Minerals in edible insects:a review of content and potential for sustainable sourcing

In response to the rapid increase in world population and subsequent demands for food,edible insects represent an alternative food source for humans that is rich in proteins,amino acids and minerals.Entomophagy is a tradition in many countries including China and Thailand,and edible insects have attracted a lot of attention in Western World due to their suitable nutrient composition,high mineral content(e.g.,Fe,Zn,Ca,Mg)and potential use as a supplement in human diet.In this study,we surveyed mineral content in seven insect orders and 67 species of mass produced and wild-harvested edible insects.The total content of essential elements in edible insects was very high in Tenebrio molitor,Bombyx mori,and Zonocerus variegatus.The heavy metal content(summarized for eight species)was below the maximum limit allowed for safe consumption.Sustainable supply of minerals derived from insect biomass is complicated due to the high variations of mineral content in insects and the potential of its change due to processing.

31 Plant Species against Blood Feeding and Disease Vectors Insects: Beyond Anti-Insect Properties, Unvalued Opportunities and Challenges for Health and Sustainability

Plants with bioactive properties are greatly useful in preventing and controlling blood-sucking and disease-vector invertebrates, particularly in developing countries and low-income communities. Their application is a promising alternative to synthetic compounds whose use remains a health, environmental, and economic challenge. However, many are still unknown and unvalued, while others are becoming ignored and threatened. The main objective of this ethnobotanical study is to identify and characterize indigenous and locally grown plants against blood-sucking and disease-vector insects. Salient opportunities and challenges of using these plants are documented and discussed. Semi-structured interviews, using a prepared questionnaire, were conducted with 228 informants. The consensus index (CI) was calculated to analyze the reliability of the collected information. The identified 31 anti-insect plant species belong to 20 botanical families, four morphological categories, and six habitat types. They can be categorized as insecticidal plants (42% of the total), insect repellent (42% of the total), and both insecticidal and insect repellent (16% of the total). More than 54% of these are still abundant in the study area, while about 35.5% have become rare and difficultly accessible. Based on the numerical importance of related anti-insect plant species, the seven targeted blood-sucking insects range in the following decreasing order: Jiggers (16 species) > Fire Ants (9 species) > Flies (8 plants) > Mosquitoes (4 species) > Fleas (2 species) > Bedbugs (1 species) > lice (0 species). The three most commonly used plants, with the highest confirmation indices, are Tetradenia riparia (ICs = 0.712), Eucalyptus globulus subsp. maidenii (ICs = 0.302), and Solanum aculeastrum (ICs = 0.288). The antimicrobial role of many locally grown anti-insect plants and the multiple other associated valorization possibilities are ignored by most informants. Domesticating, propagating, protecting, and promoting the sustainable use of these plants would be an appropriate route for their conservation and continued availability.

An Adaptive Hate Speech Detection Approach Using Neutrosophic Neural Networks for Social Media Forensics

Detecting hate speech automatically in social media forensics has emerged as a highly challenging task due tothe complex nature of language used in such platforms. Currently, several methods exist for classifying hatespeech, but they still suffer from ambiguity when differentiating between hateful and offensive content and theyalso lack accuracy. The work suggested in this paper uses a combination of the Whale Optimization Algorithm(WOA) and Particle Swarm Optimization (PSO) to adjust the weights of two Multi-Layer Perceptron (MLPs)for neutrosophic sets classification. During the training process of the MLP, the WOA is employed to exploreand determine the optimal set of weights. The PSO algorithm adjusts the weights to optimize the performanceof the MLP as fine-tuning. Additionally, in this approach, two separate MLP models are employed. One MLPis dedicated to predicting degrees of truth membership, while the other MLP focuses on predicting degrees offalse membership. The difference between these memberships quantifies uncertainty, indicating the degree ofindeterminacy in predictions. The experimental results indicate the superior performance of our model comparedto previous work when evaluated on the Davidson dataset.

