Epileptic brain network mechanisms and neuroimaging techniques for the brain network

Epilepsy can be defined as a dysfunction of the brain network,and each type of epilepsy involves different brain-network changes that are implicated diffe rently in the control and propagation of interictal or ictal discharges.Gaining more detailed information on brain network alterations can help us to further understand the mechanisms of epilepsy and pave the way for brain network-based precise therapeutic approaches in clinical practice.An increasing number of advanced neuroimaging techniques and electrophysiological techniques such as diffusion tensor imaging-based fiber tra ctography,diffusion kurtosis imaging-based fiber tractography,fiber ball imagingbased tra ctography,electroencephalography,functional magnetic resonance imaging,magnetoencephalography,positron emission tomography,molecular imaging,and functional ultrasound imaging have been extensively used to delineate epileptic networks.In this review,we summarize the relevant neuroimaging and neuroelectrophysiological techniques for assessing structural and functional brain networks in patients with epilepsy,and extensively analyze the imaging mechanisms,advantages,limitations,and clinical application ranges of each technique.A greater focus on emerging advanced technologies,new data analysis software,a combination of multiple techniques,and the construction of personalized virtual epilepsy models can provide a theoretical basis to better understand the brain network mechanisms of epilepsy and make surgical decisions.

An Insight Survey on Sensor Errors and Fault Detection Techniques in Smart Spaces

The widespread adoption of the Internet of Things (IoT) has transformed various sectors globally, making themmore intelligent and connected. However, this advancement comes with challenges related to the effectiveness ofIoT devices. These devices, present in offices, homes, industries, and more, need constant monitoring to ensuretheir proper functionality. The success of smart systems relies on their seamless operation and ability to handlefaults. Sensors, crucial components of these systems, gather data and contribute to their functionality. Therefore,sensor faults can compromise the system’s reliability and undermine the trustworthiness of smart environments.To address these concerns, various techniques and algorithms can be employed to enhance the performance ofIoT devices through effective fault detection. This paper conducted a thorough review of the existing literature andconducted a detailed analysis.This analysis effectively links sensor errors with a prominent fault detection techniquecapable of addressing them. This study is innovative because it paves theway for future researchers to explore errorsthat have not yet been tackled by existing fault detection methods. Significant, the paper, also highlights essentialfactors for selecting and adopting fault detection techniques, as well as the characteristics of datasets and theircorresponding recommended techniques. Additionally, the paper presents amethodical overview of fault detectiontechniques employed in smart devices, including themetrics used for evaluation. Furthermore, the paper examinesthe body of academic work related to sensor faults and fault detection techniques within the domain. This reflectsthe growing inclination and scholarly attention of researchers and academicians toward strategies for fault detectionwithin the realm of the Internet of Things.

Improving the Segmentation of Arabic Handwriting Using Ligature Detection Technique

Recognizing handwritten characters remains a critical and formidable challenge within the realm of computervision. Although considerable strides have been made in enhancing English handwritten character recognitionthrough various techniques, deciphering Arabic handwritten characters is particularly intricate. This complexityarises from the diverse array of writing styles among individuals, coupled with the various shapes that a singlecharacter can take when positioned differently within document images, rendering the task more perplexing. Inthis study, a novel segmentation method for Arabic handwritten scripts is suggested. This work aims to locatethe local minima of the vertical and diagonal word image densities to precisely identify the segmentation pointsbetween the cursive letters. The proposed method starts with pre-processing the word image without affectingits main features, then calculates the directions pixel density of the word image by scanning it vertically and fromangles 30° to 90° to count the pixel density fromall directions and address the problem of overlapping letters, whichis a commonly attitude in writing Arabic texts by many people. Local minima and thresholds are also determinedto identify the ideal segmentation area. The proposed technique is tested on samples obtained fromtwo datasets: Aself-curated image dataset and the IFN/ENIT dataset. The results demonstrate that the proposed method achievesa significant improvement in the proportions of cursive segmentation of 92.96% on our dataset, as well as 89.37%on the IFN/ENIT dataset.

