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A biometric system is essential in improving security and authentication processes across a variety of fields. Due to multiple criteria and alternatives, selecting the most suitable biometric system is a complex decision. We employ a hybrid approach in this study, combining the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) with the Analytic Hierarchical Process (AHP). Biometric technologies are ranked using the TOPSIS method according to the relative weights that AHP determines. By applying the neutrosophic set theory, this approach effectively handles the ambiguity and vagueness inherent in decision-making. Fingerprint, face, Iris, Voice, Hand Veins, Hand geometry and signature are the seven biometric technologies that are incorporated in the framework. Seven essential characteristics are accuracy, security, acceptability, speed and efficiency, ease of collection, universality, distinctiveness used to evaluate these technologies. The model seeks to determine which biometric technology is best suited for a particular application or situation by taking these factors into account. This technique may be applied in other domains in the future.

Identification of attacks by a network intrusion detection system （NIDS） is an important task. In signature or rule based detection, the previously encountered attacks are modded, and signatures/rules are extracted. These rules are used to detect such attacks in future, but in anomaly or outlier detection system, the normal network traffic is modeled. Any deviation from the normal model is deemed to be an outlier/attack. Data mining and machine learning techniques are widely used in offline NIDS. Unsupervised and supervised learning techniques differ the way NIDS dataset is treated. The characteristic features of unsupervised and supervised learning are finding patterns in data, detecting outliers, and determining a learned function for input features, generalizing the data instances respectively. The intuition is that if these two techniques are combined, better performance may be obtained. Hence, in this paper the advantages of unsupervised and supervised techniques are inherited in the proposed hierarchical model and devised into three stages to detect attacks in NIDS dataset. NIDS dataset is clustered using Dirichiet process （DP） clustering based on the underlying data distribution. Iteratively on each cluster, local denser areas are identified using local outlier factor （LOF） which in turn is discretized into four bins of separation based on LOF score. Further, in each bin the normal data instances are modeled using one class classifier （OCC）. A combination of Density Estimation method, Reconstruction method, and Boundary methods are used for OCC model. A product rule combination of the three methods takes into consideration the strengths of each method in building a stronger OCC model. Any deviation from this model is considered as an attack. Experiments are conducted on KDD CUP＇99 and SSENet-2011 datasets. The results show that the proposed model is able to identify attacks with higher detection rate and low false alarms.

This study presents and discusses the home delivery services in stochastic queuing-inventory modeling (SQIM). This system consists of two servers: one server manages the inventory sales processes, and the other server provides home delivery services at the doorstep of customers. Based on the Bernoulli schedule, a customer served by the first server may opt for a home delivery service. If any customer chooses the home delivery option, he hands over the purchased item for home delivery and leaves the system immediately. Otherwise, he carries the purchased item and leaves the system. When the delivery server returns to the system after the last home delivery service and finds that there are no items available for delivery, he goes on vacation. Such a vacation of a delivery server is to be interrupted compulsorily or voluntarily, according to the prefixed threshold level. The replenishment process is executed due to the (s,Q) reordering policy. The unique solution of the stationary probability vector to the finite generator matrix is found using recursive substitution and the normalizing condition. The necessary and sufficient system performance measures and the expected total cost of the system are computed. The optimal expected total cost is obtained numerically for all the parameters and shown graphically. The influence of parameters on the expected number of items that need to be delivered, the probability that the delivery server is busy, and the expected rate at which the delivery server’s self and compulsory vacation interruptions are also discussed.

A simple, cost effective, highly sensitive and rapid kinetic spectrophotometric method was developed for hydrazines by using Thiophene-3-carboxaldehyde (3-Thienaldehyde) and 3-Butenone (E)-1,1,1-trifluoro-4-(3-thienyl) (CF3 enone). CF3 enone was prepared by crossed aldol condensation of 3-Thienaldehyde and characterized by UV-Vis, FT-IR and NMR spectra. Reactions of 3-Thienaldehyde (with catalyst) and CF3 enone (in acetonitrile medium without catalyst) with hydrazines were followed spectrophotometrically and compared. Variables such as temperature and concentration were optimized to determine hydrazines in the concentration range of 0.1 mM to 0.1 M for 3-Thienaldehyde and 0.1 mM to 1 mM for CF3 enone. Minimum detectable limits were found to be 0.2 mM (Hydrazine) and 0.1 mM (MMH ) for 3-Thienaldehyde. For CF3 enone, Minimum detectable limits were found to be 0.007 mM (Hydrazine) and 0.01 mM (MMH). Rate of the CF3 enone reaction was studied as there is gradual decrease in absorbance for the peak at 320 nm for the interaction of hydrazines. Initial rate and fixed time methods were adopted for kinetic study. CF3 enone based kinetic spectrophotometric method is rapid and sensitive with no catalyst requirement for interaction of hydrazines when compared with the classical CHO functional group based method. Defence Science Journal, Vol. 64, No. 1, January 2014, DOI:10.14429/dsj.64.3092

Red mud waste from the aluminium industry was modified by leaching using hydrochloric acid or oxalic acid with additives, followed by precipitation or evaporation. The prepared catalysts were characterized in detail and tested for toluene total oxidation. The samples prepared by precipitation of the leachate by adding a base gave a much better performance in catalytic oxidation than the ones prepared by just evaporating the leachate. These improved performances can be correlated to the enhanced textural and redox properties of the catalysts due to the better dispersion and higher enrichment of Fe oxides at their surface. The best performing catalyst had a light-off temperature of around 310 °C and complete oxidation took place at around 380 °C.

