Predicting chronological age of 14 or 18 in adolescents: integrating dental assessments with machine learning.

AIM: Age estimation plays a critical role in personal identification, especially when determining compliance with the age of consent for adolescents. The age of consent refers to the minimum age at which an individual is legally considered capable of providing informed consent for sexual activities. The purpose of this study is to determine whether adolescents meet the age of 14 or 18 by using dental development combined with machine learning. METHODS: This study combines dental assessment and machine learning techniques to predict whether adolescents have reached the consent age of 14 or 18. Factors such as the staging of the third molar, the third molar index, and the visibility of the periodontal ligament of the second molar are evaluated. RESULTS: Differences in performance metrics indicate that the posterior probabilities achieved by machine learning exceed 93% for the age of 14 and slightly lower for the age of 18. CONCLUSION: This study provides valuable insights for forensic identification for adolescents in personal identification, emphasizing the potential to improve the accuracy of age determination within this population by combining traditional methods with machine learning. It underscores the importance of protecting and respecting the dignity of all individuals involved.

How Health Professionals Identify and Respond to Perpetrators of Domestic and Family Violence in a Hospital Setting: A Scoping Review.

There is heightened awareness that a whole-of-systems approach to perpetrator responses is key to addressing domestic and family violence (DFV). This paper reports on the findings from a scoping review which mapped the international literature on how health professionals identify and respond to perpetrators of DFV within a hospital setting. A comprehensive scoping review methodology was used. The search, spanning January 2010 to January 2022, yielded 12,380 publications from four databases. Eligibility for inclusion included peer-reviewed literature with any reference to inpatient hospital health professionals identifying or responding to perpetrators of DFV. Fourteen articles were included in the final review. The review presents the literature categorized by levels of prevention, from primary, secondary, through to tertiary preventive interventions. An additional category "other practices" is added to capture practices which did not fit into existing levels. Despite glimpses into how health professionals can identify, and respond to perpetrators of DFV, the current knowledge base is sparse. The review did not identify any mandated or formal procedures for identifying and/screening or responding to perpetration of abuse in hospitals. Rather, responses to perpetrators are inconsistent and rely on the motivation, skill, and self-efficacy of health professionals rather than an embedded practice that is driven and informed by hospital policy or procedures. The literature paints a picture of missed opportunities for meaningful work with perpetrators of DFV in a hospital setting and highlights a disjuncture between policy and practice.

Phenotypic and Genotypic Methods for the Identification and Quantification of Cyanobacteria in Lake Water.

Early monitoring of Microcystis, a cyanobacterium that produces microcystin, is paramount in order to confirm the presence of Microcystis spp. Both phenotypic and genotypic methods have been used. The phenotypic methods provide the presence of the microcystis but do not confirm its species type and toxin produced. Additionally, phenotypic methods cannot differentiate toxigenic from non-toxigenic Microcystis. Therefore, the current protocol also describes genetic methods based on PCR to detect toxigenic Microcystis spp. based on microcystin synthetase E (mcy E) gene and 16-23S RNA genes for species-specific identification, which can effectively comprehend distinct lineages and discrimination of potential complexity of microcystin populations. The presence of these microcystin toxins in blood, in most cases, indicates contamination of drinking water by cyanobacteria. The methods presented herein are used to identify microcystin toxins in drinking water and blood.

Experimental Ecotoxicology Procedures Interfere with Honey Bee Life History.