Software Defect Prediction Using Hybrid Machine Learning Techniques: A Comparative Study

When a customer uses the software, then it is possible to occur defects that can be removed in the updated versions of the software. Hence, in the present work, a robust examination of cross-project software defect prediction is elaborated through an innovative hybrid machine learning framework. The proposed technique combines an advanced deep neural network architecture with ensemble models such as Support Vector Machine (SVM), Random Forest (RF), and XGBoost. The study evaluates the performance by considering multiple software projects like CM1, JM1, KC1, and PC1 using datasets from the PROMISE Software Engineering Repository. The three hybrid models that are compared are Hybrid Model-1 (SVM, RandomForest, XGBoost, Neural Network), Hybrid Model-2 (GradientBoosting, DecisionTree, LogisticRegression, Neural Network), and Hybrid Model-3 (KNeighbors, GaussianNB, Support Vector Classification (SVC), Neural Network), and the Hybrid Model 3 surpasses the others in terms of recall, F1-score, accuracy, ROC AUC, and precision. The presented work offers valuable insights into the effectiveness of hybrid techniques for cross-project defect prediction, providing a comparative perspective on early defect identification and mitigation strategies. .

Comparison of prognosis and postoperative morbidities between standard pancreaticoduodenectomy and the TRIANGLE technique for resectable pancreatic ductal adenocarcinoma

BACKGROUND Radical surgery combined with systemic chemotherapy offers the possibility of long-term survival or even cure for patients with pancreatic ductal adenocar-cinoma(PDAC),although tumor recurrence,especially locally,still inhibits the treatment efficacy.The TRIANGLE technique was introduced as an extended dissection procedure to improve the R0 resection rate of borderline resectable or locally advanced PDAC.However,there was a lack of studies concerning postoperative complications and long-term outcomes of this procedure on patients with resectable PDAC.PDAC.METHODS Patients with resectable PDAC eligible for PD from our hospital between June 2018 and December 2021 were enrolled in this retrospective cohort study.All the patients were divided into PDstandard and PDTRIANGLE groups according to the surgical procedure.Baseline characteristics,surgical data,and postoperative morbidities were recorded.All of the patients were followed up,and the date and location of tumor recurrence,and death were recorded.The Kaplan-Meier method and log-rank test were used for the survival analysis.RESULTS There were 93 patients included in the study and 37 underwent the TRIANGLE technique.Duration of operation was longer in the PDTRIANGLE group compared with the PDstandard group[440(410-480)min vs 320(265-427)min](P=0.001).Intraoperative blood loss[700(500-1200)mL vs 500(300-800)mL](P=0.009)and blood transfusion[975(0-1250)mL vs 400(0-800)mL](P=0.009)were higher in the PDTRIANGLE group.There was a higher incidence of surgical site infection(43.2%vs 12.5%)(P=0.001)and postoperative diarrhea(54.1%vs 12.5%)(P=0.001)in the PDTRIANGLE group.The rates of R0 resection and local recurrence,overall survival,and disease-free survival did not differ significantly between the two groups.CONCLUSION The TRIANGLE technique is safe,with acceptable postoperative morbidities compared with standardized PD,but it does not improve prognosis for patients with resectable PDAC.

Treatment of bilateral developmental dysplasia of the hip joint with an improved technique:A case report

BACKGROUND Developmental dysplasia of the hip(DDH)is a common osteoarticular deformity in pediatric orthopedics.A patient with bilateral DDH was diagnosed and treated using our improved technique"(powerful overturning acetabuloplasty)"combined with femoral rotational shortening osteotomy.CASE SUMMARY A 4-year-old girl who was diagnosed with bilateral DDH could not stand normally,and sought surgical treatment to solve the problem of double hip extension and standing.As this child had high dislocation of the hip joint and the acetabular index was high,we changed the traditional acetabuloplasty to"powerful turnover acetabuloplasty"combined with femoral rotation shortening osteotomy.During the short-term postoperative follow-up(1,3,6,9,12,and 15 months),the child had no discomfort in her lower limbs.After the braces and internal fixation plates were removed,formal rehabilitation training was actively carried out.CONCLUSION Our"powerful overturning acetabuloplasty"combined with femoral rotational shortening osteotomy is feasible in the treatment of DDH in children.This technology may be widely used in the clinic.