Hyper-Tuned Convolutional Neural Networks for Authorship Verification in Digital Forensic Investigations

Authorship verification is a crucial task in digital forensic investigations,where it is often necessary to determine whether a specific individual wrote a particular piece of text.Convolutional Neural Networks(CNNs)have shown promise in solving this problem,but their performance highly depends on the choice of hyperparameters.In this paper,we explore the effectiveness of hyperparameter tuning in improving the performance of CNNs for authorship verification.We conduct experiments using a Hyper Tuned CNN model with three popular optimization algorithms:Adaptive Moment Estimation(ADAM),StochasticGradientDescent(SGD),andRoot Mean Squared Propagation(RMSPROP).The model is trained and tested on a dataset of text samples collected from various authors,and the performance is evaluated using accuracy,precision,recall,and F1 score.We compare the performance of the three optimization algorithms and demonstrate the effectiveness of hyperparameter tuning in improving the accuracy of the CNN model.Our results show that the Hyper Tuned CNN model with ADAM Optimizer achieves the highest accuracy of up to 90%.Furthermore,we demonstrate that hyperparameter tuning can help achieve significant performance improvements,even using a relatively simple model architecture like CNNs.Our findings suggest that the choice of the optimization algorithm is a crucial factor in the performance of CNNs for authorship verification and that hyperparameter tuning can be an effective way to optimize this choice.Overall,this paper demonstrates the effectiveness of hyperparameter tuning in improving the performance of CNNs for authorship verification in digital forensic investigations.Our findings have important implications for developing accurate and reliable authorship verification systems,which are crucial for various applications in digital forensics,such as identifying the author of anonymous threatening messages or detecting cases of plagiarism.

CVIP-Net: A Convolutional Neural Network-Based Model for Forensic Radiology Image Classification

Automated and autonomous decisions of image classification systems have essential applicability in this modern age even.Image-based decisions are commonly taken through explicit or auto-feature engineering of images.In forensic radiology,auto decisions based on images significantly affect the automation of various tasks.This study aims to assist forensic radiology in its biological profile estimation when only bones are left.A benchmarked dataset Radiology Society of North America(RSNA)has been used for research and experiments.Additionally,a locally developed dataset has also been used for research and experiments to cross-validate the results.A Convolutional Neural Network(CNN)-based model named computer vision and image processing-net(CVIP-Net)has been proposed to learn and classify image features.Experiments have also been performed on state-of-the-art pertained models,which are alex_net,inceptionv_3,google_net,Residual Network(resnet)_50,and Visual Geometry Group(VGG)-19.Experiments proved that the proposed CNN model is more accurate than other models when panoramic dental x-ray images are used to identify age and gender.The specially designed CNN-based achieved results in terms of standard evaluation measures including accuracy(98.90%),specificity(97.99%),sensitivity(99.34%),and Area under the Curve(AUC)-value(0.99)on the locally developed dataset to detect age.The classification rates of the proposed model for gender estimation were 99.57%,97.67%,98.99%,and 0.98,achieved in terms of accuracy,specificity,sensitivity,and AUC-value,respectively,on the local dataset.The classification rates of the proposed model for age estimation were 96.80%,96.80%,97.03%,and 0.99 achieved in terms of accuracy,specificity,sensitivity,and AUC-value,respectively,on the RSNA dataset.

Research on Metaverse Security and Forensics

As a subversive concept,the metaverse has recently attracted widespread attention around the world and has set off a wave of enthusiasm in academic,industrial,and investment circles.However,while the metaverse brings unprecedented opportunities for transformation to human society,it also contains related risks.Metaverse is a digital living space with information infrastructure,interoperability system,content production system,and value settlement system as the underlying structure in which the inner core is to connect real residents through applications and identities.Through social incentives and governance rules,the metaverse reflects the digital migration of human society.This article will conduct an in-depth analysis of the metaverse from the perspective of electronic data forensics.First,from the perspective of Internet development,the background and development process of the metaverse is discussed.By systematically elaborating on the concept and connotation of the metaverse,this paper summarizes the different views of current practitioners,experts,and scholars on the metaverse.Secondly,from the perspective of metaverse security,the social risk and crime risks of the metaverse are discussed.Then the importance of metaverse forensics is raised.Third,from the perspective of blockchain,smart wearable devices,and virtual reality devices,the objects and characteristics of metaverse forensics have been studied in depth.Taking smart wearable devices as an example,this paper gives the relevant experimental process of smart bracelet forensics.Finally,many challenges faced by metaverse forensics are summarized by us which provide readers with some exploratory guidance.