Apis mellifera was used as a model species for ecotoxicological testing. In the present study, we tested the effects of acetone (0.1% in feed), a solvent commonly used to dissolve pesticides, on bees exposed at different developmental stages (larval and/or adult). Moreover, we explored the potential effect of in vitro larval rearing, a commonly used technique for accurately monitoring worker exposure at the larval stage, by combining acetone exposure and treatment conditions (in vitro larval rearing vs. in vivo larval rearing). We then analyzed the life-history traits of the experimental bees using radio frequency identification technology over three sessions (May, June, and August) to assess the potential seasonal dependence of the solvent effects. Our results highlight the substantial influence of in vitro larval rearing on the life cycle of bees, with a 47.7% decrease in life span, a decrease of 0.9 days in the age at first exit, an increase of 57.3% in the loss rate at first exit, and a decrease of 40.6% in foraging tenure. We did not observe any effect of exposure to acetone at the larval stage on the capacities of bees reared in vitro. Conversely, acetone exposure at the adult stage reduced the bee life span by 21.8% to 60%, decreased the age at first exit by 1.12 to 4.34 days, and reduced the foraging tenure by 30% to 37.7%. Interestingly, we found a significant effect of season on acetone exposure, suggesting that interference with the life-history traits of honey bees is dependent on season. These findings suggest improved integration of long-term monitoring for assessing sublethal responses in bees following exposure to chemicals during both the larval and adult stages. Environ Toxicol Chem 2024;00:1-12. © 2024 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Cost-effective evaluation of Aptamer candidates in SELEX-based Aptamer isolation.

Aptamers are short, single-stranded nucleic acids with high affinity and specificity for various targets, making them valuable in diagnostics and therapeutics. Their isolation traditionally involves a time-consuming and costly process called SELEX. While SELEX methods have evolved to improve binding and amplification, the crucial step of aptamer identification from sequencing data remains expensive and often overlooked. Common identification methods require modification of aptamer candidates with labels like biotin or fluorescent dyes, which becomes costly and cumbersome for high-throughput sequencing data. This paper presents an efficient and cost-effective approach to streamline aptamer identification. It employs asymmetric polymerase chain reaction (PCR) to generate modified single-stranded DNA copies of aptamer candidates, simplifying the modification process. By using excess modified forward primers and limited reverse primers, this method reduces costs since only unmodified candidates need to be synthesized initially. The approach was demonstrated with an IgE protein aptamer and successfully applied to identify aptamers from a pool of 12 candidates against a monoclonal antibody. The validity of the results was further confirmed through the direct synthesis of fluorophore-conjugated aptamer candidates, yielding consistent outcomes while reducing the cost by threefold. This approach addresses a critical bottleneck in aptamer discovery by significantly reducing the time and cost associated with aptamer identification, facilitating aptamer-based research and making aptamers more accessible for various applications in diagnostics and therapeutics.

RamanCluster: A deep clustering-based framework for unsupervised Raman spectral identification of pathogenic bacteria.

Raman spectroscopy serves as a powerful and reliable tool for the characterization of pathogenic bacteria. The integration of Raman spectroscopy with artificial intelligence techniques to rapidly identify pathogenic bacteria has become paramount for expediting disease diagnosis. However, the development of prevailing supervised artificial intelligence algorithms is still constrained by costly and limited well-annotated Raman spectroscopy datasets. Furthermore, tackling various high-dimensional and intricate Raman spectra of pathogenic bacteria in the absence of annotations remains a formidable challenge. In this paper, we propose a concise and efficient deep clustering-based framework (RamanCluster) to achieve accurate and robust unsupervised Raman spectral identification of pathogenic bacteria without the need for any annotated data. RamanCluster is composed of a novel representation learning module and a machine learning-based clustering module, systematically enabling the extraction of robust discriminative representations and unsupervised Raman spectral identification of pathogenic bacteria. The extensive experimental results show that RamanCluster has achieved high accuracy on both Bacteria-4 and Bacteria-6, with ACC values of 77 % and 74.1 %, NMI values of 75 % and 73 %, as well as AMI values of 74.6 % and 72.6 %, respectively. Furthermore, compared with other state-of-the-art methods, RamanCluster exhibits the superior accuracy on handling various complicated pathogenic bacterial Raman spectroscopy datasets, including situations with strong noise and a wide variety of pathogenic bacterial species. Additionally, RamanCluster also demonstrates commendable robustness in these challenging scenarios. In short, RamanCluster has a promising prospect in accelerating the development of low-cost and widely applicable disease diagnosis in clinical medicine.

Significant influence of urban human activities and marine input on rainwater chemistry in a coastal large city, China.