Research Progress on Detection Techniques of Fungicide Residues in Chinese Chives

Chinese chive is a kind of medicinal and edible plant,with many diseases,and chemical fungicides are usually used for control.In order to find out the risk of pesticide residues in Chinese chives,this paper summarized relevant literatures published in recent years,and sorted out and analyzed the types of pesticides used and detection techniques of common diseases in Chinese chives.

Prediction of high-embankment settlement combining joint denoising technique and enhanced GWO-v-SVR method

Reliable long-term settlement prediction of a high embankment relates to mountain infrastructure safety.This study developed a novel hybrid model(NHM)that combines a joint denoising technique with an enhanced gray wolf optimizer(EGWO)-n-support vector regression(n-SVR)method.High-embankment field measurements were preprocessed using the joint denoising technique,which in-cludes complete ensemble empirical mode decomposition,singular value decomposition,and wavelet packet transform.Furthermore,high-embankment settlements were predicted using the EGWO-n-SVR method.In this method,the standard gray wolf optimizer(GWO)was improved to obtain the EGWO to better tune the n-SVR model hyperparameters.The proposed NHM was then tested in two case studies.Finally,the influences of the data division ratio and kernel function on the EGWO-n-SVR forecasting performance and prediction efficiency were investigated.The results indicate that the NHM suppresses noise and restores details in high-embankment field measurements.Simultaneously,the NHM out-performs other alternative prediction methods in prediction accuracy and robustness.This demonstrates that the proposed NHM is effective in predicting high-embankment settlements with noisy field mea-surements.Moreover,the appropriate data division ratio and kernel function for EGWO-n-SVR are 7:3 and radial basis function,respectively.

Modified Black Widow Optimization-Based Enhanced Threshold Energy Detection Technique for Spectrum Sensing in Cognitive Radio Networks

This study develops an Enhanced Threshold Based Energy Detection approach(ETBED)for spectrum sensing in a cognitive radio network.The threshold identification method is implemented in the received signal at the secondary user based on the square law.The proposed method is implemented with the signal transmission of multiple outputs-orthogonal frequency division multiplexing.Additionally,the proposed method is considered the dynamic detection threshold adjustments and energy identification spectrum sensing technique in cognitive radio systems.In the dynamic threshold,the signal ratio-based threshold is fixed.The threshold is computed by considering the Modified Black Widow Optimization Algorithm(MBWO).So,the proposed methodology is a combination of dynamic threshold detection and MBWO.The general threshold-based detection technique has different limitations such as the inability optimal signal threshold for determining the presence of the primary user signal.These limitations undermine the sensing accuracy of the energy identification technique.Hence,the ETBED technique is developed to enhance the energy efficiency of cognitive radio networks.The projected approach is executed and analyzed with performance and comparison analysis.The proposed method is contrasted with the conventional techniques of theWhale Optimization Algorithm(WOA)and GreyWolf Optimization(GWO).It indicated superior results,achieving a high average throughput of 2.2 Mbps and an energy efficiency of 3.8,outperforming conventional techniques.

Economic Power Dispatching from Distributed Generations: Review of Optimization Techniques

