by a single microstrip line. The three slots are series fed simultaneously with a radial stub on the microstrip feed line for impedance matching. The proposed antenna covers a frequency of 7.8-11.8 GHz at −10 dB impedance bandwidth. The antenna gain varies from 7.5 dB at the lowest operating frequency and increases to around 9.5 dB at the center frequency of 10 GHz. A key versatility of the proposed design is the ability to modify the ground plane size without affecting the antenna impedance and gain. This makes the antenna suitable to incorporate RADAR transceiver components without modifying the existing antenna design. The proposed antenna is fabricated and measured results show good agreement with simulated results.

Stainless steel 316 has wide applications in different industries due to its distinct mechanical and corrosion resistance properties. In this paper, a new adaptive design approach is presented and discussed to optimize the machining process when face milling of Stainless steel 316 under different cooling and lubrication strategies. The proposed approach considers three process designs, namely: balanced, cost, and quality-oriented designs. Experimental runs were modeled using a support vector regression model, which was later used as objective functions for the particle swarm optimization algorithm. Finally, the technique of order preference similarity to the ideal solution (TOPSIS) was adopted with different weightings for different design criteria. In general, MQL and higher cutting speed are favorable in all designs. In contrast, lower feed rates are recommended in the quality-oriented design, compared to the cost-oriented one. This analysis does not only provide an adaptive design, but it also utilizes this approach to come up with different designs, each biased toward one of the responses. The approach and methodology are described in a straightforward, intuitive manner, which makes it replicable in different machining operations and even in other engineering optimization problems sharing the same nature of the present problem of interest.

This article presents an experimental investigation and statistical analysis of the effects of cutting conditions on the machining performance of AISI 1045 steel using a wiper-shaped insert. Experimental findings are used to compare the machining performance obtained using wiper inserts with those obtained using conventional round-nose inserts as recently reported in the literature. In addition, the effects of process conditions, namely cutting speed, feed rate, and depth of cut, are analyzed in order to obtain optimum conditions for both types of inserts. The goal is to achieve the optimal machining outcomes: minimum surface roughness, resultant cutting force, and cutting temperature, but maximum material removal rate. A full factorial design was followed to conduct the experimental trials, while ANOVA was utilized to estimate the effect of each factor on the process responses. A desirability function optimization tool was used to optimize the studied responses. The results reveal that the optimum material removal rate for wiper-shaped inserts is 67% more than that of conventional inserts, while maintaining a 0.7 µm surface roughness value. The superior results obtained with wiper-shaped inserts allow wiper tools to use higher feed rates, resulting in larger material removal rates while obtaining the same surface quality.

In machining operations, minimizing the usage of resources such as energy, tools, costs, and production time, while maximizing process outputs such as surface quality and productivity, has a significant impact on the environment, process sustainability, and profit. In this context, this paper reports on the utilization of advanced multi-objective algorithms for the optimization of turning-process parameters, mainly cutting speed, feed rate, and depth of cut, in the dry machining of AISI 1045 steel for high-efficient process. Firstly, a number of experimental tests were conducted in which cutting forces and cutting temperatures are measured. Then the material removal rate and the obtainable surface roughness were determined for the examined range of cutting parameters. Next, regression models were developed to formulate the relationships between the process parameters and the four process responses. After that, four different multi-objective optimization algorithms, (1) Gray Wolf Optimizer (GWO) and (2) Weighted Value Gray Wolf Optimizer (WVGWO), (3) Multi-Objective Genetic Algorithm (MOGA), and (4) Multi-Objective Pareto Search Algorithm (MOPSA), were applied. The results reveal that the optimal running conditions of the turning process of AISI 1045 steel obtained by WVGWO are a feed rate of 0.050 mm/rev, cutting speed of 156.5 m/min, and depth of cut of 0.57 mm. These conditions produce a high level of material removal rate of 4460.25 mm3/min, in addition to satisfying the surface quality with a roughness average of 0.719 µm. The optimal running conditions were found to be dependent on the objective outcomes’ order. Moreover, a comparative evaluation of the obtainable dimensional accuracy in both dry and wet turning operations was carried out, revealing a minimal relative error of 0.053% maximum between the two turning conditions. The results of this research work assist in obtaining precise, optimal, and cost-effective machining solutions, which can deliver a high-throughput, controllable, and robust manufacturing process when turning AISI 1045 steel.

Improving cultivability of a wider range of bacterial and archaeal community members, living natively in natural environments and within plants, is a prerequisite to better understanding plant-microbiota interactions and their functions in such very complex systems. Sequencing, assembling, and annotation of pure microbial strain genomes provide higher quality data compared to environmental metagenome analyses, and can substantially improve gene and protein database information. Despite the comprehensive knowledge which already was gained using metagenomic and metatranscriptomic methods, there still exists a big gap in understanding in vivo microbial gene functioning in planta, since many differentially expressed genes or gene families are not yet annotated. Here, the progress in culturing procedures for plant microbiota depending on plant-based culture media, and their proficiency in obtaining single prokaryotic isolates of novel and rapidly increasing candidate phyla are reviewed. As well, the great success of culturomics of the human microbiota is considered with the main objective of encouraging microbiologists to continue minimizing the gap between the microbial richness in nature and the number of species in culture, for the benefit of both basic and applied microbiology. The clear message to fellow plant microbiologists is to apply plant-tailored culturomic techniques that might open up novel procedures to obtain not-yet-cultured organisms and extend the known plant microbiota repertoire to unprecedented levels. Keywords: Plant microbiome, Metagenomics, Plant-based culture media, Culturomics, Unculturable bacteria, Candidate Phyla Radiation (CPR)

From the past few decades, the integral inequalities have been extensively researched. Integral inequalities are applied in innumerable mathematical problems. In current paper, we obtain new versions of Ostrowski’s type integral inequalities by implementing proposed general form of 7-step quadratic kernel. Applications for cumulative distribution are also provided.