The coastal urban region is generally considered an atmospheric receptor for terrestrial and marine input materials, and rainfall chemistry can trace the wet scavenging process of these materials. Fast urbanization in China's east coastal areas has greatly altered the rainwater chemistry. However, the chemical variations, determinants, and sources of rainfall are unclear. Therefore, the typical coastal city of Fuzhou was selected for 1-year rainwater sampling and inorganic ions were detected to explore above problems. The findings depicted that rainwater ions in Fuzhou were slightly different from those in other coastal cities. Although NO3-, SO42-, Ca2+ and NH4+ dominated the rainwater ions, the marine input Cl- (22 %) and Na+ (11 %) also contributed a considerable percentage to the rainwater ions. Large differences in ion concentrations (2∼28 times) were found in monthly scale due to the rainfall amount. Both natural and anthropogenic determinants influenced the rainwater ions in coastal cities, such as SO2 emission, air SO2 and PM10 content on rainwater SO42-, NO3-, and Ca2+, and soot & dust emission on rainwater SO42-, NO3-, indicating the vital contribution of human activities. Stoichiometry and positive matrix factorization-based sources identification indicated that atmospheric dust/particles were the primary contributor of Ca2+ (83.3 %) and F- (83.7 %), and considerable contributor of SO42- (39.5 %), NO3- (38.3 %) and K+ (41.5 %). Anthropogenic origins, such as urban waste volatilization and fuel combustion emission, contributed 95 % of NH4+, 54.5 % of NO3- and 41.9 % of SO42-, and the traffic sources contribution was relatively higher than fixed emission sources. The marine input represented the vital source of Cl- (77.7 %), Na+ (84.9 %), and Mg2+ (55.3 %). This work highlights the significant influence of urban human activities and marine input on rainwater chemicals and provides new insight into the material cycle between the atmosphere and earth-surface in coastal city.

Nitroaromatic compounds in six major Chinese cities: Influence of different formation mechanisms on light absorption properties.

Nitroaromatic compounds (NACs) are important nitrogen organics in aerosol with strong light-absorbing and chemically reactive properties. In this study, NACs in six Chinese megacities, including Harbin (HB), Beijing (BJ), Xi'an (XA), Wuhan (WH), Chengdu (CD), and Guangzhou (GZ), were investigated for understanding their sources, gas-particle partitioning, and impact on BrC absorption properties. The concentrations of ΣNACs in PM2.5 in the six cities ranged from 9.15 to 158.8 ng/m3 in winter and from 2.02 to 9.39 ng/m3 in summer. Nitro catechols (NCs), nitro phenols (NPs), and nitro salicylic acids (NSAs) are the main components in ΣNACs, with NCs being dominant in particulate phase and NPs being dominant in the gas phase. Correlation analysis between different pollutant species revealed that coal and biomass combustions were the major sources of NACs in the northern cities during wintertime, while secondary formation dominated NACs in the southern cities during summertime. The contribution of ΣNACs to brown carbon (BrC) light absorption ranged from 0.85 to 7.98 % during the wintertime and 2.07-6.44 % during the summertime. The mass absorption efficiency at 365 nm (MAE365) were highest for 4-nitrocatechol (4NC, 17.4-89.0 m2/g), 4-methyl-5-nitrocatechol (4M5NC, 15.0-76.9 m2/g), and 4-nitroguaiacol (4NG, 11.7-59.8 m2/g). The formation of NCs and NG through oxidation and nitration of catechol and guaiacol led to a significant increase in aerosol light absorption. In contrast, NPs and NSAs formed by the photonitration and photooxidation in liquid phase showed high polarity but low light absorption ability, and the proportions of (NPs + NSAs) in the light absorption of ΣNACs were lower than 15.3 % in the six megacities.

A multi-strategy fusion identification model for failure mode of reinforced concrete column.