In the increasingly decentralized energy environment,economical power dispatching from distributed generations(DGs)is crucial to minimizing operating costs,optimizing resource utilization,and guaranteeing a consistent and sustainable supply of electricity.A comprehensive review of optimization techniques for economic power dispatching from distributed generations is imperative to identify the most effective strategies for minimizing operational costs while maintaining grid stability and sustainability.The choice of optimization technique for economic power dispatching from DGs depends on a number of factors,such as the size and complexity of the power system,the availability of computational resources,and the specific requirements of the application.Optimization techniques for economic power dispatching from distributed generations(DGs)can be classified into two main categories:(i)Classical optimization techniques,(ii)Heuristic optimization techniques.In classical optimization techniques,the linear programming(LP)model is one of the most popular optimization methods.Utilizing the LP model,power demand and network constraints are met while minimizing the overall cost of generating electricity from DGs.This approach is efficient in determining the best DGs dispatch and is capable of handling challenging optimization issues in the large-scale system including renewables.The quadratic programming(QP)model,a classical optimization technique,is a further popular optimization method,to consider non-linearity.The QP model can take into account the quadratic cost of energy production,with consideration constraints like network capacity,voltage,and frequency.The metaheuristic optimization techniques are also used for economic power dispatching from DGs,which include genetic algorithms(GA),particle swarm optimization(PSO),and ant colony optimization(ACO).Also,Some researchers are developing hybrid optimization techniques that combine elements of classical and heuristic optimization techniques with the incorporation of droop control,predictive control,and fuzzy-based methods.These methods can deal with large-scale systems with many objectives and non-linear,non-convex optimization issues.The most popular approaches are the LP and QP models,while more difficult problems are handled using metaheuristic optimization techniques.In summary,in order to increase efficiency,reduce costs,and ensure a consistent supply of electricity,optimization techniques are essential tools used in economic power dispatching from DGs.

The Impact of Model Based Offset Scaling Technique on the Amplitude Variation with Offset Responses from 3D Seismic Data Acquired from the Tano Basin, Offshore Ghana

Amplitudes have been found to be a function of incident angle and offset. Hence data required to test for amplitude variation with angle or offset needs to have its amplitudes for all offsets preserved and not stacked. Amplitude Variation with Offset (AVO)/Amplitude Variation with Angle (AVA) is necessary to account for information in the offset/angle parameter (mode converted S-wave and P-wave velocities). Since amplitudes are a function of the converted S- and P-waves, it is important to investigate the dependence of amplitudes on the elastic (P- and S-waves) parameters from the seismic data. By modelling these effects for different reservoir fluids via fluid substitution, various AVO geobody classes present along the well and in the entire seismic cube can be observed. AVO analysis was performed on one test well (Well_1) and 3D pre-stack angle gathers from the Tano Basin. The analysis involves creating a synthetic model to infer the effect of offset scaling techniques on amplitude responses in the Tano basin as compared to the effect of unscaled seismic data. The spectral balance process was performed to match the amplitude spectra of all angle stacks to that of the mid (26°) stack on the test lines. The process had an effect primarily on the far (34° - 40°) stacks. The frequency content of these stacks slightly increased to match that of the near and mid stacks. In offset scaling process, the root mean square (RMS) amplitude comparison between the synthetic and seismic suggests that the amplitude of the far traces should be reduced relative to the nears by up to 16%. However, the exact scaler values depend on the time window considered. This suggests that the amplitude scaling with offset delivered from seismic processing is only approximately correct and needs to be checked with well synthetics and adjusted accordingly prior to use for AVO studies. The AVO attribute volumes generated were better at resolving anomalies on spectrally balanced and offset scaled data than data delivered from conventional processing. A typical class II AVO anomaly is seen along the test well from the cross-plot analysis and AVO attribute cube which indicates an oil filled reservoir.

Predictive modeling for postoperative delirium in elderly patients with abdominal malignancies using synthetic minority oversampling technique