Chlorpyrifos (CPF) is a very toxic pesticide commonly used for controlling agriculturally important pests. The present study investigates DNA damaging effects of CPF on Clarias gariepinus was assessed using genetic assays. Fish were exposed to varying concentrations of CPF (100ppm, 200ppm and 300ppm) at 96 hours (acute exposure). LC50 of the pesticide was found to be 120ppm and one-tenth of the LC50 (12ppm) was taken for the chronic exposure. Distilled water and colchicine were used as negative and positive controls respectively. After 28 days of chronic exposure, fish were fed with inclusion of B. sapida seed (powder) for 14 days. Liver and gills of the fish were removed following 96h exposure, days 7, 14 and 28 of the chronic exposure as well as after the period of amelioration (14 days) for CA assay and histopathological analysis. The results of CA assay showed statistically significant (p ˂ 0.05) increase in CA in a dose-dependent manner for all the exposed groups after acute exposure and time dependent after chronic exposure. Also, this study showed that CPF can potentially induce genotoxic and histopathological changes in fish and other aquatic organisms.

This paper presents an approach based on radio frequency identification (RFID) and machine learning for contamination sensing of food items and drinks such as soft drinks, alcohol, baby formula milk, etc. We employ sticker-type inkjet printed ultra-high-frequency (UHF) RFID tags for contamination sensing experimentation. The RFID tag antenna was mounted on pure as well as contaminated food products with known contaminant quantity. The received signal strength indicator (RSSI), as well as the phase of the backscattered signal from the RFID tag mounted on the food item, are measured using the Tagformance Pro setup. We used a machine-learning algorithm XGBoost for further training of the model and improving the accuracy of sensing, which is about 90%. Therefore, this research study paves a way for ubiquitous contamination/content sensing using RFID and machine learning technologies that can enlighten their users about the health concerns and safety of their food.

Unfolding the structure–property linkages between the mechanical performance and microstructural characteristics could be an attractive pathway to develop new single- and polycrystalline Al-based alloys to achieve ambitious high strength and fuel economy goals. A lot of polycrystalline as-cast Al-Cu-Mg-Ag alloy systems fabricated by conventional casting techniques have been reported to date. However, no one has reported a comparison of mechanical and microstructural properties that simultaneously incorporates the effects of both alloy chemistry and mechanical testing environments for the as-cast Al-Cu-Mg-Ag alloy systems. This preliminary prospective paper presents the examined experimental results of two alloys (denoted Alloy 1 and Alloy 2), with constant Cu content of ~3 wt.%, Cu/Mg ratios of 12.60 and 6.30, and a constant Ag of 0.65 wt.%, and correlates the synergistic comparison of mechanical properties at room and elevated temperatures. According to experimental results, the effect of the precipitation state and the mechanical properties showed strong dependence on the composition and testing environments for peak-aged, heat-treated specimens. In the room-temperature mechanical testing scenario, the higher Cu/Mg ratio alloy with Mg content of 0.23 wt.% (Alloy 1) possessed higher ultimate tensile strength when compared to the low Cu/Mg ratio with Mg content of 0.47 wt.% (Alloy 2). From phase constitution analysis, it is inferred that the increase in strength for Alloy 1 under room-temperature tensile testing is mainly ascribable to the small grain size and fine and uniform distribution of θ precipitates, which provided a barrier to slip by deaccelerating the dislocation movement in the room-temperature environment. Meanwhile, Alloy 2 showed significantly less degradation of mechanical strength under high-temperature tensile testing. Indeed, in most cases, low Cu/Mg ratios had a strong influence on the copious precipitation of thermally stable omega phase, which is known to be a major strengthening phase at elevated temperatures in the Al-Cu-Mg-Ag alloying system. Consequently, it is rationally suggested that in the high-temperature testing scenario, the improvement in mechanical and/or thermal stability in the case of the Alloy 2 specimen was mainly due to its compositional design.

The plant-based-sea water culture medium is introduced to in vitro cultivation and in situ recovery of the microbiome of halophytes. The ice plant (Mesembryanthemum crystallinum) was used, in the form of juice and/or dehydrated plant powder packed in teabags, to supplement the natural sea water. The resulting culture medium enjoys the combinations of plant materials as rich source of nutrients and sea water exercising the required salt stress. As such without any supplements, the culture medium was sufficient and efficient to support very good in vitro growth of halotolerant bacteria. It was also capable to recover their in situ culturable populations in the phyllosphere, ecto-rhizosphere and endo-rhizosphere of halophytes prevailing in Lake Mariout, Egypt. When related to the total bacterial numbers measured for Suaeda pruinosa roots by quantitative-PCR, the proposed culture medium increased culturability (15.3–19.5%) compared to the conventional chemically-synthetic culture medium supplemented with (11.2%) or without (3.8%) NaCl. Based on 16S rRNA gene sequencing, representative isolates of halotolerant bacteria prevailed on such culture medium were closely related to Bacillus spp., Halomonas spp., and Kocuria spp. Seed germination tests on 25–50% sea water agar indicated positive interaction of such bacterial isolates with the germination and seedlings’ growth of barley seeds.