Accurate identification of the failure modes of Reinforced Concrete (RC) columns based on the design parameters of the structural members is critical for earthquake-resistant design and safety evaluation of existing structures. Existing identification methods have some problems, such as high cost, incomplete consideration of influencing factors, and low precision or recall in identifying shear or flexural-shear failure. In this paper, the main factors for the failure modes of RC columns are first analyzed and studied. Then, the problem of class imbalance in data samples is investigated. To identify the failure modes of RC columns, oversampling of data (BSB-FMC), model ensembling (RFB-FMC), cost-sensitive learning (CSB-FMC) and a fusion model of three strategies (BSFCB-FMC) are proposed. And finally, the SHapley Additive exPlanations (SHAP) method is used to provide a better interpretation of the designed model. The results show that the developed strategies can improve the accuracy of identifying the failure modes of RC columns compared to the models using a single Artificial Neural Network (ANN), a Support Vector Machine (SVM), a Random Forest (RF), and Adaptive Boosting (AdaBoost). The overall accuracy of the developed BSFCB-FMC model reaches 97%, and the precision and recall for the three failure modes are both above 90%. The designed model provides a solution for fast, accurate and cost-effective identification of the failure modes of RC columns.

Surveillance of mosquitoes harnessing their buzzing sound.

Mosquito surveillance for vector-borne disease management relies on traditional morphological and molecular techniques, which are tedious, time-consuming, and costly. The present study describes a simple and efficient recording device that analyzes mosquito sound to estimate species composition, male-female ratio, fed-unfed status, and harmonic convergence interaction using fundamental frequency (F0) bandwidth, harmonics, amplitude, and combinations of these parameters. The study examined a total of 19 mosquito species, including 3 species of Aedes, 7 species of Anopheles, 1 species of Armigeres, 5 species of Culex, 1 species of Hulecoetomyia, and 2 species of Mansonia. Among them, the F0 ranges between 269.09 ± 2.96 Hz (Anopheles culiciformis) to 567.51 ± 3.82 Hz (Aedes vittatus) and the harmonic band (hb) number ranges from 5 (An. culiciformis) to 12 (Ae. albopictus). In terms of species identification, the success rate was 95.32 % with F0, 84.79 % with F0-bandwidth, 84.79 % with harmonic band (hb) diversity, and 49.7 % with amplitude (dB). The species identification rate has gone up to 96.50 % and 97.66 % with the ratio and multiplication of F0 and hb, respectively. This is because of the matrices that combine multiple sound attributes. Comparatively, combinations of the amplitude of the F0 and the higher harmonic frequency band were non-significant for species identification (60.82 %). The fed females have shown a considerable increase in F0 in comparison to the unfed. The males of all the species possessed significantly higher frequencies with respect to the females. Interestingly, the presence of male-female of Ae. vittatus together showed harmonic convergence between the 2nd and 3rd harmonic bands. In conclusion, the sound-based technology is simple, precise, and cost-effective and provides better resolution for species, sex, and fed-unfed status detection in comparison to conventional methods. Real-time surveillance of mosquitoes could potentially utilize this technology.

A protein network refinement method based on module discovery and biological information.

BACKGROUND: The identification of essential proteins can help in understanding the minimum requirements for cell survival and development to discover drug targets and prevent disease. Nowadays, node ranking methods are a common way to identify essential proteins, but the poor data quality of the underlying PIN has somewhat hindered the identification accuracy of essential proteins for these methods in the PIN. Therefore, researchers constructed refinement networks by considering certain biological properties of interacting protein pairs to improve the performance of node ranking methods in the PIN. Studies show that proteins in a complex are more likely to be essential than proteins not present in the complex. However, the modularity is usually ignored for the refinement methods of the PINs. METHODS: Based on this, we proposed a network refinement method based on module discovery and biological information. The idea is, first, to extract the maximal connected subgraph in the PIN, and to divide it into different modules by using Fast-unfolding algorithm; then, to detect critical modules according to the orthologous information, subcellular localization information and topology information within each module; finally, to construct a more refined network (CM-PIN) by using the identified critical modules. RESULTS: To evaluate the effectiveness of the proposed method, we used 12 typical node ranking methods (LAC, DC, DMNC, NC, TP, LID, CC, BC, PR, LR, PeC, WDC) to compare the overall performance of the CM-PIN with those on the S-PIN, D-PIN and RD-PIN. The experimental results showed that the CM-PIN was optimal in terms of the identification number of essential proteins, precision-recall curve, Jackknifing method and other criteria, and can help to identify essential proteins more accurately.