Efficacy of Beneficial Fungi Isolates in Solanum lycopersicum L. Protection against Lepidopteran Insects through a Leaf Inoculation Technique

Helicoverpa armigera is a key insect pest of tomatoes reducing drastically yields. The effect of the endophytic colonization of tomato plants by Beauveria bassiana using leaf spray as an inoculation method on damage and survival of H. armigera was assessed in a screen house. Two B. bassiana isolates (Bb 115 and Bb 11) and two tomato varieties (a local variety Tounvi and an improved variety Padma) were included in the study. The adaxial and abaxial leaf surfaces were sprayed at a concentration of 107 conidia/ml and 109 conidia/ml for each isolate and each of the two tomato varieties. Thirty days after inoculation, five discs of tomato leaf and tomato root were cut for each isolate, each concentration per isolate and for each variety. The samples were incubated at room temperature (28°C ± 2°C) and periodically checked for fungal growth. Larval survival was checked and a damage assessment was done on tomato flowers and the leaves. The results show that the lowest Mean Survival Times (MSTs) were recorded on larvae feeding on plants inoculated with Bb 11 (4.2 ± 0.8 days against 11.5 ± 0.2 days for control). Compared to the other treatments, low damage rates of the flowers of the improved variety inoculated with Bb 11 at 109 conidia/ml were recorded from the 6th Day After Inoculation (DAI). This rate remains low until the end of treatment. Overall flower damage was lower than leaf damage. The results showed large differences in pathogenicity, with most endophytic isolate belonging to Bb 11 when inoculated at 109 conidia/ml using the leaf spraying technique. Data were discussed with regard to the use of endophytism B. bassiana in an integrated tomato pest control approach.

Forensic Study on Objective Evaluation of Visual Acuity of Ametropia with the Event-related Potential P3

Objective In this study,we aimed to assess the characteristics of the P3 component from an event-related potential(ERP)that was induced by visual acuity(VA)processing.Furthermore,we sought to provide electrophysiological evidence for the objective evaluation of VA.Methods We recruited 32 participants with myopia-related ametropia.They reported no other ocular diseases and had an uncorrected VA of 4.0 in both eyes.We used the block letter“E”at different visual angles and orientations as the graphic stimuli.The oddball paradigm,consisting of 4 modules,was used for ERP analysis.The standard stimuli of each module were identical,with a visual angle of 1°15′.The visual angles of the target stimuli were 1°15′,55′,24′,and 15′.The VA test was performed on each eye separately for all participants,and all characteristics of the P3 component were analyzed.Results There was no significant difference in the P3 peak letencies between the target stimulation angle 1°15′group and the 55′group,or between the target stimulation angle 24′group and the 15′group.There was a significant difference in the P3 peak letencies between the target stimulation angle 1°15′group and the 24′group as well as the 15′group.There was a significant difference in the P3 peak letencies between the target stimulation angle 55′group and the 24′group as well as the 15′group.No significant differences were observed in the P3 amplitude between modules.Conclusion In the oddball paradigm,P3 elicitation indicated a cognitive response to the target stimuli.These data showed that the characteristics of P3 can be used as an objective evaluation of VA.