The rapid growth of the world population has increased the food demand as well as the need for assurance of food quality, safety, and sustainability. However, food security can easily be compromised by not only natural hazards but also changes in food preferences, political conflicts, and food frauds. In order to contribute to building a more sustainable food system—digitally visible and processes measurable—within this review, we summarized currently available evidence for various information and communication technologies (ICTs) that can be utilized to support collaborative actions, prevent fraudulent activities, and remotely perform real-time monitoring, which has become essential, especially during the COVID-19 pandemic. The Internet of Everything, 6G, blockchain, artificial intelligence, and digital twin are gaining significant attention in recent years in anticipation of leveraging the creativity of human experts in collaboration with efficient, intelligent, and accurate machines, but with limited consideration in the food supply chain. Therefore, this paper provided a thorough review of the food system by showing how various ICT tools can help sense and quantify the food system and highlighting the key enhancements that Industry 5.0 technologies can bring. The vulnerability of the food system can be effectively mitigated with the utilization of various ICTs depending on not only the nature and severity of crisis but also the specificity of the food supply chain. There are numerous ways of implementing these technologies, and they are continuously evolving.

The present work aimed to study the culturable diversity of psychrotolerant bacteria persistent in soil under overwintering conditions, evaluate their ability to sustain plant growth and alleviate chilling stress in tomato. Psychrotolerant bacteria were isolated from agricultural field soil samples colleced during winter and then used to study chilling stress alleviation in tomato plants (Solanum lycopersicum cv Mill). Selective isolation after enrichment at 5°C yielded 40 bacterial isolates. Phylogenetic studies indicated their distribution in genera Arthrobacter, Flavimonas, Flavobacterium, Massilia, Pedobacter and Pseudomonas. Strains OS211, OB146, OB155 and OS261 consistently improved germination and plant growth when a chilling stress of 15°C was imposed and therefore were selected for pot experiments. Tomato plants treated with the selected four isolates exhibited significant tolerance to chilling as observed through reduction in membrane damage and activation of antioxidant enzymes along with proline synthesis in the leaves when exposed to chilling temperature conditions (15°C). Psychrotolerant physiology of the isolated bacteria combined with their ability to improve germination, plant growth and induce antioxidant capacity in tomato plants can be employed to protect plants against chilling stress.

Let H be a connected labeled graph. In this article, we characterize all the total dominating sets and the minimal total dominating sets of H-join graphs. Consequently, we compute the (multivariate) total domination polynomial and the (multivariate) minimal total domination polynomial of H-join graphs. We also compute the total domination number of H-join graphs. Finally, as an illustration, we calculate the total domination polynomial and the minimal total domination polynomial of the join of graphs, multipartite graphs, Kn-join graphs, Kn1,...,nm-join graphs, the corona product of graphs and the windmill graphs.

Let 𝐻 be a connected labeled graph. In this article, we characterize all the total dominating sets and the minimal total dominating sets of 𝐻−join graphs. Consequently, we compute the (multivariate) total domination polynomial and the (multivariate) minimal total domination polynomial of 𝐻−join graphs. We also compute the total domination number of 𝐻−join graphs. Finally, as an illustration, we calculate the total domination polynomial and the minimal total domination polynomial of the join of graphs, multipartite graphs, 𝐾𝑛−join graphs, K n 1 , … , n m −join graphs, the corona product of graphs and the windmill graphs.

Genetic variations in taste receptors are associated with gustatory perception and obesity, which in turn affects dietary preferences. Given the increasing tendency of people with obesity choosing sweet, high-fat meals, the current study assessed the cross-regulation of two polymorphisms of the sweet taste receptor ( T1R2/T1R3 ), rs35874116 and rs307355, on fat sensitivity in Indian adults. We investigated the association between taste sensitivity and BMI in the T1R2, T1R3 , and CD36 polymorphic and non-polymorphic groups. The general labelled magnitude scale (gLMS) was used to assess the taste sensitivity of 249 participants in addition to anthropometric data. TaqMan Probe-based RT-PCR was employed to determine the polymorphisms. Additionally, the colorimetric method utilizing 3, 5-dinitro salicylic acid was used to evaluate the participants' salivary amylase activity. The mean detection thresholds for linoleic acid (LA) and sucrose were greater in individuals with obesity (i.e., 0.97 ± 0.08 mM and 0.22 ± 0.02 M, respectively) than in healthy adults ( p  < 0.0001), indicating lower sensitivity. Moreover, it was found that a greater proportion of persons with obesity fall into the polymorphic groups (i.e., 52% with genotype CD36 AA, 44% with genotype T1R2 CC, and 40% with genotype T1R3 TT). All three single nucleotide polymorphisms support the Hardy–Weinberg equilibrium ( p  = 0.78). The Pearson correlation analysis between LA and the sucrose detection threshold revealed a significant ( p  < 0.0001) positive relationship with an r  value of 0.5299. Moreover, salivary amylase activity was significantly ( p  < 0.05) higher in the polymorphic sub-groups. The results of our study imply that genetic variations in T1R2/T1R3 receptors affect perception of both sweetness and fat, which may have an effect on obesity.