Computer-aided design space identification for screening of protein A affinity chromatography resins.

The rapidly growing market of monoclonal antibodies (mAbs) within the biopharmaceutical industry has incentivised numerous works on the design of more efficient production processes. Protein A affinity chromatography is regarded as one of the best processes for the capture of mAbs. Although the screening of Protein A resins has been previously examined, process flexibility has not been considered to date. Examining performance alongside flexibility is crucial for the design of processes that can handle disturbances arising from the feed stream. In this work, we present a model-based approach for the identification of design spaces, enhanced by machine learning. We demonstrate its capabilities on the design of a Protein A chromatography unit, screening five industrially relevant resins. The computational results favourably compare to experimental data and a resin performance comparison is presented. An improvement on the computational time by a factor of 300,000 is achieved using the machine learning aided methodology. This allowed for the identification of 5,120 different design spaces in only 19 h.

Revolutionizing aquatic eco-environmental monitoring: Utilizing the RPA-Cas-FQ detection platform for zooplankton.

The integration of recombinase polymerase amplification (RPA) with CRISPR/Cas technology has revolutionized molecular diagnostics and pathogen detection due to its unparalleled sensitivity and trans-cleavage ability. However, its potential in the ecological and environmental monitoring scenarios for aquatic ecosystems remains largely unexplored, particularly in accurate qualitative/quantitative detection, and its actual performance in handling complex real environmental samples. Using zooplankton as a model, we have successfully optimized the RPA-CRISPR/Cas12a fluorescence detection platform (RPA-Cas-FQ), providing several crucial "technical tips". Our findings indicate the sensitivity of CRISPR/Cas12a alone is 5 × 109 copies/reaction, which can be dramatically increased to 5 copies/reaction when combined with RPA. The optimized RPA-Cas-FQ enables reliable qualitative and semi-quantitative detection within 50 min, and exhibits a good linear relationship between fluorescence intensity and DNA concentration (R2 = 0.956-0.974***). Additionally, we developed a rapid and straightforward identification procedure for single zooplankton by incorporating heat-lysis and DNA-barcode techniques. We evaluated the platform's effectiveness using real environmental DNA (eDNA) samples from the Three Gorges Reservoir, confirming its practicality. The eDNA-RPA-Cas-FQ demonstrated strong consistency (Kappa = 0.43***) with eDNA-Metabarcoding in detecting species presence/absence in the reservoir. Furthermore, the two semi-quantitative eDNA technologies showed a strong positive correlation (R2 = 0.58-0.87***). This platform also has the potential to monitor environmental pollutants by selecting appropriate indicator species. The novel insights and methodologies presented in this study represent a significant advancement in meeting the complex needs of aquatic ecosystem protection and monitoring.

Donor whole blood DNA methylation is not a strong predictor of acute graft versus host disease in unrelated donor allogeneic haematopoietic cell transplantation.

Allogeneic hematopoietic cell transplantation (HCT) is used to treat many blood-based disorders and malignancies, however it can also result in serious adverse events, such as the development of acute graft-versus-host disease (aGVHD). This study aimed to develop a donor-specific epigenetic classifier to reduce incidence of aGVHD by improving donor selection. Genome-wide DNA methylation was assessed in a discovery cohort of 288 HCT donors selected based on recipient aGVHD outcome; this cohort consisted of 144 cases with aGVHD grades III-IV and 144 controls with no aGVHD. We applied a machine learning algorithm to identify CpG sites predictive of aGVHD. Receiver operating characteristic (ROC) curve analysis of these sites resulted in a classifier with an encouraging area under the ROC curve (AUC) of 0.91. To test this classifier, we used an independent validation cohort (n = 288) selected using the same criteria as the discovery cohort. Attempts to validate the classifier failed with the AUC falling to 0.51. These results indicate that donor DNA methylation may not be a suitable predictor of aGVHD in an HCT setting involving unrelated donors, despite the initial promising results in the discovery cohort. Our work highlights the importance of independent validation of machine learning classifiers, particularly when developing classifiers intended for clinical use.