Comparison Study and Forensic Analysis between Experiment and Coupled Dynamics Simulation for Submerged Floating Tunnel Segment with Free Ends under Wave Excitations

This paper presents dynamic-behavior comparisons and related forensic analyses of a submerged floating tunnel(SFT)between numerical simulation and physical experiment under regular and irregular waves.The experiments are conducted in the 3Dwave tank with 1:33.3 scale,and the corresponding coupled time-domain simulation tool is devised for comparison.The entire SFT systemconsists of a long concrete tunnel and 12 tubular aluminummooring lines.Two numerical simulation models,the Cummins equation with 3D potential theory including second-order wave-body interaction effects and the much simpler Morison-equation-based formula with the lumped-massbased line model,are designed and compared.Forensic analyses for mooring-line adjustments in the simulation are carried out in view of the best representation of the physical system.After that,the measured pre-tension distribution and systemstiffness of twelvemooring lines arewell reproduced in the numericalmodel.Subsequently,the dynamic responses and mooring tensions of the SFT are compared under regular and irregular waves.The measured and simulated results coincide reasonably well for both regular-and irregular-wave conditions.

Enhancing CNN for Forensics Age Estimation Using CGAN and Pseudo-Labelling

Age estimation using forensics odontology is an important process in identifying victims in criminal or mass disaster cases.Traditionally,this process is done manually by human expert.However,the speed and accuracy may vary depending on the expertise level of the human expert and other human factors such as level of fatigue and attentiveness.To improve the recognition speed and consistency,researchers have proposed automated age estimation using deep learning techniques such as Convolutional Neural Network(CNN).CNN requires many training images to obtain high percentage of recognition accuracy.Unfortunately,it is very difficult to get large number of samples of dental images for training the CNN due to the need to comply to privacy acts.A promising solution to this problem is a technique called Generative Adversarial Network(GAN).GAN is a technique that can generate synthetic images that has similar statistics as the training set.A variation of GAN called Conditional GAN(CGAN)enables the generation of the synthetic images to be controlled more precisely such that only the specified type of images will be generated.This paper proposes a CGAN for generating new dental images to increase the number of images available for training a CNN model to perform age estimation.We also propose a pseudolabelling technique to label the generated images with proper age and gender.We used the combination of real and generated images to trainDentalAge and Sex Net(DASNET),which is a CNN model for dental age estimation.Based on the experiment conducted,the accuracy,coefficient of determination(R2)and Absolute Error(AE)of DASNET have improved to 87%,0.85 and 1.18 years respectively as opposed to 74%,0.72 and 3.45 years when DASNET is trained using real,but smaller number of images.

Computer Forensics Framework for Efficient and Lawful Privacy-Preserved Investigation

Privacy preservation(PP)in Digital forensics(DF)is a conflicted and non-trivial issue.Existing solutions use the searchable encryption concept and,as a result,are not efficient and support only a keyword search.Moreover,the collected forensic data cannot be analyzed using existing well-known digital tools.This research paper first investigates the lawful requirements for PP in DF based on the organization for economic co-operation and development OECB)privacy guidelines.To have an efficient investigation process and meet the increased volume of data,the presented framework is designed based on the selective imaging concept and advanced encryption standard(AES).The proposed framework has two main modules,namely Selective Imaging Module(SIM)and Selective Analysis Module(SAM).The SIM and SAM modules are implemented based on advanced forensic format 4(AFF4)and SleuthKit open source forensics frameworks,respectively,and,accordingly,the proposed framework is evaluated in a forensically sound manner.The evaluation result is compared with other relevant works and,as a result,the proposed solution provides a privacy-preserving,efficient forensic imaging and analysis process while having also sufficient methods.Moreover,the AFF4 forensic image,produced by the SIM module,can be analyzed not only by SAM,but also by other well-known analysis tools available on the market.