BACKGROUND Postoperative delirium,particularly prevalent in elderly patients after abdominal cancer surgery,presents significant challenges in clinical management.AIM To develop a synthetic minority oversampling technique(SMOTE)-based model for predicting postoperative delirium in elderly abdominal cancer patients.METHODS In this retrospective cohort study,we analyzed data from 611 elderly patients who underwent abdominal malignant tumor surgery at our hospital between September 2020 and October 2022.The incidence of postoperative delirium was recorded for 7 d post-surgery.Patients were divided into delirium and non-delirium groups based on the occurrence of postoperative delirium or not.A multivariate logistic regression model was used to identify risk factors and develop a predictive model for postoperative delirium.The SMOTE technique was applied to enhance the model by oversampling the delirium cases.The model’s predictive accuracy was then validated.RESULTS In our study involving 611 elderly patients with abdominal malignant tumors,multivariate logistic regression analysis identified significant risk factors for postoperative delirium.These included the Charlson comorbidity index,American Society of Anesthesiologists classification,history of cerebrovascular disease,surgical duration,perioperative blood transfusion,and postoperative pain score.The incidence rate of postoperative delirium in our study was 22.91%.The original predictive model(P1)exhibited an area under the receiver operating characteristic curve of 0.862.In comparison,the SMOTE-based logistic early warning model(P2),which utilized the SMOTE oversampling algorithm,showed a slightly lower but comparable area under the curve of 0.856,suggesting no significant difference in performance between the two predictive approaches.CONCLUSION This study confirms that the SMOTE-enhanced predictive model for postoperative delirium in elderly abdominal tumor patients shows performance equivalent to that of traditional methods,effectively addressing data imbalance.

Micro-power negative pressure wound technique reduces risk of incision infection following loop ileostomy closure

BACKGROUND Prophylactic loop ileostomy is an effective way to reduce the clinical severity of anastomotic leakage following radical resection of rectal cancer.Incisional surgical site infection(SSI)is a common complication after ileostomy closure.AIM To evaluate the efficacy and safety of the micro-power negative pressure wound technique(MPNPWT)in preventing incisional SSI.METHODS This was a prospective,randomized controlled clinical trial conducted at a single center.A total of 101 consecutive patients who underwent ileostomy closure after rectal cancer surgery with a prophylactic ileostomy were enrolled from January 2019 to December 2021.Patients were randomly allocated into an MPNPWT group and a control group.The MPNPWT group underwent intermittent suturing of the surgical incision with 2-0 Prolene and was covered with a micro-power negative pressure dressing.The surgical outcomes were compared between the MPNPWT(n=50)and control(n=51)groups.Risk factors for incisional SSI were identified using logistic regression.RESULTS There were no differences in baseline characteristics between the MPNPWT(n=50)and control groups(n=51).The incisional SSI rate was significantly higher in the control group than in the MPNPWT group(15.7%vs 2.0%,P=0.031).However,MPNPWT did not affect other surgical outcomes,including intra-abdominal complications,operative time,and blood loss.Postoperative hospital stay length and hospitalization costs did not differ significantly between the two groups(P=0.069 and 0.843,respectively).None of the patients experienced adverse effects of MPNPWT,including skin allergy,dermatitis,and pain.MPNPWT also helped heal the infected incision.Our study indicated that MPNPWT was an independent protective factor[odds ratio(OR)=0.005,P=0.025)]and diabetes was a risk factor(OR=26.575,P=0.029)for incisional SSI.CONCLUSION MPNPWT is an effective and safe way to prevent incisional SSI after loop ileostomy closure.

Urethroplasty among Elderly Men, Surgical Techniques and Outcomes

Introduction: Urethroplasty remains the gold standard for the management of urethral stricture. However, the treatment of stricture disease in the elderly tends to be less invasive due to the presumption that they might not be able to stand long hours of surgery and might have higher rates of recurrence due to poor wound healing from microangiopathy. We present our experience with the outcomes of urethroplasty among elderly men seen at the Komfo Anokye Teaching Hospital from January 2012 to December 2021. Methods: This was a retrospective review of data captured in the urology database on all patients 65 years and above who underwent urethroplasty at the hospital over the study period. Data was obtained on patients’ demographics, stricture characteristics, urethroplasty technique, and outcome. A successful outcome was defined as peak flow rate > 15 mls/s, a patent urethra on retrograde urethrogram, patient satisfaction with urine stream, or restoration of the normal stream of urine with only one attempt at urethral calibration or internal urethrotomy postoperatively. Data was analyzed using PASW Statistics for Windows, Version 18.0. Results: Overall, 43 urethroplasties were done over the study period in elderly men. The age range was 65 to 87 years. The commonest aetiology was catheterization (62.79%) followed by urethritis (32.56%). Stricture length ranged from 0.5 cm to 16 cm with a mean of 3.93 cm. Most patients (60.46%) had bulbar urethral strictures. The repair methods employed were anastomotic urethroplasty (62.80%), fasciocutaneous flap (FCF) ventral onlay (13.95%), buccal mucosa graft (BMG) ventral onlay urethroplasty (4.65%), and staged urethroplasty (4.65%). Three of the patients (6.98%) had a combination of anastomotic and tissue transfer urethroplasty. The overall success rate was 88.37%. Complications included three surgical site infections, two urethral diverticula and one glans dehiscence. Conclusion: Elderly men tolerate urethroplasty well and the procedure should not be denied solely based on age.