Discovery of novel FGFR4 inhibitors through a build-up fragment strategy.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death. FGFR4 has been implicated in HCC progression, making it a promising therapeutic target. We introduce an approach for identifying novel FGFR4 inhibitors by sequentially adding fragments to a common warhead unit. This strategy resulted in the discovery of a potent inhibitor, 4c, with an IC50 of 33 nM and high selectivity among members of the FGFR family. Although further optimisation is required, our approach demonstrated the potential for discovering potent FGFR4 inhibitors for HCC treatment, and provides a useful method for obtaining hit compounds from small fragments.

Assessment of the integrated disease surveillance and response system implementation in health zones at risk for viral hemorrhagic fever outbreaks in North Kivu, Democratic Republic of the Congo, following a major Ebola outbreak, 2021.

BACKGROUND: The Democratic Republic of the Congo (DRC) experienced its largest Ebola Virus Disease Outbreak in 2018-2020. As a result of the outbreak, significant funding and international support were provided to Eastern DRC to improve disease surveillance. The Integrated Disease Surveillance and Response (IDSR) strategy has been used in the DRC as a framework to strengthen public health surveillance, and full implementation could be critical as the DRC continues to face threats of various epidemic-prone diseases. In 2021, the DRC initiated an IDSR assessment in North Kivu province to assess the capabilities of the public health system to detect and respond to new public health threats. METHODS: The study utilized a mixed-methods design consisting of quantitative and qualitative methods. Quantitative assessment of the performance in IDSR core functions was conducted at multiple levels of the tiered health system through a standardized questionnaire and analysis of health data. Qualitative data were also collected through observations, focus groups and open-ended questions. Data were collected at the North Kivu provincial public health office, five health zones, 66 healthcare facilities, and from community health workers in 15 health areas. RESULTS: Thirty-six percent of health facilities had no case definition documents and 53% had no blank case reporting forms, limiting identification and reporting. Data completeness and timeliness among health facilities were 53% and 75% overall but varied widely by health zone. While these indicators seemingly improved at the health zone level at 100% and 97% respectively, the health facility data feeding into the reporting structure were inconsistent. The use of electronic Integrated Disease Surveillance and Response is not widely implemented. Rapid response teams were generally available, but functionality was low with lack of guidance documents and long response times. CONCLUSION: Support is needed at the lower levels of the public health system and to address specific zones with low performance. Limitations in materials, resources for communication and transportation, and workforce training continue to be challenges. This assessment highlights the need to move from outbreak-focused support and funding to building systems that can improve the long-term functionality of the routine disease surveillance system.

Opening the black box: explainable deep-learning classification of wood microscopic image of endangered tree species.

BACKGROUND: Traditional method of wood species identification involves the use of hand lens by wood anatomists, which is a time-consuming method that usually identifies only at the genetic level. Computer vision method can achieve "species" level identification but cannot provide an explanation on what features are used for the identification. Thus, in this study, we used computer vision methods coupled with deep learning to reveal interspecific differences between closely related tree species. RESULT: A total of 850 images were collected from the cross and tangential sections of 15 wood species. These images were used to construct a deep-learning model to discriminate wood species, and a classification accuracy of 99.3% was obtained. The key features between species in machine identification were targeted by feature visualization methods, mainly the axial parenchyma arrangements and vessel in cross section and the wood ray in tangential section. Moreover, the degree of importance of the vessels of different tree species in the cross-section images was determined by the manual feature labeling method. The results showed that vessels play an important role in the identification of Dalbergia, Pterocarpus, Swartzia, Carapa, and Cedrela, but exhibited limited resolutions on discriminating Swietenia species. CONCLUSION: The research results provide a computer-assisted tool for identifying endangered tree species in laboratory scenarios, which can be used to combat illegal logging and related trade and contribute to the implementation of CITES convention and the conservation of global biodiversity.

"Caught in the crossfire" - women veterans' testimonies regarding excessively violent acts committed in combat zones.