Historical and current climates affect the spatial distribution of herbivorous tree insects in China

Historical and current climate impacts reshape the evolutionary trajectory and ecological dynamics of entire vegetative communities,which can drive insect species distribution.Understanding the spatial distribution of insects can enhance forest management effectiveness.The effects of historical and current climates in the spatial distribution of herbivorous tree insects in China were explored.A species distribution model simulated insect spatial distribution based on 596 species and the distribution probability and richness of these species were assessed in forest ecoregions.The explanatory power of the historical climate was stronger than that of the current climate,particularly historical annual precipitation and annual mean temperatures,for the distribution of herbivorous insects.Under both historical and current climatic conditions,herbivorous tree insects were and are mainly distributed in the North China Plain and the middle and lower reaches of the Yangtze River Plain,namely in the Huang He Plain mixed forests,Changjiang Plain evergreen forests,and Sichuan Basin evergreen broadleaf forests.The Yunnan-Guizhou Plateau and northeast China are regions with large impact differences between historical and current climates.The findings of this study provide valuable insights into herbivorous insect responses to sustained climate change and may contribute to long-term biodiversity conservation activities.

Internet of Things for Digital Forensics Application in Saudi Arabia

Despite the extensive empirical literature relating to the Internet of Things (IoT), surprisingly few attempts have sought to establish the ways in which digital forensics can be applied to undertake detailed examinations regarding IoT frameworks. The existing digital forensic applications have effectively held back efforts to align the IoT with digital forensic strategies. This is because the forensic applications are ill-suited to the highly complex IoT frameworks and would, therefore, struggle to amass, analyze and test the necessary evidence that would be required by a court. As such, there is a need to develop a suitable forensic framework to facilitate forensic investigations in IoT settings. Nor has considerable progress been made in terms of collecting and saving network and server logs from IoT settings to enable examinations. Consequently, this study sets out to develop and test the FB system which is a lightweight forensic framework capable of improving the scope of investigations in IoT environments. The FB system can organize the management of various IoT devices found in a smart apartment, all of which is controlled by the owner’s smart watch. This will help to perform useful functions, automate the decision-making process, and ensure that the system remains secure. A Java app is utilized to simulate the FB system, learning the user’s requirements and security expectations when installed and employing the MySQL server as a means of logging the communications of the various IoT devices.

Forensics: Collection of Sound Digital Evidence

This summary paper will discuss the concept of forensic evidence and evidence collection methods. Emphasis will be placed on the techniques used to collect forensically sound digital evidence for the purpose of introduction to digital forensics. This discussion will thereafter result in identifying and categorizing the different types of digital forensics evidence and a clear procedure for how to collect forensically sound digital evidence. This paper will further discuss the creation of awareness and promote the idea that competent practice of computer forensics collection is important for admissibility in court.

Characterization of insect-fauna of the free-ranging urban dog, Canis domesticus (L.) carcass in tropical region of Pakistan: A tool for forensic entomology

As the authors’ request, the following paper published in Vol.1, No.2, 29-37, 2013 has been withdrawn from the website.

A Review on Characteristics, Extraction Methods and Applications of Renewable Insect Protein

Due to the expected rise in the world population,an increase in the requirements for quality and safety of food and feed is expected,which leads to the growing demand for new sources of sustainable and renewable protein.Insect protein is gaining importance as a renewable material for several reasons,reflecting its potential contribu-tions to sustainability,resource efficiency,and environmental conservation.Some insect species are known to be able to efficiently convert organic waste into high-value products such as protein,requiring less land and water compared to traditional livestock.In addition,insect farming produces fewer greenhouse gas emissions,contri-buting to mitigating climate change.Insects are considered as a major potential alternative to animal or plant protein due to their many nutritional benefits,including high protein,mineral,and vitamin contents.On average,the protein content of insects ranges between 35%and 60%dry weight,which exceeds plant protein sources,such as cereal,soybeans,and lentils.As the acceptance of insect protein grows and technologies advance,the food and feed industries continue to explore and expand their applications,offering consumers diverse and sustainable pro-tein choices.In this review,we discuss the recentfindings relating to insect protein focusing on its characteristics,extraction methods,applications,and opportunities along with some trade-offs and uncertainties.