Detection of Knowledge on Social Media Using Data Mining Techniques

In light of the rapid growth and development of social media, it has become the focus of interest in many different scientific fields. They seek to extract useful information from it, and this is called (knowledge), such as extracting information related to people’s behaviors and interactions to analyze feelings or understand the behavior of users or groups, and many others. This extracted knowledge has a very important role in decision-making, creating and improving marketing objectives and competitive advantage, monitoring events, whether political or economic, and development in all fields. Therefore, to extract this knowledge, we need to analyze the vast amount of data found within social media using the most popular data mining techniques and applications related to social media sites.

Standardizing MI-TLIF, a Proposal for a Reproducible Technique

Background: Minimally invasive transforaminal lumbar interbody fusion (MI TLIF) is a widely known and performed technique, however its versatility among different physicians continues to hinder its replication and results. Therefore, this study aimed to provide a step-by-step surgical guide to perform a safe MI-TLIF, based on the results obtained in patients operated on by a single surgeon over a period of 12 years. Patients and methods: A retrospective, single center, longitudinal, and observational cohort study was conducted with 931 patients who underwent MI TLIF by a single surgeon between 2010 and 2022 using the technique described on this paper, each with a minimum follow-up of 12 months. Criteria included Schizas classification, listhesis according to Meyerding classification, number of levels treated, cage size, and complications (screw repositioning or cerebrospinal fluid leak). Patient clinical outcomes were assessed using the Oswestry Disability Index (ODI), Visual Analog Scale (VAS) for pre- and postoperative radicular pain. Thin slice CT scans were used to assess the progression of the fusion using the Bridwell classification. In the statistical analysis, percentages, median, and interquartile range (IQR) were calculated. Results: Nine hundred and thirty one patients underwent MI TLIF using the technique described, eight hundred and eighty (94.5%) had a single level treated and fifty one (5.5%) had a 2 level procedure (982 levels), an 8mm cage was placed on five hundred and seventeenlevels (52.7%), six hundred and sixty three levels(67.6%) achieved grade I fusion, two hundred and sixty six levels (27.1%) achieved grade II fusion, 52 levels (5.3) achieved grade III fusion and one level (0.1) achieved a grade IV fusion or non-union. Revision surgery was performed on 3 patients (0.3%) for screw repositioning, cerebrospinal fluid leak was present on 2 patients during surgery and treated before closure. VAS scores and ODI were improved at 12 months postop (VAS from 8.70 to 2.30 and ODI from 34.2 to 14.1, (p = 0.001). Conclusions: The MI TLIF technique described could be a safe and easy to replicate way to achieved lumbar interbody fusion, providingclinical and radiological benefits.

Acoustic Non-Destructive Testing Technology in Concrete Bridge Inspection and Pile Foundation Detection

This article takes the actual construction project of a certain concrete bridge project as an example to analyze the application of acoustic non-destructive testing technology in its detection.It includes an overview of a certain bridge construction project studied and acoustic non-destructive testing technology and the application of acoustic non-destructive testing technology in actual testing.This analysis hopes to provide some guidelines for acoustic non-destructive testing of modern concrete bridge projects.