As women in the Israeli Defense Forces (IDF) are increasingly placed in supportive and combat roles in active war zones, they routinely encounter and participate in violent acts. This study focusses on the centrality of gendered inequality and oppression as a factor that shapes not only women's experience in the military but also their responses in cases of excessive violence. The goal of this study was to explore the ways women veterans of combat or combat-support units conceptualize their stance regarding violent acts which they either committed or witnessed in war zones. Using a qualitative approach, we analyzed the retrospective testimonies of 58 Israeli women veterans from the archives of an NGO that documents veteran combatants exposure to excessive violence. Most women explained their violent acts as inherent to the military system and culture, which in our analysis was categorized as examples of either internalized gender oppression or as identification with the aggressor. A smaller number of women described their attempts to protest, as they took a moral stance rooted in a feminine perspective. The three explanations revealed through the analysis of the testimonies reflect the inner tension experienced by many women in the military, as they navigate between two extreme positions, either as victims of male dominance, or as aggressors that are part of a powerful military system. In this study, gendered inequality provides a framework for analyzing the data. Thus, this study contributes to the theoretical knowledge and methodological approaches concerning violent situations in combat areas, focusing on the various ways in which women veterans subjectively and retroactively conceptualize their participation in and responses to violent acts.

A machine learning based framework for IoT devices identification using web traffic.

Identification of the Internet of Things (IoT) devices has become an essential part of network management to secure the privacy of smart homes and offices. With its wide adoption in the current era, IoT has facilitated the modern age in many ways. However, such proliferation also has associated privacy and data security risks. In the case of smart homes and smart offices, unknown IoT devices increase vulnerabilities and chances of data theft. It is essential to identify the connected devices for secure communication. It is very difficult to maintain the list of rules when the number of connected devices increases and human involvement is necessary to check whether any intruder device has approached the network. Therefore, it is required to automate device identification using machine learning methods. In this article, we propose an accuracy boosting model (ABM) using machine learning models of random forest and extreme gradient boosting. Featuring engineering techniques are employed along with cross-validation to accurately identify IoT devices such as lights, smoke detectors, thermostat, motion sensors, baby monitors, socket, TV, security cameras, and watches. The proposed ensemble model utilizes random forest (RF) and extreme gradient boosting (XGB) as base learners with adaptive boosting. The proposed ensemble model is tested with extensive experiments involving the IoT Device Identification dataset from a public repository. Experimental results indicate a higher accuracy of 91%, precision of 93%, recall of 93%, and F1 score of 93%.

A systematic literature review of hate speech identification on Arabic Twitter data: research challenges and future directions.

The automatic speech identification in Arabic tweets has generated substantial attention among academics in the fields of text mining and natural language processing (NLP). The quantity of studies done on this subject has experienced significant growth. This study aims to provide an overview of this field by conducting a systematic review of literature that focuses on automatic hate speech identification, particularly in the Arabic language. The goal is to examine the research trends in Arabic hate speech identification and offer guidance to researchers by highlighting the most significant studies published between 2018 and 2023. This systematic study addresses five specific research questions concerning the types of the Arabic language used, hate speech categories, classification techniques, feature engineering techniques, performance metrics, validation methods, existing challenges faced by researchers, and potential future research directions. Through a comprehensive search across nine academic databases, 24 studies that met the predefined inclusion criteria and quality assessment were identified. The review findings revealed the existence of many Arabic linguistic varieties used in hate speech on Twitter, with modern standard Arabic (MSA) being the most prominent. In identification techniques, machine learning categories are the most used technique for Arabic hate speech identification. The result also shows different feature engineering techniques used and indicates that N-gram and CBOW are the most used techniques. F1-score, precision, recall, and accuracy were also identified as the most used performance metric. The review also shows that the most used validation method is the train/test split method. Therefore, the findings of this study can serve as valuable guidance for researchers in enhancing the efficacy of their models in future investigations. Besides, algorithm development, policy rule regulation, community management, and legal and ethical consideration are other real-world applications that can be reaped from this research.