Truth Behind Speech:Forensic Linguistic Analysis on Speech

We are living in a society constructed by many aspects as well as languages.There are many ways to deal with legal cases,language is also an active one among them.As it is proved that resultant of forensic linguistic researches do help around.

Dynamic flight stability of hovering insects

The equations of motion of an insect with flapping wings are derived and then simplified to that of a flying body using the ＂rigid body＂ assumption. On the basis of the simplified equations of motion, the longitudinal dynamic flight stability of four insects （hoverfly, cranefly, dronefly and hawkmoth） in hovering flight is studied （the mass of the insects ranging from 11 to 1,648 mg and wingbeat frequency from 26 to 157Hz）. The method of computational fluid dynamics is used to compute the aerodynamic derivatives and the techniques of eigenvalue and eigenvector analysis are used to solve the equations of motion. The validity of the ＂rigid body＂ assumption is tested and how differences in size and wing kinematics influence the applicability of the ＂rigid body＂ assumption is investigated. The primary findings are： （1） For insects considered in the present study and those with relatively high wingbeat frequency （hoverfly, drone fly and bumblebee）, the ＂rigid body＂ assumption is reasonable, and for those with relatively low wingbeat frequency （cranefly and howkmoth）, the applicability of the ＂rigid body＂ assumption is questionable. （2） The same three natural modes of motion as those reported recently for a bumblebee are identified, i.e., one unstable oscillatory mode, one stable fast subsidence mode and one stable slow subsidence mode. （3） Approximate analytical expressions of the eigenvalues, which give physical insight into the genesis of the natural modes of motion, are derived. The expressions identify the speed derivative Mu （pitching moment produced by unit horizontal speed） as the primary source of the unstable oscillatory mode and the stable fast subsidence mode and Zw （vertical force produced by unit vertical speed） as the primary source of the stable slow subsidence mode.

中文语境下“Forensic Science”界定与诠释

作为美国司法制度的一项有机组成部分,"Forensic Science"已与其诉讼法律文化紧密融为一体。美国诉讼活动中,双方当事人对于某项专门性问题进行举证时,往往需要运用被称为"Forensic Science"的科学实证活动予以证明。在将"Forensic Science"一词引入国内的同时,必须在中文语境基础上对其进行准确界定与诠释。通过中美诉讼法律文化的比较研究,并对我国现有相关专业术语进行辨析后可以发现,"司法鉴定"一词与"Forensic Science"不仅在内涵、外延上较为吻合,亦符合我国诉讼制度与司法实践的现实要求。同时,采用"司法鉴定"一词,还可以充分反映出"Forensic Science"的本质属性,避免因中文界定的偏差而造成不必要的争议与误解。

Characteristics of Pattern Visual Evoked Potential in Two Eyes with Varying Visual Acuity in One Eye and Forensic Application

In this study, we aimed to study the pattern visual evoked potentials （P-VEPs） in two eyes with varying visual acuity in one eye and to provide an objective estimation of visual acuity by comparing P-VEPs in one and two eyes. Thirty subjects were chosen, who had one eye with an acuity of 5.0, 4.85, 4.6, 4.0, or scieropia and obstructed vision and the other eye with an acuity of 5.0, respectively. P-VEPs were detected under the large grating stimuli at 3x4 spatial frequency, moderate grating stimuli （12× 16 spatial frequency） and small grating stimuli （48×64 spatial frequency）. Under large grating stimuli, there was no significant difference in P100 peak latency between the groups, nor was there a significant difference between the amplitude of two eyes and the amplitude of one normal-vision eye. Under moderate and small grating stimuli, there was a significant difference in P100 peak latency between the group with both eyes having an acuity of 5.0 and the group with visual acuity below 4.0 in one eye. There was a significant difference in P100 amplitude between the group with visual acuity of 5.0 in both eyes and the group with one normal-vision eye. There was no significant difference in the amplitude of two eyes and the amplitude of one normal-vision eye between any other two groups. In forensic identification, characteristics and variability of P-VEPs in one and two eyes can be used to identify malingering or decline in visual acuity.