Innovative Techniques Unveiled in Advanced Sheet Pile Curtain Design

This thorough review explores the complexities of geotechnical engineering, emphasizing soil-structure interaction (SSI). The investigation centers on sheet pile design, examining two primary methodologies: Limit Equilibrium Methods (LEM) and Soil-Structure Interaction Methods (SSIM). While LEM methods, grounded in classical principles, provide valuable insights for preliminary design considerations, they may encounter limitations in addressing real-world complexities. In contrast, SSIM methods, including the SSI-SR approach, introduce precision and depth to the field. By employing numerical techniques such as Finite Element (FE) and Finite Difference (FD) analyses, these methods enable engineers to navigate the dynamics of soil-structure interaction. The exploration extends to SSI-FE, highlighting its essential role in civil engineering. By integrating Finite Element analysis with considerations for soil-structure interaction, the SSI-FE method offers a holistic understanding of how structures dynamically interact with their geotechnical environment. Throughout this exploration, the study dissects critical components governing SSIM methods, providing engineers with tools to navigate the intricate landscape of geotechnical design. The study acknowledges the significance of the Mohr-Coulomb constitutive model while recognizing its limitations, and guiding practitioners toward informed decision-making in geotechnical analyses. As the article concludes, it underscores the importance of continuous learning and innovation for the future of geotechnical engineering. With advancing technology and an evolving understanding of soil-structure interaction, the study remains committed to ensuring the safety, stability, and efficiency of geotechnical structures through cutting-edge design and analysis techniques.

Optical techniques in non-destructive detection of wheat quality:A review

Wheat quality detection is essential to ensure the safety ofwheat circulation and storage.The traditional wheat quality detection methods mainly include artificial sensory evaluation and physicochemical index analysis,which are difficult to meet the requirements for high accuracy and efficiency in modern wheat quality detection due to the disadvantages of subjectivity,destruction of sample integrity and low efficiency.With the rapid development of optical technology,various optical-based methods,using near-infrared spectroscopy technology,hyperspectral imaging technology and terahertz,etc.,have been proposed for wheat quality detection.These methods have the characteristics of nondestructiveness and high efficiency which make them popular in wheat quality detection in recent years.In this paper,various state-of-the-art optical-based techniques of wheat quality detection are analyzed and summarized in detail.Firstly,the principle and process of common optical non-destructive detection methods for wheat quality are introduced.Then,the optical techniques used in these detection methods are divided into seven categories,and the comparison of these technologies and their advantages and disadvantages are further discussed.It shows that terahertz technology is regarded as the most promising wheat quality detection method compared with other optical detection technologies,because it can not only detect most types of wheat deterioration,but also has higher accuracy and efficiency.Finally,the research of optical technology in wheat quality detection is prospected.The future research of optical technology-based wheat quality detection mainly includes the construction of wheat quality optical detection standardization database,the fusion of multiple optical detection technologies and multiple quality index information,the improvement of the anti-interference of optical technology and the industrialization of optical inspection technology for wheat quality.These studies are of great significance to improve the detection technology of wheat and ensure the storage safety of wheat in the future.

Optical non-destructive techniques for small berry fruits: A review

Small berries including strawberry and blueberry are extensively consumed fruits with great economic values due to their characteristic flavor and appearance as well as potential health benefits.This review elaborated the optical non-destructive techniques viz.Vis-NIR spectroscopy,computer vision system,hyperspectral imaging,multispectral imaging,laser-induced method and thermal imaging,and their applications for quality and safety control of small berry fruits.The discussion regarding the photoacoustic technique,X-ray technique,Terahertz spectroscopy,odor imaging,micro-destructive testing and smart mobile terminal-based analyzer was also presented.Furthermore,we proposed our personal understanding of the technical challenges and further trends for these optical non-destructive techniques:1)owing to the relatively low detection limit,the so-called micro-destructive techniques may be alternative to the traditional non-destructive techniques in both practical and fundamental research;2)we suggest that the research articles like“collecting data first,and then modeling the relevant properties of agricultural products by machine learning”should be less produced in related fields.That's because such research methods are likely to be suspected of“cheating”.It is recommended that some modeling competitions can be carried out in the agricultural engineering field to avoid or reduce the“cheating”model.